esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### The Model Method Used  
Currently, the passenger flow prediction models used by scholars for rail transit stations are mainly divided into global regression models and local regression models. The traditional four-stage model has shortcomings in data accuracy and model application range. Its research area is global, and it is difficult to refine the impact of the building environment around the station on passenger flow. Given the limitations of the traditional four-stage model for predicting passenger flow, more and more scholars have begun to focus on the OLS (Ordinary Least Squares) model. [PERSON] et al. (2016) studied direct passenger flow forecasting models and concluded that the most widely used is the OLS linear regression model. However, the OLS regression model is also a global regression model, which does not take into account the spatially localized influences affecting station passenger flow or the spatial heterogeneity of the independent variables. [PERSON] (1996) ([PERSON], 1996) proposed the GWR (geographically weighted regression) model. The GWR model is based on the regression analysis of the OLS model taking into account the local characteristics of the independent variables, including spatial heterogeneity as well as spatial correlation, which can better deal with spatially variable data. [PERSON] et al. (2012) ([PERSON] et al., 2018) and [PERSON] et al. (2018) ([PERSON] et al., 2018) compared the effectiveness of fitting OLS and GWR models for rail transit passenger flow prediction, and they found that GWR was more suitable. They combined geographic and economic factors to explore the relationship between subway passenger flow and land use characteristics, providing a new research direction that justifies further research using the GWR model.  
Previous studies have focused on the comparison of model accuracy, but few studies have combined the spatial heterogeneity of passenger flow influencing factors to give policy advice. [PERSON] et al. (2020) ([PERSON] et al., 2020) classified Guangzhou rail transit stations through K-means clustering and gave policy advice for each type of station but did not give policy advice on a macrolevel. [PERSON] et al. (2023) ([PERSON] et al., 2023) ([PERSON] et al., 2023) verified that the GWR model was more accurate than the OLS model by comparing the model accuracies but did not further utilize this model to give opinions on transportation policies in Nanjing. Analyzing the influence mechanism of the factors affecting passenger flow, taking administrative districts as an analytical perspective, and giving policy advice is of guiding significance to public transportation planning and urban land use, etc., and there is a relative lack of research on such.
## 7 Open Issues and Research Challenges
### Energy Management  
UAVs are lightweight, battery-operated flying devices with a limited lifetime. The devices and infrastructure of 6G generate a large amount of data after regular intervals. So, processing such big complex data in a multi-UAV environment requires high computation power, which can drain the UAV battery and interrupt the whole operation.
## 5 Machine Learning Algorithms
### Classification Metrics Performance
#### 5.8.1 Fundamental Evaluation Measures  
In binary classification, a confusion matrix, shown in Table 2, is one of the best ways to assess the training models of the DRL and ML algorithms. The confusion matrix shows the relations between the classifier outputs and the true ones. Table 2 indicates four different combinations of predicted and actual values, explained as true positive (TP), false positive (FP), true negative (TN), and false negative (FN). In this study, samples with the presence of fraud are defined as the positive class, and samples with the absence of no-fraud are considered as the negative class. Based on the confusion matrix, accuracy, precision, sensitivity, and specificity are calculated.  
Classification accuracy is considered as one of the most popularly employed measures to the performance of classification. It is a proportion of the accurately classified samples over the total number of samples, as illustrated in (12).  
\[\text{Accuracy}=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{TN}+\text{FP}+ \text{FN})} \tag{12}\]  
Positive predictive value or precision illustrates the ratio of positive samples accurately classified over the total number of positive predicted samples as shown in (13).  
\[\text{Precision}=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Predicted Values**} \\ \cline{2-4}
**Actual Values** & Class & Negative (0) & Positive (1) \\ \cline{2-4}  & Negative (0) & True Negative & False Positive \\  & & (TN) & (FP) \\ \cline{2-4}  & Positive (1) & False Negative & True Positive \\  & & (FN) & (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Figure 10: Classification evaluation indexes.  
Sensitivity, true positive rate, or recall illustrates the rate of positive samples that are accurately classified over the total of positive predicted samples and negative predicted samples, as indicated in (14).  
\[\text{Sensitivity}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{14}\]  
Specificity is expressed as the ratio between negative samples correctly classified and the total of positive predicted samples and negative predicted samples, as shown in (15).  
\[\text{Specificity}=\frac{\text{TN}}{(\text{TN}+\text{FP})} \tag{15}\]
## 5 Results
### In-Orbit Results
#### 5.2.4 Interference with Time Structure  
In the measurements performed, interference with time structure was observed in different areas. Figure 13a shows the sub-satellite points where different types of interference were detected visually. There are two types of interference with time structure: band-limited (red crosses) and wide-band (black circles). Interference with no time structure is marked with blue stars, and the position of the type A ground radars in the 420-450 MHz band, according to Reference [21], is marked with black triangles and numbers.  
The band-limited interference was observed where the radars are present, and the bandwidth is consistent with the chirp bandwidth of these radars for the search mode (100-350 kHz for radar type A [21]). The bandwidth of the interference was estimated automatically based on two different parameters: the average power spectrum (\(m_{2k}\)) and the ratio of moments, \((m_{1k})^{2}/m_{2k}\). Both show similar results as depicted in Figure 13b. Where the algorithm did not detect a signal, the bandwidth was set to zero. It can be seen that the \(m_{2k}\) approach has slightly lower performance when the interference has low power compared to the noise. In these cases, the estimation of the bandwidth has a better performance when using the ratio of moments approach. Figure 13c shows the CV of the envelope for the cases where band-limited interference was visually detected. It was estimated by calculating the median of \(c_{k}\) over the bandwidth where the band-limited interference was detected. The cases where the ratio is zero are due to the same reason as before, i.e., where the bandwidth was not detected automatically, the CV was set to zero. The CV gives an indication of the spread and dispersion of the envelope. For AWGN, the CV is 0.52 since the spread in envelope is low. In Figure 13c, the CV is higher than the AWGN and up to 2.6. This means that the dispersion of the envelope is higher, and the probability of getting hit by stronger than average interference is higher than if it was an AWGN with the same average power.  
Figure 13: _Cont._
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## Methods
### Spatial Resampling

Table 1: The 19 storms analyzed in this study, along with the dates during their lifetimes for which available satellite overpasses were compared to HWRF data. A full list of satellite passes over each of these storms is provided in the Supplementary Materials. Figure 6: Comparison of AMSR2 and HWRF winds for Hurricane Dorian on 4 September 2019 for an AMSR2 pass at 7:32 UTC. Top panels: the HWRF winds before resampling for the HWRF analysis times before (**a**) and after (**b**) the corresponding SMAP pass. Bottom panels: the resampled HWRF winds at analysis times before (**c**) and after (**e**) the AMSR2 pass (**d**).
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
###### Abstract  
This paper describes, along with some validation results, the one-dimensional variational method (1D-Var) that is in use at the University Corporation for Atmospheric Research (UCAR) to retrieve atmospheric profiles of temperature, pressure, and humidity from the observation of the Global Navigation Satellite System (GNSS) radio occultation (RO). The retrieved profiles are physically consistent among the variables and statistically optimal as regards to a priori error statistics. Tests with idealized data demonstrate that the 1D-Var is highly effective in spreading the observational information and confirm that the method works as designed and expected, provided that correct input data are given. Tests for real-world data sets show that the retrieved profiles agree remarkably well with global weather analyses and collocated high vertical resolution radiosonde observations, and that the 1D-Var can produce value-added retrievals with respect to a priori profiles. We also find that the retrieved profiles are of exceptional long-term stability, suggesting that the 1D-Var can provide an excellent climate data record.  
UCAR 1D-Var description; GNSS RO retrieval; verification and validation Article
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 6 Experimentation and Performance Analysis  
We focus on and compare the post-quantum (Kyber-512) vs. pre-quantum (X25519 and secp256r1) ciphers that are relevant to mobile computing in 5G. We use the key-exchange ECDH utilized in the 3 GPP standardized protocol, more specifically, X25519 and secp256r1, which are used in the 5G protection scheme for key exchange as discussed in Section 2.2.1. To provide a fair comparison, we compare the ciphers that correspond to 128-bit security.  
NIST's recommendations for cryptographic algorithms often include different security levels, typically ranging from 80 bits to 256 bits. NIST analyzed such security levels based on the brute-force difficulty, as described in Section 2.1. Each level corresponds to a different expected level of security, with higher levels offering stronger protection but potentially requiring more computational resources. We focus on 128-bit security because the UE implementing and applying the ciphers is often mobile and resource-constrained.  
This section provides our experimental setup, experimentation, and performance analyses. We measure the computational time in the experimentation and analyze the performance for 100,000 samples. We present average values with a 95% confidence interval  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protection Scheme** & **Ciphertext (Bytes)** & **Public Key (Bytes)** & **Scheme Output (Bytes)** \\ \hline Kyber-512 & 768 & 800 & 847 \\ \hline X25519 & 32 & 32 & 111 \\ \hline secp256r1 & 33 & 33 & 112 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Protection scheme output comparison. Protection scheme output corresponds to the scheme output described in the last column of Table 3.  
of the sample data in the analysis, except for power/energy analysis. We also make our experimental scripts and data available in a public repository [45].
## 5 Findings of the Study
### Extrinsic Evaluation
#### 5.3.1 Evaluation Metrics  
We chose sensitivity/recall (10), precision (11), F1 score (12), Area Under Curve (AUC) and Geometric Mean (GM) [70; 71; 72] (14) as evaluation metrics because of the popularity in personality detection experiments [17; 32; 34; 36], which were quantifiable to binary and multiclass experiments [70; 71; 73] and were suitable for our asymmetric distribution datasets [74; 75]. AUC and GM metrics are also suitable for the class imbalance problem [71] as the SLDA generated datasets seems to be imbalanced. AUC is computed by plotting the recall (10) against the False Positive Rate (FPR) (13) at various threshold settings. Literally, the better generalization by the heuristic models illustrated through the high score of the three metrics will provide additional merit to the topics modeled by SLDA. Machine learning studies apply the statistical metric known as the confusion matrix to measure the strength of an algorithm in solving the given problem automatically. The confusion matrix is comprised of four elements, namely true positive (_TP_), false positive (_FP_), true negative (_TN_), and false negative (_FN_). In order to measure the time taken by the classifiers to build the model, we followed the time complexity notion stated in [66] for SMO, ref. [69] for NB and KNN, ref. [75] for DT, ref. [76] for RF, and ref. [77] for AdaBoost classifier.  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[F1=\frac{TP}{TP+\frac{1}{2}\left(FP+FN\right)} \tag{12}\]  
\[FPR=\frac{FP}{TN+FP} \tag{13}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{14}\]  
\[GM=\sqrt{Recall\cdot Specificity} \tag{15}\]
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion  
### 2D Electrochemical Process Operational Conditions  
#### 3.1.1 Effect of Anode Material on CBZ Removal  
Considering that the most important characteristics of the electrodes used in an electrochemical system are their stability, long service life, and electrocatalytic properties, and that the anode mainly controls the current efficiency, selectivity, and catalytic activity of the system [35], the influence of different anodes (BDD and MMO) on the CBZ removal was evaluated, using the same material (STS) as the cathode (Figure 1).  
The results obtained showed that the BDD anode presented a slightly better performance during the first 5 min of the electrolysis, removing 63% of CBZ after 3 min and 93% after 5 min. For the MMO anode, 44% of CBZ was removed after 3 min, and 89% after 5 min. The removal efficiency for both anodes was very similar after 5 min, reaching 100% at 10 min.  
For the removal of pharmaceuticals from water, Ti-based anodes are the most widely used, as they have extended lifetimes and significant electrocatalytic activities, and are less expensive when compared to BDD [35].  
For these reasons, and considering the results obtained, the MMO anode was chosen to perform the subsequent electrochemical experiments using the STS electrode as the cathode.
###### Abstract  
Loss of immunity is an important cause in the pathology of infectious disease. This study investigates the effect of _Litsea japonica_ fruit extract (LJFE) as a potential functional food on immunity and hematopoietic function in immunosuppressed BALB/c mice. Immunity-stimulating activity was observed in mice supplemented with LJFE at low (25 mg/kg), medium (50 mg/kg), and high (100 mg/kg) dosage for seven days after administration of cyclophosphamide. LJFE treatment significantly improved spleen injury score (\(p<0.001\)) and body weight (\(p<0.02\)) by approximately two-fold with a high dosage of LJFE (100 mg/kg). Spleen-derived lymphocyte analysis demonstrated that the numbers of clusters of differentiation (CD)4\({}^{+}\) and CD8\({}^{+}\) T-cells were notably increased by approximately two-fold (\(p<0.001\)) with a high dosage of LJFE (100 mg/kg). In mouse splenocytes differentiated into T- and B-lymphocytes, LJFE significantly induced proliferation up to approximately 90% of control for T- (\(p<0.001\)) and B-lymphocytes (\(p<0.01\)) with a high dosage of LJFE (100 mg/kg). Furthermore, LJFE significantly recovered the numbers of white blood cells, red blood cells, and platelets. Enzyme-linked immunosorbent assay revealed that serum levels of immune-related cytokines, such as tumor necrosis factor-\(\alpha\), interleukin (IL-1)\(\beta\), IL-6, IL-2, and interferon (IFN)-\(\gamma\), were notably Increased. In addition, serum levels of immunoglobulin (Ig) A, IgM, and IgG were restored by LJFE treatment. This study provides a reference to use \(L\). _japonic_ as a functional food ingredient to improve immunity and hematological function in humans.  
Litsea japonica; immunity; hematopoietic function; cytokines; mice 2 by ginsenoside treatment in murine macrophage RAW 264.7 [10]. Additionally, the fruit extracts can be beneficially applied to treat neurodegeneration, and their analgesic effects are associated with anti-inflammatory activity [11; 12]. Therefore, _L. japonica_ is regarded as a potential immunomododulator [10]. The immune system protects the body from foreign pathogens [13; 14]. To maintain physiological balance and homeostasis, the immune system recognizes and destroys antigens [14; 15]. Therefore, orchestration and maintenance of the immune system are critical for a healthy physiological state, and bioactive substances that modulate the immune response are of immense importance. However, studies on the regulation of immunity and hematopoietic activities by _L. japonica_ in animal models are scanty. In this study, for the first time, we aim to evaluate the effects of _L. japonica_ fruit extract (LJFE) on immunomododulation and hematopoietic activity in cyclophosphamide (CPA)-induced immunosuppression BALB/c mice. For this purpose, we measured levels of immunological T-cells, hematological cells, and proinflammatory cytokines.
# Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets
## 4 Results and Discussion
### Model of Grade Prediction  
Based on the obtained graphs, it could be presumed that the parameter that influenced the students' success the most in the final exam was their visit in \"Lectures\" material in Moodle. Therefore, the graph in Figure 14 was created.  
The graph shows what final grade students received when they accepted the course. According to the number of hits, they could be divided as follows:  
In the first row of Table 1, there are ranges of visit counts. They were obtained from Figure 14 by looking at the ranges of visits in \"Lecture\" material for each grade. The maximum range was determined by the highest visit number for that grade, and the minimum was determined by the last visit number that did not figure for a lower grade. The second row shows the number of students for the given lecture visit ranges. The numbers of students who received a certain grade for a given number of visits can be seen in the rest of the table. Based on the table, the following model of Petri nets could be created:  
From the start place (Figure 15), the token got to one of the categories selected based on the number of student visits to the lecture. These places were selected according to the graph in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Visits & 0.4 & 5..12 & 13 & 14..17 & 18..29 & 30..56 \\ Students & 22 & 18 & 6 & 2 & 6 & 4 \\ A & 0 & 5 & 2 & 0 & 2 & 4 \\ B & 2 & 1 & 2 & 0 & 4 & 0 \\ C & 6 & 4 & 1 & 2 & 0 & 0 \\ D & 4 & 1 & 1 & 0 & 0 & 0 \\ E & 6 & 1 & 0 & 0 & 0 & 0 \\ FX & 4 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of students based on lecture visits and final grade.  
Figure 14: Grades based on visits in “Lecture”material on the first attempt.  
In this model, transition weights were used that were set according to the number of students assigned to the categories. Subsequently, the token got to the grade place through a transition whose weight was set according to the number of students in the given visit range who received the grade. Using the model, it was possible to simulate the prediction of student grades based on their access to the lecture material [34].  
The probability of selecting a category and consequently the probability of selecting a grade for that category was calculated using Formula (8).  
\[p=\frac{w_{a}}{\sum_{i=0}^{n}w_{i}} \tag{8}\]  
\(w_{a}\) was the weight of the fired transition, \(w_{i}\) was the weight of the \(i\)-th transition, \(n\) was the number of active transitions, and \(p\) was the final probability.
###### Abstract  
Detecting relevant design patterns from system design or source code helps software developers and maintainers understand the ideas behind the design of large-scale, highly complicated software systems, thereby improving the quality of software systems. Currently, design pattern detection based on machine learning has become a hot research direction. Scholars have proposed many design pattern detection methods based on machine learning. However, most of the existing literature only reports the utilization of traditional machine learning algorithms such as KNN, decision trees, ANN, SVM, etc., which require manual feature extraction and feature selection. It is very difficult to find suitable and effective features for the detection of design patterns. In the previous research, we have initially explored a design pattern detection method based on graph theory and ANN. Based on the research work done, we speculate that if we can realize the end-to-end design pattern detection from system design or source code to design pattern with the help of the powerful automatic feature extraction and other advantages of deep learning, the detection effect can be further improved. This paper intends to first explore a UML model that extends image information, called colored UML, so as to transform the design pattern detection problem into an image classification problem; on this basis, the positive and negative sample sets and the system to be recognized are all expressed in the form of colored UML models, the convolutional neural network VGGNet is used to train the data set to extract features, and the extracted features are trained by the SVM for binary classification to judge the pattern instances. Experiments were carried out on three open-source projects. We used three non-machine learning design pattern detection methods and five design pattern detection methods based on traditional machine learning algorithms, as well as the method in this paper. In general, the method proposed in this paper achieved higher precision and recall, and for different programs and their patterns, the precision and recall were stable at more than 85% in most cases. The experimental results demonstrate that this paper can achieve a better effect in recognizing design patterns. The research is, therefore, of both theoretical significance and application value.  
design pattern detection; precision; colored UML model; deep learning; software reverse engineering +
Footnote †: journal: Applied Sciences
## 3 FMCW LiDAR Research Progress
### Progress in Signal Processing Research
The output of the high-pass filter is digitally down-converted. A dataset containing the target information is filtered after comparing the power spectrum measurements of the low- and high-pass filters. After further downsampling processing of this target dataset, an FFT is performed to determine the location of the maximum power spectrum, which is reduced by the frequency compensation value. Finally, spectrum accumulation and information resolution are employed to obtain the distance and velocity information of the target. This technical solution not only optimizes the use of hardware resources but also significantly improves the data processing speed. By introducing more advanced algorithms and system designs, researchers can effectively improve radar signal stability and optimize data processing speed. Advances in these technologies enhance the ability to adapt to environmental changes and ensure accurate transmission and processing of data, thus ensuring the reliability and practicality of radar systems in diverse application scenarios. With the continuous advancement of signal processing technology, the application fields of FMCW LiDAR have been significantly expanded and can better meet actual needs, such as industrial and safety monitoring.