esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 VAM Responses of Bolted Joints
### Acceleration Contour  
The steady global wave field is formed during the propagation and interaction of the LF and HF. Figure 4 shows the process of the VAM behavior of the Al-Al bolted joint, which presents an early propagation and a steady stage. Initially, the LF vibration and the HF probing wave appeared at the two excited nodes on the free beam (t = 5 \(\upmu\)s), transmitting to the clamped beam along the central contact region near the preloaded bolt before covering the whole structure (t = 600 \(\upmu\)s). Next, a steady acceleration contour was formed after a millisecond of reciprocating propagation of the combined waves in the structure. Then, the contours for every 0.5 ms (per half period) were similar to each other due to the energy dominant of the LF pumping vibration (period = 1 ms), and the micro variation was caused by the HF contribution (period = 0.07 ms).  
The contours of the out-of-plane acceleration (A3) of the bolted joint at the same time under different preloads are shown in Figure 5. It can be seen clearly that nonlinear waves were generated in the contact region (Figure 5a). The nonlinear responses in the contact region consisted of multiple components with different wavelengths (i.e., different frequencies) and modal shapes. However, the nonlinear responses in the joint diminished with the increase in the preload (Figure 5a-d), because the \"opening-closing\" motion was obstructed by the enhancement of the contact constraint, while the LF pumping excitation was maintained at the same level. Actually, the nonlinear features can barely be observed from 9 N-m to 13 N-m in the contours.
## 3 Methodology
### Methods of Predictive Modeling
#### 3.4.1 Models' Development  
In the proposed model, a data-splitting strategy that was discussed in [46] was applied to ensure an unbiased evaluation of the prediction performance. In the proposed model, the dataset was split into two sets: a training set with 90%, which was used to train the model, and a testing set with 10% of the data, which was used to evaluate its accuracy.  
In this model, a supervised learning approach was developed, combined with a deep learning model capable of predicting the NO\({}_{2}\) concentration with minimal error acrossvarious metrics. Our research focuses on the LSTM model because it has shown promising results in air pollution prediction [34]. Moreover, LSTM outperforms RNNs' ability to store large amounts of prior time series data without the vanishing gradient problem [25].  
Additionally, we used multiple hyperparameters to achieve the best performance in a reasonable timeframe. All models used the same batch size of parameters (32), an activation function (ReLu), and the mean square error (MSE) as a loss function to measure the error rate between the actual and predicted values, but they differed in the number of layers and the number of neurons per layer, as shown in Table 3. Moreover, we adjusted the sliding window parameters (window size, step size, and features). All the hyperparameters were adjusted to improve the performance during the experimental process. For a 5-day-ahead prediction, we used a window size of 14 days, considering the influence of the past 14 days' weather and pollution data. The step size was set to 5 days, and the features included temperature, wind speed, rainfall, humidity, and NO\({}_{2}\) concentration. These features served as input variables for the model in multi-step prediction. Studies have shown that a vector representation of time using Time2 Vec with the LSTM model improves performance in most cases and never leads to a deterioration in prediction performance [26]. We used this with the optimized model, providing better prediction results. All these hyperparameters were used with the models that were developed in this research.  
In this study, we conducted three experiments to verify the effectiveness of the proposed model. Each experiment focused on evaluating a different aspect. The first experiment used the LSTM algorithm, which has shown promising results in predicting air pollution [34]. In the second experiment, we improved the LSTM model using the batch normalization method, which was used to normalize inputs of neural network layers by re-centering and rescaling gradients for faster, stable training [47]. The last experiment included embedding a Time2 Vec layer into the improved LSTM model, which was important to verify whether our proposed framework performed well in predicting the NO\({}_{2}\) concentration in the air to make the prediction results more accurate and stable.
## 3 Results  
### Use Case 1: DNET-GG Classifier  
We evaluated the performance on the validation set, which made up 20% of the whole dataset and was not used for training. It consisted of 24 slides of ganglioglioma and seven slides of DNET. In total, 29,333 tiles were extracted from the GG slides and 6597 tiles were extracted from the DNET slides for evaluation. No hyperparameter tweaking was performed, which could have led to overfitting on the validation set. On a tile level, the accuracy was 0.936 and on a slide level 0.968. The Brier score on the tile level was 0.053 and 0.022 on the slide level. The AUC on the tile level was 0.93 and 1.00 on the slide level for the ROC curve. The average precision calculated from precision and recall was 0.88 for DNET and 0.97 for GG on the tile level. On the slide level, it was 1.00 for DNET and GG. (Figures 7 and 8)  
Model calibration was also evaluated on tile level (Figure 9). We observed tiles that were overconfidently classified by the model as DNET but were in fact GG. DNETs typically contain mucus and have a loosened-up structure. Tiles from GGs which were wrongly classified as DNETs also had a loosened-up structure, which was only artificial.  
Figure 8: Results 1 ROC (**left**) and precision recall curves (**right**) on slide level.  
Figure 7: Results 1 ROC (**left**) and precision recall curves (**right**) on tile level.
## 3 Established Connection Phase  
As stated before, the channel is assumed to be static within a frame. During the EC stage, we can have two different types of frames. The first contains only data, thus, the time spent for data transmission, \(T_{d}=M_{d}T_{s}\), with \(M_{d}\) being the number of data symbols per frame, equates to the entire frame length; that is \(T_{f}=T_{d}\) (note that, since data are transmitted only during EC, we neglect the use of the corresponding subscript on \(T_{d}\) in order to simplify the notation). Such a frame can be easily recognized, highlighted in green in Figure 1. In this sense, the transmission of frames is continuous, while the channel prediction mechanism proceeds in the background. However, still from Figure 1, it is possible to appreciate that some frames are different, since they are organized in three different fields. The first one, of \(T_{a,\text{EC}}=M_{a}T_{s}\)-length, is dedicated to the interference acquisition. The second one, of \(T_{e,\text{EC}}=M_{e}T_{s}\)-length, is used for channel estimation purposes, while the last field, of \(T_{d}\)-length, is dedicated to sending information data. The whole time duration of a frame is \(T_{f}=T_{a,\text{EC}}+T_{e,\text{EC}}+T_{d}\). Hence, for a fixed \(T_{f}\), spending time on interference and channel statistics update unavoidably leads the data transmission time to be reduced within a frame.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 5 Performance Analysis
### Simulation Result  
For different link failure scenarios, we mainly focus on two characteristics of the scenario. One is the probability of link failure per unit time. This feature determines the severity of the network environment and is used to measure the ability of the algorithm to withstand link failures. The second is the average load of the entire network link. This feature reflects the amount of data transmitted in the network and is used to measure the strength of the service provided by the algorithm. In the simulation, we set the reference value of the link failure probability to 2.5% and the reference value of the average network load to 24%, fix the value of one of the features, and analyze the influence of the other feature on the performance of the algorithm.
# A New Method of Ski Tracks Extraction Based on Laser Intensity Information
## 3 Methodology
### Ski Tracks Extraction
#### 3.4.3 Setting the Threshold for Curvature  
The setting of the curvature threshold in segmenting regions directly impacts the quality of segmentation. By selecting the curvature threshold incorrectly, one can cause oversegmentation or undersegmentation. The curvature threshold value is directly related to the complexity of the actual object; for example, the curvature threshold value of the gentler object is larger, whereas the curvature threshold value of the sharper object is smaller. The curvature threshold can be set in the \(PCL\) library by using the setCurvatureThreshold member function. Based on the points data of the Wanlong ski resort used in this article, the curvature threshold is set between 0.0016 and 0.0023.  
The growth radius and vertical distance between the neighboring points and the seed surface were judged by setting the range of spatial threshold. If the angle is less than the threshold, the current point is added to the current area. The normal vector of the fitting surface is calculated and compared with the normal vector of the seed surface to judge whether the two included angles belong to the threshold range. If the curvature is less than the critical value, the point is added to the seed point. In summary, the growth segmentation process is shown in Figure 6, and the point cloud extraction algorithm for ski tracks is in Algorithm 1.  
```
0: Point cloud = [P]; Point normals = [N];
1: Points curvatures =[K ];
2: Neighbor finding function = { \(\Omega\) (.) };
3: Curvature threshold = { \(K_{th}\) };
4: Angle threshold = { \(\theta_{th}\) };
5: Region list \(R\gets 0\);
6: Available points list \(\{A\}\leftarrow\{1,...,|P|\}\).
7: Clustering points set, \(M\)
8:while\(\{A\}\) is not empty do
9: Current region \(\{R_{K}\}\gets 0\)
10: Current seeds \(\{S_{K}\}\gets 0\)
11: Point with minimum curvature in \(\{A\}\to 0\)
12:\(\{S_{K}\}\leftarrow\{S_{K}\}\cup P_{min}\)
13:\(\{R_{K}\}\leftarrow\{R_{K}\}\cup P_{min}\)
14:\(\{A\}\leftarrow\{A\}\setminus P_{min}\)
15:for\(i=0\) to size \(<\{S_{K}\}>\)do
16: Find nearest neighbours of current seed point \(\{B_{K}\}\rightarrow\Omega(S_{K}\{i\})\)
17: Current neighbour point\(\{P_{j}\}\to B_{K}\{j\}\)
18:if\(\{A\}\) contains \(P_{j}\) and \(cos^{-1}(|(N\{S_{K}\{i\},N\{S_{K}\{j\}\})|)<\theta_{th}\)then
19:\(\{R_{K}\}\leftarrow\{R_{K}\}\cup P_{j}\)
20:\(\{A\}\leftarrow\)\(P_{j}\)
21:if\(K\{P_{j}\}<K_{th}\)then
22:\(\{S_{K}\}\leftarrow\{S_{K}\}\cup P_{j}\)
23:endif
24:endif
25:endfor
26: Add current region to global segment list \(\{R\}\leftarrow\{R\}\cup\{R_{K}\}\)
27:endwhile
28:return \(M\)
```  
**Algorithm 1**The Point Cloud Extraction Algorithm for Ski Tracks  
Figure 6: The workflow of the growth segmentation process.
## 3 Results
### Effects of Vegetation Characteristics on Reflectance, Modelled Sensitivity  
The spectral effects of four of the major model parameters, i.e., chlorophyll a+b, caroten, leaf area index, and soil moisture content, were quantified via a sensitivity analysis on in situ data (Figure 2). The chlorophyll a+b content affects a limited range of wavelengths (i.e., 500 to 750 nm), whereas the effects of caroten on the reflectance spectra were negligible. Hence, the latter was fixed to its average value. LAI and soil moisture content have a large influence on the entire range of the spectrum. LAI shows a decrease of reflectance at higher LAI values, which was expected as higher leaf coverage can absorb more light and hence lower reflectance. As expected, LAI changes the shape of the spectrum, whereas soil moisture content only increases or decreases the entire modelled spectrum. The contribution of leaf level reflectance (as modeled by 'Prospect') appears subordinate to the contribution of canopy level reflectance (as modeled by 'Sail').  
Figure 1: The effect of spatial drivers on in situ vegetation properties. The leaf properties (chl-ab and caroten) are strongly affected by flood duration and creek formation. The canopy properties (LAI and the dry/wet soil fraction (1-soil moisture content)), are affected by flood duration, wave forcing and competition. Regression lines are shown for significant relationships: p < 0.05).
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 2 Artificial Intelligence: An Overview (Fundamentals and Applications in Agriculture)
### Different AI Application in AgroSciences
#### 2.3.3 AI in Food Safety and Traceability  
AI plays a crucial role in enhancing food safety and traceability within the agro-food industry. Using advanced algorithms and machine learning techniques, AI enables precise, real-time monitoring of agricultural products throughout the supply chain. For instance, AI systems can identify and prevent contaminations in food products, thereby improving consumer safety [34].  
AI-based technologies in agriculture, such as sensors and drones, allow for detailed and real-time traceability of crops from planting to harvesting and distribution, ensuring product integrity throughout the supply chain [35].  
Moreover, the implementation of AI systems for big data analysis in the food supply chain enables early anomaly detection and efficient food safety management. These systems can analyze vast amounts of data to foresee potential risks and optimize management practices, ensuring that the food reaching our tables is safe and of high quality [36].
## 4 The Performance Analysis  
This section shows the performance analysis of the MIGM and the In-VDM. To analyze the performance of the MIGM, it was compared with an existing in-vehicle gateway to measure conversion time and an error rate when 4000 messages are converted to other messages and to analyze the performance of the IN-VDM, it was compared with a multi-layer perceptron (MLP) and a long short-term memory (LSTM) to measure the computation time and accuracy when the number of test data sets is changed.
## 3 Results and Discussion
### Sensor Performance  
#### 3.5.1 OTFT Humidity Sensor  
The use of rGO as humidity sensing material for OTFTs is based on its conductive nature and its chemical affinity toward water molecules [15]. The humidity OTFT sensor was designed for qualitative measurement purposes to detect the presence of surface moisture. It is used in conjunction with the chloride sensor to ensure the latter operates in dry conditions only to minimize false measurements.  
In this instance, the OTFT gate was functionalized with rGO which undergoes an increase in conductivity in the presence of water molecules. The sensor's transient response was measured at a constant gate bias of 1 V and drain voltage of 5 V at which the OTFT operates in the linear regime. The current transient of the sensor is depicted in Figure 7a, which displays the sensors response before, during, and after exposure to high levels of humidity.  
Figure 6: OTFT electrical characterization results: (**a**) Output characteristic at constant gate bias V\({}_{\text{GS}}\) range from 80 mV to 1 V, (**b**) parametric sweep of output characteristic against OTFT aspect ratio at V\({}_{\text{GS}}\) = 1 V, (**c**) transfer characteristic at V\({}_{\text{DS}}\) = 50 mV where I\({}_{\text{DS}}\) and \(\surd{}_{\text{DS}}\) are used for estimating threshold voltage, carrier mobility, and current ON/OFF ratio.  
As expected, the conductivity of rGO is enhanced as a result of chemical bonding between the hydroxyl groups present in rGO and water molecules. As a result, the sensor output current increases when it is exposed to humidity and the associated relative responsivity for dry to surface wetness transition (R\({}_{\text{Rel}}\)) was estimated at 122.6% using Equation (2), where \(\Delta\)I\({}_{\text{resp}}\) is the change in the sensor current due to exposure to surface wetness and I\({}_{\text{dry}}\) is the sensor output current under dry conditions. The sensor's response time and recovery time were extracted from the sensor's response shown in Figure 7b and were both estimated at 20 ms.  
\[\text{R}_{\text{Rel}}\ =\ \frac{\Delta\ I_{resp}}{\text{I}_{\text{dry}}}\times 100 \tag{2}\]  
Repeated measurements of the sensor transient response revealed that the sensor remains responsive during recurrent exposure to humidity with an unchanged response time. However, the OTFT output current decreased with repeated measurements. This is an expected behaviour of organic thin film transistors as they are prone to environmental doping, material degradation, as well as photodegradation.
## 4 Discussion
### Practical Implications for Power Plant Monitoring
In summary, the practical deployment of advanced FDD systems in power plants hinges on their ability to be:  
* Adapted to specific plant architectures and sensor networks;
* Scalable across components and subsystems;
* Transparent and interpretable;
* Integrated with control and maintenance platforms;
* Validated under realistic operating conditions. Only through attention to these factors can diagnostic research move from prototype to production, enhancing both the reliability and efficiency of power generation systems.
# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 2 Related Work
### DT-Enabled Computation Offloading with Cloud  
In recent years, DT can well replicate the network topology and the physical entities within it, which has been extended to industrial manufacturing, smart factories, and the Internet of Things. Therefore, given the technical advantages of DT, offloading locally-heavy computational tasks to DT-enabled network architectures has been widely studied. The authors in [24] proposed for the first time a digital twin dual empowered system that included end users, base stations, and cloud servers, and utilized transfer learning to solve the edge association problem. In [25], the authors presented a privacy-enhancing federated learning framework and developed DT-assisted multi-intelligent DRL-based resource scheduling for federated client association and channel allocation. The authors in [26] proposed a cloud edge device collaboration, reliable, and communication efficient DT for low carbon electrical device management. DT-empowered satellite-terrestrial cooperative edge computing networks were investigated in [27], in which computational tasks from terrestrial users could be partially offloaded to the associated base station edge servers or the associated satellite edge servers. In order to satisfy the dynamic service requirements, Ref. [28] proposed a deep reinforcement learning-based offloading mechanism for cloud edge collaborative mobile computing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Offloading** & \multirow{2}{*}{**Algorithm**} & **Optimization** \\  & **Position** & & **Objective** \\ \hline
[24] & Cloud-edge-client & DRL & Minimize latency performance \\
[25] & Cloud-edge-client & FL & Minimize the system cost \\
[26] & Cloud-edge-client & C\({}^{3}\)-FLOW & Minimize communication cost \\
[27] & Cloud-edge & MA-DATD3 & Minimize delay and energy \\
[28] & Cloud-edge-client & DRL & Minimize execution delay \\
[29] & Edge-client & SCA & Minimize the average delay \\
[30] & Edge-client & two-stage & Maximize total profits \\
[31] & Edge & SCA & Minimize latency \\
[32] & Edge & UCB & Minimize delay \\
[33] & Edge-client & non-convex & Minimize the average delay \\
[34] & Edge & optimization & \\
[35] & Edge & A3C & Minimize system energy cost \\
[36] & Edge & MINLP & Minimize the system latency \\
[37] & Edge & GatMARL & Maximize QoS \\
[38] & Edge & AAC & Minimize long term energy \\
[39] & Edge & DDQN & Minimize energy consumption \\
[39] & Edge-client & DRL & Minimize long term income \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing studies.
## 2 The Design of Hydrogen-Charging Experiment
### The Hydrogen-Charging Plans and Detection Indicators  
To analyze the electrolytic hydrogen-charging effectiveness, the material hardness, fracture morphology, tensile properties, and hydrogen embrittlement sensitivity index are evaluated during the experiments. A HV1000Z Vickers hardness tester is used to measure the specimens' material hardness, the applied load is 0.5 kgf, and the loading time is set to 10 s. Three locations on the hydrogen-charged surface are uniformly selected for hardness testing, and the average measurement result is considered as the surface hardness measure result. Additionally, the hardness layer is measured at regular intervals of 25 \(\upmu\)m along the depth to ascertain the hardening depth. In each layer, three points are selected for hardness measurement, and the average value is the hardening depth of the test results.  
To evaluate the tensile properties of the specimens before and after hydrogen charging, the UTMX4104X universal testing machine is used. During the testing process, based on the recommendation of relevant standard ISO 6892-1:2009, taking into consideration the material properties and specimens length, the strain rate used in the tensile test is selected as 0.00025 s\({}^{-1}\). Based on the formula provided by the standard, the corresponding actual tensile rate for the selected strain rate is determined to be 1.17 mm/min. Considering the specific parameters of the equipment used and the tensile parameters set in similar materials tests [33], the tensile rate used in the tensile test in this paper is finally set at 1 mm/min in the actual tensile process. The SUPRA 55 field emission scanning electron microscopy is used to observe the fracture morphology at the magnification of 1000 times.  
During the tensile process, the variations in the length between the fractured specimens before and after hydrogen charging are measured, and the post-break elongation \(\delta\) of the specimens is calculated. The hydrogen embrittlement sensitivity index \(I_{\mathrm{H}}\) is calculated based on the post-break elongation of specimens before and after hydrogen charging. The calculation process is illustrated by Equation (1).  
\[I_{\mathrm{H}}=\frac{\delta_{0}-\delta_{\mathrm{H}}}{\delta_{0}} \tag{1}\]  
where \(\delta_{0}\) is the breaking elongation of the non-hydrogen charging specimen, and \(\delta_{\mathrm{H}}\) is the breaking elongation of the hydrogen charging specimen.