esa-sceva/satcom-chunk-collection huggingface dataset:
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
###### Abstract  
Existing Searchable Encryption (SE) solutions are able to handle simple Boolean search queries, such as single or multi-keyword queries, but cannot handle substring search queries over encrypted data that also involve identifying the position of the substring within the document. These types of queries are relevant in areas such as searching DNA data. In this paper, we propose a tree-based Substring Position Searchable Symmetric Encryption (SSP-SSE) to overcome the existing gap. Our solution efficiently finds occurrences of a given substring over encrypted cloud data. Specifically, our construction uses the position heap tree data structure and achieves asymptotic efficiency comparable to that of an unencrypted position heap tree. Our encryption takes \(O(kn)\) time, and the resulting ciphertext is of size \(O(kn)\), where \(k\) is a security parameter and \(n\) is the size of stored data. The search takes \(O(m^{2}+occ)\) time and three rounds of communication, where \(m\) is the length of the queried substring and \(occ\) is the number of occurrences of the substring in the document collection. We prove that the proposed scheme is secure against chosen-query attacks that involve an adaptive adversary. Finally, we extend SSP-SSE to the multi-user setting where an arbitrary group of cloud users can submit substring queries to search the encrypted data.  
substring position search; searchable symmetric encryption; cloud computing; position heap tree  
PACS:J0101
## 1 Introduction
However, they used different schemes to model the variation of the mechanical properties, such as four-parameter power-law, Weibull, and exponential distributions. Furthermore, in the literature, the modeling of FG shell type structures or FG plates by means of commercial finite element codes is also reported. Examples of models using ANSYS are the works of [PERSON], [PERSON], and [PERSON] [40], [PERSON] et al. [41], and [PERSON] and [PERSON] [42]. In this regard, [PERSON] et al. [43] used a three-dimensional brick finite element available in ABAQUS to model the free vibration of FG sandwich plates. In the literature, several works related to the seven-parameter finite element formulation can be found [44; 45; 46; 47; 48]. However, to date, the implementations of this formulation were devoted to performing different analyses than the modal analysis of FG shell structures with nonuniform thickness. The main aim of this work is to extend the finite element analysis previously presented in [49] for the modeling of shell-type structures made of FGMs; that is, to take into account the FGM behavior in the seven-parameter finite element formulation in order to determine the natural frequencies and vibration modes of arbitrary FG shell structures with uniform and nonuniform thicknesses. Moreover, due to the few results available in the literature for FG shell-like structures with variable thickness, a user-defined routine to model these type of structures by means of the commercial code ANSYS mechanical APDL is developed and used to compare the results of the present finite element model. In addition, the routine performance is verified using natural frequencies of FG structures with constant thickness reported in the literature.
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Single-Model-Based Pain Recognition
#### 3.1.1 Physiological Signals  
Physiological signals are among the most important measures used to describe the pain level from the physiological response of the body. These signals include vital signs (such as blood pressure, respiration rate, heart rate and muscle activity) or brain dynamics.  
In addition to the lack of using physiological signals for pain detection, [PERSON] and [PERSON] in 2017 [3] proposed a model using neural network (NN) techniques. Their proposed approach implements a multitask learning method to tackle the problem of personal differences through shared layers in NNs. Their system involves multi-model data that depend on physiological signals from skin conductance (SC) and electrocardiograms (ECGs) only. They used the available data set, called a BioVid dataset, to build a model and conduct their experiments. Then, they compared their model with other machine-learning methods (logistic regression (LR) and support-vector machine with both linear kernel (SVM-L) and radial basis function kernel (SVM-RBF). They reported that this NN approach outperforms others by around 82.75%. However, they reported no experimental testing of the model on new data sets. In the future, this method can be easily adapted in real clinical settings because of the simple use of two features that can be acquired from wrist-wearable devices. Recent studies were conducted to validate the accurate of such wearable devices [4].
## 6 Simulated Results
### Data Breaches  
In Figure 6, the simulation results demonstrate the analysis of the ratio of data breaches of the EGCIR protocol in the comparison of the existing solutions. The data breach is the planned or unplanned release of confidential information to untrusted nodes and is therefore compromised to network performance. It is noted that the EGCIR protocol reduces the fraction of data breaches by an average of 30% and 45%. The reason behind this reduction is such that the EGCIR protocol is extremely energy-efficient and reliable in terms of clustering and data security processes. Moreover, unlike other solutions that manage data transmission in a distributed manner, the EGCIR protocol uses the BS as a KDC, which guarantees secure key generation and distribution among sensors. In addition, the integration of nonce in key sharing, data encryption and decryption reduces the probabilities of malicious nodes to perform misbehaving actions in the observing area. Moreover, unlike other solutions where routing paths are more prone to failure due to heavy load network setup, the EGCIR protocol uses a graph learning-based intelligent routing decision to extract the shortest, energy-efficient  
Figure 4: Impact of the nodes and data latency.  
and most trustworthy nodes from the undirected graph--which results in reducing data breaches and increases the level of security for sensor data.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline  & **S1** & **S2** & **C** \\ \hline RMS TA (μV) & 3.24 & 3.2 & 1.72 \\ SD (μV) & 0.77 & 0.62 & 0.55 \\ U (S1 vs. C) & 39 & NA & 30 \\ Z (S1 vs. C) & \(-\)5.463 & NA & \(-\)5.463 \\ p (S1 vs. C) & 0.001 * & NA & 0.001 * \\ r (S1 vs. C) & \(-\)0.758 & NA & \(-\)0.758 \\ U (S2 vs. C) & NA & & 28 \\ Z (S2 vs. C) & NA & & \(-\)5.664 \\ p (S2 vs. C) & NA & & 0.001 * \\ r (S2 vs. C) & NA & & \(-\)0.786 \\ U (S1 vs. S2) & & 335.5 & & NA \\ Z (S1 vs. S2) & & 0.037 & & NA \\ p (S1 vs. S2) & & 0.971 & & NA \\ r (S1 vs. S2) & & 0.005 & & NA \\ \hline RMS MM (μV) & 3.09 & & 2.37 & 1.78 \\ SD (μV) & 1.09 & & 0.76 & 0.58 \\ U (S1 vs. C) & 78.5 & NA & & 78.5 \\ Z (S1 vs. C) & \(-\)4.74 & NA & & \(-\)4.74 \\ p (S1 vs. C) & 0.001 * & NA & & 0.001 * \\ r (S1 vs. C) & \(-\)0.657 & NA & & \(-\)0.657 \\ U (S2 vs. C) & NA & & & 141 \\ Z (S2 vs. C) & NA & & \(-\)3.596 & \\ p (S2 vs. C) & NA & & & 0.001 * \\ r (S2 vs. C) & NA & & & \(-\)0.5 \\ U (S1 vs. S2) & & 187.5 & & NA \\ Z (S1 vs. S2) & & 2.745 & & NA \\ p (S1 vs. S2) & & 0.006 * & & NA \\ r (S1 vs. S2) & & 0.381 & & NA \\ \hline \end{tabular} (S1)—study group before compression technique; (S2)—study group after compression technique; (C)—control group; (r)—effect size; * Significant difference.  
\end{table} 
Table 2: Comparing means of bioelectric activity of temporalis anterior (root mean square (RMS) TA) and maseeter muscle (RMS MM) during resting mandibular position.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 5 Robust Watermarking Algorithms Based on Compressed Videos
### H.265-Based Watermarking Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Type** & **Watermark Preprocessing** & **Embedding Position** \\ \hline
[90] & Blind & - & NNZ difference of 4 \(\times\) 4 luma TBs of intra-predicted frames \\ \hline
[91] & Blind & Pseudorandom sequence & 4 \(\times\) 4 luma TBs with high NNZ values in homogeneous regions of 1 frames \\ \hline
[92] & Blind & Encoding & Multi-coefficients of the 4 \(\times\) 4 luminance DCT blocks of selected frames \\ \hline
[93] & Blind & Encoding & Multi-coefficients of the 4 \(\times\) 4 luma DST blocks of selected frames \\ \hline
[94] & Blind & - & QDST coefficients in 4 \(\times\) 4 luminance PU of I frames \\ \hline
[95] & Blind & - & QDCT and QDST coefficients of intra prediction residuals \\ \hline
[96] & Blind & BCH code & Residual QDCT or QDST coefficients within the different size of TUs of 1 frames \\ \hline
[97] & Blind & - & Low-frequency nonzero quantized AC coefficients in 4 \(\times\) 4 blocks of I frames \\ \hline
[98] & Blind & - & Low-frequency nonzero quantized AC coefficients in 4 \(\times\) 4 blocks of P frames \\ \hline
[99] & Blind & Exclusive OR operation & Nonzero AC residual coefficients \\ \hline
[100] & Blind & - & LSBs of selected nonzero of QTCs of I frames \\ \hline
[101] & Blind & Arnold transform and APBT &
\begin{tabular}{l} Nonzero coefficient blocks in luminance components with the size of 4 \(\times\) 4, 8 \(\times\) 8, \\ and 16 \(\times\) 16 of I frames \\ \end{tabular} \\ \hline
[102] & Blind & Binarization & Motion vectors of the smallest PUs in CTU \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary comparison of several H.265-based watermarking algorithms. At present, the research on watermarking algorithms based on original videos is relatively mature, and the research focus is on the video watermarking algorithm based on HEVC coding standard. In summary, the research status of robust video watermarking algorithms for copyright protection is shown in Figure 8.
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 4 Addressing Imbalanced Data
### Cost-Sensitive Learning  
Cost-sensitive learning is an approach that recognizes the inherent imbalance in the dataset by assigning different costs to misclassification errors of different classes. It entails adjusting the learning algorithm's parameters to penalize misclassifications in the minority class more heavily than in the majority class. By doing so, the model is incentivized to focus on accurately predicting the minority class. Cost-sensitive learning requires the specification of an appropriate cost matrix, obtained by assigning higher misclassification costs to the minority class.  
Table 2a contains a typical cost matrix for a two-class problem. Following convention, it is assumed that \(c(0|0)=c(1|1)=0\), i.e., no costs are associated with correct classifications. Therefore, only two costs necessitate specification: \(c(0|1)\), representing the cost of false negatives, and \(c(1|0)\) indicating the cost of false positives. The expected cost of misclassification (ECM) is then computed as  
\[\text{ECM}=c(0|1)p(0|1)+c(1|0)p(1|0),\]where \(p(j|k)=\Pr(C_{j}|\mathbf{x}\in C_{k})\) is the conditional probability of assigning an example from class \(C_{k}\) to class \(C_{j}\) (\(j\
eq k\)). Note that, if equal costs of misclassification are employed, i.e., \(c(0|1)=c(1|0)=1\), then minimizing ECM is equivalent to minimizing the probabilities of misclassification, which corresponds to the MAP classification principle discussed in Section 2.4.  
In general, MAP rule requires to specify the adjusted prior probabilities:  
\[\tau_{0}^{*} =\frac{c(0|1)\tau_{0}}{c(0|1)\tau_{0}+c(1|0)\tau_{1}},\] \[\tau_{1}^{*} =\frac{c(1|0)\tau_{1}}{c(0|1)\tau_{0}+c(1|0)\tau_{1}}.\]  
Notably, if we make the assumption, without loss of generality, that \(c(0|1)=1\) and \(c(1|0)=\text{IR}\), and using sample proportions to estimate \(\tau_{k}\) (for \(k=\{0,1\}\)), we obtain:  
\[\tau_{0}^{*} =\frac{\tau_{0}}{\tau_{0}+\text{IR}\tau_{1}}=0.5,\] \[\tau_{1}^{*} =\frac{\text{IR}\tau_{1}}{\tau_{0}+\text{IR}\tau_{1}}=0.5.\]  
Thus, assigning the cost of misclassification for the minority class proportional to IR is equivalent to use equal adjusted prior probabilities. The corresponding posterior probability for applying the MAP rule simplify as  
\[\Pr(C_{k}|\mathbf{x}_{i})=\frac{f(\mathbf{x}_{i}|C_{k})}{f(\mathbf{x}_{i}|C_{0})+f(\mathbf{x}_ {i}|C_{1})}\qquad\text{for }k=\{0,1\},\]  
which is equivalent to require \(f(\mathbf{x}_{i}|C_{1})>f(\mathbf{x}_{i}|C_{0})\) for classifying an observation to the minority class \(C_{1}\).
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology
### Dataset  
To date, a dataset with timing side-channel attack footprints in an SDN environment is not available. This prompted us to construct the dataset. Some of the commonly available datasets with traffic for SDN environments are reviewed in Table 2. The proposed algorithm for sampling timing probes requires the dataset to consist of the following fields: source MAC address, destination MAC address, source IP address, destination IP address, timestamp, TTL, and protocol. An examination of accessible datasets containing SDN traffic was conducted and the key findings are presented in Table 2. The results indicate that existing datasets pertinent to SDN traffic lack the necessary fields required for the effective execution of the proposed algorithm. To calculate the number of samples with unknown  
Figure 2: Workflow of Response Phase.  
destination MAC addresses, as well as the number of samples with known destination MAC addresses, we require source and destination MAC addresses. The present datasets for SDN environments are missing these fields, which are essential for the implementation of the timing side-channel algorithm. This motivated us to develop the dataset. For the collection of samples in the dataset, we develop an API, called \"Listener API\", which is a Python-based RYU application that receives specific network flows and stores them in a database. This application also ensures that the database receives network topology information. In addition, it extracts the source IP and MAC addresses and the destination IP and MAC addresses in conjunction with the timestamps for all 'packet-in' requests. It can determine whether a request is carried via the 'User Datagram Protocol' or the 'Transmission Control Protocol' (TCP). The database is also notified of the switch name and port from which the request originates.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 5 Results and Analysis
### Analysis of Variance (ANOVA)
#### 5.3.1 ANOVA of Trip Time  
Table 7 shows the factors and the levels considered for the trip time variance analysis. The factors selected for the trip time were the routes, traffic distribution, and traffic density. The route factor had three levels, traffic distribution had five (5) levels, and traffic density had two (2) levels, as denoted in the table.  
We quantify the impact of the main factors on the trip time by performing ANOVA tests. The ANOVA for the trip time is given in Table 8. We can observe that the main factors contribute about 97% to the variation of the trip time results, and the first and second-order interactions between the factors contribute around 2% and 1%, respectively. The route (R) has the most significant effect on the results with a 91.8% variation. Traffic distribution (T) shows a variation of 3.2% on the trip time results. However, traffic density (D) has the least significant effect on the results adding up to 1.6%. Our assumption that the residuals are normally distributed is proven by the Quantile-Quantile (Q-Q) for all three factors, as shown in Figure 17. The visual tests show that the residuals fall evenly around the least-squares line forming a heavy-tailed linear line.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Factor**} & \multicolumn{5}{c}{**Levels**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** & **5** \\ \hline R & Routes & R1 & R2 & R3 & & \\ T & Traffic Distribution & 0:100 & 25:75 & 50:50 & 75:25 & 100:0 \\ D & Traffic Density & 500 & 5000 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Factors and their respective levels for trip time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Sum of Squares** & **Perc. Variation** & **DF** & **Mean Square** \\ \hline \(y\) & 339,533.0005 & & 30 & \\ \(g\) & 327,189.6338 & & 1 & \\ \(y-\tilde{y}\) & 12,343.3667 & 100.00\% & 29 & \\ Main Effects & 11,928.2186 & 96.6\% & 7 & 1704.0 \\ D & 201.0703 & 1.6\% & 1 & \\ T & 400.6816 & 3.2\% & 4 & \\ R & 11,326.4667 & 91.8\% & 2 & \\ First-order Interactions & 264.6889 & 2.1\% & 14 & 18.9 \\ D-T & 92.6519 & & 4 & \\ D-R & 92.5407 & & 2 & \\ T-R & 79.4963 & & 8 & \\ Second-order Interactions & 150.4593 & 1.2\% & 8 & 18.8 \\ D-T-R & 150.4593 & & 8 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: ANOVA of trip time.
## 4 Numerical Simulations and Analysis
### Avoidance of Dynamic Obstacles on Horizontal Plane
The curves between 10 s and 30 s in each subgraph of Figure 10 demonstrate the similarities in velocity, state, and input patterns during this phase. However, a noteworthy distinction emerges in the approach adopted by the AUV when confronted with the last obstacle, which exhibits a different movement direction. This disparity is clearly evident in Figure 10d. Specifically, for situation 2, two distinct peaks of \(\psi\) can be observed, measuring 1.723 rad and 1.709 rad, respectively. These simulation results highlight the adaptability of the proposed obstacle avoidance approach when dealing with dynamic obstacles. The AUV consistently performs well in avoiding the first two obstacles, with minimal deviations in trajectory, velocity, state, and input. However, the distinct movement direction of the final obstacle necessitates a unique obstacle avoidance strategy, resulting in variations in the AUV's trajectory and \(\psi\) values. These results emphasize the effectiveness of the proposed method in dynamically navigating environments, successfully avoiding obstacles while maintaining control over the AUV's motion.
# Application Study of the High-Strain Direct Dynamic Testing Method
## 2 Theory of the High-Strain Direct Dynamic Testing Method
### Basic Assumptions  
When calculating the ultimate bearing capacity of a single pile using the high-strain direct dynamic method, the pile is discretized into a series of pile segments at the nodes where strain sensors are placed along the pile shaft as shown in Figure 2. The ultimate bearing capacity of the pile equals the sum of the ultimate static soil resistance acting on each pile segment. The basic assumptions of this method are summarized as follows:  
1--The total soil resistance around the pile during longitudinal vibration under dynamic load consists of static and dynamic resistances. Static resistance is related to the  
Figure 1: Arrangement of transducers on the pile.  
Figure 2: Schematic diagram of pile segment divisions.  
relative displacement between the pile and soil, while dynamic resistance is related to the pile's velocity.  
2--When the average velocity of a pile segment is zero, its dynamic resistance is zero.  
3--When the dynamic resistance of a pile segment first drops to zero from its peak value, the total soil resistance on that pile segment is considered to have reached the segment's lateral ultimate static resistance.
## 2 Materials and Methods
### System Evaluation
#### 2.5.2 Neural Network  
To determine the best identification for each participant, we used artificial intelligent technology neural network (NN) [61], a multi-layer neural network (Figure 7), to learn and analyze the output data of the five electrodes. The output of the BCI system is the input data of the neural network. Each electrode has two pieces of data: the sound-evoked potential obtained from stimulation on the left and right ears via the headphones. Therefore, the system will generate ten data from the five electrodes after every trial. Additionally, the expected output of the multi-layer neural network is to L (=0) or R (=1).  
There are 11 neurons (N101 to N111) in Hidden Layer 1 and two (N201 and N202) in Hidden Layer 2. We used the Keras API with parameters set as activation ='sigmoid', model.compile: loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'].  
We use a 5-fold cross-validation method to train and validate the performance of the aBCI system. The NN model divides the data from 20 trials of each participant into five data sets. In each iteration of the cross-validation, four data sets out of five are used as training data, while the remaining data set is the testing data. Additionally, the NN model uses the gradient descent method to update the weights, the cross entropy function (Equation (3)) is used as the loss function (Equation (4)), and the learning rate is set as 0.001. After 10,000 iterations of training, the neural network achieved a high classification accuracy for training data. Then, the testing data is used to evaluate the accuracy of the model. Finally, the averaged accuracy among the five testing data sets obtained.  
Figure 7: The structure of neural network to learn the data from the five electrodes.  
The activation function of each neuron is defined as sigmoid function:  
\[f_{w}(x)=\frac{1}{1+e^{(-w^{2}x+b)}} \tag{2}\]  
where \(w\) is the weight vector, \(x\) is the input vector, and \(b\) is the bias.  
The cross entropy function is:  
\[\mathrm{H}(p,q)=-\sum_{x}p(x)\log q(x) \tag{3}\]  
where \(p(x)\) is the target distribution and \(q(x)\) is the predicted matching distribution.  
So, the loss function is:  
\[L(w)=-\frac{1}{n}\sum_{i=1}^{n}[y_{i}\log f_{w}(x_{i})+(1-y_{i})\log(1-f_{w}(x _{i})] \tag{4}\]  
where n is the number of training data, and when \(y_{i}\) (desired output) \(\approx\)\(f_{w}(x_{i})\) (NN output), \(L(w)\) has a minimum value.