esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Site-Specific Climate and Flux Data  
In this study, our analysis is based on continuous observations of land-surface exchanges during 2001-2006 at US-Bo1 site and during 2005-2006 at US-Ro1 site [39]. The product provides measurements of ecosystem-scale water flux (LE), CO\({}_{2}\) flux, meteorological variables including solar radiation (R\({}_{\text{g}}\)), air temperature (T\({}_{\text{a}}\)), soil temperature/moisture, vapor pressure deficit (VPD), precipitation (P), and the estimates of GPP derived from the measured NEE using the methodology proposed by [PERSON] _et al._[40] at half-hour, daily, weekly (8-day), and monthly time scales [17]. Both the marginal distribution sampling (MDS) approach and the artificial neural network (ANN) method are used to gap-fill the original CO\({}_{2}\) flux data. Generally, the ANN method is slightly superior to the MDS method [40, 41]. Thus, the gap-filled results of NEE and GPP data based on the ANN method are used. The 8-day period, level-4 data are analyzed in this study. Therefore, flux tower and MODIS observation periods are consistent for comparison. In addition, the missing data resulting from system failures, stable atmospheric conditions or rejection of poor data were gap-filled using standardized approaches to provide complete data sets [40, 42].  
Crop WUE can be characterized in many different ways in terms of scientific disciplines as well as the temporal and spatial scope of interest [43]. The ecosystem-level definition is used in this study, which is also closely related to ecosystem models. It is defined as:  
\[\text{WUE}=\text{GPP}\,/\,\text{ET} \tag{2}\]  
The seasonal variations in WUE can be described using 8-day WUE (g-Ckg\({}^{-1}\cdot\)H\(\text{2O}\)). ET (mm/day) was inferred from the observed latent heat fluxes (LE, W/m\({}^{2}\)) as:  
\[\text{ET}=\text{LE}\,/\,\lambda \tag{3}\]  
where \(\lambda\) is the amount of energy required to evaporate 1 kg liquid water to vapor (about 2,454,000 J).
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 2 What Is Interpretable Optimization?
### The Potential for Transparency in Optimization  
The first fact that needs to be acknowledged is that classical optimization has an inherent level of model transparency that does not apply to other decision-making tools (specifically ML). A classical optimization model is carefully designed to explicitly describe the problem, typically formulated with a well-defined mathematical structure. The constraints, decision variables, and objective function are precisely stated, allowing for a clear interpretation of each element. This provides optimization with phenomenal potential for interpretability, which remains largely untapped. Existing interpretability techniques in IML, as well as metaheuristics and evolutionary computation--such as Genetic Algorithms, Particle Swarm Optimization, or Simulated Annealing--and ML, do not need to explicitly express the mathematical structure of the problem. These more flexible methods require only one thing--the possibility of evaluating the fitness of solutions. From that alone, they can apply solution-improvement strategies that, without any information about the prior problem structure, can converge to an optimal solution in the best cases. We should note that this convergence will only happen in specific instances where the fitness landscape allows it, and there should be no expectation of an optimality guarantee. However, classical optimization explicitly defines the objective function, variables, and constraints, which, if exploited, can lead to intuitive decision explanations.  
While classical optimization can offer the guarantee of optimality in some particularly interesting instances, such as linear or mixed-integer programming [22], it can be impractical for high-dimensional or nonconvex problems [23], as well as, crucially, for problems where a mathematical formulation is not easily available, which is very often the case in many of the complex systems that require optimization. ML and metaheuristics introduce an additional level of flexibility, only necessitating a fitness evaluation; however, they must renounce, in general, any optimality claims.  
Regardless of the solution approach, optimization models do not assess interpretability past some initial sensitivity analyses. Once a model is solved, decision-makers receive an optimal solution but little insight into why that solution was chosen, what trade-offs were made, or what alternative options exist. The field of interpretable optimization seeks to address this limitation by embedding transparency into the optimization process, ensuring that solutions are not only mathematically sound but also comprehensible, amenable to discussion, and actionable.
## 3 UAV Applications in Climate Change Research
### UAVs in Climate Change Research
Recently, a considerable amount of research has deployed UAVs with infrared thermography, further enhancing the monitoring capabilities, identifying thermal anomalies, assessing rock mass conditions in complex environments, and identifying slope hydrology dynamics influencing instability [147; 148]. For characterizing debris flows, UAVs can capture high-resolution images and generate digital terrain models (DTMs) that identify and map debris flow paths, help estimate deposited volumes, and quantify torrent evolution [149]. UAVs have also been deployed for climate modeling and air-quality monitoring, providing access to more granular data. Combining microclimate sensors and air-quality instruments allows UAVs to relate local weather, pollution, and environmental factors to community health. For instance, a study in California gathered hyper-local air-quality data to model pollution exposure risks and guide the policies for protecting vulnerable populations. UAVs can also be integrated with advanced technologies, such as artificial intelligence and computer vision, to analyze and interpret the collected data, which can identify pollution hotspots, predict air-quality trends, and develop early warning systems for potential health risks. As climate change leads to more frequent extreme heat and air pollution episodes, UAV sensor networks can enable real-time monitoring to issue health warnings.
## 4 Experimental Analysis  
This section describes the experimental setup for evaluating the proposed RNN-based epilepsy classification model. Two publicly accessible datasets, the BONN EEG dataset and the CHB-MIT EEG database, are utilized to assess the model's performance. The BONN dataset contains single-channel intracranial EEG signals, while the CHB-MIT dataset consists of multi-channel surface EEG recordings. Different scenarios are considered in the experimental setup, involving variations in parameters such as training and testing ratios, learning rate, and frequency analysis methods. The proposed RNN-based model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Method** & **Train-Test Ratio** & **Learning Rate** & **Cross Validation** \\ \hline
1 & Frequency sub-band & & & \\ Delta-Ac4 (0.0–4.0 Hz) & 30/70 & 0.01–0.1 & 10-Fold \\ Theta-Dc-4 (4.0.0–8.0 Hz) & 40/60 & & \\ Alpha-Dc-3 (8.0–15.0 Hz) & 50/50 & & \\ Beta-Dc-2 (15.0–30.0 Hz) & 60/40 & & \\ Gamma-Dc-1 (30.0–60.0 Hz) & 70/30 & & \\  & & 80/20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: RNN simulations using CHB-MIT EEG dataset and different frequency sub-bands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Method** & **Train-Test Ratio** & **Learning Rate** & **Cross Validation** \\ \hline
2 & RNN+DWT & 10/90 & 0.000001 & 10-Fold \\  & & 20/80 & 0.00001 & \\  & & 30/70 & 0.00001 & \\  & & 40/60 & 0.0001 & \\  & & 50/50 & & \\  & & 60/40 & & \\  & & 70/30 & & \\  & & 80/20 & & \\ \hline
3 & RNN+FFT & 10/90 & 0.0000001 & 10-Fold \\  & & 20/80 & 0.000001 & \\  & & 30/70 & 0.00001 & \\  & & 40/60 & 0.0001 & \\  & & 50/50 & & \\  & & 60/40 & & \\  & & 70/30 & & \\  & & 80/20 & & \\ \hline
4 & RNN+DWT & 30/70 & 0.01–0.1 & 10-Fold \\  & & 40/60 & & \\  & & 50/50 & & \\  & & 60/40 & & \\  & & 70/30 & & \\  & & 80/20 & & \\ \hline
5 & RNN+FFT & 30/70 & 0.01–0.1 & 10-Fold \\  & & 40/60 & & \\  & & 50/50 & & \\  & & 60/40 & & \\  & & 70/30 & & \\  & & 80/20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: RNN-based simulations using CHB-MIT EEG dataset and different frequency sub-bands.  
is trained and tested on both datasets using cross-validation techniques to validate the efficacy of the proposed model in classifying EEG signals and detecting epileptic seizures.  
The experiments were carried out on a computer running Microsoft Windows 11 operating system, equipped with an AMD Ryzen 7 3700X 8-Core Processor, and 48 GB of RAM. The implementation of data preparation, frequency analysis, and feature extraction was performed in Python. On the other hand, the data normalization and classification using RNN were conducted using MATLAB, a widely used tool for data analysis and modeling.
# Modelling of Failure Behaviour of 3D-Printed Composite Parts
## 2 Materials and Methods
### Computational Modelling
#### 2.2.1 Computational Methodology-1  
In the first computational methodology (CM-1), nonlinear properties of ABS polymer from Table 2 are employed for modelling. This method is based on isotropic damage and plasticity law. The finite element models of RVE are developed for this computational method. The finite element RVE models of 3D-printed parts for this computational method are shown in Figure 3. In these finite element models of RVE, the bonding at the interface of extrudates is assumed to be perfect. Furthermore, FE models of RVE of printed composite material have the following additional assumptions: perfect bonding between fibres and matrix, fibres are perfectly in line with the direction of the extrudate, and sCF reinforcements are positioned sparsely. These assumptions in the RVE FE models make the computational model idealistic. Material properties for ABS polymer from Table 2 and for Carbon fibre reinforcements from Table 3 are employed in the computational models.  
Where the nonlinear material parameters in Table 2 represent \(\sigma_{Y}\)-yield strength, \(\sigma_{U}\)-ultimate strength, \(\delta\)-exponent for the damage evolution law, \(H\)-linear term for hardening law, \(\varepsilon_{p0}\)-equivalent plastic strain at which damage begins, \(\varepsilon_{p1}\)-equivalent plastic strain at which damage causes zero stress, \(\sigma_{mean}\)-mean stress at damage initiation, \(J_{1}\)-volumetric strain to failure. More details about the damage law are available in the manual of the Multiscale designer tool by Altair [40]. The values of nonlinear parameters are obtained from the tensile testing data of ABS polymer, which is used for 3D printing.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Property** & \
\begin{tabular}{c} **E,** \\ **in MPa** \\ \end{tabular} & \(v\) &
\begin{tabular}{c} \(\sigma_{Y}\) \\ **in MPa** \\ \end{tabular} & \(\sigma_{U}\) & \(\delta\) & \(H\) & \(\varepsilon_{p0}\) & \(\varepsilon_{p1}\) & \(\sigma_{mean}\) & \(J_{1}\) \\ \hline Value & 2230 & 0.34 & 30 & 40 & 200 & \(-100\) & 0.07 & 0.10 & 36 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of ABS polymer for nonlinear material modelling.  
This computational method is based on isotropic damage and plasticity law, which is commonly used for the damage modelling of polymers. Computational modelling for 3D-printed parts involves the following sequence of steps; firstly, linear material modelling using the homogenization technique for estimating linear material properties. Then, nonlinear material modelling uses the damage criterion and damage progression law to predict the failure of parts.
# Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model
## 4 Results
### Performance Measures
Following the prediction models established through Multinomial Logit (MNL) and Random Forest algorithms, performances of the models were obtained by calculating precision and recall, along with F1 score values. In the MNL model, precision values for property damage only, injury, and fatal classes were 64%, 57%, and 75%, respectively. Furthermore, while recall values were found to be 89%, 24%, and 2%, F1 score values were calculated as 75%, 34%, and 3%, respectively. On the other hand, the precision values of the injured class obtained in this study as 94.66% and 80.17% for the models, where all and selected attributes were analysed, respectively, are significantly higher than the value of 57% given by [PERSON] and [PERSON] [6]. These higher values clarify the fact that the accuracy of class prediction of injury-related accidents in this study is remarkably improved. It can also be seen from Table 4 that there is an approximately 5% difference between the \"accuracy values\". This quite small difference clearly indicates that there is no drawback of establishing the proposed model with the attributes selected within the scope of this study. The remaining attributes, after all the elimination and clearance processes, produced the aimed-for results of this research. Another important issue to be stated is that as the characteristics of the accidents in which the driver is killed are quite similar to the accident characteristics in which drivers are injured the model tends to predict the dead drivers as injured in accidents. \begin{table}
\begin{tabular}{l l|r r r r r r r} \hline \hline  & & \multicolumn{4}{c}{**All Attributes**} & \multicolumn{4}{c}{**Selected Attributes**} \\ \cline{3-10}  & & \multicolumn{4}{c}{**(After Pre-Processing-1)**} & \multicolumn{4}{c}{**(After Pre-Processing-2)**} \\ \hline  & & \multicolumn{4}{c}{Observed} & \multicolumn{4}{c}{Observed} \\  & & Uninjured & Injured & Dead & Precision (\%) & Uninjured & Injured & Dead & Precision (\%) \\ \cline{3-10}  & Uninjured & 2647 (DIT) & 156 & 63.42 & 3803 (DIT) & 156 & 63.14 \\ \cline{3-10}  & Injured & 10.781 & 6208 (DIT) & 9 & 94.66 & 7624 & 4442 (DIT) & 9 & 80.17 \\ \cline{3-10}  & Dead & 0 & 0 & 0 (DIT) & 0 & 0 & 0 (DIT) & 0 \\ \cline{3-10}  & Recall (\%) & 94.34 & 69.64 & 0 & 66.72 & 78.27 & 0 \\ \cline{3-10}  & & \multicolumn{4}{c}{Accuracy: 78.09\%} & \multicolumn{4}{c}{Accuracy: 73.75\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix and performance measures of the model with regard to all and selected attributes.
## 4 Results and Discussion
### Network Planning Results and Performance
#### 4.2.3 Wireless Network Planning for 1000 Mbps and a Maximal Average Received Power \(AVE_{Rx}\)  
Finally, to further showcase the advantages of the ML + GA-approach over the WHIPP tool, Table 4 shows the results of the network design for a higher throughput requirement, being reflected by higher required received power (see Table 2, 15/256 QAM/30 dB/1000 Mbps/\(-\)53 dBm). Table 4 shows that in the final proposed deployment 100% of Rx points are covered with the required signal strength. In terms of the number of APs, the ML + GA can find a better solution: 4 APs (25%) fewer for the Koutitas building and 4 APs (11%) fewer for the Library Jaume I building. Related to the time to make the deployment, the GA re-uses the preprocessed PL values calculated in the previous deployment, resulting in a calculation time that is in the worst case still 15 times faster than in the case of the WHIPP tool.  
Above scenarios show that the ML model was able to accurately estimate PL calculated by more complex models with an MAE lower than 3 dB compared to an IDP-based path loss model. The same principles could equally be applied to other complex models that are based on physical inputs. It was subsequently shown that a GA using this ML model can find a better network deployment (with a lower number of APs) without compromising coverage and taking into account additional constraints like the maximum \(AVE_{Rx}\).
# Packet-Forwarding Algorithm in DTN Based on the Pheromone of Destination Node
## 3 The Model of Pheromone and Community
### Community Model Based on Pheromone  
If the connections between the core node \(v_{0}\) and its neighbor nodes are relatively stable, then the pheromone of the core node \(v_{0}\) can be computed according to the pheromone model. The pheromone will be spread to external nodes according to the pheromone spread model. When the pheromone decreases to zero, the community around the core node \(v_{0}\) is constructed. Community based on the pheromone of \(v_{0}\) are shown in Figure 4, and the best path for packet forwarding to the core node \(v_{0}\) is along the direction where the pheromone becomes stronger.
## 2 Materials and Methods  
### Samples  
Biopsy specimens of normal gastric tissue and samples with mild-to-moderate gastritis and hyperplastic polyp were obtained from the archives of the Biotechnology Laboratory, Euro-Mediterranean Institute of Sciences and Technologies (IEMEST). Gastric samples with severe gastritis, intestinal metaplasia, dysplasia, and carcinoma were collected from the archive of the Surgical Pathology laboratory, Department of Sciences for the Promotion of Health and Mother and Child Care, University of Palermo. Formalin fixed and paraffin embedded blocks of human gastric tissue were collected (10 cases for each group). The group of normal mucosa (NM) consisted of 5 males and 5 females with an average age of \(51\pm 4\) years. The group of specimens with mild-to-moderate gastritis (MMG) consisted of 3 males and 7 females, with an average age of \(54\pm 4\) years, with no signs of activity and a negative history of cancer or polyps. The group of hyperplastic polyp samples comprised 4 males and 6 females, with an average age of \(56\pm 5\) years, and a negative history of tumors and/or familial hereditary polyposis syndromes. Endoscopically, these polypoid formations had a diameter below \(1\) cm. The group of severe gastritis comprised 8 males and 2 females, with an average age of \(52\pm 2\) years. The group of gastric dysplasia comprised 7 males and 3 females, with an average age of 51 \(\pm\) 2 years. All patients had a negative history of _Helicobacter pylori_. The grade of dysplasia was moderate to severe. The group of gastric carcinoma samples comprised 5 males and 5 females, with an average age of 58 \(\pm\) 5 years. These samples were obtained from total surgical gastrectomy and presented the histopathological diagnosis of intestinal type adenocarcinoma (type I according to [PERSON]), with a moderate degree of differentiation (G2). Together with infiltrating neoplasm, all samples also showed areas of normal mucosa and areas of intestinal metaplasia. The pathological staging (pTNM) of the gastric carcinoma group was T\({}_{2}\)N\({}_{0}\)M\({}_{\mathrm{X}}\) and T\({}_{3}\)N\({}_{0}\)M\({}_{\mathrm{X}}\) for 6 and 4 cases, respectively.
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 5 Conclusions  
This paper proposed a completion method for the missing deformation monitoring data of concrete dams. The main points are as follows:  
1. The monitoring data missing of concrete dam deformation was discussed, including deformation monitoring data characteristics and data contamination types.
2. A data completion method with high accuracy, good stability, and strong adaptability validated through a case study was proposed. By reviewing the traditional processing methods to deal with incomplete information, this paper discussed the principle and weakness of traditional missing value completion methods in the case of single-value missing and multi-value missing. For the single-value missing in monitoring data, the non-local mean method was studied, and the regression interpolation method of spatial adjacent points was improved to accomplish data completion. For the multi-value missing data completion, the nonlinear regression and the spatial adjacent point regression were used, and the BP mapping of spatial adjacent points was proposed to complete the missing data pieces. The method proposed in this work is simple and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Completion** & **Linear** & **Nonlinear** & **Space Adjacent** & **BP** \\
**Method** & **Interpolation** & **Interpolation** & **Interpolation** & **Interpolation** \\ \hline Determination & & & & \\ coefficient & 0.1094 & 0.6053 & 0.9518 & 0.9527 \\ Root Mean & & & & \\ Square error & 1.4702 & 0.8470 & 0.2753 & 0.1292 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of completion accuracy of missing values.  
Figure 8: Comparison diagram of missing value completion results.  
effective to complete long data sequences and can meet the requirements of safety monitoring during dam operation.  
Conceptualization, H.G. and T.W.; methodology, Y.Z. and D.Y.; validation, D.Y., C.W. and L.H.; formal analysis, C.W.; resources, C.W.; data curation, D.Y.; writing--original draft preparation, L.H. and Y.Z.; writing--review and editing, T.W. and D.Y.; visualization, Y.Z.; supervision, H.G.; funding acquisition, T.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 51739003, Grant 51909173 and Grant U2040223, in part by the Free Exploration Project of Hohai University under Grant B200201058, and in part by the Open Foundation of Changjiang survey, planning, design and Research Co., Ltd. (CX2019K01).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## Appendix A Pseudocode of the MDCAE-CSBD-Vol Algorithm  
```
0:\(D\): Time series dataset; \(W\): Sliding window size; \(T\): Maximum number of iterations; \(N\): Number of time series in \(D\); \(k\): Number of clusters Ensure: S: Similarity measure matrix; \(C\): Clustering results; \(SC\): Silhouette Coefficient; \(R1\): Rand Index; \(ARI\): Adjusted Rand Index; \(NMI\): Normalized Mutual Information
1: Initialize: \(S\leftarrow\infty\), current iteration \(i\gets 0\)
2: Standardize all time series in \(D\)
3:while\(i\leq T\)do
4:for each time series \(x\) in \(D\)do
5:for each time series \(y\) in \(D\), \(y\
eq x\)do
6: Compute \(d\) based on Eq. (2): \(d=d\times\frac{\mathrm{vol}_{x}}{\mathrm{vol}y}\)
7: Calculate CSBD distance between \(x\) and \(y\) using sliding window \(W\) and \(d\)
8: Update \(S\) with the computed CSBD distance
9:endfor
10:endfor
11: Apply \(k\)-medoids clustering on \(S\) to obtain \(C\)
12: Calculate \(SC\), \(RI\), \(ARI\), \(NMI\) for clustering result \(C\)
13:if\(i>0\) and \((SC,RI,ARI,NMI)\) not significantly improved then
14: Break
15:else
16:\(i\gets i+1\)
17: Randomly adjust \(W\) within a predefined range
18:endif
19:endwhile
20:return\(S,C,SC,RI,ARI,NMI\)
```  
**Algorithm A1** CSBD-Vol for Time Series Clustering