esa-sceva/satcom-chunk-collection huggingface dataset:
# Conceptual Design of a Compact Divertor Heat Load Simulation Device: HIT-PSI
## 2 Designs of the Device and Its Sub-Systems
### Target Holder Subsystem  
The target subsystem is designed to accomplish the following goals:  
* To be combined with the target probe design for measurements of the heat load flux;
* To complete tests for different sizes and shapes of materials;
* To apply proper voltage to the sample for various incident ion energy regulations;
* To realize plasma-material interaction experiments with different beam incident angles;
* To achieve effective heat dissipation for long-duration experimental tests;  
A schematic diagram of the target holder is shown in Figure 13. The front of the target is a molybdenum baffle attached to a copper cooling plate with ceramic screws. The hole in the center is for the sample exposure or target probe placement. The sample to be analyzed is placed behind the molybdenum baffle. Both the ceramic holder and the molybdenum baffle may be replaced to accommodate the varying sizes and shapes of the samples. These components are mounted on a copper base with water cooling. The copper base is isolated from the rear flange and front molybdenum baffle to allow it to link to a DC power source for bias, along with the sample.  
Sensors are installed between the molybdenum baffle and the sample to measure the temperature of the sample. The inlet and outlet water-cooling pipes are also with thermometers and flow meters, and the combination of the two can give the heat load density. The entire target part can be rotated to realize material testing with different incident angles.
# Directional Support Vector Machines
## Kernelized Symmetric Directional SVM
Then, the VC dimension of the classifier:_  
\[l(x|w_{0},w_{d})=\operatorname{sign}(f(x|w_{0},w_{d}))=\operatorname{sign}(w_{ 0}+w_{d}^{\intercal}\boldsymbol{\phi}(x)) \tag{13}\]  
_equals \(2N+1\)._  
Proof. : See Appendix A.2. The Fourier series of the triangle wave \(g_{t}\) has an infinite number of non-zero coefficients, and therefore, the classifier instanced with the triangle wave kernel \(k(x,z)=g_{t}(x-z)\) has infinite VC dimension. On the other hand, the VC dimension of the classifier instanced with the cosine kernel \(k(x,z)=\cos(x-z)\) equals three. Consequently, the SVM with the triangle wave kernel is able to express an arbitrary number of thresholds in the circular domain, unlike the SVM with the cosine kernel or with the triangle wave in the primal form, which, as proven before, can only express two thresholds in \([0,2\pi)\). Figure 2 illustrates these differences. However, depending on the relative position of the data points, even the SVM with the triangle wave kernel may fail to assign the correct label to all of them. In order to overcome this limitation, composite kernels, constructed from this baseline, can be explored. Typical cases include the polynomial directional kernel, \(k(x,z)=\left(g(x-z)+1\right)^{d}\), where \(d\) is the polynomial degree and the directional RBF kernel, \(k(x,z)=\exp\left(\kappa g(x-z)\right)\) (while the standard RBF kernel relies on the Gaussian expression, the directional RBF kernel relies on the expression of the von Mises distribution). Figure 3a shows a simple training set that is correctly learned both with the primal and kernel triangle wave formulations. On the other hand, it should be clear that the setting in Figure 3b cannot be correctly learned by these same models. In this case, setting the SVM with the kernel \(k(x,z)=\left(g_{t}(x-z)+1\right)^{2}\) achieves the correct labeling. It is important to note that standard, off-the-shelf, toolboxes can be used to solve the kernelized directional SVM directly. One just needs to properly define the kernel as discussed before. Figure 3: Example illustrating the benefits of composite kernels. (**a**) Toy example correctly solved by the primal and the kernel formulation (using the directional kernel \(g_{t}\)). (**b**) Toy example not solved correctly by the primal, nor the kernel with \(g_{t}\). Solved correctly with polynomial directional kernel of degree two.
# State-of-the-Art Internet of Things in Protected Agriculture
## 3 The Crucial Technologies of IoT in Protected Agriculture
### Machine Learning  
Machine learning (ML) is a smart way for computers to simulate people's learning activities, acquire new knowledge, continuously improve performance and implement their own perfection. In recent years, machine learning has achieved great success in algorithm, theory and application [59] and have been combined with other agricultural technologies to maximized crop yield while minimizing the input costs [60]. The main machine learning algorithms are naive bayes, discriminant analysis, K-nearest neighbor, support vector machine (SVM) [61], K-means clusterin, fuzzy clustering, gaussian mixture models, artificial neural network (ANN), deep learning [62], decision tree algorithm and so on [63].  
ML can discover the internal connection between messy, modelless and complex agricultural data, make accurate predictions and provide a theoretical basis for agricultural decision-making. Machinelearning technology is playing an important role in crop breeding, disease identification, pest and disease prediction, intelligent irrigation planning and agricultural expert systems [64; 65; 66; 67]. For example, ML technology can analyze past farmland data, including the performance of crops under different climatic conditions and the inheritance of a particular phenotype. As well, ML technology can explore the association rules and then build a probability model to predict which genes are most likely to participate in the expression of a certain good trait of the plant, which can help the breeding expert conduct a reasonable breeding experiment [68; 69; 70]. [PERSON] et al. came up with a method for detecting the maturity of a single intact tomato-based ML, which consisted of three steps: pixel-based segmentation, blob-based segmentation and individual fruit detection. In the first two steps, decision trees were generated based on features such as color, shape, texture and size and image segmentations were conducted using the generated trees. Finally, the automatical detection of individual fruit in multiple tomatoes was achieved by X-means clustering method. The results of the tomato detection image test showed that their method achieved a recall of 0.80, while the precision was 0.88 [71]. [PERSON] et al. proposed deep learning architecture for counting fruits based on convolutional neural networks. They used synthetic images to train the neural network to save time-consuming collect and annotate. The network was trained for three epochs on 24,000 synthetic images using an Adam optimizer and 100 randomly chosen images were tested. The experimental results demonstrated that their method had higher average accuracy as comparing with others [72].
## 3 Results
### Localization and Hoop Strain Profiles  
Figures 5-8 show the FEM mesh of the axisymmetric cross sections of the four sizes of cylinders considered, with one, two, four and six FRP wraps, plotted at the end of the stress-strain curves presented in Figures 3 and 4. The principal strains at each integration point are shown by a pair of vectors representing their orientation and magnitude, which clearly shows the localization of failure planes. The longer cylinders show multiple planes of failure with some planes more accentuated than others. Figures 5-8 also show the confining wrap hoop strain profiles plotted along the length of the cylinders at the point where the peak strain in the wraps reaches the ultimate strain of 1.5%. It is assumed that the FRP wrap fails once the ultimate strain is reached at any location in the wrap.  
Figure 5: Half cross-section of the 150 mm \(\times\) 150 mm cylinder with localized failure and the FRP wrap hoop strain profile at peak FRP strain = 1.5%.  
Figure 6: Half cross-section of the 150 mm \(\times\) 300 mm cylinder with localized failure and the FRP wrap hoop strain profile at peak FRP strain = 1.5%.  
The localized failure planes follow the same orientation for all configurations. It is observed that the proximity of the boundaries seems to constrain the failure planes in the shortest H/D = 1 cylinders, causing a stiffer response and resulting in higher stress levels when compared to the longer cylinders. As failure planes localize at the specimen upper and lower corners and develop towards the center, they meet before reaching that center, leaving an intact core where additional failure planes can localize, requiring additional fracture energy. The levels of damage and the density of the highly strained elements increase with the confinement level or number of wraps.  
The apparent ultimate strength and strain of the confined concrete system depend on the strength of the FRP wrap and the geometry of the mechanism of failure that causes strain concentrations in the wrap. After the localization of the failure planes inside the confined concrete, the movement of solid wedges along those planes causes high hoop stress concentrations in the FRP wrap. At the locations of maximum lateral displacement, the strain in the FRP wrap reaches its limit strength, causing premature failure of the confined cylinder. The hoop strain profiles in the FRP wraps plotted next to each cross-section in Figures 5-8 show the large stress variations caused by the localization of concrete failure and the movement of the solid wedges. The ratios of the average FRP hoop strain to the maximum hoop strain were calculated for all configurations, giving numbers between 0.692 and 0.874, as shown in Table 1 alongside a summary of the results.  
Figure 8: Half cross-section of the 150 mm \(\times\) 900 mm cylinder with localized failure and the FRP wrap hoop strain profile at peak FRP strain = 1.5%.  
Figure 7: Half cross-section of the 150 mm \(\times\) 600 mm cylinder with localized failure and the FRP wrap hoop strain profile at peak FRP strain = 1.5%.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### Case Study 1--Pick-and-Place-Based Bi-DMP  
As the most common industrial step, pick-and-place is always used as a benchmark trajectory to validate algorithms. In this part of the study, operators of different ages and genders were selected to perform pick-and-place experiments to collect their demonstration trajectories. Specifically, the operators were asked to use a gripper to catch an object, drag the robot to move, and place the object into an orange box. Figure 12 shows the details:  
Figure 12 shows the experimental platform, while Figure 12 presents the demonstrations from operators. These demonstrations were collected via the TM collaborative robot, and the trajectories are shown in Figure 13. It can be observed that there are discrepancies in the demonstration trajectories when performing the same task, due to factors such as gender, height, and arm span [35; 36]. These factors should not be ignored, as operators prefer to work with a robot that matches their habits and physical characteristics in HRC. For example, the trajectory demonstrated by a person of short stature may collide with the body of a person of tall stature. Therefore, using only one DMP to generalise this  
Figure 11: The results of Bi-DMP. (**a**) Bi-DMP for A-shaped trajectory. (**b**) Bi-DMP for M-shaped trajectory. (**c**) Bi-DMP for S-shaped trajectory.  
Figure 12: Experimental platform for pick-and-place. (**a**) Main hardware equipment. (**b**) Demonstration trajectories’ collection.  
pick-and-place task is not appropriate. Bi-DMP, on the other hand, can model and retain the differences in all demonstrations.  
In the experiment, the red curve trajectory is chosen as a benchmark demonstration for the construction of DMP, as shown in Figure 13b. The purple dashed curve in Figure 13c is the path generated by the DMP. In order to preserve the discrepancies between different operators, Bi-DMP is used to learn the two new trajectories based on the constructed DMP as shown in Figure 13d. The orange and blue dashed curves are generated by Bi-DMP.  
According to the above results, Bi-DMP can effectively extend the force item of DMP. Starting from the constructed DMP, the force item is continuously updated so that the differences in the demonstrations can be fully learned.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### AI-Based Voice Assistant Module
#### Response Handling Logic  
The system enters a listening mode for a predefined time window (15 s) using the speech_recognition library. During this period, it processes the user's vocal response and classifies the input into predefined categories, as shown in Table 5. This rule-based classification provides a lightweight yet effective intent recognition mechanism, suitable for embedded systems or low-resource environments.  
This sequence of operations, as illustrated in the terminal output in Figure 3, is supported by a set of integrated software components. Table 6 provides an overview of the technical stack used to implement the voice assistant and its related functionalities.  
The voice assistant module significantly enhances the human-in-the-loop approach of the fall detection system. By allowing the user to confirm or deny a fall via speech, the system achieves the following:  
* Reduces false positives from misclassified motions;
* Adds an empathetic interaction layer;
* Ensures fast emergency response when needed.  
Figure 3: Terminal output showing the system’s runtime behavior.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**User Response Type** & **Example Phrases** & **System Action** \\ \hline \multirow{2}{*}{Positive Acknowledgement} & “I’m fine,” “I’m okay,” “No & No further action; reset fall \\  & help needed” & alert \\ \hline \multirow{2}{*}{Emergency/Help Request} & “I need help,” “Need & Trigger emergency \\  & assistance,” “I’m not okay” & protocol; send alert \\ \hline \multirow{2}{*}{No Response or Incoherent} & Silence, unclear phrases, & Assume fall is real; trigger \\  & long delay & alert \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification of user vocal responses and corresponding system actions.
###### Abstract  
Plant growth is directly related to levels of photosynthetic photon flux density, \(Q_{p}\). The improvement of plant-growth models therefore requires accurate estimations of the \(Q_{p}\) parameter that is often indirectly calculated on the basis of its relationship with solar irradiation, \(R_{S}\), due to the scarcity of ground measurements of photosynthetic photon flux density. In this experimental campaign in Burgos, Spain, between April 2019 and January 2020, an average value of the \(Q_{p}/R_{s}\) ratio is determined on the basis of measurements at ten-minute intervals. The most influential factor in the \(Q_{p}/R_{s}\) ratio, over and above any daily or seasonal pattern, is the existence of overcast sky conditions. The CIE standard sky classification can be used to establish an unequivocal characterization of the cloudiness conditions of homogeneous skies. In this study, the relation between the CIE standard sky type and \(Q_{p}/R_{s}\) is investigated. Its conclusions were that the \(Q_{p}/R_{s}\) values, the average of which was \(1.93\pm 0.15\) umol\(\cdot\)J\({}^{-1}\), presented statistically significant differences for each CIE standard sky type. The overcast sky types presented the highest values of the ratio, while the clear sky categories presented the lowest and most dispersed values. During the experimental campaign, only two exceptions were noted for covered and partial covered sky-type categories, respectively, sky types 5 and 9. Their values were closer to those of categories classified as clear sky according to the CIE standard. Both categories presented high uniformity in terms of illumination.  
photosynthetic active radiation; CIE standard skies; solar irradiance +
Footnote †: journal: Applied Physics Letters
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 3 System Design  
In this section, we propose the main design of the edge-aided data transmissions in sensor networks.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 4 An Algorithm for Reducing Contact Information Overhead
### PoiFord Design
#### 4.2.3 Phase 3: Forwarding Utility
The former can be computed for nodes that have an encounter history with the destination node, while the latter can be computed for every node in the network. That way, nodes that do not have an encounter history with the destination node may still have some degree of forwarding utility through the closeness-to-destination weight. This allows them to carry the message spatially closer to the destination node, thereby increasing the chances of encountering and forwarding it to nodes that have a higher likelihood of delivery via the encounter history weight. In order to achieve these weights, encounter history and closeness-to-destination are multiplied by constants \(\mu\) and \(\omega\), respectively, where \(\mu\gg\omega\). The sum of both constants equals 1, thereby acting as a slider that decides how much impact each weight has on the overall forwarding utility (cf. Equation (11)). \(\mu\) is much greater than \(\omega\) in order to give more impact to the encounter history weight, so that messages are always directed towards nodes that have encounter history with the destination. To implement this, we select the value of \(\mu\) and \(\omega\) as 0.8 and 0.2, respectively. \[\mu+\omega=1 \tag{11}\]  
The weight for encounter history, denoted by \(E_{(a,d)}\in[0,\,\mu]\), at every node \(a\) for each known destination node, \(d\), is given by Equation (12), while the weight for closeness-to-destination, denoted by \(D_{(a,s,d)}\in[-\infty,\omega]\), is given by Equation (13). From Equation (13), \(D_{(a,s,d)}\) increases as \(dMin_{a,d}\) reduces, so that a node that often visits locations closer to the location of the destination node presents a higher closeness-to-destination weight. The overall forwarding utility, denoted by \(PoiUtility_{(a,s,d)}\in[-\infty,\,1]\), is given by Equation (14). Note that \(E_{(a,d)}\) is equal to 0 if node \(a\) does not have any encounter history with node \(d\), in which case \(PoiUtility_{(a,s,d)}\) becomes \(D_{(a,s,d)}\). \[E_{(a,d)}=e_{(a,d)}\times\mu \tag{12}\]  
\[D_{(a,s,d)}=\left(1-\frac{dMin_{a,d}}{d_{s,d}}\right)\times\omega \tag{13}\]  
\[PoiUtility_{(a,s,d)}=E_{(a,d)}+D_{(a,s,d)} \tag{14}\]
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 2 Materials and Methods  
### Data and Model  
#### 2.1.1 COSMIC-1  
The FORMOSAT-3/COSMIC constellation consists of six microsatellites that orbit the Earth at an initial altitude of 800 km. The satellites have been operating since the summer of 2006, and the data set is from November 2006 [25]. The radio occultation technique can provide parameters of the ionosphere, such as total electron content (TEC), electron density, and S4 index. The main advantages of RO measurements are global coverage, high vertical resolution, and avoiding the impact of the troposphere.  
The S4 index is the scintillation index, which is measured from the signal-to-noise ratio (SNR) intensity fluctuations of the GPS L1 signal (f = 1.575 MHz). The S4 index typically maintains a low value but experiences a rapid increase when the signal passes ionospheric irregularities. [PERSON] et al. (2012) introduced the details of the S4 index calculation [26]. The period of the S4 index used in this paper is from January 2014 to December 2014. The successive S4 profiles within the altitude range of 90-130 km were selected in our research.
## 4 Lunar Antenna Types
### Wire Antennas
#### 4.4.4. Sleeve Dipole Antennas
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Antenna Type** & **Antenna Name** & **Ref** & **Frequency Band** & **Mission/Research** & **Application** \\ \hline \multirow{4}{*}{Wire} & Dipole & [69] & HF & Mission & Near-side low radio frequency imaging \\ \cline{2-5}  & Small-Sized Active Dipoles & [71] & HF & Research & Lunar radio telescopes \\ \cline{2-5}  & CHI (monopole) & [75] & VHF & Mission & Lunar penetrating radar \\ \cline{2-5}  & CH2 (bow-tie) & [75] & VHF-UHF & Mission & Lunar penetrating radar \\ \cline{2-5}  & Sleeve Antenna (dipole) & [17] & S & Mission & Lunar surface communication \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between lunar wire antennas in terms of application. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & \begin{tabular}{c} **Operating** \\ **Frequency (MHz)** \\ \end{tabular} & \begin{tabular}{c} **Bandwidth** \\ **Fractional (\%)** \\ \end{tabular} & \begin{tabular}{c} **Gain (dBi)** \\ \end{tabular} & \
\begin{tabular}{c} **Size (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Software** \\ \end{tabular} \\ \hline
[69] & 1–10 & 163.3 & n\(\backslash\)a & \(8\times 30.51\) & CST 2023 \\ \hline
[71] & 1–70 & 194.36 & n\(\backslash\)a & \(2.8\times 23\) & n/a \\ \hline
[75] & 40–80 & 66.66 & n\(\backslash\)a & n/a & FEKO \\ \hline
[75] & 250–750 & 100 & \(-7.5\) & \(336\times 120\) & FEKO \\ \hline
[17] & 2400–2700 & 17.64 & 2.23 & \(12\times 159.5\) & CST \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison between lunar wire antennas in terms of performance and simulation parameters. Figure 16: (**a**) [PERSON] rover with sleeve dipole antenna. (**b**) Proposed sleeve dipole structure. Proc. 2023 IEEE CAMA, Genoa, Italy, 2023 [17].