esa-sceva/satcom-chunk-collection huggingface dataset:
# Securely Computing the Manhattan Distance under the Malicious Model and Its Applications
## 3 Preparatory Knowledge
### Goldwasser-Micali Encryption Algorithm  
The Goldwasser-Micali (GM) encryption algorithm [34] has XOR homomorphism.  
Preparation: Two large prime numbers \(p\) and \(q\) are selected to get \(n=pq\). And \(t\in Z_{n}^{1}\) (where \(Z_{n}^{1}\) is a subset of Jacobi containing elements of \(Z_{n}^{*}\)) is selected as part of the public key. The private key of the algorithm is \((p,q)\) and the public key is \((n,t)\).  
Encryption: For \(m=m_{1}m_{2}\cdots m_{s}(m_{i}\in\{0,1\})\) in binary representation, the random number \(r\) is selected to encrypt the message \(m_{i}\):  
\(E(m_{i})=t^{m_{i}}r_{i}^{2}\text{mod}n=\left\{\begin{array}{ll}tr_{i}^{2} \text{mod}n,&m_{i}=1\\ r_{i}^{2}\text{mod}n,&m_{i}=0\end{array}\right.\)  
Decryption: The private key \((p,q)\) is used for decryption to obtain the ciphertext:  
\(m_{i}=\left\{\begin{array}{ll}0,&(\frac{E(m_{i})}{p})=(\frac{E(m_{i})}{q}) =1\\ 1,&(\frac{E(m_{i})}{p})=(\frac{E(m_{i})}{q})=-1\end{array}\right..\)  
Among them, \((\frac{a}{p})\) is defined as follows:  
\[(\frac{a}{p})=\left\{\begin{array}{ll}1,&p\text{ cannot divide }a,&p\text{ is the }\text{ }\text{sec}\text{ }\text{ond}\text{ residue of }a;\\ -1,&p\text{ is not divisible by }a,&p\text{ is a quadratic non-residue of }a;\\ 0,&p\text{ can divide }a.\end{array}\right.\]  
XOR homomorphism: \(E(m_{1})\cdot E(m_{2})=E(m_{1}\oplus m_{2})\).
## 6 Keystroke Recognition
### Preliminary Results for Five-Key Recognition  
The CSI was fed as an input to the data processing framework detailed above, and we obtained the following result for a single user. In Figure 9, we present a confusion matrix for our keystroke classification algorithm as it predicts the key pressed by our user. The rows represent true classes, and the columns represent predicted classes. Hence, the leading diagonal represents the true positive rate for each class. The TFP represents the true positive rate, and FNR represents for false negative rate.  
The overall accuracy of the classifier was 89.04%. Z performed the worst of the five classes with a true positive rate of 73.8%, with H following closely at an accuracy of 76.2%. Investigating this more closely, the letter Z was misclassified as H 16.70% of the  
Figure 8: Data processing and ML framework for keystroke detection.  
Figure 9: Confusion matrix for keystroke recognition.  
time. This result may indicate the difficulty for the classifier in creating clear boundaries between keystroke classes in the case of the H key, which sits in the middle of the keyboard. Furthermore, we note that no other keystroke was misclassified as P, as demonstrated by the zeros in the column for the predicted class P. This is symptomatic of the P key being the most spatially separated from all other keys. When cross-evaluating each set of CSI with the validation set from the alternate experimental subject, we observed the overall true positive rate drop by 5%. Overall, we derived the following key outcomes from this experiment:  
1. We can maintain a high accuracy of keystroke recognition with a single antenna system deploying a low-complexity ML framework, significantly reducing hardware costs compared to prior work.
2. The cross-validation result highlights the susceptibility of keystroke recognition systems to changes in personnel.
3. Detecting keystrokes incurs an accuracy degradation compared to our prior results on hand fingerprints. This motivates the need for more sophisticated sensing algorithms to detect finer movements.  
Overall, our system achieved 89% accuracy for a five-key recognition task using low-complexity ML with a single antenna. Keys spatially close or centred (e.g., Z and H) showed more misclassifications, emphasising the spatial limitations of CSI.
## 6 Evaluation
### Effects of Hybrid Ratio  
Figure 5 demonstrates the performance of different ratios of hybrid attacks in a topology with seven switches and three servers. When the ratio of hybrid attacks increases, the detection accuracy of LAD, RADAR, and COM becomes inferior. FAD's average detection accuracy is 75%, RADAR's average detection accuracy is 72.22%, and COM's average detection accuracy is 90%. The detection accuracy of the FDD is the most stable, with an average detection accuracy of 96.67%, which is better than previous studies.  
Figure 5: Accuracy vs. hybrid ratio.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Yes (Fact)** & **No (Fact)** \\ \hline Yes (Prediction) & TP & FP \\ No (Prediction) & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
We further explored the type I and type II errors of all studies by observing the false-positive rate (FPR) and false-negative rate (FNR). A type I error is the false rejection of the null hypothesis (FPR) such as \"normal traffic is detected as a hybrid attack\", while a type II error is the false acceptance of the null hypothesis (FNR) such as \"a hybrid attack is detected as normal traffic\". Figure 6 describes the reason causing the difference in average detection accuracy. FDD-FPR and FDD-FNR are both lower than 10%, which is better than all the other detection systems because the FDD detects target link congestion in small-size topologies more easily.  
It should be noted that the accuracy rate of previous studies shown in Figure 5, which are LAD, RADAR, and COM, dropped between the 20% and 80% hybrid ratio. On the other hand, the FPR and FNR of previous studies, which are LAD, RADAR, and COM, changed abnormally between the 20% and 80% hybrid ratio. This is because the hybrid attacks were a combination of an SEA and LFA. Previous detection systems only depend on one value that increases under a single-type attack, and they cannot identify hybrid attacks. However, our proposed FDD maintains high accuracy and a low FPR/FNR because it identifies the attacks according to flows through the target area, which indicates the differentiation of the service input and output flows. As long as the service is affected by the attack, whether it is link congestion or server collapse, the FDD will be determined as an attack. We can infer that the FDD can solve the problem that single-type detection systems cause a miss rate when hybrid attacks occur.
## 3 Experimental Results
### Fracture Sequence of Composite Rock Layers  
The interlayer shear slip has a significant impact on the fracturing of the rock strata, and it weakens the integrity of the rock strata. The bending section coefficient of the rock strata that is undergoing interlayer shear slip decreases from \(W_{t}=2/3 bh^{2}\) to \(W_{t}=1/6 bh^{2}\), making it more prone to fracturing. According to the maximum tensile stress strength criterion, the fracturing of composite rock layers depends on whether the internal tensile stress reaches a limit value. At the point of fracturing, the rock stratum undergoes extreme bending deformation, and the extreme curvature of the upper and lower layers of rock strata are  
\[\frac{1}{\rho_{cr1}}=\frac{2[\sigma_{t1}]}{E_{t1}h_{1}}\text{ and }\frac{1}{\rho_{cr2}}=\frac{2[\sigma_{t2}]}{E_{t2}h_{2}} \tag{18}\]  
where \(\rho_{cr1}\) and \(\rho_{cr2}\) are the limiting curvature radii of the upper and lower rock masses (m); \([\sigma_{t1}]\) and \([\sigma_{t2}]\) are the ultimate tensile strengths of the upper and lower rock strata (MPa); and \(E_{t1}\) and \(E_{t2}\) are the tensile elastic moduli of the upper and lower rock strata (GPa).  
As can be seen from Equation (18), the ultimate curvature of a rock stratum is related to its ultimate tensile strength, tensile elastic modulus, and thickness. When bending occurs, if the ultimate curvature of the upper rock stratum is greater than that of the lower rock stratum, the lower rock stratum reaches the ultimate tensile stress first and fractures. If the ultimate curvature of the upper rock stratum is less than that of the lower rock stratum, the upper rock stratum fractures first. If the ultimate curvatures of the two rock strata are the same, there is a high probability that the two rock strata will fracture synchronously. In fact, the fracturing of the upper or lower rock stratum will produce a dynamic load and may even cause the other layer of the rock strata to fracture. In particular, in the case in which the thickness and strength of the two rock strata are relatively close and the upper rock stratum fractures first, the load borne by the lower rock stratum will increase sharply in an instant. For some mining tremor events, underground workers and surface residents have described the sound characteristics as two short-interval muffled explosions followed by a continuous rumbling sound.  
Figure 15: Distributions of shear stress and friction force between the rock layers: (**a**) under a concentrated load \(P\); and (**b**) under a uniformly distributed load \(q_{0}\).
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 4 Sensor Experiment and Discussion
### FeNi Film Thickness and Aspect Ratio Effect on Sensor Performance  
The sensor response of the developed 150 MHz SAW sensors with different FeNi film aspect ratio (1:1, 1.5:1, and 2:1) and various FeNi film thickness (300, 500, and 700 nm) were characterized at current range of 0-10 A and room temperature (20 \({}^{\circ}\)C), as concluded in Figure 13. Figure 13a indicates the sensor sensitivity for different FeNi film aspect ratio (1:1, 1.5:1, and 2:1) was measured to be 8.4, 9.7, and 10.7 KHz/A. Obviously, larger aspect ratio of FeNi film is effective to improve the sensor sensitivity. On the other hand, the effect on sensor sensitivity from the FeNi film thickness is relatively complicated, as shown in Figure 13b. Compared to the sensors with FeNi film thickness of 300 and 700 nm, the senor with a 500 nm thickness FeNi film has highest sensitivity, which agrees well with the calculated result mentioned in Figure 4a. Therefore, the optimal FeNi thickness and aspect ratio were determined to be ~500 nm and 2:1, respectively.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 1 Introduction
### Main Contributions
Section 4 outlines the architecture and core procedures of the proposed edge-assisted QoS routing approach. Section 5 analyzes the complexity of the proposed algorithms. Section 5.4 presents a comparative evaluation between our offloaded DODAG computation and two heuristic-based solutions. Finally, the conclusions and future research directions are discussed in Section 6.
# Experimental Evaluation of Rigidity Center
## 3 Results and Discussion
### Results of Group A Buildings

Table 7: Rigidity center comparisons of Group A.  
Figure 15: Test results of Group A. As can be seen from the Figure 16 and Table 7, in all buildings except A5, the RCs calculated from the tests were almost compatible with the ones found from ETABS. In the symmetrical A1 building, the RC was found close to the MC as expected; in A2, the RC was on the single shear wall; and in A3, the RC was found at the conjunction of the shear wall axis. As A4 had two shear walls with equal distance to the MC, the RC was found to be between those shear walls close to the single shear wall aligned differently. In A5, although the building had two-way symmetry, test result was not compatible with the analytical result due to the change in the mechanical properties of the balsa plates and the unexpected problems in manufacturing. However, in general, it can be said that the test results were consistent with the results found from ETABS. The difference was almost 4% (except for A5).
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 6 Scheduling Scheme
### Using Genetic Algorithm to Decide the Scheduling Sequence  
Genetic algorithm is used to decide the sequence of UAVs in scheduling. To apply the genetic algorithm, one UAV is represented as a gene and one chromosome represents a sequence of the UAVs in scheduling. Hence, the size of a chromosome is equal to the number of UAVs to be scheduled.  
For the first generation, we generate \(C\) chromosomes to create different sequences of UAVs. We have a condition when generating a chromosome that for UAVs that are in the same lane, the UAV ahead should appear first in the chromosome so that it will be scheduled first. Then, the UAVs are temporarily scheduled according to their order in the chromosome. We will discuss how we schedule a UAV in Section 6.3. We temporarily schedule the UAVs since the final schedule will be based on the best chromosome. After scheduling all UAVs in a chromosome, we compute the chromosome's fitness value based on the objective function. The objective is to minimize  
\[\sum_{U=1}^{m}t_{travel_{U}} \tag{21}\]  
where \(t_{travel_{U}}\) is the travel time of \(UAV_{U}\) from the time it sent request until it exits the intersection and \(m\) is the total number of UAVs scheduled in the current scheduling instant.  
Then, we select the chromosomes with the top fifty percent highest fitness values. From the chosen top chromosomes, we perform crossovers and mutations to generate new chromosomes and complete the population of \(C\) chromosomes for the next generation. These processes are performed repeatedly to improve the fitness values of the chromosomes. The genetic algorithm is terminated when it reaches \(G\)th generation. The final scheduling sequence of the UAVs will be based on the best chromosome of the \(G\)th generation.  
Figure 7: Example red–black tree of a cube.
###### Abstract  
The grid connection of intermittent energy sources such as wind power and photovoltaic power generation brings new challenges for the economic and safe operation of renewable power systems. To address these challenges, a multi-time-scale active power coordinated operation method, consisting of day-ahead scheduling, hour-level rolling corrective scheduling, and real-time corrective scheduling, is proposed for the combined operation of wind-photovoltaic-thermal-hydro power and battery (WPTHB) to handle renewable power fluctuations. In day-ahead scheduling, the optimal power outputs of thermal power units, hydro-pumped storage units, and batteries are solved with the purpose of minimizing the total power generation cost. In hour-level rolling corrective scheduling, the power output plan of thermal power units and pumped storage units is modified to minimize the correction cost based on the on-off state of thermal power units determined in day-ahead scheduling. In real-time corrective scheduling stage, the feedback correction and rolling optimization-based model predictive control algorithm is adopted to modify the power output of thermal power units, hydro-pumped storage units, and batteries optimized in hour-level rolling correction scheduling, so as to ensure the economy of the correction plan and the static security of system operation. Finally, simulation results demonstrated that the proposed method can accurately track system power fluctuations, and ensure the economic and security operation of a multi-energy-generation system.  
multi-time-scale; wind-solar-thermal-hydro power and battery; coordinated operation; day-ahead scheduling; hour-level rolling corrective scheduling; real-time corrective scheduling
# A Study on a Mechanism of Lateral Pedestrian-Footbridge Interaction
## 2 A Dynamic Model of Lateral Pedestrian-Footbridge Interaction  
A pedestrian is considered using an inverted pendulum model with the dynamic feature of the hybrid Van der Pol/Rayleigh model, where the coupling pedestrian-footbridge vibration is presented in Figure 1. When pedestrians walk on the footbridge, the footbridge is vibrated by the pedestrians, generating vibrations. The footbridge responds correspondingly according to the pedestrians' vibrations. In addition, the pedestrians will make timely adjustments according to the vibrations of the footbridge. Therefore, the movement of the pedestrian consists of two parts: one is the lateral movement \(u_{yp}(t)\) of the pedestrians relative to the footbridge, and the other is the lateral movement \(u_{ys}(t)\) of the footbridge. The lateral movement of pedestrians is the sum of the two parts \(u_{yp}(t)+u_{ys}(t)\). Thus, when considering the pedestrian-footbridge interaction, its lateral dynamic equation is as follows:  
\[\dot{miv}_{yp}(t)+F_{y}(u_{yp}(t),\dot{u}_{yp}(t))+\dot{miv}_{ys}(t)=0. \tag{1}\]  
The external force induced by walking pedestrians is loaded on the footbridge. As a result, the lateral dynamic equation of the footbridge is:  
\[\ddot{u}_{ys}(t)+2\zeta_{s}\omega_{s}\dot{u}_{ys}(t)+\omega_{s}^{2}u_{ys}(t)= \frac{F_{y}(u_{yp}(t),\dot{u}_{yp}(t))}{M} \tag{2}\]  
\[F_{y}(t)=m\omega_{0}^{2}u_{yp}(t)-m\eta\dot{u}_{yp}(t)+m\rho\dot{u}_{yp}^{3}(t) +\xi m\dot{u}_{yp}(t)u_{yp}^{2}(t). \tag{3}\]In the above equation, \(u_{yp}(t)\) is the pedestrians' center of gravity relative to the lateral vibration displacement of the footbridge. \(u_{ys}(t)\) is the lateral vibration displacement of the footbridge. \(M\) is the modal mass of the footbridge. \(m\) is the mass of a single pedestrian. \(F_{y}(t)\) is the force loading on the footbridge structure when the pedestrians are walking. In general, Equations (1) and (2) are the dynamic equations of the pedestrian-footbridge interaction. They are coupled by the pedestrian-footbridge interaction force (related to the speed and displacement of pedestrians). It can be interpreted that Equations (1) and (2) are the lateral coupling dynamic equations of the pedestrian-bridge interaction. For the pedestrian system, the additional effect due to the movement of the footbridge is the external force. For the footbridge system, the excitation force generated by the movement of pedestrians is the external force.
## 3 Scientometric Analysis: Results and Discussion
### Science Mapping  
Science mapping can be used to evaluate the progress and innovation in a research field. Therefore, we used VOSviewer to map publication sources, countries, authors, and documents by performing a comprehensive quantitative analysis according to the number of documents and citations for each item, in addition to recording the value of the normalized citations, average year of publication, average number of citations, and average normalized citations [39]. Normalized citations are defined as the number of citations of all the articles within the same journal, or from the same author or country; the average year of publication determines when the articles were published; the average number of citations was obtained by calculating the total number of citations per article; the average normalized citations are defined as the total number of citations divided by the average number of  
Figure 2: Number of documents per year and accumulated documents published in the time horizon (2020–15 June 2022).  
citations published in the same year, which is used to correct for the fact that the old articles have more time for more citations than the new ones [39]. A simple visualization of this quantitative analysis was developed using VOSviewer, resulting in a set of figures in which the size of the node indicates the importance of the journal, author, document, and country, in terms of the number of documents, citations, or average normalized citations. The colors and thicknesses of the linking lines indicate the inter-connections between them [37]. The results are discussed below.
## 3 Methods
### Target Broadside Radiation Pattern
#### 3.1.2 Target Broadside Radiation Pattern with Maximum Directivity Factor  
The 2\(N\)-th order broadside differential beampattern in (11) can be written in vector form  
\[\widetilde{B}^{(2N)}(\theta)=\mathbf{\alpha}_{N}^{T}\mathbf{\mathrm{c}}_{2N} \tag{13}\]  
where  
\[\mathbf{\alpha}_{N}=\left[\begin{array}{cccc}\alpha_{N,0}&\alpha_{N,1}&\ldots& \alpha_{N,N}\\ \mathbf{\mathrm{c}}_{2N}=\left[\begin{array}{cccc}1&\cos^{2}\theta&\ldots&\cos ^{2N}\theta\\ \end{array}\right]^{T}\end{array}\right]^{T} \tag{14}\]  
Inserting (13) into the definition of directivity factor in (7) yields  
\[\mathrm{DF}=\frac{\pi}{\mathbf{\alpha}_{N}^{T}\mathbf{\mathrm{C}}\mathbf{\alpha}_{N}} \tag{15}\]  
where \(B(k,\theta)\) is replaced with \(\widetilde{B}^{(2N)}(\theta)\) and  
\[\mathbf{\mathrm{C}}=\int\limits_{0}^{\pi}\mathbf{\mathrm{c}}_{2N}\mathbf{\mathrm{c}}_{2N}^ {T}d\theta \tag{16}\]  
To obtain the 2\(N\)-th order broadside differential beampattern with maximum directivity factor, the optimization problem can be formulated as follows:  
\[\min_{\mathbf{\alpha}_{N}}\quad\mathbf{\alpha}_{N}^{T}\mathbf{\mathrm{C}}\mathbf{\alpha}_{N} \tag{17}\]It is easy to verify that  
\[\mathbf{\alpha}_{N}^{T}\mathbf{C}\mathbf{\alpha}_{N}=1+\widetilde{\mathbf{\alpha}}_{N}^{T} \widetilde{\mathbf{C}}\widetilde{\mathbf{\alpha}}_{N}+2\widetilde{\mathbf{c}}^{T} \widetilde{\mathbf{\alpha}}_{N} \tag{18}\]  
where  
\[\left\{\begin{array}{l}\widetilde{\mathbf{c}}=\left[\begin{array}{ccc}\cos^ {2}\theta&\ldots&\cos^{2N}\theta\end{array}\right]^{T}\\ \widetilde{\mathbf{\alpha}}_{N}=\left[\begin{array}{ccc}\alpha_{N,1}&\ldots& \alpha_{N,N}\end{array}\right]^{T}\\ \widetilde{\mathbf{C}}=\int_{0}^{T}\widetilde{\mathbf{c}}\widetilde{\mathbf{c} }^{T}d\theta\end{array}\right. \tag{19}\]  
The (\(m\),\(n\))th element of the matrix \(\widetilde{\mathbf{C}}\) is written as  
\[\widetilde{\mathbf{C}}^{mn}=\int_{0}^{\pi}\cos^{2(m+n)}\theta d\theta=\frac{(2 m+2n-1)!!\pi}{(m+n)!2^{m+n}}\quad(m,n=1,\ldots,N) \tag{20}\]  
where \((\cdot)!!\) is the double factorial and \((\cdot)!\) is the factorial.  
The optimization problem (17) can be equivalently written as  
\[\min_{\widetilde{\mathbf{\alpha}}_{N}}\quad\widetilde{\mathbf{\alpha}}_{N}^{T} \widetilde{\mathbf{C}}\widetilde{\mathbf{\alpha}}_{N}+2\widetilde{\mathbf{c}}^{T} \widetilde{\mathbf{\alpha}}_{N} \tag{21}\]  
The optimal real vector \(\widetilde{\mathbf{\alpha}}_{N}\) can be solved by using the method of Lagrange multipliers  
\[\widetilde{\mathbf{\alpha}}_{N}=-\widetilde{\mathbf{C}}^{-1}\widetilde{\mathbf{c}} \tag{22}\]  
The optimal real vector \(\mathbf{\alpha}_{N}\) can be written as  
\[\mathbf{\alpha}_{N}=\begin{bmatrix}1&\widetilde{\mathbf{\alpha}}_{N}^{T}\end{bmatrix} ^{T} \tag{23}\]
## 3 Influence of Boundary Distance in the Integral Response Deformation Method
### Influence of the Lateral Boundary Distance  
This study first investigates the impact of the lateral boundary distance on the computational accuracy of the IRDM. A dimensionless lateral boundary distance ratio \(R_{S}=L_{S}/L\) is introduced and is set to 0.5, 1.0, 2.0, 3.0, 4.0, and 5.0 for parametric analysis via the IRDM. The bottom boundary of the numerical model is fixed at the bedrock surface.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 3 Materials and Methods  
This design uses Resnet18 as the basic framework of the network and optimizes the structure and parameters of the network while introducing a self-attention module in the network to obtain the attention matrix and improve the signal quality assessment performance. This network can achieve a high level of ECG signal quality assessment without introducing a large amount of computations and parameters.  
Accuracy, sensitivity, specificity, precision, and F1-score are the five assessment criteria we use to assess the performance of the deep network model. Equations (1)-(5) below define their calculating equations. Table 1 shows the confusion matrix, with TP, TN, FP, and FN defined as true positive, true negative, false positive, and false negative.  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{1}\]\[Sensitivity=Recall=\frac{TP}{TP+FN} \tag{2}\] \[Specificity=\frac{TN}{FP+TN}\] (3) \[Precision=\frac{TP}{TP+FP}\] (4) \[F1-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{5}\]