esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**SBP**} & \multicolumn{3}{c}{**DBP**} & \multicolumn{3}{c}{**MAP**} \\ \cline{3-10}  & **MAE** & \begin{tabular}{c} **\% Error** \\ \textless{}10 mmHg** \\ \end{tabular} & \(R^{2}\) & **MAE** & \begin{tabular}{c} **\% Error** \\ \textless{}10 mmHg** \\ \end{tabular} & \(R^{2}\) & **MAE** & \begin{tabular}{c} **\% Error** \\ \textless{}10 mmHg** \\ \end{tabular} & \(R^{2}\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Full \\ \end{tabular} } & ANN (2 \(\times\) 512) & 14.0 & 46.5 & 0.110 & 6.62 & 79.4 & 0.158 & 7.34 & 72.9 & 0.250 \\  & ANN (3 \(\times\) 512) & 13.8 & 47.3 & 0.148 & 6.54 & 80.0 & 0.158 & 7.16 & 74.3 & 0.274 \\  & LSTM & 22.5 & 27.1 & \(-\)0.819 & 22.2 & 10.1 & \(-\)0.0513 & 9.24 & 60.3 & \(-\)0.0754 \\  & CNN & 16.1 & 35.8 & 0.00 & 21.6 & 10.7 & 0.00 & 8.79 & 62.4 & 0.00 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Feature \\ Extraction \\ \end{tabular} } & ANN (2 \(\times\) 512) & 5.73 & 83.7 & 0.777 & 2.96 & 95.8 & 0.692 & 3.27 & 94.5 & 0.771 \\  & ANN (3 \(\times\) 512) & 5.32 & 86.5 & 0.780 & 3.50 & 94.9 & 0.614 & 3.65 & 94.2 & 0.750 \\ \cline{1-1}  & ANN (4 \(\times\) 512) & 5.26 & 86.1 & 0.798 & 3.39 & 94.9 & 0.629 & 3.30 & 94.1 & 0.745 \\ \cline{1-1}  & Binary Tree & 7.46 & 77.0 & 0.485 & 3.58 & 90.2 & 0.404 & 4.27 & 86.7 & 0.477 \\ \cline{1-1}  & Reg. SVM & 13.8 & 44.4 & \(-\)0.769 & 6.11 & 84.5 & 0.0779 & 7.13 & 74.9 & 0.186 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SBP, DBP, and MAP Results from Trained Machine Learning Models. Figure 4: Scatter plot of predicted blood pressure against actual blood pressure for SBP, DBP, and MAP. The models used for the prediction are those that gave the best performance, specifically the 2-layered feature-trained ANN for DBP and MAP and the 4-layered feature-trained ANN for SBP. Complete ABP data was generated as per the algorithm in Section 3.3. An example of a normotensive sample and a hypertensive sample is shown in Figure 5a,b, respectively.
# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 3 Supply Chain Network Risk Propagation Model and Resilience Metric
### Supply Chain Network Risk Propagation Model
#### 3.1.2 SIS Model  
The SIS model is a classical propagation dynamics model that contains two types of node states, i.e., S (susceptible) and I (infected). It can reflect the dynamic evolution of risk propagation, can be applied to large-scale network environments, and is simple to use [4; 53]. Therefore, the SIS model was used in this study. The susceptible person corresponds to a node (in an SCN) that is not affected by risks, and the infected person corresponds to a node that is affected by risks. The mechanism of risk propagation in an SCN is similar to the mechanism of virus propagation [66]. Risk propagation in an SCN refers to the risk transmission of risk-exposed enterprises to adjacent supply chain enterprises through various media [24].  
For an SCN, if a healthy node is adjacent to a risky node, it is possible for the healthy node to be affected by the risk to become a risky node. This probability is defined as the infection rate, \(\alpha\). It is possible for a node at risk to heal itself and become a healthy node. This probability is defined as the recovery rate, \(\beta\). The effective infection rate is defined as the ratio between the infection rate and the recovery rate (i.e., \(\lambda=\alpha/\beta\)). There exists a critical value called \(\lambda c\): for \(\lambda<\lambda c\), there will be no infected nodes in the network after a long time and the dynamical system of the network will be in the absorption phase; for \(\lambda>\lambda c\), after a long time the region of density of risky nodes in the network will be stable and the dynamical system of the network will be in the active phase [67].
## 3 Types of Supplementary Dampers and Their Characteristics
### Metallic Yield Dampers
#### 3.4.2 Mathematical Model  
These dampers utilize the hysteretic behavior of metals (typically mild steel or lead) when deformed beyond their yield point. Energy is dissipated through cyclic plastic deformation. Therefore, the behavior of metallic yield dampers is typically modeled using plasticity theory. Common models include the bilinear model, the Ramberg-Osgood model, and the Bouc-Wen model.
# Strata Control by Roof Blasting for Bord and Pillar Mining Method in Mechanized Depillaring Panels
## 2 Empirical Approach to the Design of Underground Structures
### Width of Gallery
#### 2.2.1 Safety Factor of Pillar and Fender  
[PERSON] [17] developed an empirical formula (Equation (2)) for estimating of pillar strength, which is well established in Indian coalfields for pillar design.  
\[S=0.27\,\sigma_{c}h^{-0.36}+\left(\frac{H}{250}+1\right)\left(\frac{W_{e}}{h}-1 \right)\,\text{MPa} \tag{2}\]  
where S = pillar strength (MPa), \(\sigma\)c = uniaxial compressive strength of coal (MPa), h = working height (m), H = depth of cover (m), and we = effective width of the pillar (m) = 4A/P, where A is area and P is the perimeter of the pillar.  
The Tributary Area Method [18] is used to calculate load over pillar and fender. The formation of a pillar by driving galleries all around disturbs the state of virgin stresses, keeping the total magnitude of the overlying strata weight \(\gamma\)H constant. It is normally assumed that the entire weight overlying a tributary area is supported by solid pillars. The stress on pillar (P) is estimated using the Tributary Area Method as given in Equation (3).  
\[P=\frac{\gamma H}{1-e}\,\text{MPa} \tag{3}\]  
where \(\text{e}=\text{recovery}=\frac{[(W_{1}+\text{B})(W_{2}+\text{B})-(W_{1}W_{2})]} {(W_{1}+\text{B})(W_{2}+\text{B})}\), H = depth cover (m), B = width of the gallery (m), W1 = length of pillar (m), W2 = width of pillar (m), and \(\gamma\) = unit rock pressure (0.025 MPa/m).  
The abovementioned empirical formula has been used to estimate the strength of the pillar and fender that will be formed during mechanized depillaring in the panel. Here, the fender is half of the pillar after it splits into two unequal parts (Figure 5). Unequal division has been suggested to control the cut-out distance (COD) as per the geo-mining conditions. Considering the variation in the depth of the cover of the panel, the safety factor of the pillar and fender is estimated based on the maximum 266 m depth of cover. The safety factor of the pillar is estimated using the strength and load on the pillar, as mentioned in Table 3. The estimated safety factor of the pillars (4.28) and fenders (1.64 and 2.84) for the panel seems to be competent for the pillar extraction subjected to regular caring and less disturbance due to geological discontinuities in the working area.  
Figure 5: Pillar (34 m \(\times\) 29.5 m) is split into two unequal fenders.
## 2 Modelling and Methods
### Test Cases Examined
The comparison was carefully made against the wall assemblies incorporating the variable values that the algorithms were deprived of. Since the regressor models were generally trained using extreme values of insulation (with the exception of ANN 1, which utilised the full dataset), the comparison was made against wall assemblies featuring mid-range values (i.e., 50 mm of insulation or \(\epsilon=0.5\)). Although it was anticipated that ANN 1 would have an extremely good predictive score (since it was already trained with full data), it was included in the resulting graphs for comparative reasons. In addition to assessing each algorithm's performance, it was important to ensure that overfitting was avoided. Once the most accurate regressor was identified, it was considered necessary to evaluate its applicability on wall assemblies that had not been offered to the algorithm at any stage of its development and were, as such, completely unknown to the model. The variable values and basic structure of these new models had to be within the range of the algorithm's training space, as shown on Table 5; however, they featured completely new parameter value combinations. To enable the comparison of the ANN predictions with actual ground truth values, six new FE models were developed and analysed. Their output was finally compared to the regressor predictions, as shown in the results section of this study. The six new models included the following:
## 4 Experimental Results and Discussion
### Experimental Setup and Comparative Metrics  
The experimental system utilized in this study is powered by an Intel Core i9-10940X processor, featuring a base frequency of 3.3 GHz and 28 threads. The operating system is Ubuntu 22.04, using an NVIDIA RTX 3080 TI GPU and CUDA 12.2. The training environment is based on the deep learning framework PyTorch, with a training batch size of 32 and 500 iterations.  
To analyze the experimental results, this study employs mean average precision (_mAP_) and average precision (_AP_) for each category as metrics for evaluating the algorithm's performance. Average precision is determined by calculating the area under the precision (_P_) and recall (_R_) curve. The formulas for calculating P, R, and AP are given below:  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[AP=\int_{0}^{1}P_{th}(R)d_{R} \tag{4}\]  
where, _FN_, _FP_, and _TP_ denote false negatives, false positives, and true positives, respectively; \(N\) denotes the total number of categories detected; and _AP_ indicates the detection accuracy for different categories.  
Generally, the higher the detection rate for a specific category, the larger the _AP_ value. However, when evaluating multi-class performance across the entire dataset, using _mAP_ is more appropriate. The mAP is calculated by averaging the AP values for all categories in the dataset, providing a measure of the model's overall detection performance across the entire dataset. In this study, the evaluation metric is the average detection precision at an intersection over union (IoU) threshold of 0.5, denoted as [EMAIL_ADDRESS], and is computed as follows:  
\[[EMAIL_ADDRESS]=\frac{1}{N}\sum_{i=1}^{N}AP_{i}(IoU_{th}=0.5) \tag{5}\]  
Figure 9: Training dataset.
## 5 Discussion
The higher value of relative density and volumetric plastic strain of M10S\(\upmu\) under 100, 300, 500, and 700 MPa compared to M10 Sn, as shown in (Figure 5b) and (Figure 4c), respectively, confirm the superior densification of Mg-SiC composite containing the submicron size reinforcement. In addition to particle size, particle distribution is another crucial parameter affecting the densification behavior of powder composites [35]. Cross-sections of M10 Sn and M10S\(\upmu\) milled powders are displayed in (Figure 6c,d), with the Mg matrix in light grey and the SiC particles as white dots [33]. These Figures indicate a significantly finer nano SiC distribution than submicron SiC in the Mg matrix. Reducing SiC reinforcement size increases the reinforcement particles' perimeter and, consequently, a larger contact surface between the matrix and reinforcement. In composites containing nano SiC, the increased total contact surface results in higher pressure transfer to the SiC, which in turn causes a nonuniform distribution of pressure and, accordingly, a nonuniform density distribution [5; 36]. As evidenced by the FEM-predicted results (Figures 4b and 5b), the M10S\(\upmu\) exhibits a more homogeneous relative density distribution, which is attributed to its more homogeneous pressure distribution (Figure 5a). In conclusion, the Drucker-Prager cap (DPC) model effectively predicts the compaction behavior of powder composites containing different reinforcement sizes. However, there was an observed relative error of approximately 2% between the computational and experimental outcomes for relative density (Figure 4a) and also a reduction in volume (Figure 4d). As detailed in the previous work [27], the observed error is attributable to several influencing factors, such as spring back [37], the use of silicon spray as a lubricant during the CIP experiment [25], the assumed constant material parameters in the DPC model [11], and the omission of friction between the particles and die wall during the tests to obtain the DPC parameters [38]. The present study offers a solid foundation for understanding the intricate relationship between SiC particle size and the resulting composite properties in Mg-SiC nanocomposites under Cold Isostatic Pressing. The findings empower materials scientists and engineers to develop tailored strategies for enhancing Mg-SiC nanocomposite performance by shedding light on the underlying mechanisms and key parameters. Ultimately,this will pave the way for advanced materials with superior characteristics, catering to the ever-growing demands of various high-performance applications across industries.
# Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
## 2 Background  
We consider \(N\) multi-user MIMO BS antennas are serving \(K\) single antenna users where \(N\gg K\). The bottleneck of using this scenario (\(N\gg K\)) is the channel hardening phenomenon is being dominant. It is utilized to cancel the small-scale fading characteristics [25]. Symbols are transmitted individually by \(K\) users, where a symbol vector \(\mathbf{x}=[x_{1},\ x_{2},.....,\ x_{K}]^{T}\) is transmitted by all users. At the BS, a vector \(\mathbf{y}=[y_{1},\ y_{2},.....,\ y_{N}]^{T}\) is received as  
\[\mathbf{y=Hx+w}, \tag{1}\]  
where \(\mathbf{H}\) is the channel matrix and \(\mathbf{w}\) is the noise vector. (1) is very popular in detection techniques to estimate the transmitted signal (\(\mathbf{x}\)). However, a perfect channel estimation is assumed at the BS. The free-matrix-inversion methods depend on the equalization matrix (\(\mathbf{A}\)), which is expressed as  
\[\mathbf{A}=\mathbf{G}+\sigma^{2}\mathbf{I}_{K}, \tag{2}\]where \(\sigma^{2}\), \(\mathbf{I}_{K}\), and \(\mathbf{G}\) are the noise variance, the \(K\times K\) identity matrix, and the _Gram matrix_ or _Gramian_\(\mathbf{G}=\mathbf{H}^{H}\mathbf{H}\), respectively.
# A Lightweight Image Cryptosystem for Cloud-Assisted Internet of Things
## 2 Literature Review
In terms of security, KLEIN had the lowest degree of diffusion and the highest degree of confusion. This behavior of KLEIN was believed to be related to its SPN structure. A multiplatform Feistel structure-based lightweight cryptosystem called TWINE was developed by the authors of [41]. TWINE has a well-organized embedded software, a small hardware size, a block size of 64 bits, 36 rounds, and a key length of 80 or 128 bits. TWINE's rounds have unpredictable four-bit S-boxes and four-bit block permutations as substitution and permutation layers, respectively. RECTANGLE is another lightweight multiplatform cryptosystem created by the authors of [42]. RECTANGLE has a block size of 64 bits, a key length of 80 or 128 bits, and runs on only 25 rounds. It is a bit-slice block encryption algorithm, requires a small hardware area for its implementation, and achieves an outstanding software performance. A lightweight image cryptography algorithm that employed the techniques of message passing and a two-dimensional logistic chaotic map, consumes a low memory space, is highly secure, and has a high running performance was designed by the authors of [43]. The algorithm was tested and evaluated. The scheme's evaluation results demonstrated that the cryptosystem can withstand key sensitivity analysis even when its key space is insufficiently large. The results additionally showed that the encryption system is immune to statistical, differential, known-plaintext, and brute-force cryptanalysis attacks while consuming little time and space. Table 1 summarizes the various lightweight cryptography algorithms that are used today. \begin{table}
\begin{tabular}{c c c c c} \hline
**Author** & **Title** & **Algorithm used** & **Strength** & **Weakness** \\ \hline \multirow{3}{*}{[41]} & Lightweight cryptography & \multirow{3}{*}{Generalized Feistel structure (GFS)} & Less resources-intensive. The same program code can be used for encryption and decryption & It is vulnerable to related key attacks \\  & Multiple platforms & & & and decryption & \\ \hline \multirow{3}{*}{[44]} & An Ultra-Lightweight & \multirow{3}{*}{Feistel structure} & \multirow{3}{*}{Conserves memory} & Susceptible to related key attacks, and requires more rounds to ensure security \\  & Block Cipher & & & & \\ \hline \multirow{3}{*}{[45]} & \multirow{3}{*}{The Noekeon Block Cipher} & \multirow{3}{*}{SPN structure} & Consumes less power and has better performance than the software on legacy sensors & It is resource-intensive \\  & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of existing work on lightweight cryptography algorithms. From the reviewed literature, it appears that SPNs and FNs are more common structures adopted by researchers to ensure the security of LWC.
# Influence of Fiber Mixing Process on the Cracking Resistance of Cold Recycled Asphal Mixture
## 4 Results and Discussions
### Dispersion State of Fiber in the CRAM
#### 4.3.2 Micro-Distribution State of Fiber in CRAM Specimens  
The fiber micro-distribution state in the CRAM specimen was obtained using scanning electron microscope technology, as shown in Figure 15. It was found that the internal microstructure of the CRAM without fiber is relatively loose, while the overlapping microstructure of fibers is observed in fiber CRAM. As a result of a reinforcing role of fiber, the addition of fiber can enhance the fracture toughness of the mixture. However, because of the difference in fiber micro-distribution state, the toughness against cracking is also different. Due to the fiber agglomeration phenomenon in Figure 15b,d, the micro dispersion state of fiber is poor, which reduces the uniformity of fiber in CRAM. In contrast, an overlapping bridging network structure can be observed in Figure 15c, which makes the fiber CRAM transfer and disperse stress better when it is stressed. In addition, this structure allows the fiber to pass through the microcrack and weak interface of the mixture, and the weak position of the interface can be strengthened by the bridging principle, so as to make up for the weak point of the aggregate adhesion in RAP. This finding is highly consistent with the literature [26]. Therefore, uniformly dispersed fibers can optimize the internal structure of the fiber mixture and enhance its ability to resist cracking.
## 2 Materials and Methods
### Test Methods
#### 2.3.2 Chloride Ion Diffusion  
Chloride ions can affect the durability of concrete due to the penetration of sulfate ions. In particular, it is necessary to evaluate the chloride ion diffusion characteristics according to the application of the ACS aggregate because the de-icing agent can affect the rural-road pavement concrete in winter. The diffusion evaluation of chloride ions by rapid electrophoresis was performed according to the NT Build 492 test standard [29]. The chloride diffusion test setup is shown in Figure 2. A 100 \(\times\) 200 mm\({}^{2}\) cylindrical specimen was manufactured. A 50 mm section was cut from the center of the specimen and pretreated for the test as a sample. For the pretreatment, the cut specimen was maintained in a vacuum desiccator at a pressure of 1 to 5 kPa for 3 h at first, followed by a second vacuum enclosure for 3 h. Ca(OH)\({}_{2}\) was then added to all of the specimens to be immersed; the specimens were placed under vacuum for 1 h. The desiccator lid was opened to allow air exposure for 18 \(\pm\) 2 h; the specimen surface was then drained. After this pretreatment, specimens were installed in the test apparatus. For the installation method, first, the specimen was placed in a rubber tube and fixed with two stainless-steel clamps, and the surrounding area was treated with silicone to prevent the solution from leaking into the area where the specimen and rubber tube intersect. After fixing the specimen, 300 mL of 0.3 M NaOH was added to the rubber tube along with a stainless-steel circular plate inserted for use as an anode. The test sample was then placed on a device with a plastic base, where it was soaked in a container carrying 12 L of 10% NaCl (mass concentration) solution. After sample preparation, an external power supply was connected to the sample, and voltage was applied. During the experimental process, the initial current running through each specimen was measured by applying a voltage of 30 V at the beginning of the experiment. From the measured initial current value, the voltage to be applied to the specimen was selected from Table 9 and the voltage was adjusted [30; 31]. After determining the voltage to be applied from the initial measurements, the voltage was applied to the test specimen from which the associated current was recorded. The temperature of the internal solution was measured. After voltage was applied, the current on completion of the experiment and the temperature inside the rubber tube was measured and recorded. Finally, after the test was completed, the specimen was separated from the rubber tube and the surface was cleaned. Then, the depth of the discolored portion of the specimen was measured by spraying 0.1 M AgNO\({}_{3}\) on the refined surface. The depth of discoloration was measured by excluding approximately 10 mm from the end of both sides of the specimen; the mean
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 1 Introduction
VOEI performs the decision-making of a video-aware FANET by taking into account energy issues to provide energy-efficiency operations, avoiding the premature death of nodes, network disconnections, route failures, void areas, and low-quality video transmissions. VOEI supports the concept of SDN into FANET to separate the control and data plane, as well as to provide network flexibility and programmability by controlling UAVs' operation parameters on-the-fly. The main benefits of VOEI is due to the SDN controller considers global FANET context information to establish reliable and energy-efficiency routes, and to prevent UAV loss caused by energy issue and video delivery failure. In this sense, the controller must compute reliable and energy-efficiency routes, as well as trigger the UAV replacement process to keep the FANET connectivity level (dynamic paths) with a suitable quality level. Simulation results demonstrate the efficiency of the VOEI in disseminating videos with high energy-efficiency, while achieving QoE support in a video-aware FANET scenario. The main contributions of VOEI are twofold: (i) the use of a SDN controller to control all operations in a cooperative FANET system, while improving the selection of reliable, suitable and energy-efficiency routes. Thus, VOEI aims to increase the video QoE and the network lifetime; (ii) assessment of the impact of different UAV speeds on the energy level, as well as QoE. The remainder of the article is organized as follows. Section 2 outlines existing works for video dissemination over FANET, and their main drawbacks to support energy-efficiency and QoE. Section 3 presents the VOEI mechanism by describing its components and operations. Section 4 describes the simulation settings for the performance evaluation in a video dissemination scenario and also discusses the results. Finally, the concluding remarks are presented in Section 5.