esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Mmwave Communications  
The mmWave bands are already identified as enablers for network capacity and throughput enhancements for 5G V2X [25; 26]. From the 3 GPP perspective, in Rel-15 and Rel-16 the frequency range 2 (FR2: 24,250-52,600 MHz) mmWave bands were specified for 5G NR to complement frequency range 1 (FR1: 410-7125 MHz) bands, opening up new spectrum resources for demanding vertical applications, such as CAM. While FR2 provides at least an order of magnitude higher bandwidth compared to FR1, in Rel-17 there are already efforts being made to evaluate bands from the 52.6-71 GHz region with potentially larger spectrum bandwidths relative to FR2 [27].  
As future vehicular systems will demand Gbps links to upload the collected sensor data for advanced automated driving, mmWave bands have become the candidate for 5G spectrum scalability.
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
## 3 Results
### Performance of SAD Drying
The moisture content of yarn particles at various locations of the drying chamber, as a function of time for various drying methods is shown in Figure 5. The trays were categorized as Top tray, Middle tray and Bottom tray with air flowing from below the drying chamber for the SAD and SD systems, while that of the OSD are noted as Tray 1-3. For OSD drying, the trays are not stacked in a drying chamber and, therefore, there is no influence between the trays. The SAD system shows differences in moisture content for the three trays especially in the first night under low temperature drying with dehumidified air, while there are only minor differences for the trays in the SD dryer as corroborated by [PERSON] et al. [27]. The differences between the trays in SAD that arise during the first night propagate over the next days and nights. Figure 6 shows the effect of different drying methods on vitamin C at the end of drying in 'Dente'. The figure shows that, for each drying method, vitamin C content was reduced. There was no statistical difference in vitamin C retention between Solar drying (SD) and solar adsorption drying (SAD) samples. The open sun (OSD) dried samples retained the least vitamin C. Though adsorption drying involves low temperatures, because it is an addition to solar drying, the longer exposure of the food samples (44 h for SAD and 32 h for SD) to air flow might have contributed to vitamin C loss in comparison with the SD system [14; 28]. Additionally, the loss of vitamin C might also be attributed to higher temperatures for both SAD and SD while higher rates of moisture removal by SAD than SD cause a difference between them. The open sun (OSD) dried samples retained less vitamin C due to exposure of food to direct sunlight [8] in addition to moisture removal. This explains why the SAD and SD samples retained higher concentrations of vitamin C compared to the OSD samples. Figure 5: Moisture content of yarn particles (’Dente’) at different positions in the dryers as a function of time for SAD, SD and OSD. The grey portions represent night drying.
## 3 Results
### Metabolic Profiling Analysis  
Rat serum UPLC/Q-TOF MS metabolite profiling was performed in both positive and negative ion modes. The base peak intensity (BPI) chromatograms of the serum of sham, OVX, and SCE groups in the positive and negative ion modes are shown in Figure 1.  
Multivariate data analysis was performed using unsupervised PCA, and samples from different groups were scattered clearly. According to the statistical parameters from the PCA score plots, these two components explained the R2X value of 0.843 and 0.724, respectively, in positive and negative ion modes and predicted a Q2Y value of 0.675 and 0.572, respectively, in positive and negative ion modes for all sample variables (Figure 2A,B). To confirm the difference in metabolic profiles, supervised PLS-DA and OPLS-DA were performed to sharpen the established separation observed in Figure 2. To illustrate the effect of SCE, PLS-DA was performed to analyze the sham, OVX, and SCE groups. The PLS-DA score plots showed that the sham and OVX groups were separated; SCE treatment groups were closer to the sham group in both positive and negative ion modes, which showed that metabolic pattern was restored to near normal level after treatment of SCE (Figure 2C,D, ESI+: R2X = 0.487, Q2Y = 0.187; ESI-: R2X = 0.88, Q2Y = 0.867). As shown in the OPLS-DA score plots in Figure 2E,F, complete separation between the two groups was observed, which suggested that there were significant differences in metabolic profiles between sham and OVX groups. The metabolites with the VIP \(>\) 1 and \(p\) value \(<\) 0.05 were considered as potential biomarkers that distinguished the two groups.
###### Abstract  
Non-orthogonal multiple access (NOMA) is playing a pivotal role in 5G technology and has the potential to be useful in future developments beyond 5G. Although the effectiveness of NOMA has largely been explored in the sum throughput maximization, the identification of individual user data rate (IDR) still remained an unexplored area. Previously, it has been shown that reconfigurable intelligent surfaces (RIS) can lead to an overall improvement in the data rate by enhancing the effective channel gain of the downlink NOMA system. When time division multiple access (TDMA) is clubbed with multiple RISs in a distributed RIS-assisted NOMA (TDMA-RIS-NOMA) downlink system, a point-to-point communication model is created between access point-to-RIS-to-user device. Due to this point-to-point communication model, optimization of the phase shifts provided by meta-atoms of each RIS is facilitated. The optimized phase shifts of meta-atoms maximize the equivalent channel gain between the access point to the user. In this scenario, the channel becomes saturated and signal-to-interference plus noise ratio (SINR) becomes a function of power coefficients only. In this study, the power coefficients are calculated to maximize the SINR of each user belonging to a NOMA cluster using a geometric progression-based power allocation method such that IDR reaches its upper bound. These observations are also verified using the recently published magic matrix-based power allocation method. There are two observations from this study: (i) the IDR is better in the case of the TDMA-RIS-NOMA downlink system than using downlink NOMA alone and (ii) irrespective of the number of meta-atoms and total cluster power, the upper bound of IDR cannot be increased beyond a certain limit for all users except the highest channel gain user. Because of the restricted upper bound for IDR, we suggest that the RIS-assisted downlink TDMA-NOMA system is more suitable for IoT applications, where minimum IDR can also suffice  
reconfigurable intelligent surfaces (RIS); downlink NOMA; power allocation; meta-atom allocation; individual data rate maximization; fairness 202312618. [[https://doi.org/10.3390/electronics12030618](https://doi.org/10.3390/electronics12030618)]([https://doi.org/10.3390/electronics12030618](https://doi.org/10.3390/electronics12030618))  
1
## 4 Experimental Results
### Verification and Analysis of Measurement Results  
Two experimental data sets were collected for different validation purposes. First, 100 sets of data were obtained from a standard flat platform using the continuous measurement mode. These measurements were used to verify the baseline precision and stability of the system under ideal conditions. Second, 200 sets of data were collected from actual marine propeller blades, using both continuous and single-point measurement modes. This experiment was conducted to assess real-world performance and compare the two modes under practical operational scenarios.  
Each set included measurements at three designated points on the blade, with four repeated measurements per point, resulting in 12 data points per set. This approach ensures a comprehensive evaluation across both simulated and real manufacturing environments. Each data set consisted of measurements taken at three points, with four repeated samples per points, yielding a total of 1200 data points. The 1200 data points acquired from the standard platform were first analyzed using analysis of variance (ANOVA) to examine between-group differences and determine whether the measurement values were consistent and correlated. In addition, measurement values to the second and third decimal places were extracted and subjected to homogeneity testing to evaluate whether statistically similar characteristics were obtained for the different numbers of decimal places. This analysis was conducted to determine the level of decimal precision that the measurement system could consistently maintain.  
Furthermore, the marine metal propeller was evaluated under both the continuous and single-point measurement modes. The one-way ANOVA homogeneity test was conducted to compare values at the second and third decimal places, thereby assessing the reliability of measurements at each precision level. One-way ANOVA was also employed to determine whether these modes had any significant differences or stability patterns.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Generation
#### 2.1.3 Logistic Chaotic Map  
Chaos refers to the seemingly random irregular movement occurring in a deterministic system. For a system, it is described by deterministic theory while its behavior is uncertain, unrepeatable,  
Figure 4: The general model of watermark generation.  
and unpredictable, which is the chaotic phenomenon. In chaotic systems, chaotic sequences can be reconstructed accurately using initial values. The ergodic statistical characteristic of Logistic chaotic sequences [15] is similar to zero-mean white noise, which has good randomness, correlation, and complexity. It is impossible to correctly predict chaotic sequences for a long time. Logistic chaotic sequence can be defined as:  
\[x_{k+1}=\mu x_{k}(1-x_{k}), \tag{4}\]  
where \(0\leq\mu\leq 4\) is the branch parameter. In practical applications \(\mu\) is limited to [3.57, 4]. This algorithm has better confidentiality than other general algorithms, and it has high fidelity, good security, sufficient bandwidth, and strong real-time feature. However, it also has shortcomings of high computational complexity and low operation efficiency.  
In addition, there are other schemes to encrypt watermark information, such as pseudorandom number generator, encoding, spread spectrum technology, and so on. Through encryption, the difficulty of deciphering can be further enhanced, and the security of watermark can be improved.
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 3 Dynamic Network Evolution  
At present, commonly used attack strategy models include random, deliberate, and incomplete information attack. Among them, incomplete information attacks are closer to real combat. The communication network shows a certain degree of robustness [24] to confront random attacks. The robustness and vulnerability directly affect the effectiveness of the communication network system in combat. Incomplete information attacks can not only reflect the robustness of communication network research and confront random attacks but also indicate the vulnerability of deliberate attacks, which can more truly reflect the changes in the effectiveness of the communication network system in actual operations. At the same time, different from the attack strategy in the previous section, it is more scientific and reasonable to use the incomplete information attack as the attack strategy and the ranking \(I_{i}\) of the importance of the node \(v_{i}\) in the network as the basis for the basic information of the attacking nodes.  
There are a large number of various nodes in the combat SoS, but how to find the most valuable target to attack is significant, in order to achieve the maximum combat effectiveness. In the real combat, the interaction of various information and nodes is becoming more frequent. Simply destroying a node is difficult to cause greater impact and damage to the enemy's combat system. Therefore, it is necessary to determine the topological structure of the opponent's combat system and attack the key node to achieve a more ideal effect.  
Determination \(\Omega\) involves two issues: in one, \(\Omega\) nodes are included in the accuracy of the attack information; the other is the number of \(\Omega\) nodes contained in and the extent of the attack information. Therefore, the selection of nodes for incomplete information attacks can be compared to the \"unequal probability sampling problem\" without replacement. Here, \(\alpha\) is used to represent the accuracy of the attack information, and \(\beta\) is used to represent the breadth of the attack information and the sample size \(n=N\bullet\beta\), where \  
dynamic evolution of the combat network under continuous time series is shown in Figure 8. As a comparison, we use the network evolution diagram of the ER network after being attacked as a control experiment and select relevant indicators as a comparison description.
## 3 Proposed Scheme
### Decryption Process  
The decryption process is an essential component of the overall encryption scheme as it enables the recovery of the original data by reversing the operations performed during encryption. It is acknowledged that certain operations, such as modulo operations, may inherently possess irreversibility. However, within our proposed encryption scheme, these operations are strategically employed in a manner that ensures the feasibility of the decryption process. For instance, the modulo operation utilized in step 4 serves the purpose of constraining the result of the summation operation within a specific range. Although it introduces a level of non-invertibility, it does not impede the decryption process from being successfully executed. The carefully designed application of irreversible operations within our encryption scheme strikes a balance between security and practicality, allowing for a robust decryption process while maintaining the necessary security measures. The following are the steps for the decryption process.  
Step 1: perform an exclusive operation between the final image \(C\) and \(K_{1}\) to obtain an intermediary image of logistic sine map \(\beta^{x}\) to obtain \(C^{\prime}\);  
Step 2: perform an exclusive operation between secret keys from the fuzzy Henon map \(K_{2}\) and \(C^{\prime}\) to obtain intermediary image \(\beta^{xx}\);  
Step 3: divide the intermediary image \(\beta^{xx}\) into four equal blocks \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) and \(\beta_{4}\);  
Step 4: reshuffle the position of each block diagonally between \(\beta_{1}\) and \(\beta_{3}\), as well as \(\beta_{2}\) and \(\beta_{4}\);  
Step 4: subtract two diagonal pixel values in paired wise rows, means select two adjacent rows in an image, and subtract the pixel values diagonally across from each other, performing three iterations in each block;  
Step 5: shift even columns to the left-side and even rows downwards in each block;  
Step 6: reshuffle the position of each block diagonally between \(\beta_{1}\) and \(\beta_{3}\), as well as \(\beta_{2}\) and \(\beta_{4}\);  
Step 7: combine the blocks to obtain the original image \(\beta\).
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 2 Non-Invasive Monitoring Techniques
### Microwave Imaging
The measurement data will provide information about the location and size of defects [66]. This highly sensitive interaction is a function of defect size and position within the waveguide aperture [61].
###### Abstract  
Background: Hallux valgus (HV) is one of the most common foot deformities and negatively impacts plantar support. The abductor hallucis (AH) is the most important muscle in the etiopathogenesis of hallux valgus, but the effectiveness of its rehabilitation clashes with the difficulty of identifying the most suitable exercises to activate it. Therefore, the aim of this study was to compare four different therapeutic exercises in the activation of AH in these patients. Methods: In this observational case-control study, 48 patients suffering from hallux valgus of moderate/severe grade, according to traditional radiographic classification and the Manchester scale, were divided into two groups: the case group underwent a monthly rehabilitation protocol for their foot deformity, whereas the control group was only evaluated without any intervention. The exercises were as follows: Toe Spread Out (TSO), Short Foot (SF), Forefoot Adduction (FA), and Flexion of the Metatarsophalanges (FM). Both groups were analyzed at baseline and 1 month later (at the end of rehabilitation for the case group) while performing the four mentioned exercises using a surface electromyograph (sEMG) to record the muscle activity of AH in terms of Root Mean Square (RMS) and Maximum Voluntary Contraction (MVC). Results: FA was the only exercise to determine a statistically significant improvement in AH at the end of the rehabilitation cycle, both in terms of RMS (\(p\) = 0.015) and in terms of MVC (\(p\) < 0.0001), whereas the other exercises did not produce any change in muscle activity in the comparison between times and groups or in the related interaction. Conclusions: FA seems to be the best exercise to activate and train AH, so rehabilitation programs for patients suffering from hallux valgus should consider this exercise as the starting point for improving plantar support, always considering the specific characteristics of HV. Further studies are needed to deepen the effectiveness of this exercise, with the aim of implementing rehabilitation strategies and rethinking traditional HV therapies, which are currently predominantly surgical.  
**Keywords:** foot deformity; therapeutic exercise; rehabilitation; muscle
## 4 Multi-Criteria Decision-Making Method for Emergency Communication Protocol (MCDM-ECP)
### Next-Hop Node Selection  
For simplicity of illustration, we mark the weighted multi-criteria matrix \(M\) as follows:  
\[M=\left[\begin{array}{cccc}m_{11}&\cdots&m_{14}\\ \vdots&\vdots&\vdots\\ m_{n1}&\cdots&m_{n4}\end{array}\right]\]where \(m_{ij}(i=1,\ldots,n;j=1,\ldots,4)\) is a supplement of the elements in the weighted multi-criteria matrix \(M\) that we get in Section 4.2. In this section, we use cost-benefit theory [42] to calculate the next-hop node. All four decision criteria (EC, CN, HC, DIS) that we choose in Section 3 are cost types, so ideal and bad solutions of the weighted multi-criteria matrix \(M\) are respectively described as:  
\[M^{+}=(min\ m_{i1},\cdots,min\ x_{i4})\triangleq(m_{1}^{+},\cdots,m_{4}^{+}), \ (i=1,\ldots,n)\]  
\[M^{-}=(max\ m_{i1},\cdots,max\ m_{i4})\triangleq(m_{1}^{-},\cdots,m_{4}^{-}), \ (i=1,\ldots,n)\]  
where \(M^{+}\) and \(M^{-}\) stands for ideal and bad solutions of the weighted multi-criteria matrix \(M\) respectively; \(min\ m_{ij}(i=1,\ldots,n;j=1,\ldots,4)\) calculates the minimum element in column \(j\), and \(max\ m_{ij}(i=1,\ldots,n;j=1,\ldots,4)\) calculates the maximum element in column \(j\). For each solution, the Euclidean distance \(D\)[43] is calculated as:  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{4}\Bigl{(}m_{ij}-m_{j}^{+}\Bigr{)}^{2}},\ (i=1,\ldots,n) \tag{10}\]  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{4}\Bigl{(}m_{ij}-m_{j}^{-}\Bigr{)}^{2}},\ (i=1,\ldots,n) \tag{11}\]  
The preference index (\(PI\)) of the highest solution to the lowest solution that we discussed in this paper is defined as:  
\[PI_{i}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}}\ (i=1,\ldots,n) \tag{12}\]  
\(PI\) determines how ideal solutions outperform bad solutions, so we select the node with the highest \(PI\) value as the optimal next-hop node for data transmission.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 4 Design of Multi-Constraint Multi-Strategy Hopping Algorithm
### Path Similarity Constraint
#### 4.3.2 Hopping Path Weight Calculation  
Different network attacks require different network attributes. For eavesdropping attacks, it is generally required that the path can meet the continuity of data forwarding in time and that the scale of forwarding data is large enough in space so that the attacker can analyze and reorganize the data, recover the original information, and achieve the purpose of stealing user information. DDoS (distributed denial of service) and link-flooding attacks including sending a large amount of data to the destination node to consume link bandwidth so that the path has no remaining bandwidth to provide services for normal communication. Due to the finiteness of the hopping path, the intersection between paths is inevitable, so there will be many key nodes, and the amount of data forwarded by these nodes is much larger than that of general nodes. Once attacked, it will cause a large amount of data leakage; even if path hopping is performed, there is still the possibility that the hopping path will pass through this key node. At the same time, when the network is under flooding attack or when the amount of data transmitted is relatively large, the remaining bandwidth of the path becomes very important. Based on this, when selecting a path, the key node and the remaining bandwidth of the path need to be considered. The weight of the key node refers to the calculation of the number of intersecting paths of each node (except the source node and the destination node) in the path, and the path weight is calculated based on the maximum number of node intersections. The remaining bandwidth weight of the path refers to the calculation of the remaining bandwidth of each link in the path, and the path weight is calculated based on the minimum remaining bandwidth of the link. The equations are as shown in Equations (9) and (10):  
\[W_{1}(path_{i}^{S\to D})=\frac{\frac{1}{MAX(N^{S\to D}(set(path_{i}^{S \to D})))}}{\sum_{path_{i}^{S\to D}\in Space^{S\to D}} \frac{1}{MAX(N^{S\to D}(set(path_{i}^{S\to D})))}} \tag{9}\]  
\[W_{2}(path_{i}^{S\to D})=\frac{MIN(b_{k}(set(link_{j})))}{\sum_{path_{i}^{S \to D}\in Space^{S\to D}}MIN(b_{k}(set(link_{i}^{S\to D})))} \tag{10}\]  
In Equation (9), \(set(path_{i}^{S\to D})\) represents the set of nodes after removing the source node and the destination node from path \(i\), \(N^{S\to D}\) represents the number of cross paths passing through the node, and \(MAX(N^{S\to D}(set(path_{i}^{S\to D})))\) represents the maximum number of cross paths for nodes other than the source and destination nodes in path \(i\). It can be seen from Equation (9) that the more paths that intersect with the key nodes in the path, the smaller the weight of the path and the lower the probability of being selected. It can be seen from Equation (10) that the larger the remaining bandwidth of the path, the greater the weight of the path and the higher the probability of being selected.