esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of the Standard Deviation of the Particle Size Distribution of Colloidal Particles  
The median particle radii of 0.18 and 0.55 \(\upmu\)m were selected, which correspond to the median particle diameter of kaolinite SA1 and kaolinite RP2 in [38], in order to investigate the impact of the standard deviation of particle size distribution. Figure 5 illustrates simulated breakthrough curves of contaminants and the retention profiles of particles at varied \(\sigma_{c}\) and \(\mu_{c}=-1.72\) (corresponding to the median particle size = 0.18 \(\upmu\)m). It is clearly observed that the increase in the standard deviation of particle size distribution (i.e., an increase in the coefficient of uniformity) retarded the transport of contaminant (Figure 5a). This is attributed to the larger amount of retained particles at higher \(\sigma_{c}\) (Figure 5b), which is caused by larger values of sample \(r_{c}\) at higher \(\sigma_{c}\) (Figure 6). As presented in [17], in the identical median size, the amount of retained particles increased as \(\sigma_{c}\) increased. In other words, no consideration of \(\sigma_{c}\) in colloidal particle transport in porous media would lead to a significant underestimation of the amount of retained particles. The results obtained in Figure 5 indicate that \(\sigma_{c}\) can be a critical factor not only for the colloidal particle transport but also for the contaminant transport when the contaminants are favorably adsorbed to retained colloidal particles.  
The impact of \(\sigma_{c}\) became more significant when \(\upmu_{c}\) increased, as demonstrated in Figure 7. At \(\upmu_{c}\) = \(-0.6\) (corresponding to the median particle size of 0.55 \(\upmu\)m), the retardation of contaminant transport was more significant compared to the case of \(\mu_{c}=-1.72\), as illustrated in Figure 7a. This result may be explained mainly by the fact that an increase in the amount of strained particles due to increasing \(\sigma_{c}\) is greater at larger \(\mu_{c}\) (showing a more exponential retention profile according to \(\sigma_{c}\) at \(\mu_{c}=-0.6\) (Figure 7b) than at \(\mu_{c}=-1.72\) (Figure 5b). The significant increase in retained particles at the top layer of the sand medium corresponding to the increase of \(\sigma_{c}\) provided a large number of adsorption sites at the top layer, which resulted in the significant retardation of contaminant transport.  
Figure 4: Calculated \(k_{att}\) and \(k_{str}\) using Equations (5) and (6) at varied \(r_{c}\) (0.1 \(\upmu\)m interval) and \(r_{s}=360\)\(\upmu\)m.
## 2 Analysis of Imaging Properties of HDOE in Wide-Bandwidth Spectrum
### Analysis of Image Quality Degradation of HDOE Imaging
The distribution of the PSF of the SHDCIS on the focal plane was calculated according to the design value, as shown in Figure 3a. The real PSF of the SHDCIS was measured and calculated according to the method provided in [25], as shown in Figure 3c. The figure shows that the PSF of the SHDCIS is basically close to the design value. The PSF has obvious spatial variation, and there is a clear difference in the PSF between the central and off-axis fields. Consequently, off-axis fields of view will show more aberration than central fields of view. This means that the computation and reconstruction of images in the central and off-axis fields of view differ in the intrinsic process of image restoration. This conclusion provided inspiration for the design of the regional division and channel selection module in the DDZMR network. Deep Dense Zoned Multipath Residual Network for Image Restoration and Simultaneous Super-Resolution Image Reconstruction
###### Abstract  
This study addresses multi-robot distributed rendezvous controls in cluttered underwater environments with many unknown obstacles. In underwater environments, a Unmanned Underwater Vehicle (UUV) cannot localize itself, since a Global Positioning System (GPS) is not available. Assume that each UUV has multiple signal intensity sensors surrounding it. Multiple intensity sensors on a UUV can only measure the amplitude of signals generated from its neighbor UUVs. We prove that multiple UUVs with bounded speed converge to a designated rendezvous point, while maintaining the connectivity of the communication network. This study further discusses a fault detection method, which detects faulty UUVs based on local sensing measurements. In addition, the proposed rendezvous control is adaptive to communication link failure or invisible UUVs. Note that communication link failure or invisible UUVs can happen due to unknown obstacles in the workspace. As far as we know, our study is novel in developing 3D coordinate-free distributed rendezvous control, considering underwater robots that can only measure the amplitude of signals emitted from neighboring robots. The proposed rendezvous algorithms are provably complete, and the effectiveness of the proposed rendezvous algorithms is demonstrated under MATLAB simulations.  
distributed rendezvous control; unmanned underwater vehicle; signal amplitude; faulty robot; network connectivity; three dimensional environments; fault tolerant control; underwater robot Article
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 4 Results  
The models presented in Section 3 were evaluated to determine their effectiveness in predicting the subjective quality rating based on the given objective parameters (Table 1).  
The best results were obtained for the linear regression (LR) model. The LR model obtained prediction results that were most similar to those obtained during subjective assessment of image quality, as indicated by the smallest error values. The beginning of this section presents the results of classic subjective and objective video quality evaluation results and the correlations between them, while the rest of the section discusses the results of quality prediction using a machine learning (linear regression) model. The results, presented in tables and graphs, respectively, are then discussed.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Crossover and Mutation  
In evolutionary algorithms, crossover and mutation operations are used to generate new individuals in search of the optimal solution. Perturbations are inevitable in the crossover and mutation, and the degree of the perturbations is determined by the crossover probability \(P_{C}\) and mutation probability \(P_{M}\). As introduced in Section 3.2, the individual is encoded as the VTW chromosome and single-orbit mission scheduling. Correspondingly, the crossover and mutation occur on the VTW chromosome and affect the mission scheduling on the corresponding orbit. The detailed procedures of the crossover and mutation are elaborated as follows:  
(1) Multi-point crossover: In this paper, multi-point crossover is adopted. Firstly, randomly select \(P_{C}\cdot N_{pop}\) individuals from the population to form the crossover pool, where \(N_{pop}\) is the number of the individuals. Secondly, randomly select two individuals from the crossover pool as parents and, remove them from crossover pool. Then, \(P_{C}\cdot N\) genes are selected randomly to exchange between the parents. Moreover, the missions corresponding to the exchanged genes are removed from the single-orbit observation sequence and are reallocated to the unscheduled mission set of the new orbits according to the new genes.  
(2) Greedy mutation: In the greedy mutation, to improve search speed and efficiency, all the genes corresponding to the unscheduled missions will greedily mutate while the genes corresponding to the scheduled missions mutate with a mutation probability of \(P_{M}\). A new gene is randomly generated for each mutated gene. In addition, each mission corresponding to the mutated gene is removed from the single-orbit observation plan of the previous orbit and is added to the unscheduled mission set of the new orbit based on the mutated gene.
# Immersive Phobia Therapy through Adaptive Virtual Reality and Biofeedback
## 3 The PhoVR System  
### VR and Biophysical Acquisition Devices  
The PhoVR system uses affordable, consumer-level VR playback and biophysical acquisition devices readily available on the market. The system was developed to be used with both all-in-one equipment such as the Oculus Quest series [33] and head-mounted display (HMD) equipment that requires external processing from a VR-compatible laptop or desktop. In particular, we used the HTC Vive [34] and Valve Index [35] HMDs, which are also recommended, but there is the possibility of using other models available on the market. For simplicity in use and configuration, and due to the substantially reduced costs, using only all-in-one equipment, such as Oculus Quest 2, is desirable, but its processing power is much lower than desktop solutions. For this reason, the use of a dedicated HMD is still recommended in order to enhance graphic quality and an optimal video flow.  
To evaluate the degree of perceived anxiety, biometric data were collected throughout the VR session. We recorded electrodermal activity (EDA) and heart rate variability (HRV) using a portable device. After a detailed analysis of the available equipment on the market, BITalino [36] was chosen due to its ease of use, increased ergonomics, and reduced cost.
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Results and Discussion
#### 3.4.1 Analysis of Relationship Graphs between Metro Stations  
We visualize the adjacency matrices as heatmaps to better understand relationships between stations in the graphs. Darker colors in the heatmap represent higher weights. For better comprehension, we use alphanumeric code to represent each station. Figure 12 illustrates the heatmap of the network structure correlation adjacency matrix. We can see that the matrix exhibits elevated weights for neighboring stations, indicating that the neighboring relationship fosters frequent passenger movement and generates distinct spatial dependencies.  
Unlike a single network structure correlation adjacency matrix, there are six adjacency matrices for the passenger flow similarity graphs, which arise from variations in flow direction and time granularities. As illustrated in Figure 13, the passenger flows are divided into inbound and outbound directions and visualized with three time granularities. For the passenger flow relationships between different stations, we can see that the higher weighted stations have similar passenger flow trends. The darker color of each cell in the heatmaps indicates higher similarity, and the lighter color indicates lower similarity.  
Figure 13a-c show the passenger inflow similarity adjacency matrices at time periods of 10 min, 15 min, and 30 min, respectively. Some stations share similar passenger inflow patterns, probably because they serve similar demographics during specific time periods. Meanwhile, Figure 13d-f show the passenger outflow similarity adjacency matrices at different time granularities. Analyzing areas of high- and low-intensity color distribution in heatmaps contributes to identifying potential or hotspot passenger flow patterns. High-intensity areas in heatmaps usually correspond to specific passenger hotspots, such as busy commercial areas, important transfer hubs, or densely populated residential areas. In the  
Figure 12: The heatmap of network structure correlation adjacency matrix.  
above areas, passengers exhibit consistent travel behavior, resulting in a more stable and densely packed passenger flow pattern. Conversely, areas with low intensity indicate weak spatial correlation or low similarity between stations. These stations are spatially dispersed due to different transportation options and the uniqueness of the surrounding areas.
## 2 Descriptions of Conventional Histogram-Based Reversible Data Hiding
### Advantages and Drawbacks of the Conventional Scheme  
Among the requirements addressed in Section 1, the histogram-based reversible data hiding has the advantages of ease of implementation, guaranteed output image quality, and little side information produced [8]. On the contrary, the limited amount of embedding capacity is the major drawback for this algorithm. With the descriptions in Section 2.1, we observe the advantages of the histogram-based reversible data hiding, which are depicted as follows.  
* Ease of implementation: The portion between max and zero points in the histogram is intentionally shifted to the right by one. No calculation for performing this procedure is necessary.  
Figure 2: _Cont._  
* Guaranteed imperceptibility: The mean squared error (MSE) between the original and output images would be at most 1.00 [8], meaning that the Peak Signal-to-Noise Ratio (PSNR) would be at least 48.13 dB.
* Little overhead: Only the luminance values of the max and zero points are required at the decoder for extracting the data and the original, meaning that 2 bytes (or 16 bits) of overhead is necessary, regardless of the size of original images.  
On the contrary, the major drawback of the histogram-based scheme is described as follows.
* Limited amount of capacity: The embedding capacity is limited by the number of occurrences at the max point, and this value is directly affected by the characteristics of the original image. Suppose that the cover image in grayscale has the size of \(M\times N\) pixels, and each pixel is represented by 1 byte. Under the worst case when the occurrences of max points are the same for every luminance value, meaning that the histogram is flat, it represents the uniform distribution between 0 and 255, and the capacity for embedding is only \(M\times N\)\(\div\)256 bits. This implies that embedding capacity is only 0.049% of the filesize of cover image.  
From the observations above, we are able to take the characteristics of the original image into consideration, and try to increase the capacity at the expense of somewhat degraded quality of the output image. Side information should be comparable to that of the conventional scheme. Therefore, we employ the concept of quadtree decomposition for the implementation of histogram-based reversible data hiding algorithm.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Parallel Acceleration of Computational Layers in CNN
#### 2.1.1 Parallel Acceleration of Convolutional Layers
As a result, the pixel values read by the computational kernel are located in different buffer groups during the entire convolution operation, thus increasing the access bandwidth of the computational kernel. As shown in Figure 4, the similar scheme used is eight-input parallelism, eight-output parallelism, and four-pixel parallelism. The computational kernel can compute 256 multiplication and addition operations in a single clock, i.e., 512 operations. The computational kernel is intended to run at a minimum of 300 M, giving a computational power of 153.6 GOPS. The accelerator will be able to run the 2 GOPS detection model TinyYOLO in real-time. Figure 4: Input channels are 3. Input parallelism is 8. Output parallelism is 8. Pixel parallelism is 4. Internal data flow diagram of the Computational kernel.
## 7 Final Remarks  
The integration of DL with the IoMT presents a groundbreaking opportunity to revolutionize healthcare diagnostics, treatment, and patient monitoring. By leveraging advanced AI-driven analytics and real-time data processing, DL-powered IoMT solutions can significantly enhance early disease detection, personalized treatment plans, and operational efficiency. However, major challenges persist, including computational constraints, data privacy concerns, lack of interoperability, and energy-efficient processing. The heterogeneity of IoMT-generated data, combined with the demand for real-time decision making and security, further complicates widespread adoption, particularly in resource-limited healthcare environments.  
To accelerate progress in this domain, future research must prioritize key areas for innovation and practical implementation. First, there is an urgent need for the development of lightweight, energy-efficient DL models optimized for edge computing and IoMT devices. Researchers should focus on model compression techniques, including pruning, quantization, and knowledge distillation, to enable high-performance AI on low-power medical devices. Second, ensuring data privacy and security must remain a top priority, with federated learning, homomorphic encryption, and blockchain-based frameworks emerging as promising solutions to secure patient data without compromising AI performance. Moreover, standardization efforts should be accelerated to establish universal IoMT interoperability standards, allowing seamless integration of DL models across diverse healthcare systems. Collaboration between academia, industry, and regulatory bodies will be critical in creating frameworks that ensure transparency, fairness, and trust in AI-driven healthcare applications.  
Additionally, researchers should explore XAI techniques to bridge the gap between DL models and healthcare professionals, improving the interpretability, accountability, trustworthiness, and dependability of AI-assisted decision making. From an implementation perspective, IoT developers and AI practitioners should actively work on deployable solutions tailored to real-world clinical environments, addressing latency, bandwidth constraints, and real-time processing challenges. Encouraging publicly available, diverse, and high-quality IoMT datasets for training and validating DL models will further enhance generalizability and fairness across patient populations. In conclusion, while DL-powered IoMT systems offer unparalleled potential, realizing this vision requires collective efforts from researchers, engineers, and healthcare professionals to tackle computational efficiency, privacy, security, and scalability challenges. By prioritizing lightweight AI models, robust security frameworks, and scalable interoperability solutions, the research community can drive the next wave of AI-driven smart healthcare innovations, ensuring equitable and high-quality medical care for all.  
This research received no external funding.  
Not applicable, the study does not report any data.  
The authors declare no conflicts of interest.
# Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network
## 1 Introduction
The inconsistency of sample generation and sample classification can increase the complexity of the diagnosis model. This paper proposes a model for diagnosing imbalance faults with an improved CGAN to ensure consistency of generation and classification. Because GANs were originally designed for image processing, vibration signals are first transformed into feature images as inputs of the proposed model. This develops a new generative model as a generator and a discriminative model as a discriminator to generate reasonable discriminatory samples. The discriminator also identifies the fault condition with real and fake samples. The discriminative model as a classifier relies on the classification training of enough samples to improve the fault classification accuracy of the designed method. Experimental results demonstrate that the advanced strategy is sufficiently robust to synthesize high-quality samples and provides excellent diagnostics with unbalanced data. The contributions of this paper are summarized as follows:  
(1) A novel fault diagnosis method based on improved CGAN is presented to solve the problem of the multi-class imbalanced data. The improved model synthesizes more varied samples to balance the data distribution and identify fault states, which can be applied to the bearing and gearbox datasets. (2) A generative model as a generator that integrates feature convolution and cross-iteration batch normalization is proposed to overcome the training instability of CGANs. Furthermore, the discriminative model as a discriminator is improved to identify real/fake samples with skip connection, and it is also used as a classifier to define fault categories. (3) The three loss functions are reconstructed for the generator, the discriminator, and the classifier to achieve the diagnosis of unbalanced samples for enhancing the model performance, respectively. The remainder of this paper is organized as follows. Section 2 illustrates the theoretical basis and a detailed description of the proposed approach. The two cases for experimental verification against the compared methods are shown in Section 3. Section 4 reveals the conclusion.
# Effect of Diagonal Pattern Training on Trunk Function, Balance, and Gait in Stroke Patients
## 2 Materials and Methods
### Evaluation  
#### 2.7.1 Trunk Impairment Scale (TIS)  
To check the effect of the diagonal pattern training on the trunk performance, the trunk impairment scale was used. The trunk impairment scale measures the static balance, dynamic balance, and the coordination of body adjustment in a sitting position. The trunk impairment scale consists of a total of 23 points with 7 points, 10 points, and 6 points, respectively. The higher the score, the better the trunk control ability. Notably, the trunk impairment scale tool reported a high reliability of 0.96 for stroke patients [18].
## 4 Conclusions  
This work demonstrates the possibility of tracing dark I-V curves in the modules of a PV plant without disconnecting them from the string. This is a breakthrough, as the dark I-V curve gives important information about the PV device and certain defects. These measures have been possible thanks to the combination of a bidirectional inverter and the developed electronic boards installed at the PV module level.  
The results of the measurements have been satisfactory, and this allows obtaining the values of \(R_{s}\) and \(R_{slt}\), among others, of the different PV modules of a plant online, without disconnection. The values obtained for \(R_{s}\) and \(R_{slt}\), adjusted with the 2/3 Diode Fit software perfectly fit to the PV module installed.  
With regard to future work, the authors will research the combination of dark and light I-V curve measurements. All measurements will be carried out without disconnection in the PV plant when combined with the bidirectional inverter. In addition, these measures will be managed and controlled through a low-cost communications system based on power line communications. In addition, with the measurements carried out, this research group will work with models based on artificial intelligence to locate defects in modules and cells, with the aim of keeping the performance of the photovoltaic plant high. Advances in O&M are key for this research team. Models based on artificial intelligence will work with images (electroluminescence and thermography) and I-V curves (light and dark).  
Conceptualization, L.H-C. and J.I.M.-A.; methodology, L.H-C., J.I.M.-A., S.G-S., M.d.C.A.-G., V.A-G., and J.L.B.; software, J.I.M.-A. and A.R-P.; validation, L.H-C., J.I.M.-A., S.G-S., M.d.C.A.-G., V.A-G., and J.L.B.; formal analysis, L.H-C. and J.I.M.-A.; investigation, L.H-C., J.I.M.-A., S.G-S., M.d.C.A.-G., and V.A-G.; resources, L.H-C. and V.A-G.; data curation, J.I.M.-A., S.G-S., M.d.C.A.-G., and A.R-P; writing--original draft preparation, L.H-C., J.I.M.-A., S.G-S., M.d.C.A.-G., and J.L.B.; writing--review and editing, L.H.-C., J.I.M.-A., S.G-S., M.d.C.A.-G., V.A-G., J.L.B., and A.R-P.; visualization, L.H-C., J.I.M.-A., S.G-S., M.d.C.A.-G., and V.A-G; supervision, L.H-C., M.d.C.A.-G.; and V.A-G.; project administration, L.H-C.; funding acquisition, L.H-C. and V.A-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Ministerio de Industria, Economia y Competitividad\" grant number \"RTC-2017-6712.3\" with name \"Desarrollo de herramientas Optimizadas de operacion y manTenimientO pRedictivo de Plantas fotovoltaicaas--DOCTOR-PV\".  
To request the data, please, contact the corresponding author  
The authors declare no conflict of interest.