esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Research Instrument
#### 3.2.2 Experts' Analysis  
There followed the assessment of the questionnaire items in version 1, at which 10 experts--5 academics (four engineers and one psychologist) and 5 industry experts--attended. They reviewed the relevance of the items, proposed changes related to the clarity of the formulations to increase the ease of understanding of the questionnaire by the respondents and proposed the removal of some items and their replacement with others considered more appropriate.  
After consensual agreement on the changes proposed by the experts, in version 3, the questionnaire had 14 items on online training.  
Table 1 highlights the presence of the following items:  
* Two items about the frequency of internet use and personal effectiveness in using the internet (I.01, I.02);
* Three items designed to identify the advantages perceived by the participants regarding online learning using the SSM.ro platform (I.03, I.04, I.05);  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Code** & **Questionnaire Items (QIs)** \\ \hline I.01 & I use frequently the internet \\ I.02 & I use internet easily \\ I.03 & I find easy the registration/training/testing steps in the SSM.ro platform \\ I.04 & I think that I increase my knowledge through online training, compared to other forms of training \\ I.05 & I think that using an electronic signature to record OSH training is more useful than a paper signature \\ I.06 & I think that online OSH training is a better method compared to traditional (face-to-face) training \\ I.07 & I think that traditional training (face-to-face/classroom) is better than online training \\ I.08 & I think that video conference training provides a better experience than traditional (face-to-face) training \\ I.09 & I am satisfied with the online training \\ I.10 & I am satisfied with understanding the material in the online training \\ I.11 & I am satisfied with the accountability in complying with OSH requirements provided by the online training \\ I.12 & I am satisfied with the clarification of misunderstandings of OSH legislation provided by the online training \\ I.13 & I am satisfied with the online training in order to test the knowledge acquired in the online training \\ I.14 & I am satisfied with the online training in general \\ \hline \hline \end{tabular}
\end{table}
Table 2: Questionnaire items (QIs) in the analysis of OSH online training.  
* Eight items to assess the satisfaction with occupational health and safety training (I.06, I.07, I.08, I.09, I.10, I.11, I.12, I.13, I.14).
## 3 Results and Discussion  
### Weld Shape  
Weld depth of fusion (\(D\)) and effective weld width at interface (\(W_{E}\)), as illustrated in Figure 3, are used as key geometrical features in this paper to evaluate the bonding integrity between two parts. \(D\) was measured from the top surface of lower part to the root of the fusion zone and \(W_{E}\) was represented by the accumulated width of the intersection between the fusion zone and the top surface of the lower part. Acceptance limits on \(D\) and \(W_{E}\), i.e., \(D\) greater than 10% of lower part thickness; and, \(W_{E}\) greater than the summation of lower  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Processing** & \(S_{x}\) & \(f\) & \(O_{y}\) & \(P_{A}\) & \(P_{B}\) & \(P_{C}\) & **Line Energy** \\
**Window** & **[m/min]** & **[Hz]** & **[mm]** & **[kW]** & **[kW]** & **[kW]** & **[J/mm]** \\ \hline S2-F50 & 2 & 50 & 0.4 & 1.74 & 3.25, 3.13, 2.90, 2.67 & 1.45 & 72.68, 70.88, 67.43, 64.07 \\ S4-F100 & 4 & 100 & 0.7 & 2.55 & 5.28, 5.05, 4.81, 4.59 & 2.32 & 57.86, 56.14, 54.34, 52.69 \\ S6-F150 & 6 & 150 & 0.3 & 4.52 & 5.80, 5.57, 5.34, 5.10 & 3.48 & 49.00, 47.85, 46.70, 45.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Matrix of process parameters used in this paper.  
Figure 2: Representative weld cross-sections in the individual processing window (**a**) S2-F50, (**b**) S4-F100 and (**c**) S6-F150.  
part thickness and part-to-part gap, have been adapted from international standards [33] to guarantee the minimum bonding condition at the interface between the parts being welded. For each processing window as shown in Table 2, increasing laser power (and consequently the line energy) leads to a wider and deeper fusion zone. However, not all the processing windows meet the bonding condition. For example, welding sets with low line energy in S6-F150 and S2-F50 exhibit insufficient \(W_{E}\) which indicates insufficient bonding area between top and bottom part (Figure 3b).
## 2 Results and Discussion
### High Temperature-Triggered Regulatory Events in Koshiiukari Rice Seeds of Grades 2 and 3
#### 2.4.1 Cell Wall Damage Repair  
Initial response to heat shock is observed in cell wall remodeling proteins like PMEs and XETs. PMEs are involved in regulating cell wall plasticity, porosity, and modulation of Ca\({}^{2+}\) ion channels [37], while XETs are involved in secondary cell wall strengthening [38; 39]. The regulation of XETs and PMEs under heat shock is previously observed in rice, _Arabidopsis_, and many plant varieties [38; 40; 41]. During heat stress, Y2 and Y3 exhibited down regulation of PMEs genes Os09g37360.1 and Os04g46740.1, respectively. In addition, Y3 alone shows downregulation of genes related to cellulose synthesis (Os02g09930.1) and pectin lysates (Os10g26940.1 and Os02g15690.1), while upregulation of XET gene (Os06g22919.2) indicating cell wall response in Y3 is more sensitive to heat shock. An upregulation of the raffinose synthase gene (Os08g38710.1) in Y3, which prevents plants from oxidative stress damage [42; 43; 44], was also observed. Besides, the pectinase activity upregulation of cellulases like endo-glucosidase, prominently in Y3 can be seen (supplementary Table S3, Metabolism overview; and Figure 5).
# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
## 3 Results and Discussion  
In this study, the EEG signals were recorded at a sample rate of 1k Hz and a bit resolution of 22 bits. A second-order Butterworth band-pass filter was used to remove the signals with frequencies lower than 4 Hz and higher than 60 Hz. The window size of the Hamming window was 1000 points, which was then applied to obtain a sequence of trials. Subsequently, the power energies for each visual stimulus were estimated using an FFT with 4096 points and triangular windows with 2-Hz bandwidths.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 4 Proposed Approach
### Recommendation Process of Situation Rules
#### 4.2.1 Rule's Content-Based Approach
Tags are associated with location, time, role and fields for better rule content organization and discoverability. Obviously, some tags are more important than others. The vector weight \(W=\{W_{x}\mid x\in S\}\) is used to define that a tag \(x\) has their relative importance (assuming \(W_{location}=0.75\), \(W_{role}=0.45\), \(W_{field}=0.75\), \(W_{subfield}=0.35\)). We define two vectors called Preference Pair: \(PP=((term_{1},\ weight_{1})\ldots(term_{m},\ weight_{m}))\) based on the weight \(w_{i}\) of term \(t_{i}\) of each user preference (implicit and explicit). We also define the user preference \(P=(w_{1}\ldots w_{m})\), based on the weight values of each \(PP\) vector. Similarly, each rule item is presented as a vector \(C=(u_{1},\ u_{2}\ldots,\ u_{m})\) containing weights \(u_{i}\) of term \(t_{i}\). The vector \(C\) is generating using Algorithm 3. The similarity measure between the rule item and user preference is calculated as presented in Equation (4). It should be noted that the similarity between the rule item and user preference in this work is the cosine similarity since it projects the rule items of the same context dimension in the 3-D recommendation space closer together. ```
Inputs: Rule item's metadata \(R\) (item to recommend); The vector \(PP\); Output: The vector \(C\); Begin
1 : For each term value \(t_{i}\) from \(PP\)
2 : If (\(t_{i}\) is merely included in tag \(x\) of \(R\)) Then
3 : \(u_{i}\gets W_{x}\);
4 : If (\(t_{i}\) is included in two or more tags of \(R\)) Then
5 : \(u_{i}\gets Max\{W_{x}\}\);
6 : If (\(t_{i}\) isn't included in any tag of \(R\)) Then
7 : \(u_{i}\gets 0\);
8 : End For
9 : Return \(C\)
```  
**Algorithm 3**Generating the Vector C.  
For instance, location item of user context dimension: university, faculty and study room are oriented closer together in 3-D recommendation space. The closer meaning of context terms, the higher is the cosine similarity. The similarity measure is calculated as:  
For each pair of user-items, we compute weights \(w_{i}\) regard an explicit term \(t_{i}\) in order to get the normalized value as follows:  
\[Similarity=\frac{C\times P}{\|C\|\times\|P\|}=\frac{\sum_{i=1}^{m}u_{i}w_{i}}{ \sqrt{\sum_{i=1}^{m}u_{i}^{2}\sum_{i=1}^{m}w_{i}^{2}}} \tag{4}\]
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Design of Wind Turbines
#### 3.1.3 Location Selection and Environmental Impact Assessment
It should be noted, however, that the existence of legal restrictions related to land use, environmental protection and community opposition can complicate site selection. An example of such an analysis was given by [PERSON] et al. [40]. In their study, GIS and the analytic hierarchy process (AHP) are used to identify suitable sites for wind farms in Ethiopia. A similar analysis for Sudan was conducted by [PERSON] et al. [41], who used a fuzzy AHP approach in addition to GIS. * Turbine layout optimization: Once a potential site is selected, UI can be used to optimize the layout and spacing of wind turbines within that area. The algorithms take into account the interaction between the turbines (the so-called \"wake effect\") to maximize energy production and reduce losses, as found in the research of [PERSON] and [PERSON] [42] and [PERSON] et al. [22]. In this case, the ANN-based wake models speed up the design simulations by 40% compared with the traditional CFD methods. According to studies by [PERSON] et al. [43], optimizing the plant layout can reduce the area required by an average of 18% per plant. The application of AI in environmental impact assessment includes the analysis of the following:
* Impact on wildlife: AI is being used to analyze data on bird and bat migrations and the habitats of other animals to assess the potential impact of wind turbines on wildlife. Algorithms can predict risk areas and help plan mitigation measures to minimize negative impacts, such as switching off turbines at certain times or installing visual and acoustic deterrents. In their paper, [PERSON] et al. [44] present the development of an AI-powered bird detection system. This system integrates radar and camera technology to track the movements of birds with the aim of minimizing collisions with turbine blades. Using machine learning algorithms, the system can identify bird species and predict their flight paths, enabling proactive measures to avoid collisions. * Noise from wind turbines: AI is used to model and predict the noise levels generated by wind turbines. This allows the impact of noise on neighboring settlements to be estimated and, if necessary, noise reduction measures to be planned. For example, [PERSON] et al. [45] discuss in their study how AI can classify different types of wind turbine noise based on factors such as amplitude modulation and sound characteristics. The study emphasizes the potential of AI to improve noise assessment methods and provide insights into how different noise profiles affect wildlife and humans. * Impact on soil and water: AI models can predict the long-term impact of wind turbines on soil and vegetation by simulating different scenarios. These models take into account factors such as turbine location, wind conditions and environmental conditions to predict potential changes in the environment and suggest mitigation strategies [23].
## 4 Experiments and Performance Analysis
### Performance Metrics  
In order to quantitatively estimate the similarity between \(\widehat{\mathbf{Z}}\) and \(\mathbf{Z}\), some widely used performance metrics were adopted [5; 16; 25; 32], including reconstruction signal-to-noise ratio (RSNR), root mean squared error (RMSE), spectral angle mapper (SAM), erreur relative globale adimensionnelle de synth\(\,\)se (ERGAS), degree of distortion (DD) and structural similarity index for measuring image quality (SSIM), as listed below.  
* RSNR evaluates the spatial quality, defined as \[\text{RSNR}=10\log_{10}\left(\frac{\|\mathbf{Z}\|_{F}^{2}}{\|\widehat{\mathbf{ Z}}-\mathbf{Z}\|_{F}^{2}}\right).\]
* RMSE evaluates the error of global quality by \[\text{RMSE}=\sqrt{\frac{1}{ML}\|\widehat{\mathbf{Z}}-\mathbf{Z}\|_{F}^{2}}.\] (24)
* SAM evaluates the spectral distortion, defined as \[\text{SAM}=\frac{1}{L}\sum_{n=1}^{L}\arccos\left(\frac{(\hat{\mathbf{z}}[n])^{ T}\mathbf{z}[n]}{\|\hat{\mathbf{z}}[n]\|_{2}\cdot\|\mathbf{z}[n]\|_{2}}\right)\] where \(\hat{\mathbf{z}}[n]\) denotes the \(n\)th column of \(\widehat{\mathbf{Z}}\).
* ERGAS evaluates the relative dimensionless global error, defined as \[\text{ERGAS}=\frac{100}{r}\sqrt{\frac{1}{M}\sum_{m=1}^{M}\frac{\text{RMSE}_{ m}^{2}}{\mu_{\mathbf{Z}^{(m)}}^{2}}},\] where \(r\) is the blurring factor, \(\mu_{\mathbf{Z}^{(m)}}\) is the mean of the \(m\)th row vector \(\mathbf{Z}^{(m)}\) and \(\text{RMSE}_{m}\) is the RMSE of the \(m\)th band image.  
Figure 4: Flowchart of [PERSON]’s protocol.  
* DD is an indicator to estimate the spectral quality, defined as \[\text{DD}=\frac{1}{ML}\|\text{vec}(\widehat{\mathbf{Z}})-\text{vec}(\mathbf{Z}) \|_{1}^{2}.\]
* SSIM is to measure the structure similarity between the reconstructed and reference images, defined in [48].  
Generally, the higher the value of RSNR and SSIM, the better the evaluated performance. However, the lower the values of DD, RMSE, SAM and ERGAS, the less distortion. Besides theoretical complexity in order of magnitude (OM), the running time \(T\) (in seconds) is regarded as the metric of computational complexity for the fusion methods, while per-iteration running time (PRT, in seconds) is to evaluate a single time complexity of a simplified solution.
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Robotic Add-On Devices  
Robotic add-on devices are attached to conventional endoscopes to improve visual fields and enhance distal dexterity while performing ESD. The advantages of robotic add-on devices are that they are relatively inexpensive and easy to sterilize after use. They also minimize the disruption to current clinical workflows owing to the simple attachment and removal. These assistive robotic devices are expected to show greater improvements in surgical safety and dissection speed [22]. Because robotic add-on devices have limited functions, they are less capable of triangulation of instruments and tissue manipulation compared to the robotic endoscopic multitasking platform.
## 3 Results and Discussion  
### The Response of NEE and LE Fluxes to the Extreme Temperature Anomalies  
The responses of NEE and LE fluxes to temperature changes in temperate and boreal forests show a wide range of variation across different landscapes and over time due to plant species characteristics, growing conditions, plant age, and adaptation to changing environmental conditions. Intra-annual variability is determined by seasonal changes in green plant biomass, as well as by differences in assimilation processes occurring in woody plants during cold and warm periods. Overall, during the cold season, plant photosynthesis is completely shut down in deciduous forests, and it is strongly inhibited in evergreen forests. During the growing season, in the warm half of the year, photosynthesis and respiration of plants increase significantly under favorable temperature, solar radiation, and soil moisture conditions for plant growth for both evergreen and deciduous tree species after the appearance of leaves. Anomalous weather conditions can affect tree functioning and growth, resulting in changes in NEE and LE fluxes.  
In order to highlight the specific response of woody plants under different growing conditions in different seasons to anomalous weather conditions, we considered the response of forest ecosystems to external forcing in the warm and cold seasons separately. The beginning and end of the warm season are associated with a steady transition of the mean daily temperature through zero for at least seven consecutive days. For several stations located in warm climates with prevailing positive temperatures (e.g., station in the Mediterranean region), the seasonal transition was determined by the sharp change in gross primary production (GPP) values observed during leaf emergence and leaf fall.
# Experimental Study on Out-of-Plane Seismic Performance of New Type Masonry System
## 2 Proposed Block Systems and Material Characteristics  
### Proposed Block Systems  
The authors developed two types of concrete blocks to improve the seismic performance, in both the in- and out-of-plane directions, and enhance the workability, without the joint mortar [35]. The two types of concrete blocks only consist of main and key blocks, and they have different key block shapes. One is the peanuts shape and the other is the H shape, as shown in Figure 3.  
Since the masonry unit may expand in the process of demolding and curing, a gap of 1.5 mm was inserted between the main and key blocks, in consideration of these production errors, as shown in Figure 3. As can be found in Figures 4 and 5, the proposed block systems have half-height difference between the main and the key block. The main blocks, on both sides of the wall, have half-sized key blocks, as shown in Figure 5. Although the behavior of the masonry wall in the in- and the out-of-plane directions are different, a high seismic performance in both directions can be expected because of the height difference formed in the interlocking mechanism.  
Figure 3: Two types of concrete block systems (unit: mm): (**a**) peanuts-shaped concrete block; (**b**) H-shaped concrete block.
## 4 Simulation and Performance Evaluation  
### Simulation Model  
The simulation parameters and corresponding range of values are listed in Table 6. Then we talk about these parameters in detail. In our simulation, sensor nodes are deployed in a square area, and the side length of this area is 200 m. The number of nodes varies from 200 to 600. All nodes have the same communication range and initial energy. They are 75 m and 2 J respectively. The energy model in HEED [47] is adopted. To receive \(n_{b}\)_bits_ at the receiver, the radio expends \(n_{b}\times E_{dec}\)_J_. The energy consumption of transmitting \(n_{b}\)_bits_ at the sender has two cases. Here we define the distance between a sender and a receiver as \(d\), and threshold distance of communication is \(d_{0}\). In the first case, if \(d<d_{0}\) is occurred, the sender consumes \(n_{b}\times(E_{elec}+E_{fs}\times d^{2})\)_J_. If \(d\geq d_{0}\), \(n_{b}\times(E_{elec}+E_{mp}\times d^{4})\)_J_ is consumed. The value of \(E_{fs}\) is 10 pJ/bit/m\({}^{2}\), and \(E_{mp}\) is 0.0013 pJ/bit /m\({}^{4}\). The control message size is 25 bytes, and data packet size is 100 bytes. To simulate the uneven event source, we assume the event area is a circular area, and all the nodes in this area are appointed as source nodes. Sink moves at a constant velocity, and source nodes have a constant rate of producing sensory data. The lifetime of network is the time when the first node runs out of its energy. The performance metrics are as follows:  
* Lifetime: The time when the first node runs out of its energy.
* Average Residual Energy: The average residual energy of all nodes when the network is ended.
* Length of Sink's Movement: The length of sink's trajectory when the network is end.
* Variance of Residual Energy: The variance of all nodes' residual energy when the network is ended.
* Number of Data Packets Collected: The number of data packets collected by sink when the network is ended.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definition** & **Default Settings** \\ \hline \(L\) & Side length of deployment area & 200 m \\ \(N\) & Number of sensor nodes & 200\(\sim\)600 \\ \(r_{c}\) & Node communication radius & 75 m \\ \(r_{e}\) & Radius of event area & 20\(\sim\)60 m \\ \(E\) & Initial energy of each node & 2 J \\ \(d_{0}\) & Threshold of communication & 75 m \\ \(E_{elec}\) & Digital electronics & 50 nJ/bit \\ \(E_{fs}\) & Communication \((d<d_{0})\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{mp}\) & Communication \((d\geq d_{0})\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 3 Results
### Comparison with Copernicus Global Land Cover and ESA WorldCover
However, the presence of multiple classes results in some points of Dense Vegetation corresponding to what we grouped as Sparse Vegetation and vice versa. Consequently, an OA of 0.80, a Kappa coefficient of 0.60, and producer and consumer accuracies of 0.80 each were obtained.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 5 Conclusions  
In this work, aiming to improve the throughput of the 5G network deployed in an NSA setup, we focus on observing the performance and finding a better combination of MPTCP scheduler and CCA. We implemented a testbed based on NS3-DCE that enables simulating MPTCP's performance in both 3 GPP and non-3 GPP networks. We also designed a scenario where a UE can transfer data through both 4G and 5G network interfaces simultaneously, and the network condition changes dynamically. We implemented Redundant, ECF, and BLEST schedulers in the testbed along with the previously available SRTT and Round-robin schedulers.  
Through extensive simulation experiments, we confirm that BLEST scheduler, in combination with BALIA CCA, provides the highest throughput due to their proper adaptation to network changes. Moreover, we found a direct relation between SINR, throughput, and packet losses measured at the UE and briefly explained their relation.  
In future work, we plan to enable multipath quick user datagram protocol internet connection (MPQUIC) in the NS3-DCE testbed to enable comparison of MPTCP with MPQUIC considering 5G and 4G scenarios. We further plan to design a scheduler that considers SINR, CWND, throughput, and losses to make the proper scheduling decision to ensure high throughput by reducing HoL blocking at the receiver.  
Conceptualization, I.M.; methodology, I.M.; software, I.M. and T.L.; validation, I.M. and T.L.; formal analysis, I.M.; investigation, I.M.; resources, I.M.; data curation, I.M.; writing--original draft preparation, I.M.; writing--review and editing, I.M.; visualization, I.M. and T.L.; supervision, Y.-Z.C.; project administration, Y.-Z.C.; funding acquisition, Y.-Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Ministry of Education, 2018R1A6A1A03025109, and was funded by the Korean government, 2019R1A2C1006249.  
This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. NRF-2018R1A6A1A03025109), and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2019R1A2C1006249).  
The authors declare no conflict of interest.