esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Networks for Big Data Systems
## 3 Technical Approaches to WSN-Based Big Data Systems
### Data System
#### 3.4.3 Data Processing  
In this section, we describe in-network processing in WSN: data aggregation and fusion technologies. [PERSON] et al. [19] provided an overview and discussion of state-of-the-art data mining and data fusion techniques designed for WSNs. They emphasized that processing sensor data inside the network (in-network) before any further processing helps save limited resources and prevent excessive data duplication.  
In addition to a comprehensive survey, a specific algorithm called high-dimensional data aggregation control algorithm for big data (HDAC) is proposed in [47]. In this study, information to eliminate the dimension not matching with the specified requirements is the main source. While handling this, the principal components method to analyze the remaining dimension is employed. In the process of data aggregation, the self-adaptive data aggregation mechanism is used to reduce the phenomenon of network delay. The simulation results evaluate node energy consumption and data delay through the HDAC. Recently, a comprehensive survey of data aggregation for big data in WSN was presented in [19]. It covers research challenges in the big data area and proposes a new classification of these challenges, accordingly to the necessities of WSN. The detailed data aggregation strategies in WSNs are also discussed. They include: (1) distributed compressive data aggregation in large-scale WSN; (2) sensor data aggregation in a multi-layer big data framework; (3) lifting wavelet compression based data aggregation in big data WSN; (4) scalable privacy-preserving big data aggregation mechanism; and (5) a cluster-based data fusion technique to analyze big data in wireless multi-sensor systems.
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 3 Proposed Method
### Loss Function  
The loss function of the network we designed mainly consists of two parts, confidence loss (_conf_) and localization loss (_loc_). The weight term \(\alpha\) is set to 1 by cross-validation.  
\[L(x,c,l,g)=\frac{1}{N}\Big{(}(L_{conf}(x,c)+\alpha L_{loc}(x,l,g)\Big{)} \tag{4}\]  
where, the \(N\) represents the number of positive box. The localization loss is the smooth L1 loss between the prediction box (\(l\)) and the ground truth box (\(g\)). Parameters: (\(cx\), \(cy\)), (\(w\)), and (\(h\)) represent the center, width, and height offsets of the default bounding box, respectively.  
\[L_{loc}(x,l,g)\hat{=}\sum_{i\in Pos}^{N}\sum_{m\in\{cx,cy,w,h\}}x_{ij}^{k}smooth _{L1}\Big{(}l_{i}^{m}-\hat{g}_{j}^{m}\Big{)} \tag{5}\]  
\[\hat{g}_{j}^{cx}=\Big{(}g_{j}^{cx}-d_{i}^{cx}\Big{)}/d_{i}^{w}\ \hat{g}_{j}^{ cy}=\Big{(}g_{j}^{cy}-d_{i}^{cy}\Big{)}/d_{i}^{y} \tag{6}\]  
\[\hat{g}_{j}^{w}=\log\left(\frac{g_{j}^{cy}}{d_{i}^{w}}\right)\ \hat{g}_{j}^{h}=\log\left( \frac{g_{j}^{h}}{d_{i}^{h}}\right) \tag{7}\]  
where  
\[smooth_{L1}(x)=\left\{\begin{array}{cc}0.5(x)^{2}&|x|<1\\ |x|-0.5&\textit{otherwise}\end{array}\right. \tag{8}\]  
The confidence loss is the softmax loss over multiple classes confidences (\(c\)) given by:  
\[L_{conf}(x,c)=\sum_{i\in Pos}^{N}x_{ij}^{p}\log\left(\hat{c}_{i}^{p}\right)- \sum_{i\in Neg}\log\left(\hat{c}_{i}^{0}\right) \tag{9}\]  
where  
\[\hat{c}_{i}^{p}=\exp\left(c_{i}^{p}\right)/\sum_{p}\exp\left(c_{i}^{p}\right) \tag{10}\]  
Figure 8: Tramp materials in raw coal single-shot detector (TMRC-SSD) architecture overview.
## 3 Experiments and Analysis
### Qualitative Comparison
#### 3.4.1 TVF  
The temporal filtering performance of TVF is excellent under simple backgrounds. However, in complex backgrounds, as the background moves in the image, the intricate structures of the background may be detected as false alarms due to phase shifts. This can be observed particularly in the detection results of TVF in Figure 7(f1-f6), especially in the results of Seq3.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Video Degradations
#### 4.1.1 Coding Artifacts (Compression)  
The Advance Video Coding (AVC) H.264/ MPEG-4 and the High Efficiency Video Coding (HEVC) H.265 [50,51] standards were selected to compress the source stimuli. Throughout a visual examination, researchers selected four bitrate values for each coding standard and they labelled it as Low, Medium, High, and Very High quality. The sample sequences used for this analysis were not included in the main experiment. The selection was made by taking into account bitrate values used in previous works and picking four clear quality levels [52,53]. Table 3 presents the bitrate values used for each codec.
## 4 Simulation
### Numerical Simulation  
Comsol Multiphysics is a finite element software that can simulate multiple-physical-field coupling simultaneously. The simulation of multiple physical fields is realized by solving partial differential equations. In this study, the coupling simulation of the fine matter transfer module, chemical module, and solid mechanics module is adopted. Definition of boundaries: This paper adopts a three-dimensional prism model with six faces, so there are six boundaries. It is assumed that five boundaries have no flux and that sulfate ions can only enter the specimen through one boundary. This boundary is the tensile surface of the specimen. The boundary concentration is \(C\)(0,t) = 5% Na\({}_{2}\)SO\({}_{4}\). Material properties: The ratio coefficients are R\({}_{1}\) = 0.6, R\({}_{2}\) = 0.5, and R\({}_{3}\) = 0.35. The temperature \(T\) = 298.15 K, the density of cement \(\rho_{\mathrm{c}}\) = 3100 kg/m\({}^{3}\), the density of mortar \(\rho\)s = 2300 kg/m\({}^{3}\), and the density of concrete \(\rho_{\mathrm{c}}\) = 2600 kg/m\({}^{3}\).  
The specimen size is 40 mm \(\times\) 40 mm \(\times\) 160 mm, and the sulfate ion diffuses from the eroded surface of the cementitious material in one dimension, bending load F\({}_{1}\) = 0, F\({}_{2}\) = 0.2f, and F\({}_{3}\) = 0.4f (f is the ultimate flexural strength of the specimen). In this study, a conditional statement command is used to automatically switch between the wet and dry cycle conditions by entering the commands into the COMSOL Multiphysics finite element software physical field boundary conditions input box.  
Figure 12 presents the numerical simulations: (a) shows the mesh generation diagram. In this study, triangular meshes were used for mesh division. Triangular meshes have excellent adaptability and numerical stability in simulating concrete erosion, and are particularly suitable for handling complex boundaries, multi-physical field coupling, and heterogeneous material problems; (b) depicts the application of bending stress at a position 20 mm from the edge, with varying magnitudes. Figure 13 displays numerical simulations of sulfate ion concentrations, where panels (a)-(f) represent different erosion ages. As observed in the figures, an increase in erosion age correlates with a rise in sulfate ions  
within the specimen; however, the sulfate concentration at the bottom of the specimen remains low, approaching zero.
# Rotation Accuracy Analysis of Aerostatic Spindle Considering Shaft's Roundness and Cylindricity
###### Abstract  
The rotation accuracy of the aerostatic spindle can easily be affected by shaft shape errors due to the small gas film clearance. Thus, the main shaft shape errors with the largest scale--that is, the roundness and cylindricity errors--are studied in this paper, and a dynamic mathematical model is established with the consideration of the roundness, cylindricity errors, and spindle speed. In order to construct the shaft model, the discrete coefficient index of the shaft radius based on roundness measurement data are proposed. Then, the simulation calculations are conducted based on the measured cylindricity data and the constructed shaft model. The calculation results are compared with the spindle rotation accuracy measured using the spindle error analyzer. The results show that the shaft with a low discrete coefficient is subjected to less unbalanced force and smaller rotation errors, as obtained by the experiment.  
rotation accuracy; aerostatic spindle; roundness; cylindricity; dispersion coefficient +
Footnote †: journal: Medical and Precision Instrument Engineering, Xi’an University of Technology, Xi'an 710048, China; [EMAIL_ADDRESS] (G.Z.); [EMAIL_ADDRESS] (R.Z.); [EMAIL_ADDRESS] (W.S.)
# Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete
## 3 Numerical Modeling and Characterization
### Numerical Modeling with a Dense Packing Algorithm  
#### 3.1.1 Virtual Specimens with Different Gradings  
The same gradings in Section 2.1 are adopted for virtual (numerical) lightweight concrete specimens. Several random packing algorithms are proposed by different researchers [24; 25; 35]. In this study, a packing theory used by [PERSON] and [PERSON] [26] is utilized to generate the virtual specimens. In their model, particle packings with three different size distributions, such as mono size, bimodal, and Gaussian, can be produced; here, the modified bimodal distribution model is adopted, and a detailed description of the model can be found in [26; 36].  
To produce the virtual specimens with different gradings, the LWA is classified into four classes as 4, 2, 1, and 0.5 mm particle sizes, and \(\pm\)5% differences in each class are set to be allowed. In each virtual specimen, every Liaver(r) particle is assumed to be non-overlapping spheres and are randomly distributed by considering the packing algorithm and interparticle reactions. Figure 3 shows the sample of the virtual LWC specimens with different gradings. In each sample, the ratios between different sizes of LWA are assumed to be the same as those of the real LWC specimens in Section 2.3, and designed volume content of LWA in each specimen is about 73% of the whole specimen. The remaining part is considered as the matrix (cement paste). The material properties of LWAs with different sizes and matrices are presented in Table 6. The characteristics of the LWA size distribution and material responses are evaluated using these samples as well as numerical approaches and compared with those from experimental results.
## 1 Introduction
The advantages of this new algorithm include a guaranteed closed-loop stability for optimization-based trajectory tracking control. Nevertheless, most of the above trajectory tracking control algorithms have been applied to AUVs models and they were demonstrated based on numerical simulations. The propulsion model of the biomimetic spherical robot differs from an AUV and the hydrodynamics are complex, which makes trajectory tracking for biomimetic sphere robot (BSR) difficult to achieve experimentally. This paper focuses on the achievement of trajectory tracking with thrust constraints for the biomimetic spherical robot in an underwater plane. Inspired by the guidance in [24], a trajectory tracking controller that is based on a model predictive control algorithm is designed in this study. The main contribution of this paper is three-fold. First, the kinematic and dynamic models of the BSR are established. To establish the predictive model of the BSR, the identification of the dynamic model in yaw and surge is proposed. Second, an improved MPC control algorithm is developed in order to solve the BSR trajectory tracking problem. Third, simulation and experimental evaluations of the developed algorithm are conducted. The remainder of this paper is organized, as follows. Section 2 describes the prototype of the biomimetic spherical robot and the established model of the robot for trajectory tracking. In Section 3, the MPC-based trajectory tracking controller is detailed. Simulation results are presented in Section 4. Section 5 describes the experimental results and discusses the comparison with the other trackingalgorithms. Finally, some concluding remarks and possible areas of future study are summarized in Section 6.
## 1 Introduction
Deep learning can automatically extract high-level features and has strong generalization capabilities, which provide unique advantages in processing radar spectrograms. However, deep learning models typically require extensive computational resources and storage, conflicting with the size, power, and cost constraints of radar devices. This makes it challenging to deploy complex neural networks directly on radar devices. Furthermore, existing lightweight models often compromise the model's representational capacity during compression and acceleration, impacting recognition accuracy. To address this issue, this paper proposes TG2-CAFNet, a through-wall radar-based HAR method leveraging a lightweight network. The proposed method constructs a lightweight feature extraction module, TG2, based on GhostV2 convolution, which parallelly extracts RTM and TDM features. Then, a CA-attention-based AFF fusion method is used to capture correlations between multi-domain features and adaptively fuse inter-map and intra-map spatial features. In summary, the innovations of this paper can be summarized as follows:  
* This paper demonstrates that, under through-wall conditions, the exponential weighting method is more suitable than traditional moving target indication (MTI) for suppressing wall clutter. By employing exponential weighting, the multipath effect of a wall can be effectively mitigated, enhancing the resolution of target signals in the frequency domain, and thereby improving the signal quality and recognition accuracy of through-wall radar systems. * The proposed TG2 module, based on GhostV2 convolution, is capable of generating rich feature representations with a small number of parameters and FLOPs. Unlike most existing lightweight models that are based on a single feature domain, the TG2 module is able to extract features from both range-time map (RTM) and time-Doppler map (TDM) domains in a parallel manner, fully utilizing multi-domain information and effectively enhancing feature representation capabilities. This approach addresses the limitation of the single-domain feature expression found in current lightweight models. * This paper proposes a feature fusion method based on coordinate attention (CA), named CAF. CAF improves upon the traditional attention feature fusion (AFF) by extending the adaptive weighting mechanism to the spatial dimension, enabling information from different feature domains (RTM and TDM) to be fused at a finer spatial granularity. This method not only enhances the effectiveness of feature fusion but also significantly improves the model recognition accuracy, while maintaining lightweight model properties.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 6 Experiment Results
### Training and Verification of CNN  
The training set size in this experiment is 50, and pairs of adjacent modules from the 50 barcode images consist of a CNN training and validation set. The number of total pairs of adjacent modules of a barcode image can be calculated by the formula: \(\text{F}=\text{n}^{*}(\text{n}-1)^{*}2\), where n is the size of the barcode. The CNN training and validation set, finally, contains 20,810 samples. Then, we manually mark these samples into two categories. One has 10,040 samples, and the other has 10,770 samples.  
We use five CNN models for the CNN training and validation set, such as AlexNet, GoogleNet, VGG16, VGG19, and ResNet50. The period of the training experiment is 5, and the learning rate is 0.01. In addition, the batch size is 128. The classifier is tested in five-fold cross-validation. To evaluate the loss value of the training period and the accuracy of verification, they are displayed graphically, as shown in Figure 15. It can be found that the validation score follows the training score, which indicates that our results avoid overfitting in the training procedure and retain the generalization characteristics of the CNN.  
Figure 14: Some samples of our database.  
Figure 15: Change in accuracy and loss of five CNN models.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 4 Results and Discussion
### Scenario 1: BLE-Based Communication with Cluster-Based Mitigation and a Jammer Node
The absence of an intelligent coordination mechanism means that affected nodes persist in unsuccessful transmission attempts, delaying network stabilization. In contrast, the network employing the proposed mitigation algorithm exhibits a considerably lower recovery time. The key factor contributing to this improvement is the dynamic reconfiguration of network clusters, which ensures that disrupted nodes swiftly identify new cluster heads or alternative routing paths, thereby restoring connectivity more efficiently.
## 4 Development of the Model
### SGB Model  
For the SGB model, the boosting iterations (\(\alpha\)), max tree depth (\(\beta\)), shrinkage (\(\chi\)), and minimum terminal node size (\(\delta\)) were specifically fine-tuned for developing the SGB model. Like the RF model, a 10-fold cross-validation method was also applied to avoid over/underfitting. A trial and error procedure with various parameters of 100 SGB models was performed. The best modelling result was achieved by using \(\alpha=141\), \(\beta=4\), \(\chi=0.1899\), and \(\delta=5\). Figure 7 shows the root-mean-squared error (RMSE) of the SGB model for estimating the content of ilmenite in this study with different parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Rule** & **Anatase** & **Leucoxene** & **Zircon** & **Monazite** & **Ilmenite** \\ \hline \hline Rutile & \(1\) & & & & & \\ Anatase & 0.659115 & \(1\) & & & & \\ Leucoxene & 0.516591 & 0.447161 & \(1\) & & & \\ Zircon & 0.65455 & 0.632258 & 0.326362 & \(1\) & & \\ Monazite & 0.101687 & 0.037384 & 0.084057 & 0.052774 & \(1\) & \\ Ilmenite & 0.476926 & 0.650812 & 0.04358 & 0.668171 & \(-0.00484\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation matrix of heavy minerals.  
Figure 6: Performance of RF model for estimating ilmenite content.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Reactive Routing Protocols  
Reactive routing protocols (RRP) are also known as on-demand or passive routing protocols, which can be used to discover or maintain a routing path on demand for data transmission. The routing table here is updated only if there is some data to send. As a result, there is no need to calculate a route if there is no connection between the two UAVs. Thus, these routing protocols update routing tables only for those paths that are presently in use [27]. Consequently, it overcomes the overhead issues regarding proactive routing protocols. In this routing model, two type of message are generated: (i) _RouteRequest_; and (ii) _RouteReply_ messages. A _RouteRequest_ message is initiated from the source UAV to all adjacent UAVs, using the flooding process to calculate the optimal route. Alternatively, a _RouteReply_ message is originated by the receiving UAV and transmits to the sending UAV in a unicast communication manner. There is no compulsion to refresh all the routing tables of the network in this routing approach. RRP is bandwidth efficient, due to the absence of periodic updates. The main failing of RRP is the high latency during the optimal path finding phase.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 2 Related Works
### The Allocation Method
Some studies have applied the potential game to solve the spatial spectrum access problem [34] and power allocation problem [35]. We can also find this game method widely used in resource allocation, especially cooperating with other methods in some complex situations. Ref. [36] modeled the inter-cell channel allocation as a potential game and the intra-cell one as a many-to-one matching game. Ref. [37] solved the bitrate adjustment problem by the large deviation principle and the spectrum allocation problem by a potential game. Although the above two studies incorporated and solved two issues together, they may tend to neglect some details while achieving overall targets and we can find that neither of them considered the characteristics of specific videos. Thus, that's what we need to do. In this paper, we will discuss the adoption of potential game in rate allocation, especially from the perspective of application layer videos' particularity and their transmission quality assurance. In a word, the rate allocation in the UAV cluster for video transmission is not a simple algorithm for resource divisions. The mechanism should consider to improve the video QoE, take the flying cluster as the object, and adopt a distributed method like game theory. In previous related works, these three aspects have not been fully studied and we will discuss about them further in this paper.
## 5 Implementation Scheme of Injection Harmonic Current Differential Protection
### Principle of Injection Starting of Characteristic Harmonic Currents  
In order to avoid harm to users from the CHCs injected under normal operation of the distribution network, this paper designs a corresponding starting module to initiate the injection of CHCs only after a fault. From Figure 4, the fault is detected by collecting the phase voltage and current at the IBDG outlet. When the starting criterion is satisfied, an enable signal is injected into the internal harmonic injection unit of the IBDG controller.  
In Figure 4, the starting criterion 1 based on the mutation of phase current is as follows:  
\[||i(k)-i(k-N)|-|i(k-N)-i(k-2N)||\geqslant K_{\mathrm{S}}I_{\mathrm{N}} \tag{17}\]  
where \(i(k)\) is the sampling value of phase current at time \(k\); \(N\) is the total number of sampling points per cycle; \(K_{\mathrm{S}}\) is the starting coefficient and takes the value of 0.1-0.3; \(I_{\mathrm{N}}\) is the rated current of the protected feeder; and \({}^{\prime\prime}\mid\mid^{\prime\prime}\) are the signs for absolute value. The starting criterion 2 based on the mutation of phase voltage is similar to the starting criterion 1 and will not be repeated here.