esa-sceva/satcom-chunk-collection huggingface dataset:
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 5 Centralized Key Management Scheme for LoRaWAN
### Key Generation Scheme (KGS)  
In the LoRaWAN v1.1 that the join server is playing the role like CKMS. However, In the CKM scheme that the CKMS is playing a much more important role than the join server in the LoRaWAN, due to the fact that a much heavier key management task to the CKMS would be assigned. At first, The CKMS will have a Key Generation Scheme (KGS) to deal with key generation regarding the session keys derivation.  
The KGS uses the Modified Rabbit PRNG (refer to Section 6 for details), a proposed more efficient (v1.1 uses AES, which can be considered to be computing-heavy) key generation algorithm to generate each session key based on the previous session key or the root key; which key to use for key updating depends on which rounds of the key updating has reached. In Section 6, the paper will also analyze and evaluate the significance of using the Modified Rabbit PRNG compared to the AES-ECB or the original Rabbit PRNG, since it shows a much faster computing speed and achieves a similar degree of randomness to other PRNGs, which means the energy required to generate each key is much lower. Also, it allows the key to be updated more frequently. It is essential for the LoRaWAN devices due to the majority of them are battery-based. Therefore, it would be much harder for cryptanalysts to crack the session keys, meanwhile, the lifetime of the battery in the device is extended.
## 3 Dataset  
This study uses four different datasets, namely the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Toronto Emotional Speech Set (TESS), Emotional Database (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE). Details of the dataset and their limitations can be seen in Tables 1 and 2 respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Description** \\ \hline \multirow{4}{*}{RAVDESS} & The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) is a publicly available dataset designed for affective computing and emotion recognition research. It contains audio and video recordings of 24 actors performing eight different emotions in both speech and song. The dataset provides a diverse set of emotional expressions for study and is widely used in emotion recognition research. \\ \hline \multirow{4}{*}{TESS} & The Toronto Emotional Speech Set (TESS) is a comprehensive and publicly available collection of 2803 audio recordings, featuring professional actors portraying seven distinct emotional categories. The dataset includes balanced representation from both male and female actors, making it valuable for developing and evaluating emotion recognition models. \\ \hline \multirow{4}{*}{SAVEE} & The Surrey Audio-Visual Expressed Emotion (SAVEE) dataset is designed for research into speech emotion recognition, featuring 480 audio recordings with a single male actor portraying seven emotional states. The dataset provides a standardized resource for studying emotional speech and has been widely used in affective computing research. \\ \hline \multirow{4}{*}{EmoDB} & The Emotional Database (EmoDB) is utilized for studying emotional speech recognition and consists of recordings from ten professional German actors portraying different emotions. The dataset, developed by the Technical University of Berlin, is valuable for developing and evaluating algorithms for automatic emotion classification from speech signals. \\ \cl  
Despite limitations in some datasets, they remain valuable for emotional speech analysis research. The TESS dataset, in particular, excels due to its diverse emotional expressions, large actor pool, natural recording conditions, high-quality data, detailed metadata, and multimodal features, making it a robust resource in emotional speech analysis research. Researchers should be aware of dataset-specific considerations when interpreting results and generalizing findings beyond a dataset's scope.
# Accurate Constant Phase Elements Dedicated for Audio Signal Processing
## 4 Applications of CPE  
This section is aimed at giving the reader some ideas, i.e., practical examples, of analog systems that utilize one or several of the CPEs provided in Section 3 of this paper. Figure 5 demonstrates two fabricated printed circuit boards (PCBs) dedicated for realization of CPEs. The first is fully passive in the sense of Figure 1a, where each resistor and capacitor connected in a single branch is composed of a series/parallel combination of three resistors and three capacitors, respectively. Note that, if necessary, shunt components are employed to make a proper connection. Board organization and SMD components minimize the parasitic properties of the PCB and its negative effects on CPE approximation. This renders the PCB useful for frequencies up to tens of MHz. A second PCB shows active realization of CPEs based on two-ports, given in Figure 1i.  
The upcoming demonstration examples require a very good approximation of CPEs. For audio phase shifters, accuracy is a key property, because a smooth transition between two points located on the phase frequency response of a designed two-port is our goal. A reduced Baxandal's circuit, as well as subsonic oscillator, is an example of less demanding applications of the designed CPEs. However, heavily rippled CPEs can cause uncertainties in the specification of the oscillation frequency. Practical experience with applications of derived CPEs shows, once again, that all approximations are extremely sensitive to the numerical values of resistors and capacitors. Thus, the general recommendation is to make careful selections and measure real values of all passive components before assembling on the PCB.
# Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
## 3 Methodology
### Proposed EL Method  
This section describes the implemented DL architecture based on the ensemble learning technique. The objective of the proposed method is to learn and extract medical image representation using three well-known DL models, including MobileNetV2, DenseNet169, and Vision Transformer (VIT). As shown in Figure 1, the input image to the ensemble method is fed to three functional layers simultaneously. At this stage, each functional layer represents a pretrained model that relies on MobileNetV2, DenseNet169, and VIT, respectively. For dimensionality reduction, each functional layer's output (learned representations) is fed to the global average pooling layer. After applying the pooling operation on each parallel flow, the output is flattened and concatenated to generate a single feature vector of each inputted image. To fine-tune the overall network, overcome overfitting, and boost the classification accuracy, a sequential set of layers were placed on top of each other, including batch normalization (BN), fully connected layer (dense), and dropout layer, as shown in Figure 1. The final output of the ensemble method is generated using a fully connected layer to output the classification result.  
Using chest X-ray image data sets, the ensemble method was fine-tuned to learn and extract feature vectors from input images of size 224 \(\times\) 224. The three models MobileNetV2, DenseNet169, and VIT were pretrained on the ImageNet [51]. In our experiments, the pre-trained ensemble method was employed and fine-tuned on the data sets having chest X-ray images. As an output, these models generate a feature vector after flattening of sizes 1280, 1664, and 768, respectively. Thus, the concatenated feature vector is of size 3712. During the fine-tuning of the ensemble method, the weights of the three models were fixed to accelerate the training process.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 4 Results
### Validation of Land-Use Classification and Abandoned Farmland Detection  
A total of 150 verification points were used to evaluate the performance of the land-use classification and abandoned farmland detection. As seen in Table 2, all metrics reached at least 0.7 and above, within the acceptable range. The proposed method achieved outstanding performance for wheat and water body classification, with all metrics higher than 0.93. This is because the temporal VI patterns of wheat and water bodies are significantly different from those of other land uses, making them easier to classify. The confusion matrix in Figure 14 shows that pixels in residential areas were easily misclassified. This is because the components of the residential area are complex, which makes the pixels in them have a high degree of spatial variability. Overall, the proposed method yielded a feasible result with a kappa coefficient of 0.876.  
Figure 11: The spatial distribution of saline classes in Hetao: (**a**) 1989, (**b**) 1999, (**c**) 2010, and (**d**) 2019.  
Figure 12: The trend of the area of sand dunes and deserts and the area of abandoned salinized farmland from 1988 to 2019.  
Figure 14: The confusion matrix of land-use classification and abandoned salinized farmland detection results. The ASF represents the abandoned salinized farmland.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline Wheat & 0.987 & 0.955 & 0.955 & 0.955 \\ Corn and sunflower & 0.960 & 0.914 & 0.970 & 0.865 \\ Other crops & 0.947 & 0.833 & 0.800 & 0.870 \\ Forest & 0.973 & 0.846 & 0.786 & 0.917 \\ Water body & 0.993 & 0.966 & 1.000 & 0.933 \\ Sand dune area & 0.980 & 0.880 & 0.917 & 0.846 \\ Residential area & 0.947 & 0.778 & 0.700 & 0.875 \\ Abandoned salinized farmland & 0.987 & 0.909 & 1.000 & 0.833 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics of land-use classification and abandoned salinized farmland detection results.  
Figure 13: The spatial distribution of the detected abandoned salinized farmland from 1988 to 2018.
## 2 Methodology
### Numerical Simulation
#### 2.2.2 Modeling Process  
The numerical model of this case generally follows the steps: establishing a coal mining process model, meshing, model calculation, and result analysis. Geological parameters, load conditions, and boundary conditions should be determined in advance. Apart from the known values of parameters, to simulate the self-weight, the body force \(\gamma=20\) kN/m\({}^{3}\). To simulate the lateral pressure in actual engineering, the lateral pressure coefficient K\({}_{0}=1\). Boundary conditions on two lateral sides are free in the vertical direction and zero in the horizontal displacement, i.e., u\({}_{1}\)(x\({}_{1}=0\)) = 0, u\({}_{2}\)(x\({}_{1}=0\)) = 1. The bottom layer sets as a fixed-end constraint, and the displacements in all directions are limited, i.e., u\({}_{1}\)(z\({}_{1}=0\)) = u\({}_{2}\) = 0. In order to simulate the mining process, four steps are set to study the variation of stress and displacement as time changes. The advancing distance is gradually increasing as the units in specific regions are gradually deactivated. Optimal meshing is a prerequisite for numerical convergence. An appropriately refined grid not only accelerates convergence but also enhances solution accuracy. In the mesh part, the element shape is set as a quadrangle due to its ability to achieve higher computational efficiency. The model is expressed as a regular rectangle, which can meet the requirements of calculation efficiency by a structured meshing technique. To enhance the computational accuracy, global seeding is implemented. During the seeding process, the approximate global element size is set to 0.5 m.  
Four groups of numerical models, as listed in Table 2, are used to perform the parametric study with a control group X. These groups have a similar setup of geometry and basic properties, only being changed by some parameters, such as Young's modulus, mining height, and advancing rate. The Mohr-Coulomb criterion is utilized as the failure model.
# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 6 Engagement Analysis
### Correlation Analysis among the Engagement Dimensions  
Here we analyze the pairwise intersections of the six engagement dimensions. Although we considered the pairwise intersections in Figure 5, we show only the most representative ones. The numbers in the matrices represent the number of students who have the characteristics of the corresponding areas, where \(H=\)_High, \(M=\)_Medium, and \(L=\)_Low.  
The intersections between _Autonomy_ and _Confidence_ offer valuable insights into how these two indicators interact. Notably, when _High Autonomy_ aligns with _Low Confidence_, we observe a dimensionality of 5. Interestingly, the most substantial intersection occurs when _High Autonomy_ combines with _High Confidence_, resulting in a dimensionality of 65. This indicates a strong correlation, implying that individuals with high autonomy levels often coexist with high confidence levels, potentially reinforcing each other.  
Conversely, _Autonomy_ and _Understanding_ are independent. This shows that help interventions, while they are generally sufficient to solve the specific task for what they were requested, are not always effective for supplying a more comprehensive level of understanding, applicable to all the tasks. Furthermore, they show that the perceived need for external support is very personal and not always related to the actual need.  
Similarly, _Reflection_ and _Understanding_ are not correlated.  
_Confidence_ positively influences _Reflection_. Specifically, 66 of the students who have _High Confidence_ also have _High Reflection_. Conversely, only 26 of the students have _Low Reflection_. This is justifiable because self-confidence helps students to rely on their own  
Figure 4: The seven most frequent students’ profiles.  
capability and to address problems with a reflective approach (compared to a trial-and-error one).
# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 4 Performance and Experimental Results
### Analysis of the Trained YOLO Model Based on Real-Time Experiments
#### 4.2.5 Experiment 5: Blurry Scenes  
The present study successfully demonstrated the ability to detect the presence of a safety belt in images with low clarity. This phenomenon was observed in situations where the camera experienced rapid movement due to factors such as a rough road surface or vibrations. The camera was affixed in accordance with the methodology outlined in Experiment 1, and its stability was ensured through the meticulous adjustment of the holder screws. Following that, we proceeded to traverse a speed bump at a velocity of 40 km per hour, resulting in an observation of significantly distorted visual perception, as illustrated in Figure 18.  
Figure 16: The safety belt detection status of (**A**,**B**) was represented when occupants were wearing clothing that was of a similar colour to the webbing.  
Figure 17: This illustrates the detection status of safety belts in both well-lit conditions (**A**) and low-light conditions (**B**).  
Analysing Statistical Insights and Robustness of Safety Belt Buckle Detection across Diverse Experimental Scenarios  
The results we present here are based on a total of 15 tests carried out for each of the five distinct experimental scenarios. These scenarios were designed to cover a wide range of situations that could affect the detection performance of the safety belt buckle. The outcomes of these tests are depicted in Figure 19, which illustrates the object detection model's accuracy and performance across different conditions. This comprehensive approach ensures that our findings are grounded in a robust and representative dataset, allowing us to draw meaningful insights about the model's effectiveness in real-world scenarios.  
The line plot visualization of experimental outcomes shown in Figure 19 provides a comprehensive overview of the YOLOv7-based object detection model's accuracy in identifying safety belt buckles. The analysis highlights the model's proficiency in distinguishing between buckled and unbuckled belts across diverse scenarios while also pinpointing opportunities for refinement. These insights contribute to the advancement of intelligent safety systems, encouraging further exploration and enhancements in safety belt detection technology for safety applications.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 3 Network Coding Design  
### Review of Network Coding  
Network coding was first introduced by [PERSON] et al. [25] to improve the throughput and capacity of the network. In network coding, the nodes in the network can combine the received data in a linear or non-linear process and each node is able to receive the combined data packets from different nodes. It has been proved by [PERSON] et al. [26] that linear network coding can achieve the max flow bound on information transmission. [PERSON] et al. [27] present an instant primer of network coding that can achieve near maximum throughput without the knowledge of network topology. However, traditional network coding is not suitable in our proposed protocol for the computation complexity: the decoding process of random linear coding needs matrix inversion, and the complexity is \(O\left(K^{3}\right)\) for \(K\) data packets. It will be very complex for a large number of original packets. The complexity is not practical for real-time decoding at each node in the sensor network. To solve this issue, we propose a novel network coding scheme based on fountain code in this paper.
## 3 A Successful Single-Station Alarm for Nearby Moderate-Magnitude Earthquakes  
To examine and explain the relationship between the electric field signal and crustal activity, Chinese Meridian Project researchers have established a regional monitoring network of the atmospheric electric field in Sichuan Province, China. All these stations use EFM100 electric field meters with a measurement range of \(\pm\)50 kV/m, a measurement accuracy of <5% and a resolution of 10 V/m. Minanning Station (longitude = 102.17\({}^{\circ}\) E, latitude = 28.55\({}^{\circ}\) N), where a period of negative anomalies appeared before the Minning EQ, is part of this monitoring network.  
The negative atmospheric electrostatic field Ez signal before the Ms 3.6 Minanning EQ, which occurred at 20:44 LT on 4 October 2022 (longitude = 102.14\({}^{\circ}\) E, latitude = 28.91\({}^{\circ}\) N), is shown in Figure 2. The red circles and stars represent the same features as in Figure 1. The distance between the station and the epicenter is 40 km. The electric field started to show a significant negative anomaly at 20:10 LT on October 2 nd (47.4 h before the EQ), and the negative anomaly lasted for approximately 7 h. The negative anomaly peak was -4.42 kV/m at 22:37 LT on October 2 nd. Based on the meteorological data obtained from the Central Meteorological Observatory, the weather was very fair during the period of the negative electrical field.  
There is a very obvious period of electric field signal anomalies caused by lightning activities before the EQ shown in Figure 2, with a sharp jump in positive and negative values. Compared with the previous meteorological conditions, there was 0.4-8.5 mm of continuous precipitation in the period from 21:00 LT on October 3 rd to 04:00 LT on October 4 th, with a maximum relative humidity of 95% and 10 min average visibility not exceeding 10 km. This confirmed that the electric field signal resulted from the effect of lightning activities.  
To provide an early warning of an EQ by detecting abnormal signals within the atmospheric electric field during fair weather, researchers have used artificial intelligence recognition technology to build a system that can compare meteorological conditions and electric field values to discern whether there are negative anomalies. The system currently uses a simple algorithm that still needs to be improved. At 22:33 LT on October 2 nd (46.2 h before the EQ), this system sent an alarm, which indicated the presence of negative anomalies in the fair weather and warned people to stay alert. This was an example of a successful single-station alarm indicating a nearby moderate-magnitude EQ.
# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 1 Introduction
It has been described that data mining approaches analyze an organization's historical data and figure out the required pattern or information that is otherwise impossible [9]. In DM, both classification and regression are used to build predictive models. The classification techniques are used on pre-classified data to characterize the unclassified data [10]. The classification can be performed using different learning schemes such as decision trees (ID3, REP Tree and C4.5, etc. ), logistic regression (LogitBoost), backtracking (MLP), and probability (Naive Bayes, Bayes Network). These algorithms are known as single classifiers which have some limitations for the accuracy of the model. It is therefore important to improve the performance of single classifiers. To achieve such a goal, the ensemble method has been introduced which combines different classifiers into a single unit. However, in the ensemble structure, many areas must be explored to increase the accuracy of prediction models. A research study has been conducted to discover other algorithms of learning schemes in different ensemble methods which can help in predicting students' academic performance with greater accuracy [11]. This research intends to propose a classification model developed by fusion of single-based classifiers and ensemble-based classifiers to predict the academic performance of secondary-level students based on their academic and personal traits. The prediction of students' grades from their academic data along with other features is a useful application in EDM; it is therefore becoming a suitable source of information that can be utilized in multiple ways to improve the quality of education. The major contributions of this research study towards EDM include (a) the identification of the personal traits of secondary school students. (b) Development of a dataset by gathering data from four different secondary schools located in three different cities. The academic data were collected through a student information system and data related to personal characteristics and socioeconomic conditions was gathered using online and physical surveys. (c) Building of a model which analyzes students' academic and personal data and predicts their academic progress with higher accuracy and precision. The rest of the paper is organized as follows: Section 2 presents a literature review that highlights the findings of prior research studies in order to identify the set of the most common factors affecting students' academic performance. Secondly, it determines the most frequently used data mining techniques in previous research. Section 4 presents the research methodology, and the section is about data collection and data preprocessing. In Section 5, the conclusion and future prospects of this research study have been presented.