T-Mobile (NASDAQ: TMUS) is taking its leading 5G network to the next level for hundreds of thousands of people in Utah. Today, the company announced that it's upped its 5G game in the Beehive State over the last two years, expanding wireless coverage across a whopping 85,000 square miles to now cover nearly 99% of Utahns.

T-Mobile Expands 5G Network Coverage in Utah, Delivering Supercharged Speeds Across the State

T-Mobile (NASDAQ: TMUS) is taking its leading 5G network to the next level for hundreds of thousands of people in Utah. Today, the company announced t

Malaysia has the third fastest 5G network in the world, with the best performance in South-East Asia as of September 2023, according to a report from Internet access and performance analytics platform Ookla.

Malaysia has the third fastest 5G network in the world, says Ookla

Globe Telecom Inc.’s fixed broadband network was declared as the most consistent in 33 towns and two provinces in the country for the third quarter of 2023 based on data from network insights platform Ookla.

Globe’s broadband network ‘most consistent’ in 35 areas in Q3

He also said this is based on the latest results from Ookla's Speedtest Intelligence data for the third quarter of 2023...

M'sia's median 5G speed ranked fastest in Southeast Asia, third globally

At Network X 2023 we caught up with Sylwia Kechiche from Ookla who told us a bit more about their latest report on the EU’s progress towards a Gigabit Society. We spoke about fixed and 5G network performance and the targets of European Commission’s Digital Compass.

Ookla’s Sylwia Kechiche on achieving a Gigabit Society

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Malaysia's median 5G speed is at 485.25Mbps, ranked the fastest amongst Southeast Asian countries andthird globally.

Malaysia’s median 5G speed ranked fastest in Southeast Asia, third globally

Minister Fahmi Fadzil says 5G adoption rate at end of September stood at 7.4%, with 2.5 million mobile users subscribing to the 5G network.

Malaysia’s median 5G speed ranks fastest in Southeast Asia, third globally

Malaysia’s median 5G speed is at 485.25Mbps, ranked the fastest amongst South-east Asian countries and third globally, said Minister of

Fahmi: Malaysia’s median 5G speed ranked fastest in SE Asia, third globally

Ookla

ookla

Authors:
(1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Table of Links Abstract & Introduction Related Work and Background Challenges in TD Detection Measurement Setup Development Case Study : Wehe - TD Detection Tool for Mobile Environment Shortcoming of Wehe on HTTPS Traffic TD Detection of HTTPS Traffic Conclusion & References VII. CONCLUSION Net neutrality violation detection is a need of an hour. As many of the ISPs are also content providers these days, they compete with each other, which can lead to one deliberately discriminating the services of the other to gain market share. However, users should have the freedom to choose services as per their wishes. Our work considered various challenges in the detection of traffic discrimination in HTTPS traffic. As a case study, we validated Wehe, one of the latest tools available to detect traffic differentiation. The described challenges helped us divide the entire tool into multiple interdependent components and validate them independently. Our validation using commercial traffic shaper revealed that traffic in Wehe setup may not mimic the characteristics of HTTPS traffic accessed from the original servers. Hence, middle-boxes may not subject them to intended discrimination. Thus, Wehe may not detect discrimination of HTTPS traffic. Our new method that uses the appropriate SNI parameter value in the initial TLS handshake message overcomes this shortcoming. Hence our work provided a mechanism to detect a wide range of possible discriminations on the Internet. REFERENCES [1] T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, and E. P. Duarte, “Monitoring network neutrality: A survey on traffic differentiation detection,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 2486–2517, 2018. [2] V. Nguyen, D. Mohammed, M. Omar, and P. Dean, “Net neutrality around the globe: A survey,” in 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 480–488. [3] Ramneek, P. Hosein, W. Choi, and W. Seok, “Detecting network neutrality violations through packet loss statistics,” in 2015 17th AsiaPacific Network Operations and Management Symposium (APNOMS), 2015, pp. 404–407. [4] D. Li, F. Tian, M. Zhu, L. Wang, and L. Sun, “A novel framework for analysis of global network neutrality based on packet loss rate,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 297–304. [5] M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi, “Detecting bittorrent blocking,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Oct. 2008, pp. 3–8. [6] M. B. Tariq, M. Motiwala, N. Feamster, and M. Ammar, “Detecting network neutrality violations with causal inference,” in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, ser. CoNEXT ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 289–300. [Online]. Available: https://doi.org/10.1145/1658939.1658972 [7] R. Ravaioli, G. Urvoy-Keller, and C. Barakat, “Towards a general solution for detecting traffic differentiation at the internet access,” in 2015 27th International Teletraffic Congress, Sep. 2015, pp. 1–9. [8] Y. Zhang, Z. M. Mao, and M. Zhang, “Detecting traffic differentiation in backbone isps with netpolice,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ser. IMC ’09. New York, NY, USA: ACM, 2009, pp. 103–115. [Online]. Available: http://doi.acm.org/10.1145/1644893.1644905 [9] P. Kanuparthy and C. Dovrolis, “Shaperprobe: End-to-end detection of isp traffic shaping using active methods,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Nov. 2011, pp. 473–482. [10] U. Weinsberg, A. Soule, and L. Massoulie, “Inferring traffic shaping and policy parameters using end host measurements,” in Proceedings of IEEE INFOCOM, 2011. [11] M. Dischinger, M. Marcon, S. Guha, K. Gummadi, R. Mahajan, and S. Saroiu, “Glasnost: Enabling end users to detect traffic differentiation,” in 7th USENIX Conf. Networked Systems Design and Implementation, Apr. 2010, pp. 27–27. [12] V. Bashko, N. Melnikov, A. Sehgal, and J. Schonw ¨ alder, “Bonafide: A ¨ traffic shaping detection tool for mobile networks,” in 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), 2013, pp. 328–335. [13] A. Molavi Kakhki, A. Razaghpanah, A. Li, H. Koo, R. Golani, D. Choffnes, P. Gill, and A. Mislove, “Identifying traffic differentiation in mobile networks,” in Proceedings of the 2015 Internet Measurement Conference, Oct. 2015, pp. 239–251. [14] R. Narisetty and D. Gurkan, “Identification of network measurement challenges in openflow-based service chaining,” in 39th Annual IEEE Conference on Local Computer Networks Workshops, 2014, pp. 663– 670. [15] E. Balestrieri, L. De Vito, F. Lamonaca, F. Picariello, S. Rapuano, and I. Tudosa, “Research challenges in measurement for internet of things systems,” ACTA IMEKO, vol. 7, p. 82, 01 2019. [16] E. Karoly. (2000, Feb.) Qos performance validation in real life scenarios. [Online]. Available: http://archive.opengroup.org/tech/qos/ conference/q101/karoly.pdf [17] S. Molnar, P. Megyesi, and G. Szab ´ o, “How to validate traffic gen- ´ erators?” in 2013 IEEE International Conference on Communications Workshops (ICC), 2013, pp. 1340–1344. [18] G. Lu, Y. Chen, S. Birrer, F. E. Bustamante, C. Y. Cheung, and X. Li, “End-to-end inference of router packet forwarding priority,” in IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications, 2007, pp. 1784–1792. [19] R. Mahajan, M. Zhang, L. Poole, and V. Pai, “Uncovering performance differences among backbone isps with netdiff,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 205–218. [20] (2008, Jun.) Ookla, measuring and understanding broadband : Speed, quality and application. [Online]. Available: https://www.ookla.com/ docs/UnderstandingBroadbandMeasurement.pdf [21] V. S. Khandkar and M. K. Hanawal, “Detection of traffic discrimination in the internet,” in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), 2020, pp. 677–679. [22] Tcpreplay: Pcap editing and replay tools for *nix. [Online]. Available: https://github.com/appneta/tcpreplay [23] W. Cui, V. Paxson, N. Weaver, and R. Katz, “Protocol-independent adaptive replay of application dialog,” in 13th Annual Network and Distributed System Security Symposium (NDSS), Feb. 2006, pp. 27–27. [24] R. T. El-Maghraby, N. M. Abd Elazim, and A. M. Bahaa-Eldin, “A survey on deep packet inspection,” in 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017, pp. 188–197. [25] M. AlSabah, K. Bauer, and I. Goldberg, “Enhancing tor’s performance using real-time traffic classification,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 73–84. [Online]. Available: https://doi.org/10.1145/2382196.2382208 [26] Z. Hu, L. Zhu, J. Heidemann, A. Mankin, D. Wessels, and P. Hoffman, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Requests for Comments, IETF, RFC 7858, August 2018. [Online]. Available: https://tools.ietf.org/pdf/rfc8446.pdf [27] C. Zhou, C. Lin, and Z. Guo, “mdash: A markov decision-based rate adaptation approach for dynamic http streaming,” IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 738–751, Apr. 2016. [28] T. Joe, L. Eliot, M. Allison, K. Markku, O. Kumiko, S. Martin, E. Lars, M. Alexey, E. Wes, Z. Alexander, T. Brian, I. Jana, M. Allison, T. Michael, K. Eddie, and N. Yoshifumi. (2020, Sept) Service name and transport protocol port number registry. [Online]. Available: https://www.iana.org/assignments/ service-names-port-numbers/service-names-port-numbers.xhtml [29] G. Keinan. (2018, January) Optimizing enterprise networks through sd-avc (software define application visibility and control). [Online]. Available: https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2018/ pdf/BRKCRS-2502.pdf [30] A. Rao, J. Sherry, A. Legout, A. Krishnamurthy, W. Dabbous, and D. Choffnes, “Meddle: middleboxes for increased transparency and control of mobile traffic,” in CoNEXT Student Workshop, ACM, 12 2012, pp. 65–66. [31] R. Liu and X. Yu, “A survey on encrypted traffic identification,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, ser. CIAT 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 159–163. [Online]. Available: https://doi.org/10.1145/3444370.3444564 [32] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, ser. SIGCOMM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 130–144. [Online]. Available: https://doi.org/10.1145/3341302.3342092 [33] P. Srisuresh and K. Egevang, “Traditional IP Network Address Translator (Traditional NAT),” Internet Requests for Comments, IETF, RFC 3022, Jan 2001. [Online]. Available: https://tools.ietf.org/pdf/rfc3022.pdf This paper is available on arxiv under CC 4.0 license. Authors: (1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Authors: Authors: (1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Table of Links Abstract & Introduction Abstract & Introduction Related Work and Background Related Work and Background Challenges in TD Detection Measurement Setup Development Challenges in TD Detection Measurement Setup Development Case Study : Wehe - TD Detection Tool for Mobile Environment Case Study : Wehe - TD Detection Tool for Mobile Environment Shortcoming of Wehe on HTTPS Traffic Shortcoming of Wehe on HTTPS Traffic TD Detection of HTTPS Traffic TD Detection of HTTPS Traffic Conclusion & References Conclusion & References VII. CONCLUSION Net neutrality violation detection is a need of an hour. As many of the ISPs are also content providers these days, they compete with each other, which can lead to one deliberately discriminating the services of the other to gain market share. However, users should have the freedom to choose services as per their wishes. Our work considered various challenges in the detection of traffic discrimination in HTTPS traffic. As a case study, we validated Wehe, one of the latest tools available to detect traffic differentiation. The described challenges helped us divide the entire tool into multiple interdependent components and validate them independently. Our validation using commercial traffic shaper revealed that traffic in Wehe setup may not mimic the characteristics of HTTPS traffic accessed from the original servers. Hence, middle-boxes may not subject them to intended discrimination. Thus, Wehe may not detect discrimination of HTTPS traffic. Our new method that uses the appropriate SNI parameter value in the initial TLS handshake message overcomes this shortcoming. Hence our work provided a mechanism to detect a wide range of possible discriminations on the Internet. REFERENCES [1] T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, and E. P. Duarte, “Monitoring network neutrality: A survey on traffic differentiation detection,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 2486–2517, 2018. [2] V. Nguyen, D. Mohammed, M. Omar, and P. Dean, “Net neutrality around the globe: A survey,” in 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 480–488. [3] Ramneek, P. Hosein, W. Choi, and W. Seok, “Detecting network neutrality violations through packet loss statistics,” in 2015 17th AsiaPacific Network Operations and Management Symposium (APNOMS), 2015, pp. 404–407. [4] D. Li, F. Tian, M. Zhu, L. Wang, and L. Sun, “A novel framework for analysis of global network neutrality based on packet loss rate,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 297–304. [5] M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi, “Detecting bittorrent blocking,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Oct. 2008, pp. 3–8. [6] M. B. Tariq, M. Motiwala, N. Feamster, and M. Ammar, “Detecting network neutrality violations with causal inference,” in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, ser. CoNEXT ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 289–300. [Online]. Available: https://doi.org/10.1145/1658939.1658972 [7] R. Ravaioli, G. Urvoy-Keller, and C. Barakat, “Towards a general solution for detecting traffic differentiation at the internet access,” in 2015 27th International Teletraffic Congress, Sep. 2015, pp. 1–9. [8] Y. Zhang, Z. M. Mao, and M. Zhang, “Detecting traffic differentiation in backbone isps with netpolice,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ser. IMC ’09. New York, NY, USA: ACM, 2009, pp. 103–115. [Online]. Available: http://doi.acm.org/10.1145/1644893.1644905 [9] P. Kanuparthy and C. Dovrolis, “Shaperprobe: End-to-end detection of isp traffic shaping using active methods,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Nov. 2011, pp. 473–482. [10] U. Weinsberg, A. Soule, and L. Massoulie, “Inferring traffic shaping and policy parameters using end host measurements,” in Proceedings of IEEE INFOCOM, 2011. [11] M. Dischinger, M. Marcon, S. Guha, K. Gummadi, R. Mahajan, and S. Saroiu, “Glasnost: Enabling end users to detect traffic differentiation,” in 7th USENIX Conf. Networked Systems Design and Implementation, Apr. 2010, pp. 27–27. [12] V. Bashko, N. Melnikov, A. Sehgal, and J. Schonw ¨ alder, “Bonafide: A ¨ traffic shaping detection tool for mobile networks,” in 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), 2013, pp. 328–335. [13] A. Molavi Kakhki, A. Razaghpanah, A. Li, H. Koo, R. Golani, D. Choffnes, P. Gill, and A. Mislove, “Identifying traffic differentiation in mobile networks,” in Proceedings of the 2015 Internet Measurement Conference, Oct. 2015, pp. 239–251. [14] R. Narisetty and D. Gurkan, “Identification of network measurement challenges in openflow-based service chaining,” in 39th Annual IEEE Conference on Local Computer Networks Workshops, 2014, pp. 663– 670. [15] E. Balestrieri, L. De Vito, F. Lamonaca, F. Picariello, S. Rapuano, and I. Tudosa, “Research challenges in measurement for internet of things systems,” ACTA IMEKO, vol. 7, p. 82, 01 2019. [16] E. Karoly. (2000, Feb.) Qos performance validation in real life scenarios. [Online]. Available: http://archive.opengroup.org/tech/qos/ conference/q101/karoly.pdf [17] S. Molnar, P. Megyesi, and G. Szab ´ o, “How to validate traffic gen- ´ erators?” in 2013 IEEE International Conference on Communications Workshops (ICC), 2013, pp. 1340–1344. [18] G. Lu, Y. Chen, S. Birrer, F. E. Bustamante, C. Y. Cheung, and X. Li, “End-to-end inference of router packet forwarding priority,” in IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications, 2007, pp. 1784–1792. [19] R. Mahajan, M. Zhang, L. Poole, and V. Pai, “Uncovering performance differences among backbone isps with netdiff,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 205–218. [20] (2008, Jun.) Ookla, measuring and understanding broadband : Speed, quality and application. [Online]. Available: https://www.ookla.com/ docs/UnderstandingBroadbandMeasurement.pdf [21] V. S. Khandkar and M. K. Hanawal, “Detection of traffic discrimination in the internet,” in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), 2020, pp. 677–679. [22] Tcpreplay: Pcap editing and replay tools for *nix. [Online]. Available: https://github.com/appneta/tcpreplay [23] W. Cui, V. Paxson, N. Weaver, and R. Katz, “Protocol-independent adaptive replay of application dialog,” in 13th Annual Network and Distributed System Security Symposium (NDSS), Feb. 2006, pp. 27–27. [24] R. T. El-Maghraby, N. M. Abd Elazim, and A. M. Bahaa-Eldin, “A survey on deep packet inspection,” in 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017, pp. 188–197. [25] M. AlSabah, K. Bauer, and I. Goldberg, “Enhancing tor’s performance using real-time traffic classification,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 73–84. [Online]. Available: https://doi.org/10.1145/2382196.2382208 [26] Z. Hu, L. Zhu, J. Heidemann, A. Mankin, D. Wessels, and P. Hoffman, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Requests for Comments, IETF, RFC 7858, August 2018. [Online]. Available: https://tools.ietf.org/pdf/rfc8446.pdf [27] C. Zhou, C. Lin, and Z. Guo, “mdash: A markov decision-based rate adaptation approach for dynamic http streaming,” IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 738–751, Apr. 2016. [28] T. Joe, L. Eliot, M. Allison, K. Markku, O. Kumiko, S. Martin, E. Lars, M. Alexey, E. Wes, Z. Alexander, T. Brian, I. Jana, M. Allison, T. Michael, K. Eddie, and N. Yoshifumi. (2020, Sept) Service name and transport protocol port number registry. [Online]. Available: https://www.iana.org/assignments/ service-names-port-numbers/service-names-port-numbers.xhtml [29] G. Keinan. (2018, January) Optimizing enterprise networks through sd-avc (software define application visibility and control). [Online]. Available: https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2018/ pdf/BRKCRS-2502.pdf [30] A. Rao, J. Sherry, A. Legout, A. Krishnamurthy, W. Dabbous, and D. Choffnes, “Meddle: middleboxes for increased transparency and control of mobile traffic,” in CoNEXT Student Workshop, ACM, 12 2012, pp. 65–66. [31] R. Liu and X. Yu, “A survey on encrypted traffic identification,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, ser. CIAT 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 159–163. [Online]. Available: https://doi.org/10.1145/3444370.3444564 [32] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, ser. SIGCOMM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 130–144. [Online]. Available: https://doi.org/10.1145/3341302.3342092 [33] P. Srisuresh and K. Egevang, “Traditional IP Network Address Translator (Traditional NAT),” Internet Requests for Comments, IETF, RFC 3022, Jan 2001. [Online]. Available: https://tools.ietf.org/pdf/rfc3022.pdf This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Ensuring Fair Internet Access: Challenges and Solutions in Traffic Discrimination Detection

We’ve all been there: very politely asking the internet for a video of a cat engaging in some wacky antics. And then … the internet pauses, seeming to consider the request. What’s the problem here? It might be you (sorry). It might be the infrastructure you’re working with. It might be your internet provider. Let’s explore some causes and solutions together. What Is a “Good” Internet Speed? Step one is to conduct a so-called speed test. Jonathan Schwantes, senior policy counsel at Consumer Reports, who works on the organization’s , which is collecting consumers’ internet bills and speed test results as part of its advocacy work, suggests using a pair of tests, one from and another from . Fight for Fair Internet initiative Ookla MLab Each uses slightly different methods, so Schwantes recommends trying both to make sure they’re consistent. The tests show two important results: download speed (how quickly you’re receiving data from the internet) and upload speed (how quickly you’re sending data out to the internet), measured in megabits per second. A bit of data is a single unit of information, expressed in the binary code that computers can understand as either a one or a zero. A megabit is one million bits of data. If a speed test says your download speed is 100 megabits per second, your connection is pumping 100,000,000 ones and zeros into your device each second, and short kitty antics videos . should stream fine What qualifies as a “good” internet speed depends on what you’re using it for, however. If you have multiple users and need stable connections (think kids doing online schooling while you work in the next room), education advocacy group Common Sense Media recommends speeds of 200 mbps download and 10 mbps upload. Those recommended speeds can be high relative to available levels across much of the United States. For February of this year, Ookla median speeds in the U.S. for wired broadband connections at 146 mbps download and 20 mbps upload; speeds on mobile internet—63 mbps download and 9 upload—were far slower. pegged Amina Fazlullah, the organization’s director of equity policy, insists that stable video conferencing connections are crucial for things like virtual school. “What does it mean when a 7-year-old has an interruption?” Fazlullah said, noting that 25 mbps download and 3 mbps upload will technically work but likely with frequent service disruptions. “How long can you wait for them to get back up to speed? Will they be able to get back on track after those interruptions?” Netflix, on the other hand, a mere 15 mbps download speed for streaming content in 4K/Ultra HD. recommends What speed is available to you at home depends on the infrastructure available: the tubes through which your desired digital data flows. That infrastructure may seem intangible, but it exists both inside the walls of your home and in a corporeal series of tubes transmitting data across the globe. Understanding those factors, and how they work together, is crucial to understanding how the modern internet works—and how to get that cat video faster. Is the Slowdown Coming from Inside the House? When Schwantes gets a slow speed test result, his first suspicion is usually along the lines of “Something is up with my router.” If you’re reading this on a device connected to Wi-Fi, the first connection you have to the larger internet are your modem and wireless router or your modem/wireless router combo beaming out Wi-Fi to the immediate area. The first two things to try if your internet is slow are restart the router and get your device closer to the router. Like a computer, routers store information in their memory, and sometimes an error gets saved mistakenly. Restarting is a way to shake out the cobwebs. And Wi-Fi signals degrade the farther they travel. If distance is slowing your speed, consider your options: Your Wi-Fi router may offer the choice between a 2.4 GHz connection and a 5 GHz connection; 5 GHz offers faster speeds over shorter distances, and 2.4 GHz provides slower speeds over longer distances. The 2.4 GHz frequency can also get congested with traffic from other wireless networks in the area, so sometimes switching to 5 GHz will provide a less obstructed path for your cat video. If your device has an ethernet port, you can also plug directly into the router and avoid the Wi-Fi issue altogether. Around the start of the pandemic, Schwantes noticed his internet slowing down. In this case, the problem was him. He was using a decade-old wireless router. After replacing it, he said, his speeds improved dramatically. What’s Happening on the Other Side of the Wall? The biggest speed variances come from the different ways data flows through the tubes directly outside your home. Data can travel on different types of tubes—fiber optic, coaxial cable, and copper wire, and even on wireless cellular networks—each providing very different speeds for uploading and downloading. Sometimes the technology that leads to your home is too out of date to support faster speeds. The effects of this fall more on communities of color and poorer residents, an . AT&T, Verizon, Earthlink, and CenturyLink charged customers in 38 cities the same price for sluggish DSL connections as they did for zippy fiber connections. investigation by The Markup found In 36 of those cities, the worst deals tended to go to households located in neighborhoods that were lower-income, less-White, or historically redlined. Fiber optic cables consist of a core of ultra-thin glass surrounded by a reflective coating that transmits information as pulses of light at the speed of, well, light traveling through glass. The fastest internet connections available today are termed “fiber to the home” because the fiber optic cables begin where you live and run all the way to where they join the larger maze of fiber optic tubes making up the internet. (There are also systems called “fiber to the node” or “fiber to the curb,” where fiber runs to a connection hub somewhere in your neighborhood, and then the signal moves to slower wires the rest of the way.) If the internet is a road, fiber is like an expressway with five lanes in each direction and a 75 mph speed limit. Not everyone in the United States even has access to fiber internet. According to a study released in January by the Fiber Broadband Association, of U.S. homes have that option. less than half At the other end of the spectrum, copper wire is like a one-lane road where you can’t go above 25. Not only can Fiber Expressway get you home faster than Copper Lane, but it can also handle far more traffic without congestion, which is important because your data runs through the same tubes as those of your neighbors who use the same ISP. The longer you can stay on fiber before having to turn off to something that’s not fiber, the faster your connection. What is copper wire? DSL, which stands for Digital Subscriber Line, operates on the same copper wires that phone companies use for traditional telephone service—so-called landlines—albeit on a higher frequency range that doesn’t necessitate tying up a phone line the way dial-up service does. ( .) Kids, ask your parents Phone lines were built to transmit voice, which doesn’t require a whole lot of data, so they tend to be s‑l‑o‑w. DSL speeds are dependent on the distance from where they hook into the fiber connections leading to the internet’s backbone. They need to be boosted frequently to push data over longer distances. In the middle is cable internet, which is generally slower than fiber but can provide relatively high speeds. Copper wire is like a one-lane road where you can’t go above 25. Cable internet is piped through coaxial cables carrying signals on a copper conductor surrounded by layers of plastic and metal coating to shield it from interference (an improvement over DSL). Since the coaxial cables were originally developed to transmit cable TV, which is fairly data-intensive video and audio information, they can move a lot of data. Since cable TV is traditionally a one-way broadcast medium, cable connections generally have faster downloads than uploads. A relatively new entrant to the residential broadband market is fixed wireless. As the name implies, this type of connection doesn’t involve a tube running directly into your home. Getting a fixed wireless connection typically involves installing a receiver pulling in cellular service that is then converted to Wi-Fi. Fixed wireless home service across the country, but some services advertise speeds that can give cable connections a run for their money. isn’t universally available For remote locations, where it’s not feasible for ISPs to install any kind of wired connection between the internet’s fiber backbone and your home, satellite internet, which allows data to be transmitted up to a satellite and then beamed down to an antenna at your residence, is an option. Satellite internet is generally not as fast as fiber or cable internet. Ookla found the median speed for SpaceX’s Starlink, the fastest satellite internet available in the U.S., was around 100 mbps download and 13 mbps upload. Can I Just Switch to a Different Provider and Get Faster Internet? You can certainly give it a shot! However, a dearth of competition in the broadband market across the United States may make that tricky. “The U.S. market for internet service is dominated by just four companies: AT&T, Charter, Comcast, and Verizon,” reads the 2020 edition of the Open Technology Institute’s Cost of Connectivity about the state of the U.S. broadband market compared to that in other developed countries. report “In most cities, there were only, like, two providers, and there are large swaths of addresses that only had one provider,” said Claire Park, one of the report’s authors. A 2020 from the Institute for Local Self-Reliance found that 83.3 million Americans are only able to purchase broadband internet from a single provider. report PSA: If your household income is lower than , you may qualify for the (ACP), which was created by the 2021 infrastructure bill and provides a $30 monthly subsidy for internet service. these guidelines Affordable Connectivity Program That subsidy increases to $75 for folks living on tribal lands. If you’re already signed up for free and reduced lunch for your dependent at school, the , , , or a number of other public-benefit programs, you . Supplemental Nutrition Assistance Program Medicaid Supplemental Security Income likely qualify (But hurry: An analysis by the Institute for Local Self-Reliance suggests that the pool of money Congress allocated for the ACP as soon as 2024 if it is not replenished.) could run out Many ISPs have created their that would allow households that qualify for the ACP to effectively get monthly internet service for free when combined with the governmental benefit. own reduced-cost internet plans Even if you can afford your internet bill, you can still shop around to see if you can get faster and/or cheaper service. To check your options, enter your address into the to see which ISPs serve your address. FCC’s National Broadband Map What’s listed as available for every address isn’t always accurate, so after seeing your search results, go to each ISP’s website, search for your address and see what prices and speeds they offer you. Maybe, just maybe, you can get an upgrade that will get you that cat video a whole lot faster. Update This story has been updated to include the findings of an investigation by The Markup. Written by: Aaron Sankin Also published here Photo by on Pascal van de Vendel Unsplash

Reasons Why Your Internet Is Slow

Razones por las que su Internet es lento

Raisons pour lesquelles votre Internet est lent

कारण आपका इंटरनेट धीमा क्यों है

インターネットが遅い理由

Razões pelas quais sua Internet está lenta

Lý do tại sao Internet của bạn chậm

互联网速度慢的原因

Do you think 4g phones will work in 2022? Future of 4g mobiles in 2022- After the recent announcements of the 5g band. Many countries are already launching it and some are in the legal queues to get it in their hands. There is a question that also popping into our heads: what will happen to our 4g devices aka our current mobile phones. Is there a chance that our current 4g mobiles phones will be phased out? There was a time when 3g was a thing then 4g came and replaced it. After the release of 4g, suddenly 3g became outdated and new phones were released with 4g enabled chipsets. To experience 4g data speeds in the palm of our hands. 4G made our lives easy and very dependent on our mobile phones. Cuz we know that now YouTube can play full HD videos without buffering. Buying and selling on the internet is now getting common. Internet speeds greatly improved our lives and then IoT came in and this eco-system of devices started emerging. Like smart devices for the home, online teaching on zoom and office meetings on google meet and so many things. Now in 2022, the time of 5g is here sooner than we expected. The future is everchanging and needs are kept on growing. In the technology industry after the pandemic, we have seen a massive boost in our internet industry. We had to switch to digital and make it our new normal. From traditional to digital change was not easy but it made us realize. That is how far the technology has come from keypad phones to current time touch-sensitive smartphones. Following the launch of 4G in late 2010 in the mobile internet industry, something has changed. A revolution occurred, and high-speed internet became increasingly widespread and accessible. Every other country wants to be a part of the 4G revolution now that it has begun. Slowly other countries have also achieved this milestone. 4g phones were 10 times faster and more accurate than 3g phones. Soon after the release mobile companies also started releasing the latest smartphones with 4g connectivity. All of a sudden the mobile phone market also saw a big boom in its sales numbers. Even I remembered when I got my first 4g phone I was so excited to just test my internet speed on . speedtest.com 3G was also a big player back then and most commonly used after the broadband service. but in 3g phones, we see buffering issues in YouTube videos. If you ever wanted to upload a big file on the internet, broadband was the best choice to do so. After the release of  4G Download was much faster than  3G and streaming videos on the internet were buffer less. If we go technical and talk about the speed of 4G it was five times faster than 3G. On average a user was getting approx. 8 MBPS on speed test apps, meaning 1 MBPS pure download speed in real-time results. It was a good experience back then but now after a decade the internet is again getting better and moving towards a 5G band. 5G band on the other hand will be 10x times faster and will eventually replace 4G and the fast data needs of the user. In some countries, the testing of the 5G band is already done. They have released their version of 5g in major cities and townships. It will be a matter of time before 5g will become the new normal for us. With a continuous supply of 5g phones in the world. It was already a hint for many of us that the 5g will be here sooner than later. So now what will be the future of 4g phones? In my opinion, at present, the 4g phones are not going anywhere, not until this 5g becomes our new normal and every compatible device becomes available in the market at affordable prices. The market speculation says that at the end of 2022. we will begin to see the downfall of 4g phones and all the related products with 4g’s will be unavailable or become out of stock. In response, there will only be 5g products that are going to be manufactured. As we all know now the 3g is officially dead and the majority of people are shifted on 4g or 5g devices (depending on availability). Some of the major network carriers have already released the list for their phones which are going to work on 3g carriers. If your phone name is not in the list then it means AT&T will no longer going to provide you 3g service on that particular device or app. the 5g band is already announced and running in major cities with variable speeds**. in Q3 of 2021** conducted a test comparing the speeds of these countries to know which country has the fastest 5g. . In the US, China, Canada and many other 12+countries, Speedtest.com Surprisingly Oslo and Norway were the fastest in the 5g speed test and Cape Town was the slowest in the world For more details check the source- . OOKLA Is it necessary to have a 5G phone in 2022 or is now a good time to get one? If you’re in the market for a new phone in 2022, you should search for one that supports the majority of 5G carrier bands because you’re going to need it sooner or later. If we look at the mobile market right now, we can see that every mobile device manufacturer is producing 5g devices and attempting to provide all 5g supported bands. If we go back to when 4g was first released, 4g phones were in short supply and 3g phones were still selling well. However, the situation has altered, and mobile phone providers are now swift and astute. In response to these new developments, they have preserved the same price and are now giving us 5g devices at the same price. With a little bit of cost-cutting and replacing some parts with old technology, it doesn’t matter to the majority of consumers. These low-cost 5g gadgets are keeping the same price tag by giving us a lower resolution display in our new 5g device. The display will be changed from AMOLED to LCD, and the 5G band will be included. And if you want the very same gadget with a higher resolution or an AMOLED display, you’ll only get a 4g band, with no option for a 5g variant. Originally published . here

Do I Need A 5G Phone In 2022? 

Recently, SpaceX launched Starlink in Portugal. So, naturally, our CTO got one for Altar as soon as the pre-orders opened. A couple of weeks ago, the sleek satellite dish finally arrived. In no time at all, Claudio and a couple of devs from the team had unboxed it and set it up in the garden outside our offices. Fast forward to today, and he's had the opportunity to use Starlink, as well as run some tests. Here's the full rundown of those tests, in Claudio's words, starting with his initial impressions Starlink Unboxing & First Impressions Inside the large grey Starlink packaging, you’ll find: The Starlink, motorised, satellite dish – connected to a 30-meter (100 foot) Power-over-Ethernet (PoE) cable A metal tripod stand A power adaptor A small metal Wi-Fi Router with its own PoE cable. The initial set-up was incredibly simple: Click the dish into the tripod mount, plug the PoE cables from both the router and the dish into the power adaptor, plug it into the wall socket, download the app and you’re ready to go. The hardware is well designed, especially the metal Wi-Fi router, which looks like it’s been freshly carved out of a Cybertruck. The dish itself is plastic mounted on a metal pole. The PoE cable is permanently attached to the dish. I feel this last part is worth noting – without a way to easily detach the PoE cable from the dish, you could face having to replace the whole dish if the cable gets damaged. To run it out of your house you’re looking at either drilling a hole in the wall or permanently leaving a window open as the cable is quite thick (7mm) and includes an even thicker ferrite bead (19mm). The app is also well designed, with the consistent UX you would expect from a company like SpaceX. The seamless setup continues here with no login requirements – keeping the experience truly “plug and play”. This brings me to the crux of this article, the internet tests. Starlink Setup & Internet Tests To cut a long story short, Starlink offers a moderately fast, if somewhat inconsistent, broadband connection – under the right circumstances. I initially set up Starlink in the garden outside our offices. Despite the fact there were no obstructions directly above the dish, the garden is located in the centre of the building, and is surrounded on all sides. This was enough of an obstruction to mean that I wasn’t able to connect to the internet using Starlink (after 30 minutes of adjusting the dish positioning). It’s worth noting here, however, that Starlink’s value proposition is not for inner-city use. Rather, its goal is to bring high-speed internet to rural areas. So, I packed Starlink back up, popped it in the back of my car and headed home to run some further tests. I set Starlink up in my garden initially, 5 meters away from the house. Despite it being much more of an open space vs. the office, the height of my house (~7 meters) still obstructed the satellite dishes view. So, once I had run all the intended tests, I moved Starlink to an open field with no obstructions and repeated my tests. Aside from the placement of the dish (garden/obstructed & field/unobstructed) I kept all other aspects of the testing parameters the same. Here are the results: Testing Information SpaceX Services, Inc. ISP: 217.65.138.***. External IP address: Access to 1700 Starlink satellites orbiting at 550km of altitude, operating downlinks between 10.7Ghz and 29.1GHz. 2m Distance between Device (Android 11 Smartphone) and Access Point WiFi: -62dBm  (Good) RSSI: -80dBm (Good) Noise: using the Native Android Application. Method used: Ookla SpeedTest A Speed Test estimates the available capacity of the network between the client and the server. The throughput of the tests depends on the distance between the 2 endpoints (Round Trip Time), meaning the closest server will give the most accurate results. The Ookla app automatically chose my server. Per the Association for Computing Machinery (ACM) Recommendation, I executed the tests at randomly selected times throughout the day (including peak and offpeak time). Test #1: Throughput (Downstream, Upstream) Using the Native Ookla Application, I took 31 measurements at random intervals over two days against the fastest nearby server (automatically selected by the Ookla app). Here are the throughput results for both the obstructed and unobstructed conditions: Once I had completed the throughput tests, I moved on to test Latency & Timeouts. Test #2: Latency & Timeouts For this test, I carried out a continuous measurement over 260 mins. The goal here was to measure the number of timeouts that occur under both the obstructed and unobstructed scenarios. In the obstructed scenario (in my garden, 5 meters from my ~7 meters tall house) I observed 576 timeouts over the 260-minute test. Meaning, there was the chance of an outage every minute: In contrast, in the unobstructed scenario (open field) I observed 47 timeouts over the 260-minute test. Now, the chance of an outage had reduced to once every ~5 minutes: Key Takeaways There are no two ways about it, for optimal connection, where you position the dish is paramount. In an unobstructed scenario, I had no problem working using Starlink. I was able to keep a database connection most of the time, whilst streaming Spotify, downloading files and browsing the internet. In an obstructed scenario, however, it was almost unusable for work. There were simply too many outages. My connection was interrupted too often, my database connection was unstable and I often had to refresh web pages and wait before they would work. That being said, for normal smartphone usage, there were no noticeable differences between the obstructed and unobstructed scenarios. By “normal smartphone usage” I mean music/video streaming and using social media/chat apps). In terms of power consumption, Starlink runs at about 100W – although this can vary if the dishes motors are active. I haven’t observed any overheating or thermal issues with any of the kit parts and the dish seems to sit at a comfortable 23ºC – 25ºC when in use. Is Starlink a True Competitor to Rural Cable Broadband? In rural areas with a good internet infrastructure (basic fibre optic for example), Starlink can’t compete at this time. In areas where minimum cable broadband connections are unstable, however, is where Starlink starts to compete. The other bonus of Starlink is the fact it doesn’t come with a contract. Therefore, if you’re heading to a rural area temporarily (where a 24-month contract wouldn’t be worth it)  then it may be work installing a Starlink kit. After all, once you’ve bought the kit it’s yours, and although you can’t resell it, if you don’t end up using it, it could make a nice (if very expensive) coffee table. Wrapping Up Starlink is a remarkable feat of engineering and SpaceX’s ability to turn it into a consumer product is impressive. That being said, traditional cable companies won’t be overthrown by Starlink any time soon. Firstly, it carries a hefty price tag. The equipment costs €500 plus an added monthly service fee of €99 here in Portugal. It’s a lot to pay for, in the best-case scenario, the reasonable broadband speeds with inconsistent connectivity and large swings in latency that Starlink offers. Perhaps this will change as SpaceX launches more satellites. We may well see it work better when surrounded by tall trees or buildings, but that remains to be seen. However, I do believe that the traditional telecom companies should take note of Starlink. The execution of the product and the hype and excitement of a democratised internet connection should serve as a warning. A warning that people don’t want to be tied down to a 24-month contract just to get a decent internet connection – and that disruption is coming. This review was originally published here using  original data collected by our Chief Technology Officer, Cláudio.

Does Starlink Live Up to Its Hype? 

As communities worldwide grapple with the reality of an extended , Internet usage has, understandably, significantly increased. As more and more countries are forced into lock-down in an effort to curb the spread of the deadly virus, the growing number of people forced into working remotely and finding online entertainment is seeing the Internet absolutely explode. In fact COVID-19 has pushed up Internet use by a whopping 70 percent in some countries while streaming services are up by more than 12 percent, . COVID-19 induced lock-down figures last month revealed The Internet is getting overwhelmed. And, while Internet service providers (ISPs) like Comcast, Vodafone, Verizon and Telefónica have been building their networks for years to account for increased demand, they never anticipated it would happen quite so suddenly. Enrique Blanco, CTO at Telefónica, a Spanish telecommunications company, said that growth the industry had expected to take a year is “happening over days”, due to COVID-19. “Increased consumption of digital content from mobile apps to free TV streaming and gaming has already been observed in China and Italy,”a statement by Omdia, the tech research arm of Informa Tech, said. “Online streaming services provided by brands such as Netflix and now Disney+ are likely to see 12% growth, while Ecommerce will be the other sector that will see a revenue boost as a result of the pandemic, adding $175 billion in revenue in 2020, which represents a 5% increase.” But while growth is usually seen as a good thing, things in the digital space aren’t exactly golden. The Internet is buckling under the pressure of unprecedented heavy use, and some ISPs are . ISPs look after a number of very important Internet-related things including Internet access, Internet transit, domain name registration, web hosting, Usenet service, and colocation. In other words, ISPs are the guys tasked with making sure you can access the Internet whenever you like, as well as routing Internet traffic, resolving domain names, and maintaining the network infrastructure that makes Internet use possible. Without ISPs, there would be no Internet. But amidst staggering rates of Internet use, we are beginning to see service providers strain under the pressure. struggling to deal with the additional demand for connectivity In Italy, , Internet traffic increased by 109.3 percent over the roughly one-month period from February 20 to March 31, while download speed decreased by 35.4 percent. In India, the mean fixed broadband speed has been steadily declining since the beginning of lock-down too. one of the nation's worst hit by the global coronavirus pandemic "When networks are under usage strain like they are in this unprecedented time of lockdown in India due to COVID-19, it is natural that they experience some level of slowdown. It is important to note that while the Internet itself should handle elevated usage, there may be impacts to speed as people continue to move their daily activities increasingly online. While the core of the Internet remains stable, some ISP networks may struggle to keep up,” CEO of Ookla, a web service that provides free analysis of Internet access performance metrics, , said. As per the latest Ookla's Speedtest Global Index report, the mean download speeds on fixed broadband have decreased from 39.65 Mbps in February to 35.98 Mbps in March - a direct result of increased global Internet use. Doug Suttles A few weeks ago, five of Spain's major operators (including Orange and Vodafone) warned that services could potentially soon collapse amidst a 40 percent spike in traffic through IP networks and 50 percent hike in voice calls. At around the same time, Netflix recognised it couldn’t continue to keep up with the growing demands of viewers while not impacting other Internet capacities, and so in a bid to support the sudden growth in streaming. Apple TV+, Disney+ and Amazon have followed suit. it reduced its streaming bit rates Interestingly, ISPs are coping at varying levels depending on the location of traffic congestion. which looked at the Internet congestion of Australia's major cities from February up until March 30, exposed huge differences in the speed from city to-city. It found that Internet speed was strongest in Perth, while Canberra and Melbourne were experiencing the worst average Internet congestion, according to data supplied by Monash University. A recent study led by KASPR datahaus, The good news is there are . If Internet users are struggling to get speedy and efficient Internet, and they are approaching the end of a contract, it is possible to swap to a new provider free of charge. However doing that won’t necessarily guarantee enhanced quality of Internet speed. options in the ISP space The other good news is, current effects on Internet speed are entirely regional, and we have already seen the worst of it. Speeds are unlikely to decrease much more, as most places around the world have already been issued stay-at-home orders. In other words, the most extreme cases of speed degradation have already occurred.

As Internet Usage Spikes During COVID-19 Pandemic: How Are ISPs Holding Up?

The other day, I saw this TV ad from Airtel claiming it was ‘officially’ India’s fastest 4G network. The speed was tested on Ookla, whose speedtest.net (website and app) is widely used all over the world. The ad was obviously a dig by Airtel at its competitor, Jio. In fact, a complaint of misleading advertising has been filed by Jio against Airtel for this very ad. Since I use Jio and have a spare Airtel SIM, I decided to test the two networks for myself, on speedtest.net. And compare results on an alternate speed test site, fast.com. Here’s what I found. Airtel clocked 9.88Mbps on speedtest but only 3.1Mbps on fast.com. Jio clocked 7.1Mbps on both speedtest.net and on fast.com Okay, this was just a random test, done by an amateur. But that 9.88Mbps higher speed on speedtest.net for Airtel is a reminder of the old saying - there’s no smoke without a fire. Airtel (Android Mi4i): 9.88Mbps on speedtest.net and 3.1Mbps on fast.com Jio (iOS iPhone 6s+): 7.1 Mbps, on both speedtest.net and fast.com Skewed results This wasn’t the first time I had an issue with Ookla. In fact, I find that using speedtest.net usually raises questions, instead of answers. Take the time I installed ACT Fibernet at my house. I had signed up for their 40Mbps broadband plan (75GB cap at ₹1206/month). The engineer who was installing the system verified the speed for me with a check on Ookla’s speedtest.net which indicated a speed of 42.59Mbps. I tested it myself on my phone and that’s when I noticed that speedtest.net was doing the test on a server hosted by ‘ACT Fibernet.’ It seemed to me a case of ‘Accused’ and ‘Judge’ being the same person. So I quietly did a second test on fast.com, and found the speed to be 15Mbps. On seeing the result, the engineer got a bit defensive, and said he had never heard of fast.com. So I asked him if he heard of Netflix, and he nodded, and I informed him that fast.com belonged to Netflix. At this, he went all techy on me, and told me speeds vary according to distance of my house from the speedtest server, and fast.com must be far away. He also said download speed for a file will be a lot less than streaming speed, and the international norm is to rate speed on streaming. he talked about bytes and bits and kpbs and kilobits, all of which went over my head. What I did understand was speed test on speedtest.net depends on the server. So I tried changing to another nearby server at the same distance (3km from my house). This time, the speed dropped even lower to 6.64Mbps. The engineer gave me some new excuse. But speedtest.net had just lost a fan. The wide variance in my ISP’s speed when checked on different servers on speedtest.com and fast.com Officials question ‘Officially’ Going back to Airtel’s fastest network claim, there was another twist. TRAI (Telecom Regulatory Authority of India) said their own data showed that Jio’s speed is twice as fast as Airtel. In fact, the whole ‘officially India’s fastest 4G network’ sounds fishy as Ookla is not recognised by TRAI. The Advertising Standards Council of India (ASCI) has also found the Ookla ad to be misleading, and has asked Airtel to modify or withdraw the commercial by 11 April. Airtel has protested this, and ASCI has said they will give them a hearing. Which is where matters currently stand. Money talks So what’s the big deal? This is just marketing warfare, and all’s fair in love and war, right? Well, there’s a bit more to it, and that’s the bit that concerns me. While I was doing all this testing, Airtel quietly picked my pocket. The Airtel SIM that I was using is a prepaid one with ₹300 loaded on it as talktime. The speedtests quietly used up around 20Mb, before Airtel messaged me that my balance was down to ₹200. (Airtel charges extraordinary high rates for data used without a data pack, and your talktime balance can disappear in a matter of minutes). I quickly loaded a small 3G data pack of 120MB for ₹28. That 120MB too was wiped out in no time by the remaining speed tests. In five minutes, Airtel had swiped ₹128 from me. What about Jio? I have a ₹303 pack that gives me 1GB per day, every day for the next 3 months. If I cross the 1GB limit, my speed is throttled but I’m still not charged anything. All those speed tests cost me nothing extra. Just because most networks do what Airtel did, does not make it right. And then there’s Airtel’s crafty use of the ethically-challenged speedtest.net to fool customers into believing Airtel has the fastest network. As the old marketing saying goes, the customer is not a fool; she’s your wife. Having said that I don’t really care if Airtel is marginally faster than Jio, or vice versa. But what I do care is that I can’t let my guard down when I’m using Airtel. As they are likely to whack my undies if I don’t keep an eye on them. No such issues with Jio. I quietly removed the Airtel SIM from my phone. End of story.

Jio, Airtel and a fishy speed test

FTC v. Amazon Court Filing, retrieved on Sep 26, 2023, is part of . You can jump to any part in this filing . This is part 72 of 80. HackerNoon’s Legal PDF Series here COUNT XVI: VIOLATIONS OF OKLAHOMA STATE LAW 518. Plaintiff State of Oklahoma repeats and re-alleges and incorporates by reference each and every preceding paragraph and allegation of this Complaint as if fully set forth herein. 519. Amazon was at all times relevant hereto engaged in trade and commerce within the State of Oklahoma. 520. The acts alleged in the Complaint constitute violations of the Oklahoma Antitrust Reform Act, 79 O.S. §§ 201, et seq. (a) The acts alleged in the Complaint constitute unlawful monopolization of a part of trade or commerce in a relevant market within the State of Oklahoma pursuant to 79 O.S. § 203. 521. Plaintiff State of Oklahoma seeks relief under the Oklahoma Antitrust Reform Act, 79 O.S. §§ 201, et seq., including, without limitation, the following: (a) Injunctive and other equitable relief pursuant to 79 O.S. § 205; (b) Disgorgement and restitution pursuant to 79 O.S. § 205; (c) Costs and attorney’s fees pursuant to 79 O.S. § 205; and (d) Other remedies as the Court may deem appropriate under the facts and circumstances of the case. 522. The acts alleged in the Complaint also constitute violations of the Oklahoma Consumer Protection Act, 15 O.S. §§ 751, et seq. (a) Amazon is a person within the meaning of 15 O.S. § 752; (b) The acts alleged in the Complaint occurred in connection with consumer transactions within the meaning of 15 O.S. § 752; and (c) The acts alleged in the Complaint constitute unfair or deceptive trade practices, within the meaning of 15 O.S. § 752, and were committed in violation of 15 O.S. § 753. 523. Plaintiff State of Oklahoma seeks relief under the Oklahoma Consumer Protection Act, 15 O.S. §§ 751, et seq., including, without limitation, the following: (a) Injunctive and other equitable relief pursuant to 15 O.S. § 756.1; (b) Disgorgement and restitution pursuant to 79 O.S. § 756.1; (c) Costs and attorney’s fees pursuant to 15 O.S. § 761.1; and (d) Other remedies as the Court may deem appropriate under the facts and circumstances of the case. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 2:23-cv-01495 retrieved on October 2, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ftc.gov

Oklahoma Accuses Amazon of Breaching State Regulations

Table of Links Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 5. Acknowledgements and References APPENDIX A. Appendix APPENDIX A. APPENDIX A.1. The mantel coefficient does not strictly increase with the proliferation of life We investigated how changing the parameters of our model influenced the Mantel coefficient as a function of the ratio of terraformed planets. We find a few characteristic shapes of curves observed: flat, increasing then decreasing, and mostly increasing. A.1.1. Additional Parameters Mutation. Depending on the scenario, we include either a 0% or 10% chance of mutation for each element of the planet’s new composition, meaning that in the 10% scenario, with our length 10 composition vector, on average, 1 of the 10 elements is mutated during each terraformation event. Mutated elements are chosen from a continuous uniform random distribution of [0, 1], just like the initial compositions were chosen. Mutation is meant to represent the change in the kind of planetary observable characteristics which are compatible with life as life evolves over time. Number of origins of life (OoL). Simulations are initialized with either 1 or 10 OoL (Fig. A2). Compositions inherited from pre-terraformed planet. We vary the number of elements that the post-terraformed planet inherits from the pre-terraformed planet, n ∈ [0, 1, 2, 5], with the rest of the composition inherited from the incoming life. A.1.2. Flat curve A.1.3. Increasing then decreasing curve When simulations are initialized with a single OoL, the progression of planet terraformation over time leads to an increasing then decreasing shaped Mantel coefficient curve, where the Mantel coefficient peaks after some of the planets have been terraformed, and then decreases afterwards (Fig. A1, data in green). Because the Mantel coefficient is measuring the correlation between positional and compositional distance matrices of planets, this correlation will decrease if either distance matrix becomes too homogeneous. With a single OoL, this is exactly what begins to happen with the compositional distance matrix, with the decline especially pronounced in the scenario with perfect replication (Fig. A1B, green box). A.1.4. Mostly increasing curve Authors:
(1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp);
(2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 4. Summary and Discussion 5. Acknowledgements and References 5. Acknowledgements and References APPENDIX APPENDIX A. Appendix A. Appendix APPENDIX A. APPENDIX A.1. The mantel coefficient does not strictly increase with the proliferation of life A.1. The mantel coefficient does not strictly increase with the proliferation of life The mantel coefficient does not strictly increase with the proliferation of life We investigated how changing the parameters of our model influenced the Mantel coefficient as a function of the ratio of terraformed planets. We find a few characteristic shapes of curves observed: flat, increasing then decreasing, and mostly increasing. A.1.1. Additional Parameters Additional Parameters Mutation . Depending on the scenario, we include either a 0% or 10% chance of mutation for each element of the planet’s new composition, meaning that in the 10% scenario, with our length 10 composition vector, on average, 1 of the 10 elements is mutated during each terraformation event. Mutated elements are chosen from a continuous uniform random distribution of [0, 1], just like the initial compositions were chosen. Mutation is meant to represent the change in the kind of planetary observable characteristics which are compatible with life as life evolves over time. Mutation Number of origins of life (OoL). Simulations are initialized with either 1 or 10 OoL (Fig. A2). Number of origins of life (OoL). Compositions inherited from pre-terraformed planet. We vary the number of elements that the post-terraformed planet inherits from the pre-terraformed planet, n ∈ [0, 1, 2, 5], with the rest of the composition inherited from the incoming life. Compositions inherited from pre-terraformed planet. A.1.2. Flat curve Flat curve A.1.3. Increasing then decreasing curve Increasing then decreasing curve When simulations are initialized with a single OoL, the progression of planet terraformation over time leads to an increasing then decreasing shaped Mantel coefficient curve, where the Mantel coefficient peaks after some of the planets have been terraformed, and then decreases afterwards (Fig. A1, data in green). Because the Mantel coefficient is measuring the correlation between positional and compositional distance matrices of planets, this correlation will decrease if either distance matrix becomes too homogeneous. With a single OoL, this is exactly what begins to happen with the compositional distance matrix, with the decline especially pronounced in the scenario with perfect replication (Fig. A1B, green box). A.1.4. Mostly increasing curve Mostly increasing curve Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp); (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Authors: Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA ( hbs@elsi.jp ); hbs@elsi.jp (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan ( lana.sinapayen@gmail.com ). lana.sinapayen@gmail.com This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. available on arxiv

A Biosignature Based on Modeling Panspermia and Terraformation: Appendix

Table of Links Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 5. Acknowledgements and References APPENDIX A. Appendix 5. ACKNOWLEDGEMENTS The authors would like to thank Estelle Janin and Cole Mathis for encouraging and productive conversations, and for feedback on the manuscript. REFERENCES Affholder, A., Guyot, F., Sauterey, B., Ferri`ere, R., & Mazevet, S. 2021, Nature Astronomy, doi: 10.1038/s41550-021-01372-6 Balbi, A., & Grimaldi, C. 2020, Proceedings of the National Academy of Sciences, 117, 21031, doi: 10.1073/pnas.2007560117 Bartlett, S., Li, J., Gu, L., et al. 2022, Nature Astronomy, 6, 387, doi: 10.1038/s41550-021-01559-x Benner, S. A. 2010, Astrobiology, 10, 1021, doi: 10.1089/ast.2010.0524 Bich, L., & Green, S. 2018, Synthese, 195, 3919, doi: 10.1007/s11229-017-1397-9 Bixel, A., & Apai, D. 2021, The Astronomical Journal, 161, 228, doi: 10.3847/1538-3881/abe042 Carroll-Nellenback, J., Frank, A., Wright, J., & Scharf, C. 2019, The Astronomical Journal, 158, 117, doi: 10.3847/1538-3881/ab31a3 Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., et al. 2018, Astrobiology, 18, 709, doi: 10.1089/ast.2017.1737 Checlair, J. H., Villanueva, G. L., Hayworth, B. P. C., et al. 2021, The Astronomical Journal, 161, 150, doi: 10.3847/1538-3881/abdb36 Cleaves, H. J., Hystad, G., Prabhu, A., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2307149120, doi: 10.1073/pnas.2307149120 Cleland, C. E. 2012, Synthese, 185, 125, doi: 10.1007/s11229-011-9879-7 Cleland, C. E., & Chyba, C. F. 2002, Origins of Life and Evolution of the Biosphere, 32, 387, doi: 10.1023/A:1020503324273 Cockell, C. S. 2022, in New Frontiers in Astrobiology (Elsevier), 1–17, doi: 10.1016/B978-0-12-824162-2.00009-9 Gobat, R. 2021, The Astrophysical Journal, 16 Green, J., Hoehler, T., Neveu, M., et al. 2021, Nature, 598, 575, doi: 10.1038/s41586-021-03804-9 Grimaldi, C., Lingam, M., & Balbi, A. 2021, The Astronomical Journal, 162, 23, doi: 10.3847/1538-3881/abfe61 Hahsler, M., Piekenbrock, M., & Doran, D. 2019, Journal of Statistical Software, 91, 1, doi: 10.18637/jss.v091.i01 Harman, C. E., & Domagal-Goldman, S. 2018, in Handbook of Exoplanets, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 1–22, doi: 10.1007/978-3-319-30648-3 71-1 Janin, E. 2021, Astronomy & Geophysics, 62 Kim, H., Smith, H. B., Mathis, C., Raymond, J., & Walker, S. I. 2019, Science Advances, 5, eaau0149, doi: 10.1126/sciadv.aau0149 Kinney, D., & Kempes, C. 2022, Biology & Philosophy, 37, 22, doi: 10.1007/s10539-022-09859-w Kovacevic, A. B. 2022, arXiv:2202.07347 [astro-ph, q-bio]. http://arxiv.org/abs/2202.07347 Lenardic, A., Seales, J., & Covington, A. 2022, International Journal of Astrobiology, 1, doi: 10.1017/S1473550422000222 Lenardic, A., Seales, J., Moore, W. B., & Jellinek, A. M. 2023, Nature Astronomy, 1, doi: 10.1038/s41550-023-02031-8 Lin, H. W., & Loeb, A. 2015, The Astrophysical Journal, 810, L3, doi: 10.1088/2041-8205/810/1/L3 Lingam, M., Grimaldi, C., & Balbi, A. 2021, Monthly Notices of the Royal Astronomical Society, 509, 4365, doi: 10.1093/mnras/stab3108 Lynas, M., Houlton, B. Z., & Perry, S. 2021, Environmental Research Letters, 16, 114005, doi: 10.1088/1748-9326/ac2966 Mariscal, C., & Doolittle, W. F. 2020, Synthese, 197, 2975, doi: 10.1007/s11229-018-1852-2 Marshall, S. M., Mathis, C., Carrick, E., et al. 2021, Nature Communications, 12, 3033, doi: 10.1038/s41467-021-23258-x Mix, L. J. 2015, Astrobiology, 15, 15, doi: 10.1089/ast.2014.1191 Moore, W. B., Lenardic, A., Jellinek, A. M., et al. 2017, Nature Astronomy, 1, 1, doi: 10.1038/s41550-017-0043 Olejarz, J., Iwasa, Y., Knoll, A. H., & Nowak, M. A. 2021, Nature Communications, 12, 3985, doi: 10.1038/s41467-021-23286-7 Pacetti, E., Turrini, D., Schisano, E., et al. 2022, The Astrophysical Journal, 937, 36, doi: 10.3847/1538-4357/ac8b11 Rideout, J. R., Caporaso, G., Bolyen, E., et al. 2023, biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release, Zenodo, doi: 10.5281/zenodo.8209901 Satopaa, V., Albrecht, J., Irwin, D., & Raghavan, B. 2011, in 2011 31st International Conference on Distributed Computing Systems Workshops (Minneapolis, MN, USA: IEEE), 166–171, doi: 10.1109/ICDCSW.2011.20 Schwieterman, E. W., Meadows, V. S., Domagal-Goldman, S. D., et al. 2016, The Astrophysical Journal, 819, L13, doi: 10.3847/2041-8205/819/1/L13 Schwieterman, E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, Astrobiology, 18, 663, doi: 10.1089/ast.2017.1729 Smith, H. B., & Mathis, C. 2023, BioEssays, 2300050, doi: 10.1002/bies.202300050 Smith, H. H., Hyde, A. S., Simkus, D. N., et al. 2021, Life, 11, 498, doi: 10.3390/life11060498 Sol´e, R. V., & Munteanu, A. 2004, Europhysics Letters (EPL), 68, 170, doi: 10.1209/epl/i2004-10241-3 Tasker, E., Tan, J., Heng, K., et al. 2017, Nature Astronomy, 1, 0042, doi: 10.1038/s41550-017-0042 Tasker, E., Unterborn, C., Laneuville, M., et al., eds. 2020, Planetary diversity: rocky planet processes and their observational signatures, AAS-IOP astronomy (Bristol, UK: IOP Publishing) The scikit-bio development team. 2020, scikit-bio: A bioinformatics library for data scientists, students, and developers. http://scikit-bio.org Totani, T. 2023, International Journal of Astrobiology, 22, 347, doi: 10.1017/S147355042300006X Vickers, P., Cowie, C., Dick, S. J., et al. 2023, Astrobiology, ast.2022.0084, doi: 10.1089/ast.2022.0084 Walker, S. I., Bains, W., Cronin, L., et al. 2018, Astrobiology, 18, 779, doi: 10.1089/ast.2017.1738 Wong, M. L., Prabhu, A., Williams, J., Morrison, S. M., & Hazen, R. M. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007658, doi: 10.1029/2022JE007658 Wright, J. T., Mullan, B., Sigurdsson, S., & Povich, M. S. 2014, The Astrophysical Journal, 792, 26, doi: 10.1088/0004-637X/792/1/26 Authors:
(1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp);
(2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 4. Summary and Discussion 5. Acknowledgements and References 5. Acknowledgements and References APPENDIX APPENDIX A. Appendix A. Appendix 5. ACKNOWLEDGEMENTS The authors would like to thank Estelle Janin and Cole Mathis for encouraging and productive conversations, and for feedback on the manuscript. REFERENCES Affholder, A., Guyot, F., Sauterey, B., Ferri`ere, R., & Mazevet, S. 2021, Nature Astronomy, doi: 10.1038/s41550-021-01372-6 Balbi, A., & Grimaldi, C. 2020, Proceedings of the National Academy of Sciences, 117, 21031, doi: 10.1073/pnas.2007560117 Bartlett, S., Li, J., Gu, L., et al. 2022, Nature Astronomy, 6, 387, doi: 10.1038/s41550-021-01559-x Benner, S. A. 2010, Astrobiology, 10, 1021, doi: 10.1089/ast.2010.0524 Bich, L., & Green, S. 2018, Synthese, 195, 3919, doi: 10.1007/s11229-017-1397-9 Bixel, A., & Apai, D. 2021, The Astronomical Journal, 161, 228, doi: 10.3847/1538-3881/abe042 Carroll-Nellenback, J., Frank, A., Wright, J., & Scharf, C. 2019, The Astronomical Journal, 158, 117, doi: 10.3847/1538-3881/ab31a3 Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., et al. 2018, Astrobiology, 18, 709, doi: 10.1089/ast.2017.1737 Checlair, J. H., Villanueva, G. L., Hayworth, B. P. C., et al. 2021, The Astronomical Journal, 161, 150, doi: 10.3847/1538-3881/abdb36 Cleaves, H. J., Hystad, G., Prabhu, A., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2307149120, doi: 10.1073/pnas.2307149120 Cleland, C. E. 2012, Synthese, 185, 125, doi: 10.1007/s11229-011-9879-7 Cleland, C. E., & Chyba, C. F. 2002, Origins of Life and Evolution of the Biosphere, 32, 387, doi: 10.1023/A:1020503324273 Cockell, C. S. 2022, in New Frontiers in Astrobiology (Elsevier), 1–17, doi: 10.1016/B978-0-12-824162-2.00009-9 Gobat, R. 2021, The Astrophysical Journal, 16 Green, J., Hoehler, T., Neveu, M., et al. 2021, Nature, 598, 575, doi: 10.1038/s41586-021-03804-9 Grimaldi, C., Lingam, M., & Balbi, A. 2021, The Astronomical Journal, 162, 23, doi: 10.3847/1538-3881/abfe61 Hahsler, M., Piekenbrock, M., & Doran, D. 2019, Journal of Statistical Software, 91, 1, doi: 10.18637/jss.v091.i01 Harman, C. E., & Domagal-Goldman, S. 2018, in Handbook of Exoplanets, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 1–22, doi: 10.1007/978-3-319-30648-3 71-1 Janin, E. 2021, Astronomy & Geophysics, 62 Kim, H., Smith, H. B., Mathis, C., Raymond, J., & Walker, S. I. 2019, Science Advances, 5, eaau0149, doi: 10.1126/sciadv.aau0149 Kinney, D., & Kempes, C. 2022, Biology & Philosophy, 37, 22, doi: 10.1007/s10539-022-09859-w Kovacevic, A. B. 2022, arXiv:2202.07347 [astro-ph, q-bio]. http://arxiv.org/abs/2202.07347 Lenardic, A., Seales, J., & Covington, A. 2022, International Journal of Astrobiology, 1, doi: 10.1017/S1473550422000222 Lenardic, A., Seales, J., Moore, W. B., & Jellinek, A. M. 2023, Nature Astronomy, 1, doi: 10.1038/s41550-023-02031-8 Lin, H. W., & Loeb, A. 2015, The Astrophysical Journal, 810, L3, doi: 10.1088/2041-8205/810/1/L3 Lingam, M., Grimaldi, C., & Balbi, A. 2021, Monthly Notices of the Royal Astronomical Society, 509, 4365, doi: 10.1093/mnras/stab3108 Lynas, M., Houlton, B. Z., & Perry, S. 2021, Environmental Research Letters, 16, 114005, doi: 10.1088/1748-9326/ac2966 Mariscal, C., & Doolittle, W. F. 2020, Synthese, 197, 2975, doi: 10.1007/s11229-018-1852-2 Marshall, S. M., Mathis, C., Carrick, E., et al. 2021, Nature Communications, 12, 3033, doi: 10.1038/s41467-021-23258-x Mix, L. J. 2015, Astrobiology, 15, 15, doi: 10.1089/ast.2014.1191 Moore, W. B., Lenardic, A., Jellinek, A. M., et al. 2017, Nature Astronomy, 1, 1, doi: 10.1038/s41550-017-0043 Olejarz, J., Iwasa, Y., Knoll, A. H., & Nowak, M. A. 2021, Nature Communications, 12, 3985, doi: 10.1038/s41467-021-23286-7 Pacetti, E., Turrini, D., Schisano, E., et al. 2022, The Astrophysical Journal, 937, 36, doi: 10.3847/1538-4357/ac8b11 Rideout, J. R., Caporaso, G., Bolyen, E., et al. 2023, biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release, Zenodo, doi: 10.5281/zenodo.8209901 Satopaa, V., Albrecht, J., Irwin, D., & Raghavan, B. 2011, in 2011 31st International Conference on Distributed Computing Systems Workshops (Minneapolis, MN, USA: IEEE), 166–171, doi: 10.1109/ICDCSW.2011.20 Schwieterman, E. W., Meadows, V. S., Domagal-Goldman, S. D., et al. 2016, The Astrophysical Journal, 819, L13, doi: 10.3847/2041-8205/819/1/L13 Schwieterman, E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, Astrobiology, 18, 663, doi: 10.1089/ast.2017.1729 Smith, H. B., & Mathis, C. 2023, BioEssays, 2300050, doi: 10.1002/bies.202300050 Smith, H. H., Hyde, A. S., Simkus, D. N., et al. 2021, Life, 11, 498, doi: 10.3390/life11060498 Sol´e, R. V., & Munteanu, A. 2004, Europhysics Letters (EPL), 68, 170, doi: 10.1209/epl/i2004-10241-3 Tasker, E., Tan, J., Heng, K., et al. 2017, Nature Astronomy, 1, 0042, doi: 10.1038/s41550-017-0042 Tasker, E., Unterborn, C., Laneuville, M., et al., eds. 2020, Planetary diversity: rocky planet processes and their observational signatures, AAS-IOP astronomy (Bristol, UK: IOP Publishing) The scikit-bio development team. 2020, scikit-bio: A bioinformatics library for data scientists, students, and developers. http://scikit-bio.org Totani, T. 2023, International Journal of Astrobiology, 22, 347, doi: 10.1017/S147355042300006X Vickers, P., Cowie, C., Dick, S. J., et al. 2023, Astrobiology, ast.2022.0084, doi: 10.1089/ast.2022.0084 Walker, S. I., Bains, W., Cronin, L., et al. 2018, Astrobiology, 18, 779, doi: 10.1089/ast.2017.1738 Wong, M. L., Prabhu, A., Williams, J., Morrison, S. M., & Hazen, R. M. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007658, doi: 10.1029/2022JE007658 Wright, J. T., Mullan, B., Sigurdsson, S., & Povich, M. S. 2014, The Astrophysical Journal, 792, 26, doi: 10.1088/0004-637X/792/1/26 Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp); (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Authors: Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA ( hbs@elsi.jp ); hbs@elsi.jp (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan ( lana.sinapayen@gmail.com ). lana.sinapayen@gmail.com This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. available on arxiv

A Biosignature Based on Modeling Panspermia and Terraformation: References

Table of Links Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 5. Acknowledgements and References APPENDIX A. Appendix 4. SUMMARY AND DISCUSSION We showed how a positive Mantel correlation coefficient with a low p-value can be used to identify when a region of space contains life, under the postulates that life can spread between planetary systems and terraform planets. Going beyond (or below) a population-scale biosignature, we demonstrated how to select, from a population of planets, clusters which have high-likelihood to contain terraformed planets. More importantly, these selected clusters have a low-likelihood to contain non-terraformed planets. This is increasingly important as the field of astrobiology reconciles with unavoidable false positives for more traditional biosignatures. We assumed that the background non-terraformed distribution of planetary compositions is uncorrelated and drawn from a flat random distribution of compositions (a reasonable prior given the high sensitivity of planet formation to initial conditions of the planetary disk; (Tasker et al. 2020; Pacetti et al. 2022)). In the event that underlying abiotic planets are correlated in position and composition space (perhaps from some undiscovered astrophysical phenomena), then the value of the Mantel correlation coefficient considered to be a statistically significant biosignature would have to increase. It’s possible that if the underlying coefficient of non-terraformed planets were too high, the spread of life would actually increase the Mantel p-value, even early in the simulation. If planetary compositions are actually clustered around a few characteristic planet types, we could represent the initial non-terraformed planets as being drawn from a distribution punctuated with narrow Gaussians in composition space, and the results might be different yet. Our results underscore the importance of better understanding the baseline abiotic diversity of planetary populations, including better constraining which features of planet formation best predict planetary characteristics over time. Our model demonstrates that such information could be used to aid life detection in the absence of making progress on a theory of living systems. Our approach here also provides an alternative to specific chemical biosignatures at the level of planets, showing the promise of detecting anomalous features at the population-scale. Critically, these anomalous features can be explained by a model with simple hypotheses about what life does, rather than what life is, and is agnostic to our ignorance of living systems as they might exist outside of Earth. This is only an introduction to the kinds of questions which could be investigated with this technique. The basic idea of taking key features of life, and understanding how they might manifest on the scale of planets leads us to imagine properties of life that may manifest across scales, like “planetary phylogenies”—that is, heredities and lineages of planets—and how they could shed light on better understanding living systems generally. Such hypothetical characteristics of planets (and life) could be investigated with our of model. Perhaps one of the biggest limitations in our model is assuming we can reliably map observable characteristics of a planet to something as comprehensible and malleable as a vector of real numbers. For example, how the chemistry of the atmosphere, biosphere, geosphere, etc. could be reflected via the atmosphere of a planet, and what the mappings are from observed atmospheric properties (the planet’s “phenotype”) to the underlying chemistries that put constraints on the compatibility of planets (the planet’s “genotype”), and the kinds of life which can emerge on or coexist with a planet. This would further strengthen the realism of our agnostic approach with minimal assumptions, and in future work we would like to explore these ideas more. While we only walk through a simulation under a single set of parameters here, our goal was to identify a best possible case scenario for applying a technique where we assume life should be able to drive correlations in planetary position-composition space based on hallmarks of life like proliferation and environmental bi-directional feedback. Our model results show promise that life could be detected at the scale of a population of planets, using information from only ≈ 1000 (perhaps fewer) planetary atmospheres, even in the absence of any information about what kinds of planetary environments are most suitable to life, or without knowing anything about the origins of life, or the peculiarities of life’s metabolic outputs. We showed how our distributed biosignature can be further refined to detect specific clusters of terraformed planets in this space, even when they only comprise a few percent of all planets. Again, this approach does not require an independent way to “detect” life, like a smoking gun biosignature atmospheric gas. Instead this approach depends on two key assumptions about what life can do, and derives observable consequences directly from them, providing a statistical approach which can be refined by astronomical surveys, and therein lies its biggest promise. Authors:
(1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp);
(2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 4. Summary and Discussion 5. Acknowledgements and References 5. Acknowledgements and References APPENDIX APPENDIX A. Appendix A. Appendix 4. SUMMARY AND DISCUSSION We showed how a positive Mantel correlation coefficient with a low p-value can be used to identify when a region of space contains life, under the postulates that life can spread between planetary systems and terraform planets. Going beyond (or below) a population-scale biosignature, we demonstrated how to select, from a population of planets, clusters which have high-likelihood to contain terraformed planets. More importantly, these selected clusters have a low-likelihood to contain non -terraformed planets. This is increasingly important as the field of astrobiology reconciles with unavoidable false positives for more traditional biosignatures. non We assumed that the background non-terraformed distribution of planetary compositions is uncorrelated and drawn from a flat random distribution of compositions (a reasonable prior given the high sensitivity of planet formation to initial conditions of the planetary disk; (Tasker et al. 2020; Pacetti et al. 2022)). In the event that underlying abiotic planets are correlated in position and composition space (perhaps from some undiscovered astrophysical phenomena), then the value of the Mantel correlation coefficient considered to be a statistically significant biosignature would have to increase. It’s possible that if the underlying coefficient of non-terraformed planets were too high, the spread of life would actually increase the Mantel p-value, even early in the simulation. If planetary compositions are actually clustered around a few characteristic planet types, we could represent the initial non-terraformed planets as being drawn from a distribution punctuated with narrow Gaussians in composition space, and the results might be different yet. Our results underscore the importance of better understanding the baseline abiotic diversity of planetary populations, including better constraining which features of planet formation best predict planetary characteristics over time. Our model demonstrates that such information could be used to aid life detection in the absence of making progress on a theory of living systems. Our approach here also provides an alternative to specific chemical biosignatures at the level of planets, showing the promise of detecting anomalous features at the population-scale. Critically, these anomalous features can be explained by a model with simple hypotheses about what life does, rather than what life is, and is agnostic to our ignorance of living systems as they might exist outside of Earth. This is only an introduction to the kinds of questions which could be investigated with this technique. The basic idea of taking key features of life, and understanding how they might manifest on the scale of planets leads us to imagine properties of life that may manifest across scales, like “planetary phylogenies”—that is, heredities and lineages of planets—and how they could shed light on better understanding living systems generally. Such hypothetical characteristics of planets (and life) could be investigated with our of model. Perhaps one of the biggest limitations in our model is assuming we can reliably map observable characteristics of a planet to something as comprehensible and malleable as a vector of real numbers. For example, how the chemistry of the atmosphere, biosphere, geosphere, etc. could be reflected via the atmosphere of a planet, and what the mappings are from observed atmospheric properties (the planet’s “phenotype”) to the underlying chemistries that put constraints on the compatibility of planets (the planet’s “genotype”), and the kinds of life which can emerge on or coexist with a planet. This would further strengthen the realism of our agnostic approach with minimal assumptions, and in future work we would like to explore these ideas more. While we only walk through a simulation under a single set of parameters here, our goal was to identify a best possible case scenario for applying a technique where we assume life should be able to drive correlations in planetary position-composition space based on hallmarks of life like proliferation and environmental bi-directional feedback. Our model results show promise that life could be detected at the scale of a population of planets, using information from only ≈ 1000 (perhaps fewer) planetary atmospheres, even in the absence of any information about what kinds of planetary environments are most suitable to life, or without knowing anything about the origins of life, or the peculiarities of life’s metabolic outputs. We showed how our distributed biosignature can be further refined to detect specific clusters of terraformed planets in this space, even when they only comprise a few percent of all planets. Again, this approach does not require an independent way to “detect” life, like a smoking gun biosignature atmospheric gas. Instead this approach depends on two key assumptions about what life can do, and derives observable consequences directly from them, providing a statistical approach which can be refined by astronomical surveys, and therein lies its biggest promise. Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp); (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Authors: Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA ( hbs@elsi.jp ); hbs@elsi.jp (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan ( lana.sinapayen@gmail.com ). lana.sinapayen@gmail.com This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. available on arxiv

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