esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Performance Evaluation of eLoran Monitoring System
## 3 Near-Field Monitoring Station Performance Analysis
### Transmission Control Precision Data Analysis  
According to the test method of transmission control accuracy of the near-field monitoring station, the test was carried out in the BPL time service system. After the calculation and analysis of the test data for a long time, as shown in Figure 4, the standard deviation of the time difference between the 1 PPS signal output by the monitoring receiver and the UTC (NTSC) standard 1 PPS signal is 6.24 ns, which is less than 30 ns. It meets the requirement of the transmission control precision index of the eLoran system.
## 3 Results
### Forest Ecological Security Index and Its Spatial Distribution  
The _ESI_ spatial distribution is illustrated in Figure 4. Nujiang and Diqing had \"larger\" _ESI_s (0.753\(-\)0.877), Lijiang and Xishuangbanna had \"large\" _ESI_s (0.681\(-\)0.752), Dali, Chuxiong, Yuxi, and Lincang had \"medium\" _ESI_s (0.645\(-\)0.680), Dehong, Puer, Honghe, and Wenshan had \"small\" _ESI_s (0.604\(-\)0.644), and Baoshan, Kunming, Zhaotong, and Qujing had \"smaller\" _ESI_s (0.565\(-\)0.603).  
Figure 3: Forest security pressure index in Yunnan province.  
Figure 2: Forest ecological state index in Yunnan province.  
The _ESI_ values of prefectures are shown in Figure 5. In 2017, the mean _ESI_ value in Yunnan was 0.654, with Nujiang obtaining the highest value (0.877) and Qujing obtaining the lowest (0.565). These findings indicate an imbalanced _ESI_ spatial distribution. Specifically, the northwest and south regions had the largest _ESI_s, while the northeast region had the smallest _ESI_.  
Nujiang and Diqing had the largest area of natural forests in the entire Yunnan Province, with their forest coverage rate reaching up to 75%. The natural forest acreage and natural forest accumulation in these prefectures accounted for more than 90% of the total forest acreage and accumulation in the province (Table 2).  
These two prefectures, Nujiang and Diqing, also had the highest concentration of primitive forest distribution in Yunnan and preserved the authenticity of their ecosystems. They also had low population densities of 37 and 18 people/km\({}^{2}\), respectively. Nujiang and Diqing also had underdeveloped industrial production, which explained their relatively low degree of environmental pollution, undisturbed forest ecological environments, minimal pressures on their forest ecological systems, and highest forest ecological security index among all prefectures in Yunnan.  
Figure 4: Forest ecological security index in Yunnan province.  
Figure 5: Forest ecological security index in different prefectures.  
The low forest ecological security evaluation index of Qujing and Zhaotong can be ascribed to several reasons. First, these prefectures have a long history of development that is accompanied by high population density, extensive damage to their forests, low forest coverage, high pressure on their forest ecosystems, and threatened forest ecological security. As shown in Table 2, Qujing had a population density of 209 people/km\({}^{2}\) and a forest coverage of 43.33%. The secondary industry in this prefecture accounted for 39.39% of its entire economy, and its resource consumption was second only to the provincial capital of Kunming. Meanwhile, Zhaotong had a population density of 242 people/km\({}^{2}\), a forest coverage of 34.98%, and a natural forest acreage of only 52.12%, most of which were residual secondary forests. The secondary industry in this prefecture accounted for 43.5% of its entire economy, hence producing huge amounts of environmental pollution that greatly disturbs its forest ecological environment [24].
## 4 Results and Discussion
### Analysis of Experimental Results
The integrated classifier consists of eight methods. Consequently, individual classification results for each method are required, followed by a comparison of the results. The best classification result is then output. This process necessitates training for each method, leading to high computational complexity. The design of lightweight classification methods can be considered in later studies. To verify the efficacy of our methodology, we carried out a thorough comparative study, considering various techniques. Notably, every study in our review used a variety of techniques to detect unknown threats, with encouraging results. Table 6 illustrates the effectiveness of our LVAE approach in detecting unknown attacks, with substantial improvements in various metrics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **Methods** & \begin{tabular}{c} **Training** \\ **(Accuracy)** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **(Accuracy)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(Accuracy)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(Accuracy)** \\ \end{tabular} & \
\begin{tabular}{c} **F1** \\ **(Recall)** \\ \end{tabular} &
\begin{tabular}{c} **F1** \\ **(F1)** \\ \end{tabular} \\ \hline \multirow{7}{*}{Training (10,000)} & MLP & 95.44\% & 89.65\% & **98.63\%** & 86.68\% & 92.27\% \\  & NB & 80.84\% & 97.82\% & 98.15\% & 97.82\% & 97.98\% \\  & DT & 88.29\% & 98.95\% & 98.03\% & **98.95\%** & 98.49\% \\  & RF & **99.95\%** & 98.87\% & 98.03\% & 98.90\% & 98.45\% \\  & SVM & 93.36\% & 94.07\% & 98.06\% & 94.08\% & 96.03\% \\  & LR & 85.09\% & 96.40\% & 98.02\% & 96.41\% & 97.21\% \\  & GB & 99.65\% & 98.88\% & 98.04\% & 98.88\% & 98.46\% \\  & GRU & 80.97\% & **99.01\%** & 98.12\% & 98.90\% & 98.52\% \\ \hline \multirow{7}{*}{Training (20,000)} & MLP & 95.19\% & 98.63\% & **99.27\%** & 92.49\% & 95.76\% \\  & NB & 63.60\% & 94.37\% & 99.11\% & 94.37\% & 96.68\% \\  & DT & 91.57\% & 97.56\% & 99.06\% & 97.56\% & 98.31\% \\  & RF & **99.98\%** & 99.47\% & 99.01\% & **99.50\%** & **99.25\%** \\  & SVM & 92.95\% & 97.12\% & 99.03\% & 97.13\% & 98.07\% \\  & LR & 83.35\% & 97.23\% & 99.02\% & 97.23\% & 98.12\% \\  & GB & 99.24\% & 97.55\% & 99.01\% & 97.55\% & 98.27\% \\  & GRU & 87.30\% & **99.50\%** & 99.01\% & **99.50\%** & 99.25\% \\ \hline \multirow{7}{*}{Training (30,000)} & MLP & 96.13\% & 99.02\% & **99.42\%** & 99.30\% & 99.36\% \\  & NB & 79.27\% & 3.88\% & 93.21\% & 3.88\% & 7.46\% \\ \cline{1-1}  & DT & 91.13\% & 98.37\% & 99.37\% & 98.37\% & 98.87\% \\ \cline{1-1}  & RF & **99.93\%** & 99.66\% & 99.34\% & **99.66\%** & **99.50\%** \\ \cline{1-1}  & SVM & 93.62\% & 98.08\% & 99.35\% & 98.08\% & 98.71\% \\ \cline{1-1}  & LR & 84.39\% & 98.17\% & 99.34\% & 98.18\% & 98.76\% \\ \cline{1-1}  & GB & 99.09\% & 98.91\% & 99.35\% & 98.92\% & 99.13\% \\ \cline{1-1}  & GRU & 88.04\% & **99.67\%** & 99.34\% & **99.66\%** & **99.50\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of various methods of detection between 10,000 and 30,000 samples.
## 4 Results
### Obtaining the Peak Load Profile
#### 4.1.2 Smoothing the Distribution of Load  
Generally, broken curves (like the one in Figure 2) do not describe the real load, so the proposed method uses a selected smoothing technique. In the described method, the kernel smoothing for the distribution of the optimal \(f_{i}\) (vector \(F\_opt\)) is proposed.  
Figure 1: Change of values of \(f_{i}(i=1\ldots 48)\) in each iteration of work steps of **fmincon**.  
Algorithm 2, which was developed for calculating the smoothed distribution, is presented below:  
```
(input: F_opt; output: sF_opt)
01 // F_opt - vector of f_i for i = 1... n
02 // sF_opt - smoothed vector of f_i for i = 1... n
03 f_low = find the smallest value of f_i from F_opt
04 j = 1
05 sample = []
06 for i = 1 to n
07 i_size = ceil (F_opt[i]/f_low)
08 // i_size-how many times index i should be represented in a sample
09 for k = 1 to i_size
10 sample [j++] = i
11 sF_opt = ks (sample, n)
12 // ks-generates a kernel-based density (n - values) using sample vector
```  
**Algorithm 2** Calculating the smoothed load profile using kernel estimator.  
Algorithm 2 generates a sample containing indices, i.e., a set of \(i\) values of time intervals \(I_{i}\) (see the beginning of Section 4.1.1). A value of the \(i\) index is repeated in the sample such (lines 07-10) that the interval \(I_{i}\) is represented in the sample proportionally to the \(f_{i}\) values from _F_opt_. Invoking the function **ks** (line 11) allows for the creation of an internal representation of the kernel-based density using the sample and then returns its values at points \(1,\ldots,n\).  
For the considered real example, the MATLAB function--**ksdensity**[42] with a Gaussian kernel--played the role of **ks** in Algorithm 2. The obtained smoothed distribution of \(f_{i}\) (interpolated _sF_opt_) is presented in Figure 3.  
Figure 2: Obtained optimal values of \(f_{i}(i=1\ldots 48)\)—the linear interpolation of normalized load.
## 3 Results
### Capacitance and Conductance Changes with NIR Irradiation  
The capacitance and conductance were measured in real time with NIR irradiation, as shown in Figure 4a. To maximize the contribution of cellular capacitance in the impedance measurement, the full coverage of electrodes with the cells was confirmed before the experiment. Data with a 1 kHz alternating current (AC) were used to investigate the electrical changes in the cells with NIR irradiation [20]. The parallel relative capacitance and relative conductivity were the values divided by the control value before the NIR irradiation. Figure 4b shows that the capacitance started to decrease when the NIR energy density increased above 2 J/cm\({}^{2}\). In contrast, the conductance increased immediately upon NIR irradiation, and the value remained almost constant (Figure 4c). The conductance dropped slightly at approximately 1.3 J/cm\({}^{2}\) and then increased again, which might be related to the function of membrane proteins, as shown in Figure 1c. However, the change was within the error range. At the end of 3 J/cm\({}^{2}\) NIR irradiation, the capacitance decreased by 0.4% from the beginning, and the conductance increased by 0.6%. This implies that NIR induces alterations in the electrical properties of the cells. The cell-free capacitance decreased and conductivity slightly increased, which may be due to photoreceptors in FBS-containing media. These reference values were subtracted from the measured data of the cells.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.2.1 Antinflammatory Drugs
[PERSON] et al. found that the presence of nanoscopic pores on certain polymer surfaces, i.e., poly(4-acryloylmorpholine) (PACM) and poly(2-carboxymethyl acrylate) (PCEA), each cross-linked with divinylbenzene, led to order-of-magnitude faster aspirin nucleation rates when compared with surfaces without pores [67]. [PERSON] et al. investigated the effects of polymeric additives HPMC and PVP used in pharmacy to stabilize crystalline suspensions on the growth kinetics of naproxen (non-steroidal anti-inflammatory drug [68]. The influence of PVP on the crystal growth of the drug was explained based on hydrophobic--hydrophilic intermolecular interactions occurring at the crystal--solution interface. Two years later, they released a publication on the utilization of the molecular dynamics simulations to model the interactions among PVP, naproxen and ethanol/water mixture occurring at the crystal/solution interface [69]. The presented results were consistent with experimental observations and enabled the understanding of the mechanism behind the anisotropic growth behavior of a naproxen crystal in the presence of HPMC and PVP additives.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.7 C3S V2 LAI  
The 1 km C3S V2 LAI product is derived from PROBA-V observations with a 10-day temporal resolution, based on a 31-day observation window with asymmetric weights [13]. The Two-stream Inversion Package (TIP), which relates Bi-Hemispheric Reflectances to various canopy parameters, is applied to visible and NIR broadband albedos to retrieve the LAI. TIP is based on the Two-stream Model developed by [PERSON] et al. [55], which implements the two-stream approximation of radiative transfer for a homogeneous canopy. The TIP algorithm provides effective LAI. For efficient processing, the retrievals are taken from LUTs, generated with the TIP model [13].
## 4 Results and Discussion
### Surface Water and Groundwater Interactions
#### 4.2.2 Seasonal Variations of the SGIs and Interactions Near the Soil Zones
The gravity drainage to the unsaturated zones and the recharge from the unsaturated to the saturated zones are highly sensitive with the variations of rainfall. Note that the results in Figure 13 only provide the overall performance of the system responses for different components. In the study, the GSFLOW model accumulates the results from HRUs in the PPGB. The spatial variations might be limited by the sizes and shapes of the HRUs defined in the model.
## 3 Results and Discussion
### Batch Tests
#### 3.2.2 Adsorption Isotherms
All the B values for aniline adsorption were higher than 1.0, which means that the adsorption mainly occurs on the heterogeneous surface of the loess via multilayer adsorption supplemented with monolayer adsorption. In this regard, aniline adsorption on the loess is mainly chemical adsorption followed by physical adsorption, which agrees with the results obtained from the kinetic analysis. Thirdly, the values of q\({}_{\text{m}}\) from the Langmuir model for aniline adsorption did not increase as TNT adsorption but decreased from 208.77 to 167.32 mg/kg, with the temperature increasing from 278 to 318 K, proving that the aniline adsorption process is exothermic and benefits from low temperatures.
## 1 Introduction  
Biometrics refer to the use of intrinsic physical or behavioral traits in order to identify humans. Besides regular features (face, fingerprint, iris, DNA and retina), human gait, which can be obtained from people at larger distances and at low resolution without subjects' cooperation has recently attracted much attention. It also has a vast application prospect in crime investigation and wide-area surveillance. For example, criminals usually wear gloves, dark sun-glasses, and face masks to invalidate finger print, eyes, and face recognition. In such scenarios, gait recognition is the only useful and effective identification method. Previous research [1; 2] has shown that human gait, specifically the walking pattern, is difficult to disguise and unique to each person.  
In general, video sensor-based gait recognition methods are divided into two families: appearance-based [3; 4; 5; 6; 7] and model-based [8; 9; 10]. Appearance-based methods focus on the motion of human body and usually operate on silhouettes of gait. They extract the gait descriptors from the silhouettes. The general framework of appearance-based methods usually consists of silhouette extraction, period detection, representation generation, and recognition. Model-based gait recognition focuses more on the extraction of the stride parameters of subject that describe the gait by using the human body structure. The model-based methods usually require high resolution images as well as being computationally expensive, while gait recognition needs to be real-time and effective at low resolution. Our proposed work falls in the category of appearance-based methods. It differs from the majority of contributions in the field in that the Deep Learning (DL) framework is used to extract gait representation compared with well engineered features such as the widely used average silhouette representations: Gait Energy Image (GEI) [3], Gait Flow Image (GFI) [5], Gait Entropy Image (GEnI), Masked GEI based on GEnI (MGEI) [4], and Frequency-Domain Feature (FDF) [6; 7]. However, the performance of gait recognition is often influenced by several covariates such as clothing, walking speed, observation views, and carrying bags. For appearance-based methods, view changes are the most problematic covariates. Therefore, we propose a more discriminative appearance-based representation, DeepGait and introduce Joint Bayesian to deal with the view change problems. Numerous experiments were conducted for both non-view variance and cross-view settings on the OU-ISIR large population (OULP) dataset [11] to validate the effectiveness of our proposed method.
## 2 Preliminaries of State Estimation Methods Using Kalman Filter  
The Kalman Filter (KF) uses noisy observed data and data with other inconsistencies to estimate unknown states by the use of a mathematical model. It is a standard optimal estimation algorithm based on Bayesian filter theory ([PERSON] and [PERSON], 2016) and was proposed early in the 1960s by [PERSON] (1960). As the algorithm offers quick, efficient, real-time and powerful anti-interference estimation, it has been widely used and accepted in the course of target tracking, orbit calculation and navigation. It has also gained importance within the fields of dynamic positioning, microeconomics, sensor fusion and digital image processing. The Extended and Unscented Kalman Filters are other major variants of Kalman Filters. Along with these, we present a comparison study with the particle filter (PF).
## 4 Data Analysis and Discussion of Results
### Condition Configuration Analysis  
The configuration analysis aims to explain the sufficiency of the different configurations formed by each condition variable to produce the result variable. At present, the consistency threshold used to measure sufficiency is often different due to the differences in research situations. In accordance with the suggestion of [PERSON] [37], the consistency threshold is determined to be 0.8 and the frequency threshold is determined to be 1 to obtain a complex solution, a parsimonious solution, and an intermediate solution. Referring to previous studies, the condition variables that appear simultaneously in the intermediate solution and the parsimonious solution are regarded as core conditions, and the condition variables that appear only in the intermediate solution are regarded as edge conditions. Each condition variable interacts to form three condition configurations, and the overall consistency is higher than 0.8, indicating that these three configurations can be regarded as a sufficient condition combination for the efficient construction performance of intelligent coal mines. The overall coverage can effectively explain nearly 63.1% of cases (Table 4).
# SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration
## 2 Related Work
### Student Dropout Analysis  
A common approach to analyzing student dropout is determining the importance of features for a particular model. In this vein, a new application of data mining called educational data mining (EDM) is an interdisciplinary research area in the educational field that uses different methods and techniques from machine learning, statistics, data mining, and data analysis to analyze data collected during teaching and learning [16; 17; 18; 19; 20; 21]. A great deal of research on student dropout using EDM has centered on predicting student dropout using different machine learning models such as random forest [3; 6; 10], neural networks [3; 7], support vector machines [3; 6], deep learning [22; 23], long short-term memory (LSTM) [24; 25], generative adversarial networks (SC-GAN) [26], and logistic regression [3; 8; 27; 28]. Many of these studies are designed to influence policy decisions and provide recommendations for interventions to prevent student dropouts [6; 8; 28]. For instance, [PERSON] et al. [28] aimed to help in designing better courses and improving learning effectiveness by answering the following questions. How can students be retained on a course? How can the completion rate of a course be estimated? Moreover, how do we evaluate the learning performance of different students?  
Another important goal of these studies is not only to predict whether students will leave the university but also when they will leave [7; 10]. Researchers have used machine learning techniques with success, but an area that has been gaining strength is survival analysis [29; 30]. In this context, survival analysis is a set of statistical methods for longitudinal data analysis, for identifying students at risk of dropping out [9; 31; 32; 33]. For instance, based on the semi-parametric proportional hazard Cox model, [PERSON] et al. [32] aimed to determine the factors that influence student dropout and to discover how long it takes for a student to drop out of university.  
As discussed above, these methods aim to explore, analyze, discuss, and predict student dropouts by computing the importance of the characteristics of the environment (academic, social, and economic). However, do not offer alternatives of variables alteration to chance predefined student situation. In contrast, our approach enables the generation of synthetic instances based on real examples, to obtain the desired output.
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Robotic Endoscopic Multitasking Platform
#### 2.1.3 Flex Robotic System (Medrobotics, Raynham, MA, USA)  
The Flex Robotic System (Medrobotics, Raynham, MA, USA) is a robotic platform for lower GI tract procedures. It is composed of a robotic endoscope, mechanical single-use flexible instruments, a master console, an HD screen, and a joystick (Figure 3) [18]. This system was originally developed for head and neck surgery [19], but has been recently modified to perform procedures in the lower GI tract. The scope has two working channels and can accommodate several therapeutic accessories. The robot can access the lesions up to 25 cm from the anal verge. The robotic endoscope is controlled by an operator at the master console. This device provides dual manual control, which makes it easy to use two instruments. In an ex vivo porcine colon model experiments, when using FLEX, ESD novices performed ESD faster and safer than using conventional ESD methods [18]. This system was approved by the FDA for lower GI tract procedures in 2017.  
Figure 2: Virtual rendering of Endoluminal Assistant for Surgical Endoscopy. (**A**) Slave unit: mobile cart with robotic modules. (**B**) Master unit [17]. The ergonomic design of the master unit allows a single operator sitting at the console to control both the endoscope and the instruments.