esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
### Practical Inspiration
#### 6.2.1 Priority Investment Targets: Factors Positive Affecting SCR  
(1) Supply Chain Risk Management  
Research findings indicate that supply chain risk management has a positive impact on supply chain resilience. This suggests that enterprises can significantly enhance their ability to manage uncertainties through systematic risk identification, assessment, mitigation, and monitoring measures. Therefore, companies should prioritize the establishment of a comprehensive risk management framework [44], develop standardized risk management processes, and embed them into daily operations. For example, enterprises can implement supply chain risk mapping tools to systematically identify potential risk sources [88]. Additionally, companies should regularly conduct risk drills, simulating emergencies (such as natural disasters or policy changes) to test their response capabilities and optimize contingency plans. Furthermore, enhancing risk information-sharing mechanisms with suppliers and logistics partners ensures a quick and coordinated response in the event of risks [10], thereby improving overall supply chain responsiveness and risk resistance.  
(2) Digital Capability and Digital Analysis Capability  
Research findings reveal that digital capability and digital analytics capability play a significant moderating role between supply chain risk management and resilience. This implies that digital capabilities can enhance the effectiveness of risk management measures, thereby further strengthening supply chain resilience. Consequently, enterprises should prioritize investment in data analytics tools, such as AI-driven predictive analytics platforms and real-time monitoring systems [89], to improve the accuracy and speed of risk identification and response. Simultaneously, companies should focus on cultivating digital talent and forming cross-functional data analytics teams to ensure that data-driven insights effectively support risk management decision making. For instance, in the EV supply chain, machine learning models can be used to predict price fluctuations of battery raw materials [3], enabling proactive procurement strategy adjustments. Additionally, enterprises should promote digital collaboration within the supply chain by leveraging blockchain technology or cloud-based collaboration platforms [90] to achieve data sharing across different supply chain segments, thereby enhancing transparency and coordination.
## 2 Materials and Methods  
### Proposed Method  
Figure 1 presents a sequential schematic of the proposed method, which iteratively processes image-based CNNs and k-space corrections, with the CNNs representing a priori image space information. The method aims to preserve information pertaining to tissue structures and to suppress aliasing artifacts to a larger degree compared to standalone image-based CNN and noniterative k-space correction methods. As indicated in Figure 1, first, undersampled k-space data, \(y_{0}\), are extracted from the corresponding fully sampled data using a binary sampling mask, \(\mathbf{R}\), and zero-filling images, \(x_{0}\), are obtained using the inverse Fourier transformation, \(\mathcal{F}^{-1}\). Subsequently, the CNNs are trained to map these zero-filling images onto the corresponding fully sampled images, and k-space corrections are performed to replace the unsampled data by the original k-space data [23]. Finally, \(\mathcal{F}^{-1}\) is used to obtain the corrected images from the corrected k-space data. The above-mentioned processes are performed iteratively. Note that in all iterations except the first, CNNs are trained to map the \(i\)th output onto the corresponding fully sampled image. The calculations performed by the proposed method can be expressed by the following:  
\[\begin{cases}z^{i}=f_{\theta}(\left|\mathcal{F}^{-1}(y_{0})\right|)&i=0\\ z^{i}=f_{\theta}(x^{i})&\text{otherwise}\end{cases} \tag{1}\]\[x^{i+1}=\left|\mathcal{F}^{-1}\Big{(}y_{0}+\mathcal{F}\Big{(}z^{i}\Big{)}\circ \overline{\mathbf{R}}\Big{)}\right| \tag{2}\]  
Here, \(x^{i}\) denotes the \(i\)th output of a reconstructed image, \(\overline{\mathbf{R}}\) denotes the logical negation of binary sampling mask \(\mathbf{R}\), \(\circ\) denotes element-wise multiplication, and \(f_{\theta}\) represents the CNN with network weights \(\theta\). \(\mathcal{F}\) denotes the Fourier operator.
# Fully Open-Source Meeting Minutes Generation Tool
## 5 Evaluations
### Performance of Information Extraction Function  
The _information extraction function_ uses _BART LM_ for _abstractive summarization_ and _RaKuIn2_ for _keyword extraction_. _BART LM_ is integrated into the proposed system due to its strong text generation capabilities, as evidenced by prior selections and reported in several studies [61; 62]. Similarly, _RaKuIn2_ was chosen based on its representative keywords' extraction, supported by previous selection criteria and confirmed by [63]. Table 9 shows that _BART LM_ models perform variably across slides and topics, with _ROUGE-1_, _ROUGE-2_, and _ROUGE-L_ scores peaking on slides 2, 3, and 4 across most topics, reflecting the typical presentation structure. _ROUGE_ scores assess summary quality by comparing generated summaries to reference ones. Specifically, _ROUGE-1_ measures unigram (single-word), _ROUGE-2_ evaluates fluency through bi-gram (two-word) overlap, and _ROUGE-L_ checks the longest common sub-sequence (continuous adjacent words) to assess structural coherence. High scores across technical and general topics indicate that the system produces coherent, reliable summaries across varied domains. This consistency suggests that _BART LM_ is suitable for various summarization tasks. The keyword extraction measures the similarity between the extracted and reference keywords using _cosine similarity_[64]. Table 10 illustrates the cosine similarity of the extracted keywords for each slide from every topic. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} \\  & & & & & & & & & & & & \\ \hline Slide 1 & 0.714 & 0.503 & 0.669 & 0.462 & 0.487 & 0.721 & 0.874 & 0.801 & 0.394 & 0.566 & 0.619 \\ Slide 2 & 0.504 & 0.597 & 0.453 & 0.423 & 0.481 & 0.356 & 0.447 & 0.496 & 0.429 & 0.499 & 0.469 \\ Slide 3 & 0.478 & 0.577 & 0.516 & 0.484 & 0.549 & 0.499 & 0.522 & 0.592 & 0.509 & 0.462 & 0.519 \\ Slide 4 & 0.571 & 0.559 & 0.526 & 0.577 & 0.649 & 0.539 & 0.547 & 0.416 & 0.725 & 0.362 & 0.547 \\ Slide 5 & 0.306 & - & 0.316 & 0.340 & 0.360 & 0.399 & 0.416 & 0.547 & 0.269 & 0.441 & 0.377 \\ \hline \hline
\begin{tabular}{l} Average \\ (per topic) \\ \end{tabular} & 0.515 & 0.559 & 0.496 & 0.457 & 0.505 & 0.503 & 0.561 & 0.570 & 0.465 & 0.466 & 0.509 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Keywords’ _cosine similarity_ from each slide and each topic.
# Terahertz Inspection of Buildings and Architectural Art
## 2 Terahertz Uses in Architectural Art
### Wall Paintings
B-scans revealed the presence of two major in-depth anomalies. The first one was located at about 1 cm below the surface and positioned transverse at a steep angle into the plaster. The sign of the THz signals reflected at the anomalous interface indicated that the related material must have had a lower refractive index than the surrounding mortar. ToF offered additional insight by imaging, in detail, its surface morphology. The extremely sharp and regular texture of the buried interface indicated that it was not an accidental air gap, but rather a building element. The second anomaly was found at about 7-8 mm depth inside the plaster and it has been visualized in a volume rendering after data processing, first by windowing the relevant region of interest of the signals. Volume rendering allowed localizing the buried feature with respect to the wall painting surface. The sign of the reflected THz signal suggested that the material had a higher refractive index than the surrounding one, i.e., higher density than the mortar, excluding an air gap. A certain identification of the building elements found in depth in plaster was rather difficult, but their surface profiles have been 3D imaged in detail, demonstrating the high potentials of the technique for identifying hidden subsurface inclusions in lime-based plaster. All of this was possible by combining the proper data processing with the use of 3D visual rendering. The localization of buried or concealed structures is important in the conservation field, since the presence of degrading, deforming, or moving components inside plaster layers can affect their integrity and, consequently, the conditions of the painting layers. Characterizing position, geometry, and appearance of internal structural elements may also help in understanding the building history and its construction phases, which is valuable information for building archaeology and architecture field. A THz time domain system was used in 2019 by [PERSON] et al. [32] for scanning a wall painting \"Annunciation\" by [PERSON], one of the masterpieces of the early Italian Renaissance, located at the Museum of San Marco in Florence, Italy. The system has been used as complementary inspection tool of a multi-technique examination protocol, which included the use of technical photography (fluorescence induced by ultraviolet radiation), infrared refectography, and ground penetrating radar. THz C-scans allowed for imaging the fine decorative gold lines, the surface cracks, and repaintings in correspondence of painting lacunas. Two-dimensional (2D) time-of-flight plots at increasing depth towards the interior layers of the painting highlighted the presence of in-depth inhomogeneities.
## 2 Background and Methods
### Machine Learning Methods for 3D BM Prediction  
#### 2.2.1 Random Forest  
A random forest (RF) (also known as a random decision forest) is an ensemble learning method that shows satisfactory performance in classification, regression, and other tasks that operates by assembling many decision trees. In general, the output of random forest is the label chosen by the majority of trees for classification tasks. For regression problems, RF outputs the mean or average predicted value of those decision trees. The bagging ensemble of RF corrects a decision tree's habit of overfitting its training set, so RF outperforms decision trees but is less accurate than gradient boosted trees. Figure 3 shows an illustration of the RF algorithm. RF is a decision tree-based ensemble model which creates many data subsets from the training set. Then, RF improves the accuracy and stability of predictions by combining multiple decision trees into a single model [31]. Based on a resampling strategy, RF generates many sub-datasets with the same sample size from a given training sample. Next, each decision tree can be well-trained by using the recursive-partitioning-based selected features for each new training set. A decision tree search is applied to accomplish  
Figure 1: Illustration of proposed ML-enabled framework in 3D BM estimation.  
Figure 2: Standard Position (Male: Right; Female: Left).  
the best split from the selected features. All decision trees passed the predicted mean to the final output. The RF can be formed in the following steps:  
1. Using K-bootstrap to sample sub-datasets from the original dataset.
2. Individual learning from Tree 1 to Tree K via splitting on best feature for corresponding sub-dataset.
3. Majority voting of the output from all learning trees.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 3 Results  
### Discrimination of Labeled Categories  
We analyzed the signal values for the labeled categories in the Sentinel satellite imagery that contributed most to the between-class differentiation to better understand the feasibility of discriminating between selected categories of crops and ultimately to interpret the results of the model. The RF algorithm outputs a standardized measure of the importance of each of the features used in the model [40]. In our case, the S2-B3 and S2-B4 features were the most important for discriminating between the selected seven classes, followed by the S1-VV value. Box plots of the training data for each of the classes were created using the S2-B3, S2-B4, and S1-VV signal values (Figure 3a-c). Figure 3d shows a two-dimensional (2D) plot in the S2-B3 and S2-B4 coordinates for representative training points. Although there is significant overlap in the spectral signatures of beans, cassava, and other vegetation, maize, bananas, trees, and the non-vegetative class appear separable.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**S2 Flight Date** & **35 MQU** & **35 MRU** & **35 MQT** & **35 MRT** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: S2 imagery cloud cover by frame (information from Sentinel-hub EO-Browser); Rwanda falls within four S2 frames (35 MQU: Kaberege, Ngarama; 35 MRU: Cyampirita, Ngarama; 35 MQT: Kinyaga, Rwakigarati; 35 MRT: Kabarama).
## 6 Conclusions  
The rapid and accurate estimation of wheat production is important to national food security and sustainable agricultural development. In this study, we developed a new GPP estimation model named the Agriculture Crop Photosynthesis Model (ACPM) for the winter wheat productivity estimation, based on the light-use efficiency theory and environmental stress factors from the Himawari-8 and MODIS satellite observations. The main conclusions were cited as follows:  
1. We developed the new modified ratio vegetation index (MRVI), which could reveal the detailed spatiotemporal distribution of the leaf C\({}_{\text{ab}}\) and N status of the winter wheat in Hengshui City.
2. We newly developed the ACPM for the winter wheat productivity estimation based on light-use efficiency theory and environmental stress factors from the Himawari-8 and MODIS satellite observations. This model described the joint effects of heat, soil moisture, and N on the crop photosynthesis performance. The ACPM model used a quantic additivity of the environmental factors in order to improve the minimum form or multiple multiplication form in the previous models. The light was determined from the MODIS FPAR data and Himawari-8 PAR data. The heat was determined from the MODIS LST data. The soil moisture was obtained from the inversion, using a visible and shortwave infrared drought index (VSDI). The N stress of the winter wheat was detected using MRVI.
* Based on the newly developed GPP model (ACPM), the DAM and yield were well estimated within a 10% and 12% error of the situ data in Hengshui City in 2017. Comparing the DAM and yield results based on the ACPM, GPP1, and GPP2 models, the ACPM model improved the underestimation of the DAM and yield results, based on the previous GPP1 and GPP2 models.  
This new ACPM model was simple, economical, timely, robust, and easily operational. Considering the easy operation of the ACPM model and the accessibility of the corresponding satellite images, the model can be expected to provide information on the winter wheat shortfalls and surplus ahead of the availability of the official statistical data.  
[PERSON], [PERSON] and [PERSON] proposed the idea and participated in the writing of the paper; [PERSON], [PERSON] and [PERSON] participated in the collection of the data, processed the data, and analyzed the data and the results; [PERSON] and [PERSON] contributed to the writing of the paper.  
This work was supported by the National Science Foundation for Young Scientists of China (No. 51709003), the Major National Research and Development Project of China (No. 2016 YFD0300603-2), and the Science and Technology Facilities Council of UK Joint Program Project (No. 61661136006).  
The authors declare no conflict of interest.
# Investigation on Aerodynamic Noise Characteristics of Coaxial Rotor in Hover
## 3 Results and Discussion
### Comparison of Different \(C_{t}\) Levels  
The research is conducted at three different \(C_{t}\) levels, as listed in Table 2. Figure 15 shows the comparison of loading sound pressure at different \(C_{t}\) levels. For the A0 case, as the airfoil of blades is symmetrical and the collective pitches of the twin rotors are both zero, the rotor thrusts almost remain constant most of the time, while they show impulsive fluctuations at blade-meeting azimuths. The thrust of the upper rotor spikes down, while the lower rotor thrust spikes up, as illustrated in Ref. [18]. Correspondingly, the sound pressure fluctuations of the upper and lower rotors are almost antisymmetric in the A0 case. As \(C_{t}\) increases, the impulsive fluctuation of the upper rotor becomes stronger, while for the lower rotor, the change is not obvious. This is consistent with the changes in impulsive thrust fluctuation with different \(C_{t}\) levels, as discussed in Ref. [18]. Combined with the above results, it can be concluded that the impulsive noise of the coaxial rotor is mainly caused by the blade-meeting interaction, rather than the interaction between the upper rotor vortex and the lower rotor blade.  
Figure 14: Total noise SPL contour maps. (**a**) Single rotor; (**b**) Coaxial rotor.  
Figure 15: Sound pressures of loading noise at different \(C_{t}\) levels. (**a**) A0 case; (**b**) A1 case.  
Figure 16 shows the total noise distribution of the coaxial rotor at different \(C_{\rm T}\) levels. It is easy to find that the total noise SPL becomes stronger with the increase in \(C_{\rm T}\). This is because the load fluctuations caused by the aerodynamic interaction are stronger. For the A0 case, there is a low-SPL region near the latitude angle of \(-30^{\circ}\), between every two blade-meeting azimuths, marked in blue in the figure. This is because the thickness noise mainly radiates in the rotor disk plane, and the loading noise is only strong at blade-meeting azimuths for the A0 case. Thus, the superposition of the thickness and loading noises leads to the four low-SPL regions. For the A1 case, the loading noise caused by the interaction is stronger and spreads from the blade-meeting azimuths to the surrounding region. The result is that the low-SPL region lies near the rotor disk plane, between every two blade-meeting azimuths.
## 3 Results and Discussion
### Plasma Erosion on the Dielectric Surfaces  
Figure 8 shows the surface degradation of the dielectric samples during the prolonged operation. Figure 8a shows an enlarged photograph of the post-discharge solid-plate surface. The dotted line of the photograph shows the boundary line between the high-voltage electrode and the ground electrode. The surface was damaged on the inner surface of the dotted line due to the plasma generation. The operating-condition current of each solid plate is approximately 3 mA (V = 1.0 kV for alumina, V = 1.6 kV for glass, and V = 2.0 kV for quartz), as shown in the graph of Figure 6a. The damage on the solid-plate dielectric surface is barely apparent, even after the long operating run of 10 h. A very clean solid-plate surface was maintained after the discharge, even for the alumina, showing a slight yellow discoloring on the plasma-generation boundary surface. The solid plates, however, did not show the dielectric breakdown or the surface degradation.  
Figure 8b is an enlarged photograph of the polymer-film surface after the long-run discharge. The operating-condition current of the polymer-film sample is also approximately 3 mA, as shown in Figure 6b, where the operating voltage is 0.9 kV for the PI, 1.2 kV for the GP, and 2.0 kV for the PP. On the surface of the polymer film, a severe plasma-erosion was observed. The plasma erosion caused persistent damage on the film surface and the dielectric breakdown on the damaged surface. After 19 min of the PP-film discharge operation, the dielectric breakdown occurred and the film-surface damage was observed. The erosion pattern on the PP-film surface is deep and clear. The PP film is vulnerable to the surface damage and the dielectric breakdown since the PP heat resistance is low.  
Figure 7: The equivalent circuit of the current–voltage measurement.  
The dielectric breakdown occurred in the PI film after 30 min of the operation. The pinhole appeared due to the dielectric breakdown on the PI film. The GP film did not show the dielectric breakdown even after the operation lasted for more than 10 h; however, a slight crack and the plasma erosion were also observed on the GP surface.  
For the purpose of preventing the plasma erosion in Figure 8c, the polymer film was reinforced by the Si-paste coating on the film surface. The KE-441K Si paste (Shientsu, Tokyo, Japan) was used. Since the Si chemical resistance is high, the coating prevents the plasma degradation and erosion and enhances the durability of the flexible polymer film, even for the PP film. On the surface of the polymer films coated with the Si paste of the thickness of approximately 40 \(\upmu\)m, the erosion and dielectric breakdown did not occur, even after the long-run 10-h operation, as shown in Figure 8c. In the case of the Si-coated polymer films, however, the film surfaces were not degraded even after 10 h of the operation.
## 3 Conventional and Proposed Methods
### Proposed Method
#### 4.2.2 Dynamic Power Control Mechanism
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline Transmission Power & 5 W \\ Number of Antenna Elements & 16 \\ Transmission Gain & \(-\)8 dB \\ Modulation Method & 16 QAM \\ Carrier Frequency & 2.4 GHz \\ Number of Subcarriers & 48 \\ Doppler Frequency & 5 Hz \\ Number of Multipaths & 5 \\ Time Step & 1 s \\ BER Threshold & 0.1\% \\ Power Adjustment Step & 0.5 W \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter setting. Figure 7: Time during movement—throughput characteristics. tiveness in reducing system total power consumption while maintaining performance in dynamically changing channel environments.