esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result Analysis
### Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sampling** & **Model** & **F-Score** & **Type I Error** & \begin{tabular}{c} **Type II** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Geometric** \\ **Mean** \\ \end{tabular} \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Non- \\ sampling \\ \end{tabular} } & LR & 0.8554 & 0.9756 & 0.0159 & 0.5043 & 0.1550 \\  & MLP & 0.8551 & 0.9823 & 0.0167 & 0.5005 & 0.1319 \\  & Deep MLP & 0.8736 & 0.9824 & 0.0358 & 0.4909 & 0.1303 \\  & RF & 0.8130 & 0.9676 & 0.1169 & 0.4578 & 0.1692 \\  & GBT & 0.7874 & 0.9472 & 0.1525 & 0.4502 & 0.2115 \\  & KNN & 0.8154 & 0.9474 & 0.1017 & 0.4755 & 0.2174 \\  & SVM & 0.8299 & 0.9356 & 0.0807 & 0.4919 & 0.2433 \\ \hline \multirow{8}{*}{SMOTE} & LR & 0.9047 & 0.5263 & 0.0339 & 0.7199 & 0.6765 \\  & MLP & 0.8348 & 0.4211 & 0.1864 & 0.6963 & 0.6863 \\  & Deep MLP & 0.8202 & 0.4595 & 0.2067 & 0.6669 & 0.6548 \\  & RF & 0.8065 & 0.7895 & 0.1525 & 0.5290 & 0.4224 \\  & GBT & 0.7627 & 0.7368 & 0.2373 & 0.5130 & 0.4480 \\  & KNN & 0.6275 & 0.5789 & 0.4576 & 0.4818 & 0.4779 \\  & SVM & 0.7833 & 0.7368 & 0.2034 & 0.5299 & 0.4579 \\ \hline \multirow{8}{*}{ADASYN} & LR & 0.9134 & 0.5263 & 0.0169 & 0.7284 & 0.6824 \\  & MLP & 0.9000 & 0.3684 & 0.0847 & 0.7735 & 0.7603 \\ \cline{1-1}  & Deep MLP & 0.8671 & 0.3903 & 0.1356 & 0.7371 & 0.7260 \\ \cline{1-1}  & RF & 0.8160 & 0.7895 & 0.1356 & 0.5375 & 0.4266 \\ \cline{1-1}  & GBT & 0.8130 & 0.7368 & 0.1525 & 0.5554 & 0.4723 \\ \cline{1-1}  & KNN & 0.6214 & 0.6316 & 0.4576 & 0.4554 & 0.4470 \\ \cline{1-1}  & SVM & 0.7934 & 0.7368 & 0.1864 & 0.5384 & 0.4628 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results of the prediction models among women. with the MLP classifier obtained the best prediction model, followed by ADASYN with deep MLP and LR classifiers presented the high scores in a data sample of women, as shown in Figure 5. Similar to the other samples, single LR, MLP, deep MLP and RF classifiers illustrated the lowest performance due to severe imbalance ratio and small sample data of women. To summarize, single models were inferior to the synthetic oversampling-based models over each sample. Figure 4: Boxplot of the geometric mean over prediction models among men. Figure 3: Boxplot of the geometric mean over prediction models among all subjects.
## 3 Experimental Verification
### Comparison between Theory and Experiment  
Formula (6) shows that the influence of the eddy current effect is related to the train speed \(v\) and suspension current \(I\), which is consistent with the experimental results. Because the formula is derived based on the two-dimensional model and ideal situation, it will be different from the real situation. After several empty-load experiments, the results shown in Table 3 are obtained. This result will be used for rectifying the formula calculation. The parameter rectification is mainly about permeability \(\mu_{0}\), \(v\), and 2\(d\). The rectification method is to multiply these three parameters by a certain coefficient. The rectified results are shown in Figure 9. Then, according to the rectified formula, the variation of the suspension force with the rail width 2\(a\) at different speeds can be calculated, as shown in Figure 10. It can be seen that the greater the rail width 2\(a\), the stronger the attenuation of the suspension force. Therefore, changing the rail width 2\(a\) is a feasible scheme to suppress the influence of eddy current.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type** & **Current (0 km/h)** & **Current (60 km/h)** & **Current Variation** & **Suspension Force Variation** \\ \hline Point 1 (empty-load) & 29 A & 27.2 A & \(-\)6.21 \% & 13.67 \% \\ Point 1 (full-load) & 33.2 A & 30.8 A & \(-\)7.23 \% & 16.19 \% \\ Point 2 (empty-load) & 27 A & 34.5 A & 27.78 \% & \(-\)38.75 \% \\ Point 2 (full-load) & 32 A & 42 A & 31.25 \% & \(-\)41.95 \% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of eddy current effect on suspension system.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Speed** & **Current** & **Current Variation** & **Suspension Force Variation** \\ \hline
0 km/h & 27 A & – & – \\
55 km/h & 31.37 A & 16.19\% & \(-\)25.92\% \\
60 km/h & 34 A & 25.93\% & \(-\)36.94\% \\
75 km/h & 36.5 A & 35.19\% & \(-\)45.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of eddy current effect on suspension system under different speeds.
## 3 Results
### ISB Estimates Derived from Low-Cost Receivers
As to the accuracy improvement coming with a priori ISB constraints, it is confined to several centimeters in terms of the 3D component under the cutoff elevations of 10\({}^{\circ}\) and 20\({}^{\circ}\) and reaches the optimal level at an elevation mask of 30\({}^{\circ}\). The 3D accuracy improvement is less than 2 dm when the cutoff elevations are further increased to 40\({}^{\circ}\) and 50\({}^{\circ}\), due to the inclusion of more position solutions at a lower accuracy level for the SPP processing with a priori ISB constraints. Figure 14 illustrates the SPP results under a cutoff elevation of 30\({}^{\circ}\). The accuracy improvement is obvious in the up direction after taking the a priori ISB constraints into consideration, and the epochwise position errors usually vary within a range of 10, 10, and 20 m in the east, north, and up directions, respectively. Compared with the SPP solutions without a priori ISB constraints, the positioning accuracy (i.e., RMS statistics of epochwise position errors) for those with a priori ISB constraints is improved by approximately 1.5 and 6.3 dm (10% and 11%) in the east and up directions, respectively, but slightly degrades by about 3 cm in the north direction. The limited performance enhancement that benefits from the a priori ISB constraints may be attributed to the combination of only two satellite systems and the worse ISB estimates with the low-cost receiver. Figure 13: Epochwise ISB estimates between GLONASS and GPS derived from low-cost u-blox MST receiver at an elevation mask angle of 10\({}^{\circ}\) on DOY 304 and 305 of 2021.
## 5 End-to-End Solutions for M2M Communication
### Technology Integration for M2M Communications
In [116], a mapping between the QoS requirements of mHealth services and the existing service classes defined by 3 GPP standards is proposed, aiming to provide guidelines for network operators. A cross-layer design for QoS of medical video streaming over mobile WiMAX (IEEE 802.16e) and the 3G High-Speed Packet Access (HSPA) cellular technology is proposed in [117], and a comparison between the performance of the two technologies is given, opening the road to further investigation on LTE-based mHealth applications.
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
## 2 Background
### Fock Matrix Computation
#### 2.2.1 Two-Electron Repulsion Integrals
The evaluation of ERIs requires calculating the integral values for all possible combinations of four basis functions, resulting in a computational complexity of \(O(M^{4})\) for \(M\) basis functions. The value of \(M\) is determined by factors such as the number of atoms in a given system, their atomic numbers, and the chosen basis set. However, due to the symmetry of basis functions in Equation (11) as follows:  
\[(\mu\
u|\lambda\sigma) =(\
u\mu|\lambda\sigma)=(\mu\
u|\sigma\lambda)=(\
u\mu|\sigma \lambda)\] \[=(\lambda\sigma|\mu\
u)=(\lambda\sigma|\
u\mu)=(\sigma\lambda| \mu\
u)=(\sigma\lambda|\
u\mu), \tag{18}\]  
the number of unique integral values is not \(M^{4}\) but rather \(M(M+1)(M^{2}+M+2)/8\), approximately reducing to \(1/8\) of the total. Despite the symmetry in Equation (18), the evaluation of ERIs requires computational effort of the order of \(O(M^{4})\). However, screening techniques have been developed to significantly reduce the number of integrals requiring computation. Among these, a prominent approach involves calculating an upper bound for each ERI based on the Schwarz inequality [36], which is a simple yet powerful technique widely used in various libraries and applications in quantum chemistry [37; 38; 39; 40; 41; 42]. If this bound is negligibly small, the corresponding integral evaluation is omitted. By applying the Schwarz inequality to Equation (11), an upper bound for the absolute value of each ERI is given by  
\[|(\mu\
u|\lambda\sigma)|\leq\sqrt{(\mu\
u|\mu\
u)}\sqrt{(\lambda\sigma|\lambda \sigma)}. \tag{19}\]  
If the Schwarz upper bound falls below a predefined cutoff threshold \(\theta\), we can approximate the ERI as \((\mu\
u|\lambda\sigma)\approx 0\) and skip the complex integral computation. The factors of the upper bound are ERIs over basis function pairs, and since their combinations scales only with \(O(M^{2})\), we can compute all factors required for the screening at low cost. Once these factors are stored in memory, the significance of the desired ERI over the quartet of basis functions can be determined simply by reading two factors, \((\mu\
u|\mu\
u)\) and \((\lambda\sigma|\lambda\sigma)\). The cutoff threshold \(\theta\) control the trade-off between accuracy and computational cost. While a larger value significantly reduces computational expense, it can also introduce non-negligible errors in computed quantities such as the orbital coefficients \(\mathbf{C}\) and energies of a system. The upper bound using the Schwarz inequality can be calculated not only for contracted ERIs but also for primitive ERIs as follows:  
\[\chi_{\mu}(\mathbf{r})=\sum_{a=1}^{K_{\mu}}w_{a}G_{lmn}(\mathbf{r},\mathbf{A},\rho_{a}), \tag{12}\]0  
where the factors \(\sqrt{[ab|ab]}\) and \(\sqrt{[cd|cd]}\) are calculated from primitive ERIs for a pair of the Gauss-type orbitals. By utilizing the upper bound of the each primitive ERI given by Equation (20), we can perform a finer screening of integrals that cannot be reduced by Equation (19) alone.
###### Abstract  
Elastomeric bearings are commonly used in base-isolation systems to protect the structures from earthquake damages. Their design is usually developed by using nonlinear models where only the effects of shear and compressive loads are considered, but uncertainties still remain about consequences of the tensile loads produced by severe earthquakes like the near-fault ones. The present work aims to highlight the relapses of tension on the response of bearings and superstructure. To this end, three-, seven- and ten-storey r.c. framed buildings are designed in line with the current Italian seismic code, with a base-isolation system constituted of High-Damping-Rubber Bearings (HDRBs) designed for three values of the ratio between the vertical and horizontal stiffnesses. Experimental and analytical results available in literature are used to propose a unified nonlinear model of the HDRBs, including cavitation and post-cavitation of the elastomer. Nonlinear incremental dynamic analyses of the test structures are carried out using a homemade computer code, where other models of HDRBs considering only some nonlinear phenomena are implemented. Near-fault earthquakes with comparable horizontal and vertical components, prevailing horizontal component and prevailing vertical component are considered as seismic input. Numerical results highlight that a precautionary estimation of response parameters of the HDRBs is attained referring to the proposed model, while its effects on the nonlinear response of the superstructure are less conservative.  
b -  
of the bearings are often presented. Most of these only consider compressive and shear stresses and describe the behavior of the bearings by referring to one or more springs with constant stiffness. However, large vertical loads and lateral displacements produced by strong EQs can be responsible for substantial reductions in the horizontal and vertical stiffnesses of the elastomeric bearings and of their load-carrying capacity.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Data Representation
#### 4.8.2 Obstacle Structure  
For each encountered obstacle, we define a structure that contains all of the nodes that have been generated on that obstacle, at any instant of time. The set \"ExpandedObsts\" contains the structures of the expanded nodes and the set \"EndObsts\" contains those of the end nodes that are not yet expanded. The obstacle structure has the following four fields.  
* 'ObstNo': the integer identification number of the obstacle.
* 'EndNodes': the set of the structures that represent the nodes without successors that reside on this obstacle, in the increasing order of their position in the contour of the obstacle.
* 'ExpandedNodes': a similar set as the preceding one for the nodes that have been expanded.
* 'FreeTangents': the set of tangent structures that represent the tangents leaving this obstacle toward other obstacles, while they have not yet been assigned to a node on this obstacle. _Obst_(\(x\)) will denote the obstacle structure for the obstacle on which resides the node \(x\). Note that all of the nodes on expanded obstacles are not necessarily expanded, but all of the nodes on end obstacles are necessarily end nodes. A set called ObstNo contains all of the obstacle numbers. When there are \(N\) obstacles, the obstacle number \(N+1\) is assigned to the starting point \(S\) and \(N+2\) to the target point T.
## 4 Model Description
### System Model  
There are three types of characters as shown in Figure 1 in our system. Users offer their attempt histories to join in the training of policy for same problem or only ask for the policy on server for operation suggestions. If it is needed, users could also ask for trained policy. These different actions are shown in Figure 1 as UserA, UserB and UserC.  
Server collects encrypted data from users offering it, and fuse those data from different users and protected by different secret key to a unified encrypted state database. After that, the Server could act further calculation to update the policy matrix. This policy matrix is usable but only generate encrypted results so that it can not be used by the person who do not hold the correct secret key.  
Key Generation Center (KGC) is also the manager for this system and act as a trusted third party but it did not join in the operation too much. After the collecting of encrypteddata from users by the Server, the KGC should help the Server to transform ciphertext that can support the needed calculation. It also helps to convert results set for those users who legally ask for calculation result. Except what mentioned above, KGC do not undertake the calculation cost. It would distribute keys for all participators including itself and tasks. In fact, the data from users are converted to a domain protected by the key of KGC by default by the Server, which offers the right of decrypt them to KGC.
## 6 Experiments and Discussion
### Semi-Physical Simulation Experiments
#### 6.2.2 Navigation Accuracy
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Selected Satellites ID** & **GDOP** & **3D Positioning Error** \\ \hline STC & 10, 13, 21, 29 & 2.430 & 1.072 m \\ ATC & 10, 13, 18, 24, 29 & 2.159 & 0.728 m \\ MATC & 15, 10, 18, 21, 29 & 2.168 & 0.461 m \\ RMNCE-TC & 15, 10, 18, 21, 29 & 2.168 & 0.205 m \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Satellite selection results of the different schemes at the 2050-th second. Figure 7: 3D positioning errors of the selected schemes during [1900 s, 2500 s].
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Globally Optimal Algorithm (Goma)  
The _GOA_[43] technique is an upgraded version of _NAA_. It was developed to decrease the delay of the real event. _GOA_ provides each node with a timer that is defined by a pseudo-random number generator. This timer is utilized to allow the node to transmit real packets. If the time count reaches zero, and there is no real packet to transmit, a fake packet is transmitted instead. All nodes must use the same pseudo-random number generator to confuse the adversary and obtain the seed used by other nodes. This concept allows _GOA_ to decide the shortest path to the sink. The _GOA_ technique improves the throughput and power consumption when compared to _ConstRate_. However, the primary issue in _GOA_ is that it should have a knowledge of the entire topology of the network, which is not required by _ConstRate_.
###### Abstract
Dynamic programming in [20] is applied for DG allocation in such a way as to increase the DisCo benefits. Additionally, the teaching-learning-based optimization algorithm is proposed in [21] to reduce energy losses and improve voltage profile and voltage stability. A hybrid evolutionary algorithm with game theory to maximize DG owner net income and reduce energy losses; voltage variation stability index and energy cost is presented in [22]. Pareto frontier differential evolution to reduce energy losses and voltage variation and to improve voltage stability is proposed in [23]. Finally, mixed-integer linear programming via CPLEX to increase the DG owner's net income and reduce DisCo cost is considered in [24]. As a contribution to this line of research, a multi-objective analysis for the DG sizing and siting problem in distribution networks and three simple decision making strategies are carried out in this work, aiming at common good among perspectives of concern. The main features and contributions of this work are described as follows:  
* Three objective functions are considered regarding the proposed multi-objective model: energy losses as a technical aspect; DG plant installation costs as an economic aspect; and equivalent CO\({}_{2}\) emission as an environmental concern of society. To the best of the authors knowledge these objective functions have not been simultaneously considered in the way they are approached in this paper. * A new multi-objective solution methodology based on Genetic Algorithm combined with Maximin metric, named in this work as Multi-Objective Hybrid Genetic Algorithm (MOHGA), are implemented to obtain optimal Pareto sets of solution alternatives. * Max-Min approximation as non-preference criterion and Minimal power losses and maximum net present value as preference criteria are applied as decision making strategies to select final solutions from obtained optimal Pareto sets. * The approach robustness allows the application of proposed methodology in real-size distribution networks, as evidenced by the results with 918-bus Brazilian network. To address the proposed modeling for DG sizing and siting problem and the adopted solution process, the following assumptions are made in this work:  
* Only renewable energy sources are considered (hydraulic, wind, and photovoltaic). * Network connection buses and maximum capacity (divided in modules) of generators are known data of the problem, in order to take into account proper conditions such as available source of energy, environmental restrictions, and occupation area. * Maximum capacity for DG plants is set in 5 MW, since it covers common regulations (specifically, in Brazil, this value corresponds to the maximum allowed for DG's). This paper is organized as follows: the proposed multi-objective optimization model and solution method are addressed in Section 2; Section 3 presents the results and analysis performed in the two networks as case studies, whose main conclusions are drawn in Section 4.
## 2 System Model
### LEACH Protocol
#### 2.2.1 Setup Phase  
The selection of CHs follows a probabilistic model, aiming to allocate energy usage evenly throughout the network according to the mathematical formulation [39]:  
\[\mathbb{P}(\mathsf{CH})=\frac{1}{1-P\cdot(r\cdot\mathtt{mod}\cdot\frac{1}{p})} \tag{1}\]  
where \(\mathbb{P}(\mathsf{CH})\) denotes the probability threshold dictating whether a specific node will act as a CH in the ongoing round, while \(P\) signifies the percentage of nodes designated to become CHs--a parameter representing the optimal proportion of CHs at any point. The variable \(r\) denotes the current round number, and the operation mod refers to the modulus operation. CHs are responsible for collecting data from member nodes. To balance energy consumption, CHs rotate periodically.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 6 Result Analysis and Discussions
### Robustness
#### 6.4.2 Machine Learning  
Nowadays, intruders apply various machine learning algorithms to guess whether a media contain stego contents or not. They even try to extract the secrets as well through their machine learning algorithms. To evaluate the robustness of our scheme, we implement a one-hot encoder for using nucleotides in a convolution neural network. The method first takes the cover nucleotides and the stego nucleotides of the proposed method, [PERSON] et al. [30], [PERSON] et al. [31] and [PERSON] et al. [32] in five separate files. We first extracted the nucleotides from the cover file. As the nucleotides are one of 'A', 'C', 'G' and 'T', we generated four bits as one-hot codes for each nucleotide. 'A', 'C', 'G' and 'T' are used for label/class values. Thus, we had a long list of data where each one consisting of 4 bits. Suppose that this is \(X\). Similarly, we have the same length of class labels, i.e., nucleotides. Let these data be \(Y\). We divided both \(X\) and \(Y\) in a ratio of 9:1. Let the first 90% of data be \(trainX\) and \(trainY\). In the same way, let the remaining 10% of data be \(testX\) and \(testY\). We built a convolution neural network (CNN) model to train \(trainX\) and \(trainY\). Afterwards, we applied \(testX\) to predict \(testY\). We measured the accuracy in decoding the one-hot code. We performed the same tasks for the other four files, i.e., for the stego nucleotides of the proposed method, following [PERSON] et al. [30], [PERSON] et al. [31] and [PERSON] et al. [32]. The results are demonstrated in Table 15. The table states that the accuracies in stego nucleotides are very close to those in cover. This means that detecting stego contents in a stego DNA sequence by deep learning is quite hard.  
Another noticeable point is that the accuracy is 0 in [PERSON] and [PERSON] for \(OurDNA.2\) cover sequence. It happens as a result of the small size of the sequence. That sequence contains only 10 nucleotides. For such a small dataset, a deep learning or machine learning algorithm cannot provide accurate results. Rather, any result can be produced by a machine. Hence, it would not be rational to believe the accuracy of the other three pieces of data of the \(OurDNA.2\) cover sequence as well.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **Cover** & **Proposed** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline AC153526 & 51.79 & 48.83 & 48.83 & 48.80 & 48.79 \\ \hline MN908947.3 & 48.37 & 48.40 & 48.40 & 48.64 & 48.24 \\ \hline MW738481 & 47.29 & 47.39 & 47.26 & 47.57 & 47.20 \\ \hline NC045512.2 & 48.37 & 48.40 & 48.32 & 48.64 & 48.20 \\ \hline OU055787.1 & 48.09 & 48.14 & 48.11 & 47.30 & 47.96 \\ \hline OurDNA.1 & 36.77 & 36.79 & 36.83 & 36.96 & 36.89 \\ \hline OurDNA.2 & 33.33 & 33.33 & 50.00 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Decoding accuracy of CNN.