esa-sceva/satcom-chunk-collection huggingface dataset:
# The Subsea Micro-Drilling Vehicle's Dynamic Analysis during Landing
###### Abstract  
Whether the subsea micro-drilling vehicle (SMDV) can perform its subsequent operations safely depends on the quality of the landing procedure. [PERSON] creates the SMDV dynamic model for this study. A model of the interaction between the SMDV and deep-sea sediment is built, and a simulation of the SMDV falling on the sea's sediment substrate is developed. The water resistance is applied to the model by equivalent height replacement, and the in-situ soil data is measured with a triaxial undrained unconsolidated (UU) compression test and a load-sinkage experiment. When the landing surface is a flat sediment substrate, the release height is 5 m, the sinkage amount is 347 mm, and the center of mass's impact acceleration is less than seven gravitational accelerations. Three states can occur when the vehicle lands on a sloped surface: stability, slip, and overturning. The risk of slipping and overturning is the least when the vehicle is landing on the ground in the forward direction, and the risk is equal when it lands on the ground in the backward and sideways directions. The ultimate overturning angle drops, and the final slip angle remains relatively constant as the vehicle's release height increases. Our findings offer a theoretical foundation for the SMDV's safe landing and the scientific formulation of rational release intervals.  
subsea micro-drilling vehicle; multi-body dynamic model; landing characteristic; numerical simulate +
Footnote †: journal: Applied Sciences
# GNSS Positioning Using Mobile Devices with the Android Operating System
## 3 Materials and Methods
This application is a RINEX parser and an observation analyzer which allows defining specific parameters to extract only the useful data for the user. In particular, it is possible to choose which constellation to store and the RINEX format version. The methodology of this work is summarized in Figure 2.
## 4 Discussion
### Uncertainty Reliability Analysis for Operational Deployment  
MC-Dropout Sampling Efficiency  
The computational overhead of Monte Carlo sampling is not negligible. In Experiment E, we quantified the trade-off between the number of forward passes (N = [10, 20, 50, 100]) and uncertainty calibration for both regions. As shown in Table 4, the ECE in Region 1 decreases by 35.9% (from 12.34% to 7.91%) as N increases from 10 to 50 and decreases slightly beyond N = 50. Region 2 shows a nonlinear cost-benefit trade-off: while increasing N from 50 to 100 further reduces the ECE from 4.08% to 3.56% (\(\Delta\) = 0.52%), this requires a doubling of the inference time (1.25-2.50 s). Thus, N = 50 achieves the best calibration efficiency--95.3% of the maximum possible ECE improvement at 59% of the computational cost. Notably, the reconstruction accuracy (PSNR/SSIM) remained stable (\(\pm\)0.1 dB deviation) across the number of samples, confirming that 50 forward passes establish a cost-effective balance between computational tractability and uncertainty reliability.  
Noise Robustness Evaluation  
Field-deployed sonar systems frequently encounter signal degradation from turbulent flows and sensor artifacts. To simulate these conditions, we injected additive white Gaussian noise (AWGN) at SNR levels of [10,20,30] dB into test data from both regions. As illustrated in Table 5, Experiment E maintained superior performance under severe noise compared to the TfaSR.  
Across varying signal-to-noise ratios (SNRs), Experiment E consistently outperforms TfaSR in balancing reconstruction accuracy and uncertainty reliability. At SNR = 10 dB (Region 1), E reduces the RMSE by 19.7% (33.15 vs. 41.27 m) while improving the PSNR by 4.34 dB (42.86 vs. 38.52 dB), with the error variance and ECE decreasing by 40.8% and 36.6%, respectively. These gains persist at higher SNRs: at 30 dB, E achieves near-identical SSIM (0.984) but with a 41.4% lower error variance (793.43 vs. 1353.91 m\({}^{2}\)) and 36.5% reduced ECE (7.91% vs. 12.45%) compared to TfaSR. In Region 2, E demonstrates even stronger robustness--at SNR = 20 dB, it attains a 50.66 dB PSNR (7.1% improvement over TfaSR) alongside a 46.9% reduction in error variance (489.33 vs. 921.45 m\({}^{2}\)). Notably, E's uncertainty quantification remains stable across noise levels, with ECE consistently being below 8% versus TfaSR's 10-20% range.  
The integrated evaluation confirms that Experiment E's hybrid framework satisfies both accuracy and operational reliability requirements. By achieving real-time inference speeds while maintaining sub-meter uncertainty calibration, the model establishes a new benchmark for deployable AI-driven bathymmetric systems. These advancements directly address the U.N. Decade of Ocean Science priorities for trustworthy marine AI, particularly in hazardous environments where uncertainty awareness enables collision risk mitigation.
## 7 Results
### Experiment 2: Combinations of Descriptor Types  
The discrimination capacity of the classifiers may be enhanced by the combination of different types of descriptors. In Figure 8, morphological and statistical descriptors provide high accuracy rates. These descriptors bring together the two main handcrafted discriminating features: shape and texture. Thus, morphological and statistical descriptors constitute a baseline for comparison. The results show that statistical descriptors combined with morphological descriptors provided together an improvement of 3%. The combination of statistical with texture (LBP) improves the performance of the morphological descriptors. This proves the relevance and importance of textural features versus morphological properties. The combination of statistical with the space-frequency descriptors provided an overall accuracy improvement of 2%, while the combination of all features (textural + morphological + frequency + statistical) is the most accurate with an improvement of 4%. Note that the moments were discarded due to the low individual performance they gave and also the low relevance obtained when calculating the SFFS (see Section 5.2).  
Thus, the combination of morphological + textural + space-frequency + statistical descriptors provided the lowest error rate, with an overall accuracy (_Acc._) equal to 98.11% using a bagging DT with 10 fcv. The combination of textural and space-frequency (LBP + Log Gabor), that is using the data represented without any augmentation bias, provided an overall accuracy of 97.63% using bagging DT with 10 fcv. This is highlighted in Figure 8.  
Figure 7: Comparison of different categories of descriptors under the bagging DT classifier.
## 3 Results
### Pattern Recognition Modeling for Authenticating Poppy Seed Oil
For instance, [PERSON] and [PERSON] (2020) successfully identified the adulteration of virgin coconut oil with canola, corn, sunflower, and soybean oils. They adulterated the coconut oil samples in incremental steps of 1% ranging from 10% to 40% for each adulterant oil and successfully applied SIMCA to detect adulteration [42]. Similarly, FT-IR and SIMCA were used in another study to detect the adulteration of Moroccan organ oil with soy and sunflower oils at \(w/w\) adulteration levels ranging from 5% to 100% [43]. This method allowed the correct classification of authentic Argan oil samples despite the large interval of the adulterant concentrations. Additionally, [PERSON] (2022) utilized FT-IR and SIMCA to predict the adulteration of 40 different commercial black cumin oils, demonstrating the potential of FT-IR for detecting adulteration in real samples [44]. These studies underscore the reliability and versatility of FT-IR spectroscopy for food authentication and its successful application in detecting adulteration across various oils. Figure 2: (**a**) Three-dimensional (3D) projection plot of Soft Independent Modeling of Class Analogy (SIMCA) calibration model for multiple class approach applied to the spectral data of commercial poppy seed oil samples acquired with a portable FT-IR unit. (**b**) SIMCA discriminating power plot based on the FT-IR spectra of authentic and tainted poppy seed oil samples using a portable FT-IR unit. * arbitrary units. (**c**) SIMCA 3D projection plot for the external validation set. (**d**) Three-dimensional projection plot of SIMCA calibration model for single class approach applied to the spectral data of commercial poppy seed oil samples acquired with a portable FT-IR unit. Brown dots represent authentic poppy seed oil samples, red dots represent adulterated poppy seed oil samples, and green dots represent common adulterants.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 2 BitTorrent-Like Systems and Network Coding Background
### Network Coding Overview  
Network coding is a recent technique that changes the traditional idea of store-and-forward routing by allowing a network node to linearly combine incoming packets and then send the combined packet to the next hub node. The idea was introduced by [PERSON] et al. [20], showing that the network coding can increase network throughput and improve robustness. The basic idea is relatively simple: the intermediate nodes combine the received packets, then send a linear combination of these packets, creating a new representation of the original packets. In order to decode and rediscover the original packets, the recipient must have a sufficient number of independent linear combinations of these packets. The benefits of network coding in computer networks have been demonstrated by the study of different aspects such as throughput and end-to-end delay [20], wireless resources [21; 22; 23], and problems of security and resilience [24; 25; 26; 27; 28; 29]. The following subsection demonstrates the benefit of using network coding.
## 2 Materials and Methods
### Algorithm for Downscaling
#### 2.3.1. Problem Formulation  
Let \(GPP_{R}(x_{i})\) represent the random variable of pixel \(R\) centered at \(x_{i}\) (j = 1, 2,..., N, where N is the number of pixels) in 1 km resolution image of GPP, whereas \(GPP_{r}(\mathbf{x}_{i})\) represents the random variable of pixel \(r\) centered at \(x_{i}\) (\(i\) = 1, 2,..., M, where M is the number of pixels) in the 500 m resolution image of GPP. The notations \(R\) and \(r\) represent the 1 km and 500 m pixels, respectively. The purpose of the downscaling method based on topographic factors and the LAI is to predict \(GPP_{r}(\mathbf{x})\) at a 500 m resolution. \(GPP_{r}(\mathbf{x})\) can be described as  
\[GPP_{r}(\mathbf{x})\ =\ GPP_{r1}(\mathbf{x})+GPP_{r2}(\mathbf{x}) \tag{2}\]  
where \(GPP_{r1}(\mathbf{x})\) and \(GPP_{r2}(\mathbf{x})\) represent the predictions of the regression and ATPK processes, respectively. More descriptions about their calculation can be found in the following sections.
## 3 Results and Discussion
### In Silico Colorimetric Analysis of the Grape Seed Peptide-Mv3 glc Interaction  
Table 3 shows the results of the nine mv3 glc-grape seed peptide (mv3 glc-GSP) conformations (Figure S1) based on the obtained docking parameters (binding energy, mv3 glc dihedral angle, and type of intermolecular interaction), spectral parameters (\(\lambda_{\text{max}}\) and A\({}_{520}\) nm), and CIELAB color parameters (\(L^{*},a^{*},b^{*},C^{*}_{ab},h_{ab}\)). The energy values of the nine mv3 glc-GSP conformations obtained in the docking analysis (Figure 2) ranged from \(-4.1\) to \(-3.9\) kcal/mol, indicating that all of them are energetically probable. As observed in Table 3, the protein-anthocyanin complexes were characterized by the presence of intermolecular forces established between the aromatic rings of the anthocyanin and the peptide (Figure 1), including hydrogen bonding, \(\pi\)-alkyl, and \(\pi\)-\(\pi\) interactions. Among them, the \(\pi\)-alkyl forces are common in all the complexes (Table 3). The colorimetric results showed qualitative and quantitative differences for the reproduced colors between the nine systems. The predicted colors for the conformations 2, 4, 6, 8, and 9, as indicated by the reproduced \(h_{ab}\) values, corresponded to unusual tonalities of the flavylium cation in acidic conditions. Particularly, the \(h_{ab}\) values of 45\({}^{\circ}\) and 90\({}^{\circ}\) of these conformations correspond to orange and yellow tonalities, respectively. Thus, although all these conformations are energetically probable, the _in silico_ colorimetric methodology applied in these conformers inaccurately reproduced the resulting color from the molecular \begin{table}
\begin{tabular}{c c c c c} \hline
**Dihedral Angle (0)** & \(\Delta E^{*}_{ab}\) & \%\(\Delta L\) & \%\(\Delta C\) & \%\(\Delta H\) \\ \hline
0–10 & 4.03 & 0.01 & 0.94 & 99.05 \\
0–20 & 6.05 & 0.85 & 0.06 & 99.09 \\
0–30 & 14.24 & 0.17 & 0.89 & 98.94 \\
0–40 & 27.27 & 0.04 & 2.62 & 97.34 \\
0–50 & 40.77 & 1.92 & 1.19 & 96.89 \\
0–60 & 50.75 & 12.07 & 3.56 & 84.38 \\
0–70 & 63.72 & 38.47 & 40.12 & 21.41 \\
0–80 & 65.19 & 44.19 & 47.92 & 7.89 \\
0–90 & 67.22 & 49.58 & 50.35 & 0.07 \\ \hline \end{tabular}
\end{table} 
Table 2: Color differences (\(\Delta E^{*}_{ab}\)) calculated between the simulated colors of the different mv3 glc conformations at increasing dihedral angles (from 10\({}^{\circ}\) to 90\({}^{\circ}\)) respect to mv3 glc conformation at 0\({}^{\circ}\), together to the relative differences in lightness, chroma, and hue (%\(\Delta L\), %\(\Delta C\), and %\(\Delta H\)) that make up each \(\Delta E^{*}_{ab}\).
# Projections of Climate Change Impact on Stream Temperature: A National-Scale Assessment for Poland
## 2 Materials and Methods
### Stream Temperature Validation  
In the implementation of the SWAT model for simulating stream temperature in this study, a conscious decision was made not to engage in a hard calibration process, owing to the inherent simplicity of the equation employed, which is Equation (1). Its clarity and transparency provide a straightforward and easily interpretable method for estimating stream temperature. Unlike more complex models requiring extensive parameterization and calibration, the equation used here involves a limited set of parameters, eliminating the need for fine-tuning. This decision is supported by the equation's alignment with established methodologies found in the scientific literature, ensuring consistency with proven approaches [40; 41]. Additionally, the high-quality input temperature data utilized in this study, representative of the study area, contribute to the reliability of the equation. Instead of calibration, a thorough validation process was conducted to assess the equation's performance. The validation process (2000-2019) of daily temperature time series were carried out at 369 water quality monitoring points (Figure 1B) obtained from the Chief Inspectorate for Environmental Protection. The data collected for this study originate from a network of monitoring stations distributed across the entire study area. The temporal resolution and frequency of data measurements varied depending on the specific monitoring point. Measurements were sampled irregularly within the monitoring network, reflecting operational schedules and monitoring objectives unique to each site. Specifically, the frequency of data collection ranges from once a month at some locations to several times per month at others. During the validation period, each point was assessed, and the total number of observations ranged from 100 to 690 (148 on average). The assessment of the SWAT model's performance involved three statistical measures, KGE (Kling-Gupta Efficiency) [42], percent bias (PBIAS) measuring the average tendency of the simulated values to be larger or smaller than their observed ones, and the coefficient of determination (R\({}^{2}\)). To assess the accuracy of SWAT model temperature simulations, a comparison was conducted between the daily mean simulated outcomes and the observed stream temperatures (continuously throughout the entire validation period) at 369 water quality monitoring points spanning the entire country.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification
### Classification of RL-Based Routing Protocols Based on Routing Algorithm
#### 5.2.2 Network Topology  
In this section, routing methods are divided into two groups based on the network topology: hierarchical and flat. These two techniques indicate how to execute the routing process in the network. In the following section, we have explained these approaches. Furthermore, Table 4 has made a comparison between these routing algorithms.  
* **Hierarchical routing:** In this routing technique, UAVs are divided into several hierarchical levels shown in Figure 15. At each level, UAVs can be directly connected to each other. Furthermore, they are connected to a node called the parent node (at their upper level) to communicate with other UAVs at the upper level. The parent node is responsible for managing its children (UAVs at the lower level) and sending their data to the UAVs at the upper level. In this method, determining the different roles for nodes leads to the efficient consumption of the network resources in the route calculation process and reduces routing overhead. This method is scalable. However, there are important research challenges, including the management of different roles and the selection of parent nodes, especially if the UAVs have high mobility in the network. These challenges should be considered in these methods.  
Figure 14: Multi-path routing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Routing Scheme** & \
\begin{tabular}{c} **Routing Table** \\ **Management** \\ \end{tabular} & **Packet Loss** & **Fault-Tolerance** &
\begin{tabular}{c} **Network** \\ **Congestion** \\ \end{tabular} & **Routing Loop** \\ \hline
**Single-path** & Simple & High & No & High & Low \\ \hline
**Multi-path** & Complex & Low & Yes & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of single and multiple paths.  
* **Flat routing:** In a flat routing scheme, all UAVs play a similar role in the network, meaning that, dissimilar to hierarchical methods, it does not define any different roles such as parent node and cluster head node in the network to manage the routing process. The flat routing scheme is shown in Figure 16. In this approach, each UAV executes a simple routing algorithm and makes its routing decisions based on its status and neighbors on the network. These routing methods suffer from low scalability and high routing overhead.
## 2 Materials and Methods  
### Protein and Ligand Preparation  
In this study, the HSP90 inhibitor MEY (N-{[1-(5-chloro-2,4-dihydroxyphenyl)-2-oxo-2,3-dihydro-1H-benzimidazol-5-yl]methyl]naphthalene-1-sulfonamide), crystallized with the HSP90 protein (PDB code: 3 OWD), was utilized as a reference structure for identifying structurally related compounds. A detailed search of the PubChem database was conducted by using the MEY structure as the query. This was achieved by inputting its SMILES notation and performing a similarity search to retrieve compounds with similar chemical scaffolds. The search results yielded 30,994 compounds that shared structural features with MEY, with a minimum similarity of 80%. These compounds were then filtered using [PERSON]'s rule of five to assess their drug-likeness and pharmacokinetic properties. The filtering criteria included the molecular weight (less than 500 Da), the octanol-water partition coefficient (LogP less than 5), hydrogen bond donors and acceptors, and the number of rotatable bonds. Compounds that did not meet these criteria were excluded from further analysis, ensuring the selection of drug-like candidates for subsequent molecular docking and dynamics simulations.  
The crystal structure of the human HSP90 protein (PDB code: 3 OWD, resolution 1.63 A), obtained from the Protein Data Bank [17], was also used in this study for both protein and ligand preparation. LigPrep ([PERSON], 2018) was employed to prepare the ligands by optimizing their geometry and protonation states, ensuring their suitability for further computational analysis. Additional methodological details can be found in previous publications from our research group [18].
# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 4 Industrial Pilot Case
### Scheduling Problem Description  
The use case specifications and description should be mapped into production variables, formulating the scheduling problem's parameters. The scheduling problem is defined below.  
* Product types: The set of product types is \(P=\{P_{1},P_{2},\ldots,P_{i}\}\), with \(i=1,2,\ldots,maxP\), where \(maxP=12\).
* Process plan: There are three operation categories in the system, which create two different process plans based on the product type. The three operations are \(Pr_{1}=manual\)_process_, \(Pr_{2}=assembly\) and \(Pr_{3}=tire\)\(fitting\). Thus, there are two types of process plans: \(H_{1}=\{Pr1,Pr2,Pr3\}\) and \(H_{2}=\{Pr1,Pr2\}\).
* Resources: The assembly lines can be modeled as resources, since each production job can be allocated to one and only one assembly line during production. Thus, \(M=\{M_{1},M_{2},\ldots,M_{r}\}\) with \(r=1,2,\ldots,maxM\), where \(maxM=4\).
* Tasks: As a task is considered, a wheel process plan must be executed. This means that a product is represented as task \(T_{b}\), with \(b=1,2,\ldots maxT\), where \(maxT=1500\). The workload consists of a maximum of 1500 wheels to be produced.
* Jobs: As a job is considered, a certain number of units must be produced for a certain wheel type. A job \(J_{c}\), with \(c=1,2,\ldots,maxJ\), where \(maxJ=60\), consists of 25 tasks or less.
* Order: An order is considered as a certain amount of jobs to be executed. An order \(O_{g}\) with \(g=1,2,\ldots,maxO\), where \(maxO=30\), consists of two jobs or less.
* Suitability and processing times: A processing time for each task \(T_{b}\) is defined for each resource (assembly line) \(M_{r}\). If the value is 0, it means that the task is not suitable for the resource: \(I(M_{r},Pr_{ki})\geq 0\). This is presented in Table 1.
* Scheduling workload: For this scheduling problem, the maximum number of orders is \(maxO=30\), the maximum number of jobs is \(maxJ=60\) (each order consists of two jobs) and the maximum number of tasks is \(maxT=1500\) (each job has a maximum number of 25 tasks). The scheduling workload consists of all orders \(O_{g}\), jobs \(J_{c}\) and tasks \(T_{b}\) that need to be executed. The scheduling solution refers to the job's allocation to resources (\(J_{c}\) allocation to \(M_{r}\)).