esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Equipment and Protocols
### Characterisation of the Chip Seal Surface Characteristics  
The Mean Profile Depth (MPD) and Mean Texture Depth (MTD) calculations were done using the data obtained from the handheld 3D scanning equipment. The surface characteristics of the different seals before and after APT loading were determined and compared using the 3D scans. The MPD is calculated as the average profile depth of two sections (using the highest peak on each section) over a 100-mm-long baseline [36].  
The sand patch test [37] uses a volumetric approach of measuring pavement macrotexture. The MTD [15] can be expressed as the ratio of the volume of material that is required to fill the surface, divided by the area that is covered by the volume of material. Unlike the American Society for Testing Materials (ASTM) method, the surface area of the sample remains fixed when using 3D scanning technology, with the volume of the material filling the surface texture serving as the variable quantity. The benefit of this calculation is that an equivalent sand patch value can be calculated directly using the software. The volume of both the cuboid surrounding the sample (area multiplied by the greatest difference in height) and the volume of the aggregate are known. The difference in the volume is occupied by the virtual sand particles that would fill the surface until the entire surface is covered uniformly.  
Two quantitative metrics were used for describing the relevant surfacing characteristics. For each sample, the kernel size was configured to be between 25% and 100% of the nominal aggregate dimension. Smaller kernel sizes provided improved representative statistics of the samples. The two metrics that were found to provide good results are curvature and roughness.  
The built-in curvature tool provides an assessment of the extrinsic mean curvature that is derived from a differential geometry. The curvature of each point is estimated by  
Figure 7: Typical example of an imported mesh of the 20 mm single seal.  
best fitting a quadratic around that particular point. A histogram provides the distribution of the metric. The roughness distribution is based on a best-fitting plane of the surrounding points within the kernel. The roughness will thus be highly dependent on the volume and shape of the aggregate protruding from the seal or binder surface. The statistical data can be expressed using either a Weibull distribution with a- and b-parameters describing the scale and shape, respectively, or an exponential distribution. The goodness-of-fit is highly dependent on the aggregate dimensions and shape for each test.
## 3 System Implementation
### Sampling and Recording  
The aim of this step is to measure the sound levels and obtain the audio files. The frequency range of sound audible to the human ear is 20-20 kHz [18], so we use a sample with 44.1 kHz, quantization with 8-bit, and coding with PCM to capture sounds. A-weighting is now commonly used for the measurement of environmental noise and industrial noise among the five different frequency weightings (called A, B, C, D, and Z) for measuring the sound pressure level [19]. The A-weighted equivalent sound level LAeqT[20; 21] is given by:  
\[\text{LA}_{\text{eqT}}=10\log_{10}\left(\frac{1}{T}\int_{0}^{T}(v_{A}(t))^{2}dt \right)+\Delta \tag{1}\]  
where vA(t) is the result of passing the induced voltage in the microphone through an A-weighting filter [22] and \(\Delta\) is a constant offset determined by calibrating the microphone against a standard sound level meter. The measurement of noise levels is a continuous process of real-time implementation, but the recording is not continuous. Only when the level reaches a threshold will it be triggered. The delay of the device itself may result in the loss of audio samples, thus we chose the data point in the buffer for 500 ms before the trigger moment as the beginning of recording to solve the problem. To reduce the amount of data, an endpoint detection algorithm based on the sub-band selection spectral variance [23; 24] is adopted in this work. We can discriminate the useful segment to be recognized from the noisy signal according to the variation of the spectral variance. The sub-band spectral variance is expressed as follows:  
\[X_{i}=\left\{X_{i}(1),X_{i}(2),\cdots,X_{i}\left(\frac{N}{2}+1\right)\right\} \tag{2}\]  
where \(X_{i}\) is the amplitude of the \(i\)-th frame, and the length of each frame is N.  
\[M_{i}(m)=\sum_{k=1+(m-1)p}^{1+(m-1)p+(p-1)}\left|X_{i}(k)\right| \tag{3}\]  
Figure 2: The block diagram of the proposed system.  
where \(M_{i}(m)\) is the magnitude of the m-th band of the \(i\)-th frame, and m is the sub-band index, p is the spectral line number in each sub-band.  
\[Ei=\frac{1}{q}\sum_{k=1}^{q}XX_{i}(m) \tag{4}\]  
where \(E_{i}\) is the mean value of the sub-band of the \(i\)-th frame, and q is the number of the sub-band.  
\[D_{i}=\frac{1}{q-1}\sum_{k=1}^{q}[XX_{i}(m)-E_{i}]^{2} \tag{5}\]  
where \(D_{i}\) is the sub-band spectral variance of the \(i\)-th frame.  
The experimental results are introduced in Section 4. After sampling and recording, the data mentioned above is saved in the SD card and transferred to the remote server for further processing.
## 3 Methodology
### Adaptive Graph Convolutional Network  
A predefined graph is most used in spatial feature extraction for the graph convolution network, and the predefined graph can be constructed by distance or similarity [46; 47; 48; 49]. However, the predefined graph depends on prior knowledge and contains limited spatial dependencies.  
To overcome the above limitations, an adaptive graph is used to automatically learn the connectivity relationships between nodes via gradient descent. Inspired by the adaptive graph convolution in Graph WaveNet and adaptive graph convolutional recurrent network (AGCRN) [50; 51], a learnable node embedding matrix, \(E\in\mathbb{R}^{N\times d}\), is proposed, where \(d\) represents the node embedding dimension. The adaptive graph can be computed as follows:  
\[A_{\text{adp}}=\text{Softmax}\Big{(}\text{ReLU}\Big{(}EE^{\text{T}}\Big{)} \Big{)} \tag{8}\]  
where Softmax (\(\cdot\)) normalizes the adjacency matrix of the graph, and ReLU (\(\cdot\)) is used to eliminate weak connections.  
Based on the adaptive graph, the adaptive graph convolution can be given as follows:  
\[Z_{\text{AGCN}}=A_{\text{adp}}XW \tag{9}\]  
where \(X\in\mathbb{R}^{T\times N\times C}\) is the input multi-sensor signals, and \(W\) is the weight matrix.
# Mobile Internet Technology Adoption for Sustainable Agriculture: Evidence from Wheat Farmers
## 3 Study Hypothesis for the MIT Adoption
### Farmer's Characteristics  
[PERSON] and [PERSON] [49] analyzed the MIT by American farms and determined that the adoption rate of the Internet and computers is declining with age. Indeed, as the descriptive statistics show, young people have a higher Internet access rate. [PERSON] et al. [50] found that the use of MIT is higher among young people. Therefore, in terms of the adoption and usage of MIT, it is not surprising that some studies have shown that the adoption rate decreases with age [51]. Therefore, the following conditions are assumed:  
**Hypothesis 1a.**_(Age): Older farmers are less likely to adopt MIT._  
Gender plays a crucial role in information technology adoption decisions [49; 52]. Compared with male, female farmers are less likely to adopt modern technologies [49; 53]. However, when it comes to adopting the internet for agriculture, the results are mixed. For example, [PERSON] et al. [54] found no association between gender and Internet adoption, while [PERSON] et al. [29] found that gender was correlated with internet adoption. Specifically, male farmers are more likely to become MIT adopters. [PERSON] et al. [55] pointed out that more and more men use MDs to access the internet. Regarding the adoption of MIT, [PERSON] et al. [56] found that the adopter is more likely to be male. Hence, the following assumption was made:  
**Hypothesis 1b.**_(Gender): Male farmers are more likely to adopt MIT._  
Education develops the ability of individuals to understand, learn and adopt new technologies, such as the internet [29], and therefore is a significant socio-economic factor for information technology adoption [57; 58; 59]. Based on this insight, [PERSON] et al. [60] pointed out that more formal schooling is optimistically associated with the use of IT in agricultural development. Generally speaking, well-educated adults are more likely to access the internet [20]. In addition, MIT adoption and education are also positively correlated [23]. Additionally, [PERSON] et al. [61] believed that education is an important factor influencing IT adoption. Therefore, the following assumption was made:  
**Hypothesis 1c.**_(Education): farmers with higher education are more likely to adopt MIT._  
Innovation is manifested in the willingness to check the latest technology [62] and is an important factor affecting the latest technology adoption. [PERSON] et al. [53] revealed that innovation is positively associated with precision agriculture adoption in the agriculture field. Regarding MIT, [PERSON] et al. [63] pointed out evidence that innovation is associated with MIT adoption. Therefore, the following assumption was made:  
**Hypothesis 1d.**_(Innovativeness): More advanced farmers are more likely to adopt MIT._
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Training Process  
Figure 1 depicts the training process, starting with the cloud server distributing the latest model parameters to the clients. Once it has received the global model parameters, each client performs local training to update model parameters based on its own dataset and then transmits the updated model parameters to its associated edge server, taking into account client location and available bandwidth. An example is given in Figure 2, where \(ESI,ES2,\ldots\) represent edge servers and \(c1,c2,\ldots\) represent the clients participating in this round of edge aggregation, \(\mathbf{\omega}^{(t)}\) represents the model parameter in the cloud server in round \(t\), and \(\mathbf{\omega}^{(t)}_{j}\) represents the parameter in the edge server \(j\) in round \(t\). Each edge server performs edge aggregation on the received model parameters after a predefined time interval of \(\Delta T\). Then, the edge servers upload edge model parameters to the cloud server and the cloud sever performs cloud aggregation during the time interval of \(\Delta T_{aggregation}\).  
Figure 1: A snapshot of the considered system model.  
Additionally, the edge server optimizes bandwidth allocation to improve the achievable transmission rate and reduce the uploading time of model parameters. Subsequently, the edge server uploads the aggregated model parameters to the cloud server for cloud aggregation. Notably, edge aggregation and model uploading operations can occur concurrently on the edge servers. Such a process is named a round and is repeated many times until convergence.
## 4 Survey Results
### Experimental Datasets and the Context of Performance Prediction
Overall, the studies that included data points of more than 10,000 students amounted to 11 (17.74%), three of which used a sample size greater than 100,000 students (i.e., [85; 78]). The remaining seven (11.29%) studies did not specify the sample size of their student dataset. When we inspected the models' prediction accuracy based on the size of the dataset, we found varied results. For example, datasets containing less than 100 students gave weak predictions (e.g., 83 students resulted in an accuracy = (48-100%) [69]; 134 students resulted in an accuracy = 81.3% [64]) and acceptable predictions (e.g., 100 students resulted in an accuracy = 90%, recall = 90%, and precision = 74% [59]). Similarly, datasets of more than 100,000 students gave mixed findings. For instance, a pool of 597,692 students gave an impressive accuracy = 98.81%, AUC = 99.73%, sensitivity = 98.46%, and specificity = 99.20% [85]. However, a sample of 130,000 students gave an accuracy = 48-55%, RMSE = 8.65-10.00, and MAE = 6.09-7.74 [78]. Similarly, a sample of 142,438 students gave an RMSE = 0.34 and AUC = 0.81 [102]. It is worth pointing out here that classifying the training datasets into two pools, small or sufficient sample size, to draw meaningful conclusions about the significance  
Figure 10: Frequency of studies based on the number of students in the experimental datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Number**} & **Number of Studies** & **Studies** \\  & & **(Percentage of Occurrence)** & **Studies** \\ \hline \multirow{2}{*}{School} & One & 1 (1.61\%) & [50] \\ \cline{2-4}  & Multiple & 11 (17.74\%) & [47; 48; 49; 51; 52; 53; 54; 55; 56; 57; 58] \\ \hline \multirow{2}{*}{University} & One & 36 (58.06\%) & [59; 42; 44; 59; 59; 59; 59; 59; 57; 58] \\ \cline{2-4}  & Multiple & 2 (3.22\%) & [45; 93] \\ \hline Not Specified & & 12 (19.35\%) & [46; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Source of educational dataset in the studies. of the results is no simple matter. This is because such a division is influenced by several intertwined factors, including the diversity of input features impacting the outcome variable, the tolerance for errors, and the type of prediction (e.g., statistical analysis or learning) implemented. Moreover, comparing the performance of models that were trained on differing datasets (i.e., characteristics and sizes) might not be conclusive. There is no disagreement that larger the sample size we have to train the predictive models, the stronger is the predictions we obtain. However, this was not evident from our analysis of the synthesis.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 5 Performance Evaluation  
The performance of OTPP is evaluated by three metrics (1) number of beacon points, (2) average localization errors, and (3) localization coverage. In Section 5.1, we describe the three metrics and compare three common localization techniques. The settings of experimental parameters are introduced in Section 5.2. Finally, we change the shapes of obstacles and the communication range to see the impact of blocking rate and the impact of resolution. The results are presented in Section 5.3.  
Figure 10: The straight-line path from \(c_{1}\) to \(c_{2}\) is blocked by an obstacle; therefore, swap the positions of \(c_{2}\) and \(c_{3}\). The red arrows are the trajectories while the shadow area is the obstacle.  
Figure 9: The straight-line path from \(c_{1}\) to \(c_{2}\) without blockage by an obstacle. The red arrows are the trajectories while the shadow area is the obstacle.
###### Abstract  
In the context of Internet of Things (IoT) for Smart City (SC) applications, Mobile Data Collectors (MDCs) can be opportunistically exploited as wireless energy transmitters to recharge the energy-constrained IoT sensor-nodes placed within their charging vicinity or coverage area. The use of MDCs has been well studied and presents several advantages compared to the traditional methods that employ static sinks. However, data collection and transmission from the hundreds of thousands of sensors sparsely distributed across virtually every smart city has raised some new challenges. One of these concerns lies in how these sensors are being powered as majority of the IoT sensors are extremely energy-constrained owing to their smallness and mode of deployments. It is also evident that sensor-nodes closer to the sinks dissipate their energy faster than their counterparts. Moreover, battery recharging or replacement is impractical and incurs very large operational costs. Recent breakthrough in wireless power transfer (WPT) technologies allows the transfer of energy to the energy-hungry IoT sensor-nodes wirelessly. WPT finds applications in medical implants, electric vehicles, wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), mobile phones, and so on. The present study highlights the use of mobile collectors (data mules) as wireless power transmitters for opportunistic IoT-SC operations. Specifically, mobile vehicles used for data collection are further exploited as wireless power transmitters (wireless battery chargers) to wirelessly recharge the energy-constrained IoT nodes placed within their coverage vicinity. This paper first gives a comprehensive survey of the different aspects of wireless energy transmission technologies--architecture, energy sources, IoT energy harvesting modes, WPT techniques and applications that can be exploited for SC scenarios. A comparative analysis of the WPT technologies is also highlighted to determine the most energy-efficient technique for IoT scenarios. We then propose a WPT scheme that exploits vehicular networks for opportunistic IoT-SC operations. Experiments are conducted using simulations to evaluate the performance of the proposed model and to investigate WPT efficiency of a power-hungry opportunistic IoT network for different trade-off factors.  
Internet-of-Things; smart city; opportunistic data collection; wireless sensor networks; wireless power transfer (WPT); mobile data collectors; electromagnetic wave; energy harvesting +
Footnote †: journal: _Electronics_  
0000-0001-5000-00001-5000][PERSON]. [PERSON]
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion
### Evaluation Metrics  
In this research, the root mean squared error (RMSE) and mean absolute error (MAE) are employed as evaluation metrics to measure model performance and analyze prediction accuracy. The RMSE and MAE values can be considered the absolute Celsius error between the true and predicted values. The calculation formula is described as follows.  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}^{\prime}-y_{i})^{2}} \tag{10}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\bigl{|}y_{i}^{\prime}-y_{i}\bigr{|} \tag{11}\]  
Here, \(y_{i}^{\prime}\) is the predicted value. \(y_{i}\) is the actual value. \(N\) is the sample number of the test set. Smaller \(RMSE\) and \(MAE\) indicate higher prediction accuracy of the model; conversely, higher evaluation metrics indicate lower prediction accuracy.
## 2 Background
This review will discuss the performance of many different machine learning and deep learning systems with physiological and behavioral biometric models. ML and DL algorithms are a type of artificial intelligence (AI) that mimic human intelligence to make inferences on groups of data. DL algorithms are different from ML in that they make use of unsupervised learning strategies and can analyze unlabeled datasets [5]. Using DL strategies, algorithms learn hierarchical representations from large amounts of data with less human intervention. Prevalent DL algorithms in this review include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep neural networks (DNNs). DNNs are a type of multilayer perceptron and utilize many hidden layers with fully connected weights in their architectures. RNNs and CNNs are both different types of DNN [6]. CNNs are a type of feed-forward network and are made up of stacks of layers that contain neurons with learnable weights and biases. These layers each transform 3D input volumes into 3D output volumes using a differentiable function [6]. RNNs can work with both supervised and unsupervised learning tasks and are commonly known for performing well with sequential data such as text. In unsupervised learning, RNNs use previous data samples to predict future data sequences, continuously updating and making predictions in a recurrent manner [6]. An example of a popular RNN used in many current studies would be a long short-term memory network (LSTM). Prevalent machine learning algorithms in this review include support vector machine (SVM) and random forest (RF). SVM is commonly used in classification and regression tasks. To perform classification, SVM constructs an n-dimensional space on which the datapoints lie and attempts to find the optimal hyperplane in the training dataset. The optimal hyperplane is a decision boundary that separates two classes of data and is used to properly classify new input data. RF is also used for classification and regression tasks. Unlike SVM, RF utilizes an ensemble learning technique. Multiple models, referred to as \"trees\", are constructed from the data samples and a majority vote is taken on their outputs. Both ML and DLalgorithms can provide acceptable classification performance depending on the needs of the authentication problem and each have their respective strengths and weaknesses.
_applied_  
_sciences_  
_Article_  
**Restenosis Investigation of Two-Stent Placement in the Artery Bifurcation with Different Stenting Techniques Using Computational Fluid Dynamics Analysis**  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2,*(c)**  
1 Department of Electrical Engineering, I-Shou University, No. 1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan; [EMAIL_ADDRESS] (B-S.C.)  
2 Department of Biomedical Engineering, I-Shou University, No. 8, Yida Rd., Jiaosa Village Yanchao District, Kaohsiung City 82445, Taiwan  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +886-7-6151100-7473  
**Abstract:** The in-stent restenosis may still occur in patients with a coronary bifurcation stenting. The purpose of this study is to investigate the restenosis effect in the relationships between the bifurcation angles of the coronary artery and three types of stenting techniques (T-stenting, Culotte, and Kissing techniques), for comparing two kinds of arterial shapes using computational fluid dynamics (CFD) analysis. The finite element models of two types of the coronary arteries were reconstructed using stereolithography (STL) data of an artificial artery from reverse engineering laser scanning, and then each artery was further modified to add three kinds of the bifurcation angles, including 80\({}^{\circ}\), 113\({}^{\circ}\), and 142\({}^{\circ}\). The low TAWSS of the two arteries were mainly concentrated at the LCX branch; a more essential discovery was that the low TAWSS area ration was in proportion to the bifurcation angle. The smallest low TAWSS of the T-stenting and Kissing techniques was detected in the arteries A and B, respectively. Both Culotte and Kissing techniques always showed a higher OSI than the T-stenting, and a high OSI was meaningfully located at the LCX branch. The CFD study concluded that the large bifurcation angle of an artery using a long stent at the LCX side branch with the T-stenting technique has a high risk of stenting restenosis.  
**Keywords:** computational fluid dynamics; in-stent restenosis; coronary stenting; hemodynamics; time-averaged wall shear stress; oscillatory shear index