esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem and Algorithm Description  
### Problem Description  
The existing free-to-move node self-deployment algorithms usually aim at the event coverage and deploy the node according to the network coverage. However, these algorithms do not consider the problems related to network connectivity, network reliability, and network energy consumption. Furthermore, these algorithms determine the final position of each node after the node moves several rounds according to the result of every iteration of the algorithm. This process induces each node to move to the final position not in a straight line, thereby increasing the moving distance of the node and the energy consumption. In these algorithms, the network coverage is increased by forcing each node to move an appropriate distance in each of the iterations so that the node can stay away from each other as far as possible. This process decreases the area of the overlapping region in the target area; however, the probability of deploying the node near the sink node is also decreased. As a result, holes appear in the locations near the sink node more easily, and the network service may be degraded regardless of the network routing protocol. In addition, positioning the nodes far from other nodes, may lead to a segmented network, which detrimentally also affects network connectivity. Thus, in this study, the problem is defined as follows: given \(N\) sensor nodes that are thrown randomly and uniformly in the deployment space, design an algorithm to maximize network coverage, maintain network connectivity while minimizing energy consumption.  
This study proposes the pigeon-based self-deployment algorithm (PSA) for UWSNs to solve the drawbacks of the existing free-to-move node self-deployment algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes network coverage in its one-hop region. The one-hop nodes then divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, each cluster head node regards the ratio of the movement distance of the node to the change in CRR of the node as the target function and uses pigeon swarm optimization to solve its position. As a result, the network coverage is expanded, the deployment energy consumption is reduced, and the number of node in the region near the sink node is increased.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 1 Introduction
### Turbo Encoder Interleaving in DVB-RCS2 and Broader Applicability  
The DVB-RCS2 system is a standardized specification developed to support interactive satellite communication services, primarily in broadband applications [20]. One of the most demanding components of DVB-RCS2 is its Turbo Encoder, which provides strong Forward Error Correction (FEC) capabilities to ensure reliable transmission over high-latency, noisy satellite links [13; 21].  
The Turbo Encoder in DVB-RCS2 requires data interleaving through permutation functions, which depend on parameters \(P,Q_{0},Q_{1},Q_{2},Q_{3}\). These permutations are algebraically defined and dynamically vary depending on payload size and configuration [13]. This results in a computational burden, especially for real-time embedded systems such as satellite modems or onboard processing units [22].  
Our algorithm specifically targets the permutation block in the Turbo Encoder, offering * Low-latency, real-time execution on vectorized DSPs (e.g., CEVA-XC4500) [23];
* A modular phase-based structure (Load-Execute-Store) which facilitates reconfigurability and portability;
* Efficient memory access patterns and alignment with hardware instructions, improving both throughput and resource usage.  
Broader Applicability  
Beyond DVB-RCS2, the core structure of our algorithm--bit-level manipulation, dynamic permutation, and vectorized execution--can be adapted to similar systems, including the following:  
* DVB-S2X: This is an advanced broadcast and interactive satellite communication standard that also relies on interleaved FEC coding [20; 24].
* 5G NR Uplink Control Channel (PUCCH): This is where interleavers and permutation-based coding are used in HARQ feedback [25].
* WiMAX and LTE Turbo Coding: They use similar permutation structures and may benefit from hardware-optimized interleaver implementations [26].
* SpaceWire and CCSDS standards: They may require error-correction schemes in onboard satellite data links [27].
# Review on the Recent Numerical Studies of Liquid Atomization
## 3 Interface Capturing
### Summary  
Liquid atomization modeling faces three critical challenges in interface-capturing methods: (1) numerical dissipation during microscale interface evolution, addressed through CLSVOF with Gaussian curvature correction and THINC/QQ algorithms, reducing droplet volume errors to <1% while maintaining 2-3 cell interface sharpness; (2) high Weber number oscillations mitigated by tensor-based CSS formulations and IMEX-BDF2 temporal schemes, achieving 68% velocity fluctuation suppression and CFL stability enhancement to 0.5; (3) multiphysics computational bottlenecks overcome via DRL-AMR and transformer-accelerated models, delivering 8-100\(\times\) speedups validated on 100 M-grid aerospace atomizers. Emerging quantum-AI hybrid paradigms and digital twin frameworks (EU H2020 AMPHIBIAN case) demonstrate potential for 1000\(\times\) efficiency gains and <5% cross-scale errors, positioning these solutions as foundational for next-generation atomization design across energy and propulsion systems.
###### Abstract  
Myocardial Perfusion Imaging (MPI) has played a central role in the non-invasive identification of patients with Coronary Artery Disease (CAD). Clinical factors, such as recurrent diseases, predisposing factors, and diagnostic tests, also play a vital role. However, none of these factors offer a straightforward and reliable indication, making the diagnosis of CAD a non-trivial task for nuclear medicine experts. While Machine Learning (ML) and Deep Learning (DL) techniques have shown promise in this domain, their \"black-box\" nature remains a significant barrier to clinical adoption, a challenge that the existing literature has not yet fully addressed. This study introduces the Deep Fuzzy Cognitive Map (DeepFCM), a novel, transparent, and explainable model designed to diagnose CAD using imaging and clinical data. DeepFCM employs an inner Convolutional Neural Network (CNN) to classify MPI polar map images. The CNN's prediction is combined with clinical data by the FCM-based classifier to reach an outcome regarding the presence of CAD. For the initialization of interconnections among DeepFCM concepts, expert knowledge is provided. Particle Swarm Optimization (PSO) is utilized to adjust the weight values to the correlated dataset and expert knowledge. The model's key advantage lies in its explainability, provided through three main functionalities. First, DeepFCM integrates a Gradient Class Activation Mapping (Grad-CAM) algorithm to highlight significant regions on the polar maps. Second, DeepFCM discloses its internal weights and their impact on the diagnostic outcome. Third, the model employs the Generative Pre-trained Transformer (GPT) version 3.5 model to generate meaningful explanations for medical staff. Our dataset comprises 594 patients, who underwent invasive coronary angiography (ICA) at the department of Nuclear Medicine of the University Hospital of Patras in Greece. As far as the classification results are concerned, DeepFCM achieved an accuracy of 83.07%, a sensitivity of 86.21%, and a specificity of 79.99%. The explainability-enhancing methods were assessed by the medical experts on the authors' team and are presented within. The proposed framework can have immediate application in daily routines and can also serve educational purposes.  
fuzzy cognitive maps; particle swarm optimization; convolutional neural networks; classification; feature selection; Grad-CAM; coronary artery disease; natural language processing +
Footnote †: journal: Medical Image Analysis
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 5 Experiment, Results, and Analysis  
The recognized performance of CNN-ST for randomly overlapping dual-component radar signals under different SNR is evaluated. To demonstrate better classification performance over others, CNN-DQLN and CNN-Softmax in [49] are also utilized for conducting comparative analyses.
# Integrating Biofeedback in Dynamic Biomechanical Gait Training for Chronic Ankle Instability
###### Abstract  
Gait analysis was performed in patients with chronic ankle instability (CAI). Despite advancements in rehabilitation techniques, the integration of gait-training strategies with concurrent feedback (visual and auditory) remains underexplored. This study aimed to investigate the dynamic biomechanical characteristics of CAI patients using a gait-training device following a 6-week intervention program. Thirty patients with CAI (Intervention Group; Sex: Male 8, Female 7, Age: 30.6 \(\pm\) 4.08, Height: 170.49 \(\pm\) 11.09 cm), (Control Group; Sex: Male 9, Female 6, Age: 30.49 \(\pm\) 4.39, Height: 171.63 \(\pm\) 9.90 cm) participated in this single-blind, randomized controlled trial. The intervention group completed six weeks of gait training using novel devices. Following the intervention, dorsiflexion angles increased significantly from 88.66 \(\pm\) 5.47% to 92.60 \(\pm\) 4.45% (\(p\) = 0.002) for the FAAM-ADL score, while FAAM-Sport scores improved from 80.79 \(\pm\) 8.20% to 85.67 \(\pm\) 6.41% (\(p\) = 0.000). Increased dorsiflexion and aversion angles were observed during the early to mid-late stance and late swing phases of gait for both walking and running. Joint moments demonstrated significant changes, with dorsiflexion, eversion, and abduction tendencies increasing throughout the gait cycle after the intervention. The newly developed inertial measurement unit (IMU) proved to be a viable gait-training device for CAI, highlighting that concurrent feedback may allow for greater improvements in deficiencies associated with CAI.  
chronic ankle instability (CAI); gait analysis; inertial measurement units (IMU); gait training; concurrent feedback +
Footnote †: journal: Applied sciences
## 2 Spectrogram of Infrasonic Seismic Signals
### Signal Processing
#### 2.3.2 Identification of Frequency Ridges  
A ridge filter is first applied to identify the ridge structures of the spectrogram. Then, the filtered spectrogram is applied to the original spectrogram as a weighting function to enhance it further. At each spatial position \((t,f)\) in the spectrogram \(S(t,f)\), where \(t\) represents time and \(f\) denotes frequency, the partial derivatives (gradients) of the spectrogram are calculated concerning both time and frequency. Then, the gradients are subjected to a Gaussian smoothing process. The structure tensor, which captures the local intensity distribution structure in the spectrogram's neighborhood, is then constructed from the smoothed gradients as follows [8]:  
\[T(t,f)=\begin{bmatrix}\
abla_{t}^{2}&\
abla_{t}\cdot\
abla_{f}\\ \
abla_{t}\cdot\
abla_{f}&\
abla_{f}^{2}\end{bmatrix}, \tag{3}\]  
where \(T(t,f)\) is the structure tensor at \((t,f)\), \(\
abla_{t}\) is the partial derivative with respect to time, and \(\
abla_{f}\) is the partial derivative with respect to frequency. From the structure tensor, we derived the eigenvalues \(\lambda_{1}\) and \(\lambda_{2}\) using the following expression  
\[\lambda_{1,2}=\frac{1}{2}\Big{[}\
abla_{t}^{2}+\
abla_{f}^{2}\pm\sqrt{( \
abla_{t}^{2}-\
abla_{f}^{2})^{2}+4(\
abla_{t}\cdot\
abla_{f})^{2}}\Big{]}. \tag{4}\]  
When there is an edgelike structure such as a frequency contour in the local neighborhood of the spectrogram, the condition \(\lambda_{1}>\lambda_{2}\) is fulfilled. In the case of a perfect edge, \(\lambda_{2}\) becomes zero while the condition \(\lambda_{1}>\lambda_{2}\) is still fulfilled. If \(\lambda_{1}=\lambda_{2}\), it indicates that the underlying structure is rotationally symmetric, such as an isolated spectral peak. If both eigenvalues become zero, it suggests that the underlying structure is homogeneous throughout the local neighborhood. From the eigenvalues of the structure tensor at a given position \((t,f)\) in the spectrogram, the coherence is computed as follows:  
\[c=\frac{\lambda_{1}-\lambda_{2}}{\lambda_{1}+\lambda_{2}}. \tag{5}\]
## 4 In-Loop Testing of the Control Model  
Hardware in the Loop (HIL) is a method to test and calibrate the controller through the virtual environment of the whole machine operation. By building a simulation model of the hybrid tractor unit and designing the whole vehicle controller, we develop a hardware-in-the-loop test platform for the whole vehicle controller of the hybrid tractor and conduct HIL testing to verify the effectiveness of the energy management control strategy.  
The dSPACE host of the DS1005 series is used to build the hardware-in-the-loop platform of the hybrid tractor, as shown in Figure 9. In the figure, a closed-loop communication is formed between the host computer, the DS1005 HIL device, and the whole vehicle controller. The host computer configures the hardware-in-the-loop test software environment according to the simulation model and uses ControlDesk software to monitor the data changes in the test process. The DS1005 host computer is a standard component system, with the DS1005 as the core processing board and the Power PC processor. The expansion boards mainly include the DS4002 board to send and receive digital signals, the DS2003 board to receive. The DS4002 board sends and receives digital signals, the DS2003 board receives analog signals, the DS2103 board outputs switching signals, and the DS4302 board is used to realize the CAN communication function of the HIL platform. The host computer is equipped with a PowerPC Board communication card for entering the simulation model. After entry, the simulation model is automatically converted into the file form recognized by ControlDesk software. The complete controller and dSPACE communicate via the CAN bus. The controller and dSPACE supply provide external DC power for the complete controller and DS1005, respectively.  
Figure 8: Multi-layer energy management strategy.  
In order to verify the superiority of the multi-layer decoupling control strategy established in this article, a single-layer control strategy with power-following, fuzzy control is selected for experiments and compared.  
The method of building the random load module in the article [13] was used to obtain a load demand power signal that fits the power spectrum of the tractor load fluctuations. The load demand power is shown in Figure 10.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 3 The RPL and Problem Description  
RPL is a distance vector routing protocol for LLNs compliant with the 6 LoWPAN protocol. It operates at the networking layer and supports several link layer technologies, including IEEE 802.15.4.
## Appendix A  
### Film Consumption  
A mathematical model describing the consumption of the stretch film to wrap a cylindrical bale using combined 3D technique was derived in [14]. It was assumed that the subsequent film strips, which are wrapped in \(p_{b}\) bottom layers on the bale's lateral surface, overlap one another, creating the overlap \(k_{fb}b_{fr}\), where \(k_{fb}\) is a dimensionless relative ratio determining the width of the contact between adjacent film strips and the film width after stretching \(b_{fr}\) is described by [12]:  
\[b_{fr}=b_{f}\Big{(}1-v_{f}\epsilon_{lf}\Big{)}, \tag{1}\]  
where \(b_{f}\) is the width of un-stretched film, \(v_{f}\) and \(\epsilon_{lf}\) are the Poisson's ratio and unit deformation of the film. The extreme film strips are overlapped at the bases of the bale for \(\delta\), as shown in [14] (Figure 3). It was assumed that \(0<k_{fb}<\frac{1}{2}\), for which only one film layer results for one wrapping cycle. For the wrappings of \(p_{u}\) upper layers it was assumed that the subsequent film strips are wrapped along the bale's longitudinal axis with the overlap ratio \(k_{f}\)[14] (Figure 2).  
The global film consumption measured by the index \(FC\) defined as the ratio of the surface area of un-stretched film used to wrap the bale to bale volume, is described by the function [14]:  
\[FC=\frac{4p_{b}b_{f}}{D_{b}H_{b}\Big{(}\epsilon_{lf}+1\Big{)}}\left[\frac{H_{ b}+2\delta-b_{fr}k_{fb}}{b_{fr}\Big{(}1-k_{fb}\Big{)}}\right]+\frac{8(D_{b}+H_{ b})b_{f}}{\pi D_{b}^{2}H_{b}\Big{(}\epsilon_{lf}+1\Big{)}}\left[\frac{\pi D_{b}p_{u}}{2 b_{fr}\Omega\Big{(}k_{f}\Big{)}}\right], \tag{2}\]  
where \(\lceil x\rceil\) denotes ceiling function [45], function [20]  
\[\Omega\Big{(}k_{f}\Big{)}=\Big{(}1-k_{f}\Big{)}\left[\frac{1}{1-k_{f}}\right], \tag{3}\]  
is introduced for brevity of the notation; \(\lfloor x\rfloor\) is the floor function [45]. The above formula indicates the dependence of the film usage on film parameters \(\epsilon_{lf}\), \(v_{f}\), \(b_{f}\), bale diameter \(D_{b}\) and height \(H_{b}\), the overlaps \(\delta\), \(k_{fb}\) and \(k_{f}\) of the bottom and upper film layers, and the numbers of film layers \(p_{u}\), \(p_{b}\). Second summand of \(FC\) describe film used for upper layers wrappings provided that the following applicability condition [14]:  
\[\frac{p_{u}}{\left\lfloor\frac{1}{1-k_{f}}\right\rfloor}=m,\ m\in\mathcal{N}, \tag{4}\]  
holds, where \(\mathcal{N}\) denotes the set of positive integer numbers. Global number of basic film layers wrapped on the bale's lateral surface is \(p_{l}=p_{b}+p_{u}\).
Furthermore, the research has found that the type of LNAPL, the initial soil moisture content, the specific location of the leak, and the abrupt interfaces in geological structures all significantly affect the migration pathways and rates of the LNAPL. These findings are crucial for predicting and simulating the migration of LNAPLs. Researchers have also further explored how natural conditions such as rainfall events and changes in groundwater levels affect the redistribution of LNAPLs through simulations. In reality, fluctuations in the groundwater table can significantly impact the redistribution of a residual phase LNAPL within the smear zone. These secondary sources often lead to persistent contamination of both groundwater and soil [28; 29]. Although research on LNAPLs has been conducted by scholars in both field investigations and laboratory studies, the medium arrangement in existing experimental sandboxes is relatively simple, with most focusing on the migration patterns of LNAPL under homogeneous medium conditions, and few considering the conditions under different lens-shaped bodies. Moreover, these research methods often rely solely on direct observation or high-density electrical methods, which have their limitations. To investigate the impact of lens-shaped structures on the migration of light non-aqueous phase liquids (LNAPLs), and to understand how different types of these structures affect the distribution characteristics of LNAPL migration, this paper builds upon the research of numerous scholars. It simulates the LNAPL leakage process using a two-dimensional indoor sandbox model. Diesel fuel is taken as a typical representative of LNAPLs. We conduct three distinct test groups: the no lens-shaped aquifer test group (using fine sand of uniform particle size), the low-permeability lens test group (where fine sand is locally interspersed with pulverized clay), and the high-permeability lens test group (where fine sand is locally interspersed with coarse sand). This study employs a combination of oil front curve mapping and high-density resistivity imaging techniques to systematically assess how the presence of lens-shaped structures affects the migration behavior, distribution patterns, and corresponding resistivity anomalies of LNAPL. Through this integrated analytical approach, we are able to gain a comprehensive understanding of how lens-shaped structures influence the migration dynamics of LNAPL and how they alter the response of resistivity. This research not only helps to reveal the regulatory mechanisms of lens-shaped structures on LNAPL migration but also holds significant implications for optimizing groundwater remediation strategies and assessing environmental risks.
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 2 Background
### Optimized Link-State Routing (OLSR) Protocol  
The optimized link-state routing protocol (OLSR) is a proactive protocol that recurrently contacts other network nodes and shares topology information. The set of neighboring nodes chosen by the node are called multipoint relays (MPRs). The MPRs forward the control information anticipated for dispersal in the whole network; this facilitates a decrease in the required transmissions. MPR nodes provide the link-state information and are used to create the route between the source and destination nodes. OLSR makes the shortest path routes available for all destinations using the link-state information issued by MPRs. MPRs are selected by nodes amongst their one-hop neighbors, establishing bidirectional links; this helps in overcoming the issues of unidirectional links related to the transfer of data packets. The OLSR design facilitates how to work autonomously from other protocols, and makes no assumptions related to the underlying link layer.
## 5 Discussions and Contributions
### Computational Requirements and Feasibility in Low-Resource Environments  
While our approach significantly improves translation quality for low-resource languages, its computational demands must be carefully considered, especially in settings with limited technological infrastructure. Fine-tuning the mT5 model on the English-Twi dataset took approximately 2 h on an NVIDIA Tesla T4 GPU when trained for 100 epochs with a batch size of 8. The memory usage during training reached approximately 15 GB of VRAM, which may pose a challenge for training on lower-end GPUs. During inference, the model processed around 100 sentences per second on a GPU, but this dropped to approximately 10-15 sentences per second on a CPU, underscoring the need for hardware acceleration in real-time applications.  
Despite these demands, the feasibility of deploying this method in low-resource environments can be enhanced through optimization techniques. Model compression methods, such as quantization (e.g., 8-bit or 4-bit), can significantly reduce the memory footprint, making it possible to run the model on devices with lower computational capacity. Furthermore, offline fine-tuning strategies allow for local adaptation of the model, eliminating the need for continuous access to cloud-based resources and making the model more accessible in areas with limited internet connectivity. By optimizing the model for mobile devices and leveraging AI accelerators, such as Google's Edge TPU, real-time translation applications can be deployed even in computationally constrained environments.  
These optimizations ensure that our model remains practical and accessible for low-resource languages, addressing both linguistic challenges and the technological limitations often faced by underrepresented language communities.