esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Identification of Principal Variables
#### 3.2.1 ANOVA Analysis  
In this section, the hypothesis test of one-way ANOVA was employed to identify the relation between the variables and the type of accident that happened. In particular, a hypothesis test with a significance level of 0.05 and the obtained results are summed up in Table 1.  
Figure 11: Curve fitting of the training, validation and test processes.  
From Table 1, it was concluded that the type of accident is associated with the type of ship A, construction year, wind force, sea conditions and both ship's dimensions. The same happened with the number of crew members in ship A and the TRB of ship A and if it obeyed the minimum crew members requirements. In a decreased level, the type of ship B was associated with the type of accident.  
It is of interest to highlight that visibility, the crew members and the TRB of ship B were not associated with the type of accident.
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 3 Nonlinear Analysis Algorithms
### Implementation of Perturbation Method in Structural Engineering  
To use improved HPM, computational software is implemented for reducing the cost of analysis. The new approach uses Equations (13) to (20). Consider  
\[\left\{g_{(u)}\right\}=\left\{F\right\}-\left\{f_{(u)}\right\} \tag{21}\]  
where vector [13] denotes generalized coordinates established a base on the translations of nodes, \(\left\{\text{f}\right\}\) is resultant internal forces matrix and \(\left\{\text{F}\right\}\) is the external force matrix. Then  
\[\left[f_{(u)}\right]=-[T] \tag{22}\]  
where \(J(u)\) is a Jacobian matrix that is required at each time increment and describes the local material behavior, \(\left[T\right]\) describes stiffness equations; therefore, Equation (22) can be rewritten as  
\[\Delta u\approx[T]^{-1}\left\{P-f_{(u)}\right\}+[T]^{-1}\left(\left\{P\right\} -\left\{f_{(u+[T]^{-1}\left\{P-f_{(u)}\right\})}\right\}\right) \tag{23}\]  
First, an approximate solution is guessed similar to the Newton-Raphson method. An iterative algorithm based on previous formulations is developed for improving nonlinear analysis using the perturbation method. This algorithm is shown as follows:  
Step 1: Input variables and parameters are set.  
Step 2: The general assumptions related to the structural systems are considered (e.g., material properties, joint coordinates, boundary conditions, etc.).  
Step 3: \([T]\) is generated and assembled to summarize the stiffness equations to develop the system tangent stiffness matrix \([\tau]\).  
Step 4: For the first iteration in \((i+1)\) loading step, the displacement is calculated.  
\[\Delta u_{1}^{i+1}=\left[T^{1}\right]_{i+1}^{-1}\left\{\Delta P^{i+1}\right\} \tag{24}\]  
Step 5: The new node coordinates are determined.  
\[\left\{u_{1}^{i+1}\right\}=\left\{u^{i}\right\}+\left\{\Delta u_{1}^{i+1}\right\} \tag{25}\]  
Step 6: The unbalanced forces in \(j\)th iteration is determined for \((i+1)\)th loading step \(\left\{\Delta\text{Q}_{j}^{i+1}\right\}\).  
\[\left\{\Delta Q_{j}^{i+1}\right\}=\left\{P^{i+1}\right\}-\left\{f_{(u_{j}^{i+ 1})}\right\} \tag{26}\]  
Step 7: The \(\left[Du_{j}^{i+1}\right]\) is calculated.  
\[\Delta u_{\beta}^{i+1}=\left[T^{j}\right]_{i+1}^{-1}\left\{\Delta Q_{j}^{i+1 }\right\} \tag{27}\]  
Step 8: Calculate the new geometry of the joints:  
\[\left\{u_{j}^{i+1}\right\}=\left\{u_{j-1}^{i+1}\right\}+\left\{\Delta u_{j}^ {i+1}\right\} \tag{28}\]  
Step 9: Repeat steps 6-8 considering the convergence criterion is satisfied:  
\[\sqrt{\frac{\sum_{i}\left(\Delta u^{i}\right)^{2}}{\sum_{i}\left(u^{i}\right) ^{2}}}\leq e \tag{29}\]  
Step 10: Steps 6 through 8 are repeated to satisfy the convergence tolerance.
# Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
## Validation of the GA-Based SDM Parameter Identification Method
### Test Point Selection  
The maximum variation of the I-V curvature occurs in proximity to the MPP, while the I-V curve is almost linear near \(I_{sc}\) and \(V_{oc}\). Thus, the number and positions of the test points must be chosen as a trade-off between the minimum distance from the MPP (reduced power loss) and the right I-V curvature identification (high precision in the SDM parameter calculation). As a reference, the authors of [7] focused on the identification of \(R_{s}\), and they suggested selecting test points up to 60% \(\div\) 75% of \(I_{MPP}\). On the other hand, a 15% voltage reduction to the left of \(V_{MPP}\) is usually enough to enter the other nearly linear portion of the I-V curve. Since the PV source is usually controlled by regulating the PV voltage, it is very simple to acquire the PV voltage and current by increasing (or decreasing) the control voltage reference in a step-by-step manner, thus moving with high precision around the MPP. For the case under study, the test points are equally spaced by \(\Delta V=1V\). To cover the desired range, N = 8 points are enough, and 4 V is the maximum distance from the MPP.  
As highlighted in the previous sections, the main benefit of the proposed approach is the reduction of the power loss during the measurement of the test points. For the case under study, in the worst case, the power delivered during the acquisition of the test points is only 11% less than the one delivered in the MPP, as shown in Figure 7.  
Figure 6: Comparison of the I-V and P-V photovoltaic curves for Test #1. (**a**) Photovoltaic I-V curves; (**b**) photovoltaic P-V curves.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 4 Mathematical Analysis
### Probability Analysis  
The probability equations of BFD and TNFD communications are derived here, and it is described in this section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol of the Variable** & **Description of the Variable** \\ \hline \(n\) & Total number of nodes including MS \\ \hline \(M\) & Percentage of average number of nodes within a node’s transmission range \\ \hline \(\lambda\) & PGR by each node \\ \hline \(\lambda_{tot}=n\lambda\) & Total PGR \\ \hline \(\alpha\) & Percentage of total hidden nodes \\ \hline \(T_{RTS}\) & Time duration for RTS frame \\ \hline \(T_{CTS-AI}\) & Time duration for CTS-AI frame \\ \hline \(T_{CTS-SRA}\) & Time duration for CTS-SRA \\ \hline \(T_{ACK}\) & Time duration for acknowledgement frame \\ \hline \(T_{S}\) & Time duration for short interframe space (SIFS) \\ \hline \(T_{data}\) & Time duration for data packet \\ \hline \(P_{BFD}\) & Probability of BFD communications \\ \hline \(P_{STNFD}\) & Probability of STNFD communications \\ \hline \(P_{DTNFD}\) & Probability of DTNFD communications \\ \hline \(P_{TNFD}=P_{STNFD}+P_{DTNFD}\) & Total probability for TNFD communications \\ \hline \(L_{UIP}\) & Uplink data length \\ \hline \(L_{DW}\) & Downlink data length \\ \hline \(Th_{BFD}\) & Throughput for BFD communication \\ \hline \(Th_{S-TNFD}\) & Throughput for STNFD communication \\ \hline \(Th_{D-TNFD}\) & Throughput for DTNFD communication \\ \hline \(Th_{HD}\) & Throughput for HD communication \\ \hline \(T_{BFD}\) & Transmission time for BFD communication \\ \hline \(T_{S-TNFD}\) & Transmission time for STNFD communication \\ \hline \(T_{D-TNFD}\) & Transmission time for DTNFD communication \\ \hline \(T_{HD}\) & Transmission time for HD communication \\ \hline \(T_{D}\) & Transmission time for uplink or downlink data \\ \hline \(T_{RT}\) & Time of random timer \\ \hline \end{tabular}
\end{table}
Table 1: Declaration of variables.
# Effect of Autochthonous Lactic Acid Bacteria-Enhanced Fermentation on the Quality of Suancai
###### Abstract  
The lactic acid bacteria (LABs) used for fermentation have an extremely vital impact on the quality of Suancai, a fermented vegetable. The bacterial diversity and metabolites of inoculated Suancai with LABs, including _Lictiplintihucillus plantarum_ (_Lb. plantarum_), _Levilactabacillus brevis_ (_Lb. brevis_), and _Leuconostoc mesenteroides_ (_Leu. mesenteroides_), were investigated. The inoculation of LABs significantly decreased the pH and the content of nitrite. The Suancai inoculated with LABs had a higher content of the total tittatable acidity (TTA) and organic acids than spontaneous fermentation. The LABs inoculation significantly influenced the bacterial community structures, which directly or indirectly caused changes of metabolites. The bacterial community profiles of Suancai inoculated with Lb. plantarum were more similar to spontaneous fermentation. The inoculation of _Lb. plantarum_, _Lb. brevis_, and _Leu. mesenteroides_ could increase its abundance in Suancai. Whatever the species inoculated, _Lb. plantarum_ was always the predominant bacterium in Suancai after fermentation. The inoculated LABs were positively correlated with most volatile compounds and amino acids. The inoculated LABs significantly improved the volatile compounds and amino acid content of Suancai. This study could contribute to understanding the function of starters in Suancai fermentation and promote the selection of applicable starters for high-quality Suancai production.  
Suancai; lactic acid bacteria; inoculated fermentation; _Lictiplantihucillus plantarum_; _Levilactabacillus brevis_; _Leuconostoc mesenteroides_
# THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
## 2 Experimental Setup  
This measurement system has been designed to measure the transmission of samples in the frequency range between 18 and 40 GHz. An outline of the experimental setup is shown in Figure 1. Millimeter wave radiation emitted from a Yttrium-Iron Garnet (YIG) tunable source is sent, using a transmission line, to a horn antenna, which launches it into free space. The radiation then passes through the sample and, after being collected by another horn antenna, is detected by a Schottky diode, which gives a voltage signal proportional to the transmitted radiation power. The voltage signal is measured by a digital multimeter.  
Since the electromagnetic radiation at the edge of the horn antenna is partially reflected, both in launching and collecting, due to the impedance mismatch between the antenna and free space, the optical system, throwing horn-sample-collection horn, can be seen as a system composed of three partially reflecting mirrors. The intensity of the transmitted radiation therefore depends not only on the absorption or scattering of the sample but also on the transfer function of the composite system of the three Fabry-Perot, which depends clearly on the frequency in use. As a function of frequency, we will have a transmitted oscillating signal similar to the Etalon's transfer function eventually modified by the presence of the sample. Thus, a comparison of the transmitted signals as a function of frequency with and without the sample provides information about the optical properties of the studied sample. Figure 1A shows an image of the system with its components, while Figure 1B shows an image of the layout.  
The millimeter wave source is a YIG oscillator MLOS-184 (Micro Lambda Wireless, Fremont, CA, USA) operating in the region between 18 and 40 GHz with a maximum power of about 20 mW. The detector used in this system is a Schottky diode, which is capable of detecting frequencies in the spectral region emitted by the source.
## 5 Discussion  
Traditional water inrush early warning systems have predominantly relied on static geological parameters (e.g., permeability coefficients) or single-modality sensor data (e.g., resistivity measurements). In contrast, the Precise Microseismic Geophysical Integrated Monitoring (PMGIM) method has achieved quantitative characterization of fracture network evolution through real-time microseismic event monitoring coupled with spatial clustering analysis using [PERSON]'s index. At Wutongzhuang Mine, this approach successfully identified high-risk water inrush zones within fault areas, as evidenced by statistically significant HH-type clustering patterns.  
However, mine water hazard prediction remains a complex systems engineering challenge. The reliability of PMGIM-based diagnostics is inherently constrained by the scientific rigor of field data acquisition protocols. For instance, suboptimal sensor deployment or sampling frequency may introduce spatial bias in clustering results. Thus, PMGIM applications should be synergistically augmented with auxiliary geophysical surveys (e.g., transient electromagnetic imaging) and drilling validation to enhance operational accuracy.  
With the continuous development of artificial intelligence (AI), a new direction for future scientific and technological development is to combine microseismic signals, such as PMGIM signals, with advanced technologies like neural networks for on-site early warning analysis. Based on the comprehension of this research, we can look forward to some related research directions.  
1. AI-Enhanced Early Warning: Integrating PMGIM signals with neural network architectures could enable real-time hazard prediction. This involves deploying edge-computing optimized models for instantaneous waveform analysis and automated risk classification;
2. Multisensor Fusion Framework: A unified platform should be developed to harmonize PMGIM with fiber-optic strain sensing and InSAR-derived deformation data, thereby constructing 4D geomechanical risk profiles;
3. Digital Twin Optimization: Physics-informed digital twins could simulate fluid-rock interactions under varying stress regimes, providing scenario-based decision support for emergency response.  
Figure 12: The 182602 working face electrical geophysical survey results.  
These advancements would not only extend PMGIM's applicability to non-coal mining (e.g., metal mines with shear zones) and high-hydrostatic-pressure environments (e.g., deep-sea mining) but also establish it as a cornerstone technology for intelligent mine safety management.
###### Abstract  
Corporate performance is increasingly impacted by environmental issues, but their specific role in business failure remains underexplored, which leads to a gap in research that is often focused exclusively on financial metrics. By investigating the relationship between environmental financial exposure and business failure, this study addresses this gap, integrating financial ratios and environmental variables to understand how environmental performance affects financial viability. A novel dual-stage methodology was employed, first using Extreme Gradient Boosting (XGBoost) for feature selection to identify the most significant predictors of failure from a dataset of Spanish companies (N = 38,456) using 2022 ORBIS data. Next, a fuzzy-set qualitative comparative analysis (fsQCA) was applied to analyze the sufficient causal configurations leading to a high propensity for business failure. The analysis identified three distinct causal configurations associated with failure. All highlighted poor financial performance indicators, such as low results per employee and low profit per employee. Notably, one configuration identified high environmental risk (measured by TRUCAM) as a core condition contributing significantly to financial distress. These findings highlight the critical link between environmental responsibility and financial health, demonstrating the benefits of combining fsQCA with machine learning to identify intricate causal configurations and providing information to companies and governments who want to support long-term financial stability and corporate sustainability.  
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has heightened the importance of integrating environmental performance into corporate financial strategies.  
This study examines how environmental risks interact with financial metrics to influence business failure in Spanish firms. Prior studies highlight standalone financial predictors [6]; however, emerging evidence suggests that environmental factors augment financial distress through regulatory penalties [7] and consumer backlash [8]. Our analysis addresses this gap by integrating machine learning (XGBoost) and fuzzy-set qualitative comparative analysis (fsQCA) to identify failure pathways involving environmental risk.  
This study begins with a brief introduction and a review of relevant literature, underscoring key contributions related to the research focus. We then outline the study's objectives, describe the sample and data sources, and define the variables employed in the analysis. After providing a thorough description of the methodology, we provide the research findings, the key conclusions, and offer ideas for further study.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 2 Technology Reviews  
### Federated Learning  
In a conventional deep learning setup, a central server collects data from geographically distributed devices and consumes all the collected data for training, testing, and updating models. This can cause communication efficiency problems and various security problems [8]. An emerging federated learning (FL) method is expected to resolve parts of the problems by not requiring raw data to be transmitted to the central server. Instead, it allows distributed clients (participants) to train a model at their side without sending data to the server. As the model is trained locally, participants only send the updated parameters to the server. Figure 1 shows the action processes of federated learning [9].  
1. A server coordinator randomly selects clients to participate in federated learning. The selected participants download a current global model from the coordinator. Figure 1 assumes all four clients are selected as participants.
2. Each participant collects data locally and uses them to train the received models.  
3. After training, each participant transmits the parameters of the locally updated global model to the coordinator. Then, the coordinator integrates the values received from all of the participants and learns (updates) the global model again.
4. The process is repeated from step 1 unless a predefined condition on the model is satisfied. Otherwise, the global model is finalized and distributed to all the participants.  
The most notable feature of federated learning is the distribution of data and workload among training participants. Thus, local data are not easily exposed to large amounts of data breaches or sharing with third parties. This allows various parties to share learning models while protecting sensitive information. Distributing the workload of learning algorithms across participants can substantially improve performance. Due to these benefits, federated learning has been applied to a variety of scenarios.
# Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining
## 5 Discussion  
In this paper, the precoding jamming scheme has been proposed to enhance the security of AF relay-aided power monitoring and communication systems, where the CSI uncertainty and colluding eavesdroppers are considered. Such a system can be used in an underground mining process to guarantee the communication with management offices to ensure the safety. The scheme combined cooperative precoding for users and cooperative jamming for eavesdroppers. Numerical results have shown that the proposed scheme outperforms the scheme without jamming. Furthermore, the effectiveness of the proposed scheme with high CSI uncertainty has been proven.  
Methodology, W.M.; writing--original draft, Y.G. and L.G.; formal analysis, J.B.; validation, T.H. and Z.K.; writing--review and editing, T.H. and Z.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant 62173256.  
Not applicable.  
The authors declare no conflict of interest.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 3 Results  
### TEMPO, GEMS, and SENTINEL-4 Synthetic Observations  
This section presents typical synthetic observations for the TEMPO, GEMS, and SENTINEL-4 geostationary constellation. Solar and sensor zenith angles were limited to less than 80\({}^{\circ}\). This simulates the expected retrieval opportunities for each region. Figure 9 shows how the spatial coverage of the geostationary constellation evolves over 15 h. Depending on the time of day, two or more of the instruments are observing simultaneously, which will help constrain intercontinental transport of longer lived tracers. Figure 10 shows a simulated true color image for one full TEMPO scan for 15 June 2006 at 21 UTC in the G5 NR simulation. The bottom image shows the reflectance from the surface alone, while the top image includes atmospheric scattering from molecules, aerosols, and clouds as well. The figure illustrates the fidelity of the simulated observations with respect to realistic Earth observations in the visible spectral range.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Species** & **Dry Particle Density [kg m\({}^{-3}\)]** \\ \hline Organic Carbon & 1800 \\ Black Carbon & 1000 \\ Sulfate & 1700 \\ Sea Salt Bins 1-5 & 2200 \\ Dust Bin 1 & 2500 \\ Dust Bins 2-5 & 2650 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dry particle densities for the GOCART species.  
Figure 11b shows a simulated true color image of a portion of the TEMPO domain covering the Northwest United States for the 20 UTC model time step on 27 July 2006. This image was created by combining the 470, 550, and 670 nm simulated TOA reflectance. Figure 11c,d also show the simulated 388 nm TOA reflectance, as well as the simulated ratio of 354 to 388 nm TOA reflectance. The TEMPO synthetic observations indicate several hot spots of enhanced 388 nm TOA reflectance corresponding to high aerosol concentrations from fires, which are apparent in the simulated true color image. These hotspots also correspond to lower values of 354:388 nm TOA reflectance ratio, indicating the presence of absorbing aerosol.  
Figure 10: _Cont._  
Figure 9: Simulated 550 nm TOA reflectance as would be observed by the geostationary constellation over 15 h on 28 July 2006.  
radiative energy to amount of fuel burned. Figure 11a shows the observed MODIS Aqua true color image for 27 July 2006 at approximately 20 UTC. Although the simulated and observed true color images indicate the same locations of fire hot spots and smoke emissions, as G5 NR is not constrained by meteorological observations, the transport of the fire plume differs between the free running model and observations.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
###### Abstract  
The scarcity of open SAR (Synthetic Aperture Radars) imagery databases (especially the labeled ones) and sparsity of pre-trained neural networks lead to the need for heavy data generation, augmentation, or transfer learning usage. This paper described the characteristics of SAR imagery, the limitations related to it, and a small set of available databases. Comprehensive data augmentation methods for training Neural Networks were presented, and a novel filter-based method was proposed. The new method limits the effect of the speckle noise, which is very high-level in SAR imagery. The improvement in the dataset could be clearly registered in the loss value functions. The main advantage comes from more developed feature detectors for filter-based training, which is shown in the layer-wise feature analysis. The author attached the trained neural networks for open use. This provides quicker CNN-based solutions implementation.  
Synthetic Aperture Radar (SAR); Convolutional Neural Networks (CNN); Deep Neural Networks (DNN); autoencoders (AE); transfer learning; Automatic Target Recognition (ATR) +
Footnote †: journal: 28 July 2022  
Accepted: 24 August 2022  
Published: 29 August 2022