esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bulk Crystal Characteristics
### Thermal Characteristics, Laser Damage Threshold and Mechanical Characteristics
The density at diverse temperatures for Yb\({}^{3+}\):ScBO\({}_{3}\) was determined utilized the following formula:  
\[\rho=\frac{m}{abc}=\frac{m}{a_{0}b_{0}c_{0}}\frac{1}{\Big{(}1+\frac{\Delta a}{a_{0 }}\Big{)}\Big{(}1+\frac{\Delta b}{b_{0}}\Big{)}\Big{(}1+\frac{\Delta c}{c_{0}} \Big{)}}=\frac{\rho_{0}}{\Big{(}1+\frac{\Delta a}{a_{0}}\Big{)}\Big{(}1+\frac{ \Delta b}{b_{0}}\Big{)}\Big{(}1+\frac{\Delta c}{c_{0}}\Big{)}} \tag{4}\]  
where theoretical density \(\rho_{0}\) is 3.812 g/cm\({}^{3}\). The data were fitted to a linear formula, as shown in the result: \(\rho=(-5\times 10^{-5})\Gamma+3.83\). To measure the laser damage threshold, a Q-switched pulsed laser with an emission wavelength of 1064 nm was utilized. Then, the damage threshold of the Yb\({}^{3+}\):ScBO\({}_{3}\) crystal was 509 MW/cm\({}^{2}\). The threshold value of Yb\({}^{3+}\):ScBO\({}_{3}\) was almost half that of Yb\({}^{3+}\):YVO\({}_{4}\) and Yb\({}^{3+}\):YAG. For the purpose of acquiring the hardness of the sample, a digital microhardness tester was utilized. The hardness was determined using the following equation:  
\[H_{v}=1.8544\ P/d^{2} \tag{5}\]  
where \(H_{v}\) is the Vickers hardness, \(d\) is trace length along the diagonal, and \(P\) is the load. The Vickers hardness of the as-grown crystal along the \(a\), \(b\), and \(c\) axes was tested to be 7.05, 6.85, and 10.67 GPa, respectively. The hardness along the \(a\) and \(b\) axes was anisotropic with comparable values. The Yb\({}^{3+}\):ScBO\({}_{3}\) crystal had modest hardness, which was smaller than that of YAG (12.7 GPa) [41] and bigger than that of YVO\({}_{4}\) (4.8 GPa) [42]. Thus, the Yb\({}^{3+}\):ScBO\({}_{3}\) crystal may be incised efficiently.
## 3 The Separation Method Based on Multi-Low-Rank Constrained  
According to the multi-low-rank model and its solving method in Section 2.3, the multi-low-rank constrained vibration source signal separation method is proposed. The separation method is mainly composed of two parts: determining the vibration source and separating the vibration source signal. The schematic diagram of the separation method is shown in Figure 2.  
The purpose of the determination vibration source part is to find the vibration source signal in the collected vibration signal. To determine the vibration source, firstly, the Matrix Profile algorithm [20] is used to find some similar fragments in the vibration signal. The Matrix Profile algorithm is an algorithm for finding similar fragments in a time series, which can quickly find the most similar subsequence pair (motif) in the time series. Then, the frequency of similar fragments that have been found and the frequency of possible vibration source signals are checked. If the frequencies are equal, it means that the vibration source signal exists in the mixed signal.  
In the separating vibration source signal part, the vibration source signal can be separated from the collected vibration signal by the multi-low-rank model. When this method is used to separate the vibration source signals, \(\lambda_{i}\) is the regularization parameter for balancing, and is fixed to \(1/\sqrt{max(o_{i},p_{i})}\). \(\beta\) is usually set to 1.
###### Abstract  
The accurate perception and prediction of traffic parameters like vehicles is essential to transportation land planning and management. Video satellites launched in recent years have brought promising opportunities into this field, providing a wide perspective and high frame frequency for extracting moving vehicles. However, detecting moving vehicles remains a challenge due to their small size, which diminishes shape and texture details, often causing them to blend with noise or other objects. To address this issue, we propose an effective method for moving vehicle detection in video satellites by integrating road maps. Experiments conducted on videos sampled from Jilin-1 and Skysat satellites show that our approach achieves F-scores of 0.98 and 0.87, respectively, which are superior to the three traditional methods, Gaussian mixture model (GMM), improved frame difference (IFD), and visual background extractor (ViBe). Our method can be used for accurate parameter estimation in real traffic, which paves the way for the application of video satellites in transportation land planning and management.  
video satellite; transportation land planning and management; road mapping; optical remote sensing 202517444  
M 1C 2024  
31 October 2024  
13 December 2024  
19 December 2024  
Published: 8 January 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; Sustainable Applications of Satellite Video Technology in Transportation Land Planning and Management.  
_Sustainability_**2025**, _17_, 444
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 5 Parametric Study
### Effect of Sitting Position  
It is not reasonable to use only one value to evaluate the comfort of all passengers on the bus. In order to study the influence of the sitting position on the vibration acceleration of the human body and passenger comfort, three representative positions in the front, middle, and rear rows are selected. The vertical accelerations of the human body at the three positions are shown in Figure 24.  
As can be seen in Figure 25, the vertical acceleration is sensitive to the sitting position. The vertical acceleration of the human body at a specific row is obtained by multiplying the pitch angle with the distance to the bus body center. The vertical acceleration amplitude of human bodies of the front, middle, and rear row are 0.9474, 0.3393, and 1.9524 m/s\({}^{2}\), respectively. The accelerations of the front row and rear row are much greater than that of the middle row. This is because the front and rear rows are farther away from the center of the bus. Although the distance of the front row to the center of gravity of the bus is close to that of the rear row, the stiffness of the rear suspension is greater than that of the front suspension. Therefore, the vertical acceleration in the rear row is larger than that in the front row.  
Figure 24: Time history of human body vertical accelerations in different positions.  
Figure 25: The vertical acceleration amplitude in different positions.  
It is found in Figure 26 that the weighted acceleration RMS of the human body at various positions are not too much different. On the one hand, the weighted acceleration RMS of the human body is a comprehensive consideration of the vibration in three directions, and the contribution of the vibration in each direction depends on the corresponding weight. On the other hand, according to the simplification of the bus-human model, the roll and pitch accelerations of the bus are directly used as the human acceleration, while the vertical vibration of the human body is transferred from the bus through a spring-damping system. In the calculation of the weighted acceleration RMS of the human body, the ratio of vertical acceleration to roll and pitch accelerations is relatively small. The same observation can be obtained in the comfort evaluation, as shown in Table 8. According to the ISO 2631 evaluation standard, the passengers at all the three positions are \"slightly uncomfortable\", and the annoyance rates are close.
## 3 The Proposed FedSAGE Framework for Federated DGA Detection
### Client Selection with Data-Aware Scheduling  
In practical FL deployments, clients exhibit substantial heterogeneity in terms of computation power, energy availability, and network bandwidth. This results in the well-known _straggler problem_, where slow clients delay aggregation and prolong convergence. To mitigate this, many existing methods adopt a computation-centric client selection policy, wherein only clients with high processing speed or sufficient resources are included in each training round. While this improves training efficiency, it introduces the risk of _excluding clients that hold rare or critical data_, a significant issue in intrusion detection scenarios where novel threats, such as DGA-based attacks, may only appear in specific regions or under certain traffic patterns.  
FedSAGE addresses this challenge by coupling data-aware clustering with efficient client scheduling. After deriving latent vectors (\(\mu\)) from the pre-trained VAE, clients are grouped into clusters based on the semantic similarity of their local data. Within each cluster, only the client with the lowest estimated execution time is selected to participate in the training round. Formally, let \(\mathcal{C}_{1},\ldots,\mathcal{C}_{K}\) denote the \(K\) clusters. For each client cluster \(\mathcal{C}_{k}\), we select the top \(\rho\%\) fastest clients based on estimated execution time. The selected subset \(S_{k}\) is defined as follows:  
\[S_{k}=\big{\{}c\in\mathcal{C}_{k}\;\big{|}\;\text{Rank}_{\mathcal{C}_{k}}( \text{ExecTime}(c))\leq\lfloor\rho\cdot n_{k}\rfloor\big{\}} \tag{6}\]  
where \(\text{ExecTime}(c)\) denotes the expected local training time for client \(c\)--estimated based on prior communication rounds or resource profiles. \(n_{k}=|\mathcal{C}_{k}|\) and the rank is computed in ascending order of execution time.  
This strategy ensures that each latent data region is represented in every training round while minimizing the overall training delay. Unlike computation-only selection strategies, FedSAGE maintains _semantic diversity_ across clients and avoids the blind spots caused by excluding slower but potentially more informative nodes, also introducing fault tolerance,ensuring that the failure or dropout of a single client does not compromise training progression. Furthermore, FedSAGE reduces communication overhead, making it suitable for deployment in resource-constrained environments such as edge-based IDS systems.
## 5 Results and Analysis
### EEG Signal Classification Results  
Table 1 presents five-fold cross-validation results of ADMM-pruned models across binary, quadratic, and quintuple classification tasks. The data reveal a strong negative correlation between model accuracy and pruning ratio: when pruning ratios exceed 0.8, quintuple and quadratic classification accuracies degrade to random guessing levels (20% and 25%, respectively), while binary classification maintains 50% baseline accuracy even at a 0.99 pruning ratio (Figure 8). This demonstrates ADMM pruning's stronger robustness for low-dimensional tasks (binary) versus higher sensitivity in high-dimensional scenarios (quadratic/quintuple).  
Following the accuracy-efficiency tradeoff principle, we select a 0.6 pruning ratio as optimal (67.4% quadratic accuracy v.s. 70.6% at 0.1 ratio), achieving 500% model compression with merely 3.2% absolute accuracy loss. This balance enables real-time EEG decoding on resource-constrained devices.  
Figure 9 further illustrates the model's generalization capacity in classical control scenarios: quadratic outputs map to up, down, left, right action spaces for FrozenLake navigation, while binary classification's high stability suits rapid-decision environments like CartPole. Experiments confirm the method's extensibility to CliffWalking and Snake game scenarios, establishing a new paradigm for cross-domain brain-computer interface applications.
# On the Use of the Multi-Agent Environment for Mobility Applications
## 3 Environment for Non-Spatial Interaction in Mobility Applications
### Example Application: Environment-Centred System for Traveller Information
The agent \(u\), when he/she receives the message, analyses it and displays the result on the user's TAA. Then, the agent \(u\) restrains his interaction to the messages concerning events coming up on his way. To do so, he/she executes the following action:  
\[look(\emptyset,\{event\gets x\},\{x.subject=\"alert\"\},x.line=line\wedge x.number\geq number)\]  
The agent \(u\) is interested in the alerts concerning his transportation plan, which are expressed by the preceding \(look\) action. Let us assume that the agent representing the alert service adds an alert message concerning an accident on the way of \(u\), resulting in a serious delay for him. The traveller, via his representing agent \(u\), is notified concerning this alert event. Since the properties \(line\) and \(number\) are updated (with an \(update\) action) at each move of \(u\) (each time he/she moves from stop to stop), the segment concerned by the alert messages gets gradually reduced until the end of the trip. The use of the environment, the constant update of the properties of the PTA agents, and the use of \(look\) actions allowed us to maintain a continuous awareness of the traveller about problems occurring during his trip without relying on continuous requests to the server. The proposal of an environment-centred system for traveller information shows how our model allows for the design and implementation of a dynamic and open transportation system. Agents join and leave the system freely and have complex interaction constraints. In this application, the interaction constraints concern the current positions and travellers' itineraries.
# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 3 Research Methodology
### Rapid Turn  
Rapid turns can be detected using gyroscope and acceleration data [16]. While previous research utilized only the z-axis gyroscope to detect rapid turns, this study considers both the Y and z-axis acceleration values in addition to the z-axis gyroscope values to differentiate between stages of rapid turns. Figure 3 illustrates the changes in z-axis gyroscope, y-axis acceleration, and z-axis acceleration values across different stages of rapid turns. As the stage of rapid turning increases, the z-axis gyroscope value changes sharply, and both Y and z-axis acceleration values demonstrate increased fluctuations. As the level of aggressiveness increases, z-axis gyroscope, y-axis acceleration, and z-axis acceleration all exhibit a decrease in the time interval within which a single segment is estimated based on the maximum values. Additionally, these maximum values demonstrate an upward trend. Furthermore, a distinct skew pattern towards the right is observed, which effectively captures the maneuvering patterns associated with rapid turns in two-wheeled vehicles. Finally, the z-axis gyroscope values indicate an inclination effect within the vehicle, particularly noticeable from Stage 3 of aggressiveness onwards, as it returns to regular driving following the completion of a rapid turn. This study defines the criteria for rapid turns based on these observations, as shown in Table 1.  
Figure 3: Inertial sensing data used for rapid turn detection. Data obtained from Carla simulator, and (**a–d**) are the stages (1–4) of rapid turn defined in the survey, respectively.
# Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
### The Three-Dimensional CNN Architecture Analysis  
Although the gesture feature maps come from the same motion information, they are not the same when viewed from different angles [35]. Considering the uniqueness of each feature set, different feature sets representing the same gesture are trained, and features from different angles are fused [36]. The three-dimensional CNN combines spatial and temporal elements, and after training these three branch structures, different gestures can be obtained by integrating the features.  
Based on the three-dimensional CNN to recognize micro-motion gestures, the network structure is shown in Figure 6. The network architecture contains four layers of three-dimensional convolutions and two fully connected layers. In the third and fourth convolutional layers, the convolutional operation of the stacked structure is introduced. All convolution operations use a \(3\times 3\times 3\) convolution kernel structure with the smallest dimension. They introduced the excitation function to enhance the network learning ability. The network uses the ReLu activation function. As shown in Figure 6, the input data format is \((N_{f},h,w,c)\), and \(N_{f}=16\) represents 16 consecutive data frames \(h,w,c\) represents the data frame's length, width, and height, respectively, and they are set to 112 \(\times\) 112 \(\times\) 3. The three-dimensional convolution operation parameters are \((l_{k},h_{k},w_{k},c_{i},c_{o})\). \(l_{k},h_{k},w_{k}\) represent the convolution kernel's three-dimensional length, width, and height. Set to \(3\times 3\times 3\). \(c_{i}=3\) means that the three-dimensional convolutional layer has three input channels \(c_{o}=64\) implies that the three-dimensional convolution operation has 64 output channels. As the number of convolution layers deepens, the number of output channels of the three-dimensional convolution operation will also increase layer by layer. The number of output channels of the second and third layers is set to 128, 256. To avoid the  
Figure 5: The process of the three-dimensional CNN.  
problem of too many training parameters caused by the deepening of network layers, Three-dimensional max pooling is added after each convolutional layer to reduce the dimension of feature units. The configuration parameters are \((p_{t},p_{h},p_{w})\), \(p_{t}\) represents the compression ratio in the time series dimension, and \(p_{h}\), \(p_{w}\) represent the spatial compression ratio of a single data frame. Feature serialization is performed on the output unit of the last convolutional layer by two fully connected layers, then combined with the softmax function, combined with the minimum information entropy criterion to achieve network training, time-space joint feature extraction, and multi-gesture classification.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods
### Candidate Boxes Enhanced Filtering (CEF)
#### 2.1.1 Score Update  
The video frames were input into the detection algorithm, resulting in \(m\) detection boxes \(B=\{b_{1},b_{2},\ldots,b_{m}\}\) along with their corresponding scores \(S=\{s_{1},s_{2},\ldots,s_{m}\}\); meanwhile, the tracking algorithm generated \(n\) prediction boxes \(P=\{p_{1},p_{2},\ldots,p_{j}\}\) using the KF, in which \(b_{i}\) represents the \(i\)th detection box of the current frame, \(s_{i}\) represents the score of the \(i\)th detection box of the current frame, and \(p_{j}\) represents the \(j\)th prediction box of the current frame. Next, we calculated the IoU values between the detection boxes and the prediction boxes. To simplify the computation, we only focused on the prediction box that has the greatest impact on each detection box \(b_{i}\), resulting in a vector \(X\) of length \(m\). \(X\) can be formulated as follows (1):  
\[X=\{x_{i}|x_{i}=\max_{1\leq j\leq n}\text{IoU}(b_{i},p_{j}),i=1,2,\ldots,m\} \tag{1}\]  
\[S_{new}=\lambda S+(1-\lambda)S\cdot X \tag{2}\]  
As from Equation (2), we updated the original score \(S\) to obtain \(S_{new}\) by using the vector \(X\), which contains the maximum IoU values for each detection box and all prediction boxes. This process integrates predictive information to adjust the scores of detection candidate boxes, allowing those closer to the predicted boxes to receive higher weights. Figure 2a shows that weight \(\lambda=0.7\) is the best parameter.
## 2 Materials and Methods
### Handle Detection Module  
The handle detection module aims to identify the bounding boxes of handles in the RGB image and publish the prediction results. To achieve this, an object-detection neural network is employed. Several networks were tested, including SSD [4] with an Inception V2 encoder [26], EfficientDet [15], CenterNet architecture [27] with a ResNet-v1-50 encoder [13], and CenterNet with a ResNet-v1-101 encoder. The goal is to select the most efficient architecture for handle detection.  
The CenterNet architecture represents objects as a single point, denoting the center of their bounding box, instead of utilizing a list of proposed rectangular regions. The network employs keypoint estimation to locate the center point and regress the object's size, location, and orientation. This approach is claimed to be simpler, faster, and more accurate compared to detectors based on a list of proposed regions. In the articulated object information system, the handle detector takes an RGB image as input and produces a list of bounding boxes, class membership information, and class probability predictions. This prediction format facilitates the determination of handle locations in three-dimensional space, enabling easier robot interaction with the object.  
Figure 1: Architecture of the proposed method for the detection and estimation of articulated objects that is applied to build a 3D model of the scene enhanced with information about articulated objects.
# Acoustic Applications of a Foamed Geopolymeric-Architected Metamaterial
###### Abstract  
The paper compares and evaluates the influence of the presence of perforations on the sound absorption coefficient (SAC) of a negative stiffness metamaterial based on a foamed ceramic geopolymer. Chemical-physical, microstructural, dynamic-mechanical, and sound characterisations are presented. A rigid, lightweight geopolymeric porous material has been prepared using an inorganic/organic monomeric mixture containing oligomeric sialates and siloxanes foamed with aluminium powder. This process results in an amorphous rigid light foam with an apparent 180 Kg/m3 density and a 78% open-pore. The viscoelastic characterisation by dynamic mechanical analysis (DMA) carried out from \(10^{-3}\) to \(10^{3}\) Hz indicates the behaviour of a mechanical metamaterial with negative stiffness enabling ultrahigh energy absorption at straining frequencies from 300 to 1000 Hz. The material loss factor (the ratio of dissipative/elastic shear moduli) is about 0.03 (essentially elastic behaviour) for frequencies up to 200 Hz to suddenly increase up to a value of six at 1000 Hz (highly dissipative behaviour). The corresponding storage and loss moduli were 8.2 MPa and 20 MPa, respectively. Impedance tube acoustic absorption measurements on perforated and unperforated specimens highlighted the role of perforation-resonant cavities in enhancing sound absorption efficiency, particularly within the specified frequency band where the mass of the negative stiffness foamed geopolymer matrix magnifies the dissipation effect. In the limits of a still exploratory and comparative study, we aimed to verify the technological transfer potentiality of using architected metamaterials in sustainable building practices.  
geopolymer; architected metamaterials; negative stiffness; foamed element; impedance tube; sound absorption; dynamic mechanical analysis; morphological characterization +
Footnote †: journal: Applied Sciences