esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a parallel Cartesian mesh generation algorithm based on the traditional ray casting method is proposed and discussed. In this parallel algorithm, all three main steps of ray casting method were paralleled and executed on the GPU. Except for the necessary initial data and mesh data transmission, no data exchange occurred between the host memory and device memory, which largely reduced the data transmission cost. The lattice grid method was used to reduce the time cost in primitives searching. In additional, degenerated cases were analyzed and extra criterions are added to ensure the efficiency and stability of algorithm. The performance of parallel Cartesian mesh generation algorithm was analyzed and compared with the traditional one in theory and through numerical experiment. Through the performance test, the advantages of the parallel algorithm are presented. The efficiency of the proposed parallel algorithm was promoted by 15 times in average. The number of mesh cells was the major factor affecting the efficiency of both mesh generation algorithms. In the process of mesh generation, the utilization ratio of the GPU could reach 60% on average. The highly parallel architecture of the GPU had been fully used. The examples of Cartesian mesh generation showed that the mesh generated was sufficient for large-scale numerical simulation.  
In the future work, more efficient parallel primitive searching methods need to be developed. In addition, further applying the synchronization mechanism of GPU may increase the utilization rate of GPU parallel ability.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and P.L.; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, P.L.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant Nos. 11822203 and 11532012).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 15: Total running time of mesh generation using two algorithms with different number of facets (the red line and symbols represent the total running time of traditional serial algorithm, and the blue line and symbols represent the total running time of parallel algorithm.)
## 7 Performance Evaluation
### Simulation Results  
Since SCODE_plus_ is targeted to multiple and mobile sinks in sensor networks, we study the impact of the number of sinks, sink's speed, and the density of the network. We measure the energy consumption, average delay (average response time to users), and success ratio (total number of packets has been delivered successfully).
## 2 Materials and Methods
### Data Collection
During all experiments, three quantities are monitored and recorded using the monitoring system embedded in the control unit of the SARIX(r) machine: the average amount of normal discharge pulses per time unit (\(\dot{f}_{\text{P}}\)), average tool feed rate (\(\dot{\gamma}_{t}\)), and average gap voltage (\(u\)). These quantities are recorded in order to be used as tool wear and material removal predictors since they are highly correlated to the wear of the tool electrode. This has been demonstrated by numerous experimental and theoretical studies in literature, for instance references [17; 25; 26; 27; 28]. The values of \(\dot{f}_{\text{P}}\), \(\dot{\gamma}_{t}\), and \(u\) are computed using an averaging window corresponding to steps of 50 \(\upmu\)m in drilling depth, similarly to what was done in [17; 29]. The value of 50 \(\upmu\)m refers to the nominal drilling depth as read by the \(z\)-axis encoder of the machine tool. Due to the longitudinal wear of the tool electrode, it does not correspond to the depth of the hole being drilled. The computed values of \(\dot{f}_{\text{P}}\), \(\dot{\gamma}_{t}\), and \(u\) at every drilling step are automatically stored into a comma-separated value (.csv) text file. The material removal rate (MRR) and tool wear rate (TWR) for each experimental run are computed at the end of the micro-EDM drilling experiments. The MRR is calculated as the ratio between the volume of material removed from the workpiece (\(V_{w}\)) and the total machining time (\(T\)), approximating the holes to a cylinder:  
\[\text{MRR}\ \ =\ \ \frac{V_{w}}{T}\ \ =\ \ \frac{\frac{\pi}{4}HD_{in}^{2}}{T} \tag{1}\]  
where \(H\) is the thickness of the workpiece and \(D_{in}\) is the inlet of a drilled hole, which is measured by means of a VideoCheck HA coordinate measuring machine (Werth Messtechnik GmbH, Giessen, Germany) in optical mode. Similarly, the TWR is computed as the ratio between the volume of material removed from the tool (\(V_{t}\)) and the total machining time (\(T\)). To compute \(V_{t}\), the nominal inner (\(d_{t}\)) and outer (\(D_{t}\)) diameters of the brass tubular tool are used. As shown in Equation (2), \(V_{t}\) is calculated by subtracting the workpiece thickness (\(H\)) from the monitored value of the drilling depth when the breakthrough is observed (\(Z_{b}\)). \[\text{TWR}\ =\ \frac{V_{t}}{T}\ =\ \frac{\frac{\pi}{4}(D_{t}^{2}-d_{t}^{2}) \left(Z_{b}-H\right)}{T} \tag{2}\]
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 3 The Experimental Design Process
An example of an LHS with two factors, nine design points, and nine levels in each factor is depicted in Figure 6. One  
Figure 5: Examples of response surface plots: (**a**) Response surface plot of a first-order regression model, and (**b**) response surface plot of a second-order regression model. challenge of the LHS is to place the chosen design points evenly within the design space in order to cover as much space as possible. The next step is about the creation of a response surface for prediction purposes, based on the results from the DOE. The Polynomial, the Kriging, the Support Vector, the Feedforward neural network, and the Sparse Grid are some of the methods that can be used for the regression analysis. In this research, the Kriging method was applied. Kriging is a Gaussian regression process that is dependent on all raw data and fits automatically through all the existing points [28]. A general form of the Kriging model is the following:  
\[y(x)=f(x)+Z(x)+\varepsilon(x) \tag{12}\]  
The \(Z(x)\) is the Gaussian process and the term that differentiates the Kriging method from the polynomial regression model. Thus, the Kriging model interpolates the sampled design points and quantifies their interpolation errors. In the fifth step, the designer can conduct a sensitivity analysis of the results. A sensitivity analysis is the calculation of the objective function uncertainties in possible factors fluctuations. This analysis can be used as a diagnostic tool by the designer and can help him/her to identify and screen the most crucial parameters among them. These parameters, in their turn, can be used as a new focus in the PO of the structure [29]. An example of a sensitivity analysis diagram is shown in Figure 4.0 This diagram presents the norm of the partial derivatives of the chosen objective, in this case, the mass, with respect to the selected variables, herein: Length, radius, height, and thickness. Figure 4.1 An example of a Latin Hypercube Sampling (LHS) with two factors and both nine design points and factor levels. Finally, the created prediction models can be used for design optimization. At this point, Pareto fronts can be developed for multi-objective optimization of the structures. In Pareto optimality, the plot (surface) of the objective functions, whose non-dominated vectors compose the Pareto optimal set, is called Pareto front [30]. Pareto fronts help the designer identify useful trade-offs and potential solutions among the used objective functions in PO. The most used algorithms in solving multi-objective optimization problems are Screening, Genetic, Nonlinear Programming, and Adaptive Optimization. The Multi-Objective Genetic Algorithm (MOGA), as was presented by [PERSON] and [PERSON] [31], was used to optimize the three structures in this paper. The mass and the maximum stress of the structures were used as an objective function and constraint, respectively in the creation of the Pareto fronts.
# Factors Influencing Appliance Wearing Time during Orthodontic Treatments: A Literature Review
## 4 Results and Discussion
### Self-Reported Patient Compliance: Myths and Facts  
Since \"orthodontic compliance\" does not involve a single general aspect of compliance [27], (i.e., a patient who keeps high levels of oral hygiene does not necessarily wear the appliance for the required time, and vice versa), conducting accurate measurements of the patients' level of adherence to the treatment represents a very challenging task [27; 40]. Compliance can be subjectively assessed with self-reported questionnaires filled by patients or parents; however, many authors suggest that subjective evaluations are usually unreliable [14; 15; 27; 34].  
In fact, patients are often unrealistic about the wear-time of the removable appliances: they tend to overestimate their wear-time [15; 34] by one-third when they do not know that it is monitored [34].  
In the past decades, the introduction of several devices and monitoring tools has helped to overcome these limitations and has provided an objective evaluation of the level of patient compliance [3].
# Separate Syntax and Semantics: Part-of-Speech-Guided Transformer for Image Captioning
## 3 Approach
### Training Details  
As shown in Figures 3 and 4, the SAT-based and DAT-based multimodal decoder have the same input visual features, language signals, and PoS information as well as the same output vectors. The two outputs of our models are utilized to predict the next word and its PoS tag, which, respectively, correspond to two different objective functions. Thus, in practice, the network weights of these two models can be trained concurrently by a supervised multitask learning.  
For an input image, assume its region-based visual feature vector as \(V\), the corresponding ground-truth caption \(Y^{*}=\{y^{*}_{0},\cdots,y^{*}_{T}\}\) and the ground-truth PoS tags \(S^{*}=\{s^{*}_{0},\cdots,s^{*}_{T}\}\). For the self-attention PoS predictor, the cross-entropy (XE) loss for the PoS prediction is:  
\[L_{PoS}=-\sum_{t=0}^{T}\log(p_{\varphi}(s^{*}_{t}|Y^{*}_{t-1})), \tag{15}\]  
Figure 4: The structure of the dual attention based multimodal decoder model with part-of-speech guidance. The multimodal representations are first learned based on the dual attention with PoS guidance. Then, the gated controller is introduced to adaptively measure the contribution of visual and language cues for predicting words.  
where \(\varphi\) represents the parameters of the SAPP network.  
The parameters \(\theta\) of our image captioning model (including dual-way encoder and PoS-guided multimodal decoder) is optimized via minimizing the following cross-entropy loss \(L_{word}\) between the generated captions and the ground truths:  
\[L_{word}=-\sum_{t=0}^{T}\log(p_{\theta}(y_{t}^{*}|Y_{t-1}^{*},V). \tag{16}\]  
Combining the word prediction loss \(L_{word}\) with the PoS prediction loss \(L_{PoS}\), the total loss function for our proposed PoS-Transformer framework can be defined as:  
\[L=L_{word}+\lambda\cdot L_{PoS}, \tag{17}\]  
where \(\lambda\) is a trade-off factor between the PoS loss and the word loss. Thus, all the parameters of the PoS-Transformer network can be optimized by minimizing the total loss function.  
As can be seen from Figure 2, when minimizing the XE loss \(L_{word}\), the parameter \(\varphi\) of the SAPP network will also be optimized, which indicates that the word prediction can be considered as the leading task of the whole model. At the same time, when the XE loss \(L_{PoS}\) is minimized, only the PoS predictor in the whole framework will be updated. Thus, the training of SAPP plays a role of auxiliary task for the main task. By means of the ground-truth PoS tags, the PoS prediction model can be well optimized, which provides the main task with the auxiliary optimization direction of the parameter \(\varphi\). Consequently, with the guidance of SAPP, the image captioning part of our whole framework can be encouraged to generate more grounded and fine-grained captions.  
At inference time, PoS-Transformer needs not employ any PoS tagger to tag each word in the generated sentences since it actually utilizes the current hidden state of the SAPP network as the PoS information to guide the caption generation.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 2 Study Area and Data
### Data  
The data used in this study mainly include meteorological reanalysis data, remote sensing data, and statistical data (Table 1). The meteorological reanalysis data were taken from the ERA5 dataset of the European Center for Medium-Term Weather Forecast (ECMWF 2017). The total  
Figure 1: Location of the study area.  
precipitation of ERA5 monthly averaged data and the 2 m temperature of ERA5 hourly data were used in this study. The spatial resolution of the data is 0.25\({}^{\circ}\), and the time series covers the period from 1979 to 2018 [48]. The GPP (gross primary production) data were obtained from the National Science and Technology Basic Conditions Platform-National Earth System Science Data Sharing Service Platform [49], which provides GPP data with a resolution of 0.05\({}^{\circ}\) from 1982 to 2018 with a temporal resolution of 8 d. This data set was estimated by the EC-LUE model [50], and the verification results of the data set showed that its simulation capability exceeded that of the MODIS-GPP product. The NDVI data, which has a spatial resolution of 0.05\({}^{\circ}\) from 1981 to 2018 on a time scale of 1 d, were obtained from the long time series AVH13C1 dataset of the LTDR project [51]. The Chinese land use and land cover data at a 1 km resolution were provided by the Resource and Environmental Science Data Center of the Chinese Academy of Sciences ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)), accessed on 16 May 2021). This dataset was used to extract the range of cultivated land. The statistical data mainly included the time series maize yield at the municipal level (1996-2018 in Jilin Province and 1997-2018 in Liaoning Province) and agricultural disaster statistics from 1990 to 2016 provided by the China Bureau of Statistics, as well as crop phenological calendar from the planting management department of the Ministry of Agriculture.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 7 Threat Agents in HE
### Nation States and Advanced Persistent Threats (APT)  
As one of the most severe threat agents to HE, _Nation state-backed actors_ and _Advanced persistent Threats_ (APT) are frequently trying to gain access to sensitive intellectual property [3; 5; 6; 55]. State actors can both be foreign states' intelligence services and private actors operating on behalf of foreign states [70]. Besides, foreign intelligence services can target HE to acquire knowledge and technology (cyber espionage). To be defined within this category, the threat agent must have resources available to work methodically over time, often for months or even years, and are primarily motivated by political, economic, military, security and technological ambitions [69].  
Typical state-sponsored groups are the Iranian _Silent Librarian Campaign_[8; 9; 10]. [PERSON] also describes a North Korean group, named _Stonel pencil_, who targeted academics specifically. Regarding sophisticated attacks, [PERSON] et al. [58] documents the persistence of espionage and attempts of illegal data extraction for a small percentage of the respondents. Verizon lists _Espionage_ as the motive behind 3% of the recorded incidents in 2020 [64].
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 4 Depth Adjustment Based Target Tracking in UWSNs
### Node Depth Adjustment Problem Formulation  
Since relation between FIM and nodes' depth has been introduced, this subsection formulates the optimization problem of adjusting node depth. The FIM is a matrix that is not easy to analyze; thus, the popular D-optimality criterion is adopted in this paper, and the determinant of FIM is calculated as the metric of tracking. The larger the determinant is, the more information we will get from measurements, and the better tracking performance will be achieved. In addition, the D-optimality criterion, the A-optimality and E-optimality criterion are also widely used in the optimization problem. They respectively minimize the trace and the largest eigenvalue of the PCRLB matrix. However, they are variant under scale changes in the parameters and linear transformations of the output, and it is not convenient to calculate PCRLB matrix [36; 37]. Hence, the D-optimality criterion will be used in this paper.  
We assume that there are \(N_{s}\) nodes participating in tracking tasks at each time. The depth of sensor \(s\) is \(z_{k}^{s}\) at time \(k\). The sensor will move vertically downward, and its depth at time \(k+1\) will be  
\[z_{k+1}^{s}=z_{k}^{s}-d_{k+1}^{s}, \tag{13}\]  
where \(d_{k+1}^{s}\) is the distance to move down at time \(k+1\). It can be seen that, given node depth at time \(k+1\), the FIM of target location is a function of movement distance vector \(\mathbf{D}_{k+1}=\left[d_{k+1}^{1},\cdots,d_{k+1}^{N_{s}}\right]^{\mathsf{T}}\).  
In this paper, the problem we address is how to determine nodes' optimal depth to maximize determinant of FIM under the constraints on movement of nodes. Based on the relation between FIM and node depth, the optimization problem can be formulated as:  
\[\begin{split}&\min_{\mathbf{D}_{k+1}}-\det\left(\mathbf{\mathbf{J}}_{k+1} \left(\mathbf{D}_{k+1}\right)\right),\\ &\text{subject to}\quad d_{min}\leq d_{k+1}^{s}\leq d_{max},\\ & d_{k+1}^{s}\in\mathbf{D}_{k+1},\quad s\in\{1,\cdots,N_{s}\},\end{split} \tag{14}\]  
where \(\det(\cdot)\) denotes the determinant calculation of matrix, \(d_{min}\) and \(d_{max}\) are the lower and upper bound on moving distance, which is a dynamic variable according to target location.  
So far, we have converted the node depth adjustment to an optimization problem. We need to find a solution to the dynamic optimization problem. It is extremely difficult to find a global optimal analytical solution. The computation will be too large if we discretize the moving range and calculate the determinant of FIM with an exhaustive search. It is necessary to find a method applied to this problem with proper computation. The results will be presented in the next section.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 5 Conclusions  
In this study, we proposed the ADSC-U-Net by improving the base U-Net architecture and integrating the DSC and CBAM modules. The ablation experimental results indicate that the inclusion of both the DSC and CBAM modules enhances the network's crack detection capabilities. Comparative experimental results demonstrate that our network detects cracks with greater completeness and continuity. Compared to U-Net, SegNet, DeepLab, and DeepCrack, ADSC-U-Net improves \(R\) by 8.32%, 8.01%, 4.70%, and 59.58%, respectively, thereby enabling more comprehensive crack detection. Additionally, the proposed network not only increases \(R\) but also enhances the overall evaluation metric \(F1\), showing improvements of 5.21%, 5.73%, 4.02%, and 55.87% over U-Net, SegNet, DeepLab, and DeepCrack, respectively. Although the proposed method has a \(P\) slightly lower than U-Net, with a decrease of 1.22%, it outperforms SegNet, DeepLab, and DeepCrack, with improvements in precision by 1.90%, 2.49%, and 11.64%, respectively. Considering all the evaluation metrics comprehensively, the proposed method outperforms the other four methods, making it the most optimal approach.
# Research on Hybrid Vibration Sensor for Measuring Downhole Drilling Tool Vibrational Frequencies
## 2 Structure and Principle  
### Structure Composition of HDV-TENG  
Figure 1a illustrates the schematic diagram of the drilling working conditions, with the sensor installed within the measurement short section near the drill bit on the drill string. During the drilling process, the HDV-TENG can realize the collection of the vibration parameters of the drill pipe and the collection of vibration energy. Figure 1b shows a schematic diagram of the structure of the HDV-TENG. As shown in the figure, the sensor is cylindrical in shape overall, with a housing made of polyetheretherketone (PEEK) material in the form of a tube, with an outer diameter of 80 mm and a height of 120 mm. The HDV-TENG consists of three parts: TENG, PENG, and EMG. The TENG part consists of a set of copper electrodes (with a thickness of 0.05 mm) and a floating block. The floating block has a cylindrical protrusion in the middle, with its surface covered with polytetrafluoroethylene (PTFE) material (with a thickness of 0.05 mm). Additionally, each of the magnet assemblies 1 and 2 has a magnet (EH brand neodymium magnet) mounted on the surface of the floating block. The PENG part includes a piezoelectric ceramic sheet and a group of silver electrodes. The EMG part includes copper coils, and magnet groups 1 and 2. During the manufacturing process, the piezoelectric ceramic piece is first placed at the bottom of the PEEK tube. Once the TENG part of the sensor is installed, it is placed  
on top of the piezoelectric ceramic piece. Next, magnets 1 and 2 are installed based on the principle of like poles repelling each other so that the floating block is suspended inside the sensor. Finally, coils are wound around the outer shell of the sensor, completing the assembly of the sensor.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 2 Related Work  
### Deep Learning Deblurring  
Recently, deep learning has been the main approach in the image-deblurring field. [PERSON] et al. [6] proposed a CNN-based model to estimate the blur kernels to remove blurriness. However, the idea that every blurry image is only caused by one blur kernel is too idealized. In fact, most blur problems may be influenced by multi-blur kernels. Thus, it is hard to predict the number of real blur kernels even when using CNN methods. Later, [PERSON] et al. proposed DeepDeblur [7], the pioneer of direct adoption of the coarse-to-fine structure to recover image sharpness. However, this method suffered from a high computation time; this is because the design did not share parameters across multi-scales. To address this issue, the encoder-decoder structure with skip connection was introduced to share parameters and capture context, such as MPRNet [2] and MIMO-UNet+ [8]. In addition, [PERSON] et al. introduced DeblurGAN [9] and DeblurGAN-v2 [10] successively by employing a generative adversarial approach. [PERSON] et al. designed SDWNet [16] with wavelet transformation and some dilated convolutions to enlarge the receptive fields. In addition, [PERSON] et al. proposed BANet [17] by adopting the multi-kernel strip pooling attention structure to extract multi-scale features. These models have achieved promising performance; however, they failed to obtain the global feature, which is also important in the image-deblurring field. Only considering local features limits the accuracy of the reconstruction results.