esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Due to the rapid movement of LEO satellites, the communication between the target to be jammed and the LEO satellites is frequently switched, making it difficult for traditional ground-based distributed jammers to effectively implement targeted jamming. Distributed lift-off jammers are more effective in interfering with LEO signals than single jammers and traditional distributed jammers [20]. However, limited resource optimisation has not been reasonably possible for distributed lift-off interference sources [21]. When facing complex, dynamically changing LEO signals, the existing distributed jammer optimisation method makes it difficult to guarantee the jamming success rate while achieving simultaneous improvement in multi-dimensional jamming effectiveness. In addition, these interference optimisation methods are often limited to optimisation in a single dimension, and it is difficult for the optimisation results to fully meet expectations. In order to address the above issues, this study provides an in-depth study of the LEO satellite communication system model and the characteristics of its signals in terms of time, frequency, and power. Considering the interference performance and spatial distribution of the distributed interference sources, an LEO satellite multi-dimensional interference optimisation method based on hybrid game theory is proposed. The main innovations of this study are as follows:  
1. In this study, a distributed and collaborative LEO satellite jamming architecture with jammers is established to meet the fast, time-varying demand for LEO satellite signal jamming. 2. In this study, we propose a multi-dimensional adaptive optimisation method based on a hybrid game in the air-temporal-frequency-energy domain to achieve the optimal relative jammer allocation strategy under the Nash equilibrium for millisecond-level interference optimisation. 3. In this study, an LEO satellite interference verification system based on full-sky star fitting is constructed in a large microwave darkroom, and the verification of an LEO satellite multi-dimensional interference optimisation method based on hybrid game theory is completed. Compared with the existing interference optimisation methods, it has shown improvements in the BER and real-time response speed. The rest of the article is organised as follows: Section 2 presents the related work on LEO satellite jamming techniques and optimisation methods, Section 3 describes the system model, Section 4 introduces the relative jammers screening algorithm, based on the hybrid game, and Section 5 presents the simulation verification and analysis. Section 6 is a summary of the whole paper.
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 1 Introduction
The proposed back propagation neural network forest fire identification (BPNNFire) algorithm calculates the processing speed and delay rate of the video images. The data are collected and preprocessed to remove noise. The model recognizes the forest fire images, and the classifier classifies them to distinguish between images with fire and those without fire. A fire image is classified locally for feature extraction of forest fire images and is transmitted to a remote server for storage.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 4 Numerical Results
### Simulation Results  
We considered a statistic channel simulation model in a mobile communication scenario, where the channel gains followed an independent Rayleigh distribution. The simulation's parameter settings are shown in Table 3. The maximum multi-path delay was set to \(\tau_{\text{max}}=100\) ms. There was a total number of eight random multi-paths within the maximum delay range, in which the channel gain followed an independent Rayleigh distribution. The moving speed was set to \(v_{m}=3.8\) knots (1 knot is 1 nautical mile per hour, which is equal to 1.852 kilometers per hour), and the corresponding maximum Doppler spread was \(f_{D}=(v_{m}f_{c})/c=7.8\). The Doppler coefficient of each path was generated in \([-f_{D\text{max}},f_{D\text{max}}]\) with equal probability.  
Figure 6 shows the BER comparison of multiple signal detection methods for UWA OTFS. At the BER of \(5\times 10^{-3}\), the proposed SC-CNN-BiLSTM could achieve about 5.5 dB, 3 dB and 1.5 dB of improvement compared with the MP, FC-DNN and 2D-CNN, respectively.  
All deep learning-based signal detection methods (SC-CNN-BiLSTM, 2D-CNN and FC-DNN) perform better than conventional linear ZF, LMMSE and nonlinear MP, as DL-based OTFS signal detection methods can use nonlinear operations in neural networks to better fit data in the DD domain compared with the linear-based method. Compared with nonlinear MP, DL-based OTFS detection can fit the input-output relationship through iterative optimization of the parameters and avoid falling into a local optimum for better performance.  
The two CNN-based signal detection methods, SC-CNN-BiLSTM and 2D-CNN, performed better compared with FC-DNN-based signal detection. As the neurons in a CNN are connected to each other, the weights of neurons on the same feature mapping layer are shared. Therefore, the CNN can learn in parallel to avoid overfitting and achieve faster convergence. This is the major advantage of CNNs compared with other neural networks. Moreover, the CNN uses the ReLU activation function to prevent gradient disappearing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Params** & **Value** \\ \hline Channel gains \(h_{i}\) & Rayleigh distribution \\ Maximum multi-path delay \(\tau_{\text{max}}\) & 100 ms \\ Number of multi-paths & 8 \\ Moving speed \(v_{m}\) & 3.8 knots \\ Maximum Doppler spread \(f_{D}\) & 7.8 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of statistic simulation channel.  
Figure 6: BER performance comparison for simulation channel.  
SC-CNN-BiLSTM outperformed the 2D-CNN. A single CNN can only extract local features and cannot process time series data efficiently when used. As in SC-CNN-BiLSTM, we added symmetric skip connections to the six-layer CNN. The SC-CNN can provide more efficient information through interactions of convolutional and deconvolutional layers. After the SC-CNN extracts the important features of input vector \(y\), BiLSTM can further focus on effective information in the data sequences to mitigate ICI and ISI.
## 8 Experiments
### Datasets  
The experiments used five datasets: GosVL [47], Gos [48], Artur_J, Artur_N, and Artur_P[49]. All of them resulted from transcribing original speech records. Transcriptions are openly accessible through the CLARIN.SI repository [50]. They are provided in the form of an XML structure. The GosVL dataset contains 22 h of public academic speech. The Gos dataset is the reference speech corpus for Slovenian. It includes 112 h of speech across various real-life scenarios: radio and TV shows, school lessons, private conversations, work meetings, and more. Artur is the newest and largest database of Slovenian speech collected recently. It comprises 1000 h of recordings. We used three subsets of the Artur database, which contain the highest degree of non-standard language. Artur_J has utterances of public speech, Artur_N utterances of non-public speech, and Artur_P parliamentary speech.  
The datasets are published in the XML-TEI format, where all tokens are basic XML elements. The literal form of the words is written as the element's content, whereas the standard forms are given as attributes. If the attribute is not given, the standard form is identical to the literal form. The datasets were preprocessed to extract the literal and standard forms.  
After preprocessing, each dataset was split into three parts: 80% was used for training, 10% for development, and 10% for evaluation. The segments were selected randomly with no overlap between different parts. Information about the datasets is presented in Table 4. The Gos dataset was the largest dataset in our experiments, and GosVL was the smallest. We conducted experiments on each dataset separately and on all the data combined; we named the latter set All.
## 3 Artificial Intelligence (AI) and Machine Learning (ML)  
AI and ML are two related but separate topics in computer science. AI seeks to replicate human intelligence in machines, while ML focuses on creating algorithms that allow computers to learn from data (Figure 1 and Table 1). In the succeeding sections of this article, the differences and characteristics of these two complementary fields will be explored more in-depth.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Aspect** & **AI** & **ML** \\ \hline Description & AI is the emulation of human intelligence processes by machines, particularly computer systems. It entails creating systems or algorithms capable of carrying out activities that usually demand human intelligence, like comprehending natural language, identifying patterns, acquiring knowledge from experience, and making judgements & ML is a branch of AI that concentrates on creating algorithms and statistical models to empower computers to carry out tasks without absolute instructions. \\ \hline Goal/objective & To create systems with the ability to carry out activities that usually demand human intelligence & To allow machines to enhance their performance on a particular activity by learning from data without the need for explicit programming \\ \hline Focus/scope & Has a broader scope, encompassing a wide range of approaches, including ML & More focused on extracting patterns and insights from data to enhance the performance of a certain activity or application. \\ \hline Techniques and methodologies & Incorporates a variety of methods and techniques, including computer vision, natural language processing, expert systems, etc. & Primarily focuses on mathematical and statistical techniques algorithms for analyzing data and forecasting outcomes \\ \hline Data dependency & May or may not depend strongly on acquired data & Strongly data-dependent for model training and predictions \\ \hline Human intervention & Capable of functioning autonomously or with human involvement & Dependent on human involvement for training, validation, and adjusting parameters \\ \hline Decision making & Can make decisions based on predetermined rules or acquired patterns & Utilizes data-driven patterns to make decisions. \\ \hline Adaptability & Capable of adjusting to novel circumstances by following predetermined guidelines or assimilating information from fresh data & Enhances and refines its performance through increased data and iterations \\ \hline Examples & Chatbots, expert systems, virtual assistants, autonomous cars, robotics, natural language processing, computer vision, game-playing AI, etc. & Regression, classification, clustering algorithms, neural networks, decision trees, SVM, and reinforcement learning algorithms are examples of ML techniques \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between AI and ML.
# Multiscale Analysis for Improving Texture Classification
## 4 Experimental Results
### Experiments with HI Datasets
Thus, combining the descriptors mentioned above increased the accuracy by nearly 1%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Approach** & **10-fold** & **5-fold** \\ \hline [PERSON] et al. [35] & Shallow & 87.40 & – \\ \hline [PERSON] and [PERSON] [36] & Shallow & 73.60 & – \\ \hline [PERSON] and [PERSON] [9] & Shallow & 92.96 & – \\ \hline TiO+LightB & Shallow & **94.50** & **92.77** \\ \hline [PERSON] et al. [37] & CNN & – & 92.60 \\ \hline [PERSON] [38] & CNN & – & 84.00 \\ \hline [PERSON] et al. [39] & CNN & 92.40 & 92.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average accuracy (%) of shallow and deep approaches on the CRC dataset. The best results are marked in boldface. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Magnification**} & \multicolumn{4}{c}{**Classification Algorithms**} \\ \cline{2-5}  & **HistoB** & **LightB** & **LDA** & **CatBoost** \\ \hline
40\(\times\) & **98.64** & 98.12 & 97.83 & 98.04 \\ \hline
40\(\times\) (CV) & **98.47 (0.021)** & 97.64 (0.015) & 98.12 (0.029) & 97.28 (0.026) \\ \hline
100\(\times\) & **97.85** & 97.50 & 96.81 & 96.67 \\ \hline
100\(\times\) (CV) & **97.28 (0.021)** & 96.91 (0.015) & 95.56 (0.027) & 96.46 (0.024) \\ \hline
200\(\times\) & 97.62 & **98.76** & 94.63 & 97.28 \\ \hline
200\(\times\) (CV) & 96.82 (0.022) & **97.92 (0.014)** & 95.93 (0.027) & 97.13 (0.024) \\ \hline
400\(\times\) & **98.22** & 98.06 & 94.92 & 96.98 \\ \hline
400\(\times\) (CV) & **97.81 (0.018)** & 97.48 (0.022) & 94.11 (0.027) & 96.07 (0.025) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Image-level accuracy (%) of monolithic classifiers and ensemble methods with the TiO descriptor on 8 balanced classes of the BreakHis dataset. The best result for each magnification is shown in boldface. CV denotes the average accuracy and standard deviation (%) using a 10-fold cross-validation experimental setup.
###### Abstract  
Objective: Our objective was to investigate the effect of the rehabilitation system using augmented reality (AR) on upper extremity motor performance of patients with stroke. Methods: The system using AR applying mirror therapy mechanism provides the intervention protocol for the patient with hemiplegia after stroke. The system consists of a patient positioning tool (a chair), a white surface table, an image acquisition unit, an image processing unit, an image displaying unit, an arm holder, a Velcro-strap, and two blue circle stickers. To assess the feasibility of our system in motor function recovery, a stroke patient was recruited to receive the AR intervention. The treatment was performed two times a day for ten minutes over two weeks (ten days of treating weeks), except for the time of installation, calibration, and three minute breaks. [PERSON] hand function test and Arm Motor Fugl-Meyer assessment were used as primary and secondary outcome measures, respectively, to evaluate the effect of motor function recovery. Additionally, stroke impact scale, Korean version-Modified Barthel Index (K-MBI), active range of motion of wrist joint (ROM), and the grasp force in Newtons were measured. Participants' feedback and adverse effects were recorded as well. Results: Motor function improvements were exhibited in wrist and hand subtest of Arm Motor Fugl-Meyer (baseline: 19; post-intervention: 23), proximal arm subtest of Fugl-Meyer (baseline: 31; post-intervention: 34), ROM (extending ROM: \(10^{\circ}\) and \(3^{\circ}\) for flexion and extension, repeatedly), stroke impact scale (baseline: 46; post-intervention: 54), K-MBI (baseline: 92; post-intervention: 95), nine-hole pegboard (baseline: 30 s; post-intervention: 25 s), and grasp force in Newtons (baseline: 12.7; post-intervention: 17.7). However, the adverse effects were reported after the intervention. Conclusion: The system using AR applying mirror therapy mechanism demonstrated the feasibility in motor function recovery for the stroke patient.  
augmented reality; stroke; rehabilitation; mirror therapy
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the IoT-23 Dataset Using a Supervised Machine Learning Approach
#### 4.1.2 Research Analysis According to [31]  
Other research [31] has presented novel and known approaches to anomaly detection in IoT networks using the IoT-23 dataset. The methodology involves several key steps.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **ML Approach** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Source** \\ \hline Random & Supervised & 99.9\% & 99.9\% & 99.9\% & - & [45] \\ Forest & Supervised & 99.887\% & 99.891\% & 99.987\% & 99.880\% & [31] \\ \hline Random & Supervised & 99.99\% & 100.00\% & 100.00\% & 100.00\% & [24] \\ Forest & \multicolumn{1}{c}{} & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification report summary on the IoT-23 dataset (own elaboration).  
Step 1: Data Preparation and Processing  
The initial phase focused on cleaning and preprocessing the IoT-23 dataset to ensure its quality and suitability for analysis. This included removing duplicates, handling missing values, correcting errors, and standardizing the data using the z-score normalization method.  
Step 2: Feature Selection  
A collaborative ML approach was employed to identify the most relevant features for anomaly detection. Four ML techniques (decision tree, Extra Tree, Random Forest, and XGBoost) were used to independently nominate features based on their detection performance. Features with high scores were incorporated into the optimal feature set.  
Step 3:Model utilization: Random Forest  
An ensemble learning technique utilizing the Random Forest algorithm was implemented. This involved creating a collection of decision trees from random samples of the training data. The Gini importance measure was used to assess the significance of each feature in the decision trees. The ensemble of decision trees was then used to classify traffic flows as normal or anomalous, with a majority vote determining the final prediction.  
Step 4: Model evaluation  
To assess the performance of the proposed method, two experiments were conducted. In Experiment 1, the dataset was divided into 75% for training and 25% for testing. The model's performance was evaluated using metrics such as Accuracy, Precision, Recall, Net Present Value(NPV), Matthews Correlation Coefficient(MCC), and F1-score. In Experiment 2, the dataset was split into 10% for training and 90% for validation, with the same evaluation metrics used. The proposed method was compared to baseline algorithms like bagging, Gradient Boosting, and RF.  
Step 5: Result verification  
The performances of the proposed method and baseline algorithms were evaluated for both binary classification (normal vs. anomalous) and multi-class classification (predicting different types of attacks). The results demonstrated that the Random Forest model, as part of the ensemble learning technique, consistently outperformed the baseline algorithms in terms of Accuracy, Precision, Recall, and F1-score.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 3 RARZ Protocol
### Message Formats Used in RARZ  
To meet the needs of RARZ, we have defined some unique message formats, as shown in Figure 5. The header of each of the message is shown below.  
When MsgKind is set to 0, the message is broadcast by the BS for ring formation. A value of 1 for MsgKind represents the send route message, i.e., the node's own message, and 2 denotes a re-route message, i.e., a message that is received by an intermediate node for further relaying. MsgSeqNo field is used to keep track of the packet ID. Initially, it is set to 1. Once the message is received by nodes from the BS, they set their ringID to the MsgSeqNo. In case of a re-route message, the node that receives the packet first checks the packet ringID and compares it to its own ringID. When its ringID is less than the ringID of the received packet and the node is selected as the next hop, it updates the ringID field of the received packet by its own ringID, resets the MsgKind from 1 to 2, and sends it further. The same mechanism is applied at each hop. A beacon message with MsgKind '3' is used to remove a scheduled packet that has already been sent by another node in the same ring. In this way, the packet is forwarded from upper-level rings to lower rings and routed to the BS. All the messages defined in the protocol and their fields are mentioned in Figure 5.  
Figure 4: Finding the next hop node in RARZ.  
Figure 5: Message formats used in RARZ.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 1 Introduction
This work utilizes a data fusion strategy so that spatiotemporal features of the cracks in videos are efficiently used, showing improved achievement of 98.3% hit rate toward the traditional inspection system of a nuclear power plant. More recently, [PERSON], et al. [17] propose a semi-supervised approach for steel surface defect recognition based on CNN. The method has better performances with 17.53% improvement compared to the baselines. Also, it has been applied in a real-world detection scenario with a limited labeled dataset. Although several studies have been conducted to enhance the defect detection performance in the steel surface, there are still challenging issues for practical use, which motivates this study. Firstly, the optimization of a deep neural network-based model should be conducted. Tuning the hyper-parameters and building the optimized architectural structure should be carried out to maximize the classification performance for detecting the steel surface defects. Besides, training the network and an over fitting problem could be a practical issue while operating for both observed and unobserved data. Secondly, decisions made by deep learning algorithms should be interpretable. Connections between input and output of a deep neural network-based model could not be described using specific mathematical analyses or mapping functions. When the defect is detected during the process, the explanation should be given to demonstrate the basis of the decision-making of a black-box model. Imbued by the above-mentioned challenges, this paper proposes a convolutional neural network (CNN) based detection method for improving vision-based diagnostics model and enhancing its explainability while classifying steel surface defects. The CNN classification model is developed to learn high-dimensional characteristics of spatial information, being developed to discriminate among 6 different kinds of surface defects, i.e., rolled-in scale, patches, crazing, pitted surface, inclusion, and scratches. Furthermore, class activation map (CAM) is localized to describe the most significant parts from the images, providing as an interpreter of the results. To validate our proposed method, the performance resulted from the proposed model is compared with conventional machine learning-based classification algorithms, i.e., support vector machine (SVM) and logistic regression.
# Probabilistic Analysis as a Method for Ground Freezing Depth Estimation
## 2 Material and Methodology
### Estimation of Gumbel Distribution Parameters
#### 2.3.2 Maximum Likelihood Method  
Maximum likelihood estimation is recommended due to its many advantages. In this method, the assessment of the estimated parameters is based on values maximizing the credibility of the sample. Due to the lack of analytical solution for assessment of the distribution parameters, numerical methods are used. Estimators obtained with this method are consistent, asymptotically most efficient and they have normal asymptotical distribution. In practice, this method is rarely used due to calculation complexity, as the values of parameter distribution are assessed iteratively. The implementation of the maximum likelihood method (Max LM) consisted of seeking the maximum of the likelihood function given by the formula:  
\[l=\prod_{i=1}^{n}\alpha\cdot\exp\{-\alpha(Z_{i}-U)-\exp[-\alpha(Z_{i}-U)]\} \tag{7}\]  
The values \(\alpha\), \(U\) for which the Function (7) reaches its global maximum are the parameters of the Gumbel distribution estimated by the maximum likelihood method.