esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Designing an Ontology Framework: A Case Study on Asthma
### Selection of Relevant Parameters
#### 4.4.5 Evaluation of AI Algorithms Using Specific Metrics  
To evaluate the performance of the algorithms, the author calculated several metrics, including accuracy, precision, recall, and F1-score. Based on the results of these metrics, a suitable algorithm was chosen for identifying the optimal number of relevant parameters.  
* Precision Precision = True Positives/(True Positives + False Positives) Precision measures how many of the positive predictions made by the model are correct.
* Recall Recall = True Positives/(True Positives + False Negatives) Recall measures how many of the actual positive instances the model correctly identifies.
* F1-score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall) The F1-score is the harmonic mean of precision and recall and provides a single metric that balances both concerns.
* Accuracy Accuracy = (True Positives + True Negatives)/(True Positives + True Negatives + False Negatives) Accuracy measures the proportion of correctly predicted instances out of the total instances.  
When comparing decision tree, neural network and logistic regression using the four metrics (accuracy, precision, recall and F1-score), the decision tree consistently achieved higher values. As shown in Figure 7, the decision tree emerged as a suitable method for feature selection based on the calculated metric values. Specifically, the decision tree used 19 relevant parameters to demonstrate the best overall performance across all metrics, making it the most effective choice based on the data provided. Using the selected relevant parameters, decision trees can perform rule extraction as the following step based on these results.
## 4 Numerical Results
### Datasets Used
This dataset having multi-platform images is important because users often share images through these platforms, and images from different platforms reflect a variety of real-world usage patterns. Therefore, the application of camera model identification to post-processed images can provide valuable insights into more realistic application scenarios, thereby increasing the relevance and applicability of the research results obtained from this dataset. The SHIDR [32] dataset comprises 5415 images captured using 23 mobile devices under various conditions. All devices were set to their default camera configurations, and images were taken without the use of flash, in a range of environments, including both indoor and outdoor settings. [PERSON] et al. [13] further expanded the SHIDR dataset by introducing nine widely used social media platforms (Twitter, Telegram, WhatsApp, Instagram, Facebook, Weibo, QQ, Dingding, and WeChat), simulating image transmission across these platforms. This extension allows for a more comprehensive evaluation of the robustness of source camera identification approaches in real-world scenarios. The Vision [33] dataset comprises a total of 34,427 images, both in their original formats and in versions shared through social media platforms such as Facebook and WhatsApp. These media files were collected from 35 portable devices across 11 major brands. The VISION dataset serves as a benchmark for evaluating various image forensic tools. The dataset includes two main categories of images: 'Flat', which features scenes like skies or walls with minimal texture, and 'Nat', which includes more diverse scenes without restrictions on orientation or content. Of the 11,732 original images, 7565 were shared through Facebook and WhatsApp, bringing the total image count to 34,427. For Facebook, two separate albums were created to host the 'Nat' images--one for high-quality (FBH) and one for low-quality (FBL) versions, as permitted by the platform. This setup allows for a comprehensive evaluation of image forensics across different compression and sharing settings. As shown in Table 2, we introduce a summary of the number of images, camera models, and distinct camera devices included in the datasets used for the experiments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Total Images** & **Total Models** & **Total Devices** \\ \hline Dresden & 16,816 & 25 & 73 \\ Alaska & 80,000 & 40 & 40 \\ Forchheim & 3851 & 25 & 27 \\ Vision & 34,427 & 29 & 35 \\ SIHDR & 5415 & 21 & 23 \\ SOCRatES & 9721 & 65 & 103 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of datasets used in the experiments.
## 3 Materials and Methods
### Experimental Design  
To verify the effectiveness of various materials in damping vibrations, three vibration-damping strategies were selected for experimental verification. The vibration frequency of the vibration motor was set and multiple sets of vibration source frequencies were simulated, including the actual vibration frequency of the harvester. A vibration meter was used to record the amplitude and frequency of each test set. Additionally, a charge amplifier was employed to measure the voltage value of each piezoelectric ceramic after vibration, with a sampling frequency of 10 Hz and a sampling time of 30 s. Through this experimental design, the damping effect of vibration-damping materials on vibration sources with different frequencies can be accurately assessed, ultimately leading to the optimal selection of the ideal vibration-damping material.  
The test procedures are described in detail below:  
1. The impact plate of the loss detection sensor is fixed on the test bench without any damping material, and the charge amplifier is connected to the piezoelectric ceramic lead and can work normally.
2. Turn on the vibration motor, set the vibration frequency as 5 Hz, the charge amplifier collects vibration data at 30 s, and the sampling frequency as 10 Hz. A total of 300 groups of test data are collected, including voltage value and vibration amplitude value, which are recorded and saved.
3. Take the adhesive rubber material below the impact plate of the loss detection sensor as the vibration reduction material, and repeat step 2.
4. Take the EVA sponge material attached to the bottom of the impact plate of the loss detection sensor as the damping material, and repeat Step 2.
5. The three groups of test data are processed and saved, and used for vibration reduction effect analysis.  
Figure 7: Vibration test bench.
## 4 Case Study
### Case Study 1: Performance Comparison between ATD and TD  
In this part, the influences of the speed factor \(\kappa\) and the filter factor \(h_{0}\) on the performance of the TD are analyzed through simulation studies while the input signal exhibits noise disturbance, firstly. Then, the proposed ATD is simulated with the same input signal to verify the availability of the ATD. The tracking target is \(\widetilde{v}_{d}(k)=0.1\pi(\sin(2t)-\cos(t))+Gau_{noise}\), where \(Gau_{noise}\) is the external Gaussian noise and the signal-to-noise ratio (SNR) is 40. \(v_{d}(k)=0.1\pi(\sin(2t)-\cos(t))\) is the signal without any noise and \(dv_{d}(k)=0.1\pi(2\cos(2t)+\sin(t))\) is the derivative signal.  
Tracking outputs of the given signal and derivative signal via TD are shown in Figure 5a,b, while \(\kappa=1.5\) and \(h_{0}=0.002\). The results depicted in Figure 5 show that \(v_{1}\) and \(v_{2}\) have phase lags of \(\widetilde{v}_{d}\) and \(dv_{d}\), respectively, when the speed factor is small. In addition, the phase lag of \(v_{2}\) is small when \(dv_{d}(k)\approx 0\) so the waveform is obviously distorted. Additionally, tracking outputs of the given signal and derivative signal via TD are shown in Figure 6a,b, while \(\kappa=15\) and \(h_{0}=0.002\). As shown in Figure 6, the tracking effects of \(v_{1}\) and \(v_{2}\) are good without phase lags by increasing the speed factor. However, noise disturbance is introduced into \(v_{2}\), and the filtering effect is poor. Moreover, an error spike exists when \(dv_{d}(k)\approx 0\), which greatly affects the tracking performance of the derivative signal. Then, two tracking curves of the TD are shown in Figure 7a,b, while \(\kappa=15\) and \(h_{0}=0.2\). It is clear from Figure 7 that \(v_{2}\) has filtered out the noise by increasing the filter factor. Unfortunately, \(v_{1}\) and \(v_{2}\) have phase lags of \(\widetilde{v}_{d}\) and \(dv_{d}\) again, and the amplitude of the \(v_{1}\) and \(v_{2}\) becomes smaller, resulting in waveform distortion. Moreover, two tracking outputs of the proposed ATD are shown in Figure 8a,b, while \(K=300\), \(L=15\), \(\sigma_{1}=3.5\) and \(\sigma_{2}=10.25\). Figure 8 shows that \(v_{1}\) and \(v_{2}\) track well without phase lags. As shown in the partial amplification in Figure 8b, the introduction of noise in \(v_{2}\) has been greatly reduced, and the error spike when \(dv_{d}(k)\approx 0\) is basically eliminated. The adaptive adjustment curves of \(\kappa(k)\) and \(h_{0}(k)\) in the ATD are shown in Figure 9.
## 3 Materials and Methods  
The proposed semantic segmentation network, PVM, mainly consists of an encoder, a time-series processing module, and a decoder (as shown in Figure 4). In the encoder, we employ the Vision Transformer (ViT) as the backbone to extract spatial features from the input remote-sensing images. To capture the temporal dynamics inherent in time-series data, we employed the Multi-Task Attention Fusion (MTAF) mechanism [31], which effectively integrates multi-modal and multi-temporal features. Additionally, we introduced the Phenology-Aware Module (PAM) to incorporate phenological stage information, dynamically adjusting attention to different time steps based on the crop growth cycle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Type** & **Pixels** \\ \hline
1 & Cropland & 564,117,840 \\
2 & Oilseed rape & 44,745,893 \\
3 & Tea & 30,280,657 \\
4 & Cotton & 2,537,294 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical data of the Hunan non-food crop dataset.  
Figure 4: Illustrates the overall architecture of the PAM-MTAF-ViT model.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 6 Discussion  
### Effect of the Particle Diameter on Fabrication  
Figure 18 shows the average particle diameter of nano powders in the reviewed transparent nano-ceramics. As depicted in Figure 18, the average particle diameter ranges from 50 to 1000 nm, and the average is close to 150 nm. At present, most of the existing powders are of nano scale. Further reducing the particle diameter means that the manufacturing cost of powder is increased. For example, by laser reprocessing, the particle diameter of commercial powder can be further reduced, resulting in the desired specific surface areas. This is because polycrystalline transparent ceramics need to reach a relative density of more than 99.9%; that is, the porosity should be less than 1/10,000. It is difficult to eliminate these micropores via the conventional sintering process because the pores trapped in the crystal during grain growth are very difficult to discharge by diffusion at the end of sintering. Therefore, it is necessary to use ultra-fine nano powder with high purity and high activity and to adopt a multistage sintering process or sintering for a long time under low temperatures and a vacuum to eliminate pores.
###### Abstract  
The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.  
smart anti-theft system; intruder detection; unsupervised activity monitoring; smart home; partially/fully covered faces
# Silver Sintering for Silicon Carbide Die Attach: Process Optimization and Structural Modeling
## 2 Materials and Methods
### Experimental Analysis for Reliability Assessment  
The porosity of a silver layer, already mentioned in Section 2.1, is measured for each analyzed sintering pressure by scanning electron microscopy (SEM) equipped with back-scattered detector. This investigation is performed on dedicated metallographic cross-sections of samples without reliability stress (just after the sintering process). Microscopic pictures are processed by a dedicated software to quantify the porosity inside the sintered layer. Voids can be distinguished by silver material due to the different gray tonality: voids appear darker. SEM is also employed to detect crack occurrence in sintering joints before the reliability assessment starts and after 3000 TS for the three considered pressure parameters.  
Considering that SEM is a destructive and time-consuming technique because a non-negligible amount of time is needed for sample preparation, there is the need to search for a non-destructive and quicker methodologies. In this framework, scanning acoustic microscopy (SAM) in reflection mode is considered. In order to have accurate control  
Figure 1: Schematic of the process flow for silver sintering process and the stack of the material at the end of sintering process.  
over wave propagation, SAM samples are dipped in water. Due to SAM characteristics, it is possible to analyze the degradation of sintering interfaces at intermediate steps of durability stress without altering the samples.  
Another methodology adopted to evaluate the degradation during reliability stress is the monitoring of a temperature-sensitive electric parameter. For MOSFET devices and fixing a sufficiently moderate current value that cannot heat the device, it has been proven that the drop voltage of body diode ( \(V_{F}\) ) tends to decreases proportionally to the device temperature. Assuming to provide a given power pulse to heat the device, the difference between the \(V_{F}\) before and after the power pulse, named \(\Delta V_{F}\), tends to increase with higher temperature difference. This parameter is widely considered for temperature estimation of power packages and modules [20; 21]. In this specific framework, \(\Delta V_{F}\) is measured 100 us after the application of a predefined power pulse. Comparing \(\Delta V_{F}\) at the different TS aging time, it is possible to have an indication of sintering layer degradation during the reliability stress. A more delaminated layer implies the worst thermal dissipation and therefore, a higher \(\Delta V_{F}\) value directly linked with temperature swing. All the considered equipment for sintering manufacturing and reliability evaluation are resumed in Table 1.
# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 3 Methodology
### Transferred Models
#### 3.1.2 Models Based on Graph Convolution  
While \(SpX\)-model specification corresponds to well-established and widely used spatial filtering techniques for video processing, the following specification is based on one of the most recent techniques of video prediction--the graph convolutional neural networks (GCNN) [22]. One of the key reasons for the success of conventional CNNs is their ability to learn hierarchical patterns and extract high-level features from image and video data. The GCNN architecture extends CNN by introducing the convolution operator for non-Euclidean graph-based spaces. There are two popular approaches to the implementation of graph-based convolutions:  
* Spectral graph convolution [52], and,
* Spatial graph convolution [53].  
The spectral graph convolution is based on the spectral graph theory and is calculated via the Laplacian matrix of the graph. Conversely, the spatial graph convolution aggregates information from the local spatial neighborhood of every graph vertex (road segment). Thus, the spectral graph convolution deals with the entire graph are more computationally intensive, while the spatial convolution is local and potentially more effective. In this study, we arbitrarily selected the spectral graph convolution approach for testing.  
Let \(W\) be the matrix of weights that defines the graph structure, and \(D\) be the diagonal matrix introduced as \(D=diag\big{(}W1^{T}\big{)}\), where \(1\) is the all-ones vector. Then, the spectral-based graph convolution is defined as [22].  
\[g\circ Y=\theta\Big{(}l_{k}+D^{-\frac{1}{2}}WD^{-\frac{1}{2}}\Big{)}Y, \tag{10}\]where \(I_{k}\) is an identity matrix, and \(g_{\theta}\) is a filter, represented by the trained matrix \(\theta\). Given the graph convolution filter, the architecture of GCNN is similar to classical CNN and includes several sequentially connected convolutional and pooling layers and the final fully connected layer. The general architecture of GCNN is presented in Figure 2.  
In this study, we utilized the GCNN architecture, developed by [PERSON] et al. [44], as a reference implementation of spectral GCNN and tuned it for our purposes. The resulting architecture includes two sequentially internal blocks of three layers: graph convolution, pooling, and LSTM; other parameters are tuned by cross-validation as described below.
# An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark
## 3 OpenACC Programming Model
Available memory spaces are usually physically separated in heterogeneous CPU/GPU architectures. Data transfers are needed to feed the data to the device and collect the results. OpenACC specification offers support for memory management and memory transfers. There are directives, such as copyin, copyout, update, and similar, to control the data movement and define data regions. To minimize the frequency of data transfers between the CPU and the GPU, OpenACC offers programmers the data directive. This directive allows programmers to create data regions in which memory transfers are executed before parallel regions, thus reducing the memory footprint in cases where multiple parallel regions are executed for the same data. Multiple memory spaces are the source of performance-related problems in heterogeneous systems. Generally, the capacity of the GPU memory is smaller than the CPU memory. For that reason, large datasets are usually split into chunks which are constantly transferred to the device. In such cases, memory bandwidth limits the performance of the code executed on the device. For that reason, it is important to reuse data on the device, as much as possible, via the hardware- and software-managed caches on the device [40]. OpenACC offers the cache directive for such purposes. It is shown that proper usage of the cache directive can lead to the performance close to the hand-written CUDA code [40]. Further performance optimizations can be achieved with the directives and clauses added in the OpenACC 2.0 specification. The atomic directive adds support for atomic updates on global variables, similar to the feature present in CUDA via intrinsic functions. The tile clause of the loop directive can be used to control the locality of the loop nests. Several other features are available to the programmer to control the data movements, asynchronous execution, and similar. OpenACC specification has been constantly improving. The current specification 3.2 includes a number of new features [35], such as enhanced support for C++ lambda functions, array reduction operators, new constructs, and similar. New features have also been proposed by the research community, such as the concept of static graphs [41].
## 4 Experiments and Analysis
### Comparison to Other Methods  
To compare the differences between the skeleton-line-based graph convolutional neural network method and other similar methods, this paper conducted comparative experiments using the contour-line-based graph convolutional neural network method [29] and the turning angle function method.  
On using the contour-line-based graphical convolutional neural network method, four features are extracted from the nodes: 1.the length of contour line AB is \(L_{1}\), 2.the azimuth of contoured line segments is \(\alpha\) (Figure 12a), 3.the length of the line segment OP is \(L_{2  
In the comparison experiment of the turning angle function, the principle is shown in Equation (11). For polygon \(A\), the tangent angle of any point O on its boundary along the reference direction (e.g., \(x\)-axis) is v. The turning angle function \(f(s)\) means the change in the tangent angle along the boundary of \(A\) is inverse or counterclockwise relative to the arc length \(s\). A total of 120 \(\times\) 9 = 1080 experimental data in 9 classes were subjected to accuracy calculations, yielding a matching accuracy of 0.438.  
\[L_{P}(A,B)= ||f_{A}(s)-f_{B}(s)||_{P}=\left(\int_{0}^{1}|f_{A}(s)-f_{B}(s)|^{p}ds \right)^{\frac{1}{p}} \tag{11}\]  
A comparison of the experimental results of the skeleton-line-based graph convolutional neural network, the contour-line-based graph convolutional neural network, and the turning angle function is shown in Table 2.  
The main reason for this discrepancy is that the skeleton line accurately portrays the overall orientation and convexity of the settlement, whereas the contour line cannot accurately portray the convexity of the settlement. The features of the skeleton line achieve an apparent classification effect after dimensional expansion and multilayer convolution of the graph convolutional neural network. In contrast, the classification of the graph convolutional neural network is not affected by spatial factors such as the starting point and the starting angle of the areal settlement, so the accuracy of the graph convolutional neural network based on skeleton line is higher than that of the turning angle function.