esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Problem Statement
### Problem Statement  
It can be noted that by factorizing the average consensus matrix, while constraining the factor matrices to be in the form \(\mathbf{I}_{N}-\alpha_{t}\mathbf{L}\), the eigenvalues of the Laplacian matrix as the inverse of \(\alpha_{t}\) can be deduced. The uniqueness has been proved in [1].  
**Lemma 1**.: _[_1_]_ _Let \(\lambda_{2},\cdots,\lambda_{D+1}\
eq 0\) be the \(D\) distinct non-zero eigenvalues of the graph Laplacian matrix \(\mathbf{L}\), then, up to permutation, the sequence \(\{\alpha_{i}\}_{i=1,\cdots,D}\), with \(\alpha_{i}=\frac{1}{\lambda_{i+1}}\), \(i=1,2,\cdots,D\), is the unique sequence allows getting the minimal factorization of the average consensus matrix as \(\frac{1}{N}\mathbf{1}\mathbf{1}^{T}=\prod_{i=1}^{D}(\mathbf{I}_{N}-\alpha_{i} \mathbf{L})\)._  
Therefore, in order to implement the proposed method, the knowledge of the network should be known. Meaning that, the number of the components \(N\) of the given network should be known by adding a learning mechanism as a configuration step. Practically, in most systems where communications are involved, learning sequences are used for communication channel identification or for synchronization. In [20], the authors have proposed a method using random walks to estimate the global properties of large connected undirected graphs such as number of vertices, edges, etc. However, it is not in the scope of this paper. Indeed, assuming that the number of agents \(N\) is known in a-priori, a consensus protocol in [10] is to be uploaded to each agent to compute the average consensus value \(\tilde{x}\). The main task in our study is to estimate the whole Laplacian spectrum.
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 2 Preliminaries
### Spatial-Temporal Data  
It describes the location and time of an observation. The data changes as the location and time change [44], as shown in Figure 2. It is common in different domains, including climate science [45], transportation [46], manufacturing [47], ecology [48], and social sciences [49], among others [50]. Examples of spatial-temporal data include climate data that tracks weather patterns across regions and time [45], traffic data that records vehicle movement and traffic patterns across different times and locations [51], and social media data that captures the space and time of user activity [52]. The characteristics of spatial-temporal data typically result in more complex data correlations than conventional methods can handle. Additionally, they are frequently firmly self-correlated, and unlike traditional data, data samples are often not produced independently. It is more challenging to process spatial-temporal data than traditional stationary data. For instance, interpreting spatial temporal data is more complicated than interpreting pictures, where researchers could rely on visual inspections [44]. The data can be represented as raster images, trajectories, and many more. Understanding and analyzing spatial-temporal data is difficult due to the complexity and interdependent nature of the spatial and temporal dimensions. Traditional statistical methods have been applied, but they are not the final panacea, which calls for more advanced machine learning techniques, such as the DNNs.
## 5 Precoder and Combiner Design Based on Reinforcement Learning
### Action Space  
The action output of one agent constitutes the elements of one row of the matrix. The action outputs of all \(2N_{s}\) agents compose the whole matrix. The elements of the matrix are continuous real numbers, and in the agent action design, the action output range is \([-1,1]\), that is, the matrix elements are normalized to the range \([-1,1]\), and the action dimension of the agent is designed according to \(dim_{action}\). In the \(t\)-th iteration, the actor network of the \(i\)-th agent outputs the action \(a_{t}^{i}\in\mathbb{R}^{dim_{t}^{action}}\) according to the current policy. The joint action is denoted as \(\boldsymbol{A}_{t}=\left\{a_{t}^{1},a_{t}^{2},\cdots,a_{t}^{2N_{t}}\right\}\). The agents interact with the environment through the joint action and obtain the reward \(\boldsymbol{R}_{t}\) and the \((t+1)\)-th state information \(\boldsymbol{S}_{t+1}\). For the reward, all of the agents obtain the same reward from the interaction with the system environment to prompt cooperative behaviors among the \(2N_{s}\) agents.
## 4 Proposed Hybrid FVIMDE Algorithm
### Hybrid FVIMDE Algorithm Stages
#### 4.5.1 Stage 1: Differential Evolution (DE) Phase  
The first stage of the algorithm is the Differential Evolution (DE) phase, which focuses on exploring the global search space. This phase is executed over the first half of the total iterations. The process begins with the initialization of a population of candidate solutions within the defined search space bounds. Each individual in the population is then evaluated based on the objective function. During each iteration of the DE phase, new candidate solutions are generated using mutation and crossover operators. Mutation involves creating a mutant vector by perturbing existing solutions, as described by the DE/rand/1 strategy. The crossover operator then combines elements from the mutant vector and the current solution to create a trial vector. This trial vector undergoes boundary handling to ensure it remains within the permissible search space. The selection process follows, where the trial vector replaces the current solution if it offers a better fitnessvalue. Throughout the DE phase, the algorithm continuously tracks the best solution found, setting it up as the foundation for the next stage.
# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 3 Data Collection and Preparation
### Data Collection  
The data sample consisted of the combined plethysmographic and ECG signals of twenty healthy subjects, seven of which were females, with an average age between 25 and 35 years. The calculated body mass index (BMI) for all the participants was in the range between 19 and 23 [3]. This dataset, consisting of physiological measurements, was collected by researchers at the Center for Exercise Science and Sports, University of Ferrara, as part of the study published in [3]. The study received approval from the Ethics Committee, with the corresponding approval number and date documented therein.  
After positioning the sensor around the neck of the patient and placing the ECG electrodes in their designated positions, the patient was allowed to assume the required posture (supine, sitting, and upright). Data acquisition was started only half a minute later  
Figure 2: A picture of the plethysmograph applied to a patient during data acquisition (**left**) and the real-time visualization of the JVP and ECG traces (**right**). The picture is taken from [3].  
to minimize artifacts and allow the patient to relax. For each subject, three traces of the JVP waveform were recorded, one for each of the three postures. Each sample recorded the timestamp, the plethysmographic signal, and the ECG signals, captured at a sampling frequency of 66 Hz and lasting approximately 30-35 s. The raw plethysmographic signals were processed with a Wavelet filter [35] to remove low-frequency artifacts caused by respiratory movements and to enhance the cardiac contribution. Instead, the ECG signal was sufficiently clear and did not require any filtering.  
Figure 3 shows an example of signals collected from a supine patient during a 5 s interval. The QRS complex from the ECG trace can be visually correlated with the peaks of the plethysmographic trace. As in Figure 1, the \(c\) peak is generally more prominent than the \(a\) peak, with the former being easier to identify. This difference is attributed to the pressure exerted by the carotid artery.  
The ECG signal serves only as a visual reference to validate the plethysmographic trace. As this study aimed to conduct a comprehensive and independent analysis of the plethysmographic signal, the ECG trace was not utilized in the subsequent steps.
## 3 Results and Discussion
### Bolt Load Transfer  
The load-bearing parts of the bonded-bolted hybrid joint include the adhesive layer and the bolt. The load is transferred to the bolt through the adhesive layer when the structure is loaded. Compared with bonded joints, hybrid joints replaced the adhesive layer at the hole in the center of the lap area with a bolt. The adhesive layer in the central region had little effect on the adhesive bearing capacity, such that the load shared by the bolt directly affected the bearing performance of the hybrid joint. The variations in the ratio \((P_{b}/P_{t})\) of the load shared by the bolt to the total load with displacement are shown in Figure 13. The load shared by the bolt is the sum of nodal forces on the middle plane of the bolt [31]. It can be seen that the value of \((P_{b}/P_{t})\) increased nonlinearly with the displacement. This is because the stiffness of the bolt was greater than that of the adhesive, so the bolt shared more load with the same deformation. The rate at which the value increased decreased. The probable cause is the eccentricity of the load resulting in the bolt deflecting slightly, which caused the bolt head to produce the pressure perpendicular to the adhesive layer. The pressure slowed down the crack propagation in the adhesive layer and increased the bearing capacity of the adhesive layer.  
Figure 12: Progressive damage of the hybrid joint.
## 3 Experiments
### Evaluation Criteria  
The performance of the model was evaluated by the confusion matrix [39], accuracy, precision, recall, and F-score [40]. The confusion matrix shows information about the correct classification and misclassification of the test data. Figure 6 shows an example of the confusion matrix; it has four quadrants, and here are what they mean: True Negative (TN) is an outcome where the model correctly predicts the negative class; False Positive (FP) is an outcome where the model incorrectly predicts the positive class; False Negative (FN) is an outcome where the model incorrectly predicts the negative class; True Positive (TP) is an outcome where the model correctly predicts the positive class; the larger the percentage of TN and TP, the higher the classification accuracy of the model.  
Based on the results of the confusion matrix, the corresponding accuracy, precision, recall, and F-score can be calculated as follows:  
\[\begin{array}{l}\text{Accuracy}=\frac{\text{TN}+\text{TP}}{\text{TN}+ \text{FP}+\text{FN}+\text{TP}}\\ \text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\\ \text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\\ \text{F-score}=\frac{2\left(\text{Precision}\text{ - Recall}\right)}{\text{Precision}+\text{Recall}}\end{array} \tag{12}\]  
where accuracy represents the number of correct classifications made by the model as a proportion of all data, precision represents the accuracy of the model in judging poten  
Figure 5: Experimental flow.  
Figure 6: The confusion matrix.  
tial events as occurring blinks, and recall refers to the percentage of correctly predicted classifications labelled 1 as a proportion of the true categories. Precision and recall are sometimes contradictory, and they are considered together using the F-score [35], which is taken as a weighted average of them, with a higher F-score indicating a more effective classification model.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 2 Materials and Methods
### Polarimetric GB-SAR System  
A Vivaldi antenna with a size of 7.5 cm in width and 7.8 cm in height is employed to configure a polarimetric antenna array (Figure 2). Theoretically, this Vivaldi antenna can transmit/receive linearly polarized electromagnetic waves, and operates over an unlimited frequency band above its lower cut-off frequency of about 1 GHz, which is determined by its opening width. This traveling-wave  
Figure 1: Designs of sample wooden walls. (**a**) Type I; (**b**) Type II. One survey line and two survey areas are marked by red color. The unit is in millimeter.  
antenna has a favorable waveform in the time domain [22]. The polarimetric antenna array consists of four Vivaldi antennas, of which two are horizontally (H-channel) and the other two are vertically (V-channel) oriented. The spacing between them is 9.5 cm as shown in Figure 2. By switching the combinations of two active antennas as a pair of transmitting and receiving sensors, we can acquire a full-polarimetric dataset--i.e., in HH, HV, VH, and VV polarizations, respectively.  
We have set up a stepped frequency GB-SAR system based on a four-port vector network analyzer (VNA), as shown in Figure 3. The polarimetric antenna array is mounted to a 2D positioning system. The movement of the antenna and the data acquisition are synchronized by a measuring program in a PC. The main measurement parameters are given in Table 1. 801 frequency samples are recorded over the range from 1 to 20 GHz with a frequency step of 23.75 MHz. The antennas are connected to the four-channel VNA by coaxial cables. A full-polarimetric dataset can be simultaneously recorded at each sampling position. The dimension of the positioning system is 1.5 m by 1.5 m. This system moves the antenna in both x and y directions. The maximum movement range of the antenna array is about 1 m in both directions, due to the spatial limitation.  
Figure 3: Developed polarimetric GB-SAR system. The line with an arrow indicates the position of the 1D survey line in Figure 1a.  
Figure 2: Polarimetric antenna array consisting of four Vivaldi antennas.
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 3 Control Area Network (CAN)  
A Controller Area Network, or CAN, is the most commonly used network for control in automotive and manufacturing applications [42]. The CAN interconnects a network of nodes and it is a serial, multimaster, multicast protocol, so when the bus is free any node can send a message, and all nodes may receive and act on the message. When a node begins to transmit messages, it prioritizes the messages. This allows you to transmit until the bus becomes inactive or until it is replaced by a node with a higher priority message.  
There are four types of CAN messages: data frame (CAN 2.0A and CAN2.0B), which is the standard CAN message broadcasting data from the transmitter to the other nodes on the bus; remote frame, a message that is broadcast by a transmitter to request data from a specific node; an error frame may be transmitted by any node that detects a bus error; overload frames are used to introduce additional delay between data or remote frames. In this research, CAN 2.0B data frame [40] was taken into consideration (Table 2). The difference between a CAN 2.0A and a CAN 2.0B message is that CAN 2.0B supports both 11 bit (standard) and 29 bit (extended) identifiers.
# Advances in Computer-Aided Medical Image Processing
## 5 Discussion
### Future Directions  
To overcome these challenges, we propose the following future directions:  
1. Improve data accessibility: Promote the sharing of medical image datasets, develop open-source data repositories, and leverage synthetic or augmented data and transfer learning techniques to overcome data-related challenges.
2. Given the heterogeneity of medical imaging data across different institutions and modalities, data harmonization techniques need to be developed. These will help to standardize datasets, reducing technical variations and making aggregated data more reliable and useful. Implement machine learning algorithms, such as ComBat or DeepHarmony, to minimize batch effects and other sources of nonbiological variation in datasets acquired from different sources. Harmonization can help to standardize datasets, making aggregated data more reliable and useful.
3. Enhance model interpretability: Explore the use of explainable AI techniques, such as layer-wise relevance propagation (LRP), SHAP (SHapley Additive exPlanations), or attention mechanisms, which can make the model's decision-making process more transparent. Additionally, techniques such as feature visualization and saliency maps can be utilized to identify key input features responsible for predictions, thereby building trust with clinicians.
4. Foster generalizability and robustness: Employ data augmentation, domain adaptation, adversarial training, and novel model architectures to enhance the generalizability and robustness of AI-based medical image recognition methods.
5. Optimize computational efficiency: Explore lightweight models, pruning techniques, and distributed training approaches to reduce the computational resource requirements of AI-based medical image recognition methods.
6. Active learning techniques: Utilize active learning strategies to identify the most informative samples for manual annotation, addressing the limitations of annotated data.
7. Clinical integration and interpretability: Work closely with clinical teams to understand their workflows and needs. Focus on developing models that not only have high predictive performance but also provide interpretable outputs. For instance, developing a model that can output a heatmap showing areas of concern in an image, alongside its predictions, could make it more useful to clinicians.  
By focusing on these future directions, we can pave the way for the successful integration of AI in medical imaging, ultimately improving the speed and accuracy of diagnoses and enhancing patient care.
## 1 Introduction  
With patient demand for whiter teeth, dental bleaching has become a widespread conservative therapy for treating teeth presenting discoloration, as a single treatment or before placing a ceramic or resin composite restoration to improve the final overall color.  
Among the bleaching products for vital teeth currently on the market, hydrogen-peroxide-based and carbamide-peroxide-based agents are the most widely used. The products vary in concentration (high concentrations for in-office use, low concentrations for use at home) and format, intended for different whitening procedures (in-office or at home) with different application methods [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. In-office applications of bleaching agents with high concentrations of hydrogen peroxide (HP) are described as an effective and fast option indicated in cases of moderate or severe vital tooth discoloration that avoid prolonged home treatments with carbamide peroxide and dietary restrictions [3; 4; 5; 6; 7; 8; 9; 10; 14; 15; 16; 17; 19; 21; 22; 23].  
Several authors have demonstrated the efficacy of high concentration hydrogen peroxide when used as a bleaching agent in the office. [PERSON] et al. verified the effectiveness of a 38% hydrogen peroxide treatment [9]. [PERSON] et al. evidenced the effectiveness of a 37.5% hydrogen peroxide treatment [5]. [PERSON] et al. used a light-curing 35% hydrogen peroxide treatment and obtained good results quickly [17]. [PERSON] et al. studied the stabilization of the color with different whitening techniques in the office and achieved an important change in the color of the treated teeth [6].  
Nevertheless, the wide range of products available for in-office whitening makes it difficult to decide which one to choose, as each could be more or less effective as a result of variations in concentration and application time. Moreover, no ranking of these products comparing their efficacy has ever been published. This would make it possible to opt for one or another according to the individual case and the patient's characteristics and needs.  
In this context, there is a need to make objective, quantifiable evaluations of the tooth color obtained through the use of bleaching products. This would allow comparison of these products on the basis of valid data reported in clinical trials. In order to evaluate color, spectrophotometers have been introduced to identify the correct and objective color change in enamel surfaces [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38].  
The aim of this randomized quadruple-blind clinical trial was to objectively quantify the effects of four high-concentration dental whitening products of hydrogen peroxide for in-office use on vital teeth, following the application protocols recommended by the manufacturers.
## 4 Monitoring System Framework
### Cloud Service System Construction
#### 4.3.2 Cloud Server  
With the advent of cloud computing and big data, traditional data servers will gradually be replaced by cloud computing data centers. The Elastic Compute Cloud Service (ECS) is used in the monitoring system outlined in this paper. A cloud server has the following remarkable advantages over traditional data servers:  
(1) Stability: The disk data reliability of cloud servers reaches 99.9% and automatic snapshot backup and data recovery are convenient and fast, while traditional servers are limited by their hardware configurations, are affected by computer room conditions and require manual backup and difficult and time-consuming manual data recovery.  
(2) Flexibility: It is possible to freely configure the CPU, memory and bandwidth of Cloud servers and upgrade their configuration at any time to ensure that data is not lost. Traditional servers have a  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Hierarchy** & **Description** \\ \hline Customer Layer & The human–computer interaction mode includes a web platform and ensures dynamic access to more platforms. \\ \hline Application Layer & Based on a JavaServer Pages (ISP) system architecture divided into a foreground presentation, background business and basic service layers. The foreground is the presentation layer and the interface layer. The background provides specific management services such as for users, permissions and so on. The basic services include regular SMS and email messages \\ \hline Data Layer & Data operation and storage using an SQL Server database, Redis queue and storage services and a scalable cloud disk based on Alibaba Cloud. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Levels of the cloud service platform for the pillar-free self-formed roadway big data monitoring system.  
Figure 12: Topology of the big data monitoring system.  
fixed configuration and, if hardware upgrade is required for configuration modification, it will take a long time to implement and service downtime is inevitable.  
(3) Security: Cloud servers have DDoS protection and incorporate security measures such as trojan horse removal to ensure the safety and reliability of monitoring data, while traditional servers need to purchase and deploy additional security measures.  
(4) Scalability: Cloud servers can seamlessly connect with a wide variety of cloud products that can sustainably provide complete computing, storage, security and other solutions for system applications, while traditional servers cannot guarantee the scalability of data growth and sustainability.