esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 4 Results and Discussion  
### Datasets and Implementation Details  
Our evaluation was conducted across four datasets: TRANCOS [33], CARPK [34], PUCPR+ [34] and UCSD [35]. The TRANCOS, CARPK, and PUCPR+ datasets are specifically designed for vehicle counting in traffic scenarios, while the UCSD dataset is primarily used for crowd-counting tasks. These datasets provide a robust experimental foundation for testing and validating the proposed method. To ensure consistency across these diverse datasets, the data are partitioned into training, validation, and testing sets in an 8:1:1 ratio. During the training phase, data augmentation techniques such as flipping, rotation, and gamma correction are applied to enhance sample diversity. Additionally, all input images are resized to a uniform dimension to standardize processing. Table 1 provides a comprehensive overview of the statistical characteristics of these datasets. Partial images of these four datasets are shown in the Figure 3.  
The model was built in Ubuntu 20.04 and the pytorch 1.10 experimental environment. The main hardware supports Intel CoreTM i5-6500 3.20 GHz and GeForce RTX3060. The experiments utilized mean-square error (MSE) loss and employed the Adam [36] optimizer with an initial learning rate set to 0.00002. It should be noted that although the proposed methodology in this paper is capable of addressing FSC scenarios, the experiments were constrained to 1-shot setups due to hardware limitations. Additionally, for computational expediency, the dimensions of the query image, query feature map, and support feature map were standardized to 128 \(\times\) 128, 128 \(\times\) 128, and 3 \(\times\) 3, respectively.  
In addition, the evaluation metrics selected for the experiments in this paper are Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Images**} & \multicolumn{4}{c}{**Count Statistics**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Average**} \\ \cline{3-3} \cline{5-6}  & & **Max** & & **Min** & & **Total** \\ \hline TRANCOS & 1244 & 107 & 9 & 46,796 & 1 & 640 \(\times\) 480 \\ CARPK & 1448 & 188 & 1 & 89,772 & 1 & 720 \(\times\) 1280 \\ PUCPR+ & 125 & 331 & 0 & 16,915 & 1 & 720 \(\times\) 1280 \\ UCSD & 2000 & 46 & 11 & 49,885 & 1 & 238 \(\times\) 158 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of the dataset.  
Figure 3: Partial illustration of the datasets. From top to bottom, the pictures are some of the original ones from TRANCOS, CARPK, PUCPR+ and UCSD datasets in sequence.
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 4 SPN-Based Modeling and Performance Analysis
### Modeling and Analysis of Traditional Satellite Network with SPN
Therefore, the average system throughput is:  
\[\bar{u}_{ready} =1\times P(M(P_{ready})=1)=p_{0}\]3  
Utilization of on-board bandwidth resources, which is used to measure the consumption of bandwidth resources on satellite networks, is calculated as:  
\[\bar{u}_{ready} =1\times P(M(P_{ready})=1)=p_{0}\]4  
Since the stochastic process used in the SPN model is a Poisson process, the occurrence of the transition satisfies the Poisson distribution, so the reciprocal of the average firing time rate of each transition in the model is its average implementation delay, i.e., \(\tau_{i}=1/\lambda_{i}\). To facilitate the analysis of the factors affecting each performance indicators, the following explanation is given: \(\lambda_{2}^{-1}\) denotes request delay \(\tau_{2}\), \(\lambda_{3}^{-1}\) denotes waiting service delay \(\tau_{3}\), \(\lambda_{6}^{-1}\), \(\lambda_{7}^{-1}\), and \(\lambda_{8}^{-1}\) denotes uplink propagation delay \(\tau_{6}\), inter-satellite link propagation delay \(\tau_{7}\), and downlink propagation delay \(\tau_{8}\), respectively. It can be seen that the average delay and throughput of satellite network communication process are closely related to the request duration, service latency, propagation delay of uplink, downlink, and inter-satellite link. The average network delay is the sum of each process delays, while the throughput rate is inversely proportional to this, which is consistent with the actual situation. As a result, in traditional satellite networks, accelerating service response, processing speed, and improving the transmission efficiency of the links between users and satellites can effectively reduce the average network delay, increase the throughput, and improve the network performance.
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### The Framework of the NSGA-II-MOEA/D-CIS Algorithm  
Figure 2 illustrates the process framework of the NSGA-II-MOEA/D-CIS algorithm, with its specific execution steps described as follows:  
Step 1: Initialize the algorithm, which includes setting upper-level and lower-level parameters, maximum iteration count, etc. Additionally, the initialization of the upper-level population is performed, generating all chromosomes for the upper-level population.  
Figure 2: Flowchart of the proposed NSGA-II-MOEA/D-CIS.  
Step 2: Perform non-dominated sorting on all individuals in the upper-level population based on their fitness values, followed by crowding distance calculation.  
Step 3: Apply genetic operators to the upper-level population, including selection, crossover, and mutation, to generate the offspring population.  
Step 4: Merge the initial population with the offspring population and generate a new population based on non-dominated sorting and crowding distance calculation.  
Step 5: Traverse the upper-level population; if any individual results in an infeasible solution in the lower level, return to Step 2. Specifically, each upper-layer individual represents a particular order splitting scheme. Before being passed to the lower layer, these individuals are evaluated based on structural feasibility, i.e., whether their corresponding order splits can be reassembled into valid production plans.  
Step 6: Transfer the upper-level population that meets the requirements to the lower level, initialize the MOEA/D population, and generate weight vectors for each individual.  
Step 7: Apply the Chebyshev decomposition method to decompose the bi-objective lower-level model into multiple subproblems. Define the neighborhood for each subproblem and perform neighborhood updates.  
Step 8: Solve the lower-level problem iteratively to obtain the Pareto front.  
Step 9: Execute the collaborative iteration strategy, where the Pareto front of the lower level is evaluated for satisfaction. Unsatisfactory solutions are fed back to the upper level for parameter adjustments. Once all satisfactory individuals are collected, check if the maximum iteration count has been reached. If so, output the final global Pareto solution set; otherwise, return to Step 2.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Comparison of BFRP Tendons and Steel Strands
#### 4.3.3 Total Prestress Loss  
As shown in Figure 3, for a specific slab width (3.0 m, 4.5 m, 9.0 m, and 12.75 m) and distribution angle (20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\)) of the prestressed tendon, the total prestress loss of the BFRP tendon was always lower than that of the steel strand. The difference between the two prestress losses varied from 6.4% to 16% and it decreased with the increase of the slab width. When the slab width was 3.0 m and the distribution angle was 20\({}^{\circ}\), the difference peaked at 17.9%. When the slab width was 12.75 m and the distribution angle was 20\({}^{\circ}\), the difference reached the minimum value of 6.4%.  
Figure 3: Difference in prestress loss between BFRP tendons and steel strands given various slab widths and distribution angles.  
Figure 2: Comparison of prestress loss of BFRP tendons and steel strands during the in-service stage at various slab widths and distribution angles (%).  
In summary, the use of the BFRP tendon in the cross-tensioned concrete pavement can effectively reduce the prestress loss compared to the steel strand, let alone its corrosion resistance, which can increase the durability of the cross-tensioned concrete pavement. Further lab tests and test sections are still needed for better evaluation of prestress loss of BFRP tendons and the performance of the BFRP-reinforced cross-tensioned concrete pavement.
## 3 Stress Measurements and Data Updating Based on Grey Theory
### Data Updating  
According to Ref. [19], the true stress arrays measured on continuous concrete girder bridges under construction using the symmetric cantilever casting method can be accurately described by mathematical models based on the grey theory. Using the mathematical models based on the grey theory, all the data obtained on Huangsha Harbor Bridge under construction are assessed for their accuracies, and some without the agreement with the grey models are updated accordingly. In addition to the grey theory, there are other  
Figure 4: Numerical model of a 0-block of Huangsha Harbor Bridge.  
Figure 5: Calculated temperature field on the 0-block (Unit: \({}^{\circ}\)C).  
approaches that can be utilized to update the measured stress data, such as the interval analysis, the genetic algorithms and other evolutionary approaches. With respect to the interval analysis, the results predicted using the model are given in intervals, but the definite values (stresses) are required by the present data updating. Therefore, the grey theory is preferable in dealing with the present issue, which can predict the definite stresses for use. We suppose that the genetic algorithms and evolutionary approaches are basically optimization methods. For optimization methods to work, surrogate mathematical models have to be established to deal with the inverse mathematical problems, which generally use large samples. However, we do not obtain large stress samples from Huangsha Harbor Bridge under construction, as we have only collected one datum from each transducer in each construction stage, not enough to build usual surrogate models. On the other hand, the grey theory regards all random variables are grey variables within certain variation ranges, and related predictive models can be established based on limited data (small samples) without exploring the uncertainties influencing the system behaviors or separating the system behaviors from the noise behaviors. Therefore, the grey models are also preferable for use in small sample cases, and they are chosen to deal with the present scientific issue of data updating.  
Figure 6: Calculated thermal stress field on the 0-block (Unit: Pa). (**a**) View from the top; (**b**) view from the bottom.
###### Abstract  
The purpose of this study was to determine the power produced by knee muscles in normal adults when performing self-selected walking. The power of a single knee muscle is not directly measurable without invasive methods. An EMG-to-force processing (EFP) model was developed, which scaled muscle-tendon unit (MTU) power output to gait EMG. Positive power by each muscle occurred when force was developed during concentric contractions, and negative power occurred with lengthening contractions. The sum of EFP power produced by knee muscles was compared with the kinematics plus kinetics (KIN) knee power at percent gait cycle intervals. Closeness-of-fit of the EFP and KIN power curves (during active muscle forces) was used to validate the model. Key findings were that most knee muscles have a characteristic eccentric-then-concentric contraction pattern, and greatest power was produced by the Semimembranosis, with peak magnitude nearly matched by two vastus muscles (VL, VMO). The EMG-to-force processing approach provides reasonable estimates of active individual knee muscle power in self-selected speed walking in neurologically intact adults. Further, a prolonged period of the gait cycle showed substantial knee flexion or extension in the absence of power produced by muscles acting at the knee.  
muscle power; concentric contraction; eccentric contraction; normal gait; energy transfer; force measurement +
Footnote †: journal: Applied Sciences
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 3 Results and Discussion  
The system described in Section 2 has been implemented in a prototype model which includes several remote nodes, the local coordinator node and the web application in the cloud. Figure 9 shows a global view of an RICS node in the field and a captured sample image. In this case, the application corresponds to a lettuce crop of variety Little Gem in the field of Cartagena, Spain.  
The selection of the Arduino DUE platform presents several advantages, in addition to its processing capabilities and connection buses available. An important aspect is the internal memory size of the microcontroller, which allows storing up to two images of VGA size (640 \(\times\) 480) in JPEG format.
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
###### Abstract  
Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (_BI_) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models' performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (\(R^{2}\)) of 0.971 provides higher performance capacity for prediction of the rock _BI_ compared to KNN model with \(R^{2}\) of 0.807 and ANN model with \(R^{2}\) of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.  
rock brittleness index; machine learning techniques; artificial neural networks; random forest; K-nearest neighbors; support vector machine +
Footnote †: journal: Applied Sciences
# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 2 Edge Computing Overview
### Main Characteristics  
The main characteristics of edge computing that are mentioned in several literature surveys are highlighted below (see Figure 1):  
* Proximity to data sources: Edge computing brings computation and data storage close to the location where data are generated, allowing real-time processing [2; 14; 15].
* Reduced latency: One of the most significant advantages of edge computing is its ability to offer low latency by processing data locally rather than sending them to centralized cloud data centers far from the data source. This is critical for delay-sensitive applications [3; 4; 6].
* Bandwidth reduction: Edge computing reduces the amount of data that must travel over the network, by employing data processing locally, thereby saving bandwidth and reducing network congestion [2; 16].
* Enhanced security and privacy: Processing data locally on edge devices can enhance data security and privacy, as sensitive information does not need to traverse the internet to reach a centralized cloud server. With proper security protocols, data breaches or leaks can be mitigated [10; 16].
* Mobility support: Due to the dynamic nature of edge locations and the devices connected within these networks, edge computing offers robust support for mobility, effectively handling the changing conditions and locations of devices [2; 15].
* Location awareness: Edge computing systems are aware of their geographical location, which can be leveraged to deliver localized services such as content delivery, local resource sharing, and regional data processing [17; 18].
* Heterogeneity: Edge computing can support a diverse range of devices, applications, and services through specific standards and data models [2; 19].  
Figure 1: Edge computing characteristics.
## 6 Experiments and Results
### Experiment Environments and Parameter Setting
#### 6.1.3 The Energy Consumption Model of the Simulated and Real Networks  
To evaluate the energy consumption of the network, we adopt the energy consumption model in [38] in our experiments, in which the node transmitting and reception energy consumption \(E_{T_{x}}(d)\) and \(E_{R_{x}}\) are defined as follows:  
\[E_{T_{x}}(d)=E_{l}+\epsilon_{a}d^{2}. \tag{19}\]  
\[E_{R_{x}}=E_{l}. \tag{20}\]  
where \(d\) is the transmitting distance, \(E_{l}\) is the energy consumed of radio electronics, \(\epsilon_{a}\) is the power amplifier. In our experiments, all nodes are initialized with \(2\times 10^{4}J\) of energy and the parameters \(E_{l}=50 nJ/bit\), \(\epsilon_{a}=10 pJ/bit/m^{2}\). The sensing packet is assumed to be a fixed size of 1000 bits.  
Based on the above energy consumption model, given a node sampling rate, the network lifetime is defined as the maximal data collection rounds if the network energy can ensure the sensing data being transferred to the sink node.  
Figure 3: The wireless network of Intel Berkeley Research Lab [37].
## 4 ERPCAWF-Based OMA Method for LTV Structures
### Limitations and Application Score of ERPCAWF-Based OMA for LTV Structures  
The proposed ERPCAWF-based OMA for LTV structures has the following limitations:  
(1) Each order of modal shapes obtained by PCA is ortho-normalized, resulting in the loss of amplitude information. This is a characteristic of PCA-based OMA, and is retained in ERPCAWF-based OMA for LTV structures. The order of the modes identified by PCA depends on their contribution, rather than their frequency values. Hence, modal deletion may occur when a mode makes only a small contribution.  
(2) The forgetting factor is selected through prior knowledge of the time-varying speed of the LTV structure. However, the forgetting factor should be fully self-adaptive, with the ability to change according to the nonstationary vibration response signals when there is no prior knowledge of the time-varying speed.  
(3) PCA-based OMA methods can identify mode shapes, modal frequencies, and mode damping ratios for LTI structures using only stationary vibration response signals. The proposed ERPCAWF method for OMA can also identify time-varying transient mode shapes, modal frequencies, and mode damping ratios from non-stationary vibration response signals. However, theoretical analysis and numerical simulations indicate that a decreasing or moving mass will generate additional damping [45,46] in the LTV structure. The time-varying transient mode damping ratio identified by ERPCAWF cannot be compared with the mode damping ratio calculated by finite element methods.  
The scope of application of the proposed ERPCAWF-based OMA for LTV structures is as follows:  
(1) For real modal analysis in the modal coordinates, the vibration response signals of weakly damped mechanical structures can be decomposed into the inner product of the modal shapes matrix and modal responses matrix. This method is only suitable for the real modal identification of linear and weakly damped structures. When the damping is too large, the modal parameter identification for LTV structures will entail complex modal analysis and complex parameter identification.  
(2) The proposed method is suitable for both SLTV and FLTV structures. However, ERPCAWF-based OMA is currently confined to LTV structures [47] and is unsuitable in scenarios where modal jumping phenomena may occur [48,49].
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 3 Results and Discussions
### Carbonyl-GSH Adduct Detection  
Carbonyls are electrophiles that can form adducts with thiol-containing compounds via 1,2-carbonyl addition (Schiff base formation) or 1,4-conjugated addition (or Michael-type addition) [28]. A target search for potential formation of adducts between two selected carbonyls (formaldehyde and _trans_-2-hexenal) and GSH was conducted as shown in Figure 3. Formaldehyde as a simple carbonyl (with only one active site for nucleophilic attack, i.e., Ccarbonyl) can undergo 1,2-addition to form adducts with GSH. To compare, _trans_-2-hexenal as an \(\alpha\),\(\beta\)-unsaturated carbonyl can form adducts with GSH via both 1,2-carbonyl (nucleophilic attack on Ccarbonyl) and 1,4-conjugated (nucleophilic attack on C\({}_{\beta}\)) addition reactions. Analytes represented as [M+H]\({}^{+}\) were used to plot EICs of formaldehyde-GSH (\(m/z\) 338) and _trans_-2-hexenal-GSH (\(m/z\) 408).
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 5 Practical Test on USV
### Dynamic Obstacle Avoidance  
In this experiment, we bind a red balloon on a remote-controlled (RC) boat to simulate a dynamic obstacle. We plan a simple path with a straight line. When the USV is autonomously navigating according to the planned path, we remotely control the RC  
Figure 23: Live video from the USV.  
Figure 22: Planned and actual path.  
bout to move ahead to the USV (Figure 24a). The Lidar in the front of the USV detects the moving RC boat and a new path is planned immediately between its current position and the goal position. The USV turns the left according to the new path to avoid the RC boat (Figure 24b). Affected by the safe distance, the USV passes through the RC boat with a safe distance (Figure 24c). Finally, the USV turns right, returning to its original path (Figure 24d), and continues its autonomous navigation. The Supplementary Videos (Video S1) show the entire procedure.