esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Detection Performance  
This paper discussed in detail the relationship between \(\mathrm{P_{d}}\) and \(\mathrm{N_{th}}\) in the third part. Figure 4 displays a group of theoretical and simulation detection probabilities with different \(\mathrm{N_{th}}\)(N = 4096, F = 10). The curve moves to right about 1.3 dB for every \(\mathrm{N_{th}}\) increase, and the approach with \(\mathrm{N_{th}}=1\) has more advantage in \(\mathrm{P_{d}}\). The greater the threshold, the lower the false alarm probability and detection probability. Therefore, applications with different requirements should consider the balance of \(\mathrm{P_{d}}\) and \(\mathrm{P_{fa}}\). In addition, Figure 4 shows that even though the detection probability declines when \(\mathrm{N_{th}}\) increases, DC-XFAST does not deteriorate badly when the number of verification times reaches 4. The reason is that the optimal structure design assures proper use of partial peaks, and the magnitude of peaks which contain two partial peaks is the same as that of a full peak.  
The detection performance of folding methods like XFAST, DF-XFAST and DC-XFAST is degraded from folding. The proposed method uses a max-value tactic to ensure higher detection probability than XFAST and DF-XFAST, which are based on a constant false alarm probability. Besides, DC-XFAST considers partial peaks, so multi-peak verification can effectively avoid peak ambiguity.  
Figure 5 reports the advantage of the DC-XFAST method over XFAST and DF-XFAST with regard to detection probability when the number of verification times is from 1-4. The figure demonstrates that, compared with XFAST, with the same local folding number, DC-XFAST has improved detection performance and the gain is about 4 dB. The greater the folding number, the more obvious the comparative advantage DC-XFAST can achieve, and when the folding number is 20, the gain is 6 dB  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathrm{N_{th}}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) \\ \hline \(\mathrm{P_{fa}}\) & \(10^{-5}\) & \(1.46\times 10^{-8}\) & \(2.38\times 10^{-12}\) & \(1.46\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The false alarm probability \(\mathrm{P_{fa}}\) of each threshold \(\mathrm{N_{th}}\).  
Figure 4: Theoretical (“T”, continuous line) and simulation (“S”, dotted line) results for detection probability under 1–4 verification times (\(\mathrm{N_{th}}\)) of DC-XFAST (“DX”) with folding number equals to 10.  
and 7 dB, respectively, when Nth = 3 and 4. Even when the folding number of XFAST is five times more than that of DC-XFAST, the acquisition performance improvements of DC-XFAST are noticeable. DC-XFAST outperforms DF-XFAST in detection performance as well. Especially when Nth = 4 and F = 20, the gain is about 2 dB. To conclude, as the folding number and number of verification times increase, DC-XFAST shows greater advantage over the other folding methods XFAST and DF-XFAST.
# A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
## 2 Technical Foundation
### Design of QKP  
Quantum keys are a very valuable resource and to manage and store them, a QKP is usually configured between each pair of neighboring nodes. i.e., a QKP is a repository containing multiple pre-generated quantum keys [34; 35]. The sender and receiver use these keys to encrypt and decrypt the communication data during the QKD process.  
Designing an adjustable QKP size requires a combination of factors. Firstly, there is a trade-off between security and availability. Larger key pools can meet more demands but also increase the security risk and resource management burden. Therefore, there is a need to balance security and availability to ensure that the critical pool is large enough and that the keys are effectively managed and protected [36]. Secondly, the speed of key generation is an important consideration. Generating quantum keys takes time and is algorithm and hardware-device-dependent. Larger key pools may take longer to generate enough keys. Therefore, when sizing the essential collection, the time required to create new keys must be considered to ensure that the necessary keys are available promptly. In addition, the rate of key consumption needs to be considered. Smaller key pools tend to be depleted, requiring frequent generation of new keys. Conversely, larger key pools reduce the need to generate new keys but may also increase the risk of crucial obsolescence. Therefore, there is a trade-off between the vital consumption rate and the key pool size to ensure that keys are always available and valid.  
In summary, resizing a QKP is a complex problem that requires a combination of security, availability, key generation speed, and key consumption rate. Trade-offs and evaluations need to be made when making decisions to meet specific requirements and ensure system stability and security.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 2 Related Work
The size of the ciphertext is independent of the number of ciphertext attributes, and the number of bilinear coupling evaluations is reduced to a constant value. Using the general Diffie-Hellman exponent assumption, they establish that the scheme is semantically secure in the selective-set model. [PERSON] et al. [26] have proposed attribute-based encryption and decryption algorithms. This study focuses on expressive systems without system-wide limits or restrictions, such as limiting the number of attributes used in a ciphertext or a private key. This is the first key-policy ABE system that allows decryption using a constant number of pairings. The study conducted on the related work helped to understand the use of RSA in hybrid methods. The utilisation of RSA in hybrid cryptography presents various benefits. The key exchange and distribution features of RSA are particularly notable because of its ability to safely facilitate the exchange of symmetric keys between communication parties. This enables the subsequent encryption of data using the shared key. This solution achieves a harmonious equilibrium between security and efficiency by leveraging RSA's asymmetric encryption for secure key exchange while simultaneously capitalising on quicker symmetric encryption algorithms for expedited data transmission. Moreover, the adaptability of RSA enables it to effectively execute diverse cryptographic operations, including encryption and digital signatures, rendering it highly adaptable to a wide range of security demands. Hybrid cryptography is a method that leverages the benefits of both RSA encryption and symmetric encryption to achieve enhanced performance and solid security measures for safeguarding data and facilitating communication. From this literature, it is observed that the hybrid model is a suitable method for enhancing the robustness of secured data. The previous work [13] concentrated on combining AES and ECC to achieve hybrid and multi-key encryption techniques. However, there is notable work on user-attribute-based key generation techniques. Furthermore, from the literature review, it is evident that there has been no work exploring multi-equation-based key generation techniques to achieve higher security. Hence, this work aims to develop a hybrid multi-key encryption technique.
## 1 Introduction
### Three Principal Methods for Rotor System
#### 1.1.1 Transfer Matrix Method  
[PERSON] and [PERSON] [1] originally proposed the TMM to calculate critical speed in the linear frequency domain and extended it [2] to determine the unbalance response, the modal response, the stability, and the damped critical speeds. Moreover, the TMM has been applied by [PERSON] and [PERSON] [3] to deal with transient analysis in the time domain using the discrete time technique. [PERSON] [4] proposed an improved TMM to study the rotor behaviors.
## 5 Performance Evaluation
### Load on SDN-Centered KMS  
Group rekeying occurs in the event of the user joining or exiting the group. For static applications of IoT like smart home, the devices joining and leaving the smart home network are less frequent. For dynamic applications like retail, healthcare, etc., the rekeying frequency is high. Figure 12a-c depict the communication load on SDN-centered KMS for different rekeying frequencies \(\mathrm{f}=1/120,1/480\), and \(1/1920\) respectively.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**KMS** & **OFT [19]** & **Ku and Chen [37]** & **[PERSON] [38]** & **ROFT [39]** & **MOFT** \\ \hline Comm. Cost & \((2 log_{2}n+1)\times L\) & \((2 log_{2}n+1)\times L\) & \((2 log_{2}n+1)\times L\) & \((2 log_{2}n+1)\times L\) & \((log_{2}n+2)\times L\) \\ KMS & \((2 log_{2}n+1)\times C_{e}+\) & \((2 log_{2}n+1)\times C_{e}+\) & \((log_{2}n+1)\times C_{h}\) & \((2 log_{2}n+1)\times C_{e}+\) & \((log_{2}n+1)\times C_{h}\) & \((log_{2}n+1)\times C_{e}+\) \\ KMS & \((log_{2}n+1)\times C_{h}\) & \((log_{2}n+1)\times C_{h}\) & \(((log_{2}n^{2})^{2}+2 log_{2}n+1)\times C_{e}+\) & \((2 log_{2}n)\times C_{h}\) & \((2 log_{2}n-1)\times C_{h}\) \\  & & & & \((log_{2}n^{2})^{2}+log_{2}n+1)\times C_{h}\) & \(2C_{d}+log_{2}n\times C_{h}\) & \(2C_{d}+(2 log_{2}n-1)\) \\ \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\(2C_{d}+log_{2}n\times C_{h}\)} & \multirow{2}{*}{\(2C_{d}+log_{2}n\times C_{h}\)} & \multirow{2}{*}{\(2C_{d}+(2 log_{2}n-1)\)} \\  & & & \(0.5\times(log_{2}n)\times C_{h}\) & \(\times C_{h}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: User join event.  
Figure 11: Comparison of proposed MOFT communication cost with OFT [19], Ku & Chen [37], Xu [38] and ROFT [39].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**KMS** & **OFT [19]** & **Ku and Chen [37]** & **Xu [38]** & **ROFT [39]** & **MOFT** \\ \hline Comm. Cost & \((2 log_{2}n+1)\times L\) & \((2 log_{2}n+1)\times L\) & \((2 log_{2}n+1)\times L\) & \((2 log_{2}n+1)\times L\) & \((log_{2}n+1)\times L\) \\ KMS & \((2 log_{2}n+1)\times C_{e}+\) & \(((log_{2}n^{2})^{2}+(2 log_{2}n+1))\times L\) & \(((log_{2}n^{2})^{2}+(2 log_{2}n+1))\times L\) & \((log_{2}n+1)\times C_{e}+\) \\ Comp. Cost & \((2 log_{2}n+1)\times C_{e}+\) & \((2 log_{2}n+1)\times C_{e}+\) & \((log_{2}n+1)\times C_{h}\) & \((2 log_{2}n+1)\times C_{e}+\) & \((log_{2}n+2)\times C_{e}+\) \\ KMS & \((log_{2}n+1)\times C_{h}\) & \((log_{2}n+1)\times C_{h}\) & \(((log_{2}n^{2})^{2}+2 log_{2}n+1)\times C_{e}+\) & \((2 log_{2}n)\times C_{h}\) & \((2 log_{2}n)\times C_{h}\) \\  & & & & \((log_{2}n^{2})^{2}+log_{2}n+1)\times C_{h}\) & \((log_{2}n+1)\times C_{e}+\) & \((log_{2}n+1)\times C_{e}+\) \\ \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\(2C_{d}+log_{2}n\times C_{h}\)} & \multirow{2}{*}{\(2C_{d}+log_{2}n\times C_{h}\)} & \multirow{2}{*}{\(2C_{d}+(2 log_{2}n-1)\)} \\  & & & \(0.5\times(log_{2}n)\times C_{h}\) & \(\times C_{h}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: User leave event.
## 4 Discussion
### Possibility of rPMS for Recovery from UE Paresis  
The use of rPMS can be characterized by penetration into deeper regions of muscles without pain. Given that peripheral stimulation for affected muscles, such as NMES, can enhance motor recovery, rPMS could improve the motor function of UE paresis. So far, however, there have been no studies to support the effectiveness of rPMS on upper extremity paresis despite some efforts using various parameters [42; 43; 44; 45; 46; 47]. Recently, a study reported the effectiveness of rPMS in UE motor function recovery when applied to affected UE muscles (shoulder, elbow, wrist, fingers) in the early acute phase of stroke (mean stroke duration of 9.2 days) [11]. The PR of the FMA-UE scores after 7.8 session-rPMS treatment (n = 10; mean scores 14.6/66 before intervention) significantly differed from the standard care (SC) group (n = 9; mean scores 19.0/66 before treatment), i.e., 2.65 for the rPMS group and 1.10 for the SC group, respectively (p = 0.003). The rPMS group demonstrated a large effect size (r = 0.68) on FMA-UE, suggesting high clinical significance in FMA-UE.  
The advantages and disadvantages of rPMS have been summarized [48]. The authors of this summary pointed out that the advantages of rPMS over NMES were the absence of pain, deeper penetration, the generation of higher muscle torque and its applicability to children, while the disadvantages were the overheating of the coil and the exposure of a larger area stimulated with increased intensity. So far, however, no recommendations have been provided regarding the parameters of rPMS application, such as coil design, duty cycle, duration, frequency and intensity.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 2 Materials and Methods
### K Sorption Experiments with Seawater  
The seawater sample was provided by the Doosan Heavy Industries & Construction (Changwon, Korea). The seawater samples with different K concentrations were prepared by concentrating or diluting the original seawater sample. The concentrated sample was prepared by reducing the sample volume by about 50% via evaporation, while the diluted sample was prepared by adding deionized water to double the sample volume. The initial pH values of the seawater samples were 8.33 \(\pm\) 0.15. Moreover, the seawater pH was adjusted to pH 5.08 \(\pm\) 0.04 to study the effect of pH conditions on K recovery from seawaters. The average initial K concentrations of the diluted, original, and concentrated seawater samples were 23, 50, and 120 mg L\({}^{-1}\), respectively. For comparison of K recovery at different pH conditions, the initial K concentrations of 23 mg L\({}^{-1}\) were used. The batch sorption tests were conducted by placing each of the three adsorbents used in this study (0.1 g) and the prepared seawater samples (100 mL) in a 100 mL polypropylene bottle. Duplicate samples were used for each condition. The sorption tests were carried out for 72 h, and the residual K concentrations in the supernatants were measured after filtration.
# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 4 Simulation Results and Validation
In order to evaluate the communication performance of different sensors, the channel capacity at different positions of four different floor is also simulated and shown in Figure 7. For the sensors on the first floor, it can be seen that the channel capacity is the highest at coordinate  
Figure 3: Simulated and clustered CIRs: (**a**) Floor 1; (**b**) Floor 4; (**c**) Floor 7 and (**d**) Floor 10.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 2 5G System
### Network Functions  
The 5 GC leverages the principles of Network Function Virtualization (NFV) and Software Defined Networks (SDNs) to virtualize network functions, including those related to transport, access, and core, which were traditionally run on closed hardware [29]. An example of the interaction between MEC and 5 GC is evident in the network function (NF) called User Plane Function (UPF). This function serves as a central point in the user plane of the 5 GC, connecting the UE to the data network (DN) and performing tasks like routing, inspection, and Quality of Service (QoS) management [8].  
The Session Management Function (SMF) manages the UPF, configuring the connection between the UE and DN, including the allocation of IP addresses and IP PDU sessions [8]. The Access and Mobility Management Function (AMF) is a network function that interacts with the data network and the UE, facilitating registration, authentication, and mobility in the network through secure signaling [8].  
The Network Exposure Function (NEF) monitors the 5G network to expose events to authorized applications and other network functions (NFs), allowing the management of QoS rules, priorities, and UE-related information [8]. These functions collaborate to ensure the effective operation of the 5 GC, using the virtualization and flexibility offered by the architecture.  
Network Slice is a core technology in the context of 5G, enabling the creation of logical networks on top of a shared infrastructure [8]. In essence, network slices aim to meet a customer's specific needs, resulting in dedicated logical networks where required resources are configured and allocated [8, 30].
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 2 Related Work
The accuracy of a stations' clocks are affected by several inner and external factors, like changes in temperature and power supply voltage, shocks and aging, which are all present in an underwater networks. The accuracy of the clock crystal is given in parts per million (ppm). Typical accuracies found in simulations for time synchronization methods are 40 ppm. In [20], time synchronization protocols are analyzed. The UWSO-TDMA proposed in this paper is a simple mechanism that uses TDMA. In contrast to the other MAC protocols, this approach can be used for real-time communications, as it is possible to bound the transmission delay. The distributed MAC protocol works with static nodes in a multi-hop network.
# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 3 System Architecture and Design
### Benchmarking Sensors
\[result=\frac{1}{a}\left(\sum_{1\leq i\leq a}\frac{c}{\sigma_{i}}\right) \tag{1}\]  
Step increment is the difference between two consecutively measured values per sensor axis, as shown in Equation (2). The constant c is divided with each sensor's respective minimum step increment. These partial results are added, provided the sensor has more than one axis. The respective results are added and finally divided by the number of axes. \[result=\frac{1}{a}\left(\sum_{1\leq i\leq a}\frac{c}{\left|\min(x_{i,k}-x_{i,k -1})\right|}\right) \tag{2}\]  
where \(2\leq k\leq n\), \(a=\#\) axes, \(c=33\). Time continuity refers to the time difference between two successive sensor events. The average time difference \(\overline{t}\) is calculated and is divided by the average time difference during the initialization phase, as shown in Equation (3). This results in a relative ratio \(v\) with a value between 0 and 1, depicted in Equation (4). \[\overline{t}=\frac{1}{n}\left(\sum_{2\leq k\leq n}t_{k}-t_{k-1}\right) \tag{3}\]  
\[v=\frac{\overline{t}_{initialization}}{\overline{t}_{Benchmark}} \tag{4}\]  
Then, the constant value is divided by the standard deviation \(\sigma\) of measured timestamps during the benchmark, as per Equation (5). Finally, the score depends on the partial result of the initialization process and the ratio of the average time differences to the initialization and benchmark, calculated as per Equation (6). \[res_{\sigma,\ Benchmark}=\frac{c}{\sigma_{Benchmark}} \tag{5}\]  
\[result=\left\{ \begin{array}{ll}\left(\frac{res_{\sigma,\ Initialization}+ res_{\sigma,\ Benchmark}}{2}\right)v,\text{ when }v<1\\ \left(\frac{res_{\sigma,\ Initialization}+res_{\sigma,\ Benchmark}}{2} \right),\text{ otherwise}\end{array} \right. \tag{6}\]  
Count of unique values is only used for light and proximity sensors. This is the number of unique x-axis values among the measured values during the benchmark. Gravity accuracy is used only for the gravity and accelerometer sensors. First, the arithmetic mean is calculated for the Z axis values (Equation (7)). A division is used to calculate the percentage deviation from the standard acceleration g = 9.80665 m/s\({}^{2}\). Finally, this percentage is multiplied with the maximum achievable score (4000) for this category to obtain the partial score for the overall rating (Equation (8)). \[\overline{x}=\frac{1}{n}\ \sum_{1\leq k\leq n}x_{k} \tag{7}\]  
\[result=\left\{ \begin{array}{l}4000\big{(}\frac{\overline{x}}{8}+3\big{)},\ when\ g\ \leq\ \overline{x}\\ 4000\big{(}\frac{\overline{x}}{8}+3\big{)},\ otherwise\end{array} \right. \tag{8}\]  
Table 5 summarizes the different metrics used in the sensor benchmark, which sensors they apply to, and their impact on the final quality score. The 'Others' column refers to all other sensors not mentioned in the previous two columns.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Integration with IoT for Remote Monitoring of Port Cranes
#### 4.3.1 Vibration Sensors in IoT-Based Remote Monitoring of Port Cranes  
Vibration sensors play a crucial role in monitoring the structural health of the port cranes. When integrated with IoT, these sensors can provide continuous and real-time data on the dynamic response of cranes to various stimuli such as lifting operations and environmental factors. It applies as follows:  
* Data collection and transmission--Vibration sensors, such as accelerometers and strain gauges, are strategically placed on critical components of port cranes. These sensors convert mechanical vibrations into electrical signals that are then amplified and conditioned. The data is collected by IoT-enabled data acquisition systems, which can be located on the crane itself or at a nearby monitoring station. The data is transmitted over wireless or wired networks to a central IoT platform. Wireless networks, such as Wi-Fi, Bluetooth, and cellular networks, are often preferred in marine environments because of their flexibility and ease of installation.
* Data Processing and Analysis--Once the vibration data reaches the IoT platform, it undergoes processing and analysis. Cloud and edge computing can also be used for data processing. Cloud computing offers virtually unlimited storage and computational resources but may introduce latency issues. Edge computing processes data closer to the source, reduces latency, and enables faster response times. The processed data is analyzed using various algorithms and techniques to detect patterns, anomalies, and trends. Machine learning algorithms can be trained to identify the early signs of structural damage or mechanical failure based on historical data and predefined thresholds.
* Automated Monitoring and Alerts--IoT platforms enable the implementation of automated monitoring systems that trigger alerts when abnormal conditions are detected. For example, if the vibration levels exceed predefined thresholds, the system can automatically send alerts to engineers and technicians via email, SMS, or mobile applications. These alerts provide valuable time to investigate and address potential issues before they escalate to catastrophic failure. Automated monitoring systems can also generate reports and dashboards, thereby providing a comprehensive overview of the structural health of the port cranes.