esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Data
### Data and Solutions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Models/Methods** \\ \hline PPP model & static mode, conventional PPP model using undifferenced dual-frequency \\  & code and phase ionosphere-free linear combination \\   Signals & L1 and L2 for GPS; E1 and E5a for Galileo \\   Stochastic modeling & different weighting functions shown in Table 2 \\   Constellation & GPS, Galileo, GPS+Galileo \\   Cut-off elevation angle & 7\({}^{\circ}\) \\   Interval estimation & 30-s \\   Periods & one week: from 38 DoY to 44 DoY of 2021 \\   Reference frame & IGS14 \\   Orbit & sp3 CODE MGEX with 5-min intervals \\   Clock & clk CODE MGEX with 30-s intervals \\   PCO and PCV for satellite antenna &
\begin{tabular}{c} igs14.atx; for Galileo used model from GPS \\   Ionospheric delay \\ \end{tabular} \\  & & ionosphere-free linear combination \\  & a priori value: Saastamoinen model with GPT2 \\  & & estimated: wet component \\  & & mapping function: GMF \\  & & IERS convention 2010 \\   Ambiguities & Estimated float value with remaining bias as constant for arc \\   ISB between GPS and Galileo & Estimated as random walk: \(1.0\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods and models used. \begin{table}
\begin{tabular}{c c c c} \hline \hline  
for the second GPS+Galileo solution, hereinafter referred to as GE1, the lower precision for Galileo observations was applied.
## 1 Introduction
Explanations for how and why a model outputs what it does are crucial in overcoming these challenges [19]. For this reason, explainability and the related concepts of interpretability and transparency have become central issues of concern for ML in medicine over the last few years [20]. It has been argued that despite the existence of much evidence supporting their usefulness, ML-based systems are unlikely to be adopted in routine medical practice beyond a limited number of niche applications unless these challenges are addressed, most likely by having systems provide satisfactory explanations for their decisions [20; 21]. Unfortunately, secondary factors also complicate solutions, for instance, different applications usually have different interpretability and explainability needs [20] working against generalizable solutions that span a number of situations. Clinical Decision Support Systems (CDSS) are computer systems designed to assist in the delivery of healthcare, and ML is being exploited for their development. The explainability of such systems is a relatively new area of study and this work aims to present its application, benefits, gaps, and future opportunities by conducting a systematic literature review. Our hypothesis is that despite the plethora of ML-based CDSS, there is only a limited number of systems that have been specifically developed with explainability as one of their features, and that there are still challenges that need to be addressed. Future systems should be created according to current reported benefits and gaps. As a result, this study aims to first identify the state-of-the-art in explainable ML-based CDSS, in terms of the area of use and current prevalent methodologies, and then discover what benefits have been reported as a result of this combination and what the areas for improvement are. The remainder of the paper is structured as follows. Section 2 presents a background of CDSS, XAI, and the considerations of applying XAI to CDSS including the need for explainability, its application in medicine, types of explanations, the matter of interpretability vs. performance, and the needs of clinicians in terms of explainability. Section 3 describes our materials, methodology, and research questions. Section 4 presents findings and answers to the research questions. Section 5 discusses these findings, along with guidelines for the future implementation of explainable ML-based CDSS. Section 6 presents our conclusions.
###### Abstract  
The use of graphene oxide (GO) in combination with mesoporous materials has gained interest in the development of adsorbents. In this study, GO was impregnated into zeolite at three concentrations (ZGO2.5, ZGO5, and ZGO10) through a simple thermal process to enhance the adsorption of methylene blue (MB). Characterization of the resulting materials was performed using spectroscopic techniques such as UV-Vis and FT-IR spectroscopy, SEM, and EDS, confirming the presence of GO on zeolite. Batch experiments were conducted to evaluate their performance, analyzing contact time, pH effect, and adsorption kinetics. Pseudo-first-order, pseudo-second-order, and Elovich kinetic models were applied, and the adsorption mechanism was studied using Langmuir, Freundlich, Temkin II, and Dubinin-Radushkevich (D-R) isotherms at different temperatures. Optimal adsorption was achieved at 273 K, 100 mg L\({}^{-1}\) of MB, adsorbent mass of 100 mg, 250 rpm, and pH 5-9, with 90% removal efficiency after 70 min. The pseudo-second-order, Freundlich, and D-R models best described the process (R\({}^{2}\) > 0.98), suggesting a mixed physisorption-chemisorption mechanism. The maximum adsorption capacity from the D-R isotherm reached 119 mg g\({}^{-1}\) at 333 K. Thermodynamic studies showed that adsorption was a spontaneous and endothermic process. These findings highlight the potential of GO-impregnated zeolite as an effective adsorbent for MB.  
graphene oxide; zeolite; methylene blue; adsorption kinetics; thermodynamic studies 2025
# Towards Robust Representations of Spatial Networks Using Graph Neural Networks
## 4 Methods
### Learning on Heterogeneous Graph Representations of Spatial Networks
#### 4.2.1 Graph Structure  
The heterogeneous graph representation is one that has different types of nodes and edges. Training this representation using a neural model, we need to consider that there could be differences between the characteristics of the node and edge attributes such as the size and meaning. For example, with the node type street, we may consider the _length_ and _width_ of the street segment as input features, whereas, for buildings, we may consider the _age_ of the building, _number of levels, area_ as input features. The number of node classes being inferred could vary across node types. Hence, the problem will be to design an architecture that is able to efficiently handle these intricacies that exist in heterogeneous graph representations [28]. We discuss a suitable neural architecture in Section 4.2.3.
## 5 Practical Engineering Applications  
The Daye iron deposit occurs in a fractured zone. In general, the contact-zone between rock and ore is unstable with joint development, and the ore body aquifer has characteristics of karstification and fracture development, causing difficulty with roadway excavation and support. After consulting published literature and performing on-site surveys, we selected 10 groups of roadway surrounding rocks from the Jianlinshan mining area of the Daye iron mine as samples for discrimination, investigation, collection, and counting of the stability evaluation indexes, and built judgement samples of roadway surrounding rocks from the Daye iron deposit, as shown in Table 6.  
Figure 6: Evolution of fitness curve.  
Figure 7: Training effect of \(S\alpha S-PNN\) and the corresponding error graph.  
By incorporating the statistics of the indiscriminate samples in Table 5 into the concept lattice of roadway surrounding rocks stability and the prediction model of the improved probabilistic neural network, this paper reports the prediction result of laneway surrounding rock stability shown in Figure 8 and the prediction accuracy rate for all prediction models shown in Table 7. The results show that the accuracy rate of prediction is 90%, which is coincident with the actual engineering situation. The accuracy rate of prediction result before index reduction is 80%, and that of non-improved probabilistic neural network prediction model is 70%, and that of mean value method is 50%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Roadway**} & **Rock Quality** & **Uniaxial** & **Integrity** & **Groundwater** & **Joint** & **Grade** \\  & **Designation** & **Compressive** & **Coefficient of** & **Seepage(/L/(min-10 m))** & **Condition** & **Grade** \\  & **(RQD)** & **Strength (Rc/MPa)** & **Rock Mass** & & & \\ \hline
1 & 55 & 98 & 0.5 & 10 & 5 & III \\
2 & 40 & 60 & 0.3 & 12 & 5 & IV \\
3 & 20 & 20 & 0.3 & 12 & 3 & V \\
4 & 96 & 135 & 0.8 & 9 & 9 & I \\
5 & 50 & 100 & 0.5 & 10 & 6 & III \\
6 & 42 & 63 & 0.5 & 10 & 3 & III \\
7 & 100 & 150 & 0.7 & 10 & 8 & I \\
8 & 78 & 125 & 0.6 & 10 & 5 & II \\
9 & 30 & 80 & 0.6 & 20 & 6 & IV \\
10 & 25 & 35 & 0.3 & 20 & 2 & V \\ \hline \hline \end{tabular}
\end{table}
Table 6: Judgement samples of roadway surrounding rocks from the Daye iron mine.  
Figure 8: Prediction results of roadway surrounding rock stability.  
In summary, the prediction model of roadway surrounding rock stability, based on concept lattice and improved probabilistic neural network, can better satisfy the practical requirements of engineering, and provide a new method for the prediction of roadway surrounding rock stability.
## 1 Introduction
The main contributions of this study can be summarized as follows:  
* _A framework for robust and interpretable MI classification:_ We propose and validate a novel framework that, for the first time, integrates channel-wise Monte Carlo dropout (MCD) for uncertainty-aware robustness with LayerCAM for neurophysiologically relevant interpretability, addressing two critical challenges in BCI simultaneously. * _Comprehensive evaluation across multiple architectures and conditions:_ We conduct a rigorous evaluation of our framework on a 52-subject dataset, testing its efficacy across three distinct deep learning architectures (_ShallowConvNet_, _EEGNet_, _TCNet Fusion_) and under varying channel montage densities (8, 16, 32, and 64 channels). * _Statistically validated performance improvement:_ We provide statistically significant evidence (\(p<0.05\)) that our MCD-enhanced models consistently outperform their baseline counterparts, with particularly notable gains for low-performing subjects, thereby enhancing both accuracy and inter-subject consistency. * _Enhanced interpretability through CAMs:_ We demonstrate through LayerCAM visualizations that our uncertainty-aware approach transforms diffuse, difficult-to-interpret spatial attention maps into focused, neurophysiologically plausible topograms, significantly improving model transparency and clinical trust. The remainder of this paper is organized as follows: Section 2 reviews related work in the field of EEG-based BCI systems. Section 3 details the methods, including baseline feature extraction with _FBCSP_ and the implementation of three DL models (_ShallowConvNet_, _EEGNet_, and _TCNet Fusion_) tailored to EEG signal characteristics, alongside channel dropout and CAM integration. Section 4 outlines the experimental setup, covering the MI-EEG dataset, preprocessing, montage reduction, and evaluation metrics for robustness and interpretability across subject groups. Section 5 presents results and discussion, analyzing classification accuracy, dropout effects on stability, and LayerCAM-based spatiotemporal patterns. Finally, Section 6 concludes with key findings and future directions for uncertainty-aware MI classification.
## 4 Discussion  
### Spatiotemporal Variations in Cropland CWUE  
In this study, we analyzed the spatiotemporal distribution of cropland CWUE in Henan Province from 2001 to 2019. Elevated multi-year average values of the CUE and WUE\({}_{\rm NPP}\) were predominantly observed in the western region, while lower values were concentrated in the northeastern area. These spatial patterns align with the distributions and trends of the cropland WUE\({}_{\rm NPP}\) and CUE reported by [PERSON] et al. [53] and [PERSON] et al. [54]. The multi-year average values of the WUE\({}_{\rm GPP}\) were mainly higher in the central and southwestern regions, which is consistent with previous studies on the spatial distribution and dynamics of the WUE\({}_{\rm GPP}\), GPP, and ET in Henan Province [55]. Additionally, the present study found that cropland areas exhibiting significant increases in both the GPP and ET constituted the majority of the total cropland, corroborating the results of [PERSON] et al. [36].  
[PERSON] et al. [56] reported that the GPP values of cropland in Asian terrestrial ecosystems ground monitoring stations ranged around 1209.3 \(\pm\) 670.5 g C m\({}^{-2}\) y\({}^{-1}\), within which the multi-year mean GPP value of 861.23 g C m\({}^{-2}\) y\({}^{-1}\) observed in Henan Province in this study falls. [PERSON] et al. [34] monitored WUE\({}_{\rm GPP}\) values at the Jinzhou Agricultural Ecosystem Field Experiment Station in Shenyang, reporting a range of 2.1-3.6 g C m\({}^{-2}\) mm\({}^{-1}\) and a multi-year mean of 2.8 g C m\({}^{-2}\) mm\({}^{-1}\), closely matching the multi-year mean WUE\({}_{\rm GPP}\) of 2.93 g C m\({}^{-2}\) mm\({}^{-1}\) found for cropland in Henan Province here. [PERSON] et al. [57] observed ET processes at the Duolun cropland site, recording an ET value of 327.6 mm y\({}^{-1}\), which is very close to the multi-year average ET of 348.05 mm y\({}^{-1}\) reported for Henan Province cropland in this study. Additionally, [PERSON] et al. [32] systematically monitored the annual mean WUE\({}_{\rm GPP}\) values of four crops, soybean (1.92 \(\pm\) 0.52 g C m\({}^{-2}\) mm\({}^{-1}\)), corn (2.48 \(\pm\) 0.69 g C m\({}^{-2}\) mm\({}^{-1}\)), winter wheat (2.00 \(\pm\) 0.39 g C m\({}^{-2}\) mm\({}^{-1}\)), and rice (1.88 \(\pm\) 0.63 g C m\({}^{-2}\) mm\({}^{-1}\)), all of which closely align with the multi-year average WUE\({}_{\rm GPP}\) value of 2.8 g C m\({}^{-2}\) mm\({}^{-1}\) found in this study.
# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
## 2 Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Math Symbol** & **Description** \\ \hline  & **Time-Independent Variables** \\ \hline \(S\) & The service provider \\ \(V=\{1,\dots,n\}\) & The set of \(n\) users \\ \(T\in\mathbb{N}_{>0}\) & The number of rounds in the simulation \\ \(Q\) & The total quality of service after \(T\) rounds \\ \(\mathcal{A}=\{D,C\}\) & The action set \\ \hline  & **Functions** \\ \hline \(f:V\rightarrow\mathbb{R}\) & The quality function \\ \(S:\mathbb{R}\times\mathbb{R}\rightarrow\mathcal{R}\) & The success function \\ \(G:\mathbb{R}\times\mathbb{R}\rightarrow\mathbb{R}\) & The payoff function for successful rounds \\ \(B:\mathbb{R}\times\mathbb{R}\rightarrow\mathbb{R}\) & The payoff function for unsuccessful rounds \\ \hline  & **Time-Dependent Variables** \\ \hline \(r_{i}\) & The resource produced by user \(i\) \\ \(v_{i}\in\mathbb{R}\) & The value associated to disclosing \(r_{i}\) \\ \(c_{i}\in\mathbb{R}\) & The cost associated to transmitting \(r_{i}\) \\ \(p_{i}\in\mathbb{R}\) & The privacy leaked when disclosing \(r_{i}\) \\ \(a_{i}\in\mathcal{A}\) & The action of user \(i\) \\ \(A_{+}\subseteq V\) & The set of contributors \\ \(q\in\mathbb{R}\) & The service quality \\ \(\tau\in\mathbb{R}\) & The quality requirement \\ \(G\in\mathbb{R}\) & The payoff for a successful round \\ \(B\in\mathbb{R}\) & The payoff for an unsuccessful round \\ \(U_{i}\) & The utility of user \(i\) \\ \hline  & **Scenario: Smart Grid** \\ \hline \(\pi_{i}\in\mathbb{R}\) & The energy production of household \(i\) \\ \(\beta_{i}\in\mathbb{R}\) & The baseline consumption of household \(i\) \\ \(\sigma\in\mathbb{R}\) & The energy surplus \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mathematical notation, in order of appearance. The individual goal of the users is to maximize their individual utility over \(T\) rounds (see Table 2):  
\[U_{i}= \begin{cases}G(\tau,q)&\text{if }q\geq\tau\\ -B(\tau,q)&\text{if }q<\tau\end{cases} - \begin{cases}c_{i}&\text{if }a_{i}=C,\\ 0&\text{otherwise.}\end{cases} \]  
The social goal, i.e., the goal of the service provider, is to maximize the total quality of the service over \(T\) rounds \(Q=\max\sum_{t=1}^{T}f(A_{+})\). The model relies on the following assumptions:  
* Users contribute their resources to a central entity, which uses them to provide a service. * Contributions cannot be doctored, e.g., to reduce the cost. * Users are not allowed to communicate with each other; this is generally the case for simple devices such as smart meters. * The system implements a privacy-preserving resource-management algorithm that optimizes the use of the resource.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Pest Control  
This service includes a UAV equipped with specialized nozzles intended for the controlled dispersion of biological and chemical pesticides. This solution prevents humans from venturing into potentially dangerous, remote areas. The timely detection and intervention facilitated by the UAV's capabilities allow for early treatment of pests while their population is still limited, consequently minimizing the quantity of environmentally harmful pesticides required for effective pest control.
## 3 Principle of the Proposed Eios-TPS  
Under the same transmitted power requirement, there exist infinitely many solutions for the combination of \(D_{1}\), \(D_{2}\), and \(\Phi\). The current stress depends not only on \(D_{1}\), \(D_{2}\), and \(\Phi\) but also on the voltage conversion ratio \(k\). For a given transmitted power p and k, the objective is to determine the combination of \(D_{1}\), \(D_{2}\), and \(\Phi\) that minimizes the current stress. This problem is a constrained optimization problem, where the equality constraint enforces transmitted power consistency, and the inequality constraints include the ZVS condition and mode-selection restrictions. To solve this problem, an enhanced integrated optimization strategy based on TPS (EIOS-TPS) is proposed in this paper. Karush-Kuhn-Tucker (KKT) conditions and the Lagrange multiplier method (LMM) are applied to address the formulated optimization problem. The resulting solution space is then further refined to extract the optimal trajectory, which ensures ZVS for all switches over the entire load range while simultaneously minimizing current stress.
## 1 Introduction
* The examination of DR's impact on pollution resulting from generation units and full capabilities of distributed energy resources has not been performed [7; 8; 9; 10; 18; 19; 20; 21; 25; 26; 27; 29; 30; 37]. * No mathematical modeling of DR schemes based on DRE, DRP, and DRPE in an efficient manner, simultaneously, has been implemented [7; 8; 9; 10; 18; 19; 20; 21; 22; 26]. * No simultaneous optimization of a multi-carrier energy system including thermal and electrical energies is used [13; 14; 27; 29; 30; 31; 32; 33; 34; 38; 39]. In this paper, an EMS has been proposed based on pollution and cost reduction of neighborhood H-MGs. In the proposed algorithm, each H-MG can form a coalition with each other and cooperatively exchange power. Depending on the objective functions of each H-MG, they can share their power and price with other players on the market, based on their DP strategies. The presence of ES and Electric Vehicle (EV), while enhancing reliability, allows the H-MGs to have a significant impact on emission reduction, in addition to maintaining their independence. EMS has tried to achieve its goals by means of a DR strategy applied at three levels: DRP, DRE and, DRPE. It should be noted that the DR schemes applied in this paper belong to the time-based programs. The major contributions of this paper can be summarized as follows:  
* It provides a comprehensive and flexible DR strategies based on price, emission, and price-emission. By DRP, DRE, and DRPE, the H-MGs' owners are able to choose dynamically the most appropriate DR programs based on their appliances situations. * It proposes a multi-objective EMS based on optimization methods and DR strategy to enhance energy independence, decrease air pollution and costs for subscribers, as well as gaining more productivity of non-dispatchable units. * It provides a solution to achieve the collective consumers' payoffs, despite the presence of players with different levels of ownership and goals. The remainder of this paper is organized as follows: In Section 2, we briefly describe the general idea. In this section, the network under study and the primary assumptions are specified. Then, in Section 3, the structure of the proposed algorithm is presented. In Section 4, the mathematical formulation of problem is introduced. Finally, in Section 5, the results obtained from the proposed approach are shown, and conclusions are given.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 2 Theoretical Method for Determining Prestress Loss of BFRP Tendons
### Prestress Loss from the Relaxation of Prestress Tendons  
For BFRP tendons, plastic deformation appears with time under high stress or constant tensile stress. Therefore, the stress in BFRP tendons reduces with time, given the unchanged length of BFRP tendons, resulting in the relaxation of BFRP tendons. In addition, the deformation of BFRP tendons also increases with time if the stress of BFRP tendons keeps the same, which is the creep of BFRP tendons. For cross-tensioned concrete pavements, both the relaxation and the creep of BFRP tendons can give rise to the prestress loss [23; 24], as shown in Equation (3).  
\[\sigma_{\text{\tiny{IA}}}=r\sigma_{\text{\tiny{con}}} \tag{3}\]  
where \(r\) is the relaxation loss rate. The prestress loss resulted from the relaxation of BFRP tendons can be calculated with Equation (4) on the basis of the relaxation mechanism of BFRP tendons.  
\[\sigma_{\text{\tiny{IA}}}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{4}\]  
where \(\sigma_{1}\) is the prestress loss arising from the creep of the BFRP tendon, and can be obtained with Equation (5); \(\sigma_{2}\) is the prestress loss caused by fiber straightening, relating to the pultrusion process of the BFRP tendon, being around \(0.01\sigma_{\text{\tiny{con}}}\) to \(0.02\sigma_{\text{\tiny{con}}}\); and \(\sigma_{3}\) is the loss resulted from the relaxation of the prestressed fiber-reinforced polymer tendon itself and is related to the type of the fiber.  
\[\sigma_{1}=\frac{\
u_{\text{\tiny{r}}}\sigma_{\text{\tiny{con}}}E_{\text{r}}} {E_{\text{f}}} \tag{5}\]  
where \(\
u_{\text{r}}\) is the volume content of the resin in BFRP tendons, and \(E_{\text{f}}/E_{\text{f}}\) is the ratio of the elastic modulus of resin to the elastic modulus of filament of BFRP tendons.