esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Data
### Data Selection  
Our approaches rest upon the analysis and comparison of different devices as well as battery systems. Since we rely on outside data sources, we occasionally had to deal with missing signals, name changes, change of sampling rate, etc. Thus, we established a canonical data model to ensure reliability and efficiency. All signal names were standardized. We also normalized the battery signals to cell level, such that they can be compared to laboratory tests and valued at specific parameters. This also eases visualizations and comparisons.  
Not all sections are suitable for SOH estimation. If there are data gaps at the start of a section, the initial battery state (e.g., SOC) is difficult to determine. Also, sections with small DODs only provide limited information for capacity estimation. Thus, only sections without data gaps and with a minimum DOD are used for SOH estimation. Furthermore, we exclusively use operation/discharge sections. There are three reasons for this: firstly, charge sections usually are not logged, because the BMS is shut down when the vehicle is turned off. Since we want to establish a universal approach, it should be independent of the existence of charge sections. Secondly, the dynamics during operation and charging are very different (strong varying currents vs. constant currents). Capturing both in one model  
Figure 1: Typical usage profile of a bus that uses opportunity charging. Operation sections are shown in green. Charging times are marked blue, idle times are yellow and data gaps are red.  
adds unnecessary complexity to the model. Thirdly, this allows us to use charge sections with large DOD to evaluate the capacity estimations.  
There is a twofold split of the data (see Figure 3). Firstly, whole sections are partitioned into a training and a validation data set. The validation sections are not used during the training process in any way and solely serve validation and evaluation purposes. Secondly, the data in the training set are once more split into training and validation points. These \"training validation points\" are used to perform early stopping. This prevents overfitting by terminating the training process if the fit on this second set of validation points does not improve.  
Figure 2: Logged full cycles of the selected subfleet 3 (one bus per row).
###### Abstract  
This study aimed to evaluate the healing of function when repaired with Chitosan (CS) scaffold impregnated or not with Simvastatin (SIM) compared with CollaCote (CL) in goat premolar teeth. Root canal treatment was performed in 52 mandibular premolars followed by fural perforation induction. The perforation was repaired with CL, CS, or CS with SIM after leaving it untreated for 4 weeks. White mineral trioxide aggregate was carried into the fural site followed by a 2-3 mm resin-modified glass monomer. The perforation was left untreated, and the access cavity was left open without coronal filling in the control group. The animals were sacrificed after one and three months. Block sections of the premolars were prepared and examined histologically to evaluate the inflammation and type of healing. Hard tissue formation was found in CL, CS, and CS/SIM groups in both periods. At one month, no significant differences were detected among the experimental groups, whereas at three months, CS without SIM showed significantly better performance compared to CL and CS/SIM groups (\(p\) = 0.040). Therefore, repairing fural perforation with CS scaffolds shows desirable biological responses and healing characteristics in favor of bone regeneration at three months.  
perforation repair; chitosan; simvastatin; osteocl activity; bone formation Article
# Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves
## 3 Results
Although Gaussian PDA showed a slightly better fit (slightly lower MSE), Figure 8 (leave one out prediction) makes it evident that the sech PDA better explained BP. The results of Figure 10 may suggest that the sech PDA was a better fit for the pulse contour region, and was more relevant for estimating blood pressure [16]. The small MSE values come from having normalized the PPG pulse amplitudes to one, for both Gaussian PDA and sech PDA, for a fair comparison. Figure 8: Scatter plots for each subject data as test data by multiple regression using Leave One Out. (**a**) Result based on Gaussian PDA. (**b**) Result based on sech PDA. In Leave One Out, we estimated systolic blood pressure with an 8-s segment based on the multiple regression model learned with the other segments of the subject data, and we repeated the process to test all 60 segments data comprehensively. Each scatterplot contains 600 data points. For both plots, \(p=0.0000\). Figure 7: Scatter plots for each subject data as training data in multiple regression. (**a**) Result based on Gaussian PDA. (**b**) The result based on sech PDA multiple regression was performed with 60 simultaneous 8-s segments of continuous systolic blood pressure vs. PPG pulse decomposition features for each subject data. Each scatterplot contains 600 data points. For both plots, \(p=0.0000\). We also conducted a multiple regression using only the coefficient's ratio a2/a1, as suggested by [3]. Specifically, we conducted multiple regression with each subject data, for all ten subject data, to calculate a multiple correlation coefficient, and averaged the ten multiple correlation coefficients. For Gaussian PDA the multiple correlation coefficients gave 0.20 \(\pm\) 0.17 (mean \(\pm\) standard deviation), with the range 0.031, 0.50, and for the sech PDA, the multiple correlation coefficients gave 0.26 \(\pm\) 0.17 (mean \(\pm\) standard deviation), with range 0.034, 0.59. No statistically significant difference was found between the Gaussian PDA and the sech PDA results, but the low multiple correlation coefficient discourages the use of the coefficient's ratios for PDA-based BP estimation.
## 3 RUL Prediction Methodology
### First Detection Time, End-of-Life Time and RUL Prediction Time  
#### 3.3.1 First Detection Time (FDT) of Fault  
Based on the fault occurrence time, the bearing health condition during the whole lifespan can be divided into two stages: the healthy stage and the degradation stage. The change point of the bearing health state is the earliest time when the bearing fault is detected, which is defined as the first detection time (FDT) \(t_{FDT}\) and determined through identifying the FCFs based on the envelope analysis in Section 3.1.3.
## 3 System Implementation and Performance Evaluation
### Execution Procedure  
This study designed a user interface, a command line interface (CLI), and has designated six kinds of system instructions for the purpose of controlling and monitoring the system at any time. All instructions of the system are listed in Table 4. Users can access all functions of the system through this interface, such as an SQL command that is input via this interface. There are over 45 Java programming routines have been coded to accomplish the optimization of the integration of three proposed big data tools and a UML diagram of the code architecture is shown in Figure 14.  
Figure 12 shows the flow chart of a SQL query execution. In Figure 12, the administrator uses the user interface to monitor the status of each node in the server farm. Platform selection started to check the remaining amount of memory available instantly on every computing node, and then it chose the appropriate tool to normally proceed a dispatched job. The proposed approach platform selection was denoted as PS in the experiments. Once a job was finished, the results were stored back in HDFS and the whole process was terminated. This work presents a kind of first-come-first-serve (FCFS) manner to proceed with SQL queries. FCFS was applied to dispatch a single job to a single tool--Hive, Impala, or SparkSQL--denoted as FCFS-SP-Hive, FCFS-SP-Impala, or FCFS-SP-SparkSQL, respectively, in the experiments. Similarly, platform selection with FCFS, denoted as FCFS-PS, was also applied in the experiments where the actions in platform selection checked the remaining amount of memory in every single computing node.
## 2 Materials and Methods
### Datasets and Preprocessing  
Various complex and diverse factors, including pollutant emissions and vegetation cover, contribute to changes in ecological conditions [42; 43]. In response, our approach involved integrating socio-economic data and remote sensing products to establish a systematic framework for assessing alterations in ecological quality. It is important to note that all the datasets utilized in our study can be accessed by clicking on the Data Source column in Table 1. Specific links to the datasets can be obtained in the Data Availability Statement section located at the end of this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **The Role of Ecological Quality Assessment** & **Temporal Resolution** & **Spatial Resolution** & **Data Source** \\ \hline Gross domestic product (GDP) & Quantify the contradiction between urbanization and ecological protection & Yearly (1992–2019) & 1 km & Real GDP \\ Human density (HD) & Reveal the threat of population and population growth to the ecological environment & Yearly (2001–2020) & 1 km & WorldPop \\ Normalized difference & Quantify the degree of vegetation cover, reflect the growth trend and horizontal structure of vegetation & Yearly (2001–2019) & 1 km & MOD13A1 v061 \\ Annual mean temperature (Tmp) & Evaluate the climate suitability and urban heat island effects & 16 Days (2001–2020) & 500 m & National Tibetan Plateau \\ Annual precipitation (Pre) & Evaluate the climate suitability and land desertification & Monthly (2001–2020) & 1 km & National Tibetan Plateau \\ Air quality (PM2.5) & Monitor inhalable particulate matter to reflect atmospheric pollution & Monthly (2001–2020) & 1 km & Data Center \\ Gross primary productivity (GPP) & Measure the strength of vegetation photosynthesis and the amount of carbon sequestration & (2001–2020) & 1 km & ChinaHighPM2.5 dataset \\ Leaf area index (LAI) & Reflect vegetation growth trend and complexity of vertical structure & 16 Days (2001–2020) & 500 m & GLASS GPP \\ Land cover (LC) & Changes in LC drive the increase or decrease of ecosystem services & Yearly (2001–2020) & 500 m & GLASS LAI \\ Soil nutrient availability & Calculate soil nutrient content to reflect recovery capacity & Yearly (2008) & 10 km & Harmonized-world-soil-database-v12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction to indicator data and their attributes.
## 1 Introduction  
The environment plays a crucial role for living beings, providing essential resources for life and raw materials for industry, maintaining biodiversity, and regulating the climate. However, it is heavily impacted by industrial activities, which contribute to increased pollution, resource depletion, and climate change. So, in contemporary society, sustainable development stands as one of the foremost challenges given the mounting concern for reconciling the needs of the present generation with those of future generations without compromising the latter [1].  
Generated waste has a major negative impact on society in general and can lead to devastating consequences, such as health problems, loss of natural resources, and increased waste management costs, and it can even damage the economy itself [2].  
Textile industry companies are highly developed and distributed around the world but far from sustainable, especially given the technical waste generated as a result of their various processes [3]. The situation is increasingly complex compared with previous years, so over the years, the pressure has been increasing, especially from customers, given the need for ecological production that reduces environmental impacts [2].  
Textile consumption in the European Union (EU) ranks as the fourth most environmentally impactful sector, with notable repercussions regarding climate change. Furthermore, it ranks third in terms of water and soil resource utilization and fifth in primary raw material consumption and greenhouse gas emissions [4].  
Considering these burgeoning industrial processes, the heightened generation of waste throughout the product life cycle can be observed. The viable recourse for such waste centers on recycling when reusability is unfeasible or on promoting reutilization. However, only a marginal fraction of textile material waste is reutilized or recycled, whether during the manufacturing process or via consumer utilization [5].  
As the textile industry has limited tools to assess the environmental impact it generates, the objective of this study is to develop a decision support method utilizing the Excel software 2016 that enables producers to conduct an environmental impact analysis related to the production of their textile products. Furthermore, it facilitates consumers in decision making by creating an \"ecolabel\" with varying levels of environmental impact.  
This manuscript is divided into several sections. Firstly, a brief theoretical framework of the environmental impacts caused by the textile industry is presented, followed by a presentation of the decision support system developed by the authors and its later practical application. The results obtained from three case studies are also shown, and finally, the conclusions are presented.
## 4 Discussion
### Comparison with the Previous Studies  
A comparison of the current testing results with the previous studies is summarized in Figure 15. Previous studies have primarily focused on silty clay with plasticity index \(I_{\rm p}\) ranging from 11.7 to 14.43, which was significantly lower than the \(I_{\rm p}=37\) of the clay used in this study. These studies with silty clay soils showed that similar to clay soils, their mechanical behaviors were also highly dependent on \(w\). In the case of silty clay, the peak strength qmax exhibited varying trends (either increasing then decreasing ([PERSON] et al.) [37], continuously increasing ([PERSON] et al. [16]), or continuously decreasing ([PERSON] et al. [17])) as the water content shifted from the dry side to the wet side of \(w_{\rm opt}\). These discrepancies might be attributed to the differences in sample dry density. In contrast, the clay used in this study was controlled at one dry density level. The effect of dry density on the relationship between \(q_{\rm max}\) and \(w\) was not clear and should be studied in the future.
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
## 2 Methods  
To systematically investigate the influence of the source geometry on SATEM measurements, comprehensive forward modeling approaches incorporating both 1D and 3D frameworks are essential. In this study, we implement the 1D modeling methodology developed by [PERSON] et al. [25], which efficiently handles layered earth structures while accounting for source geometry variations. For more complex geological scenarios requiring full 3D representation, we employ the advanced computational framework proposed by [PERSON] et al. [22], which integrates finite volume spatial discretization with model order reduction techniques to achieve computationally efficient yet accurate 3D electromagnetic field simulations. This dual modeling approach enables the rigorous quantification of source geometry effects across varying levels of geological complexity and provides a robust foundation for subsequent analysis.
###### Abstract  
Organizations in the agroindustry sector face shorter delivery schedules; therefore, they are seeking ways to conduct more effective and less costly product distribution. Supply chain management efforts have focused on improving the flow of both products and information. Thus, the aim of this case study was to build a graphical user interface to enable decision-making based on quantitative information for a food distribution process. The problem to be solved was associated with the development of a technological solution to reduce and control variations in transportation times, delivery costs and capacities in cold and dry food distribution. An eight-step system for a dynamics methodology was used: (1) distribution process analysis, (2) route description, (3) variable and parameter description, (4) causal loop diagram creation, (5) current model simulation, (6) validation, (7) quantitative scenario construction based on key performance indicators, and (8) graphical user interface development. The main findings of this research were that the graphical user interface and simulation showed information that represented on average 56.49% of the total distribution costs regarding fuel and that maintenance and tire wearing costs had less of an impact on total costs, representing 9.21% and 3.66% of the total costs, respectively. Additionally, the technological solution--created for the supply chain in the distribution process against the background of changes in policies--makes it possible to improve decision-making based on different scenarios supported by a graphical interface according to key performance indicators. This solution could be used by different organizations who aim to reduce logistics and transportation costs. The main implications of this research were the available and organized information and the restructuring of the distribution process.  
system dynamics; distribution process; key performance indicators; logistics; supply chain; graphic interface +
Footnote †: journal: Applied Physics Letters  
1 June 2020; Accepted: 21 June 2020; Published: 28 June 2020
# Software Fault Localization Based on SALSA Algorithm
## 1 Introduction
In response to this, this paper proposes a software fault localization method based on the SALSA algorithm (SA-SBFL), which aims to address the tie problem that exists in traditional SBFL techniques. Additionally, we consider the control dependency relationships between statements during the execution of the tested program, enabling more precise fault statement localization. Specifically, first, we need to construct the Root Set Control Dependency Graph (RSCDG) based on the execution trajectory of the tested program. This graph is a part of the entire control dependency graph and includes the process of fault propagation. Then, we employ the Stochastic Approach for Link-Structure Analysis (SALSA) algorithm, leveraging the algorithm to analyze the importance of nodes and rank them, thereby achieving accurate localization. The main contributions of this paper are as follows:  
1. In response to the situation where previous researchers directly abstracted source code into graph structures, which often included a lot of unimportant information, this paper proposes the Root Set Control Dependency Graph (RSCDG). The RSCDG is a part of the control dependency graph, which not only reduces the computational burden on the computer but also contains key information related to the propagation of software faults. 2. This paper proposes SA-SBFL, a method based on the principles of the SALSA algorithm, which analyzes the statement link relationships in software fault propagation to achieve accurate fault localization. Additionally, SA-SBFL can effectively address the tie problem present in traditional SBFL techniques. 3. This paper conducted experiments on SA-SBFL using five benchmark projects from Defects4] and evaluated the experimental results with two metrics: Top-N (N = 1, 3, 5) and Expected Additional Mistakes. The experimental results demonstrate that, compared to traditional SBFL techniques, SA-SBFL has better performance. The remaining research work of this paper is arranged as follows. Section 2 provides a review of related research on fault localization and introduces the technologies used in this paper; Section 3 details the specific framework of SA-SBFL; Section 4 introduces the experimental dataset and evaluation metrics used in this paper; Section 5 discusses the main research questions and the experimental results; Sections 6 and 7 offer a summary and provide an outlook on future research work.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 2 Model Description
### Track Model  
The track is modelled as a beam supported on spaced sprung mass systems (Figure 2) corresponding to the railway sleepers. The rail is modelled by beam elements in a finite element framework. In this three-layer sprung mass system, the masses represent the sleeper, \(m_{S}\) and ballast, \(m_{BA}\). The various layers are connected by spring and damping systems which represent pad, ballast and sub-ballast, with respective properties: \(k_{p}\) and \(c_{p2}\), \(k_{BA}\) and \(c_{BA}\); and \(k_{SB}\) and \(c_{SB}\). A surface profile is included on the track. Similar models have been used by other researchers [38; 39].
## 3 Proposed Work
### Elective Course Domain Prediction  
The proposed method involves three steps in recommending personalized elective courses based on the student's domain expertise. The first one predicts the target domain of each elective course; the second step is to identify the domain expertise of the student; and third step is to recommend personalized elective courses based on the domain expertise of each student.  
The deep learning models such as long short-term memory (LSTM) and GRU are used for training and predicting the target domain of each elective course. Predicting target domains involves the setting up of the target domains of core courses, the generation of word embeddings by training contents of the core courses, and predicting the domain of elective courses as specified in Algorithm 1.  
Let \(C=\{c_{1},\ c_{2},\dots,c_{n}\}\) denote the set of \(n\) core courses and \(E=\{e_{1},\ e_{2},\dots,e_{m}\}\) denote the set of \(m\) elective courses in a university curriculum. \(S=\{s_{1},\ s_{2},\dots,s_{k}\}\) denote the set of \(k\) students for whom the elective courses have to be recommended. For predicting the target domain of each elective course, every core course \(c\in C\) is set with the target domain as per the recommendations of SME. Let \(D=\{d_{1},\ d_{2},\dots,d_{t}\}\) denote the set of \(t\) target domains identified by the SME where \(t=\{1,\ 2,\dots,\ 9\}\). The SME now analyses the contents of the core courses and decides on which domain the course belongs to. Core courses in the university curriculum are then sub grouped into \(t\) number of domains identified by SME. Let \(G=\{SG_{1},\ SG_{2},\dots,SG_{t}\}\) denote the set of courses in each subgroup that comes under domain \(d\in D\). After grouping the core courses, the contents of the courses are trained based on the domain \(d\) set by SMEs using deep learning models. The steps involved during the prediction of target domain are as specified in Algorithm 1.  
Figure 4: The proposed architecture diagram of the elective course recommendation framework.  
```
Input : Set of students \(S=\{s_{1},\ s_{21},\ldots,s_{k}\}\) Set of core courses \(C=\{c_{1},\ c_{2},\ldots,c_{n}\}\) Set of elective courses \(E=\{e_{1},\ e_{2},\ldots,e_{m}\}\) Set of target domains \(D=\{d_{1},\ d_{2},\ldots,d_{t}\}\) Set of subgroups \(G=\{SG_{1},SG_{2},\ldots,SG_{t}\}\)\(SG\) is the set of all core courses of domain \(d\in D\)\(LE\) is the set of all elective courses of domain \(d\in D\) 1. for each course \(c\in C\)do. 2. Assign any one target domain \(d\in D\) as per the SME and add the courses into the respective subgroup \(SG\) which come under the target domain \(d\). 3. Generate Word embeddings for the content of the course \(c\) and map into the vectors using GloVe and spaCy. 4. Semantically train the contents of \(c\) with the assigned target domain \(d\) using LSTM, and GRU models 5. endfor 6. for each elective course, \(e\in E\)do. 7. Predict \(d\) using the deep learning model with maximum training accuracy. 8. Add \(e\) to \(LE\) of domain \(d\). 9. endfor
```  
**Algorithm 1**Algorithm for the prediction of target domain