esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Renewal and Revocation Protocols  
Our contribution is the design of secure key revocation and key renewal protocols, based on two key establishment protocols presented in Section 2. In this context, each node \(N\) has several keys that should be renewed: a symmetric shared key between \(N\) and \(S\) (\(K_{DH}(N,S)\)), a pair of public/secret keys (\(pk(N),sk(N)\)), the public key of the sink (\(pk(S)\)) and a symmetric network key (\(NK\)). We start by presenting the key revocation protocol (KR). Then, we present the protocol RSK for renewing symmetric keys (\(K_{DH}(N,S)\)) between a node \(N\) and the sink. Then, we propose four protocols to renew asymmetric keys of a node (RAKnk\({}_{a}\), RAKnk\({}_{b}\), RAKdh\({}_{a}\) and RAKdh\({}_{b}\)) using different cryptographic primitives. Finally, we propose a protocol to renew the network key (RNK) and two multihop shared key protocols (MSK\({}_{a}\) and MSK\({}_{b}\)) to establish via the sink a shared symmetric key between any two nodes of the network. In order to achieve mutual authentication of the sender and the receiver, we use nonces  
Figure 1: Join protocols. (**a**) DJS: direct join to the sink. Node \(I\) directly joins the network by communicating directly with the sink \(S\). (**b**) IJS: indirect join to the sink. Intermediate nodes between \(R\) and \(S\) forward messages without any encryption or decryption.  
in our protocols. Each time we reduce to the minimum the number of needed nonces. For example, in some cases, we use a fresh generated key as a nonce in order to minimize it. Moreover removing a single nonce or a part of a message in any of our protocols will create a flaw either in authentication or secrecy. All nonces play a crucial role in the mutual authentication in order to avoid several classic attacks, like man in the middle, replay or reflection attacks. Finally, we decide to use nonces instead of complex and time-consuming cryptographic primitives, like, for instance, signatures or MAC (message authentication codes).
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology  
The research methodology assesses the performance of interior residential building components by using a standardized performance scale, identifying failure mechanisms that impact performance and developing deterioration pattern curves to predict the life cycle expectancy in standard and various failure conditions. This approach is based on the methodology presented by [5; 6; 40], which estimates building component life expectancy, and predicts ongoing performance based on physical and visual conditions while providing a systematic survey of failures at a given age. Deterioration patterns are central to this methodology, as they reflect typical degradation mechanisms, enabling statistical data analysis to determine each component's predicted life cycle expectancy.  
The research focuses on characterizing the wear patterns of common lightweight partitions in residential construction in order to compare gypsum board partitions, gypsum block partitions, autoclaved aerated concrete partitions, and conventional hollow concrete block partitions.  
The main stages of the methodology are:  
* Systematic review of the physical and visual condition of components at different ages.
* Determination of the actual performance level of the components and the failure mechanisms that appear in them.
* Development of wear patterns for the statistical analysis of various deterioration paths.
* Prediction of component service life expectancy and performance level in standard service conditions.
* Quantification of the impact of failures on the component's predicted life cycle.
# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 4 Results and Discussion
### Upload Latency  
As seen in Figures 7 and 8 above, we once again notice several fluctuations in the time taken to upload each MB. These are artifacts of the latency variance over the total time taken. We also notice a similar disparity in Azure, with the upload time sharply rising after batch 80. Moreover, the fluctuations seen in Figure 7 are rather large, indicating an inconsistent nature of the latency for AWS. On the other hand, as shown in Figure 8, the local fluctuations are smaller, but the large discrepancy at the start and end of the upload leads to a large variance in the latency times.  
As seen in Table 3 above, the average and median upload time for AWS is far lower and has a much lower standard deviation (four times lower) than Azure. The high variance for Azure is primarily due to the increase in times after batch 80.  
Figure 8: Azure upload latency.  
Figure 7: AWS upload latency.  
Clearly, as in Figure 9, AWS has a far lower average upload time and a much more stable latency over the period of the upload compared to Azure. AWS provides a stable and fast connection to nonlocal locations compared to Azure. The increase in upload times after batch 80 may be because the Azure rate limited the uploads due to multiple requests through the Python SDK. We can statistically compare the distribution of the normalized upload times as in the boxplot of the upload times per platform shown in Figure 10 below.  
Figure 10: Boxplot comparison of the distribution of upload times per platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Average Upload Time (s/MB)** & **Median Upload Time (s/MB)** \\ \hline AWS & \(1.84\pm 0.12\) & 1.82 \\ Azure & \(3.26\pm 0.68\) & 2.85 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of upload times for AWS and Azure platform.  
Figure 9: Normalized upload time per batch for both platforms.  
Clearly, as shown in Figure 10, AWS has a lower upload time overall and has very few outliers, which are still below the minimum of the Azure batch upload time. Next, we perform the Kruskal-Wallis test to statistically determine if the distributions are different. We obtain a statistic of 171.753 and a \(p\)-value of 0.000 \(<\) 5%, allowing us to reject the null hypothesis and conclude that the distributions are actually statistically different and AWS does indeed have a lower upload time compared to Azure.
## 2 Related Work
Since these systems perform computationally intensive algorithms and manipulate confidential information such as passwords, the minimization of detection latency is very important. [PERSON] et al., in 2022, presented a firewall based on the Advanced eXtensible Interface (AXI) for System-on-Chip security [40]. The objective was to design a system to detect attacks against microprocessors and the designed firewall was located on an AXI bus to protect an AXI slave device from the attacks of a fraudulent AXI master, such as accessing non-allowed registers or carrying out a denial of service (DoS) attack. The proposed AXI firewall was implemented on a Xilinx Zync7 FPGA device. Experimental results showed that the system can efficiently work at a maximum frequency of 166 MHz and, due to the implementation of the AXI to AXI path with combinatorial logic, it can achieve a latency of zero clock cycles. [PERSON] and [PERSON], in 2022, proposed a novel switching method for multicomponent communication architectures on FPGA systems on chips to enable safe and secure bus access and minimize the impact on performance and resource usage [41]. The proposed technique was implemented on the Zynq Ultrascale+ (ZCU102) and Zynq Z-7020 (PYNQ) platforms and the results showed the feasibility of detecting a wide range of software attacks with significant benefits in terms of low resource usage and processing latency minimization.
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 4 Comparison of Analytical and Numerical Results
### Steel Beam Height of 600 mm  
Figure 21 shows the comparison of analytically calculated results, for different approaches with numerically obtained bending capacities, of a steel beam height of 600 mm. In order to avoid the influence of spot welds, all steel elements are tied.  
Figure 21: Comparison of analytical and numerical results of a steel beam height of 600 mm: (**a**) full shear connection; (**b**) shear connectors-in pairs; (**c**) shear connectors in a staggered arrangement.  
Figure 20: Comparison of analytical and numerical results of a steel beam height of 500 mm: (**a**) full shear connection; (**b**) shear connectors in pairs; (**c**) shear connectors in a staggered arrangement.  
After observing the results shown in Figure 21a, it is evident that the values of the calculated characteristic bending resistances are reached using numerically computed capacities for full shear connections in cases when the shear connectors are in a staggered arrangement. In cases when shear connectors are positioned in pairs, Figure 21b shows that the value of the bending resistance calculated with \(\mathrm{k_{t}}\) is similar to the bending capacity calculated using the numerical model; however, the value of the bending resistance without \(\mathrm{k_{t}}\) was not reached. The values of the bending resistances calculated in accordance with non-linear elastic resistance are reached in cases of both partial shear connections in pairs and in staggered arrangements. In the case of staggered arrangements, as shown in Figure 21c, the value of the maximum bending capacity in the numerical model is the most similar to the analytical values calculated without \(\mathrm{k_{t}}\).
## 2 System Model
### Energy Harvesting Phase  
In this phase, node B uses all of the antennas to support the energy for the source and the relays. Then, the energy harvested by the source and the relay \(\mathrm{R}_{m}\) can be given, respectively, by (see [15])  
\[Q_{\mathrm{S}}=\eta\alpha\mathrm{T}P_{\mathrm{B}}\sum\limits_{k=1}^{K}\gamma_{ \mathrm{B}_{k}\mathrm{S}}, \tag{5}\]  
\[Q_{\mathrm{R}_{m}}=\eta\alpha\mathrm{T}P_{\mathrm{B}}\sum\limits_{k=1}^{K} \gamma_{\mathrm{B}_{k}\mathrm{R}_{m}}, \tag{6}\]  
where \(P_{\mathrm{B}}\) is the transmit power of B, and \(\eta\) is the energy conversion efficiency at S and \(\mathrm{R}_{m}\), \(\alpha\mathrm{T}\) is time used for the EH process, \(\sum\limits_{k=1}^{K}\gamma_{\mathrm{B}_{k}\mathrm{S}}\) and \(\sum\limits_{k=1}^{K}\gamma_{\mathrm{B}_{k}\mathrm{R}_{m}}\) are channel gains of the EH links, i.e., \(\mathrm{B}_{k}\to\mathrm{S}\) and \(\mathrm{B}_{k}\to\mathrm{R}_{m}\) links, respectively..  
From Equations (5) and (6), the average transmit power that the nodes S and \(R_{m}\) can utilize is formulated, respectively, by  
\[\mathrm{E}_{\mathrm{S}}=\frac{Q_{\mathrm{S}}}{(1-\alpha)\mathrm{T}/2}=\kappa P _{\mathrm{B}}X_{0}^{\mathrm{sum}}, \tag{7}\]  
\[\mathrm{E}_{\mathrm{R}_{m}}=\frac{Q_{\mathrm{R}}}{(1-\alpha)\mathrm{T}/2}= \kappa P_{\mathrm{B}}X_{m}^{\mathrm{sum}}, \tag{8}\]  
where  
\[\kappa=\frac{2\eta\alpha}{1-\alpha},X_{0}^{\mathrm{sum}}=\sum\limits_{k=1}^{K }\gamma_{\mathrm{B}_{k}\mathrm{S}},X_{m}^{\mathrm{sum}}=\sum\limits_{k=1}^{K} \gamma_{\mathrm{B}_{k}\mathrm{R}_{m}}. \tag{9}\]
## 3 Results
### Dural Sac  
The dural sac had an average area of 4.83 mm\({}^{2}\) (\(\pm\)2.1) between vertebral segments L1 and L7. The minimum recorded value was 1.11 mm\({}^{2}\) and the maximum value was 10.05 mm\({}^{2}\). The dural sac in the L7 verteb had the lowest value with a 1.22 mm\({}^{2}\) (\(\pm\)0.1) area, and the L4 verteb had the highest average with a 7.42 mm\({}^{2}\) (\(\pm\)1.6) area (Table 1,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Morphometric Measures**} & \multicolumn{6}{c}{**Vertebral Segment**} \\ \cline{2-7}  & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7** \\ \hline Dural sac & 59 (47.7–67.4) & 61.3 (49.1–69.4) & 68.8 (58.8–76.6) & 72.2 (62.6–77.8) & 52.6 (46.8–75.3) & 20.4 (17–21.2) & 13.8 (11.3–15.7) \\ IVVP & 8.9 (6.4–11.8) & 8.3 (5.5–10.3) & 7.1 (4.8–8.7) & 6.3 (4.8–8.8) & 7.4 (5.3–10.8) & 7.7 (4.5–9.5) & 7.9 (4.5–11.2) \\ Epidural space & 41 (32.6–52.3) & 38.7 (30.6–50.9) & 31.2 (23.4–41.2) & 27.8 (20.8–37.4) & 47.4 (24.7–58.9) & 79.6 (75.6–83) & 86.2 (84.3–88.7) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average percentage (%), minimum and maximum of the vertebral canal corresponding to the dural sac, internal vertebral venous plexus (IVVP), and epidural space between vertebrae L1 and L7 in six adult dogs.  
Figure 5: Area dimension of the internal vertebral venous plexus (IVVP) between L1 and L7 vertebral segment in six adult medium size dogs.  
Figure 6: Area dimension of the vertebral canal between L1 and L7 vertebral segment in six adult medium size dogs.  
Figure 7). The dural sac occupied between 13.8% and 72.2% of the vertebral canal between the L1 and L7 vertebral segments in these dogs (Table 2). It is also possible to appreciate that there is an increase in the occupancy rate toward L4, where it reaches its maximum value, and then it decreases toward L7, where the lowest average value is recorded.
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 5 Influence of Structure Parameters on Torque Density
### Circumferential Rotor Teeth Length  
Another influencing factor of the torque density is the circumferential rotor teeth length, which influences the modulated field distribution of the armature current field. In order to analyze the circumferential rotor teeth length more conveniently, a rotor teeth ratio is introduced here:  
\[k_{\text{rt}}=\frac{l_{\text{rt}}}{\tau_{\text{m}}}. \tag{26}\]  
The rotor teeth ratio \(k_{\text{rt}}\) is analyzed as the unique variable by 3D FEM, and the results are shown in Figure 14. From Figure 14, the maximum average torque is generated when the rotor teeth ratio is in the range of 0.6-0.7, and the minimum torque ripple is 0.9% when the ratio is 0.6.  
In addition, the overload capability of the motor is influenced by the circumferential rotor teeth length. If the circumferential rotor teeth length is small, the core will become more saturated when the motor works under overload conditions. Thus, a larger rotor teeth ratio should be chosen when the overload capability is considered.
# Influence of Fastener Failure on Dynamic Performance of Subway Vehicle
## 2 Model of Vehicle-Structure System  
### System Model  
#### 2.1.1 Model of Track  
The subway vehicle will have an impact on the track system during subway line operation, and long-term operation may cause track structure fatigue damage. The dynamic deformation of the track system is similar to a type of dynamic track irregularity, which will affect the vehicle's dynamic response. Excessive deformation can cause passenger discomfort and even jeopardize vehicle operation safety. They are usually considered together in a comprehensive analysis of the vehicle and track system, and a system model is established for analysis; that is, the coupling effect between the vehicle and track system is considered.  
Soil layer-shield segment, structure-track, and plate-rail make up the majority of today's subway track structures. The FEM can be used for modeling in the numerical simulation, with the track plate and rail simulated by plate and beam elements, respectively. A spring stiffness viscous damping element could be used to simulate the fastener between the track plate and rail. When considering fastener failure, both stiffness and damping are assumed to be zero, as shown in Figure 1. The dynamic equation of a subway structure can be calculated using the dynamic equation as following:  
\[\mathbf{M}_{t}\ddot{\mathbf{X}}_{t}+\mathbf{C}_{t}\dot{\mathbf{X}}_{t}+ \mathbf{K}_{t}\mathbf{X}_{t}=\mathbf{F}_{t} \tag{1}\]  
where \(\mathbf{M}_{t}\), \(\mathbf{C}_{t}\) and \(\mathbf{K}_{t}\) denote mass, damping and stiffness matrix of subway track structure, respectively; \(\ddot{\mathbf{X}}_{t}\), \(\dot{\mathbf{X}}_{t}\), and \(\mathbf{X}_{t}\) denote acceleration, velocity, and displacement vector, respectively. \(\mathbf{F}_{t}\) denotes force vector, and it mainly comes from the wheel-rail force of the vehicle. The mass matrix \(\mathbf{M}_{t}\) of the track system will not be changed if some fasteners are damaged. The stiffness matrix \(\mathbf{K}_{t}\) will be changed if a fastener spring fails. The damping matrix includes structural damping and material viscous damping, where structural damping is assumed to be the proportional damping of stiffness and mass, and Rayleigh damping is used here, and the material viscous damping includes fastener viscous, track interlayer member viscous, and other material viscous damping. As a result, if the fastener fails, the track's damping \(\mathbf{C}_{t}\) will alter proportionately.
# Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles
###### Abstract
The angular velocity is a function of the angular velocity of the virtual leader, orientation error, and the position error along the axle of the robot wheels, transformed by a non-linear function and activated periodically by a square persistently exciting function. The stability proof is based on the decomposition of the error dynamics into position and orientation subsystems that are ISS (input to state stable). Simulations confirm the effective error reduction for stadium-circuit shape trajectories composed of two straight lines and two half circumferences. The collision avoidance was not taken into account. Collision avoidance with other robots and static obstacles was added in [8]. The position errors were replaced by correction variables, which combine the position errors and the gradients of APFs surrounding obstacles. When robots are outside of collision  
Figure 1: Cleaning robot. avoidance regions of other robots/obstacles (APF takes the value zero) the analysis given in [24] is actual. For the other cases, stability analysis based on the Lyapunov-like function was presented. Numerical verification was performed on a formation of fifteen robots in the environment with five obstacles. The desired trajectory was composed of straight lines and arcs. The trajectory tracking algorithm proposed in [8] was based on [24]. In its original form, the collision avoidance was not included. It was assumed that the initial positions of robots guarantee that no collisions would occur. The extension proposed in [24] removes this limitation. The method guarantees the stability of the closed-loop system and collisions avoidance. A tracking method with similar computational complexity was proposed in work [25]. It does not involve a persistent excitation block. The stability of the system was also proven. Another method that was considered is the VFO (vector field orientation) algorithm [26]. This approach uses a special vector field and an auxiliary orientation variable. Stability was also proven for it, but its implementation on a real robot is more complex. In [27], this approach was extended by collision avoidance capability. There is tracking and collision avoidance for the multiple robots, but it takes into account the robot's dynamics (mass, inertia) and is very complex to implement [20]. Section 2 shows the mechanical design and electronics parts of the robot. The control algorithm is presented in Section 3. Section 4 describes the methodology, and the experiment results are presented in Section 5.
# Is Malware Detection Needed for Android TV?
## 3 Methods
### Package Analysis for Labeling  
In order to label the application packages in the collection as malicious or benign, we used the security analysis reports of the antivirus vendors on the VirusTotal website, which contained more than 70 scanners for file and URL analyses. We developed a Python (v.3.12.3) script using VirusTotal's API to upload the application packages as a file and to obtain their analysis results. For each application package, our script counted the number of antivirus scanners that categorized the given file as malicious. In order to eliminate false detections, we decided to label an application package as malware if it was categorized as malicious by at least 3 antivirus scanners. Otherwise, we labeled the application package as benign.  
Figure 2 shows the analysis results from the VirusTotal site for a benign TV application. According to the analysis report obtained for the benign application, the application package is not classified as harmful by any antivirus scanner. Figure 3 shows the analysis results obtained from the VirusTotal site for a malicious TV application. The malicious application package is classified as malicious by 16 antivirus scanners.  
We observed that out of 1107 Android TV applications collected from the Androzoo [20] dataset, only 3 of them were labeled as malicious based on the analysis results. These 3 application packages were categorized as malicious by more than 10 antivirus scanners on the VirusTotal website. According to the analysis results for the remaining applications, we observed that 34 of them were categorized as malicious by at most 2 antivirus scanners, and the rest of them were not categorized as malicious by any antivirus scanner. We concluded that a threshold value of 3 was acceptable, as there was a significant range in the number of antivirus scanners for malicious applications. As a result, if an application was categorized as malicious by fewer than 3 antivirus scanners, we assumed that it was a false detection by these scanners.  
Similarly, we observed that all of the applications collected from APK Mirror were labeled as benign based on the analysis results. When we investigated the analysis reports, we observed that only 8 application packages were categorized as malicious by at most 1 antivirus scanner, and we assumed these to be false detections. The other application packages were not categorized as malicious by any antivirus scanner. As a result, we could not obtain any malware for Android TVs from the APK Mirror market.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset Name** & **Minimum File Size** & **Maximum File Size** & **Average File Size** \\ \hline Androzoo & 23.5 kB & 145.2 MB & 26.4 MB \\ \hline APK Mirror & 3.2 kB & 191.1 MB & 28.2 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: File size statistics of the TV application packages in the datasets.
## 3 Proposed SPMIPv6 Protocol
### SPMIPv6 Protocol Architecture
#### 3.1.2 Smag  
The SMAG acts like a sink node in a traditional sensor network. In SPMIPv6 it acts like an access gateway router with a main function of detecting sensor node movement and initiating mobility-related signaling with the sensor node's SLMA on behalf of the sensor node. It can move with its member sensor node as a small IP-WSN domain similar to the body sensor network of a patient. It consists of different functional module such as routing, neighbor discovery, sensor information table, adaptation module and interfacing module to the sensor node and SLMA. Routing module performs the efficient data transmission among individual sensor node and facilitates the end to end communication. Neighbor discovery module performs neighbor discovery and duplicate address detection functionality. Adaptation module performs the task of transmitting IPv6 packet over IEEE 802.15.4 link as mentioned in 6 LoWPAN adaptation layer. The sensor information table provides the up to date information of sensor nodes to the SLMA. It works with the close association of the binding cache entry of the SLMA, and the two interfacing modules communicate with the SLMA and the sensor nodes.
## 5 Discussion
### Technology Standardization  
As Table 1 shows, various technologies and approaches have been used for V2P systems. These systems vary in their architecture and abilities of communication range, latency, and bandwidth. This makes it difficult to predict the results if these systems were deployed in other scenarios or for other group of VRUs. Hence, it is necessary to standardize the V2P technology keeping various parameters of V2P systems in mind. Also, there is a need to define and standardize the messages that are being exchanged between vehicles and VRUs [17].