esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cerebral small vessel disease (CSVD) is a progressive syndrome related to the small vessels of the brain and manifests as white matter hyperintensities (WMH), lacunes, enlarged perivascular spaces (EPVS), cerebral microbleeds (CMBs), and atrophy on conventional brain magnetic resonance imaging (MRI) [1, 2]. The incidence of CSVD increases with age [3]. Thus, with the aging population, the clinical symptoms caused by CSVD have received more attention. Recently, in addition to stroke, cognitive decline, gait disturbance, and mood disorders, growing evidence also suggests that CSVD is associated with sleep disorders [4, 5, 6, 7, 8].  
Sleep is increasingly recognized as an important factor in overall health and well-being. Sleep disturbance is common among older adults [9]. Poor sleep can profoundly impact an individual's quality of life and negatively influence cognition and emotion regulation [10, 11]. A study by [PERSON] et al. demonstrated that insomnia in older adults with CSVD is higher than that among older adults in general [5]. In addition, community-based studies have shown that WMH and EPVS were associated with self-reported poor sleep quality [6, 12, 13]. However, the underlying neural mechanisms of the relationship between poor sleep quality and CSVD remain poorly understood.  
The advent and development of MRI techniques have provided a safe, non-invasive tool for investigating brain structure and function [14]. Voxel-based morphometry (VBM) is a widely used automated technique that examines regional brain volume on a voxel-wise basis and may potentially identify subtle structural abnormalities which appear normal on conventional MRI [15]. Utilizing VBM may improve understanding of the brain pathophysiology of the relationship between CSVD and poor sleep quality.  
The present study aimed to explore gray matter structural differences between CSVD patients with and without sleep disturbance using VBM analysis. We hypothesized that CSVD patients with poor sleep quality would exhibit alterations in brain structure (gray matter atrophy or hypertrophy) compared to CSVD patients with good sleep quality and that alterations in regional gray matter volume (GMV) would be associated with sleep quality.
# Video-Based Parking Occupancy Detection for Smart Control System
## 2 Materials and Methods
### Streetlight Judgment  
During the day, we could record and process parking parts and not activate part of the streetlight until night, when the streetlight system could assist in parking and lighting. Settings were divided into two parts: (1) when no objects were detected on the street, the streetlight remained on a dim settings, and (2) when an object appeared or entered the streetlight's illumination range, the streetlight would brighten in advance of the object from the dim to the high stage, as shown in Figure 13. Smart streetlight automation may assist in parking safety and facilitate finding empty spaces.  
Figure 12: Illustration of voting mechanism.  
```
1:Input: \(P_{K}\)\(\rightarrow\) bounding box of parking grid, \(\forall\kappa=1,\ldots,\kappa\)
2:Input: F \(\rightarrow\) images of streaming camera
3:Set number keep record the voting status, voting status
4:Set number record voting status, V
5:Set number of the threshold \(\rightarrow\) T
6:Set number Boolean \(\rightarrow\) P
7:While the streaming video does not stop:
```  
* bounding box F = YOLO.predict (F)
* for bounding box F in every single \(P_{K},\ \forall\kappa=1,\ldots,\kappa\)
* Intersection area \(\rightarrow\) calculate Intersection area (bounding box F,\(P_{K}\))
* Union area \(\rightarrow\) calculate Union area (bounding box F,\(P_{K}\))
* IOU ratio \(\rightarrow\) Intersection area/Union area
* Overlapping ratio \(\rightarrow\) Intersection area/area of \(P_{K}\)
* end for
* for every single \(P_{K},\ \forall\kappa=1,\ldots,\kappa\) finds which value is bigger than threshold by IOU or overlap:
* if the value of IOU ratio bigger than the threshold T
* 1. afterward, enter the voting mechanism, and P = 1
* else check the value of Overlapping ratio bigger than threshold T
* 1. afterward, enter the voting mechanism
* end for
* enter the Voting mechanism:
* if P == 1:
* update V from IOU  
* update V from Overlapping
* end if
* end the Voting mechanism
* per five F check the result of the Voting mechanism
* if V keeps the certain status
* 1. \(P_{K}\) has been occupancy
* end if
* end while ```  
**Algorithm 1**Pseudocode for occupancy and voting mechanism.
# Routing Design for Pipe System Considering Natural Frequency
## 6 Simulations and Results
### Multi-Pipe Path Planning in Two-Dimensional Environment  
After validating the correctness of routing a single pipe, further validation of the algorithm for multiple pipes was conducted. The environment and simulation results are presented in Figure 18. In this particular case of the multi-pipe routing algorithm, there are three pipes and each pipe has an initial population of 5. The parameters used in this case are the same as in case 1. The fitness value of the current optimal path is outputted every 10 generations. The fitness curves of the simulations are shown in Figure 19. At the beginning of the iteration, the evaluation function has a relatively high value due to the issue of initial paths of several pipelines crossing each other. As the iteration progresses, the phenomenon of intersecting disappears, resulting in a rapid decrease in the fitness value until convergence. The results are summarized in Table 5. In Figure 18, the red, green, and blue lines represent the centerlines of pipes P1, P2, and P3. It can be observed from Figure 18 that the multi-pipe algorithm proposed in this paper effectively achieves obstacle avoidance among multiple pipes in complex environments. By considering the natural frequency of the longest straight segment and the shortest segment in the evaluation function, the path planning result is no longer solely focused on achieving the shortest path. From Figure 18 and Table 5, it can be observed that the total path length of the red pipe is 650.2 mm, with the shortest segment length of 79.7 mm, which exceeds the set constraint of 30 mm. The natural frequency of the longest segment is 2686.6 Hz, and there are two clamp support positions. Convergence is achieved after 2520 generations. The total path length of the green pipe is 544.6 mm, with the shortest segment length being 46.7 mm. The natural frequency of the longest segment is 20,862.5 Hz, and there are five support positions. Convergence is achieved after 3510 generations. The total path length of the blue pipeline is 77.8 mm, with the shortest segment length being 43.4 mm, the natural frequency of the longest segment being 46,053.8 Hz, and having one support position. The convergence algebra is 2200 generations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Length (mm)** & **Frequency (Hz)** &
\begin{tabular}{c} **Minimum** \\ **Segment (mm)** \\ \end{tabular} & **Fitness Value** & **Generations** \\ \hline
1 & 239.6 & 8079.57 & 30 & 0.71161 & 825 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information of the piping paths in case study 1.  
Figure 17: Fitness value in case study 1.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 4 Experiment Results
### Comparison of Experimental Results for 'PI' and 'EI'  
In BayesOpt, the acquisition function is a function that recommends the next candidate to be explored based on the probabilistic estimation of the objective function by the Surrogate Model. We conduct experiments with two types, PI and EI, and compare the results. We utilized the base network which was trained with 70 classes among 100 classes in CIFAR-100 and updated the branch new network with the rest of 30 classes. Then we set the threshold accuracy to 1% less than the baseline accuracy, which did not share any base network layer. Figure 3a shows the result of the selected optimal number of sharing layers through BayesOpt using 'PI' as the acquisition function and Figure 4a shows the result of that through BayesOpt using 'EI' as the acquisition function. The shaded area represents the uncertainty of unsampled points calculated through GP regression, and the black line is the posterior mean value of unsampled points. The red points denote the normalized loss value of sampled points, and the red line is drawn as the 'PI' values based on GP regression results. In Figures 3 and 4b,c, the L2 distance of the consecutive observed points and the value of the calculated best-selected sample are represented for every iteration, respectively. Since 'PI' considered only the probability of increasing the function values, while 'EI' considers both that and the magnitude of improvements. BayesOpt followed a different path depending on which acquisition function was used as shown in Figures 3 and 4. Therefore, we chose 'EI' as an acquisition function that found the better optimal point with the smaller number of attempts.  
Figure 3: Visualization results of the optimal sharing layer configuration through BayesOpt using ‘PI’ as the acquisition function. (70 classes/30 classes) (**a**) the result of the selected optimal number of sharing layers. (**b**) The L2 distance of the consecutive observed points. (**c**) The value of the calculated best-selected sample for every iteration.  
Figure 2: Incremental learning model: the network needs to accommodate its capacity by updating it with sequential new classes.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### Music  
Another important DoA estimation method is MUSIC [11; 16]. The MUSIC spatial spectrum is computed as  
\[P_{MUSIC}(\Omega)=\frac{1}{\left|\left|E_{n}^{H}\mathcal{V}^{H}(\Omega)\right| \right|^{2}} \tag{41}\]  
where \(E_{n}\) is the noise matrix of the array obtained from eigenvalue decomposition of covariance matrix estimate \(\hat{\mathbf{R}}_{aa}\) as  
\[\hat{\mathbf{R}}_{aa}=E\lambda E^{H}=\mathbf{E}_{s}\lambda_{s}\mathbf{E}_{s}^{H}+\mathbf{E}_{n }\lambda_{n}\mathbf{E}_{n}^{H} \tag{42}\]  
where \(\lambda\) represents the eigenvalue matrix, \(\mathbf{E}\) incorporates the right eigenvectors, and subscripts \(n\) and \(s\) are the noise and signal subspace, respectively. MUSIC needs a covariance matrix with a full-rank signal. Hence, when we employ MUSIC in localizing U signals, the covariance matrix of the estimated signal should have U eigenvalues that differ from the noise. However, if there are few snapshots or coherent signals of the array covariance matrix, this assumption does not hold. The snapshot refers to frequency- and time-domain spherical harmonic coefficients.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 4 Discussion  
### Ablation Experiment Results  
This study validates the superiority of the CNN-BiLSTM-Transformer model through ablation experiments, highlighting the effectiveness of combining local feature extraction, bidirectional sequential dependency modeling, and global dependency capture in improving prediction accuracy and generalization ability. The model first extracts local features through the CNN layer, then captures sequential neighborhood information using the BiLSTM layer, and finally employs the Transformer layer with a self-attention mecha  
Figure 9: Prediction effect comparison histogram: (**a**) XGBoost; (**b**) LSTM; (**c**) BiLSTM; and (**d**) CNN-BiLSTM-Transformer.  
nism to focus on temporal information and input features, capturing global dependencies. By integrating these features, the model enables accurate porosity prediction for complex carbonate reservoirs with deep-sequence well logging data. The R\({}^{2}\) values for the training and test sets reach 0.99 and 0.98, respectively. This method demonstrates strong feature extraction capabilities, high generalization ability, and suitability for highly heterogeneous reservoirs. The CNN component enhances local feature extraction, BiLSTM handles deep sequential dependencies, and Transformer focuses on global information, significantly improving prediction accuracy. Compared to XGBoost and standalone LSTM models, CNN-BiLSTM-Transformer provides more accurate porosity distribution predictions in complex reservoirs. The high R\({}^{2}\) on the test set confirms that the model maintains strong stability and accuracy across different well logging data combinations. In conclusion, the CNN-BiLSTM-Transformer network structure provides a novel and effective reference for accurately evaluating porosity in complex and highly heterogeneous carbonate reservoirs.
# A Finite State Machine-Based Improved Cryptographic Technique
## 3 Proposed Methodology
### Cryptographic Turbo Code  
The MCTC is a proposed modification of the FSM that provides additional reliability and security for data while it is in transit. The MCTC system is composed of the transmitter, which has an MCTC encoder and decoder as well as an FSM, and the receiver, which contains an FSM. Before transferring the data through a transmission channel, the MCTC encoder first encodes and encrypts it using the private key that it has been given.  
Figure 2: Flowchart of the proposed work.
## 3 Results
### Experimental Results of the Obstacle Avoidance Approach  
Figure 12 presents the experimental results of the ROV's multiple obstacle avoidance. The obstacles are identified by purple upper-case letters, the points of the scan map are marked by blue lower-case letters, and the red line signifies the trajectory of the ROV with the results of obstacle avoidance by optimum value computing. In the obstacle avoidance experiment, the target heading was due north according to the map orientation, and the radius \(R\) of the obstacle avoidance approach was 1.7 m. The ROV started toward the destination at point \"a\". From point \"b\" to point \"d\", the ROV successfully avoided the first obstacle. Between points \"d\" and \"e\", the ROV continued to travel toward the destination. From point \"e\" to point \"g\", the ROV smoothly avoided the middle row of obstacles \"B\" and \"C\". At point \"g\", the third row of obstacles, \"D\", \"E\", and \"F\", was detected. The ROV chose to turn right 60\({}^{\circ}\) and traveled between obstacles \"E\" and \"F\" from point \"h\" to point \"k\". Finally, the ROV again traveled toward the destination from point \"l\".  
Figure 11: Experiment with multiheading control. The horizontal axis is time (s). The vertical axis is the heading degree. The black line is the heading angle of the ROV. The red dash line is the target angle.  
Figure 12: Trajectory of the obstacle avoidance of the ROV. The obstacles are identified by purple upper-case letters. The points of the scan map are marked by blue lower-case letters. The red line signifies the trajectory of the ROV.  
Figure 13 presents the cost function values of optimum value computing. In total, seven points were defined as simulation parameters. The total time of the ROV obstacle avoidance experiment was 96 s. The lowest cost function values made the ROV change direction to avoid the obstacles. As shown in Figure 13, obstacles were avoided three times by the ROV. At 14 s to 26 s, the ROV turned right 60\({}^{\circ}\) because the point at 60\({}^{\circ}\) (purple) was calculated as having the lowest value of the cost function. After passing around obstacle \"A\", the middle row of obstacles made the ROV turn left 60\({}^{\circ}\), because the point for \(-60^{\circ}\) (red) had the lowest cost function value. Finally, the third row of obstacles made the ROV turn right 60\({}^{\circ}\) again on the basis of the cost function for 60\({}^{\circ}\) (purple). Figure 14 reproduces images of the scanning sonar.
## 4 Simulation Results and Discussions  
Because of the limited experimental conditions, only simulation verification was carried out rather than actual experiment. Therefore, the simulation results may not be so accurate. After the follow-up funds are sufficient, some practical verification studies will be done. As for the selection methods of these parameters, they are artificially selected according to the conditions calculated in the theoretical part, which is not necessarily the best. In the follow-up, it is considered to use those optimization algorithms such as genetic algorithm and particle swarm optimization algorithm for adaptive selection.  
In this section, the surface-mounted three phase PMSM is used as the simulation model. The parameters are shown in Table 1. The solve method is selected as variable-step ode23 tb algorithm, the relative tolerance is set as 0.0001, and the simulation duration is set as 0.4s.  
The flowchart and the Simulink block diagram of speed control and estimator are shown in Figures 1 and 2. As seen from Figure 1, estimator is used to reconstruct the disturbance and uncertainties and the combined controller is designed to achieve better dynamic performance and reduce the influence of the disturbances.  
It can be seen from Figure 2 that the control method proposed in this paper is based on \(i_{d}=0\) strategy. The vector control of three-phase PMSM mainly includes four parts: state observer, speed loop combined controller, current loop PI regulator, and SVPWM algorithm. The design of this paper is mainly embodied in the state observer and speed loop combined controller, and its control effect will be reflected in the following simulation. According to the existing examples [21] with good control effect, the traditional PI control strategy is adopted for current control, and the SVPWM control strategy is adopted for pulse width demodulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Pole-pairs \(P_{n}\) & 4 \\ Stator inductance \(L_{s}\) & 8.5 mH \\ Stator resistance \(R\) & 2.875 \(\Omega\) \\ Flux linkage \(\psi_{f}\) & 0.175 Wb \\ Rotational inertia \(J\) & 0.003 kg\(\cdot\)m\({}^{2}\) \\ Damping Ratio \(B\) & 0.008 N\(\cdot\)m\(\cdot\)s \\ DC side voltage \(U_{dc}\) & 311 V \\ Switching Frequency of PWM \(f_{purm}\) & 10 kHz \\ Sampling period \(T_{s}\) & 10 \(\upmu\)s \\ Rated output torque & 3 N\(\cdot\)m \\ Rated output current & 5 A \\ D axis current \(i_{d}\) & 0 A \\ Speed at low speed \(Nr\) & 300 rpm \\ Speed at high speed \(Nr\) & 1000 rpm \\ Load torque at 0-0.02 s \(T_{L}\) & 0 N\(\cdot\)m \\ Load torque at 0.02–0.04s \(T_{L}\) & 10 N\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of PMSM.  
Figure 1: The speed control and estimator flowchart.
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model  
In this section, we propose an anonymous communication model SACT based on SDN, which is composed of NTP server, SDN controller, host, and SDN link. As shown in Figure 3, there are two participants: an illegal attacker and a legitimate user. By embedding watermark, the data transmission is invisible for the attacker.  
Controller \(X\) embeds the watermark \(W\) into the packet sent by \(A\) and sent it to the communication network. After the packet arrives at the network switch where \(B\) is located, the flow table action is triggered to notify controller \(Y\) to extract the watermark \(W^{\prime}\) from it. If the similarity between \(W^{\prime}\) and \(W\) is less than the threshold \(\eta\), then \(A\) and \(B\) are considered to have communication behavior.  
The controller modulates and demodulates the watermark information, including three general modules as shown in the Figure 4.  
We describe the application components of our system in the Figure 5. Mapping synchronization, convolution expansion, compression conversion, and distributed simulation modules are deployed in Controller X, and mapping synchronization, correction unit, and compression conversion modules are deployed in Controller Y. The modules are as follows:  
* Mapping synchronization: Communicate with the \(NTP\) server to get the current encoding mapping relationship. The module is deployed in both controller \(X\) and \(Y\), and the corresponding mapping is required for both encoding and decoding.
* Convolution expansion: As the core component of the system, convolution expansion module carries out convolutional encoding of the watermark information. After completion, this module sends the watermark to the compression conversion module.
* Compression conversion: In order to solve the problem of channel capacity reduction caused by the introduction of convolutional code, this module compacted and converted the watermark information after convolutional coding to improve the efficiency.
* Distributed simulation: In order to make the arrival delay of the packet interval after adding the watermark close to the normal network, the module fitted the delay sequence corresponding to the watermark information after compression conversion.
* Correction unit: After controller \(Y\) receives the packet, its arrival delay is corrected to obtain the initial sequence.
* Viterbi decoding: Decode the watermark to judge the communication relationship between \(A\) and \(B\).  
Figure 4: Controller module.  
Figure 3: SDN-based Anonymous Communication Tracking (SACT) system model.
## 3 Calibration  
### Boresight Alignment  
Georeferenced LiDAR returns and optical observations are computed as a function of lever arm offsets, boresight angles and optical measurements. Bias in any of these parameters and GPS-INS errors will result in offset point clouds and distorted images. Boresight biases are computed using data collected over buildings with peaked roofs, where overlapping data are collected with different headings. Boresight corrections are computed between campaigns and consistently result in an accuracy of \(\sim\)10 cm (1 \(\sigma\)).
## 4 Results and Discussion
### Feature Selection  
The Enhanced Mutual Information Features Selection (EMIFS) built in our previous work was used to select the most informative features for the pre-encryption phase of crypto-ransomware lifecycle. The experiments were conducted using several epochs sets with different number of features, i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 features. The results show the accuracy at different numbers of epochs. The dataset was divided into a training set and testing set using a 10-fold cross validation approach. The testing set was then used to determine the classification accuracy of those classifiers.