esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education
## 4 Materials and Methods
### Machine Learning Models  
Based on previous works with a similar dataset from the same institution [33], the chosen reference method for building classification models for the three-time phases used was SMOTE as a re-sampling strategy for the imbalanced classes, followed by the use of Random Forest to build the classification models (SMOTE + RF). Two other methods were used that deal with the imbalanced nature of data: SVMSMOTE, followed by Random Forest (SVMSMOTE + RF) and the RusBoost algorithm (RB). Additionally, two algorithms that incorporate the strategies to deal with imbalanced datasets were used. These are the Balanced Random Forest classifier (BRF) and the Easy Ensemble classifier (EE). Therefore, fifteen different models were built, five for each of the datasets representing the data in the three different moments.  
Following the usual procedure, the datasets were divided into training (90% of total records) and test sets, including a stratification procedure to ensure that the original proportion of the samples in each class was preserved in each set. In the case of SMOTE + RF, the SMOTE algorithm was applied to the training datasets. For all the datasets, a 10-fold cross-validation approach was used for model training, meaning that the training dataset was divided into 10 blocks, and the training of each model was conducted with 9 of the blocks, with the remaining one being used for validation purposes. The process was repeated 10 times, once for each block, thus enabling the maximization of the total number of observations used for validation while avoiding over-fitting. The cross-validation procedure also included the class stratification procedure. The test sets, kept aside until this point, were used for validation of the models.  
Figure 1: Distribution of the records among the three defined labels and for the datasets built with: (**a**) information at the time of enrollment (S0); (**b**) information at the end of first academic semester (S1); (**c**) information at the end of second academic semester (S2).
# General Moving Object Localization from a Single Flying Camera
## System Design
### Object Detection  
Our system is not restricted to work on only one class of objects. It can be modified to work on any class of objects, but we need to have a reliable object detection algorithm for it. In this paper, we demonstrate our system on pedestrians, and we make use of the Histogram of Oriented Gradients (HOG) pedestrian descriptor from the OpenCV 2.4.10 library (\"peopledetect.cpp\"). Figure 5 shows an example of the result of the pedestrian detection algorithm in OpenCV, which displays a bounding box over the detected pedestrian.  
The pedestrian detector from OpenCV can detect more than one person in a scene. As with any other object tracking algorithm, if we have multiple objects in a scene, we need to make use of additional information (e.g., color, size, motion vectors, etc.) to correctly match the objects in different scenes. False positives can happen too. In our experiments, we will manually select the correct pedestrian if a false positive happens. The design of a more accurate pedestrian detector is not in the scope of our work.  
We could have chosen YOLO (You Only Look Once) [37] or used popular object tracking algorithms such as TLD (Tracking-Learning-Detection) [38] or KCF (Kernelized Correlation Filter) [39], etc., in an attempt to make the bounding box more accurate. However, we believe that newer and better detection and tracking algorithms will always be developed, so the accuracy in our method will only increase and not decrease as we use more sophisticated detectors.
## 4 Simulation Results
### Experimental Settings
Without timely rescue, the health condition of a victim could deteriorate continuously. Survival probability \(v_{sp}\) is assumed to deteriorate with time until the victim gets rescued. In this paper, three curves are adopted to estimate survival probability for different injury severity [24]. \[ \begin{split} v_{sp\text{-hi}}&=\mathrm{e}^{-(t/3.324 )^{371}}\\ v_{sp\text{-si}}&=\mathrm{e}^{-(t/26.59)^{371}}\\ v_{sp\text{-ni}}&=\mathrm{e}^{-(t/66.48)^{371}} \end{split}  \tag{11}\]  
where \(v_{sp\text{-hi}}\) denotes survival probability of victims who are heavily injured, \(v_{sp\text{-si}}\) denotes survival probability of victims who are slightly injured, and \(v_{sp\text{-ni}}\) denotes survival probability of victims who are not injured. \(t\) denotes trapped time (h). Three curves, shown in Figure 4, from left to right, represent survival probability of victims with heavy injury, slight injury and no injury, respectively. Relative survival probability \(\overline{p}_{rs}\) is the ratio of average survival probability to initial average survival probability. When disaster happens, there always exist some survivors under the rubbles. Some of them would be rescued, and some of them would die without timely rescue, \(\overline{p}_{r}\) is the ratio of survivors getting rescued in the end. In the rescuing process, a lot of time is required until one site gets rescued, and \(\overline{t}_{a}\) is the average required time.
## 5 Experimental Verification  
In order to verify the feasibility and effectiveness of the proposed feature recognition and depth calculation of charging port image based on the SIFT algorithm and the SGBM algorithm, the image feature recognition experiment and binocular ranging experiment were carried out in turn. The binocular camera model DUAL-200M-030T160 is used in the experiment. Before acquiring the image of the charging port, the binocular camera needs to be calibrated and corrected first.
## 3 Materials and Methods
### Quality Control Data (QCD)
#### 3.4.2 Pollution Load Index (PLI)  
The Pollution Load Index (PLI) was calculated as the nth root of the product of n Contamination Factors (CF). This empirical index offers a straightforward, comparative method for evaluating the extent of metal pollution. A PLI value greater than 1 indicates the presence of pollution, whereas a value less than 1 suggests no significant metal contamination [45].  
PLI = (CF1 \(\times\) CF2 \(\times\) CF3 \(\times\ldots\times\) CFn)\({}^{1/n}\)
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 5 Short-Landing-Strategy Design  
### Short-Landing Procedures  
The four-stage short-landing process of the flying wing UAV, shown in Figure 19, is designed based on the analyses presented earlier. The process commences at Point A, where the UAV is at a height of 50 m and a speed of around 64 m/s, with a non-zero sink rate. The stages of the process are as follows:  
Stage I: The UAV reduces its nozzle pitch angle at a rate of 15 \({}^{\circ}\)/s, with the throttle fixed at the current value. The bank angle is adjusted to minimize the cross-track error by using the zero sideslip angle command, and the sink rate is controlled by the pitch attitude angle, as discussed in Section 4.4. The sink rate command is based on the distance to the target touchdown point, as explained in Section 5.2. When the nozzle pitch angle reaches 45 degrees, the short landing proceeds to the next stage.  
Stage II: In this stage, the lateral and sink rate controls are the same as those in Stage I. The nozzle pitch angle is utilized to control the velocity, and the velocity command is set to 35 m/s to prevent the inefficiency of the elevator under low dynamic pressure. The short landing advances to Stage III when the velocity decreases to below 36 m/s or the deceleration process lasts more than 20 s.  
Stage III: In this stage, the angle of attack is set to 6 degrees based on the analysis presented in Section 3, and the sink rate is regulated by the throttle, which is different from the scenarios in Stages I and II. The lateral control remains the same as in Stage II. Additionally, the maximum bank angle command is restricted to 5 degrees below 2 m above the ground to avoid wing strike. The UAV maintains its speed by nozzle pitch angle deflection until it touches down.  
Stage IV: Upon touchdown, the longitudinal control mode switches to pitch angular rate control, and the command is set to zero. The objective of this stage is to slow down the UAV by turning off the engine and braking while keeping it on the runway via front-wheel steering.
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 4 Evaluation
### Results  
The results of the BER in relation to the eff. data rate for different distances and codings are shown in Figure 6.  
Each diagram shows a different distance. The blue curve shows the results for _no coding_, the red curve for the _Reed-Solomon code_ and the green curve for the _Hamming code_. For the codings, the results show the BER after the messages were corrected, but the detection of faulty messages is not considered.  
It can be seen that the BER increases with an increase in the eff. data rate, which means that more bit errors occur. A \(BER>50\%\) results from data loss.  
In most cases, the BER is zero for lower data rates, followed by a second area with alternating \(BER=0\) and \(BER>0\) at some points, ending with an area with \(BER>0\) at all points. The maximum eff. data rate with \(BER=0\) in the first area is denoted as the \"worst case\" and the maximum eff. data rate with \(BER=0\) in the second area (which is the maximum eff. data rate with \(BER=0\) overall) is denoted as the \"best case\". These points are extracted and shown in Figure 7 in relation to the distance.  
In the case of _no coding_ the maximum eff. data rate at the best case is almost always the highest, while the maximum possible eff. data rate in the case of the _Hamming code_ is always the lowest. This reflects the effective rates calculated in Section 4.3.  
It can also be seen that in the worst cases, the maximum eff. data rate for _no coding_ and the _Hamming code_ are more reduced than that of _Reed-Solomon_ in comparison to the best cases.  
Figure 6: Results of the Bit Error Ratio (BER) in accordance with the selected eff. data rate and coding. The diagrams show the results for the distances at (**a**) 5 cm, (**b**) 10 cm, (**c**) 15 cm, (**d**) 20 cm and (**e**) 25 cm.  
Figure 7: Max eff. data rate, where \(BER=0\) vs. distance. (**a**) Worst case (**b**) best case.  
The possible eff. data rate is reduced with shorter distances. The highest eff. data rate is 94.3 kbit/s (no coding), 77.6 kbit/s (RS) and 28.4 kbit/s (Hamming) at a distance of 20 cm. At the distances of 5 and 10 cm, the eff. data rate reduces to a range of 100 bit/s.  
When configured as a transmitter (OPA off, LED on), the power consumption of the transceiver is 32.6 mW. Configured as a receiver (OPA on, LED off), the transceiver requires 38.7 mW. In case where not much processing is needed and the MCU can enter sleep mode, these values are reduced to 19.7 mW and 31.2 mW, respectively.
## 4 Experiments
### Attack Detection  
In the first experiment, we aimed to train classifiers that distinguish only two classes--no attack versus any attack. Table 4 presents the results of the trained models.  
Regardless of model architecture, it is possible to identify an attack using diagnostic attributes. The highest balanced accuracy is obtained for a one-attack-out scenario in both classifiers. It shows that we are able to recognize the fact of attack even for previously unseen attacks. The lowest performance is observed for a one-data-set-out scenario with a random forest classifier; for a new data set, it was more difficult to identify the attack than e.g., a new model type.  
For each scenario and classifier, we extracted feature importance and created the ranking of these variables--higher rank means higher importance. We calculated the correlation coefficients between these ranks and scenarios. The results show that the order of features is highly correlated within classifier groups. For example, the correlation between the feature importance ranks between 10-fold cross-validation and one-data-set-out scenarios for the random forest classifier is equal to 0.97. The correlation coefficient for XGB and RF is rather small, varying from 0.19 to 0.30. It shows that completely different variables are significant in attack identification for analyzed classifiers. Figure 3 shows the top 20 most important variables for each classifier.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario Name** & **RF** & **XGB** \\ \hline
10-fold cross-validation & 0.9107 (0.1050) & 0.9557 (0.0679) \\ one-attack-out & 0.9495 (0.0437) & 0.9596 (0.0350) \\ one-data-set-out & 0.9015 (0.1599) & 0.9520 (0.1167) \\ one-model-out & 0.9364 (0.0441) & 0.9164 (0.0621) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Balanced accuracy with its standard deviation for each scenario in attack detection.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario Name** & **Description** & **Application** \\ \hline
10-fold cross-validation & Cross-validation stratified by variable of interest & detection, isolation \\ one-data-set-out & One data set is treated as a test data set and the rest of data is a training data set & detection, isolation \\ one-model-out & Data for one model type is treated as a test data & detection, isolation \\  & set and the rest of data is a training data set & detection \\ one-attack-out & Data for one attack type is treated as a test data & detection \\  & set and the rest of data is a training data set & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Considered scenarios.  
The main role in both cases has a balanced accuracy calculated on the test data set. The second and third places contain an _uncertainty_ diagnostic attribute for the random forest classifier, while for XGBoost there is the _neighborhood size_ diagnostic attribute. The most frequent variable in the top 20 for RF is _prediction consistency with targets in neighborhood_, while for XGB it is _neighborhood size_. It shows that each diagnostic attribute contains valuable information that helps distinguish the occurrence of an attack in data.
## 4 Results and Discussion  
### Experimental Setup  
This work analyzes email body content, identified as the most effective approach [54]. In our proposed framework, we train traditional ML and DL models to compare their effectiveness in classifying phishing attempts. The experiments use Python and scikit-learn on Google Colab Pro, leveraging 50 GB of RAM to handle large datasets efficiently. We divided each dataset into training and testing sets using an 80-20 split. All models are trained and tested using the scikit-learn library. The parameters used for ML models are as follows: In Naive Bayes classifier, we set MultinomialNB (\(alpha=0.1\)). In logistic regression solvers, Limited-memory Broyden-Fletcher-Goldfarb-Shanno (lbfgs) was applied, and to prevent model overfitting, we used \(penalty=L2\). In SGD Classifier, we used \(loss=hinge\), which gives a linear SVM, \(penalty=l2\), \(alpha=0.0001\), and the \(learning\_rate=``\)optimal\". In XGBoost, \(learning\_rate\) was set to 0.3, and the Maximum depth of a tree \(=6\). In Decision Tree Classifier, \(criterion=``\)gini\" and \(splitter=best\). In Random Forest Classifier, the criterion is \"gini\". In Extra Trees Classifier, the \(criterion=``\)mini\", \(max\_features=``\)sqrt\(\). To prevent overfitting, we implemented regularization techniques, dropout in DL models and L2 regularization in ML classifiers. The hyperparameters used for DL models are illustrated in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} Automatically tuned \\ by RoBERTa model \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of Deep Learning Models.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 5 Intrusion Detection Method
### Model QBER vs. [PERSON]'s Interception Density
#### 5.2.3 Impact of the Interception Density  
The previous analysis considers the cases of knowing the presence or absence of [PERSON]. The extension of the study is to evaluate the impact on the BB84 performance of the interception density parameter, indicated as \(p\), which represents the percentage of qubits (and, hence, bits) that [PERSON] intercepts. This parameter is considered in QS and can be set before starting the simulation, as was already highlighted in Section 4.1. If referring to the previous analysis, this parameter impacts the BB84, as described in Figure 7:  
* With probability \(p\), [PERSON] intercepts the qubit; consequently, the BB84 performance can be computed following the analysis of Case 2 in this subsection.
* With probability \(1-p\), [PERSON] does not intercept the qubit; i.e., the scenario is equivalent to Case 1 in this subsection.  
With respect to the previous analysis, the introduction of \(p\) does not change Equation (1), i.e., \(\mathbb{P}\{CB\}=\frac{1}{2}\), while it impacts the probability of some other events as follows.  
Figure 7: Scenario with interception density \(p\).  
Recall Equation (2), \(\mathbb{P}\{Sb\}=\mathbb{P}\{Sb|+\}\), which can be calculated by considering Equations (3) and (5), weighted according to the conditioning probability \((1-p)\) and \(p\), respectively:  
\[\mathbb{P}\{Sb|+\}=\frac{3}{4}\left(1-p\right)+\frac{5}{8}\,p=\frac{3}{4}-\frac {1}{8}\,p. \tag{9}\]  
\(\mathbb{P}\{Sb|CB\}\) can be easily derived from Equation (6), taking into account that \(\mathbb{P}\{Sb\bigcap CB\}\) is equal to \(\frac{3}{8}\) or \(\frac{1}{2}\) if [PERSON] does or does not intercept the qubit, respectively:  
\[\begin{split}\mathbb{P}\{Sb|CB\}&=2\,\mathbb{P}\{Sb \bigcap CB\}\\ &=2\left(\mathbb{P}\{Sb\bigcap CB|\text{No intercept}\}\,\left(1-p \right)+\mathbb{P}\{Sb\bigcap CB|\text{Intercept}\}\,p\right)\\ &=2\left(\frac{1}{2}\left(1-p\right)+\frac{3}{8}\,p\right)=1- \frac{1}{4}\,p.\end{split} \tag{10}\]  
This result allows for deriving a key relation between the computable (by [PERSON] and [PERSON]) \(QBER\) and Eve's interception density, \(p\), in the case of a partial intercept-and-resend attack:  
\[QBER=0.25\,p, \tag{11}\]  
which confirms previous results [15] and represents the starting point of the proposed intrusion detection method. It is worth pointing out that Equation (11) is obtained when assuming ideal channel conditions, i.e., no system noise.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 4 Discussion  
The experimental results in Section 3 indicate that the proposed scheme can achieve HRWS 3D imaging. In the experiment, the simulation mode of hardware in the loop is used to verify that the multi-channel distributed SAR mode proposed in this paper can achieve 3D imaging by using airborne array measured data. For the range ambiguity problem in this mode, our proposed range ambiguity resolution method based on multi-beam forming can effectively eliminate the range ambiguity. In the study area, a large area of buildings is selected, and the buildings in this area are overlapping targets. First, the range ambiguity is simulated with hardware in the loop by using the measured data obtained from the airborne array data. It can be seen from Figure 10 that the targets in different range units are mixed. Meanwhile, due to the different modulation frequencies of different range units, the ambiguity signal is defocused in the azimuth direction. Then, the range ambiguity is calculated. In this area, the flat ground is the area with low ambiguity, so it is selected as the registration point for image coarse registration. For the overlapping targets, their DOA is not unique. The proposed method first filters the multiple DOA and then selects typical buildings for range ambiguity resolution. As shown in Figures 10-13, the ambiguity signal is effectively eliminated, and the coherence coefficient is improved from 0.5845 to 0.9864, which significantly improves the quality of imaging. Subsequently, range ambiguity resolution and 3D imaging are performed on the whole area. The experimental results are shown in Figure 15, where the color represents the height. It can be concluded that our proposed method can realize high-division and wide-width 3D imaging of a large area. It should be noted that azimuth refocusing is required after range ambiguity resolution.  
To sum up, the proposed method can realize both HRWS imaging and 3D imaging, which is of great significance to SAR imaging.
# Structural Hierarchy-Enhanced Network Representation Learning
## 2 Problem Statement  
We first describe the notations for our problem. Let \(G=(V,E)\) denote a network, in which \(V\) is the node set (\(n=|V|\) is the number of nodes), and \(E\) is the edge set. We construct a structural hierarchy \(\mathcal{H}\) from a network \(G\): \(\mathcal{H}=\{H^{0},H^{1},\ldots,H^{\tau-1}\}\), where \(\tau\) is the number of levels in hierarchy \(\mathcal{H}\), and \(H^{\tau}\) is the level-\(\tau\) graph. Level-0 graph is the original network, i.e., \(H^{0}=G\). Level-(\(h+1\)) graph \(H^{h+1}\) is constructed from level-\(h\) graph \(H^{h}\). We present the list of all notations used in this paper in Table 1.  
**Structural Hierarchical-Enhanced NRL (SHE-NRL).** Given a graph \(G=(V,E)\), in which each node's embedding vector \(\mathbf{x}_{v}\in\mathbb{R}^{1\times n}\) (\(v\in V\)) is initialized by a unit vector, along with its structural hierarchy \(\mathcal{H}\), SHE-NRL is to learn a mapping function \(f:V\rightarrow\mathbb{R}^{k}\) from nodes to low-dimensional embedding vectors so that nodes sharing similar connections in the graph, i.e., having a larger overlap on their neighbor sets, are projected as closely as possible in the embedding space. Here \(k\) is the embedding dimension, and \(f\) is a matrix of size \(n\times k\), where \(k\ll|V|\).
## 2 GNSS-Based Positioning Terminal for Road Applications  
In any road application, the knowledge of the position, velocity, and attitude of the mobile vehicle is fundamental. The availability of more GNSS satellites and constellation with respect to the past, and the increased reliability of GNSS terminals are making GNSS-based solutions the most cost-effective ones to provide an absolute reference of the vehicle with respect to a global reference frame. Such a GNSS terminal is then part of a platform where other sensors complement the weaknesses of GNSS positioning, and provide the additional information needed. The main functional components of a GBPT are depicted in Figure 1.  
Such a system outputs navigation data in the form of vehicle positions and velocities. These positioning metrics are delivered to the application module, and they are based on specific architectural and design implementation choices. For instance, the GBPT might be either a standalone GNSS receiver or, in most cases, a GNSS receiver hybridized with sensors according to a loose/tight coupling scheme [1].  
With respect to the positioning terminal depicted in Figure 1, in this work we focus only on the performance of the GNSS module. The different sensors, which might be involved within the GBPT architecture, are thus not considered during the test phases addressed in this paper. This choice is not restrictive, since the GNSS module is the only one able to provide the absolute position of the vehicle, which is the basis for the overall positioning procedure.