esa-sceva/satcom-chunk-collection huggingface dataset:
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Senders' Side
#### 4.1.1 Generation of Chaotic Matrices  
Chaotic sequences are used to generate measurement matrices and concealing matrices, and, to increase the security level of the proposed method, we use two heterogeneous chaotic systems to generate measurement matrices and concealment matrices separately. Here, we choose the tent system for measurement matrix generation and the logistic system for concealing matrix generation. Other chaotic systems, the Chebyshev system for instance, can also be used to generate such deterministic matrices.  
Based on tent equation Equation (4), we get a chaotic sequence \(z^{\prime}_{l},l=1,2,3\cdots\). Simultaneously, another chaotic sequence \(z_{l},l=1,2,3\cdots\) could be generated by logistic equation Equation (5).  
Figure 2: The main workflow of the proposed method. The left side shows the data compression, encryption, and concealing and masking processes done by a sender. The right side shows the data demasking, decryption, and retrieving processes done by receivers. The red part in the middle represents an open or insecure channel that might leak information to eavesdroppers.  
Next, a measurement matrix teeming with chaotic sequences could be generated with the following two steps:  
Step 1: After the chaotic sequence \(z_{l}^{{}^{\prime}}\) is generated, we sample this sequence using the sampling initial position \(n_{0}\) and sampling distance \(d\). Therefore, the sampled sequence \(x_{n}^{{}^{\prime}}\) is obtained as Equation (6).  
\[x_{n}^{{}^{\prime}}=z_{n_{0}+nd}^{{}^{\prime}},n=1,2,3\cdots \tag{6}\]  
Step 2: We arrange the elements in the sampled sequence to generate a matrix whose order is \(M\times N\) with some mapping relationships, and the formed matrix can be used as the CS measurement matrix. The following mapping function is available, but is not limited to,  
\[a_{ij}=x_{i(n-1)+j}^{{}^{\prime}},n=1,2,3\cdots \tag{7}\]  
where the measurement matrix is \(A\in\mathbb{R}^{M\times N}\) (\(M<N\)) and \(a_{ij}\) is the entity in matrix \(A\) with a position of row \(i\)th and column \(j\)th.  
Similarly, a concealing matrix could be generated based on chaotic sequence \(z_{l},l=1,2,3\cdots\). Suppose \(s\in\mathbb{R}^{N}\) is a k-sparse discrete signal that contains critical information, and \(j\) indicates the row number of elements in \(s\). We define a set \(C\) that consists of the critical entities that are contained in \(s\) and then can get a concealing matrix \(H\) using the function as below:  
\[h_{ij}=\begin{cases}0,&if\quad s_{j}\
otin C\\ &-z_{i}*a_{ij},if\quad s_{j}\in C\end{cases} \tag{8}\]  
where the concealing matrix is \(H\in\mathbb{R}^{M\times N}\)and \(h_{ij}\) is the entity in \(H\) with a position of the \(i\)th row and the \(j\)th column.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Processing and Evaluation of the Acquired Data
#### 2.6.4 ICP Transformation and Evaluation of the Transformed Clouds  
For better evaluation of the shape of the individual clouds (i.e., systematic error, see Figure 6), the iterative closest point (ICP) transformation of the entire test cloud to the reference cloud was performed. For this step, the Fine registration (ICP) function in CloudCompare was used. The Random sampling limit was increased to make sure that the entire cloud was used for the transformation and the filtering of the outliers (most distant points) was enabled. Again, we emphasize that the shape of the cloud was not changed, transformation was based solely on displacements and rotations of the entire clouds.  
Next, RMSE\({}_{\text{ICP-ALL}}\) (i.e., the RMSE of the entire cloud after ICP transformation) was calculated. In this way, the effect of the transformation to spherical targets was eliminated and (as shown in Figure 6), the RMSE was expected to drop by about 50%. This parameter,therefore, allows a qualitative assessment of the shape of the test cloud compared to the reference (note that this evaluation cannot be performed in the practical real-world setting where the reference cloud is not available).
## 3 Network Intrusion Detection Model Based on CNN and GRU  
Traditional intrusion detection models are more concerned with time series features and ignore spatial features in the process of detecting attacks. The CNN structure is more effective in extracting the spatial features of the data traffic, however, its ability to extract long-distance dependent information is mediocre; the GRU structure is more effective in extracting long-distance dependent information and can avoid forgetting during the learning process, however, its number of parameters is large and the training time is long. Therefore, this paper integrates the two to improve the model's ability to learn features, which can fully extract features from both spatial and temporal dimensions, and thus achieve a higher classification detection accuracy.  
The proposed network intrusion detection model combining convolutional neural network and GRU, referred to as the CNN-GRU model, consists of three main stages: firstly, the pre-processing stage, in which the original data is converted into numerical features and normalized, and then the dataset is balanced by the ADRDB algorithm, after which the features are extracted by the RFP algorithm and finally converted into a grey-scale map; Secondly, the training phase, in which the pre-processed data are assigned different weights to the features by the Convolutional Block Attention Module (CBAM) based on residuals firstly, then the spatial features are extracted by the CNN module, and the spatial information is further aggregated by combining Averagepooling and Maxpooling. After that, the temporal features are extracted by multiple GRU units. Finally, the classification is performed by the Softmax function; Thirdly, the testing phase, in which the test set is passed into the trained model for classification. The structure of the model proposed in this paper is shown in Figure 1.
# Plant Synthetic Promoters
## 3 Objectives and General Methodology of Synthetic Promoter Creating
### Cis-Element Juggling and Domain Swapping
used the CAMV35S core enhancer (subdomains A1 and B1-3), together with enhancers from three plant genes to stimulate CAMV35S minimal promoter activity, independently of orientation; the activity was identified by self-transcribing active regulatory region sequencing (STARR-seq). These enhancers efficiently stimulate synthetic expression systems only when positioned relatively nearly, within 500 bp from the CAMV35S minimal core promoter and outside of the 3'UTR [128]. Creating synthetic promoters opens the doorway to analyzing subtle or mutually exclusive metabolic events. The close spacing between cis-elements bound by trans-factors responding to SA and JA, or alternatively auxin or cytokinin treatment, allowed antagonistic pairs of plant regulators, such as JA/SA or auxin/cytokinin, to be studied. The obtained synthetic promoters could attenuate the antagonistic effects of the SA/JA and auxin/cytokinin pairs and strengthen responses to these stimuli [193]. Also, the feedback-regulated catabolism of SA to 2,3-dihydroxybenzoic acid, together with such physiologic effects as SA concentration in plant tissue, growth rate, leaf senescence, and pathogen resistance, was reconstructed in plants by the application of synthetic promoters containing the _SA 3-hydroxylase (S3H)_ gene under the control of the SA-inducible promoter from _SA 5-hydroxylase (S5H)_[193]. Furthermore, plant synthetic promoters based on the Q system from _Neurospora crassa_ could not only be activated by the expression of QF2 and QF2w trans-factors but also constrained by the QS repressor, enabling the precise control of gene expression rate [234; 235]. In addition, the precise control of three gene expression levels could be used to modulate photosynthesis efficiency and plant productivity by photorespiratory bypass. Such a synthetic photorespiratory circumvent, known as the GMA bypass, allows the plastitial glycolate to decompose, leading to the release of CO2 directly into the chloroplasts and an increase in the local CO2/O2 ratio. To efficiently execute such a task, two different constitutive promoters, viz. CaMV35S and the maize _UBIQUITIN_ promoter (_pUbi_), were exercised to avoid gene-silencing effects while driving _Cicturbina maxima malate synthase (CmMS)_ and _O. sativa ascorbate peroxidase_ 7 (OsAPX7), respectively. However, the third gene in the GMA bypass, known as _O. sativa glycolate oxidase 1_ (OsGLOI), should be controlled by a light-inducible Rubisco small subunit promoter to dynamically adapt the gene expression rate to changes in light conditions and significantly improve photosynthetic efficiency [194].
# Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
## 3 The Problem Formulation
### The VRPD Definition  
A complete graph \(\mathcal{G}\) is considered, consisting of a set of nodes \(\mathcal{N}=\{1,2,\ldots,N\}\), where node \(N\) is represented as a depot. All other nodes in the graph represent known customer locations with their orders. Each customer must be visited by a drone, a truck, or both, only once. To achieve this, a set of homogeneous trucks \(\mathcal{K}\) with the corresponding set of identical and equal number of drones \(\mathcal{D}_{k}\) for all \(k\in\mathcal{K}\) leaves the depot loaded with customer orders. The distance between nodes \(i\) and \(j\) is defined as \(d_{i,j}\) such that \(d_{i,j}=d_{j,j}\) for all nodes \(i\) and \(j\) in \(\mathcal{N}\). Given the constant velocities of drones and trucks, let \(v^{dr}\) and \(v^{tr}\) denote the velocities of drones and trucks, respectively, where \(v^{dr}=\pi v^{tr}\) and \(\alpha\geq 1\), as drones are not subjected to traffic congestion. Then the time to traverse from node \(i\) to node \(j\) is \(t^{dr}_{i,j}\) and \(\tau^{tr}_{i,j}\) for drones and trucks, respectively, where \(\tau^{tr}_{i,j}=\pi\tau^{dr}_{i,j}\).  
A drone can only fly a distance of \(\varepsilon\) due to its battery limits and must be retrieved by its corresponding truck to be recharged. As established in the literature [8; 9; 28], drones can serve only a single customer per launch. Additionally, they can be launched and retrieved only at customer locations. Negligible times for drone launching, retrieval, charging, and customer serving are assumed. Each drone can be launched and retrieved only by its corresponding truck. Both drones and trucks are allowed to wait at rendezvous nodes. In general, the decision variables for the VRDP include the sequence of nodes to be visited by each truck. For the drones, one needs to decide whether a drone will serve a customer, including at which node it will be launched to serve the selected customer and at which node the drone will be retrieved from its truck. In modeling the VRPD, a number of constraints must be satisfied. First, the flow preservation constraints ensure that all the drones and trucks leave the depot once and return back only once. Second, each customer node must be served only once. Third, to ensure the synchronization of the routes of drones and trucks, the launching nodes must be visited by trucks before drones serve the selected customers for drone service, followed by retrieval nodes. The objective function of the VRPD is to minimize the time to serve all customers measured from the moment vehicles (both drones and trucks) leave the depot until they all return. The full mathematical model of the VRPD can be found in Appendix A, that follows [29]'s formulation. It is also noted that the obstacle avoidance problem by drones is not considered by the VRPD.
## 3 Results
### Effect of Ca\({}^{2+}\) or HA on MFRO Permeate Flux under Electric Field Conditions  
In this study, we investigated the fouling of the RO membrane by using common contaminants found in water treatment applications, including Ca\({}^{2+}\) and HA.  
Figure 6a demonstrates that after 88 h of continuous operation, the permeate flux dropped from 5.49 L/(m\({}^{2}\cdot\)h) to 4.51 L/(m\({}^{2}\cdot\)h) and then reached a steady state. However, in the presence of an electric field, the permeate flux was still on a downward trend, the rate of decline decreased, and, after 120 h of operation, it approached the level observed without the electric field. The reduction in permeate flux can be attributed to the presence of calcium chloride (CaCl\({}_{2}\)) in the water, which dissociates into Ca\({}^{2+}\) and Cl\({}^{-}\) ions. These ions influence the effective size and charge density of other ions in the solution, leading to their adhesion to the membrane surface. Consequently, fouling occurs, resulting in a decrease in the permeate flux. However, when the electric field intensity is increased, it induces changes in the velocity and trajectory of the Ca\({}^{2+}\) ions, thus reducing their residence time on the surface of the membrane. Additionally, applying the electric field creates a fluid mixing effect that helps to reduce the fouling of the RO membrane.  
Figure 6b illustrates the effect of HA on permeate flux with and without an applied electric field. In the absence of an applied electric field, the permeate flux shows an initial rapid decrease, followed by a slower decrease until it finally stabilizes. From the beginning, the permeate flux decreases from 5.49 L/(m\({}^{2}\cdot\)h) to 4.23 L/(m\({}^{2}\cdot\)h). The decrease can be attributed to the dissolution of the HA in the water, as it begins to attach and adsorb to the surface of the membrane, leading to the fouling and clogging of the membrane. As time progresses, the degree of membrane fouling gradually increases, but the permeate flux is ultimately influenced by the RO membrane's stability and approaches a balanced state. When an electric field exists, the rate of permeate flux reduction slows down, and, after 24 h of operation, it reaches a plateau. Eventually, the permeate flux stabilizes at 4.81 L/(m\({}^{2}\cdot\)h),  
Figure 6: Effect of Ca\({}^{2+}\) or HA on the Multi-field reverse osmosis permeate flux in the absence of an electric field.  
indicating that the electric field reduces membrane fouling. This can be attributed to the attraction between HA and the anisotropic charge in the solution when acted upon by the electric field. Simultaneously, it becomes more difficult for pollutants to adhere to the membrane surface, reducing fouling, which is also related to electrophoretic migration and electrostatic repulsion [28,42]. In the absence of other pollutants, the assisting effect of the electric field gradually stabilizes over time.
## 2 Applicability of Blockchain in Sports
### Anti-Doping Compliance  
Doping scandals have significantly undermined stakeholder trust in competitive sports. Blockchain's tamper-proof record-keeping ensures traceability in drug storage, testing processes, and compliance monitoring. Solutions such as a redesigned Anti-Doping Administration and Management System (ADAMS), using blockchain to enhance data integrity and privacy, showcase its utility in this domain [5; 17].  
Furthermore, innovative blockchain designs allow the anonymized storage and verification of athlete biological data, supporting a robust anti-doping ecosystem while safeguarding privacy. Thus, blockchain offers an Athlete Passport to store and verify athletes' biological data.
## 3 Research Methodology
### Sensor Fusion
The distances of each sensor are denoted by \(y\), \(x\) shows the longitudinal distance, \(\dot{x}\) is the relative velocity, and \(p\), \(q\), and \(r\) are the total number of objects that the sensors have identified. Every time a measurement is updated, a multi-rate Kalman filter (KF) is employed to account for the timing variances of each of the sensors. The measurement is updated by one sensor at its own update time, and the remaining sensors' measurements are then estimated using the KF until the subsequent measurements. The sensor fusion's update rate is time synchronized with the control loop. Figure 12: Bird’s-eye view of the vehicle with three sensors. Using three different types of sensors, the vehicle can track obstacles through multi-sensor data fusion. Effective track-to-track data fusion is implemented through decentralized data fusion. Each sensor's ability to identify objects is tracked using a fusion algorithm. \[\hat{x}_{k}= \begin{bmatrix}x&y&\dot{x}&\dot{y}\end{bmatrix} ^{T} \tag{19}\]  
where \(y\) and \(x\) are the lateral and longitudinal relative distances, respectively, and \(\dot{x}\) and \(\dot{y}\) are the leading vehicle's relative velocity. There is a validated track-to-track management for each tracker. By fusing the data, the precision of the integrated state value can be increased. The lateral velocity \(\dot{y}_{k}^{V}\) and azimuth angle (\(\theta_{k}^{V}\)) of the vision sensor, along with the longitudinal velocity (\(\dot{x}_{k}^{LR}\)) and distance (\(x_{k}^{LR}\)) of the radar and lidar, are thought to be the most important variables. Equation (20) can be used to explain the fusion track (\(\hat{x}_{k}^{VLR}\)) of the radar (\(R\)), lidar (\(L\)), and vision (\(v\)). \[\hat{x}_{k}^{VLR}= \begin{bmatrix}x_{k}\\ y_{k}\\ \dot{x}_{k}\\ y_{k}\end{bmatrix} = \begin{bmatrix}x_{k}^{LR}\\ x_{k}^{LR}\times\tan\theta_{k}^{V}\\ \dot{x}_{k}^{LR}\\ \dot{y}_{k}^{V}\end{bmatrix}  \tag{20}\]
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)
### Non-Linear Methods
The popular signal parameters extracted using the RQA method include the recurrence rate (RR), the average number of neighbors (AN), determinism (DET), the length of the longest diagonal line (Lmax), entropy (ENT), laminarity (LAM), trapping time (TT), the maximum length of vertical lines (Vmax), the average length of diagonal lines (AD), the ratio of determinism to the recurrence rate (RATIO), and so on [41]. Chaos theory, a mathematical discipline concerned with the study of intricate and unpredictable systems, has also been used for HRV feature extraction. The utilization of chaos theory principles, including fractals and non-linear dynamics, has been employed in the examination of the intricacy and fluctuation of patterns in HRV [42]. Lyapunov exponents are a sophisticated mathematical construct that originate from the field of chaos theory. These notions find utility in the realm of HRV feature extraction, enabling a deeper understanding of the intricate dynamics inherent in the cardiovascular system. The metric quantifies the speed at which neighboring trajectories in a dynamical system either move  
Figure 6: A representative DFA plot of a 5 min HRV, plotted using LabVIEW. The \(x\)-axis of a DFA plot indicates the logarithmically scaled window widths, also known as time scales. Meanwhile, the \(y\)-axis represents the logarithmically scaled root-mean-square variation of the detrended data. The analysis of the slope of the plot offers valuable insights into the existence and characteristics of correlations within the examined HRV time series data. apart or come together. A Lyapunov exponent with a larger positive value signifies the presence of chaotic dynamics, whereas a Lyapunov exponent with a negative value shows convergence towards a stable attractor. Through the utilization of Lyapunov exponents derived from time series data of HRV, scholars are able to evaluate the presence of chaotic patterns or ascertain the degree of determinism and stability within the dynamics of heart rate. The provided information can be utilized to gain insights into the regulatory mechanisms of the autonomic nervous system on the heart rate and to detect possible anomalies in cardiovascular well-being [43].
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 3 Research Methodology
### Selection of Non-Landslide Points Using the Information Value Method
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature Group** & **Feature** & **Number of Classes** & **Classification Standards** \\ \hline \multirow{5}{*}{Terrain and topography} & Elevation (m) & 23 & (1) \(<\)0; (2) 50–100; (3) 100–150; \(\ldots\); (2) 950–1000; \\  & & (21) 1000–1450; (22) 1450–1500; (23) \(>\)1500 \\ \cline{2-3}  & Slope (\({}^{\circ}\)) & 12 & (1) 0–5; (2) 5–10; (3) 10–15; \(\ldots\); (10) 45–50; (11) 50–55; \\  & & (12) \(>\)55 \\ \cline{2-3}  & & & (1) 0–22.5, 337.5–360 North (2) 22.5–67.5 (Northeast); \\  & & & (3) 67.5–112.5 Eastly; \(\ldots\); (8) 292.5–337.5 (Northeast) \\ \cline{2-3}  & Plan curvature & 16 & (1) \(<\)–2.6; (2) \(-\)2.6\(\ldots\)—2.2; (3) \(-\)2.2\(\ldots\)–1.8; \(\ldots\); (14) 2.2–2.6; \\  & & (15) 2.6–3.0; (16) \(>\)3.0 \\ \cline{2-3}  & Profile curvature & 17 & (1) \(<\)–3.5; (2) \(-\)3.5\(\ldots\)3.0; (3) \(-\)3.0\(\ldots\) –2.5; \(\ldots\); (16) 3.5–4.0; \\  & & (17) \(\downarrow\)4.0 \\ \cline{2-3}  & Terrain & 25 & (1) 2–3; (2) 3–4; (3) 4–5; \(\ldots\); (23) 24–25; (24) 25–26; (25) \(>\)26 \\ \cline{2-3}  & Normalised & & \\ coverage & difference & 20 & (1) \(<\)0.05; (2) 0.05–0.1; (3) 0.1–0.15; \(\ldots\) (19) 0.9–0.95; \\  & vegetation index & & (20) 0.95–1 \\ \hline \multirow{5}{*}{Geological conditions} & Lithology & 5 & (1) Water Body; (2) Weakly Soft; (3) Moderately Hard; \\  & & (4) Relatively Hard; (5) Hard \\ \cline{2-3}  & Distance to & 14 & (1) 0–1000; (2) 1000–2000; (3) 2000–3000; \(\ldots\); \\  & fault (m) & 14 & (13) 12,000–13,000; (14) \(>\)13,000 \\ \hline Meteorological and & Precipitation & 13 & (1) \(<\)1700; (2) 1700–1750; (3) 1750–1800; \(\ldots\); (11) 2300–2400; \\  & & (12) 2400–2500; (13) \(>\)2500 \\ \hline Human & Cut slope & 17 & (1) 0–2; (2) 2–4; (3) 4–6; \(\ldots\); (15) 28–30; (16) 30–32; (17) \(>\)32 \\ \cline{2-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification standards for feature factors. to validate the selected non-landslide points, ensuring the stability and reliability of the negative samples. Figure 1 presents the selection results.