esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation
### The Mohr-Coulomb Criterion  
To evaluate the ANNs' performances, ANNs with different architectures and neurons were built. To do this, 70% of the total data fed into an ANN was used as the training dataset. The other remaining 30% was divided into two datasets, with 15% of the data used for cross-validation and 15% for testing. Then, the ANNs showing the best predictions (in terms of maximum \(R^{2}\) and minimum \(RMSE\) and \(MAE\) values) were selected. As shown in Table 3, the 15-5-10-1 and 15-15-1 networks were, respectively, the best MLP-ANN and RBF-ANN in the prediction of \(P_{i}\) for the Mohr-Coulomb strength criterion. In this case, the overall performance of MLP-ANN was better than that of the RBF-ANN.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Range of Variation** & **Standard Deviation** & **Coefficient of Variation (\%)** \\ \hline \(r_{i}\) & 3–5.35 & 0.95 & 26.78 \\ \(GSIP^{\text{\tiny{peak}}}\) & 25–100 & 29.52 & 41.34 \\ \(GSIF^{\text{\tiny{res}}}\) & 10–100 & 35.89 & 67.07 \\ \(\sigma_{ci}\) & 27.6–  
Figure 2 (as a sample representative of the RBF-ANN) depicts the topology of the best RBF-ANN developed for the Mohr-Coulomb case (15-15-1 architecture with \(R^{2}=99.21\%\), \(RMSE=0.050576\) MPa, and \(MAE=0.040918\) MPa). Figure 2(a),b show the predicted \(P_{i}\) versus the calculated \(P_{i}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ANN Type**} \\ \cline{2-7}
**Architecture** & \multicolumn{3}{c}{**MLP-ANN**} & \multicolumn{3}{c}{**RBF-ANN**} \\ \cline{2-7}  & **R2 (\%)** & **RMSE (MPa)** & **MAE (MPa)** & **R2 (\%)** & **RMSE (MPa)** & **MAE (MPa)** \\ \hline
15-3-12-1 & 94.47 & 0.126617 & 0.09375 & 79.42 & 0.22383 & 0.1494 \\
15-3-15-1 & 93.61 & 0.136705 & 0.100808 & 96.51  
Similar to Figure 3a,b, Figure 4a shows a high correlation between the calculated and the ANN-based prediction for the RBF-ANN in the case of Mohr-Coulomb strength criterion. Figure 4b, which better describes the occurred errors in the predictions for each sample, is another demonstration of the high capability of the proposed ANNs.  
Figure 3: (**a**) ANN-based predicted \(P_{i}\) versus calculated \(P_{i}\); (**b**) differences between ANN-based predicted \(P_{i}\) and calculated \(P_{i}\) for the best MLP-ANN (15-5-10-1) applied to the testing data series for the Mohr-Coulomb criterion.
# All-Sputtering, High-Transparency, Good-Stability Coplanar Top-Gate Thin Film Transistors
## 2 Experimental  
The structure of all-sputtered TFTs is illustrated in Figure 1, in which the fabricated process is given as follows. First, a thickness of 30 nm SiO\({}_{2}\) buffer layer was deposited on the pre-cleaned glass by radio frequency (RF) sputtering that isolates the H\({}_{2}\)O or O\({}_{2}\) to penetrate the functional layers, which is critical to the sensitive channel layer as adsorbed H\({}_{2}\)O or O\({}_{2}\) could induce electrical properties that are variational. Second, an active layer about 7 nm was deposited on SiO\({}_{2}\) buffer layer with the condition of 5 mTorr, 80 W and O\({}_{2}\)/Ar ratio of about 5%. This was followed by an annealing process at 300 \({}^{\circ}\)C for 30 min in ambient for eliminating part of defects in the semiconductor. Then, the source/drain electrodes were prepared using an ITO target by DC sputtering. The channel length (L) and width (W) were 160 and 635 \(\upmu\)m, respectively. Next, a 350-nm-thick Al\({}_{2}\)O\({}_{3}\) layer was deposited by RF sputtering as an insulator layer. Subsequently, top-gate electrode was defined in the same way with S/D electrodes. All the layers were patterned by shadow masks and the sputtering conditions of functional layers were listed in Table 1. For reducing the plasma bombardment on the active layer [13], the devices were post-annealed at 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C for 1 h, respectively.  
All the functional layers were deposited by using the physics vapor deposition equipment (Kurt Lesker). The electrical characteristics of TFTs were measured using a semiconductor parameter analyzer (Agilent 4155C) in ambient condition at room temperature. TEM with an energy dispersive X-ray spectrometer (EDS) was used to analyze the distribution of elements. The optical characteristics  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Pressure (mTorr)** & **Power (W)** & **O\({}_{2}\)/Ar ratio (\%)** & **Time (s)** & **Thickness (nm)** \\ \hline SiO\({}_{2}\) & 1 & 100 & 0 & 900 & 30 \\ Nd-IZO & 5 & 80 & 5 & 300 & 7 \\ ITO & 5 & 100 & 0 & 600 & 140 \\ Al\({}_{2}\)O\({}_{3}\) & 1 & 120 & 0 & 12600 & 350 \\ ITO & 5 & 100 & 0 & 600 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed sputtering conditions of each functional layer.  
Figure 1: The cross-sectional image of the all sputtered Nd:IZO TFT with coplanar top-gate configuration.  
of fabricated devices were investigated by an Ultraviolet spectrophotometer (SHIMADZU UV2600, SHIMADZU, Tokyo, Japan).
## 5 Results and Discussion
### Cumulative Benefits of Increased V2X Intermediaries  
Our findings indicate a strong positive relationship between the number of V2X intermediaries and the overall system performance. As the number of V2X data sources increases, the detection accuracy, response times, and navigation precision improve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **VRFormer** & **BEVerse-Tiny** & **Improvement** \\ \hline Motion Prediction Accuracy (mIOU) & 92\% & 77\% & +15\% \\ \hline Accident Prediction Accuracy (APA) & 90\% & 78\% & +12\% \\ \hline
3D Object Detection Precision (mAP) & 85\% & 68\% & +17\% \\ \hline Response Time (seconds) & 1.5 & 1.8 & \(-\)0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of VRFormer vs. single-vehicle BEVerse-tiny baseline.  
Figure 11 shows the cumulative gains of VRDeepSafety's VRFormer model with increasing V2X intermediaries. Detection rate (blue), response time (red), and navigation accuracy (green) are plotted against the number of intermediaries. While all three metrics improve with more V2X agents, demonstrating enhanced object detection, faster decision-making, and improved navigation accuracy, diminishing returns are observed. Most notably, around four intermediaries, the rate of improvement significantly decreases for the detection rate (reaching \(\sim\)94% with eight intermediaries) and navigation accuracy (reaching \(\sim\)94% with eight intermediaries, with rapid initial gains to \(\sim\)86-87% at just two intermediaries). Response time decreases to \(\sim\)3.5 s with four intermediaries, with further reductions becoming less pronounced. This suggests that four intermediaries offer a substantial benefit. The substantial early gains in navigation accuracy show how impactful even limited V2X communication can be.
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
## 3 Results  
The availability of the full archive of the PlanetScope imagery allows us the selection of suitable images within the same month, setting two criteria: The absence of sunlight which poses an extra processing step and eventually could introduce additional noise to the resulting deglinted images; this could in turn reduce the suitability of the images due to the already known low signal-to-noise ratio [18]; and the cloud free scenes avoiding cloud masking. Thus, the only difference between the selected images is a slightly visible wavy water surface caused by local winds formed during the morning of the day of acquisition. No sedimentation in the water column was observed, while the bottom cover is mainly composed by two types, bright sandy bottom, and rocky formations (Figure 3). Images have been selected with approximately five days interval. By applying the method described in 2.3 using the subset of training in-situ data (\(n=2854\)) and based on the AICc values (Table 2), the image of 11 July 2017 has been selected for further analysis.  
The multiple regression results of the training, based on the coefficient of determination and the standard error on the RAW (Surface reflectance) and the transformed (\(3\times 3\) median low pass filter) results are presented in Table 3, while the validation results in Figures 4 and 5, and the produced bathymric map after the combination of the two different results from the two predefined bathymetric zones in Figure 6.  
Figure 4: The validation plot for the depth zone of 0–10 m of in-situ depth points (\(x\)-axis) against image-derived bathymetries (\(y\)-axis) implementing the Lyzenga85 model on the image of 11 July 2017. 3 rd order polynomial equation has been applied.  
Figure 3: The two seabed cover types in the project area; above: sandy soft bottom, below: carbonated rocky surfaces partially covered by brown macroalgae.  
Figure 5: The validation plot for the depth zone of 10–25 m of in-situ depth points (\(x\)-axis) against image-derived bathymetries (\(y\)-axis) implementing the Lyzenga85 model on the image of 11 July 2017. 3 rd order polynomial equation has been applied.  
Figure 6: The Satellite-derived bathymetry estimated by the combination of the two-depth zones on the image of 11 July 2017.
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
## 3 Proposal for PLC Control Logic Embedding and Anomaly Detection
### Control Logic IL Code Conversion  
The proposed PLC control logic embedding method is designed for PLC control logic represented in IL code. However, IEC 61131-3 standardizes multiple PLC programming languages, allowing control logic written in different languages to be systematically converted into IL code. Most PLC manufacturers comply with this standard, ensuring that conversion between IEC 61131-3 languages, including IL, is generally supported. Engineering Workstations (EWSs) typically provide built-in functionality to facilitate this conversion. Therefore, if the control logic follows the IEC 61131-3 standard, it can be transformed into IL code. This ensures that our method can be applied across different PLC programming languages. Figure 2 illustrates an example of converting LD code into IL code.  
Figure 1: Overview of structure-based control logic anomaly detection of control logic.  
Figure 2: Example of conversion of LD code to IL code.  
An opcode refers to the name of an LD function (symbol) or command specified by the user. Operand 1 is the first operand, designated as the input value for the operator. Operand 2 is the second operand, designated as the output value for the operator. Since an operator can have multiple input and output values, operands may contain more than two variables.  
At this stage, normal control logic before being deployed to the PLC is used. If control logic was extracted from a PLC and used, there would be a possibility that it had been tampered with, as seen in Stuxnet-like attacks. Therefore, control logic in a secure state is used as the training dataset.
## 5 Methodology for Anomaly Detection and Mitigation
### Anomaly Detection
#### 5.1.3 Anomaly Information Exchange  
The earlier sub-chapters present different anomaly detection methods and how they can detect anomalies that might look similar in their effect based on different inputs. All of these methods use their own data sources, tools, algorithms and protocols to analyze an anomaly. To gain a holistic view of an anomaly and to be able to understand the real source of an anomaly leading to the proper mitigation, an exchange of anomaly information needs to be enabled. General requirements for such an anomaly information exchange are the capability to store all relevant anomaly information by all connected detection capabilities.  
In addition, the anomaly information must be readable for all available mitigation methods in order to have all relevant information available to define proper mitigation methods.  
In the anomaly detection approach, the available robot fleet data base is used, which was designed originally to store information on robot fleet performance, such as performed jobs, driven distances, etc. To address the need for anomaly representation, two new categories were implemented in the database. The first category, anomaly information, provides general information on possible anomalies without having a real occurrence of the respective anomaly. The second category represents an anomaly occurrence, referencing an anomaly and adding the case-related information. The included information in both categories is shown in Figure 2.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 2 Construction of the SV-DC Model
### State Variable-Dynamic Capacitance Model
Integrating the EKF algorithm with the state variables model enables us to dynamically characterize the well connectivity and time-lag coefficient, offering a more accurate and temporally responsive representation of reservoir dynamics.
## 3 Analysis of Monitoring Results
### Analysis of the Measured Settlement Results  
The development trends of lateral and longitudinal surface settlement during pipe jacking construction are shown in Figure 2. Figure 2a shows that the shape of the transverse surface settlement trough conforms to the Peck settlement curve. Before the cutterhead passes the monitoring section, the soil in front of the face is compressed, causing the ground to heave slightly; as the cutterhead approaches the monitoring section, the heave increases, reaching a maximum value of 1.1 mm. After the tail of the machine passes the monitoring section, due to ground loss, the surface deformation begins to change from heave to settlement, which continues to increase throughout the construction process. The maximum settlement occurs at D1, with a settlement of 22 mm. Figure 2b shows that the settlement at all monitoring points increases with the jacking process. Owing to the presence of a 7.5 m-long ground reinforcement area, the soil near the launching shaft shows relatively small displacement changes within the range of \(\pm\)5 mm. The maximum surface settlement is observed at B8, with a maximum settlement value of 26.49 mm. Moreover, the curves in the figure indicate a significant increase in settlement at B1 and B3. According to the survey report, weak interlayers were found in the soil at boreholes B1 and B3, which may lead to excessive jacking parameters when the pipe jacking machine reaches these positions.  
Figure 1: Plane layout of monitoring points.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Dataset Preprocessing and Augmentation
#### 3.2.3 Dataset Augmentation  
Data augmentation is an important method to enhance model generalization ability, especially in situations with limited data or imbalanced class distribution. Common methods of data augmentation in financial data processing include the following:  
1. **Synthetic minority over-sampling technique (SMOTE)**: As previously mentioned, SMOTE is a popular data augmentation method used to address class imbalance issues. It generates new samples by interpolating between minority-class samples.
2. **Time series data augmentation**: For stock price data, time series-specific data augmentation methods such as time window sliding and time series segmentation can be used to increase the number and diversity of data samples.
3. **Feature expansion**: In some cases, creating new features by adding noise, transforming, or combining existing features can enhance the diversity of the dataset.  
Through the above data preprocessing and augmentation steps, the quality and diversity of the dataset can be ensured, providing a solid foundation for the training and evaluation of the Merkle-Transformer model.
## 3 Polynomial Time Algorithms for the Bandwidth Problem
We recall that for given an interval representation of an interval graph \(G=(V,E)\) and a positive integer \(k\), Algorithm KV constructs a layout \(\pi\) of \(V\) that achieves the bandwidth at most \(k\) if \(\text{bw}(G)\leq k\). The main idea is as follows. We assume that we give the interval representation \(\mathcal{I}\) and \(\text{bw}(G)\) as an input to Algorithm KV. Through the construction of the optimal layout \(\pi\), indeed, Algorithm KV does not change the ordering in \(\mathcal{J}\). We note that Algorithm KV itself does not mind the set \(\mathcal{J}\). That is, for an interval graph \(G=(V,E)\), Algorithm KV does not change the ordering of any subset \(\mathcal{J}\) if \(\mathcal{J}\) induces a proper interval graph. Basically, Algorithm KV greedily labels each interval from \(1\) to \(n\), from left to right. Unlabeled intervals are divided into two groups; the intervals incident to labeled intervals and others. The former group is again divided into sets \(S_{j}^{q}\). A set \(S_{j}^{q}\) contains unlabeled intervals that should have labels up to \((j+q)\), where \(q\) is the largest label so far. In the first saturated \(S_{j}^{q}\) with respect to \((j+q)\), the interval having the smallest left endpoint is chosen as the next interval (ties are broken by the right endpoints). To prove the lemma, we have to show the leftmost unlabeled interval \(J_{u_{i}}\) in \(\mathcal{J}\) has to be chosen before \(J_{u_{i+1}}\) when Algorithm KV chooses an interval in \(\mathcal{J}\). When Algorithm KV picks up the first saturated \(S_{j}^{q}\) in Step 7, we have \(S_{1}^{q}\subseteq S_{2}^{q}\subseteq\cdots\subseteq S_{j}^{q}\). Hence, by a simple property of proper intervals, \(S_{j}^{q}\) contains all unlabeled \(J_{u_{i}}\) with \(i^{\prime}<i\leq i^{\prime\prime}\) such that \(J_{u_{i^{\prime}}}\) is the rightmost labeled interval in \(\mathcal{J}\) and \(J_{u_{i^{\prime\prime}}}\) is the rightmost unlabeled interval in \(\mathcal{J}\) that is adjacent to a labeled interval. Among them, Algorithm KV picks up the interval that has the smallest left endpoint (in Step 2 or 8). Thus, with appropriate tie-breaking, the next labeled interval can be \(J_{u_{i^{\prime}+1}}\) before \(J_{u_{i^{\prime}+2}}\). Therefore, Algorithm KV labels all intervals in \(\mathcal{J}\) from left to right, and we have the lemma.
## 3 Plasma Application to Eliminate Organic Pollutants in Water
### Pharmaceuticals
#### 3.1.2 Recalcitrant Pharmaceuticals  
Pharmaceuticals are increasingly present in greater amounts, which burdens the ecosystem and might endanger drinking water sources. [PERSON] et al. [63] developed a plasma reactor with a coaxial geometry to generate high-volume corona discharges directly in water. They also looked into the breakdown of seven resistant medications (carbamazepine, diatrizoate, diazepam, diclofenac, ibuprofen, 17a-ethinylestradiol, and trimethoprim). According to the authors, the efficient destruction of persistent chemical compounds is the main technical advantage of corona discharge generated directly in water. For procedures lasting 15 to 66 min, the majority of the examined medications were efficiently degraded by 45% to 99%. Diclofenac and ethinylestradiol were specifically and quickly destroyed. In recent research [88], in order to improve the treatment of wastewater contaminated with diclofenac, microbubble formation was coupled with cold plasma technology. The discharge voltage, gas flow, starting concentration, and pH value all had an impact on the degradation efficiency. The best degradation efficiency was 90.9% after a 45 min plasma bubble treatment under the optimum process parameters. Another study used pulsed corona discharge on a fluid produced in O\({}_{2}\) to degrade ibuprofen in water. Interestingly, it was observed that ibuprofen disappeared entirely after 20 min of plasma exposure, and mineralization rose to 76% after one hour [89]. It is important tonote that mineralization is a crucial step in the transformation of toxic drugs into nontoxic drugs. The effectiveness of drug degradation is directly correlated with the rate of mineralization. Another compound called carbamazepine, known to be harmful to aquatic species, is the most prevalent environmental drug identified, and has endocrine-disrupting activity [90]. To deal with this issue, [PERSON] et al. [91] devised a low-power in situ plasma treatment technique that demonstrated high carbamazepine breakdown efficiency when compared to DBD plasma.
## 3 Perspectives and Outcomes of the Presented RHMS with a Focus on Diet-Related Approaches
### Nutrition Evaluation Methods from an RHMS (HepatoConect System) Perspective  
From an RHMS perspective, such as that of the HepatoConect system, dietary intake can be monitored using a 24-h dietary recall or a 3-day food intake diary. Both rely on patient's cooperation and may be difficult to implement in those with advanced disease and cognitive impairment, but novel evaluation methods integrated into the HepatoConect system, with user-friendly interfaces for high compliancy allow for effortless daily monitoring of diet and nutritional supplementation, as recalled by the patient or by their caregivers. The continuous monitoring of dietary intake further allows for prompt interventions regarding diet optimisation, thus limiting, as much as possible, prolonged fasting periods or poor nutritional intake, as both significantly impact survival.  
As previously mentioned, frailty is strongly associated with malnutrition, disability, and deconditioning in patients with advanced LC. The liver frailty index, composed of three performance-based tests (grip strength, chair stands, and balance), is an in-person tool specifically developed for patients with LC to objectively determine physical function [138]. As a novel evaluation method, a virtual telemedicine-enabled frailty assessment tool for patients with advanced LC was recently developed. The tele-liver frailty index (TeLeFi), consisting of validated functional capacity surveys and virtually-assessed chair stands, was able to accurately identify frailty in liver transplant patients via telemedicine when compared to ambulatory frailty testing [139] and may be used on a large scale as a screening tool to diagnose frailty or to perform follow-up to identify those in greatest need of additional interventions for maintaining functional independence. In this context, the HepatoConect system also incorporates telemedicine-enabled frailty assessment tools and further evaluation methods based on the patient's performance status.  
Considering these findings and the spectrum of liver disease, as well as its progressive nature, the HepatoConect system can address multiple needs in the form of a two-faceted intervention approach, which may also act as a two-step intervention:  
1. Targeted education strategies, personalized nutritional assessment, and dietary interventions in patients with obesity and MASH, with or without compensated advanced chronic liver disease, who may be at risk of decompensation and malnutrition (both over- or undernutrition), aiming to prevent decompensation by limiting disease progression through early health interventions and lifestyle changes;
2. Targeted education strategies (both for patients and caregivers), personalized nutritional assessment, and dietary interventions combined with continuous telemonitoring for patients with advanced or end-stage liver disease, aiming to proactively manage any decompensating event and improve nutritional status and quality of life, as well as prevent hospitalisation and/or institutionalisation.