esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Tier Security Analysis of Official Car Management Apps for Android
## 5 Low-Level Static Analysis
### Outdated Software Components Analysis  
For both the desktop and mobile platforms, third-party components, say, libraries, comprise one of the cornerstones of modern software development. However, as already mentioned in Section 5.5, the benefit of reusing third-party code may be largely canceled out, if that code is buggy or outdated. This may augment the attack surface of the app by far and expose end-users to security and privacy risks stemming from those external software components. Indeed, the importance of updatability of such Libraries on the Android platform has been repeatedly pinpointed in the literature [105; 106]. Simply put, it has  
Figure 2: Number of vulnerable shared libraries per app. been shown that many Android apps do not update their third-party libraries, remaining vulnerable to a range of Common Vulnerabilities and Exposures (CVE). As mentioned earlier, to look closer into this issue under the perspective of the examined apps, we employed the Ostorlab tool. The outcomes of this type of analysis per app are summarised in Table 8. As observed from the table, nearly the two-thirds of the apps (19) make use of one at least outdated library. However, as already stated, such a shortcoming is tightly connected to one or more CVEs, meaning that the respective app is susceptible to publicly disclosed security flaws. Given the plethora of the involved CVEs, in the following, we succinctly describe such issues per shared library by just enumerating the relevant CVEs. The interested reader may in addition consult the respective CVE page in [107]. For brevity, we also omit references to very well-know libraries like the _Python_ one. Specifically, Table 8 reveals that more than half of the apps, 16 and 15, respectively, utilise at least one outdated version of the well-known _SQLite_[108] and _OpenSSL_[109] libraries. The first library, implements a lightweight, full-featured, SQL database engine, while the second provides an open-source implementation of the SSL and TLS protocols. With reference to the SQLite website ([[https://www.sqlite.org/cves.html](https://www.sqlite.org/cves.html)]([https://www.sqlite.org/cves.html](https://www.sqlite.org/cves.html)) (accessed on 20 February 2021)), obsolete versions of SQLite may be vulnerable to denial of service (DoS) attacks. Some pertinent CVEs to this case are CVE-2020-13632, CVE-2019-16168, and CVE-2019-19645. Even worse, several of them, say, CVE-2020-11655, CVE-2020-11656, and CVE-2019-19646 are classified as being of high or critical severity. With respect to OpenSSL, along with DoS-related CVEs, we observed 13 others of low, medium, and high severity; CVE-2020-1968, CVE-2019-1563, CVE-2015-4000 are of low severity, CVE-2018-0737, CVE-2018-0734, CVE-2018-0735, CVE-2018-12438, CVE-2016-0703, CVE-2016-2178, CVE-2016-0800, CVE-2016-2107, CVE-2015-3195 are of medium severity, and CVE-2016-2176 is of high severity. Needless to say that these kind of vulnerabilities can be potentially weaponised for breaking the encryption between the app and the server.
# A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
## 3 Fatigue Analysis of Reinforced Concrete Silos
### Fatigue Life Calculation  
To conduct fatigue analysis, the stress time curve obtained from the previous Abaqus analysis was first exported and then imported into Fe-safe for the fatigue life calculation. The process of conducting a fatigue life analysis using Fe-safe is shown in Figure 16. The fatigue properties of the steel reinforcement and concrete in the silo wall were defined according to the Seeger algorithm in Fe-safe.  
In engineering projects, different damage criteria are typically selected based on the material properties and intended applications to evaluate structural safety. The commonly used damage criteria include the maximum shear strain criterion, maximum principal stress criterion (it is believed that if the maximum principal stress in a material or structure reaches the strength limit of the material, failure will occur regardless of the magnitude of other stress components), and the Brown-Miller criterion (a method based on the stress intensity factors, assuming a power function relationship between the fatigue life \(N\) and stress intensity factor \(\Delta K\), as shown in Formula (7)) [19]. The maximum shear strain criterion is also known as the maximum shear stress theory or Tresca yield criterion. It assumes that the maximum shear stress is the cause of material yield. When the maximum shear stress at a certain point in the material reaches the shear stress limit during uniaxial tensile yield, the material experiences significant plastic deformation or yield at that point. The Brown-Miller criterion assumes that cracks originate in a specific plane where the maximum shear strain occurs, and that its life estimation for ductile metallic materials closely aligns with reality. Therefore, this paper adopts the Brown-Miller criterion for steel fatigue analysis. For concrete materials, the maximum shear strain criterion is chosen, combined with Morrow Mean Stress correction, which is a modified method for stress analysis that is designed to more accurately evaluate the behavior of materials under different loading conditions. When analyzing material stress, it considers the influence of the direction of the stress action plane on the material stress. This combination is used to evaluate damage under complex stress states more accurately.  
\[N=\frac{c}{t}\Delta K^{m} \tag{7}\]  
where \(c\) and \(m\) are experimental parameters; \(t\) is time.  
Figure 16: The process of Fe-safe fatigue life analysis.  
The minimum fatigue life of the silo wall concrete, calculated using Fe-safe, is approximately 3027 times, which translates to about 49.8 years. The fatigue life is illustrated in Figure 17. The fatigue life of the silo meets the designed service life standard of 50 years.
## 5 Addressing Concerns and Implications of AI Algorithms in Network Routing
### Ethical and Social Implications of AI Algorithms in Network Routing  
The use of AI algorithms in network routing raises several ethical and social implications that need to be carefully considered. As AI algorithms become increasingly autonomous and pervasive in network management, it is essential to address the potential consequences of their deployment.  
1. Data collection and storage: AI algorithms necessitate large volumes of data for learning and enhancement, raising concerns regarding data privacy and protection. Network operators must guarantee that data is gathered, stored, and utilized in ways that protect individual privacy and comply with data protection laws.
2. Biased decision-making: AI algorithms can reinforce biases that exist in the data used for their training, leading to discriminatory decision-making. Network operators must ensure that AI algorithms are designed to avoid biases and promote fairness. Fairness and Equity  
1. Fair resource allocation: AI algorithms may prioritize certain users or applications over others, leading to unfair resource allocation. Network operators must ensure that AI algorithms allocate resources fairly and equitably.
2. Accessibility and inclusivity: AI algorithms may not be accessible or usable by all individuals, particularly those with disabilities. Network operators must ensure that AI algorithms are designed to be accessible and inclusive. Accountability and Transparency  
1. Explainability: AI algorithms can be difficult to interpret, complicating the understanding of their decision-making process. Network operators need to ensure that these algorithms are designed to provide transparency and explainable decision-making processes.
2. Accountability: AI algorithms can make decisions that have significant consequences, and it is essential to establish error-handling and bias-mitigation frameworks. Workforce Reduction and Skills  
1. Workforce reduction: The automation of network management tasks may lead to job displacement, notably affecting individuals in entry-level positions. Network operators must ensure that they promote upskilling to help displaced workers adapt to evolving job markets.
2. Skills gap: The increasing reliance on AI algorithms may create a skills gap, particularly in areas such as AI innovation and implementation. Network operators must prioritize the development of skills that enable effective AI-driven network management. Policymaking and Regulation  
1. Regulatory Frameworks: Policymakers must establish regulatory frameworks that address the ethical and social implications of AI algorithms in network routing.
2. Industry Standards: It is crucial for industry stakeholders to establish guidelines for AI algorithm creation and implementation in network routing to ensure fairness, transparency, and accountability.
## 4 Results and Discussion
### MCC  
The value of MCC is calculated from all CM parameters. This has an advantage for our imbalanced data. As it can be seen in Table 5, all MCC coefficients are close to 1. That means that models' predictions are matching with actual results.  
The 600 system calls sequence repeatedly shows its importance--it improves MCC value by 13% in comparison with 400 system calls sequence (in case of the relatively best performing AWSCTD-CNN-S model).
## 4 Experiment Results
### Discussion  
There are still some limitations in this work, which could be improved. We observe that the brightness of the scenes affects the frequency of the interval of the spike dataset captured by the spike camera. Meanwhile, the inter-spike interval is utilized in spike distinction. Now, the brightness is not considered in our CSFR model. In the future, the region-based distinction with brightness guidance will be studied for further distinguishing the dynamic and static region. Furthermore, although our proposed CFSR framework supports flexible and adaptive ways to reconstruct images, the patch matching method utilized in fine-grained reconstruction spends most of the time in the CFSR framework, which partly restricts the efficiency of our CFSR framework. Therefore, we can further improve the efficiency of the high-speed scene reconstruction. Moreover, the image quality of the reconstruction in the static region by the CFSR method did not reach the image quality captured by the frame-based camera. Motivated by the successful applications of the attention fusion network [12] with the frames and events as well as the unifying framework that bridges the intensity images and neuromorphic event [13], the APS data captured from a frame-based camera as the guidance may also be introduced in the CFSR to further improve the performance of the reconstructed images. As the synthesized dataset is generated with only the camera's ego-motion, it is unnecessary to further distinguish the static and motion regions, and the fine-grained reconstruction is not required. Our proposed CFSR and CSR can be utilized in high-speed motion and video-based scenes reconstruction captured by a spike camera, respectively.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 8 Conclusions
### Limitations  
This review provides an initial overview on the subject matter and in future can be expanded upon with further research on the resource optimization algorithms. We selected to exclude works that do not present a concrete optimization algorithm; however it is well-known that many conceptual papers also provide a basis for solving resource management problems. It should also be emphasized that AI-ML solutions presented in the discussion subsections do not directly consider a converged radio and optical mmWave architecture; therefore the re-interpretation of their results might be costly for some specific optimization algorithms. Despite the limitations, the contributions of this article add value to the discussion with respect to integrating ML-based data analytics solutions with converged optical and mmWave radio networks, and motivate further research towards the autonomic resource management for 5G and beyond-5G performance optimization.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 3 MAC Address Randomization
### MAC Address Randomization Implementation  
MAC address randomization enhances user privacy by generating frames with temporary and disposable source MAC addresses, thereby concealing the device's global MAC address. By decoupling the MAC address from the user device, MAC randomization adds a layer of security, preventing the disclosure of user information through the global MAC address. As depicted in Figure 1, MAC randomization is achieved by setting the second most significant bit (MSB) in the first octet of the MAC address. The implementation of MAC address randomization by the major's OS vendors is as follows:  
* Apple Inc. from iOS 8, 2014 [29].
* Linux from Kernel version 3.18, 2014 [30].
* Google LLC from Android 6.0 Marshallow, 2015 [31].
* Microsoft Corporation from Windows 10, 2016 [32].  
Over time, MAC randomization techniques have experienced significant improvements. As of the writing of this article, each operating system (OS) has implemented its approach to MAC randomization. Windows 11, for instance, now has MAC randomization enabled by default and offers two randomization options. Users can either have a single random MAC address for connecting to all networks or utilize one random MAC address specific to a particular network. In the case of iOS devices, MAC randomization is enabled by default starting from iOS 14. The generated random MAC address remains associated with the SSID, even after reconnection. If the device forgets the SSID and reconnects, a new MAC address is generated for that specific connection. Similarly, Android devices running Android 10 or higher also have MAC randomization enabled by default. The random MAC address is linked to the SSID and persists even after reconnection. If the device forgets the SSID and reconnects, the same random MAC address is utilized to establish the connection. In Linux systems, a random MAC address is generated for each SSID. If the device forgets the network, Linux generates a new random MAC address when reconnecting to that network.  
Various studies focusing on MAC randomization have revealed inconsistencies in the schemes employed by different devices. Some devices adopt a fixed company identifier (CID) while randomizing the remaining 24 bits of the MAC address. Others choose to randomize the entire 48 bits while setting the local bit. In contrast, some devices even randomize the complete MAC address using the organizationally unique identifier (OUI), thereby giving the appearance of a global address when it is not the case [7]. During this research, MAC address randomization lacked a standardized approach. In addition to the various schemes, implementing MAC randomization has evolved over the years, showcasing varying deployment strategies across devices. Observations indicate that devices may employ randomization consistently, only in idle states, in a mixed manner using both global and local addresses, or not at all, with the specific approach depending on the device vendor [33].
## 4 Results and Discussion
### Objective 1: Results in Terms of Displacements
#### 4.3.2 Combined Normalized Axial Loads and Bending Moments  
For the case of combined axial loads and bending moments, the AISC [42] interaction equations represent a useful tool to make the comparison between the responses of the buildings with the two different columns since they relate the required strength (demand) and the available strength (capacity). For doubly symmetric steel members under compression and flexure with respect to the major axis, which is the case addressed in this study, these equations are as follows [42]:  
\[\text{for}\frac{P_{u}}{\phi_{c}P_{n}}\geq 0.2 \tag{7}\]  
\[\frac{P_{u}}{\phi_{c}P_{n}}+\frac{8}{9}\frac{M_{ux}}{\phi_{b}M_{ux}}\leq 1 \tag{8}\]  
\[\text{for}\frac{P_{u}}{\phi_{c}P_{n}}<0.2 \tag{9}\]  
\[\frac{P_{u}}{2\phi_{c}P_{n}}+\frac{M_{ux}}{\phi_{b}M_{nx}}\leq 1 \tag{10}\]  
where the following apply:  
\(P_{u}=\) required axial compressive strength  
\(\phi_{c}P_{n}=\) available axial compressive strength  
\(M_{ux}=\) required flexural strength with respect to the major axis  
\(\phi_{b}M_{nx}=\) available flexural strength with respect to the major axis  
To make the comparison, the \(R_{AM}\) parameter is used, which is defined as follows:  
\[R_{AM}=\frac{R_{CM}}{R_{CL}} \tag{11}\]  
where \(R_{CM}\) represents the combined axial load and bending moment demands on the medium columns according to Equation (8) or Equation (10); and \(R_{CL}\) represents the same, but deep columns are considered instead.  
The mean values of RAM are presented in Table 7 for all the models. For the case of the three-story building, the values tend to decrease monotonically as the seismic intensity increases, being larger than unity only for the first three values of Sa/g. The greater observed value is 1.23 indicating that, on an average basis, the combined normalized axial loads and bending moments are significantly reduced when deep columns are used. For the nine-story building, as for the three-story building, the mean values of RAM tend to decrease monotonically with the seismic intensity; however, unlike for the three-story model, the values are larger than unity in all cases, with the maximum values being 1.31.  
Figure 14: Mean values of R\({}_{\text{VN}}\), 20-story building: (a) NS direction, (b) EW direction.  
For the twenty-story building, unlike the three- and nine-story buildings, the values are slightly greater or slightly lower than unity, implying the same normalized combined response for the models with medium and deep columns. This behavior was observed before for other parameters (RDT) for the twenty-story building. The results are shown to be similar for the interior and exterior columns as well as for the NS and EW directions.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
###### Abstract  
This article proposes a chip antenna on millimeter-Waves. This antenna combined with TSMC 180 nm SiGe CMOS technology has the advantage of being small in size and is suitable for wireless communications. The multilayer architecture Horn antenna implemented on M4-M6 can meet both process reliability specifications and radiation performance. The results of the simulation show that the maximum gain is \(-4.2\) dBi. The return loss measurement results are almost consistent with the simulation results, and the bandwidth range is 177.4-183 GHz. This article first describes the antenna production process and measurement results, analyses the impact of the parameters on the antenna, and further compares it with other designs. The excellence of this article is that it proposes a design that solves the problem of large millimeter wave loss and successfully reduces the area. At the same time, this article can contribute to readers' future optimization and continued research directions, and at the same time contribute simulation and measurement trends to let readers understand the stability of CMOS chip antenna simulation and measurement.  
millimeter-Wave; antenna on chip; CMOS process +
Footnote †: journal: _et al._
## 5 Research Challenges and Future Directions
### Communication Range  
A passive wake-up radio is designed based on passive circuits [64], which harvests energy from the incoming RF signal in the wake-up message to turn itself on and wake-up the main radio. This result in high energy efficiency since idle listening is eliminated and the wake-up radio does not need to be powered by a battery. However, the RF energy transmission in the form of the wake-up signal has a limited range, which is a major challenge of the system. The communication range of the passive wake-up radios is about 3 m [64], which is less than the active ones. Active wake-up radio typically achieves ~200 m in range [123]. The wake-up range is increased to almost 7.5 m in another protocol [103] with ultralow power radio triggered circuit, which is still not long enough as the normal active range. To achieve a design with longer range, the wake-up transmitter needs to send the signal at high power values. Improving the receiver sensitivity also increases the wake-up range. Unfortunately, both these approaches will increase power consumption. An RFID-based passive WuR technique is proposed in [82] for ultralong range communication, which is able to achieve a range of 22 m, although it is not fully passive design. This long range is achieved by a special design architecture, which composed of MEMSIC Iris mote, an augmented UHF RFID tag, and UHF RFID energy harvester.  
Figure 11: Description of passive wake-up radio for asset tracking application.
# Constructing Enhanced Composite Solid-State Electrolytes with Sb/Nb Co-Doped LLZO and PVDF-HFP
###### Abstract  
Composite solid-state electrolytes are viewed as promising materials for solid-state lithium-ion batteries due to their combined advantages of inorganic solid-state electrolytes and solid-state polymer electrolytes. In this study, the solid electrolytes Li\({}_{6-7-x}\)La\({}_{3}\)Zr\({}_{17-y}\)Sb\({}_{0.3}\)Nb\({}_{x}\)O\({}_{12}\) (LLZSNO) with Sb and Nb co-doping were prepared by a high-temperature solid-phase method. Results indicate that Sb/Nb co-doping causes lattice deformation in LLZO and increases the lithium vacancy concentration and conductivity of LLZO. Then, with the co-doped LLZSNO as an inorganic filler, a composite solid electrolyte of polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) was prepared with a casting method. The obtained composite solid electrolyte exhibits a high ionic conductivity of 1.76 \(\times\) 10\({}^{-4}\) S/cm at room temperature, a wide electrochemical stable window of 5.2 V, and a lithium-ion transfer number of 0.32. The Li LiiFePO\({}_{4}\) coin battery with the composite solid electrolyte shows a high specific capacity of 161.2 mAh/g and a Coulombic efficiency close to 100% at 1 C. In addition, the symmetrical lithium battery Li iLi with the composite electrolyte could cycle stably for about 1500 h without failure at room temperature.  
Li\({}_{3}\)Zr\({}_{2}\)O\({}_{12}\); co-doping; composite solid-state electrolyte; ionic conductivity +
Footnote †: journal: Applied Sciences