esa-sceva/satcom-chunk-collection huggingface dataset:
# Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
## 3 Method
### Graph Pyramid Construction on a Point Cloud  
In this section, we explain how to construct a graph pyramid on point clouds according to their neighbors. Many studies directly search for the spatial neighbors of all points and link them as a graph. After that, a graph pyramid with different spatial scales is constructed by alternately applying graph construction and coarsening techniques.  
**Graph construction on a point cloud.** Given a point cloud \(P\), which is a set of the spatial coordinates of the points, a directed graph \(G=(V,E)\) is constructed. Each vertex \(v_{i}\) is associated with a center point \(p_{i}^{*}\), and the edges are added between the point and its \(K_{l}\) neighbors \(N^{\prime}(i)=\left\{p_{i}^{*},p_{i,1}^{*},\ldots,p_{i,K_{l}}^{*}\right\}\). The direction from neighbor points to the vertex represents the direction of information transmission. In our experiments, the \(K_{l}\) neighbors are learned from its features, which performs better than random sampling.  
**Graph coarsening.** To reduce the computation cost and memory consumption, the input point cloud \(P\) is subsampled with a set of ratios using the furthest point sampling algorithm. Then, a corresponding graph \(G\) can be constructed as described above.  
**Graph pooling.** To aggregate the information from neighboring vertex to the centroid vertex, graph pooling is a necessary procedure. For each local graph, the operation of graph pooling is used to aggregate the vertex features. In this paper, the max pooling adopts a pooling function. The specific definitions are as follows:  
\[h_{i}^{l+1}=pooling\left\{h_{j}^{l}:j\in N_{l}(i)\right\} \tag{5}\]  
where \(N_{l}(i)\) is the neighbor set of the \(l\)-level vertex \(v_{i}\) and \(h_{j}^{l}\) is the corresponding feature of the neighbors. \(h_{i}^{l+1}\) is the output feature of the pooling operations.  
**The network architecture.** The architecture of AP-GCN is shown in Figure 2. The subsampled point cloud is directly used as the input, and the directed graph is constructed using the proposed method. Then, the convolution operation is performed on the vertices and edges. After each coarsening, the number of neighbors decreases. For the segmentation task, the main structure is the same as the classification network. Different from the classification network, feature interpolation is necessary to obtain the feature mapping  
Figure 1: An example of a learned graph structure.  
to each point. As a result, the learned features are restored from the coarsest scale to the original scale. At the same time, different-level features are fused with a skip connection.  
The proposed network is generally consistent with the common recognition and segmentation architecture. The main difference lies in the construction of the graph structure and convolution operation. Our graphs are constructed by calculating the affinity between points, and the convolution operation captures both vertex and edge features.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 4 Case Study: Region of Salamanca
### Measurements Results from Smart Sensors
The microcontroller uses 0.0168 mA during this period of time, so the shipment required 0.3168 mA in total. Once the estimations were obtained, we proceeded to build the system and we obtained the battery consumptions. The tests consisted of simulations to define the real consumption rates of the node under different data capture and shipment conditions. This allowed us to specify the best rate to transfer data to the CVRP system and maintain a consumption level to keep the battery working for more than a year. Table 5 describes some possible configurations of the nodes in order to estimate the consumption of the device. The first column describes the data shipment per day; these shipments are periodically performed in the same time intervals and provide the needed information for node selection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor** & **Consumption (mAh)** \\ \hline Ultrasonic sensor & 0.36 \\ Load cells & 0.06 \\ Temperature sensor & 0.002 \\ Radio module & 0.3 \\ MCUi & 0.0002 \\ MCUa & 0.068 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Developed sensor consumption. The second column shows the number of measurements taken in a day. The third column displays the battery consumption estimated with the previously explained method. The fourth column shows the actual measured consumption of the device in the lab.
However, most methods classify lane-changing operations, lane-keeping, and vertical driving intentions as aggressive and moderate [20], with low classification accuracy, and do not accurately describe the lane-changing process [21; 22; 23]. Instead of outputting discrete values, the classification results of the FCM algorithm output the probabilities belonging to different outcomes, which can improve the resolution and classification accuracy of the classification results, thus avoiding overly aggressive or conservative results of the prediction model. For example, [PERSON] et al. [24] used acceleration and deceleration behavior, overspeed behavior, and operational stability as feature parameters to classify driver driving behavior using the FCM clustering method. [PERSON] et al. [25] used speed, acceleration, and gas pedal opening as feature quantities for the FCM algorithm to classify driving styles. According to the uncertainty of driving behavior and vehicle dynamics characteristics, this paper decomposes the problem of driving behavior recognition prediction into vertical speed prediction and horizontal heading angle prediction. The FCM method was applied to classify driving intentions to improve the accuracy and resolution of driving behavior pattern recognition. The model is trained with real vehicle data for different driving intentions. To accurately predict the future actions of the leading car, the action prediction is decoupled into vertical velocity prediction and horizontal heading angle prediction instead of just predicting coordinate points. For the horizontal prediction problem, the LSTM algorithm is used to predict the heading angle of the leading car, and for the vertical prediction problem, the GPR algorithm is used to predict the speed of the leading car and combine it with the calculated future trajectory. Finally, real vehicle data are used to validate the algorithm proposed. The rest of this article is arranged as follows. Section 2 describes the proposed trajectory prediction model, including the principle of the algorithm and the framework of the trajectory prediction algorithm. Section 3 introduces the research object and evaluation criteria and analyzes the experimental results, and Section 4 concludes the paper.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 1 Introduction
In contrast, the highest frequency harmonics predominantly arise from the interaction of the permanent magnet field and the permeance fluctuation [23]. It was also established that the level of magnetic vibration of the machine is mainly determined by this lowest mode of vibration. In PM machines, the order of the lowest mode of vibration excited is equal to the GCD of the number of slots and the number of poles [13]. Given the abovementioned considerations, it would seem clear that guidelines for an optimum selection of the number of slots and the number of pole pairs are required. Different authors have each proposed a distinct strategy, which, to a certain extent, are contradictory. For this reason, a clear guideline or procedure needs to be determined. In addition, the influence of the change of the number of slots and poles on the structural behaviour remains unstudied. Therefore, the present study investigates the effect of the combination of the number of slots and pole pairs on the vibration response. To this end, the effect of the number of slots on the structural behaviour of the stator are studied. Taking into account the results of the analyses, a procedure is defined to choose the optimum combination of slots and poles. The results obtained are then verified experimentally. First, in Section 2, the analytical and Finite Element (FE) calculations are detailed and the procedure for the experimental measurements are explained. Second, in Section 3, the results of the calculations and the measurements are given. In Section 4, the discussion of the results is presented and the procedure for the optimum design is established. Finally, in Section 5, the conclusions of the work are summarised.
## 2 Materials and Methods
### Musculoskeletal Modeling and Gait Simulations  
A generic multibody musculoskeletal model including 14 rigid bodies, 20 degrees of freedom and 80 musculotendon actuators [23] was used in conjunction with experimental marker trajectories and ground reaction forces to calculate joint angles, joint moments, muscle forces and joint contact forces during walking, by leveraging OpenSim [24]. Preliminarily, marker trajectories were processed with a Woltring low-pass filter to smooth the data with a mean square error of 10 mm, then MATLAB (R2018a, Mathworks, USA) was used to process marker trajectories and ground reaction forces to generate the input files and batch process the simulations in OpenSim. First, the model was personalized for each subject, by scaling the dimensions of each body segment, mass and inertial properties, and the elements attached to the body segments, based on (i) the distances between the experimental markers from the static trial and the corresponding virtual markers on the model, and (ii) body mass. Joint angles during walking were then calculated through inverse kinematics, by minimizing the errors between experimental and virtual markers. Joint moments were calculated through inverse dynamics, from the joint angles and the ground reaction forces. Then, muscle forces were calculated by decomposing the joint moments among the musculotendon actuators through static optimization, by minimizing the sum of muscle activation squared and accounting for the force-length-velocity relationship [25]. Finally, joint contact forces were calculated from the instantaneous force equilibrium through joint reaction analysis.  
All data were normalized to percentage of gait cycle from 1% to 100%, and muscle and joint contact forces were expressed in body weight (BW). Hip contact forces, and the most influential muscle forces acting on the hip were analyzed. The muscle forces were grouped and summed in hip abductor muscles (gluteus medius anterior, middle and posterior, gluteus minimum anterior, middle and posterior) and hip flexor muscles (iliacus, psoas and rectus femoris).
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 2 Materials and Methods
### Overall Network Structure
These layers progressively decrease the dimensions of the image while expanding the number of channels to extract image features. Following this, there are four basic residual blocks in the backend, which aim to capture higher-level features and finer patterns in the image. The generator mirrors the encoder structure. It starts with four basic residual blocks at the front end, which receive the representation from the shared latent space and reconstruct features. These residual blocks are responsible for restoring details and textures of the image from the latent space. Subsequently, three transposed convolutional layers are used in the backend to progressively increase the dimensions of the feature maps while compressing the number of channels in order to restore the image to the input size and generate the final output image. The discriminator network includes discriminators \(D_{A}\) and \(D_{B}\). In \(D_{B}\), the discriminator \(D_{B}\) output TRUE for real images is sampled from domain \(B\), while its output FALSE for images is generated by \(G_{B}\). \(G_{B}\) generates two types of images: reconstructed images \(\tilde{y}^{B\to B}\) and translated images \(\tilde{x}^{A\to B}\). Since reconstructed images can be trained with supervision, only the translated images are used for adversarial training. A similar approach is applied to \(D_{A}\): discriminator \(D_{A}\) output TRUE for real images is sampled from domain \(A\), while its output FALSE for images is generated by \(G_{A}\). The structure of the discriminator is depicted in Figure 2b. It consists of six convolutional layers, which progressively decrease the dimensions of the input image while increasing the depth and complexity of the features. Notably, the network processes the input image through different scales of downsampling operations, each handled by the same discriminator network, resulting in three different scales of feature outputs: \(16\times 16\), \(8\times 8\), and \(4\times 4\). Each specific scale's output independently performs real vs. fake discrimination. This enables the network to assess the authenticity of the image from multiple dimensions, with each scale providing a different perspective and information, thereby enhancing the model's discriminative ability.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 4 Experimental Results
### Energy Consumption Comparison with WiFi Data Transmission  
In order to understand the energy consumption when using LoRa module (EK-S76 SXB) to transmit data, we conducted the other experiment with a WiFi transmission module for comparison. Except for the different of transmission method, the water sensor and RX64M controller PCB did not change. There is one more thing should be mentioned: the energy consumption comparisons are conducted away from the web-sever of collecting data (a) under a 1.7 km distance with the LoRa module and (b) under a 15 m distance with a WiFi module. It is clearly shown in Figures 15 and 16. Long-distance transmission is indeed LoRa's advantage. Regardless of the amount of data (as long as it is within the capabilities of LoRa), the energy consumption of LoRa is constant, but the WiFi is not. Moreover, there is no delay phenomenon in the transmission at a distance of 1.5 km.
## 2 Related Works  
The authors in [13] investigated the modeling of the PAPR reduction problem as a combinatorial search problem and proposed to use a Hopfield neural network (HNN) optimizer [14] to solve it. Although the technique showed promising results, it would require the transmission of a large amount of side information and suffer high sensitivity to phase noise. To achieve PAPR reduction performance close to that provided by HNN while avoiding the transmission of side information, an SLM-based radial basis function network (RBFN) solution was introduced in [15]. RBFN is essentially a multivariate interpolator that generates a phase rotation vector which is fed to an SLM encoder for phase selection and OFDM signal generation. Although no explicit side information is needed, the technique requires the adaptive updating of the weights of the network's output layer as well as the activation functions of the network's hidden layers.  
The authors in [16] proposed an active constellation extension-based (ACE) method that deploys a time-frequency neural network (TFNN) to accomplish PAPR reduction performance close to that obtainable using the original ACE technique with less complexity. However, TFNN modules operate in complex time-domain and frequency-domain modes and exhibit degraded BER performance in fading channels for high-order modulations. To overcome this problem, Ref. [17] proposed an ACE-based NN variation to achieve much better BER performance for high-order modulation transmissions over fading channels while maintaining almost the same PAPR reduction performance of the TFNN method. Although the technique requires fewer in-use computations than the TFNN approach, it would rely on the continuous training and updating of adaptive NN modules at the receiver side, based on the average received signal-to-noise (SNR) ratio, through the transmission of predefined training signals over dedicated spectral resources.  
Recently, the authors in [18] proposed a deep neural network-based (DNN) encoders and decoders deploying multi-layer DNN modules for constellation mapping and demapping at the transmitter and receiver sides, respectively. The DNN-based approach can simultaneously improve the performance in terms of both PAPR reduction and BER. This comes, however, at the expense of highly expensive computational cost due to the deployment of complex multi-layer DNNs, rendering their usage unfeasible for low-latency demanding scenarios that require strict constraints on complexity.  
Unlike the above works, the proposed SNN-CF is a low-complexity PAPR reduction approach which requires neither the active adaptation of the learned network nor the transmission of side information or the dedication of additional resource blocks. In addition, the actual merit of the proposed scheme is expected to be observed for higher-order modulations where conventional ANN schemes start to exhibit severe BER performance deterioration.
## 2 Methodology  
YOLOv8 (You Only Look Once version 8) [12] represents the latest iteration of the YOLO object detection and image segmentation model series. The YOLOv8 series offers a range of models to cater to diverse scenarios and requirements. Among them, YOLOv8n stands out as the most computationally efficient and fastest in inference speed, making it particularly suitable for deployment on resource-constrained devices. Its structure is shown in Figure 1. It consists of three components: Backbone, Neck, and Head. The Backbone module incorporates Conv, C2f, and Spatial Pyramid Pooling Fast (SPPF) components, which, respectively, enhance nonlinear expression, feature propagation, and multi-scale processing capabilities. The Neck part employs a Path Aggregation Network (PAN) [13] structure, combining the Feature Pyramid Network (FPN) [14] and PAN to achieve efficient feature fusion. In the Head module, YOLOv8n uses a Decoupled Head design to separately handle localization and classification tasks, improving prediction accuracy and efficiency, simplifying the model structure, and enhancing computational speed. Regarding the loss function, YOLOv8n employs Complete Intersection over Union loss (CIoU) combining IoU [15] and the distance between central points to measure more accurately the difference between the predicted and ground truth boxes, thereby improving localization accuracy.  
When implementing PCB defect detection on mobile devices, a lightweight model is crucial to address the limited computational resources. Although YOLOv8n excels in object detection, its model size remains relatively large for embedded devices or real-time processing scenarios, necessitating further optimization. Additionally, the structural complexity of PCBs and the issue of defects at different scales pose challenges for high-precision detection with one-stage networks. Therefore, the improvements to the YOLOv8n algorithm in this paper are as follows: Firstly, SCConv is integrated into the C2f structure of the backbone network of the original model. This enhancement reduces the computational load while improving the model's learning capability. Secondly, an adaptive feature selection module is incorporated into the model to enhance the network's ability to detect small PCB defect targets. Thirdly, a self-developed lightweight shared convolution head is used to replace the original Decoupled Head, reducing complex computations and improving the detection accuracy of the network. Lastly, the WIoU loss function is introduced to provide refined evaluation results and enhance generalization capability. The network structure of the improved model is shown in Figure 2:  
Figure 1: YOLOv8n network. P1–P5 represent the changes in feature scales extracted by the backbone network; X3–X5 are the output features; Upsample refers to upsampling; C2f is the feature extraction module. The detection layer is Decoupled Head.
# Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking
## 2 Materials and Methods
### Measures  
#### 2.2.1 Generic Scale of Phubbing (GSP)  
The GSP [7] is a 15-item questionnaire that measures the degree to which individuals engage in phubbing behavior. The survey includes four subscales: nomophobia, interpersonal conflict, self-isolation, and problem acknowledgement. Examples of items include \"I cannot stand leaving my phone alone\" and \"I pay attention to my phone for longer than I intend to do so\". Items are responded to using a seven-point Likert-type scale, ranging from never (1) to always (7). Survey scores are calculated by summing all item responses, with scores ranging from 15 to 105. A higher score reflects greater engagement in phubbing behavior.
## 2 Introduction of the Proposed Control Technique
### The Dynamic Model Analysis
According to Figure 2, performance of the proposed controller is highly influenced by the embedded characteristics of PMSG that can consequently improve the controller operation with accurately regulating the virtual parameters. Also, the coefficients of \(\alpha_{1}\), \(\alpha_{2}\), \(\beta_{1}\) and \(\beta_{2}\) are the control factors of the proposed controller.
## 2 Notation and Background
### One-Point Motions  
Consider the homogeneous space \(M=G/H\), where \(H\) is a Lie subgroup of the Lie group \(G\), and let \(\pi\colon G\to M\) denote the canonical projection. Suppose that \(G\) acts on \(M\) on the left and that \(g_{t}\) is a process in \(G\). This induces a process in \(M\). For any \(x\in M\), the induced process \(x_{t}=g_{t}x\) defines the one-point motion of \(g_{t}\) in \(M\), with initial value \(x\). The one-point motion, \(X_{t}=g_{t}x\), of a Brownian motion \(g_{t}\) in \(G\), started at \(g_{0}=e\), is only a Brownian motion in \(M\) under certain conditions (see, e.g., [24], Proposition 2.7). In the case of a bi-invariant metric, a Brownian motion on \(G\) maps to a Brownian motion in \(M\) through its one-point motion, which, in general, is not the case. For example, the one-point processes of a \(G\)-valued Markov process might not preserve the Markov property if the metric is not bi-invariant.  
In this paper, we explicitly chose metrics whose Brownian motions do not descend to Brownian motions in \(G/H\). The non-invariant metrics result in processes in \(G/H\) with an anisotropic covariance structure. The anisotropic distributions in \(G/H\) will arise from non-invariant metrics, and the induced processes will, in general, not inherit the Markov property.
###### Abstract  
This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and heuristic methods, which are sensitive to the problem scale, demand high computational expenses. Thus, an efficient approach is demanded to solve this problem, and this paper proposes a deep reinforcement learning algorithm with a local attention mechanism. A mathematical model is first established to describe this problem, which considers a series of complex constraints and takes the profit ratio of completed tasks as the optimization objective. Then, a neural network framework with an encoder-decoder structure is adopted to generate high-quality solutions, and a local attention mechanism is designed to improve the generation of solutions. In addition, an adaptive learning rate strategy is proposed to guide the actor-critic training algorithm to dynamically adjust the learning rate in the training process to enhance the training effectiveness of the proposed network. Finally, extensive experiments verify that the proposed algorithm outperforms the comparison algorithms in terms of solution quality, generalization performance, and computation efficiency.  
s 2024 24 6396 10.3390/s24196396