esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### HS-SPME Method Optimization
#### 2.3.2 HS-SPME Method Evaluation  
To evaluate the effectiveness of our method, we selected 30 BALF samples collected from 30 different subjects. Using samples from various subjects aims to determine whether the optimized method remains effective for samples with diverse biological contexts. These samples were divided into 10 sets, with each set comprising three samples. In each set, the three samples were mixed and homogenized following the same approach described above. The homogenized mixture was then divided into two equal parts and transferred to two individual vials, with each containing 0.5 mL of the mixed sample. The two equally split samples were analyzed using both pre-optimization and optimized methods.  
The pre-optimization method used a 20 mL headspace vial with a 2 cm fiber. The sample was 1:2 diluted with a PBS buffer, and no salt was added to the sample. The optimized method used a 10 mL headspace vial with a 2 cm fiber. The sample was not diluted, and 40% (% \(w/v\)) NaCl was added to the sample.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Order** & \begin{tabular}{c} **Extraction** \\ **Temperature** (\({}^{\circ}\)C) \\ \end{tabular} & \begin{tabular}{c} **Extraction Time** \\ **(Min)** \\ \end{tabular} & **Order** & \
\begin{tabular}{c} **Extraction** \\ **Temperature** (\({}^{\circ}\)C) \\ \end{tabular} &
\begin{tabular}{c} **Extraction Time** \\ **(Min)** \\ \end{tabular} \\ \hline
1 & 43.5 & 56 & 8 & 37.0 & 20 \\
2 & 52.5 & 35 & 9 & 37.0 & 60 \\
3 & 34.3 & 35 & 10 & 43.5 & 35 \\
4 & 43.5 & 35 & 11 & 50.0 & 50 \\
5 & 43.5 & 35 & 12 & 50.0 & 20 \\
6 & 43.5 & 14 & 13 & 43.5 & 35 \\
7 & 43.5 & 35 & 14 & 43.5 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: HS-SPME temperature and time tested in the central composite design (CCD).
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 3 Results
### Measurement-Level Hybridization Method Results  
The proposed measurement-level hybridization method integrates GNSS and BS pseudoranges into a unified positioning solution. Since BS pseudoranges are not directly available from the mobile network, they are computed using the geometric distance between the UE and the known coordinates of the BSs. Tables 10-12 present the estimated positions and associated standard deviations for the outdoor, urban canyon, and indoor environments. Tables 13-15 summarize the corresponding positioning accuracies relative  
Figure 8: Root sum square (RSS) of standard deviations for the position-level method across the outdoor, urban canyon, and indoor scenarios. This metric reflects the internal precision of the GNSS-only, 4G-only, and hybrid solutions.  
Figure 9: Root sum square (RSS) of positioning errors for the position-level hybridization method across the outdoor, urban canyon, and indoor scenarios. This graph summarizes the overall accuracy achieved by each solution in the three test environments.  
errors with respect to the ground-truth coordinates, providing a concise measure of the total positioning error for each method in the outdoor, urban canyon, and indoor environments.
## 3 Interference Mitigation Schemes
### Power Control Approach  
Power consumption at sensor nodes in a WBASN is an important factor that affects the lifespan of the network. Because sensors are placed in or on the human body, it is necessary to reduce power consumption and save energy. As a result, interference mitigation schemes focus on power consumption in addition to the quality and throughput in an interference environment.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### General Wiring Optimization
#### 4.2.2 Decoupling into Columns  
The optimization problems (11) and (13) are far from trivial to solve. A pragmatic approach to simplify them is to decouple the optimization of the wiring matrix columnwise.  
Let \(t_{k}\) and \(w_{k}\) denote the \(k\)th columns of \(\mathbf{T}\) and \(\mathbf{W}\), respectively. We approximate the solution to (11) columnwise as  
\[w_{k}\approx\vartheta_{k}=\operatorname*{argmin}_{\omega:\max_{\mathbb{C}[( \omega;\mathcal{K})]}\leq s}d(t_{k},\mathbf{B}\omega). \tag{14}\]  
with \(s=C/K\). This means we do not approximate the linear function \(\mathbf{T}x\) with respect to the joint statistics of its input \(x\). We only approximate the columns of the target matrix \(\mathbf{T}\) ignoring any information on the input of the linear function. While in (11), the vector \(x\) may have particular properties, e.g., restricted support or certain statistics that are beneficial toreduce distortion or computational cost, the vector \(\xi\) in (14) is not related to \(x\) and must be general.  
The wiring matrix \(\tilde{\mathbf{W}}\) resulting from (14) will fulfill the constraint \(C[\tilde{\mathbf{W}}x]\leq C\) only approximately. The computational cost operator does not decouple columnwise, in general.
## 7 Evaluation and Discussion
### Case Study
#### 7.3.1 Security of Tamper-Proofing Instruction
This is because the program jumps to the virtual interpreter when calling the critical code, so there is clear control of the transfer behavior. In the following analysis process, most attackers can quickly locate the position of the Dispatcher of a normal VM protection program through the debugging analysis (target 2). In addition, some experienced attackers can modify the structure of Dispatcher with tools (like Pin tools) and extract the execution sequence of Handlers further (target 3). However, when they reverse the analysis of _VMGuards_ protected program, they encountered obstacles. ADI and TPI make it difficult to continue the dynamic debugging process for attackers. Only some experienced attackers found the Dispatcher structure through static analysis. They also cannot extract Handler execution sequences in batches by tampering with the code structure. These task targets are steps that an attacker must go through to reverse understanding of code virtualization protection, so the fewer successful attackers, the better the method's advantage. The experimental results show that our _VMGuards_ protection method has better security.
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods
### Cyclic Statistical Functions Estimators
#### 2.3.3 Cyclic Autocorrelation  
The natural estimator of the cyclic autocorrelation Function (20a) is the cyclic correlogram [2] (Definition 5.1)  
\[R_{x}^{(T)}(\alpha,\tau;t_{0})\triangleq\frac{1}{T}\int_{t_{0}-T/2}^{t_{0}+T/ 2}x(t+\tau)\,x(t)\,e^{-j2\pi\alpha t}\,\mathrm{d}t \tag{33}\]  
Under mild assumptions, the cyclic correlogram (33) is a mean-square consistent estimator of the cyclic autocorrelation (20a) [2] (Theorem 5.15), [42] and the normalized estimation error  
\[\sqrt{T}\Big{[}R_{x}^{(T)}(\alpha,\tau;t_{0})-R_{x}^{\alpha}(\tau)\Big{]} \tag{34}\]  
is asymptotically \((T\to\infty)\) complex normal [2] (Theorem 5.18), [43].  
The results in [2] (Theorems 5.15 and 5.18) are derived with the classical stochastic approach. Their FOT counterpart can be obtained by using [36] (Theorem 3.7) for the time-average of the function of time  
\[t\mapsto x(t+\tau)\,x(t)\,e^{-j2\pi\alpha t} \tag{35}\]
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 2 Methods for Generating Cryptographic S-Boxes  
There are a large number of different methods for generating cryptographic S-boxes. In this section, we provide a brief non-exhaustive overview of existing methods. The S-box generation methods can be categorized into the following main categories:* (Pseudo-)random generation
* Stochastic search
* Mathematical construction
* Construction from smaller components.
## 1 Introduction
A laser communication model is established by oversimplifying the aero-optics impact, due to the considerable uncertainty of the flow field. The air around an airborne platform is considered to be a thin turbulent boundary layer, and the aero-optics effect is limited to the power loss caused by spot diffusion [17]. Moreover, the communication performance of aviation links has been simulated, including the influence of different channel models [18]. This approximation method creatively substitutes the effects of aero-optics into the communication model. However, only the light spot diffusion caused by the turbulent boundary layer is considered in this method, and the phase interference caused by other flows in the flow field is entirely ignored. Therefore, the impact of flight altitude and flight speed cannot be involved, and the direction of the aero-optics effect cannot be exactly reflected. Accordingly, it is imperative to simulate the flow field structure around specific moving platforms and analyze the propagation process of the beam. In this paper, we present a method for analyzing the performance of airborne platform space laser communication links based on a numerical simulation of flow fields. In Section 2, the communication system model and the wavefront distortion evaluation technique are provided. In Section 3, we obtained the flow field data around the hemisphere-on-cylinder turret installed under the belly of the aircraft by means of numerical simulation, which changed with the flight altitude and speed. Flow field data are extracted to obtain the wavefronts of laser beams emitted at different angles and their corresponding optical field distributions. In Section 4, the Monte Carlo phase screen method is used to analyze the effects of short-range aero-optics and long-range atmospheric turbulence. The energy distribution of the light spot is analyzed by considering the diffusion and phase difference of the beam, and the influence of different moving speed and moving height on the laser communication performance is analyzed. The fifth part is the conclusion. In Section 5, a conclusion is given.
# TransformingBPMN Processes toSBVR Process Rules with
## 2 Related Work
### State of the Art Research
However, these papers do not present a comprehensive transformation approach but rather the very basic principles and attempts to comprehend the problematics of such a transformation in general; a small set of basic illustrative model transformation rules were presented as well. Several other research groups presented their approaches for transforming BPMN processes to business rules expressed in other than SBVR textual representation formats. The authors of [40] showed how compliance rules could be generated from the control flow of a BPMN process model. The acquired rules were aimed at the uniform representation of the activation of tasks using a specific human-readable language based on First-Order Logic (FOL). The presented approach provides informal complementation to a process model, whereas our approach deals with a more formal representation of business knowledge by the means of SBVR. In [41], an approach for the verification of BPMN processes against the rules defined as \"IF... THEN... \" expressions is presented. The approach uses the proposed Business Rules Language (BRL) to define structural patterns for the BPMNprocess model, which can then be used to construct the BRL-compliant \"IF... THEN... \" verification rules for the specific process model. The paper lacks a better representation of the defined set of model patterns as well as an experimental investigation to prove the validity of the presented development. Compared with both [40; 41], our model transformation approach handles a greater variety of relevant BPMN concepts and process model patterns, thus providing a more comprehensive representation of the process flow in a form of SBVR process rules. Concerning our work, valuable results were presented by the researchers working with reverse transformations, that is, the automated development of BPMN process models based on the business knowledge stored in SBVR business vocabularies and business rules. The basis of all the reviewed approaches was a certain set of mapping rules for transforming SBVR business vocabularies and business rules into corresponding process model fragments composed of a single or a set of interrelated BPMN concepts forming certain structural process patterns [42; 43; 44; 45; 46]. When talking about process patterns, one can also consider the so-called recommender approaches for business process modeling [47]. Those recommendations may vary from suggesting presumably the most suitable label for the next activity being added to the sequence flow [48] up to recommending large process fragments [49] to the process model under development. Among other overviewed research items dealing with model transformations, the recommender approaches may also provide insights into the variety of possible structural business process patterns, which is a relevant subject to our approach.
# Determination of Geometrical REVs Based on Volumetric Fracture Intensity and Statistical Tests
## 3 Methodology
### Wald-Wolfowitz Runs Test  
The Wald-Wolfowitz runs test is a nonparametric statistical test that checks a randomness hypothesis for a two-valued data sequence ([PERSON], 1988). In this test, two random samples (X and Y) from different populations with different continuous cumulative distribution functions are obtained; the samples X and Y involve m observations and n observations, respectively. The procedure of the Wald-Wolfowitz runs test is as follows:  
1. The merged samples X and Y are sorted in ascending order, and sample X is coded as a \"1\", while sample Y is coded as a \"2\".
2. Each observation is replaced by a label, \"1\" or \"2\", depending upon the sample to which it originally belonged.
3. Calculate the number (run) of a consecutive sequence of identical labels (\"1\" or \"2\").
4. To determine the test statistic, when both m and n are less than or equal to 20, the test statistic is the total number of runs, \(U\). The critical value \(U\) is obtained from the table of the Wald-Wolfowitz runs test. When the number of observations is larger than 20, \(U\) follows a normal distribution asymptotically. The expected number of runs and variance are given as \[E(U)=\frac{2 mn}{m+n}+1\] (6)\[Var(U)=\frac{2 mn(2 mn-m-n)}{{(m+n)}^{2}(m+n-1)}\] (7) The test statistic \(Z\) is given by [PERSON] and [PERSON] [PERSON] and [PERSON] (1986): \[Z=\frac{U-E(U)}{\sqrt{Var(U)}} \tag{8}\]
5. The \(p\) value is calculated based on the small \(U\) value or the test statistic \(Z\) obtained from the normal distribution. In this study, the REV size of a rock mass was determined based on the statistical similarity between two compared samples. When the significance level \(p\) values exceed a threshold level of 0.05, the fluctuation in P\({}_{32}\) values significantly decreases as the sample size increases, and the P\({}_{32}\) values of two compared samples are considered statistically similar. To better demonstrate the procedure of detecting the REV size by using these two statistical test methods, a calculation flow chart is provided in Figure 5.
## 2 Background of the Proposed Method
### Speeded-Up Robust Features (SURF)  
SURF first digitizes a PCB image into a vector to enable computational calculations through image processing for learning the fault pattern. Among the various types of image processing techniques, including scale invariant feature transform (SIFT) and orientation by intensity centroid (ORB), we use SURF, which is the most robust algorithm with regard to environmental factors such as size, shape, and color. The performance of SIFT does not differ significantly compared to that of SURF. However, SIFT is not able to extract the features in a small defective area. ORB is specialized in extracting the features of a rounded or curved edge, rather than a straight line, in a circuit. Because a PCB is mainly composed in a straight formation, ORB is inappropriate for extracting PCB features. On the other hand, SURF is robust and useful for feature detection. It also improves the computational speed compared to SIFT [17]. In addition, SURF provides robust features and distinctive descriptors to the size and rotation transform. Thus, it results in a strong performance regardless of the fault size and shape of the circuit. For this reason, the proposed method applies SURF for the feature extraction. The following subsections describe the SURF algorithm, which has two phases. First, a point of interest is detected in an image using a Hessian matrix. In the second phase, SURF generates descriptors with 64 dimensions, which describe the characteristics of the features.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 6 Conclusions  
The aim of this paper was to present the energy-efficient and secure routing (ESR) protocol for intrusion defense in IoT based on wireless sensor networks. In the existing solution, most of them used a greedy algorithm for the construction of the routing path, overlooked intrusions in an infrastructure-less and unattended environment. This results in a high number of route discoveries and re-transmissions, particularly under the number of malicious nodes and high network load scenario. Basically, ESR optimized the selection process of cluster heads and used a distributed strategy to generate clusters for uniform distribution of energy consumption. Furthermore, by considering higher values of RSSI and least network congestion improved the routing performance with respect to QoS constraints and data reliability. Moreover, to achieve a secure network-wide data routing against malicious nodes, ESR protocol adopted a light-weight secret sharing scheme between cluster heads and BS. This provided data security from nodes towards the cluster heads and further to the BS against malicious threats. For future work, the proposed protocol will be extended by considering multi-hop network communication along with the mobility standards.  
Conceptualization, K.H. and N.I.; methodology, I.U.D.; validation, A.A. and Z.J.; writing--original draft preparation, K.H.; writing--review and editing, I.U.D.; visualization, N.I. and Z.J.; supervision, A.A.; funding acquisition, A.A.  
The authors are grateful to the Deanship of Scientific Research, king Saud University for funding through Vice Deanship of Scientific Research Chairs.  
The authors declare no conflict of interest.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 2 Methods
### Innovation 2: Multi-Scale Feature Extraction  
In underwater crack detection, cracks can vary in size and shape, requiring the model to capture features at different scales. DeepLabV3+ already uses atrous convolution to capture multi-scale features, but this capability was enhanced by adding additional layers (multi-scale feature extraction layers [34], as shown in Figure 4) to the decoder that focus on fine-grained details. These layers are designed to capture small-scale features, such as tiny cracks, which may be difficult to detect with standard convolution layers.  
Let \(F_{i}\in\mathbb{R}^{H_{i}\times W_{i}\times C}\) denote the multi-scale feature maps extracted from different decoder layers. This paper applies a multi-scale aggregation:  
\[F_{\text{agg}}=\ \sum\
olimits_{i=1}^{n}w_{i}\cdot\text{Upsample}(F_{i}) \tag{8}\]  
where \(w_{i}\) are learnable scale weights. This enables hierarchical crack context fusion while preserving fine geometry. This design encourages the model to balance global context (from low-resolution features) and fine-grained edge details (from high-resolution maps), enhancing its ability to detect cracks of different sizes. Compared to the standard DeepLabV3+'s ASPP-only structure, this extension introduces hierarchical attention to spatial scales in the decoder, guided by task-specific feature response.  
Theoretical Motivation: Multi-scale aggregation (Equation (8)) mitigates scale variance in cracks. Let \(\mathcal{F}_{agg}\) be the aggregated feature map and \(\mathcal{F}_{base}\) the baseline (single-scale) features. For a crack pixel \(p\) with optimal receptive field \(r_{p}^{*}\), the following is shown:  
\[\left\|\mathcal{F}_{agg}(p)\ -\ \phi(p)\right\|_{2}\ \leq\ \epsilon\cdot\left\| \mathcal{F}_{base}(p)\ -\ \phi(p)\right\|_{2} \tag{9}\]  
where \(\phi(p)\) is the ground truth feature representation, and \(\epsilon<1\) when \(r_{p}^{*}\) aligns with any \(w_{i}\)-weighted scale in \(F_{i}\). This guarantees superior representation for fine/large cracks, directly improving small-crack IoU.  
Additionally, the proposed model introduces skip connections between the encoder and decoder (ASPP module, as show in Figure 5) to ensure that fine details from the early layers of the network are preserved during the upsampling process. This multi-scale feature extraction mechanism allows the model to detect cracks of varying sizes more effectively, improving both accuracy and robustness.  
Figure 4: Multi-scale feature extraction.
## 2 Data Preprocessing  
### Variational Mode Decomposition  
Due to the instability of wind power, the forecasting error is relatively large when the original wind power sequence is used. Therefore, some scholars have used a decomposition of the original wind power sequence to reduce the complexity of the data, thereby improving forecasting accuracy. At present, commonly used decomposition methods include wavelet transform [21], collective empirical mode decomposition (EEMD) [22], and local mean decomposition (LMD) [23]. The VMD has better anti-noise ability than these decomposition methods, with fewer components; in addition, VMD is capable of separating two pure harmonic signals with similar frequencies [24; 25]. Because of to these advantages, VMD is used to decompose the original wind power sequence in this study.  
The VMD is a signal decomposition and estimation method for solving variational problems. The objective is to divide the frequency band according to the frequency domain characteristics of the original signal and decompose it into a fixed number of modes. Each mode is a band-pass signal, and its center frequency is automatically updated during the decomposition process. The bandwidth determination is performed as follows: first, the parsed signal of all modes are obtained by using the Hilbert transform; second, the parsed signal spectrum is moved to the baseband; finally, the bandwidth of each mode is estimated using the Gaussian smoothing index H1 of the frequency-shifted signal.  
Assuming that the wind power sequence \(f\) is decomposed into \(K\) modes, the constraint variation problem shown in the following equation can be constructed:  
\[\left\{\begin{array}{c}\min\limits_{\{u_{k}\},\{u_{2}\}}\left\{\sum\limits_{k =1}^{K}\left\|\partial_{t}\llbracket\big{[}\sigma(t)+\frac{j}{\pi t}\big{]}u_{k} (t)\big{]}e^{-ju_{2k}t}\right\|\right\|_{2}^{2}\right\}\\ s.t\quad\sum\limits_{k=1}^{K}u_{k}(t)=f(t)\end{array}\right. \tag{1}\]  
where \(\{u_{k}\}=\{u_{1},\cdots,u_{K}\}\) represents the set of all modes, and \(\{\omega_{k}\}=\{\omega_{1},\cdots,\omega_{K}\}\) indicates the center frequency of each mode.  
In order to solve the constrained variational model, we need to use a quadratic penalty function term and a Lagrange multiplier to transform Equation (1) into the unconstrained model as shown in Equation (2).  
\[L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum_{k=1}^{K}\left\|\partial_{t}\left[ \left(\sigma(t)+\frac{j}{\pi t}\right)u_{k}(t)\right]e^{-j\omega_{k}t}\right\| ^{2}_{2}+\left\|f(t)-\sum_{k=1}^{K}u_{k}(t)\right\|^{2}_{2}+\left\langle\lambda (t),f(t)-\sum_{k=1}^{K}u_{k}(t)\right\rangle \tag{2}\]  
In Equation (2), \(\alpha\) is the penalty factor, and \(\lambda\) is the Lagrange multiplier. By using the alternating direction multiplier algorithm to obtain the saddle point of the unconstrained model, the final solution to Equation (1) is obtained; this results in the adaptive decomposition of the wind power sequence so that the modes can be obtained.