esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Supercapacitor Simulation Accuracy Through a Novel Hybrid Modeling Approach
## 2 SC Modeling and Parameter Estimation Methods
### Supercapacitor Modeling Approaches
#### 2.1.3 Data-Driven Models Using Machine-Learning Algorithms  
These models are based on Artificial Neural Network (ANN) or fuzzy logic, and they represent an alternative to the analytical models presented before [26]. The ANNs are intelligence-based methods that can often model specific nonlinear relationships between the SC performance and its impacting factors [21]. Using training experimental data, ANNs have the benefit of performing nonlinear mapping of multidimensional functions, and it uses the obtained model to predict the process behavior. The structure of the neural network (number of hidden neurons) and training procedure both contribute to the performance of the prediction capabilities of ANNs. A multilayer feed-forward neural network typically consists of an input vector, a hidden layer, and an output vector/layer. The structure receives a mixture of input variables, such as voltage V[x \(-\) 1], current I[x], or time t[x], which propagate through the hidden layer to compute the process outputs [42]. Figure 4 shows the multilayer feed-forward neural network's simplified structure.  
Figure 4: Simplified structure of a multi-layer neural network.  
A wide range of data-intensive applications have proposed the use of ANN. This method is growing in popularity due to its apparently low dependence on any domain-specific knowledge, the availability of effective training algorithms, processing power, and the ability to model a wide range of functions, as well as its convergence speed [37; 43]. Besides advantages, this type of modeling method is sensitive to the qualitative and quantitative characteristics of the training data and has low robustness [21]. In addition, ANNs solely depend on accurate historical data for predictions. Due to the extended lifespan of supercapacitors compared to regular batteries, collecting long-term cycle life data becomes time-consuming, raising the likelihood of data noise and errors. Obtaining high-precision experimental data is a technical challenge that must be overcome to achieve precise evaluations [4].
## 1 Introduction
We prepared the polysaccharide composite fibers made from PICs using the interfacial spinning method, where the CS solution was slowly placed onto the CHI solution to make an interface, and the PIC film formed at the interface was pulled up and spun [47; 48]. The obtained fibers showed high mechanical strength and swelled in water. We also prepared PIC fibers using a microfluidic device [49; 50]. However, few examples of fabrication and the functional evaluation of fibers composed of polysaccharide PICs have been reported; there is a necessity for further development. Figure 1: Chemical structures of chitin, sodium alginate (ALG), calcium alginate (ALG-Ca), chondro sulfate C sodium salts (CS), and chitosan (CHI). For ALG and ALG-Ca, one of the representative structures is shown. In this study, we designed and fabricated a smart fiber, which is a fiber with controllable drug release ability, utilizing polysaccharide PICs. Firstly, ALG-Ca fibers were prepared by solution spinning, which were then coated with PIC films made of CS and CHI. The PIC films possess smooth structures and high mechanical strength, and thus coatings of PIC layers are expected to afford the ALG-Ca fibers not only sustained release ability but also sufficient mechanical strength. Additionally, CHI in the PICs can electrostatically interact with ALG-Ca, resulting in good mechanical properties of the fibers. The PIC thin layers were formed at the solution interface and pulled up simultaneously with ALG-Ca fibers, which resulted in the fabrication of coaxial fibers of PIC-coated ALG-Ca (ALG-PIC fiber). Further, the fabrication of ALG-PIC fibers with different thicknesses of PIC layers on ALG-Ca fiber using different concentrations of the CHI solution used to fabricate the PIC film was also conducted. Such a post-coating approach is advantageous for controlling the thickness of the coating layers compared to other techniques such as coaxial electrospinning. Also, it is technically difficult to conduct coaxial electrospinning using PIC layers as the coating layers because both anionic and cationic polyelectrolytes (in this case, CS and CHI) need to be added simultaneously in the spinning solution. Characterization of the obtained fibers and evaluation of their mechanical properties and swelling behavior were performed. In addition, to confirm the drug loading and sustained release properties of the ALG-Ca fibers and ALG-PIC fibers, ALG-Ca fibers loaded with FITC-BSA, a protein with a fluorescent probe as a model drug (FITC-BSA-loaded ALG-Ca fibers), and PIC-coated fibers (FITC-BSA-loaded ALG-PIC fibers) were prepared. The abilities of sustained release of FITC-BSA from these fibers were evaluated.
## 2 Materials and Methods
### Model Development
#### 2.2.3 Gradient Boosted Decision Tree  
Machine learning's gradient-boosted decision trees take a model's forecasting accuracy and gradually boost it through a series of incremental learning stages. To minimize the loss function, the parameters, values, or biases used to determine the target value are updated with each iteration of the decision tree. Boosting is a method of rapidly increasing the forecast accuracy of a model to an optimal level; the gradient is the incremental modification performed at each stage. The steps for developing this model are as follows:  
Step 1: Determining the average value of the output target variables;  
Step 2: Calculating the residual of each target variable by subtracting it from the average value;  
Step 3: Constructing a decision tree;  
Step 4: Predicting the target label using all the trees in the ensemble;  
Step 5: Carrying out a new residual calculation by subtracting the predicted value of step 3 from the actual value;  
Step 6: Repeating Steps 3-5 until the number of iterations meets the number given by the hyper-parameter;  
Step 7: Final prediction using the trained model.  
The model was developed using the MATLAB program. Among the 52 data points (i.e., 1970 to 2021), 35 sampling years were used to train the model and 17 sampling years were  
Figure 4: Bagged decision tree.  
used to test the developed model. A double exponential smoothing technique was used to predict the data until 2040. Figure 5 depicts the conceptual structure for the proposed AI model for predicting Saudi Arabia's GHG emissions.
# LaRoAwan and Urban Waste Management--A Trial
## 5 Evaluation
### Deployment Conditions Impact
#### 5.2.2 Underground Container  
To test the underground container radio coverage and to obtain penetration attenuation, tests were performed positioning the LoRaWAN transceiver inside and outside of the container. Figure 18 presents the probability distribution of RSSI and SNR values of received message, for each condition. It is possible to observe the influence of container on received signal level and quality.  
The underground container, as expected by its steel construction, introduces a much greater attenuation, reaching average attenuation values above 26 dB. These values are visible in Table 3. This fact will imply that in terms of dimensioning the radio network it will be necessary to account for this increase in terms of link budget, in order to allow the use of sensors without external antennas.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{5}{c}{**RSSI Value (dBm)**} \\  & **Min** & **Max** & **Avg** & **StdDev** & **Probe Message Count** \\ \hline
**Underground** & & & & & \\
**Container** & & & & & \\ \hline Inside & \(-127\) & \(-111\) & \(-121.31\) & \(2.32\) & \(49\) \\ Outside & \(-115\) & \(-87\) & \(-94.90\) & \(6.29\) & \(59\) \\ \hline Difference & \(-12\) & \(-24\) & \(-26.41\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: RSSI measurements on underground containers.  
Figure 17: Iglo container received message signal strength indication: (**a**) with the container full and (**b**) with the container empty.  
Figure 18: Underground container received messages: (**a**) radio quality RSSI; (**b**) SNR measurements.
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 6 Example Applications
### Healthcare  
There are several problems related to healthcare where the application of optimization models can be advantageous. Such problems include (among others) resource allocation, organ allocation, treatment planning, disease management, personnel planning, patient scheduling, and facility location [122].  
Digitalization technologies record and store massive volumes of historical data. Computerized models can help make the most of the data, distilling insights that would have been missed otherwise. Such models could be directly integrated into healthcare systems, and they have already proven their effectiveness in handling large datasets and providing useful practical results [123].  
Optimization models can provide useful assistance, but they cannot fully replace human intervention. Interactions between people and computerized systems are critical in clinical decisions, and it is crucial to involve patients in the decision-making processes [124]. Similarly, human insights and personal preferences are also fundamental in decisions concerning hospital management [125].  
Understanding the logic behind the results that a computer provides is especially important in decisions where the lives of patients could be at stake. Under such circumstances, it is difficult to trust black-box procedures whose logic goes beyond human capabilities, and fostering the development of interpretable optimization could provide tremendously valuable tools.  
The analysis of near-optimal solutions, along with the generation of rationale that explains why these solutions were selected from a human point of view, would provide a set of solutions where each alternative includes an explanation that people can understand. These explanations would provide people with a solid foundation on which they could base their decisions.
## 3 Results
### Satellite Observations for Long-Range Transport
By complementing the disadvantages of the potential source contribution function (PSCF) model, the RTWC model can impose a weighted concentration by the source on each grid for calculating each average concentration of grid cells. In detail, the PSCF model can indicate the possibility of arriving at a detention place after a component with some pollutants passes through the source by a specific wind. However, it has a disadvantage in that it cannot apply concentration weight by time because movements of sources are generated by the same retention time even when source materials have some concentration differences [44; 45; 46; 47]. The RTWC model can impose a weighted concentration by the source,  
Figure 4: The concentration gradient in CO observed by S-5P. thus overcoming the disadvantage of the PSCF model. Figure 6 shows the results of the RTWC model realized for the ratio of CO/CO\({}_{2}\) during the total study period. It does not display the actual contribution because its concentration of RTWC by area is expanded by the number of trajectory segments whenever concentrations are distributed in each grid cell [46]. Therefore, it is difficult to understand the actual concentration by area with the concentrations shown in the figure only. However, the RTWC model suggests a relative importance of source locations measured in the study area [46]. In the case of interpreting the relative importance of source locations, the ratio of CO/CO\({}_{2}\) was introduced from the west and northwest of the study area. A similar result for the wind field was found, as shown in Figure 6.  
Figure 5: ERA Wind Field and Meteorological Field Data (European ECMWF). As a result of identifying external influxes in the study area and analyzing data from AOD and S-5 P, high concentrations of aerosol and CO from China (west of the study area) and ERA wind fields flowing from the west and northwest of the study area were confirmed. Additionally, the ratio of CO/CO\({}_{2}\) from the RTWC model had an external influence from the same direction as the ERA wind field. Conclusively, results of back-trajectory analysis using AOD, S-5 P, ERA wind field, and RTWC confirmed that external influxes during the event period affected the study area.
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
## 2 Optimization of Borehole Angles
### Crack Initiation Criteria  
This section discusses the determination of the crack initiation criteria by numerical simulation and theoretical analysis. Crack initiation and propagation under water jet action results in brittle failure. Therefore, the damage mode of a single type-I static micro-crack was used to investigate the propagation rule of cracks in coal under the action of a high-pressure water jet. To simplify the calculations, the following assumptions were made [33]:  
(1) The damage in the coal is caused by the propagation of cracks under the combined action of a high-pressure water jet impact force and quasi-static pressure.  
(2) New cracks are not produced during the coal-slotting process. Stress concentration and crack initiation will be caused around the cleat tip in the coal.  
(3) The interaction between the slot and cleats was not considered.  
(4) The hydro-mechanical coupling between water and coal is neglected.  
Therefore, using the type-I crack initiation criterion, the criterion for crack damage under the action of a water jet is expressed as follows:  
\[K_{\mathrm{I}}^{\infty}=K_{\mathrm{Ic}} \tag{1}\]  
When \(K_{\mathrm{I}}\geq K_{\mathrm{Ic}}\), the crack is initiated; \(K_{\mathrm{I}}\) can be calculated as follows:  
\[K_{\mathrm{I}}=(\sigma_{x}\cos^{2}\theta+2\tau_{xy}\sin\theta\cos\theta+\sigma _{y}\sin^{2}\theta)\sqrt{2\pi r} \tag{2}\]  
where \(K_{\mathrm{I}}\) is a type-I stress intensity factor; \(\sigma_{x}\), \(\sigma_{y}\) and \(\tau_{xy}\) denote the stress on the cleat; \(r\) is the distance from a node, close to the cleat tip; \(\theta\) is the angle between the cleat and \(\sigma_{y}\).
## 3 Results and Discussion
### Stability Study of the Analytes  
The hydrolysis of acetylsalicylic acid into salicylic and acetic acid is a phenomenon that had to be taken seriously into account in all analytical and preparative processes of the suggested formulation (honey/chocolate). In contrast, DIP generally exhibits stability.  
According to a recent study carried out with the aim of finding the solvent that ensures the greatest stability of ACA, acetonitrile and 1,4-dioxane were suggested, while methanol and ethanol were shown to create the greatest instability [47]. The degradation of ACA, in substituted and unsubstituted polyhydric alcohols, in contrast to its stability in acetonitrile with 1% formic acid solution, has also been recorded by other researchers [48; 49]. Accordingly, in aqueous solutions, ACA exhibits stability in a mixture with polyethylene glycol, while it is largely hydrolyzed in phosphate buffer at pH 7.0 and 7.4 and a temperature  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{**\%RSD**} \\ \cline{2-7}  & \multicolumn{2}{c}{**DIP**} & \multicolumn{2}{c}{**ACA**} & \multicolumn{2}{c}{**SAL**} \\ \cline{2-7}  & **AUC** & **Tf** & **AUC** & **Tf** & **AUC** & **Tf** \\ \hline Flow rate mL/min & 9.46 & 2.00 & 9.77 & 0.66 & 9.88 & 0.71 \\ (0.9, 1.0, 1.1) & 0.29 & 2.05 & 0.85 & 0.61 & 0.18 & 1.36 \\ \hline \multicolumn{7}{c}{Mobile phase A:B} & 0.51 & 9.87 & 0.48 & 0.41 & 0.38 & 0.35 \\ (66:34, 65:35, 64:36 \(v/v\)) & & & & & & \\ \hline \multicolumn{7}{c}{\(\lambda_{\text{max}}\) (nm)} & 0.265 & 0.048 & 0.37 & 0.1 & 0.82 & 0.058 \\ (284, 285, 286 \(\kappa\)xt 229, 230, 231 & 0.265 & 0.048 & 0.37 & 0.1 & 0.82 & 0.058 \\ \(\kappa\)xt 236, 237, 238 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Robustness test of the proposed analytical method.  
of 37 \({}^{\circ}\)C [33]. Degradation studies have also been performed on mixtures of water with propylene glycol and triethylene glycol diacetate with ethanol, diethyl ether and diethylene glycol (at various concentrations and temperatures with or without sonication) [50; 51]. Considering these reports, it was considered appropriate to conduct a further stability investigation of ACA and the two other analytes, SAL and DIP.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 3 Swarm Robotics Design and Simulation
### Swarm Robotics and Formation Control  
#### 3.4.1 Strategic Development Approach  
This section outlines a strategic development approach for swarm robotics applied to mining, where each subsequent model builds on its predecessor by integrating increasingly complex and efficient mechanisms. The process begins with a baseline model that serves as a foundational platform. The subsequent models, i.e., the ant, firefly, and honeybee models, are enhancements to this standard, each incorporating specialized roles, enhanced communication capabilities, and precise localization techniques that mimic behaviors observed in nature.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.7 Construction Year  
The assessment of building vulnerability is based on the construction year, which serves as an indicator of construction quality, design methodologies used, and seismic details included in the project. Old structures cannot be expected to function well because ancient construction procedures ignore the seismic details of modern building requirements [18]. Construction years provide information about building design regulations as well as design standards that are classified as low, moderate, and high. A study by [PERSON] and [PERSON] examined the effects of the construction year on seismic design codes in Albania, and the authors concluded that this existing classification technique could be used by modifying the thresholds based on the seismic standards for the region under consideration [18].
## 5 Conclusions  
This study is based on four widely used satellite-derived vegetation monitoring indices (EVI, FVC, GPP, and LAI) and proposes a novel vegetation index, the RSVI, constructed by considering the other four distinct ecological significance indices. The assessment focused on evaluating vegetation losses caused by aridity and the recovery period of vegetation after drought in Central Asia by employing the five indices. During drought years, vegetation experienced varying degrees of aridity, ranging from mild to severe, in arid, semiarid, and Mediterranean climates, while generally exhibiting mild aridity in wet and very wet climates. Vegetation that remained in a long-term state of mild drought exhibited the most pronounced response during drought events. Under moist climate conditions, the recovery period of vegetation was shorter, and there was a decreasing trend in the required recovery period when transitioning from arid to wet climates. Different vegetation types displayed characteristic recovery periods, with croplands showing the shortest recovery period and forests exhibiting the longest recovery period. The primary reason for vegetation loss in drought years is mainly attributed to the influence of climatic factors. The RSVI developed in this study demonstrates high sensitivity, correlation, and interpretability in dry-wet changing environments, showing great potential for assessing the impact of drought on vegetation.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16224189/s1](https://www.mdpi.com/article/10.3390/rs16224189/s1)]([https://www.mdpi.com/article/10.3390/rs16224189/s1](https://www.mdpi.com/article/10.3390/rs16224189/s1)), Figure S1: Two-dimensional scatter plots of vegetation in the 2019 Central Asian region within grids affected by drought in relation to temperature, precipitation, and TVDI changes; Figure S2: Two-dimensional scatter plots of vegetation in grids in the 2019 Central Asian region where no drought occurred in relation to temperature, precipitation, and TVDI changes.  
W.H.: Conceptualization, methodology, software, writing--review and editing; J.Z.: writing--review and editing, visualization, supervision, funding; J.G.: resources, supervision; Y.L.: conceptualization, software; L.L.: methodology, resources, formal analysis, supervision; C.H.: data curation, investigation; J.L.: validation, project administration; C.L.: data curation; X.M.: formal analysis, software; R.T.: conceptualization, validation, project administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Laboratory of Xinjiang Science and Technology Department (No. 2022D04009) and the Xinjiang 'Tianshan Yingcai' cultivation plan (No. 2023 SNGGGGCC004). We are grateful for the support provided by the autonomous region's joint industry-university-research postgraduate training base, China's Xinjiang Grassland Station.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 6 Conclusions and Future Work  
In a WSN, sensor nodes are typically deployed to monitor the environment and collect data. Trust-aware data gathering involves determining which nodes can be trusted to reliably collect and transmit data. Social class assignment is a mechanism used to classify sensor nodes into different groups based on their trustworthiness or reliability. Nodes in higher social classes are considered more reliable and are given higher priority in data-gathering tasks. The proposed algorithm addresses the challenge of finding an optimal social class assignment for sensor nodes. It introduces a variable-length encoding scheme to represent the social class assignment, allowing for flexibility in the number of social classes and their sizes. This enables the algorithm to adapt to different network conditions and requirements. The optimization problem is formulated as a multiobjective problem, simultaneously considering multiple conflicting objectives. Typical objectives in trust-aware data gathering include maximizing data reliability, minimizing energy consumption, and balancing the network load. The algorithm uses a combination of evolutionary particle swarm optimization, solution generation, and addition and deletion of rendezvous points to enable moving to an upper class or a lower class. Once the algorithm converges, it provides a set of multiobjective optimization metrics, namely, set coverage, hyper-volume, and the number of nondominated solutions, representing different trade-offs between the objectives followed by statistical analysis and selecting a suitable solution based on the specific requirements and priorities of the WSN. In an effort to confirm the proposed design's performance, a simulation was run to generate the time series of E2E delay, packet delivery ratio, trust, and energy consumption for each of the proposed designs. The SC-MOPSO set coverage was 0.06 over NSGA II compared with only a domination of 0.04 of NSGA-II over SC-MOPSO, while it showed competitive performance with NSGA-III. We concluded that VL-MOSCO is a promising approach for optimizing social class assignment in trust-aware data gathering in wireless sensor networks. By considering multiple objectives and using variable-length encoding, it offers flexibility and adaptability to different network conditions, ultimately improving the overall performance and reliability of the network.  
Conceptualization, K.C.; Methodology, M.A.S.; Validation, K.C.; Formal analysis, M.A.S.; Investigation, M.A.S.; Writing--review & editing, M.A.S. and K.C.; Supervision, R.J. and K.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.