esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Stage II: Detailed Experimental Planning
#### 2.2.3 Questionnaires  
The aim of creating questionnaires for users participating in experimental studies related to the investigation of the impact of occupational exoskeletons is to gather useful insights and feedback from the users' perspective. These questionnaires facilitate the understanding of the user experience, perceptions, and potential issues or concerns associated  
Figure 2: Sample size variation concerning a power variation, obtained with GPower software [30].  
with the exoskeleton technology being evaluated. The scope of these questionnaires can be divided into three main phases: before the study, during the study, and after the study. Questionnaire administered before the study support the assessment of the participants' initial perceptions, expectations, and attitudes toward exoskeleton technology. This baseline information is essential to comprehend any preconceived notions or biases that users may have, which could potentially affect their experience during the study. Furthermore, it gives an understanding of the users' familiarity with exoskeletons, their perceived usefulness, and any concerns or hesitations they may have. Questionnaire administered during the study can capture real-time feedback and experiences of the participants as they interact with the occupational exoskeleton. This information can help identify potential usability issues, discomfort, or challenges that users may face while performing tasks wearing the device. It can also provide insights into factors such as perceived effort, fatigue levels, and overall user experience during separate phases of the study. Post-test questionnaires allow participants to provide comprehensive feedback on their overall experience with the exoskeleton technology. Users can report their perceptions on aspects such as comfort, ease of use, perceived benefits, and any limitations or concerns they experienced. This feedback can be useful in identifying areas for improvement, refining the design of the task or workplace, and addressing potential user acceptance barriers.  
Users' perceptions collected through questionnaires provide a direct and complementary source of information on the subjective experiences, preferences, and concerns of the users that quantitative data alone may not fully capture. Researchers and designers, by incorporating users' feedback throughout the study, can obtain a holistic perspective of the exoskeleton's performance, usability, and potential impact on the user's well-being and productivity. Furthermore, user feedback can inform future design iterations, ergonomic considerations, training protocols, and the overall acceptance and adoption of occupational exoskeletons in industrial settings [32].
# Application of Image Recognition Methods to Determine Land Use Classes
## 2 Related Works

Table 1: Comparison of the related works found on land use classification (Random Forest (RF), Support Vector Machine (SVM), Deep Convolutional Neural Networks (DCNN), NDVI-based ML (NDVIML), Convolutional Neural Networks (CNN), Overall Accuracy (OA), Cohen’s Kappa (CK), Precision (P), Recall (R), F1-score (F1), Global Accuracy (GA), Multi-Scale Convolutional Neural Network (MSCNN), Object-Based Image Analysis with Fuzzy Logic (OBIAFL), Vector Space Model (VSM), Gradient Boosted Machine (GBM), XGBoost (XBG), Classification and Regression Tree (CART), Lasso Regression (LR)). Summing up, the results of Table 1 indicate that the most frequently used algorithm for land use classification is Random Forest (RF) (found in 25 papers of the 30 analyzed papers (see Column 2 in Table 1)). It is also noteworthy that in the majority of cases (see Column 5 in Table 1), satellite data processing techniques are described. For instance, in [1,8,9,45], these techniques are addressed in a variety of ways in the context of the cloud problem. Despite the utilization of disparate data processing and accuracy measurement metrics, the selection of metrics was found to be dynamic. For example, the Overall Accuracy metric was the most frequently used [6,8,16,23,27,28,45], and similarly, Cohen's Kappa [5,9,22,39,40] or F1-score [1,8,23,34] were viable options for verifying the results (see Column 3 in Table 1). It is also noteworthy that the accuracy results achieved by the articles examined in the literature analysis are indeed dynamic (see Column 3 in Table 1). This distribution can be attributed to various factors, including differing research objectives, satellite utilization as input data, and different combinations of classified classes. It is also noteworthy that the geographical locations of the study sites are distributed across the globe, with a significant presence in multiple regions (see Column 4 Table 1). To conclude, further research is necessary to apply the ML algorithms identified in other studies to various types of input data, such as Sentinel-2 satellite imagery of different geographical regions, and to measure the accuracy of the results using different metrics. Extensive research involving various algorithms and input data from different regions has provided us with a more comprehensive global perspective on the accuracy and characteristics of these ML algorithms when processing regional input data. Moreover, different insights for the further classification of different regions will be obtained.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 5 Joint Resource Allocation Optimization Scheme
### Joint Power Allocation  
In the above derivation, we obtain the power allocation schemes of the BSs and the satellite separately while fixing the parameters of another. Actually, the two networks will interfere each other as the entire bandwidth is reused in the whole system. Thus, the solution of one network will effect the solution of the other. To obtain the optimal solution of the whole system, an iterative algorithm is proposed as Algorithm 3. In each iteration, the Algorithms 1 and 2 will be performed based on the power allocation scheme \(P_{B,ij}[t]\) and \(P_{S,IJ}[t]\) obtained in the last iteration. Then, the new obtained results will be used for the next iteration until the results converge. Note that in each iteration, the initialization steps in Algorithms 1 and 2 will be performed based on the results of last iteration, thus the computational complexity of Algorithm 3 is \(O(L.(K_{i}/2)^{2}+I.N^{2})\).  
```
1:Initialize:\(t=1,\theta_{B,ij}^{(1)}=\theta_{B,ij}^{(2)}=1,\beta_{B,ij}^{(1)}=\beta_{B,ij}^{ (2)}=0\);
2:Initialize\(P_{B,ij}\)=0;
3:Initialize\(t_{\delta}=1\), \(\lambda>0\),\(\mu>0\)
4:Initialize \(I_{s}\)
5:repeat
6:for\(J=1\) to \(N\) do
7: Update \(P_{B,ij}\) by using (55)
8: Update \(r_{B,ij}\) by using (56)
9:endfor
10: Update \(\lambda\), \(\mu\) by using (57) and (58)
11: Set \(t_{\delta}=t_{\delta}+1\)
12: Until \(\lambda\), \(\mu\) converges
13: Set \(P_{B,ij}[t]=P_{B,ij}[t+1]\)
14:Update\(\theta_{B,ij}^{(1)}\), \(\theta_{B,ij}^{(2)}\), \(\theta_{B,ij}^{(1)}\), \(\theta_{B,ij}^{(2)}\) by using (51) and (52)
15: Set \(t=t+1\)
16:Until \(P_{B,ij}r_{B,ij}\) converges
```  
**Algorithm 2** BS Channel Resource Allocation.  
```
1:Initialize:\(t=1\),\(P_{S,IJ,I}[t]=0\),\(P_{B,ij}[t]=0\);
2:repeat
3:for\(l=1\) to \(L\) do
4: Update\(P_{S,IJ}[t+1]\) referring to Algorithm 1
5:endfor
6:for\(i=1\) to \(I\) do
7: Update \(P_{B,ij}[t+1]\) referring to Algorithm 2
8:endfor
9: Set \(P_{S,IJ}[t]=P_{S,IJ}[t+1]\)
10: Set \(P_{B,ij}[t]=P_{B,ij}[t+1]\)
11: Set \(t=t+1\)
12:Until \(P_{S,IJ,J}\),\(P_{B,ij}\) converges
```  
**Algorithm 3** Joint Power Allocation.
## 1 Introduction
In another study, [PERSON] et al. (2016) [12] verified that increasing holding time at 400 MPa caused a considerable decline in the IgG concentration in bovine colostrum, but no significant differences were observed at 300 MPa. In another study with bovine colostrum, [PERSON] et al. (2000) [13] reported that HPP at 300 and 400 MPa for 10 min at room temperature effectively suppressed the development of total aerobic mesophiles, coliforms and psychotrophic Gram-negative bacteria under refrigeration for 9 days. The authors also stated that, at 300 MPa, the IgG of colostrum was minimally affected, as the neutralizing ability against bovine coronavirus was preserved, while 400 MPa resulted in IgG denaturation (observed qualitatively) and changes in colostrum viscosity (observed qualitatively). In another study, [PERSON] et al. (2018) [14] compared the effects of single and cycled HPP treatments (600 MPa/10 min, 100 MPa/10 min-10 min interval-600 MPa/10 min, 200 MPa/10 min-10 min interval-400 MPa/10 min and 200 MPa/10 min-10 min interval-600 MPa/10 min, at 19-21 \({}^{\circ}\)C) and traditional Holder pasteurization (62.5 \({}^{\circ}\)C, 30 min) in human milk. The results showed that the condition 200 + 400 MPa was able to better preserve the IgG levels compared to traditional Holder pasteurization (82.24 versus 50.96%, respectively), as well as the levels of hepatocyte growth factor (97.15 versus 11.28%,respectively). The authors also reported that the content of leptin increased for all HPP conditions tested compared to Holder pasteurization, as well as a better retention of insulin. Other technologies, such as ultraviolet radiation (UV-radiation) have been addressed for colour stratum pasteurization. For example, [PERSON] et al. (2013) [15] studied the effects of UV-radiation (45 J/cm\({}^{2}\) at 285 nm) for nonthermal pasteurization of bovine colostrum, with the results suggesting that this methodology performed poorly compared to conventional thermal processing (63 \({}^{\circ}\)C for 30 min and 72 \({}^{\circ}\)C for 15 s), mostly due to the low penetrance of UV-radiation due to the colostrum's high level of solids. Truly, it should be highlighted that the existence of dissolved and suspended solids has the capability to disperse UV light and create an environment for bacterial aggregation, with both factors thereby diminishing the bactericidal effectiveness of UV. Since there are no reports concerning the HPP effect on donkey colostrum, the aim of this work was to study and compare the effect of TP (62.5 \({}^{\circ}\)C, 30 min) and HPP (400, 550 and 625 MPa for 2.5, 10 and 30 min, at 8 \({}^{\circ}\)C) on the endogenous microbial load, Igs (IgA, IgM and IgG) content and lysozyme activity of donkey colostrum after processing and during subsequent refrigerated storage (4 \({}^{\circ}\)C) for 40 days.
## 6 Discussion  
The present research aims to develop the direction to establish the appropriate regulatory requirements on competence for the shore-based RO, focusing on watchkeeping based on the STCW Convention's regulations provision, taking characteristics and conditions of remote operation into account. Thus, it focused on RO at RCC and assessed the lack of ship sense and required information, then reviewed the additional competence and possible requirements to compensate for the shortage. The results of the case study in Section 5 through the proposed GBGA in Section 3 make some observations. Concerning ship sense (G2), although the original element of ship sense is various, visibility outside the bridge is the most important key factor. Examples of the objects to get ship sense are landmarks such as lighthouse and buoy, forming and colour of the sea surface, cloud movement, motion of scenery and wave and encountering ships. As listed in Table 6, visibility relates to almost elements of ship sense. Although other ship sense is also necessary for getting the required information, the most part can be compensated by visibility. For instance, body balance is used for grasping the motion of a ship, such as pitching, yawing and rolling; nevertheless, these movements can also be recognised by visually capturing the change of scenery outside the bridge. It is difficult for ROs to feel body balance at RCC. Nevertheless, they can manage to be aware of the situation by utilising the remaining information resource (i.e., restricted visibility outside the bridge), as shown in Table 7. In this sense, the extent of the constraint of data transmission and consecutive visibility significantly affects SA and safe navigation. The visibility outside the bridge was relatively clear and less intermittent in the case study since the demonstration project adopts 3G or long-term evolution (LTE) (see Table 1). However, it is likely to happen that the visibility would be less clear, and a ship could not send a moving image because of the restricted data communication capacity when the ship uses the satellite communication as a data transmission tool in the ocean. In this case, the lack of visibility would be more extensive, and RO would not be able to satisfactorily be aware of yawing and rolling. The lack of ship sense widely affects the acquirement of required information for each Level SA, as described in Tables 7-9 (G3 and G4, RQ1). The adverse effects of the lack of ship sense on the information of target ships and traffic density are comparably large through all Level SA
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 4 Design and Acceleration of Instruction Set
### Instrumentation Set Design and Acceleration Opportunities
#### 4.3.1 Micromanipulation Decomposition and SIMD Data Parallel Instructions
Since the design of this study is based on a VP with 512-bit SIMD, there are a maximum of eight complex multipliers (32 real integer multipliers), and the relatively general SIMD parallel data instructions are shown in Table 2.  
In Table 2, instruction 1, 10 operates only on different data of the same subcarrier, so it is a data parallel instruction. Instructions 2-9 can carry out either data parallelism or subcarrier parallelism. In Section 4.4, the instruction 1, 2, 10 is used for data parallelism, while 3-9 are used for subcarrier parallelism.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Handover Ping-Pong Effect  
In connected drones, the handover \"ping-pong\" effect is more likely to occur than in the terrestrial case. This is due to various factors such as the fast mobile speeds of drones, suboptimal use of handover control parameters, the use of an inefficient handover decision algorithm, and the increase in LoS connection probabilities. The false activation of the handover process may further be a contributing factor. The combined factors cause repeated handovers. Moreover, if drones act as UEs, determining the relation between the drone and BSs will be another issue.
# Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm
## 4 Methodology
### Basic Definitions of the Proposal  
QM_VMO (Quantitative Miner with the VMO algorithm) is our proposal for quantitative association rule mining based on the VMO algorithm. Basic definitions about some elements that are part of the procedures are introduced below. For this, a dataset with \(m\) numeric variables is considered, a rule scheme is defined, and a population of \(p\) individuals is generated.  
* \(ratio_{a}\): This value is calculated from the division between the range of the new interval \([L_{a}^{*},U_{a}^{*}]\) and the range of the initial interval \([L_{a},U_{a}]\) of the numerical attribute \(a\).
* Node: Let \(n_{i},i\in[1,\dots,p]\) be a representation of \(p\) nodes that are part of a population; in QM_VMO, each node organizes the definition of a rule and is composed of one m-dimensional vector, the dimensions of the vector constitute each of the attributes that are part of the rule scheme, and the \(i\)-th node is defined in the following expression:  
Figure 1: QM_VMO representation of the nodes.  
Figure 2: Rule example.  
\(n_{i}=(U_{1}^{i},L_{1}^{i},U_{2}^{i},L_{2}^{i},\ldots,U_{m}^{i},L_{m}^{i})\), where \([L_{k}^{i},U_{k}^{i}]\) are the lower bound and upper bound of the \(k\)-th attribute. * Population: In this technique, population refers to the set of \(p\) individuals that are part of a conglomerate, and is represented as follows: \(S=n_{1},n_{2},\ldots,n_{p}\). * Mesh: in the studied context, the mesh is the set of nodes that are linked to exchange information and provide decision criteria in order to achieve an objective (objective function). The mesh is not a static structure. It is variable in time and can be represented in the following expression: \(m(t)=(n_{1}^{t},n_{2}^{t},\ldots,n_{p}^{t})\); this is the mesh defined at the \(t\)-th moment (iteration). The Mesh used by the QM_VMO algorithm ranges in size between \(p_{o}\) and \(p_{f}\), and \(p_{o}<p_{f}\) when using the expansion and contraction operators. * Initial mesh size: \(p_{o}\) refers to the size of the population defined in the problem and is the number of nodes with which the mesh is initialized \(m(t)=(n_{1}^{t},n_{2}^{t},\ldots,n_{p_{o}}^{t})\). * Final mesh size: \(p_{f}\) refers to the maximum size that the mesh can reach in its expansion process; it is the number of nodes that the mesh has in a given iteration \(t\)--\(m(t)=(n_{1}^{t},n_{2}^{t},\ldots,n_{p_{f}}^{t})\). * Neighborhood: \((n_{i}^{*})\) is the neighborhood of \(n_{i}\) that is defined by its \(k\) closest neighbors (determined with the Euclidean distance calculation).
## 2 Assembly Process Modelling
### During Assembly  
In the process of grazing incidence focusing mirror assembly, the upper hanging points are used to carry out the process. In this section, according to the diameter of the grazing incidence focusing mirror, two types of hanging points are used for comparison, one with three hanging points and one with six hanging points, of which three are used for adjusting the spatial position of the mirror and the other three are used for counterweight, as shown in Figure 7, which is a schematic diagram of the mirror to be assembled. In the actual assembly process, to ensure the mechanical properties, the three hanging points are used to lift the mirror at a certain vertical height, and then the spatial position is adjusted. At this time, the mirror is in suspension, and the bottom of the mirror is located in the groove corresponding to the position of the front spider. Simulation is carried out for the mirror with different numbers of hanging points in this state to analyse the influence of gravity on the accuracy of the mirror surface shape. To maintain consistency with the data of the measured point cloud in the previous section, in the process of finite-element meshing of the mirror, the form of a shell cell is used, the number of circumferential nodes is 36, and the node spacing of the busbar along the axial direction is 0.5 mm. The corresponding material parameters of the simulation are shown in Table 2, and the results of the simulation are shown in Figure 8. The nodes on the inner surface of the mirror are extracted and then reconstructed using the NURBS surface, and the imaging quality analysis of the surface shape accuracy affected by gravity is carried out and the results are shown in Figure 9. The results of the analysis of the surface shape accuracy of the mirror affected by gravity in the six-hanging-point and three-hanging-point modes are 49.50\({}^{\prime\prime}\) and 51.37\({}^{\prime\prime}\), which are decreased by about 0.86\({}^{\prime\prime}\) and 2.73\({}^{\prime\prime}\), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Young’s Modulus** & \multirow{2}{*}{**Poisson’s Ratio**} & **Density** & **Equivalent Linear Expansion** \\  & **(Mpa)** & & **(t/mm\({}^{3}\))** & **Coefficient (\(\omega^{c}\)C\({}^{-1}\))** \\ \hline \hline \begin{tabular}{c} Hanging plate— \\ Stainless Steel \\ \end{tabular} & 210,000 & 0.3 & \(7.85\times 10^{-9}\) & 0 \\ \hline \begin{tabular}{c} Mirror—Ni \\ \end{tabular} & 207,000 & 0.31 & \(8.9\times 10^{-9}\) & 0 \\ \hline \
\begin{tabular}{c} Adhesive—GHJ-01(Z) \\ \end{tabular} & 158.62 & 0.495 & \(1.22\times 10^{-9}\) & 0.0066 \\ \hline
\begin{tabular}{c} Front spider— \\ Stainless Steel \\ \end{tabular} & 210,000 & 0.3 & \(7.85\times 10^{-9}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters.  
Figure 7: Schematic of the model of the mirror.
## 1 Introduction
### Laboratory Experiments on Fangshan Granite Samples
#### 2.1.2 Experimental Results  
The experimental results of Fangshan granite samples after high-temperature and water-cooling treatment are presented in Table 1. In order for a comparison study, the samples under natural condition were also applied in the experiments under different confining pressures, and the corresponding test results were recorded under the condition  
Figure 1: Fangshan granite specimens.  
of 20 \({}^{\circ}\)C (room temperature). According to Mohr-Coulomb criterion shown in Equation (1), cohesion and the internal friction angle of the granite samples under different treatment conditions were calculated and shown in Table 2, and the results were plotted in Figure 2.  
\[\sigma_{1}=\sigma_{3}\frac{1+\sin\varphi}{1-\sin\varphi}+\frac{2c\cos\varphi}{1- \sin\varphi} \tag{1}\]  
where \(c\) and \(\varphi\) are cohesion and the internal friction angle of the samples, respectively.  
As presented in Table 2 and Figure 1, after heating and water-cooling treatment, cohesion of Fangshan granite does not change significantly within the heating range from 20 \({}^{\circ}\)C (room temperature) to 400 \({}^{\circ}\)C, while it decreases by 15.3% and 16.6% under the conditions of 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, respectively, compared with the value under room  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Confining**} & \multicolumn{5}{c}{**Peak Deviatoric Stress (\(\sigma_{1}\)–\(\sigma_{3}\)) (MPa)**} \\ \cline{2-6}  & **20 \({}^{\circ}\)C 1** ** & **200 \({}^{\circ}\)C** & **400 \({}^{\circ}\)C** & **600 \({}^{\circ}\)C** & **800 \({}^{\circ}\)C** \\ \hline
0 & 79.9 & 77.8 & 84.3 & 51.9 & 31.0 \\
10 & 229.3 & 209.7 & 247.3 & 244.0 & 161.0 \\
20 & 317.7 & 310.7 & 341.7 & 263.0 & 246.7 \\
30 & 392.7 & 397.0 & 425.0 & 379.7 & 219.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of Fangshan granite samples after high-temperature and water-cooling treatment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Heating**} & \multirow{2}{*}{**\(c\) (MPa)**} & \multirow{2}{*}{**\(\varphi\) (\({}^{\circ}\))**} & \multirow{2}{*}{**\(\mathbf{R^{2}}\)**} \\ \cline{1-1}
20 & 15.7 & 55.3 & 0.97 \\
200 & 13.8 & 55.8 & 0.99 \\
400 & 16.0 & 56.7 & 0.97 \\
600 & 13.3 & 54.9 & 0.91 \\
800 & 13.1 & 47.2 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cohesion and the internal friction angle of Fangshan granite samples after high-temperature and water-cooling treatment.  
Figure 2: Cohesion and the internal friction angle of Fangshan granite samples after high-temperature and water-cooling treatment.  
temperature. The internal friction angle almost remains constant when the temperature is not higher than 600 \({}^{\circ}\)C, and it decreases slightly at 800 \({}^{\circ}\)C, where it is 14.6% lower than that at room temperature.
## 4 Numerical Results
In the high SNR region, the change in the effective capacity of User 3 decreases significantly with the increase in blocklength. Therefore, the performance of User 3 is better with a small blocklength. However, the effective capacities for Users 1 and 2 increase slowly with the increase of blocklength. It should be noted that, when the blocklength is equal to 650, the capacity will neither increase nor decrease. In order to comprehensively ensure the performances of the three users, the blocklength setting range can be between 600 and 650. Therefore, the blocklength affects the user's effective capacity. Therefore, we can ensure the performance of users by selecting the appropriate blocklength and SNR. Figure 5: Effective capacity vs. the delay exponent \(\theta\), with \(n=300\), \(\varepsilon=10^{-5}\).