esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### SD and SWE Distribution and Variation Characteristics in the Irtysh River Basin  
Based on the simulation results of the Noah-MP model, the annual maximum snow depth and snow water equivalent are calculated, and the spatial distribution of annual average maximum snow depth and snow water equivalent in the Irtysh River Basin is given (Figure 4). It can be seen from Figure 4 that the annual average maximum snow depth and snow water equivalent simulated based on CMFD and ERA-Interim data have good consistency in spatial distribution. Both snow depth and snow water equivalent are high in the north and low in the south of the basin. This spatial distribution feature is consistent with the topography of the basin. In the north of the basin, the altitude is high and the temperature is relatively low, which is conducive to the accumulation of snow. In the south of the basin, the altitude is relatively low, the temperature is relatively high, and the snow is easier to melt. Based on CMFD and era interim data, we also give the spatial distribution characteristics of the annual average precipitation in the basin (Figure 5). It can be seen from Figure 5 that the precipitation in the north of the basin is much higher than that in the south. High altitude and higher precipitation are the main reasons for the higher snow depth and snow water equivalent in the north of the basin than that in the south.  
When analyzing the temporal variation characteristics of snow cover, because the accuracy of snow depth based on ERA-Interim simulation is slightly lower than that based on CMFD simulation, only the simulation results based on CMFD data are selected. In addition to the average maximum snow depth (SD\({}_{\text{max}}\)) and snow water equivalent (SWE\({}_{\text{max}}\)), we also selected the average snow days, the average start time of snow accumulation (STSA), and the end time of snow melting (ETSM) to analyze the variation characteristics of snow from 2001 to 2017. The linear slope and M-K test were used to determine the trend of these time series (Figure 6). As can be seen from Figure 6, the maximum snow depth, snow water equivalent, and snow days in the Irtysh River Basin showed an insignificant decreasing trend from 2001 to 2017. The start time of snow accumulation was delayed, but  
Figure 4: Annual average maximum snow depth and snow water equivalent from 2001 to 2018 based on Noah-MP simulations. (**a,c**) represent SD and SWE with CMFD as the drive, (**b,d**) represent the results obtained with ERA-Interim as the drive.  
the change trend was not significant, while the end time of snow melting was significantly advanced.
# Spatiotemporal Dynamics and Obstacles of the Multi-Functionality of Land Use in Xiangxi, China
## 2 Data and Methods  
### Research Area  
Xiangxi (full name: Xiangxi tujia and miao Autonomous Prefecture) is located in the northwest part of the Hunan province (Figure 1). It is bordering the Hubei, Guizhou, and Chongqing provinces. It is the bridge connecting the central and western regions, and the link of communication between the Han and minority nationalities. Geographically, Xiangxi lies between 109\({}^{\circ}\)10\({}^{\prime}\)-110\({}^{\circ}\)22.5\({}^{\prime}\) E and 27\({}^{\circ}\)44.5\({}^{\prime}\)-29\({}^{\circ}\)38\({}^{\prime}\) N. The total area of the region is approximately 1.547 million hm\({}^{2}\), accounting for 7.3% of the Hunan province. It has seven counties and one city under its jurisdiction (Fenghuang county, Luxi county, Huayuan county, Guzhang county, Baojing county, Yongshun county, Longshan county, and Jishou city--a county-level city). Xiangxi is a typical winding territory of Wuling mountain, with high altitude terrain in the northwest, and low terrain in the southeast. Ethnic minorities live in compact communities in Xiangxi. In 2017, 80.73% of the total population of Xiangxi belonged to ethnic minorities, mainly Tujia and Miao.Xiangxi, with a relatively backward economy, which is one of the 14 areas included in the national pilot project for poverty alleviation and is currently in a critical period of new rural construction and modern industrial development. From 2013 to 2017, the economic and social development in Xiangxi increased rapidly, with an average annual GDP growth rate of 7.82%, a per capita GDP growth rate of about 7.26%, and an increase in the urbanization rate from 38.8% to 44.97%. The regional ecological environment is superior, with 70.24% forest coverage in 2017. The land use in Xiangxi is dominated by agriculture, with the area of land used for agriculture reaching 1.4118 million hm\({}^{2}\) (accounting for 91.26%), and an area of 0.0555 million hm\({}^{2}\) has been used for construction (accounting for 3.59%). Driven by economic interests, the land use structure has changed significantly, and the area of land used for construction has increased dramatically. For example, the total urban land in 2016 was 961.34 hm\({}^{2}\), which represented an annual increase of 15.8%. Therefore, the efficient use of land in Xiangxi currently faces two major problems. First, the agricultural land area is large but the production efficiency is low. Second, urban construction is limiting the space available for production and ecology, and the contrast between land supply and land demand is increasingly prominent. Therefore, it is necessary to improve the function of production in land use in Xiangxi, and to raise people's awareness that changes in the structure of land use lead to changes in land function.
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.3.1 Overall Siamese Framework  
The core of the proposed model is a two-tower Siamese network. Both towers share the same weights and consist of two main components: a base sentence embedding model and a sequential head model.  
1. Input Processing: Job descriptions and candidate resumes are independently fed into their respective towers.
2. Base Embedding: A pretrained Sentence Transformer model generates initial contextual embeddings for segments of the input text.
3. Sequential Head: A sequential model (GRU, LSTM, or Transformer) processes the sequence of segment embeddings generated by the base model to capture higher-level sequential or contextual information within the document.
4. Final Embedding: Each tower outputs a final fixed-size vector representation (embedding) for the corresponding job description or resume.
5. Similarity Learning: The network is trained using a triplet loss function, which compares the embeddings of an anchor (job description), a positive sample (matching candidate resume), and a negative sample (non-matching candidate resume).  
This framework allows the model to learn representations sensitive to the nuanced similarities and differences relevant for job-candidate matching [13; 16; 17].  
Figure 1 presents the end-to-end workflow of the proposed recruitment-matching pipeline. Raw recruitment data are (i) cleaned of HTML artefacts, (ii) anonymized, and (iii) split into training, validation, and test subsets. Each document is encoded with a multilingual Sentence Transformer, after which segment embeddings are processed by one of three sequential heads (GRU, LSTM, or Transformer, all with 256 hidden units). Job description and resume vectors pass through weight-sharing Siamese towers optimized with a triplet loss objective (margin 0.4). Finally, ranking metrics (Top-\(K\) Accuracy, Mean Reciprocal Rank) and t-SNE visualizations are produced; the dotted feedback arrow denotes optional iterative retraining.
###### Abstract  
The paper aims to study a multi-period maximal covering location problem with the configuration of different types of facilities, as an extension of the classical maximal covering location problem (MCLP). The proposed model can have applications such as locating disaster relief facilities, hospitals, and chain supermarkets. The facilities are supposed to be comprised of various units, called the modules. The modules have different sizes and can transfer between facilities during the planning horizon according to demand variation. Both the facilities and modules are capacitated as a real-life fact. To solve the problem, two upper bounds--(LR1) and (LR2)--and Lagrangian decomposition (LD) are developed. Two lower bounds are computed from feasible solutions obtained from (LR1), (LR2), and (LD) and a novel heuristic algorithm. The results demonstrate that the LD method combined with the lower bound obtained from the developed heuristic method (LD-HLB) shows better performance and is preferred to solve both small- and large-scale problems in terms of bound tightness and efficiency especially for solving large-scale problems. The upper bounds and lower bounds generated by the solution procedures can be used as the profit approximation by the managerial executives in their decision-making process.  
Keywords:maximal covering location problem; capacitated facility; modularity; multi-period; Lagrangian decomposition heuristic +
Footnote †: journal: Applied Sciences
## 3 PDM and Parameter Identification  
This section establishes the PDM, including a flexible dynamic model and a servo system model. At the meantime, an innovative RL-based parameter identification method is illustrated.
## 4 Hardware Circuit Design
### Voltage Stresses of Switches and Diodes  
The voltage across the switches \(S_{1a}\) and \(S_{1b}\) during the turn-off period can be found from states 9 and 1, respectively, as follows:  
\[V_{ds1a}=V_{ds1b}=V_{\text{C4}}=\frac{V_{d1}}{1-D_{a}}=\frac{16.22}{1-0.575}=38.17 \text{V} \tag{31}\]  
The voltages across the switches \(S_{2a}\) and \(S_{1b}\) during the turn-off period are given by states 3 and 1, respectively, as follows:  
\[V_{ds2a}=V_{ds2b}=V_{\text{C5}}=\frac{V_{d2}}{1-D_{b}}=\frac{7.67}{1-0.572}=18 \text{V} \tag{32}\]  
In state 2, the voltage across diodes \(D_{1}\) and \(D_{3}\) are as follows:  
\[V_{D1}=V_{\text{C1}} \tag{33}\]  
\[V_{D3}=V_{\text{C3}} \tag{34}\]  
Substituting the results of (13) and (16) into (33) and (34) yields  
\[V_{\text{C1}}=V_{\text{C3}}=\frac{(n+1)}{1-D_{a}}V_{d1}+\frac{(n+1)}{1-D_{b}} V_{d2}=224.34\text{V} \tag{35}\]  
The voltages across the diodes \(D_{2}\) and \(D_{4}\) in state 8 are as follows:  
\[V_{D2}=V_{\text{C1}}=224.34\text{V} \tag{36}\]  
\[V_{D4}=V_{o}-V_{\text{C2}}-V_{\text{C3}}+nV_{d2}=71.4\text{V} \tag{37}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Specification** & **Name or Value** \\ \hline System operation mode & Continuous conduction mode (CCM) \\ \hline First input voltage (\(V_{d1}\)) & 16.22 V \\ \hline Second input voltage (\(V_{d2}\)) & 7.67 V \\ \hline Rated output voltage (\(V_{o}\)) & 400 V \\ \hline Rated output current (\(l_{o,rated}\))/power (\(P_{o,rated}\)) & 68.8 mA/27.52 W \\ \hline Minimum output current (\(l_{o,min}\))/power (\(P_{o,min}\)) & 17.2 mA/6.88 W \\ \hline First duty cycle (\(D_{a}\)) & 0.575 \\ \hline Second duty cycle (\(D_{b}\)) & 0.572 \\ \hline First duty frequency (\(f_{s}\))/period (\(T_{s}\)) & 100 kHz/10 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 4: System specifications.
## 3 Results
### Comparative Analysis
#### 3.2.3 Distribution of Feature Points Under Different Parameters  
The present study compares feature extraction methods using GF-3 and Sentinel-1 data, as shown in Figures 7 and 8. Panels a and b demonstrate the feature point maps obtained by the SAR-SIFT algorithm with manually set threshold t, panel c shows the feature point map obtained without threshold response function, panel e shows feature points obtained by adding the SUSAN algorithm, and panel d show feature points obtained by applying Laplacian operator on multi-scale extrema. This section shows the extraction results of feature points under different parameters and those obtained by the algorithm in this paper. In Figure 7a-d, feature points are distributed not only in edges and corners, but also in the center of the road or the black area of the rice field. These feature points in the center of the road and the black area of the rice field are pseudo-feature points. For subsequent matching, matching accuracy will be reduced. Figure 7e shows the feature points extracted in this paper. The feature points are mainly distributed in the corners of the image, and the pseudo-feature points are not obtained in the black areas in the center of the road or the center of the rice field. In the experiment of Sentinel-1 data, the result is similar to that of Gaofen-3. The other algorithms have a large number of pseudo-feature points, which greatly affect the accuracy of subsequent matching.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimensionality** & \(3\times 8\times 8\) & \(3\times 8\times 6\) & \(3\times 6\times 8\) & \(3\times 6\times 6\) \\ \hline NCM & 428 & 387 & 424 & 437 \\ RMSE & 1.31 & 1.44 & 1.46 & 1.53 \\ TIME (seconds) & 21.93 & 21.66 & 21.57 & 21.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Comparison of Descriptors with Different Dimensionalities.  
Figure 7: Feature point extraction results of GF-3 data.
# Assessing Water Infiltration and Soil Water Repellency in Brazilian Atlantic Forest Soils
###### Abstract  
This study presents the results of the soil hydraulic characterization performed under three land covers, namely pasture, 9-year-old restored forest, and remnant forest, in the Brazilian Atlantic Forest. Two types of infiltration tests were performed, namely tension (Mini-Disk Infiltrometer, MDI) and ponding (Beerkan) tests. MDI and Beerkan tests provided complementary information, highlighting a clear increase of the hydraulic conductivity, especially at the remnant forest plots, when moving from near-saturated to saturated conditions. In addition, measuring the unsaturated soil hydraulic conductivity with different water pressure heads allowed the estimation of the macroscopic capillary length in the field. This approach, in conjunction with Beerkan measurements, allowed the design better estimates of the saturated soil hydraulic conductivity under challenging field conditions, such as soil water repellency (SWR). This research also reports, for the first time, evidence of SWR in the Atlantic Forest, which affected the early stage of the infiltration process with more frequency in the remnant forest.  
Beerkan method; infiltration; forest restoration; soil water repellency
## 3 Results
### Summary of Interviews after Using SAR  
We interviewed patients about the advantages and disadvantages of using the SAR after they had completed their usage period. The attitude of patients towards the SAR and any changes in their acceptance were confirmed through three stages.  
First, patients wanted to ensure that the SAR alert was reliable. Patients reported being very surprised and excited when the SAR accurately suggested ways to improve the air quality or responded immediately to changes in air quality. The following patient responses are presented as examples:  
Participant A: \"When I start vacuuming, the SAR says, 'There's a lot of fine dust in the room, so keep the doors open to ensure good air quality'. Whenever I cook something over there (in the kitchen), the SAR says, 'The air quality is poor'. Then I thought, 'Oh, it's sensing it right now'.  
Participant B: \"Before SAR began telling us about the air quality, these days, even the elderlies can know what the weather is like. I can tell what the lowest temperature is and what the current temperature is by watching TV. When SAR gets it right, it amuses me or makes me laugh\".  
Second, once the reliability was confirmed, patients paid more attention to the alerts from the SAR. The continuous air quality notifications from the SAR helped COPD patients remain constantly aware of indoor air quality. Additionally, the positive and encouraging words and healthcare tips provided by the SAR induced COPD patients to listen to the messages.  
Participant D: \"SAR always tells me about the humidity, temperature, and things like drinking more water. Because it helps me to watch out for the indoor environment, I think it's a good thing to have\".  
Third, the SAR guided patients in implementing ways to improve air quality. As the SAR functions as a talking robot, it can relieve the boredom of the elderly because they feel a sense of intimacy, despite it being a robot. Therefore, if the SAR alerts the patient about  
Figure 6: Random forest model result—Increase in Node Purity.  
air quality, they are likely to follow the suggestions. Nevertheless, compliance depends on the individual.  
Participant E: \"The temperature in this room was quite hot when I turned on the heater, so the SAR said, 'It's too hot in the room, and you may feel heavy, so leave the window open for two minutes'. I would not have thought of that unless someone had said so, so I opened the front door and this window for about 10 min. Then the temperature dropped to 20 or 21 degrees. And then, when it gets cold, the SAR tells me to close the door again\".  
Participant C: \"When SAR gives an alarm about air quality, I change the air quality in the room. When the SAR says it's hot, I increase ventilation according to its instructions\".
## 1 Introduction
While the above-mentioned features, along with a multitude of others like grid-load and available generation [5; 7], explain a significant portion of the DAM's variance, they nonetheless fail to capture the impacts of technical price drivers. Lagged price features, which consist of current and past prices, inadvertently capture the impacts of technicals [8]. However, as they are an amalgamation of all historically available information, of which technicals are just a portion, it is difficult for models to identify technical signals from lagged prices. Hence, although researchers have used lagged prices to forecast the DAM [9; 10], the identification of new technical features is nevertheless required to capture the residual impacts of technicals. To capture the impacts of technicals, an approach to forecasting future price movements using historic market data is needed. Technical analysis (TA) [4], widely used by practitioners to identify investment opportunities across financial markets, offers such an approach. Rooted in the theories of behavioural finance [11], TA, as an analysis methodology, focuses on analysing statistical trends in historical market data to forecast future price movements. TA was established around three assumptions: prices move in trends; history repeats itself; studying price fluctuations allows the prediction of future shifts in demand and supply [4]. In short, TA assumes that technicals form reoccurring patterns in market data which careful analysis can identify and predict. One of the ways in which TA identifies emerging price patterns is using technical indicators (TIs). TIs transform market data using various formulas. By transforming the data, TIs can facilitate the identification of complex price patterns; signalling when securities are overbought or oversold, when prices deviate from a central trend, or when they are near support and resistance lines (levels that prices bounce off of and break through). Moreover, in conjunction with price charts, TIs can provide leading indications of future price movements, offering additional explanatory power to models forecasting financial time series. Numerous studies, such as [12; 13; 14], have extensively demonstrated the explanatory power of TIs across stock markets. Given that technicals result from universally exhibited behavioural biases among investors, such studies motivate our examination of TIs with the DAM. Summarising the principle contribution of our paper, to the best of our knowledge, we are the first to evaluate the explanatory power of TIs in DAM price forecasting. We demonstrate that the simple inclusion of TI features can significantly reduce linear, ensemble, and deep model regression forecast errors, on average reducing the root mean squared error (RMSE) by 3.28% and the mean absolute error (MAE) by 3.32%. Describing the structure of this paper, we introduce our examined TIs, modelling approaches, and case study in Sections 2.1-2.3 respectively. Subsequently, in Section 3 we present and evaluate our results. Finally, in Section 4 we discuss possible extensions to our research.
## 3 Results and Discussion
### Cytotoxicity  
The investigated materials underwent cytotoxicity screening in order to evaluate their safe effects for human and animal health. In this context, considering that the exposure of these materials could occur through the dermal contact, fibroblast NIH-3T3 cells were utilized, and MTT test were carried out at three different exposure times.  
Data acquired evidenced that the materials, except CPNUT3, increased the redox mitochondrial activity of the tested cells time-dependently (Figure 9). CPNUT3 defined a weak inhibition of the activity, which was estimated as equal to 20%, only at the lowest exposure time considered. Indeed, although inhibitory effects were not detectable through the colorimetric cell viability assay, changes in morphology were by microscope observation. In fact, the cells appeared to result in closely interlinked changes in their cell bulk properties (Figure 10). This behavior could be due to the weak cell contact among fibroblasts and the materials, so much so that they were found to spread randomly with spindle-like morphology. This is in line with the observation that the surface of the material directly in contact with cells affect their morphology [27], while not altering their mitochondrial redox activity.  
Figure 8: Inhibition zones of samples tested in presence of _E. faecalis_ (+).  
Figure 9: MTT data on NIH-3T3 redox mitochondrial activity (RA%), acquired after the considered exposure’s time (1, 3, and 6 h). The values are expressed as mean \(\pm\) SD of three of three independent measurements.