esa-sceva/satcom-chunk-collection huggingface dataset:
# Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7
## 3 Improved YOLOv7
### C2f_SCConv  
The backbone network primarily introduces the C2f_SCConv module to replace some of the ELEN modules. The C2f_SCConv module combines the C2f structure with Spatial and Channel Reconstruction Convolution (SCConv) [24], The C2f structure originates from YOLOv8, which was designed by integrating the C3 module from YOLOv8 and the ELAN concept from YOLOv7. This ensures it being lightweight while obtaining richer gradient flow information. However, the original C2f structure still lacks a sufficient feature extraction capability for masks in complex environments, as it lacks multidimensional feature information and may exhibit feature redundancy issues when combined with ordinary convolutions. SCConv effectively mitigates feature redundancy, reducing the model parameters and computational costs while enhancing feature representation capabilities and improving network feature extraction performance.  
As shown in Figure 3, SCConv consists of two parts: the spatial refined unit (SRU) and the channel refined unit (CRU). For the input feature map, it first undergoes a \(1\times 1\) convolution to adjust to the appropriate number of channels. Then, it is processed separately by the SRU and CRU modules. Finally, the channel number is restored through another \(1\times 1\) convolution, followed by a residual operation. The SRU suppresses spatial redundancy through a separate-reconstruct method, while the CRU adopts a strategy of segmentation, transformation, and fusion to reduce channel redundancy.  
Figure 3: Network structure of SCConv.  
Figure 2: Network structure of improved YOLOv7.  
As shown in Figure 4, the C2f structure is integrated with the SCConv module. The input features undergo a \(1\times 1\) convolution to adjust the channel number. Then, instead of using a \(1\times 1\) convolution, a split operation is employed to split the input features. All bottleneck modules in the original C2f structure are replaced with Bottleneck-SCConv modules, where the value of n is set to one. Compared with the bottleneck module, the Bottleneck-SCConv module enlarges the network's receptive field, enabling the extraction of richer facial mask features.
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 2 Related Work
This power control technique is based on the generalized Nash equilibrium (NE) problem. The author proposed a distributed channel-aware power control algorithm to create opportunities to reuse the spectrum for the interfering femto BSs.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Cta-Pso  
Particle swarm optimization (PSO) is well-known for its ease of use and high efficiency. PSO is built on social behavior and generates several potential solutions to a problem caused at initialization. In the algorithm, a swarm is a collection of solutions, and each answer is a particle. The particles go across the problem search space in quest of the best solution. In [51], it is disclosed that the PSO algorithm is suitable to resolve the resource allocation problem in mmWave wireless multimedia networks.  
The authors [48] presented how CTA-PSO can handle the resource allocation problem even when there is a blockage with a live IPTV. IPTV does not cache previous frames, hence when a user changes channels, the IPTV has difficulty meeting the delay limitations. There is also a blocking issue, which is possible to overcome by incorporating a switch relay into the system. Furthermore, CTA-PSO adapts to the relay very quickly, and thus, even though there is a direct LoS blocking, CTA is assigned continuously.  
* Advantages: The CTA-PSO shows excellent performance and is known for its near-optimal solution. It can seemingly deal with resource allocation across multiple applications, overcoming the issue of assigning a fixed bandwidth across arbitrary devices. Therefore, the CTA-PSO protocol does not demand network resource planning in advance.
* Disadvantage: The implementation of PSO in CTA increases the complexities in the network. In addition, in dynamic environments, the overall scheme may need to run again because of the nature of PSO if there are any unprepared problems and the collection of solutions is not feasible.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Protocol Name** & **Scenario** & **Application** & **Simulation Tool** \\ \hline \multirow{2}{*}{[37; 38]} & Directive & \multirow{2}{*}{WPAN} & Deafness and collision avoidance & \multirow{2}{*}{OPNET Modeler Net} \\ \cline{1-1}  & CSMA/CA & & & \\ \hline \multirow{2}{*}{[39]} & \multirow{2}{*}{MRD-MAC} & WPAN, indoor office & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & & & & \\ \hline \multirow{2}{*}{[42]} & \multirow{2}{*}{CAD-MAC} & WPAN, indoor conference room & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & RD-MAC & WPAN & & \\ \hline \multirow{2}{*}{[44]} & \multirow{2}{*}{BRD-MAC} & WPAN & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & & & & \\ \hline \multirow{2}{*}{[45]} & \multirow{2}{*}{D2D-MAC} & Heterogeneous small cells, outdoor cellular & \multirow{2}{*}{Backhaul, D2D, access} & \multirow{2}{*}{Not specified} \\ \cline{1-1} \cline{3-3} \cline{5-5} \cl
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 8 Discussion
### Findings
#### 8.1.3 Memory Task  
In the data of the _Memory_ task, there are also two patterns. The first pattern can be seen for all participants. They mistakenly play a key very close to the instructed key, e.g., when the instruction sequence shows an Fa, but the user plays an Mi or a Sol, both adjacent to Fa. This is expected behaviour and can be classified as a consequence of the participants' cognitive capacity, specifically memory. The number of mistakes of this type is relatively low. The second pattern can mainly be seen in Patient 2 and to a lesser extent in Patient 3. These patients make another mistake with a relatively higher occurrence than the former type. When playing a sequence, they make more mistakes when the instruction key is in the most peripheral area of their neglect than closer to the centre, e.g., Patient 2 often plays the first or second key when the eighth or ninth are instructed. Furthermore, Patients 2 and 3 have the lowest success rates for playing a correct sequence. Exploration and reaction to visual and auditory stimuli are crucial in this task. The users can respond to the auditory stimulus to get a general idea of the location of the key, but they usually need to look for the visual cue to observe the exact key to play. The assumption is made that the average head direction during a task is a good indicator of exploration. That is, the greater the deviation of the average head direction from the centre of the playing area, the better the user is exploring that peripheral area. When looking at the evolution of the average head direction during the _Memory_ task, a gradual shift can be seen moving away from the centre. This could indicate that the participants perform better exploration as the rehabilitation sessions progress. Thus, it could be taught behaviour from the tasks that persists in between sessions. The test users made very few mistakes in general. Therefore, minimal data on their behaviour are available, and an insightful comparison is infeasible.
# Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method
## 3 Materials and Methods
### Z-Topsis  
Z-TOPSIS is an improvement based on the traditional fuzzy TOPSIS [40]. The specific steps are as follows:  
Step 1. Build a decision matrix with linguistic terms.  
Assume that there are \(m\) schemes and \(n\) criterion in the conceptual design, the DMs use Z-number Z = (\(A\), \(B\)) to make decisions, so the decision matrix is as follows:  
\[M=\left[Z_{ij}\right]=\left[\begin{array}{cccc}(A_{11},B_{11})&\cdots&(A_{1n },B_{1n})\\ \vdots&\ddots&\vdots\\ (A_{m1},B_{m1})&\cdots&(A_{mn},B_{mn})\end{array}\right]_{m\times n}, \tag{16}\]  
The Z-AHP weight \(w\) is added to the decision matrix, as shown below:  
\[M=\left[Z_{ij}\right]=\left[\begin{array}{cccc}w_{1}(A_{11},B_{11})&\cdots &w_{n}(A_{1n},B_{1n})\\ \vdots&\ddots&\vdots\\ w_{1}(A_{m1},B_{m1})&\cdots&w_{n}(A_{mn},B_{mn})\end{array}\right]_{m\times n}, \tag{17}\]  
Step 2. Transform the Z-numbers into regular fuzzy numbers.  
Through Section 3.2, the Z-numbers in the evaluation matrix are transformed into regular fuzzy numbers as follows:  
\[M=\left[Z_{ij}\right]=\left[\begin{array}{ccc}\widetilde{Z}_{11}^{\prime}& \cdots&\widetilde{Z}_{1n}^{\prime}\\ \vdots&\ddots&\vdots\\ \widetilde{Z}_{m1}^{\prime}&\cdots&\widetilde{Z}_{mn}^{\prime}\end{array} \right]_{m\times n}, \tag{18}\]  
Step 3. Identify the positive ideal solution (PIS) and negative ideal solution (NIS).  
PIS and NIS can be calculated as:  
\[I^{+}=\left\{\left(\text{max}\widetilde{Z}_{ij}^{\prime}\middle|i=1,2,\ldots,m \right)\right\}=\left\{\widetilde{Z}_{1}^{\prime+},\widetilde{Z}_{2}^{\prime+ },\ldots,\widetilde{Z}_{n}^{\prime+}\right\}, \tag{19}\]  
\[I^{-}=\left\{\left(\text{min}\widetilde{Z}_{ij}^{\prime}\middle|i=1,2,\ldots,m \right)\right\}=\left\{\widetilde{Z}_{1}^{\prime-},\widetilde{Z}_{2}^{\prime- },\ldots,\widetilde{Z}_{n}^{\prime-}\right\}, \tag{20}\]  
where \(I^{+}\) and \(I^{-}\) denote PIS and NIS, respectively.  
Step 4. Calculate the distances between criterion and both PIS and NIS.  
Define \(d\) as the gap between two fuzzy numbers. When given two TFNs, F1 (\(L_{1}\), \(M_{1}\), \(U_{1}\)) and F2 (\(L_{2}\), \(M_{2}\), \(U_{2}\)), \(d\) can be calculated by Equation (21):  
\[d=\sqrt{\frac{1}{3}\left[\left(L_{1}-L_{2}\right)^{2}+\left(M_{1}-M_{2}\right) ^{2}+\left(U_{1}-U_{2}\right)^{2}\right]}, \tag{21}\]  
Then, calculate the distance of each alternative from \(I^{+}\) (\(d_{i}^{+}\)) and \(I^{-}\) (\(d_{i}^{-}\)), respectively.  
\[d_{i}^{+}=\sum_{j=1}^{n}d\left(\widetilde{Z}_{ij}^{\prime},I_{j}^{+}\right), \;i=1,2,\ldots,m., \tag{22}\]  
\[d_{i}^{-}=\sum_{j=1}^{n}d\left(\widetilde{Z}_{ij}^{\prime},I_{j}^{-}\right), \;i=1,2,\ldots,m., \tag{23}\]  
Step 5. Calculate the closeness degrees and obtain the ranking orders.  
The closeness degree \(C_{i}\) can be calculated by Equation (24) and the optimal scheme with the maximum closeness:  
\[C_{i}=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}}, \tag{24}\]
## 6 Simulation of Meshing
### Helical Gear Pair with In-Plane Misalignment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Angular**} & \multicolumn{2}{c}{**Circular**} & \multicolumn{2}{c}{**Parabolic**} & \multicolumn{2}{c}{**Logarithmic**} \\ \cline{2-7}  & \(\mathbf{C_{c}}\) **(mm)** & \(\mathbf{\Delta\varphi_{max}}\) **(arcsec)** & \(\mathbf{c_{1}}\) **(mm\({}^{-1}\))** & \(\mathbf{\Delta\varphi_{max}}\)**(arcsec)** & \(\mathbf{a_{1}}\) **(mm)** & \(\mathbf{\Delta\varphi_{max}}\)**(arcsec)** \\ \hline \multirow{4}{*}{\(-0.05^{\circ}\)} & 2.398 & \(-0.15843\) & 0.0227 & \(-0.158389\) & 2.515 & \(-0.158352\) \\  & 2.400 & \(-0.15384\) & 0.0233 & \(-0.159286\) & 2.600 & \(-0.158858\) \\  & 2.565 & \(-0.153797\) & 0.0240 & \(-0.154701\) & 2.669 & \(-0.153776\) \\  & 1.104 & \(-0.31696\) & 0.0110 & \(-0.316706\) & 1.201 & \(-0.316326\) \\ \multirow{4}{*}{\(-0.1^{\circ}\)} & 1.150 & \(-0.316498\) & 0.0113 & \(-0.317616\) & 1.236 & \(-0.31794\) \\  & 1.173 & \(-0.316498\) & 0.0115 & \(-0.31153\) & 1.275 & \(-0.30746\) \\ \multirow{4}{*}{\(-0.2^{\circ}\)} & 0.507 & \(-0.633315\) & 0.0051 & \(-0.635564\) & 0.537 & \(-0.628879\) \\  & 0.520 & \(-0.637441\) & 0.0052 & \(-0.636932\) & 0.553 & \(-0.632365\) \\ \cline{1-1}  & 0.535 & \(-0.618792\) & 0.0053 & \(-0.625768\) & 0.572 & \(-0.611297\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Amount of modifications for different in-plane angular misalignment errors for helical gears meshing. Figure 17: TE functions with different amounts of crowning: (**a**) circular, (**b**) parabolic, and (**c**) logarithmic. From the calculations, it can be seen that the logarithmic modification gives better results than the others. The logarithmic modification TE function area is 2.33% and 0.9% smaller than the parabolic and circular modification, respectively. The path of contact for different misalignment angles is shown in Figure 19. It is found that with the increase of misalignment angle, the contact path for all three modifications moves further away from the center. Additionally, a similar contact path is obtained for all three types of crowning. However, a detailed examination of the data showed that the circular crown at 0.1\({}^{\circ}\) (1.7 mrad) and 0.2\({}^{\circ}\) (3.5 mrad) of misalignment angle is closer to the center compared to the other two, whereas the logarithmic modification performs better for the smaller misalignment angle of 0.05\({}^{\circ}\) (0.87 mrad). Figure 19: _Cont._  
Figure 18: TE of helical gear with three types of modification and 0.2\({}^{\circ}\) (3.5 mrad) of in-plane angular misalignment.
## 4 Proposed Routing Protocol  
This section begins with the network assumptions that form the basis of our protocol design, describing the network model and node capabilities. Subsequently, we detail the packet structure used for routing and data transmission. Subsequently, the routing and neighbor table structures are introduced to support efficient forwarding decisions. Next, we explain the route establishment process, including the relay node selection and route updating mechanisms that adapt to the network dynamics. Finally, the data reception procedures are outlined, completing the protocol-operation framework. Each subsection provides a clear and detailed explanation of the protocol components and their interaction.
# Genetic Algorithm for the Retailers' Shelf Space Allocation Profit Maximization Problem
## 5 Genetic Algorithm
### Improvement  
#### 5.6.1 General Steps  
This part provides the practical guidelines for improving a developed solution. The improvement procedure for regular shelves executes on products that are not assigned to any local or convenience virtual segments or not at eye level. It moves the least profitable products to other shelves and takes more profitable products from other shelves (compared to the shelf that is under correction), and assigns an appropriate number of product items to this shelf so that the criteria value is increased.  
In the product lists, the following abbreviations are used:  
* GT--product is good for this shelf.
* BT--product is bad for this shelf.
* GO--product is good for other shelf.
* BO--product is bad for other shelf.  
The improvement procedure executes as follows.  
1. Select the shelf which needs to be improved: * If the previous step of GA is less than the result of the fitness function, select the shelf with the least criteria value. * If there is no improvement of the GA fitness function on the last step, select any regular shelf except the shelf that was under improvement on the previous step.
2. Generate a list of bad products on this shelf.
3. Generate a list of good products on this shelf.
4. Apply the appropriate product movement between shelves to increase the criteria value: * Remove bad products from the shelf: * Move bad products to another shelf if the criteria value on that shelf is better. * Move good products to another shelf if the criteria value on that shelf is better.
* Add good products from another shelf to the selected shelf with the least criteria value (from step 1):
* Add GT from another shelf.
5. Add GO from another shelf.
6. Swap products on this shelf with the products on the other shelf (this\(\leftrightarrow\)other). * Swap BT\(\leftrightarrow\)GT. * Swap BT\(\leftrightarrow\)GO. * Swap GT\(\leftrightarrow\)GO. * Swap GT\(\leftrightarrow\)GT. * Swap BT\(\leftrightarrow\)BO. * Swap BT\(\leftrightarrow\)BT. * Swap GT\(\leftrightarrow\)BO. * Swap GT\(\leftrightarrow\)BT.
7. Change the number of facings on the shelf.
8. Set LB values to bad products (reduce the number of facings of the bad products to LB), set UB values to good products (increase the number of facings of good products to UB) on the shelf selected in step 1.
9. Subtract 1 facing from bad products (reduce the number of facings of bad products by 1), add 1 facing to good products (increase the number of facings of good products by 1) on the shelf selected in step 1.
10. Remove duplicates from the set of generated solutions.
11. Evaluate the set of solutions.
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### SRAM Device Characteristics
#### 4.2.3 Uniformity  
Ensuring consistency in a PUF involves generating responses with an equal distribution of '1's and '0's. However, there is no dedicated universal function specifically designed to assess uniformity in a PUF. Instead, uniformity serves as a general metric that describes the ratio of '1's to '0's in the PUF system's responses. Although individual SRAM devices may exhibit a slight preference toward '1's in their responses, the overall responses from the entire system can vary.  
In the case of a binary PUF, an ideal occurrence of '1's would be 50%. To evaluate and quantify the level of uniformity, a key comprising one million bits was generated at different temperatures, with flaky bits being filtered out. The percentage of '1's in the generated key was then calculated. At each temperature, the percentage of '1's ranged from 49.85% to 50.00%, indicating that the responses' uniformity is very close to the ideal target. However, there is a slight bias toward '0's, which leads to a reduction in the entropy of the responses.  
Figure 10: A heatmap of the cell response’s standard deviation of the calculated temperature. The cell’s standard deviation across all three temperatures was calculated to present a pixel in the heatmap. The standard deviation shows a striation pattern for chip 4; however, this pattern does not appear in the mean response. Before use as a PUF, the SRAM needs to be tested for any patterning within the chip.
## 4 Results and Discussion
### Results
The values of the SSIM and PSNR transparency metrics are similar for all tested variants and range from 0.93 to 0.95 for SSIM and from 30 to 34 for PSNR, which proves the achievement of the high transparency of the algorithms, which ensures the condition of the watermark being invisible to the recipient. In the case of BER metrics, the values for \(n=1\) and \(n=2\) oscillate around 0, which proves that the watermark was correctly decoded. With the increase in the parameters \(n\) and \(N\), the BER value increases, reaching a value of 0.168 for the variant \(N=16\),384 bits and \(n=4\). This means that almost 17% of the bits, i.e., about 2785 bits in the signature, are incorrectly decoded, which means the disqualification of the marking algorithm or the need to use redundant coding.
## 4 Model Compilation Using Machine Learning Methods
### Results and Discussion  
We tested different widely known supervised machine learning techniques to classify the articles based on the country of origin for both the Hajj and Brexit datasets. The eight models used were linear SVC, SVM, multinomial NB, Bernoulli NB, SGD classifier, decision tree, random forest, and KNN, testing different n-gram values with each. The evaluation of the model's performance is presented in the next Tables 4 and 5 based on the accuracy of each model and other performance metrics, such as precision, recall, and the F-score, where the best results were marked in bold. Based on the presented results in Table 4, we can see that the linear SVC with n-gram = 1 yielded the best performance in terms of all the considered measures, with an accuracy of up to 93%. This allowed for a robust comparison of our method against the [PERSON] et al. [12] model, which reached an 85% F-score when the authors trained their model using textual features (polarity, emotions, linguistics, and part of speech) with the bag-of-words approach using a similar ML algorithm. Perhaps more meaningful is a comparison of the training method features in both models. In their study, the application of the [PERSON] et al. [12] model resulted in an F-score of 49% in SVM and 83% in RF when their features were trained using the BoW approach. However, the model achieved the highest F-score of 93% in SVM and a low F-score of 21% in RF when training the model using n-grams, specifically n-gram = 1. This purely reflected the effect the training features had on the model's performance using the same ML algorithms. In fact, [PERSON] et al. [12] demonstrated that the textual features achieved an F-score of 69% using RF, and, when combined with BoW, the score jumped to 85%. This observation obviously implies the effect of n-grams, which we clearly see in this study, as they achieved high scores of 93% in linear SVM. Furthermore, as shown in Table 4 for the Hajj articles dataset, the change in the n-gram values did not have a significant impact on the performance of the tested models, except for Bernoulli Naive Bayes. We can see from the table that the smaller value for n-gram = 1 was better with the Bernoulli NB as the accuracy achieved with unigram was 87%; with larger values, this accuracy decreased to 81.1% and 72.5% when the n-gram values changed to 2 and 3, respectively. Moreover, the SGD classifier and SVM with any value for n-gram provided comparable high performance according to model accuracy, which was around 92% and 91% for SGD and SVM, respectively. Furthermore, despite a decrease, the results for the Brexit dataset in
## 3 Background
### Proposed Robust and Tamper-Resistant Authentication Framework
The server encrypts KS and T2 using \(\mathrm{K_{pre}}\) and AES encryption algorithm and calculate the hash value of \(\mathrm{K_{pre}}\) and T2 using HMAC-SHA-224 hash algorithm as follows:  
\[\mathrm{EM}=\mathrm{E_{K_{pre}}}\ (\mathrm{KS},\mathrm{T2}) \tag{4}\]  
\[\mathrm{H^{\prime}_{S}}=\mathrm{HMAC}\ (\mathrm{K_{pre}},\mathrm{T2}) \tag{5}\]  
The server sends \(\mathrm{H^{\prime}_{S}}\) along with encrypted message EM and T2 to the client. Upon receiving message from server, the client generates the hash value HMAC (\(\mathrm{K_{pre}}\), T2) and compares both own generated hash value and received hash value. If both hash values are not same, the client terminates the connection. If both are same, the server is authenticatedand the client then decrypts the received encrypted message EM using \(\mathrm{K_{pre}}\) and AES encryption algorithm to obtain session key KS. \[\mathrm{H^{\prime}_{C}}=\mathrm{HMAC}\ (\mathrm{K_{pre}},\mathrm{T2}) \tag{6}\]  
\[\mathrm{DM}=\mathrm{D_{K_{pre}}}\ (\mathrm{EM}) \tag{7}\]  
In this way, the sensitive agriculture data is being encrypted and decrypted between the server and client with the help of using this session key KS.