esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System modeling
### Unsupervised learning methods
#### 4.2.2 Adaptive resonance theory based neural network - type 2a (ART-2a)  
As an unsupervised learning method, ART-2a is mainly used for cluster analysis. Different from K-means algorithm where the number of clusters needs to predefined, ART-2a shows its advantageous ability to add a new cluster without disturbing the existing clusters [80], so it has a potential to be used for real-time sensor data analyses. ART-2a has become the most widely used method for mass spectrometry (MS) data cluster analysis, calibration modeling, and their applications for environmental quality studies [74, 81-83].  
Given the initial cluster center vectors which usually are randomly selected from the sample set, the learning process of ART-2a can be divided into 4 steps:  
1. Randomly select an input sample vector and scale it to unit length.  
2. Find the neuron whose cluster vector has the largest resonance to this input vector and call this neuron the \"winner\". The resonance is estimated by the dot product of the input sample vector and the cluster vector.  
3. If the resonance of the winner neuron is larger than a predefined vigilance factor (VF), \(\rho_{\text{vig}}\), adjust the cluster vector of the winner neuron toward the input sample vector. Otherwise, create a new cluster for this sample vector.  
4. Repeat the above steps for all the sample vectors, which is defined as one cycle. Continue this process, until a stopping criterion is reached.  
Clearly, the number of clusters is adaptively determined by ART-2a, which makes the cluster analysis more robust and flexible. It can be seen that the vigilance factor is a key parameter to control the cluster number. An overly large vigilance factor could result in an \"overly fine\" clustering result (the extreme case is one cluster for one mass spectral sample) by generating many homogeneous small clusters, while an overly small vigilance would result in an \"overly coarse\" result [81]. In ART-2a, a sample that does not belong to any currently existing clusters is classified into a new cluster. However, during the subsequent training process, this new cluster will only expand but never be merged with its neighbor clusters, even if this new cluster becomes very similar to its neighbor clusters. Therefore, some clusters generated by ART-2a could have a significant overlap among their sample distribution spaces, especially in cases with high vigilance factors. A possible remedy for this problem is to regroup the ART-2a clusters with the same vigilance factor.
## 4 Results and Discussion
### Coverage Prediction via Ray-Tracing
#### 4.3.3 Non-Line-of-Sight Link Nlos2  
For link NLOS2, the PL of the first-order wall reflection is 92.4 dB, with a path length of 6.4 m. This corresponds well to the angular PL measurement. The reflected path via a ceiling and wall reflection has a PL of 98 dB, and the path via a reflection on window 4, followed by a wall reflection and another window reflection (on window 5), has a PL of 104 dB. Three more paths involve a ceiling reflection and multiple wall reflections, with PL values ranging from 107 dB to 125 dB.  
The throughput estimation for the grid point corresponding to the RX location of NLOS2, is 2.5 Gbps, which is lower than the estimation based on SNR (3.85 Gbps), but higher than the throughput based on PER (1.15 Gbps).
## 4 Conclusions  
This study investigated young and mature coconut fibers as asbestos replacements in fiber-cement flat sheets. Based on the results achieved, the following conclusions can be drawn.  
1. Asbestos in flat sheets was replaced by young coconut and mature coconut, which represents innovative research on the application of natural fibers in construction.
2. Fiber-cement boards with YCF had densities of 1399-1882 kg/m\({}^{3}\), while those with MCF ranged from 1498 to 1993 kg/m\({}^{3}\). Adding coconut fiber (5-9.5%) reduced density and increased porosity. In terms of mechanical properties, the modulus of rupture peaked at 12-13 MPa for both fiber types with an optimum content of 6.5-7% by weight of binder.
3. A good relationship between the modulus of elasticity and modulus of rupture was found. The modulus of elasticity (E) and modulus of rupture (Rf) showed a strong correlation (R\({}^{2}\) = 0.71), with E ranging from 4.789 GPa to 11.801 GPa. A higher Rf led  
Figure 12: Scanning electron microscopy (SEM) of raw materials: YCF (**Left**), MCF (**Right**).  
Figure 13: SEM of fiber-cement flat sheets mixed with: YCF (**Left**), MCF (**Right**).  
to a higher E, improving deformation capacity and load redistribution. Coconut fiber type had minimal impact on this relationship.
4. The thermal conductivity of fiber-cement sheets decreased with higher YCF (0.334 to 0.224 W/mK) and MCF (0.354 to 0.245 W/mK) content. Strong correlations between bulk density and thermal conductivity were observed.
5. The flat sheets made with mature coconut fiber had denser microstructure than the flat sheets made with young coconut fiber based on SEM results.  
P.K.: data curation, conceptualization, writing--original draft; T.K.: conceptualization, formal analysis, writing--review and editing, methodology; C.C.: visualization, validation; S.D.: writing--review and editing, visualization, validation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Council of Thailand (NRCT). [PERSON] was mainly supported with NRCT funding (grant NRCT 127/2560).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors gratefully acknowledge the support from the Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand during the research.  
The authors declare no conflicts of interest.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 3 Deep Learning-Based Channel Estimation  
In wireless communications systems, coherent detection requires knowledge of the propagation channels between the transmitter and the receiver, which are possible to estimate by utilizing conventional estimation techniques. In this section, we present the two widely-used channel estimation schemes that motivate us to exploit deep learning frameworks to improve the channel estimation errors.
# Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats
## 4 Communication Methods and Architecture
### Communication Methods  
When one discusses the communication aspect of UAVs, one is directed toward several integral subcomponents such as the communication protocols, the network type, and the UAV model itself. This means that with the change of communication method, one may induct the number of components and this will change the architecture of the system [27]. Many researchers have suggested several topologies and designs. This has been illustrated in Figure 4 along with the altitude range. One may opt for a different communication module and protocol as per the mission and the nature of task type [28]. Moreover, with the advent of 5G networks, several constraints such as data rate, latency, and coverage have been resolved. This advancement in communication technology not only improved these areas, but also helps to improvise the positioning and control of drones in several critical rescue and surveillance missions; for that purpose, people have used advanced flight controls and multiple sensors as well, along with camera placement, controlled and communicated in different ways as per their altitude as shown in Figure 5. These technologies are summarized later in Table 3[29].  
Figure 5: Communication methods for high- and low-altitude levels.  
Tables 3 and 4 are very important for the readers to understand the significance of the several channels based on different bandwidths and satellites, respectively. Discussing Table 3, it communicates the different wireless communication methods, but at the same time, it shares that the communication will have a latency rate as well. The table also shares the channel width, band interval, and most importantly, the mobility support for the readers to design their drones accordingly.  
Discussing Table 4, it communicates the type of communication based on satellite type. This will help the reader to see the elevation in kilometers, number of satellites, satellite life, handoff frequency doppler gateway cost, and most importantly, propagation path loss of each satellite communication [31].
## 2 State of the Art
### Impact of PLC Repeaters on Network Performance-Related Works  
Many researchers conducted their studies on power line behavior in terms of its suitability for transmitting signals in a given frequency range or resistance towards noise which is typical for these lines, but very few of them focused on the exact impact of the repeater on network performance.  
Of the published works about a repeater's influence on network performance, two types of research methods were recognized: simulations and real measurements. An overview of related works is summarized in Table 1 including the number of HEs, REPs and CPEs involved.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Semantic Feature Matching  
After acquiring depth maps of the two domains' images, this section explains how to realize the matching method between real endoscopic images and corresponding SI images. In order to improve time performance, we extracted semantic features of the depth maps to match endoscopic images with SI images. We used an auto-encoder [34] network to extract high-level semantic features of each image. We encoded the 240 \(\times\) 320 image into the feature vector of 3840 dimensions.  
We used vector Euclidean Distance to calculate the similarity of semantic features, which was a common but effective method. In the matching process, we built a semantic feature database by using the preoperative depth map dataset, and searched for the best match in the database by using the intraoperative depth map features to realize matching between semantic features. Then, CT-video registration was re-implemented based on the mapping relationship between semantic features and preoperative and intraoperative anatomical positions. The process of semantic feature matching is shown in Figure 5. The valid frames of the intraoperative video were successively inputted to style transfer network, depth prediction, and semantic feature extraction. Similarity analysis was conducted between the calculated features and the semantic feature database constructed from the semantic features of the preoperative depth map dataset, and the top-10 images with the best matches were output after sorting. When SI-DM images were acquired in Section 2.1.1, the virtual camera's position and angle corresponding to each simulated image were saved. After obtaining the matched top-10 simulated images, the positions of the frames could be displayed in the 3D model, which could reflect whether the match was correct according to the position of the branch. When one or more of the matched top-10 images matched the correct anatomical location, the match was considered a successful case.  
Figure 4: Examples of typical ureteroscope video images. The first row shows problematic or uninformative frames due to being blurry, kidney stones, or flocculent, and the second row displays informative images without any impurity.
## 4 Results and Discussion
### Signal Subcomponents (Product Functions (PFs)) Solving the Non-Stationary Problem  
The iterative decomposition algorithm LMD has shown great potential for handling various kinds of signals. However, the number of obtained PFs is not completely consistent when applied to EEG signals. This phenomenon may be caused by the endpoint effect during the decomposition procedure. Therefore, an approach known as the mirror extending technique was applied to address this problem. This technique involves the boundaries of signals at both ends being counted as two symmetrical axes, so that the original segment can be stretched into a triple-length sequence. Finally, the LMD can be subsequently executed on this extended sequence.  
Sample EEG signals of these five EEG datasets (datasets A, B, C, D, and E) and their corresponding PFs (PF1 to PF5) are shown in Figure 4. The subcomponents from top to down are denoted by PF1 to PF5, and PF1 generally contains more high-frequency elements than the others. Considerably more noise is always contained in the highest frequency component, and similarly the lowest component always contains large amounts of useless information. The decomposed PFs of each EEG segment were considered as the high frequency component (PF1) to the low frequency component (PF5). Therefore, the subcomponents from PF1 to PF5, meaning from high-frequency to low-frequency components, were employed to represent the functional information generated by one or several neurons.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Load and Generation Forecasting in Planning  
#### 2.1.1 Load Forecasting  
Digital technologies have revolutionized load forecasting (LF) in power system planning by integrating renewable sources and evolving consumption patterns. AI and ML innovations have enhanced LF accuracy, as evidenced by the lower error metrics and setting new precision benchmarks [75]. Hybrid applications of AI and deep learning (DL) can improve power distribution forecasting, demonstrating the potential of diverse computational techniques for better reliability and efficiency [50]. This statement highlights the challenges of integrating advanced forecasting technologies into existing power grids and managing the large volumes of data they produce. It emphasizes the need for a balance between embracing innovation and ensuring practical, reliable grid operation. This balance is crucial to maintain efficiency and effectiveness in power distribution as we adopt new technologies [76]. Future research should focus on refining LF techniques for dynamic power systems to enhance renewable integration and address energy policy implications [13].
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 3 Proposed Scheme
### Encryption  
The pseudo-random matrix with the same size as the marked image bit matrix is generated by the encryption key \(Ke\), and then, XOR is performed with the marked image bit matrix to obtain the encrypted image.  
The range of the gray pixel value \(f\) (\(i,j\)) at the position (\(i,j\)) in the marked image is [0, 255]. Let the bit of each pixel be: \(b_{i,j,1}\), \(b_{i,j,2}\),... \(b_{i,j,s}\), and the relationship between it and the gray value \(f\) (\(i,j\)) is shown in (7) and (8):  
\[b_{i,j,k}=\left\lfloor\frac{f(i,j)}{2^{k-1}}\right\rfloor\text{mod}2,k=1,2,\ldots 8 \tag{7}\]  
\[f(i,j)=\sum_{k=1}^{8}\left(b_{i,j,k}\times 2^{k-1}\right),k=1,2,\ldots 8. \tag{8}\]  
Then perform the XOR operation between the pseudo-random binary array \(r_{i,j,k}\), and \(b_{i,j,k}\). As shown in (9):  
\[B_{i,j,k}=b_{i,j,k}\oplus r_{i,j,k},k=1,2,...8. \tag{9}\]  
where \(B\) is the encrypted image. The encryption key \(Ke\) is also used as the decryption key, which is reversible, ensuring that the image content before encryption is completely restored in the decryption phase.
# Statistical Analysis and Modeling for Optical Networks
## 4 A Brief Introduction to Optical Networks  
We next introduce optical networks and their components in order to establish the relationship between statistical modelling and optical network design. Optical networks are high-speed communication systems that use light to transmit data through optical fibers, offering superior bandwidth, low latency, and minimal signal loss compared to traditional electrical communication networks [1]. These networks rely on wavelength-division multiplexing (WDM) and time-division multiplexing as the main technologies to transmit multiple data streams simultaneously over a single fiber by assigning different wavelengths (thus different frequencies) of light to each channel. Optical networks can be classified into backbone networks, metro networks, and access networks, serving global, regional, and local communication needs, respectively [2]. Key components include optical switches, amplifiers, and transceivers, which enable efficient routing and signal regeneration [1]. Their advantages make them essential for internet infrastructure, data centers, and telecommunications, supporting high-demand applications like 5G, cloud computing,and artificial intelligence [42]. Passive optical networks and elastic optical networks are advanced architectures which improve scalability and flexibility [1]. As data consumption grows exponentially, optical networks continue to evolve, driving next-generation connectivity and smart infrastructure.
## 3 Results and Discussion
Despite QDs being semi-conductor particles, the energy of excited QDs also transfers to the acceptor molecule. The efficiency of the energy transfer depends on the minus six power of the distance between the QD center and the acceptor, similarly to the case of a dye molecule as a donor [51, 52]. The distance dependence of Equation (1) is derived for the FRET between a dye molecule and GO [28, 53], but is also valid for the FRET between a QD and GO [54, 55].
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 2 Adaptive Environment Partitioning Invariant Learning Framework  
### Invariant Learning  
Different from the empirical risk minimization principle of traditional machine learning algorithms, the optimization target of the invariant learning framework is invariant risk. Invariant risk minimization (IRM) was first proposed by [PERSON] in 2019. Its basic principle is to understand the causal relationship between features and outcomes that remain consistent across various training environments, so as to achieve the OOD generalization that is insensitive to environment change [18].  
Assume a dataset \(\big{\{}(x_{i}^{e},y_{i}^{e})\big{\}}_{i=1}^{n_{e}}\) extracted from multiple training environments \(e\in\varepsilon_{tr}\). \(x_{i}^{e}\in\chi\) is the input to the model, \(y_{i}^{e}\in\gamma\) is the output. \(f\) is the predictor, and the risk of \(f\) within the environment \(e\) is defined as:  
\[R^{e}(f)=E_{X^{e},Y^{e}}[l(f(X^{e}),Y^{e})] \tag{1}\]  
In the formula, \(l\) represents the loss function, which measures the difference between the predicted value \(f(X^{e})\) and the actual value \(Y^{e}\).  
Ref. [2] pointed out that, if there is a data representation \(\Phi:\chi\to H\) with a classifier (or regressor) \(\omega:H\rightarrow\gamma\) that is optimal under all environments, \(\omega\circ\Phi\) will be an invariant predictor. That is, under all environments, \(\omega\in\arg\min\limits_{\widetilde{\omega}:H\rightarrow\gamma}R^{e}( \omega\circ\Phi)\) is tenable. Then the learning target of IRM can be described as:  
\[R^{e}(f)=E_{X^{e},Y^{e}}[l(f(X^{e}),Y^{e})] \tag{2}\]  
where \(\omega=1.0\), \(\lambda\) is a coefficient for balancing invariant risk and empirical risk.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method  
The overall framework of the proposed method is illustrated in Figure 1. This method utilizes spectral features as input and integrates two independent detection frameworks: SpecMFN and ASDNet. SpecMFN combines SpecNet and MobileFaceNet to achieve high detection accuracy, particularly excelling in pAUC, making it suitable for high-sensitivity and robust anomaly detection tasks. On the other hand, ASDNet is a lightweight network specifically optimized for resource-constrained environments, such as embedded systems or real-time detection applications. It significantly reduces computational complexity while maintaining strong detection performance, providing an efficient solution for scenarios with limited resources. During the training phase, Mixup [38] is employed as a data augmentation technique to mitigate overfitting and enhance the robustness of the models. Both frameworks ultimately utilize multiple methods for anomaly scoring, including cosine similarity, GMM, K-Means, and LOF. The subsequent sections provide a detailed explanation of input feature extraction, the design of classification networks, and the methods used for calculating anomaly scores.  
Figure 1: The framework of the proposed method for anomalous sound detection.
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 3 Background Theoretical and Conceptual Framework
### Binary and Multiclass Classification  
To perform binary classification, the same choices are made to compute the activation of the output neuron as in the hidden layer neurons. However, if multiclass classification is required, different choices are made as compared to the hidden unit's activation. Figure 2 shows a typical example of a multiclass classification neural network. It has multiple output units, and each unit represents an output class. Each output unit gives the probability of the occurrence of the input class to which it represents. The softmax activation function is used in multiclass classification. In the case of multiple output units, o(a) is used to represent a vector of output values. This vector is normalized, since the denominator is the sum of the numerators, and this ensures that the sum of all the output probabilities is always 1. This function is strictly positive as the exponent of any number is greater than 0. To predict an actual class of an input x, a class with the highest estimated probability from output vector o(a) is given by:  
\[f(x)=o(a)=\Big{[}\frac{\exp(a_{1})}{\sum_{c}exp(a_{c})}\cdots\ \frac{\exp(a_{c})}{\sum_{c}exp(a_{c})}\Big{]}. \tag{4}\]  
Input vector a is the pre-activation value of the final layer of the neural network. Exponential transformation is applied on elements of a. To ensure that the resulting values lie between 0 and 1, a normalization constant c is applied. It is the sum of the exponential values of all the elements of the input vector. Each exponential value is divided by the normalization constant, and each element of the softmax output vector represents the probability of the corresponding class.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Sinkhole Attack  
The sinkhole attack is a particular black hole attack that prevents the legitimate sink node from obtaining data transmitted by underwater sensor nodes, and it will cause serious threats to higher-layer protocols and applications. To gain the goal, the attacker manages to attract almost all traffic that is destined to the sink node, which appears more attractive to neighboring nodes by disguising them as the shortest hop count path. Then, the attacker may drop the packets or get privacy data from them. As illustrated in Figure 7, the ideal location for such an attacker is near the legitimate sink node.  
AODV (Ad hoc On-demand Distance Vector Routing)-based secure routing algorithm with mobile agent is proposed to detect malicious node in network [41]. In [42], an intrusion detection system (IDS) against sinkhole attacks is proposed, in which the network area is divided into a flat grid of cells. The signature-based technique was used to detect and remove fake sink nodes. The proposed IDS considered two types of sink mobility: periodic and random. Geographic routing [43; 44] and authentication are feasible countermeasures against this attack. Unfortunately, geographic routing protocols are also a challenging research topic in UWSN.