esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Dynamic Cluster Construction
#### 4.1.2 Cluster Construction  
After the CH is selected, the cluster is established through the neighbor nodes of the CH. In order to reduce the overhead of nodes in a cluster, the number of nodes in each cluster cannot exceed the upper limit \(\beta\). When the nodes that do not belong to any cluster enter the transmission range of CH and the number of nodes in the cluster does not exceed \(\beta\), the node is selected as CM by CH.  
When a dynamic cluster moves from one RSU in a cooperative region to within range of another RSU, the request content cached in the previous RSU may be out of date, and the next RSU will not cache the requested content in advance for the upcoming cluster. Faced with this inefficient use of caching resources, we can use the mobility of the cluster to obtain the next RSU to arrive in the cluster and replace the cached content on the upcoming RSU in advance through cooperative transfer between RSUs. The created dynamic vehicle clusters can help replace cached content on RSUs, reducing content fetching latency and improving user experience.
## 5 Conclusions  
(-)-\(\delta\)-heptalactone is emitted exclusively by males of _R. batava_ flies. Only (-)-enantiomer elicited EAG responses in both sexes, whereas no response to the (+)-enantiomer was registered. Both males and females of _R. batava_ were captured in traps baited with the active compound. Based on the mode of action, (-)-\(\delta\)-heptalactone is an aggregation pheromone, with potential for application in pest management programs of _R. batava_ flies. The aggregation type of a pheromone is first reported here within the genus _Rhagoletis_.  
**Author Contributions:** Conceptualization, V.B. and D.A.; methodology, V.B., S.S., L.B.-C. and R.M.; validation, V.B., D.A. and S.S.; formal analysis, L.B.-C. and D.A.; investigation, V.A., S.R., P.S., L.B.-C. and D.A.; resources, V.B., S.S., L.B.-C. and D.A.; data curation, D.A., L.B.-C., P.S. and V.B.; writing--original draft preparation, V.B. and R.M.; writing--review and editing, all authors; visualization, L.B.-C., S.R., V.A. S.S. and D.A.; supervision, V.B. and S.S.; project administration, V.B., D.A.; funding acquisition, R.M., V.B. and S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from European Social Fund (project no. 09.3.3-LMT-K-712-01-0099) under grant agreement with the Research Council of Lithuania (LMTLT).  
**Acknowledgments:** Thanks to Povilas Sukys for their kind permission to collect puparia in their sea buckthorn plantation.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound Name** & **CAS No. 1** & **Species** & **Emitter** & **Reference** \\ \hline Lavender lactone & 1073-11-6 & _Anastrepha fafractculus_ & M & [39] \\ Suspensolide & 111351-08-7 & _Anastrepha fafractculus_ & M & [39; 40; 41] \\  & & _Anastrepha ludens_ & M & [42] \\  & & _Anastrepha suspensa_ & M & [42; 43; 44] \\  & & _Anastrepha suspensa_ & M & [45] \\ S,S-Anastrephin & 77670-94-1 & _Anastrepha fafractculus_ & M & [39; 40; 41] \\  & & _Anastrepha ludens_ & M & [42; 46; 47; 48] \\  & & _Anastrepha ludens_ & M & [42; 46; 47; 48] \\  & & _Anastrepha suspensa_ & M & [42; 44; 46; 48] \\  & & _Anastrepha sp._ & M & [45] \\ S,S-Epianastrephin & 77670-93-0 & _Anastrepha fafractculus_ & M & [49; 40; 41] \\  & & _Anastrepha ludens_ & M & [42; 46; 47] \\  & & _Anastrepha suspensa_ & M & [42; 44; 46; 48] \\ (E)-5-(3/\(\delta\)-heptalenyl)-dihydro-2(3H)-furanone & 81693-14-3 & _Bactrocera cucurbitate_ & M & [49] \\ Dihydro-3-methylfuran-2(3H)-one & 1679-47-6 & _Ceratitis capitita_ & M & [50] \\ \(\delta\)-Hexalactone & 823-22-3 & _Rhagoletis completa_ & M & [15] \\ \(\delta\)-Heptalactone & 3301-90-4 & _Rhagoletis completa_ & M & [15] \\ \hline \hline \end{tabular} \({}^{1}\) Chemical Abstract Service Number; M, male.  
\end{table}
Table 2: Lactones produced by Tephritidae species.
## 4 Calculation Results and Analysis
### Gravity on Spheres Intersecting or Outside the Asteroid
Figure 20: The corresponding accelerations by the potential of the 4 th degree and order. The axes are longitude and latitude, respectively. Figure 25 shows the gravitational acceleration in the eighth degree and order model for Castalia on the sphere 2 \(r_{0}\), which is just outside the Brillouin sphere. The unit here is mgal (1 mgal = \(10^{-8}\) km/s\({}^{2}\)). A comparison can be made with Figures 19 and 20. We can see that the 8 by 8 model is more accurate than the 4 by 4 model. Figure 26 also shows the gravitational acceleration in 16 degrees and the order model of spherical harmonic expansion on the sphere of 2 \(r_{0}\). The unit here again is also \(10^{-5}\) m/s\({}^{2}\). There is very little improvement in the accuracy of the 16 \(\times\) 16 model compared to the 8 \(\times\) 8 model. Figure 21: The gravitational acceleration based on the polyhedron model for Castalia at a distance of 3 \(r_{0}\). The unit is \(10^{-5}\) m/s\({}^{2}\). Figure 22: The corresponding acceleration by the potential of 4 \(\times\) 4 on the sphere 3 \(r_{0}\). The axes are longitude and longitude, respectively. By all these comprehensive calculations, we can conclude that only the polyhedral model works inside reference \(r_{0}\), and the \(4\times 4\) harmonic expansion is accurate enough when the position is far enough, say 4 \(r_{0}\), and when the position is near the surface but outside the reference radius \(r_{0}\), the higher-order model is better, say \(16\times 16\), or at least \(8\times 8\) or \(10\times 10\).
## 3 Methodology
### Data Collection and Analysis  
The research was designed with the goal to verify the effectiveness of the camp by observing how participants' perception of SW and AI changed after taking part in the Digital New SW \(\cdot\) AI camp. For this, they were asked to respond to identical surveys before and after the camp. The survey was structured in Google Forms for data collection. Each student was subject to an online test via their own digital devices. IBM SPSS Statistics 24.0 was used as the test result analysis tool, and the significance level for statistical analysis was 5% based on which significance was determined.  
Responses to the survey were either in multiple-choice or in short-answer questions, of which the former relied on quantitative research methodology for analysis. Average and standard deviations were calculated for the pre-test and post-test results, and difference in the score of the same group was analyzed through pre-post matching sample \(t\)-test. Moreover, an independent sample \(t\)-test was performed to check the differences in the post-test analysis results by gender and school grade, which are background variables. For these analytical results, degrees of freedom and Cohen's d values were calculated to determine the effect size. [PERSON]'s d values are interpreted as: 0.2 represents a small effect size, up to 0.5 is a medium effect size, up to 0.8 is a large effect size, and up to 1.5 is a very large effect size [29]. For analysis on short-answer questions, text network analysis, which is a text mining analysis method, was carried out to understand the array structure of concepts in the text and its implications. To this end, data pre-processing work such as checking typos, eliminating stop words, and treating synonyms was preceded. Key words in answers in the short-answer questions were identified via term frequency-inverse document frequency (TF-IDF), while latent Dirichlet allocation (LDA) topic modeling was conducted to extract groups of words with similar meaning, which were then visualized. The tool used for text mining was Textom.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### FastAPI Web Service Interface  
The proposed study involves incorporating a transfer learning algorithm for disease detection into a web service architecture. The web service provides a user-friendly environment for disease detection by acting as an interface between end-users and the DL model. The architecture consists of multiple components, such as a client-side interface, a server-side application, and an API, built on FastAPI, to facilitate communication between the web service and the machine learning model. The API specifies a collection of end-points that permit the transmission of data and requests between the client and the server. Through these endpoints, the web service can receive the prediction results of the TL model and provide a determination of the plants' healthy or diseased status. In addition, the API enables the retrieval of model information, including performance metrics and training status, which can be displayed to the user via the web service interface.  
Figure 7: A close-up (**left**) and a distant photo (**right–top**) of the proposed Raspberry Pi integrated system, with the RGB camera, for the online disease detection on grapevine plants, placed on the UGV and a photo during the online measurements on the field (**right–bottom**).  
The incorporation of the transfer learning algorithm for disease detection into the web service enables real-time disease detection on patient data. Through the web service interface, the decision of the TL algorithm can be easily accessed remotely and be interconnected to other agricultural web services that would use the signal of the detection of unhealthy plants as a trigger to enable procedures, such as spraying of appropriate pesticides.  
To facilitate communication between ROS and FastAPI, a communication bridging component was developed to send the decision message published by the ROS node to the FastAPI web service using HTTP requests. A schematic representation of the whole IoT integration is given in Figure 8.
## 2 End-to-End Intelligent Parking Model Architecture
### Activation Function, Loss Function, and Training Algorithm  
The \(ELU\)[24] (Exponential Linear Unit) is used as the activation function, and the functional Equation (1) is as follows:  
\[ELU(x)=\left\{\begin{matrix}x&x>0\\ \gamma(\exp(x)-1)&x\leqslant 0\end{matrix}\right. \tag{1}\]  
where \(x\) is the weighted sum of the inputs; \(\gamma\) is an adjustable hyperparameter defined as the value to which the \(ELU\) function approaches when the input \(x\) is a large negative number, which is usually set to 1. The role of the fully connected layer is twofold: first, there is dimensional transformation, which reduces the high-dimensional feature vector to a lower dimension; second, there is the integration of the extracted image features to obtain the high-level meaning that the image features have. The primary purpose of the dropout layer is to address the overfitting issue that emerges during the deep network training process.  
The decision values outputted from the CNN-LSTM neural network model constructed in this paper are continuous and belong to the regression problem, so the Mean Squared Error Loss (\(MSE\)) loss function is used, and its defining function Equation (2) is as follows:  
\[MSE\big{(}Y,\hat{Y}\big{)}=\frac{1}{n}\sum_{i=1}^{n}(\hat{g}_{i}-y_{i})^{2} \tag{2}\]  
where \(n\) is the sample size; \(\hat{g}_{i}\) is the output prediction of the \(i\)th sample; and \(y_{i}\) is the corresponding actual value.  
The Nadam [25] (Nesterov-accelerated Adaptive Moment Estimation) algorithm is used as the training algorithm. This algorithm can calculate the adaptive learning rate for each parameter, it has more substantial constraints on the learning rate, it learns faster, and it possesses better optimization results, and its Equation (3) is as follows:  
\[\begin{array}{l}m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}\\ \hat{\widetilde{m}}_{t}=\beta_{1}m_{t}+(1-\beta_{1})g_{t}\\ v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2}\\ \hat{m}_{t}=\frac{\widetilde{m}_{t}}{1-\beta_{1}^{2}}\\ \hat{\theta}_{t}=\frac{\widetilde{v}_{t}}{1-\beta_{1}^{2}}\\ \theta_{t+1}=\theta_{t}-\eta\cdot\frac{\widetilde{m}_{t}}{\sqrt{\widetilde{ \theta}_{t}+\varepsilon}}\end{array} \tag{3}\]  
where \(\theta\) is the network weight; \(g_{t}\) is the gradient, and the sub-script \(t\) indicates the \(t\) th iteration process; \(m_{t}\) is the first-order moment, i.e., the expected value of the gradient, and \(\widetilde{m}_{t}\) indicates the Nesterov acceleration value of the first-order moment; \(v_{t}\) is the second-order moment, i.e., the expected value of the gradient squared; \(\hat{m}_{t}\) indicates the corrected first-order moment; \(\hat{\theta}_{t}\) indicates the corrected second-order moment; \(\hat{\theta}1\) and \(\hat{\theta}2\) indicate the decay rate; \(\eta\) indicates the learning rate; and \(\varepsilon\) indicates the minuscule quantities. \(\beta 1\)  
Figure 2: Multi-tasking prediction sub-network.  
\(\beta\)2, \(\eta\), and \(\varepsilon\) are hyperparameters set before the start of training and they do not need to be learned by training.
# Monitoring Safron Crops with UAVs
## 5 Discussion
### Interpretation of Results  
The methodology followed included the application of two different image-analysis methods (pixel-based and object-based) and the training of two machine learning models (Random Forest and Multilayer Perceptron) in a set of field images obtained by the observation of six cultivation fields. All images obtained where annotated by the cultivators that pointed out four field attributes (saffron, weeds, mammal traces, and soil). The dataset  
Figure 10: Predicted images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Soil** & **Weed** & **Saffron** & **Mammal** \\ \hline
**RF** & 88.5\% & 0.16\% & 6.7\% & 4.6\% \\ \hline
**MLP** & 98.3\% & 0.05\% & 1.5\% & 0.15\% \\ \hline
**Real Data** & 83.6\% & 0.4\% & 8\% & 8\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Rate of each category (OBIA).  
used to train the model was a saffron crop field for which the images were obtained by UAV flights within 120 m. The main conclusions from this pilot study are as follows:  
* The estimation accuracy of both pixel-based and object-based methods was promising with pixel-based methods presenting overall higher accuracy. The average accuracy rate for the Random Forest (RF) algorithm was at 100%, and for the Multilayer Perceptron algorithm, it was at 95%. The object-based analysis, despite the weaknesses that existed, also presented encouraging results with respect to accuracy (RF at 80% and for MLP at 76%).
* It is important to note that, in order to evaluate machine learning algorithms, we need to use other performance metrics, such as the recall metric, in addition to the accuracy metric. This is very important because our dataset is not balanced; that is, the soil category outweighs the other categories (saffron, weed, and mammal). Thus, it can be seen that the basic method for evaluating the algorithms is the recall percentage for each category. The recall rates were at a medium level for both ML algorithms.
* Regarding the object-based analysis method, it is important to mention that it presented relatively low percentages in recognizing each class separately.
* One thing that should not be taken for granted is that the method per pixel gave much better results on higher flights than the method per object. In this regard, we can estimate that the method per pixel, although time-consuming, has the potential to improve the detection of weeds, saffron, and overall various types of diseases.
* The results of this study are encouraging. It is possible to estimate the production of saffron, the detection of weeds, and the existence of mammals through images collected by UAV. In particular, by applying these ML models in real-time data, it is possible to detect and even predict from a single image the accurate position of the weed to be removed, the amount of production, and the total resources needed to grow and protect a saffron cultivation.
## 2 Materials and Methods
### Performance Indicators  
To indicate the localization capability and resolution capability of imaging methods for damage, an Array Performance Indicator (API) is introduced to indicate the size of the damage imaging hotspot area, defined as [39]:  
\[\text{API}=\frac{Area_{-6 dB}}{\lambda^{2}} \tag{15}\]  
The equation shows the ratio of the area of the imaging hotspot region (where the amplitude exceeds the \(-6\) dB threshold) to the square of the wavelength. In this paper, the wavelength corresponds to the guided wave at the center frequency. A smaller index indicates that the hotspot region of this imaging method is more concentrated on the actual location of the damage, providing better resolution at the edges of the damage.  
The peak-to-center intensity difference indicator \(\tau\)[37] is used to evaluate the resolution capability of damage imaging methods for closely spaced double damages, which is defined as the attenuation multiple of the intensity at the center of the double damage imaging relative to the peak intensity of the damage, measured in dB. Figure 5 shows the resolution capabilities of damage imaging under different indicators.  
Figure 4: Flowchart of the DORT-MUSIC method.  
Figure 5: The peak center intensity difference \(\tau\) evaluates the damage resolution capability. (**a**) When \(\tau<-6\) dB, damages can be clearly resolved. (**b**) When \(\tau=-6\) dB, damages can be barely resolved. (**c**) When \(\tau>-6\) dB, damages cannot be resolved.  
The threshold for distinguishing damage is \(-6\) dB, which means the peak center intensity is 25% of the damage peak intensity. When \(\tau<-6\) dB, the two imaging peaks of the double damage can be clearly distinguished, while when \(\tau>-6\) dB, the two imaging peaks overlap and are identified as a single damage. This indicates that a smaller threshold reflects better resolution capability of the imaging method.
## 4 Experimentation and Results
### Experiment System
#### 4.2.3 Evaluation Criteria  
In this section, we establish the criteria utilized to evaluate the performance of our YOLOv5 and DeepSORT models for product detection and customer tracking in a retail environment. These standards were selected to guarantee efficiency in real-time application and detection accuracy. As seen in Table 4, the primary metrics are accuracy, precision, recall, F1-score, latency, and resource consumption, and they are assessed across training, validation, and test datasets.
## 3 Results and Discussion
### NEXAFS Spectroscopy Research of the Nanocomposites and Initial MWCNTs
However, the HRTEM studies showed (Figure 4) that tungsten carbide coatings have good adhesion to the MWCNT outer surface, and the WC1-x nanoparticles inner part consists entirely of tungsten  
Figure 7: Normalized TEY signal of MWCNTs (red) and nanocomposite sample 2 (black) in arbitrary units in near the Near Edge X-ray Absorption Fine Structure (NEXAFS) C1 absorption edge (**a**). The arrows indicate the fine-structure elements energy positions of the carbon oxygen’s [68; 69] and WC [70; 71; 72; 73] near the NEXAFS C1 edge. Dash-dotted lines indicate the partial C1 absorption cross sections isolation. Partial C1s normalized TEY signal of MWCNTs (red) and nanocomposite sample 2 (black) in arbitrary units (**b**). The dashed lines indicate the absorption levels in the continuum. carbide. This suggests the presence of chemical bonds between the MWCNT carbon atoms, tungsten atoms from the coating layers, and oxygen atoms from the oxides on the nanotube surface. When the composite is transferred into the air, the tungsten oxide formation due to its contact with atmospheric oxygen should be expected. Therefore, the good adhesion of WC1-x to MWCNTs can be explained by the formation of a chemical bond through tungsten of pyrolytic tungsten carbide coating and the MWCNT outer surface. However, in the initial stage of the W-metal atom deposition on the MWCNT surface at a temperature of 300 \({}^{\circ}\)C, carbon oxides can also be formed. Tungsten carbide and carbon oxide spectral study is impossible due to the superposition of their structures in the NEXAFS C1 spectra. However, carbon oxides, metal oxides and metal carbides separate studies can be performed using the XPS method.
## 3 Numerical Examples
### Shape Optimization
Combining Figure 4 and Table 2, it can be seen that as the optimization progresses, the structural strain energy decreases, while the height increases and the cross-section approaches the catenary shape, which is mechanically reasonable. The volume of the optimized structure increases by about 26%, which is acceptable compared to the performance improvement, indicating that the material utilization rate of the optimized structure is improved. It is worth noting that although the increase in structural height resulting from optimization reduced the strain energy of the structure, it may worsen its other indicators, such as weight. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Iterations** & **0** & **3** & **10** & **50** & **197** \\ \hline Relative Strain Energy 1 & 100.00\% & 37.09\% & 23.19\% & 7.66\% & 6.94\% \\ Relative Volume 2 & 100.00\% & 99.56\% & 99.24\% & 119.07\% & 126.24\% \\ Structural Height (m) & 5 & 4.336 & 4.994 & 12.055 & 14.401 \\ \hline \end{tabular} 1  
Relative strain energy: the ratio of the volume of the structure at the current iteration to the initial structure’s strain energy.[ENDFOOTNOTE]  
Relative volume: the ratio of the volume of the structure at the current iteration to the initial structure’s volume.  
\end{table} 
Table 2: Changes in structural response during the optimization process. Figure 4: Typical shapes of the structure during the optimization process. (a) Initial structure (5 m); (b) Step 3 (4.336 m); (c) Step 10 (4.994 m); (d) Step 50 (12.055 m); (e) Step 197 (14.401 m). Figure 5: Von Mises stress of the initial structure.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Comparison with Other Algorithms  
The performance of the proposed inpainting method was compared with four state-of-the-art algorithms in the literature, including Context-Encoder [22], SpeechPainter [27], Audio-Visual [31], and TF-Masks [23]. The comparison results are listed in Table 10.  
The Context-Encoder, originally designed for inpainting music signals, was adapted to our established speech datasets. We conducted MOS tests for two gap lengths, specifically 64 ms and 128 ms, considering both NB and WB scenarios. Human raters were used in [27] for accessing the MOS, we categorised it under MOS(WB). The studies by [23; 31] only exhibited the Perceptual Evaluation of Speech Quality (PESQ) scores to indicate the quality of the inpainted signals. To facilitate comparison, we employed a mapping method to convert the raw PESQ scores into MOS(WB) [50].  
The results indicate that our methods outperformed the TF-Mask method across all gap lengths. In the case of the Context-Encoder method, our approaches demonstrated superior performance when the gap length was 128 ms. When the gap length was 64 ms, there were no significant differences in the MOSs for the NB scenario, and they maintained comparable performance for the WB scenario. The SpeechPainter and Audio-Visual methods exhibited slightly higher MOSs compared to our methods. This is because our proposed approaches only used the speech features preceding the gap for inpainting, without utilising any additional information. In contrast, SpeechPainter utilised the entire speech transcripts, whereas the Audio-Visual method combined both speech and visual features, leading to higher MOSs. Nevertheless, it is worth noting that in real-world communication scenarios, there will not always be support and time for additional information (i.e., the transcript) to be considered, particularly when a two-way live conversation is ongoing between communicators. As such, our proposal fills in this gap to greatly enhance the user experience.