esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Posttest ANCOVA of the Experimental and Control Groups for Biomarkers and Health Beliefs  
According to the subsequent ANCOVA, the two groups showed substantial differences in the 30-sec chair stand test, one-leg standing test, 2-min step test, and left-foot bone density results after the intervention. This result indicated that whole-body vibration training in the experimental group was more effective than the DVDs on resistance training and themanuals provided to the control group on preventing frailty. The result of this study is similar to those of previous studies. [PERSON] et al. (2019) allocated 23 young adults to either an experimental (30-40 Hz, 1.7-2.5 mm) or control group (without vibration). Leg strength and jump performance were discovered to be improved by the intervention compared with traditional strength training. [PERSON] et al. (2019) adopted a quasi-experimental, single-group, pre-test-post-test design. Seventeen participants underwent three sessions each week, and each session comprised 10 repetitions, each lasting 60 s followed by a 30-s rest period. The results of the 2-min step test and foot bone density were improved by this intervention compared with the control training. In addition, according to the health beliefs analysis results, participants with higher scores for self-perceived morbidity and severity of frailty and lower scores for self-perceived obstacles were more prone to perform preventive health activities. After the intervention program, the posttest results for self-perceived morbidity and severity of frailty, the benefits of preventing frailty, and cues to action were higher than the pretest results, whereas the posttest results for self-perceived obstacles to frailty were lower than the pretest results. This indicated that most of the participants responded positively to the intervention program.  
Our study has a number of limitations. First, this study lacked blinding of researchers and participants as well as random allocation of the participants. Additionally, because we recruited the experimental and control groups from different locations, the two study groups may have had not only different demographics but also different psychosocial conditions. Hence, the adjustment only of sex in the ANCOVA may not have been sufficient, and the generalizability of the study results may be limited. Second, older adults who underwent surgery in the previous month, those whose mobility and walking capabilities were affected by surgeries, and those with severe cardiovascular diseases (received coronary stent or bypass surgeries) were excluded from the study, thus affecting the external validity of the study and lowering its generalizability to all prefrail community-dwelling older adults. Third, since this research is not a randomized experimental research design, but a quasi-experimental design, it cannot fully follow the Intention-To-Treat analysis. Finally, because the attrition rate, or drop out, in an observational study with intervention is nearly always not at random, exclusion of missing data is always biased and affects the validity of the results.
## 6 Analysis of the Workspace Taking into Account the Interference of the Links  
The coverage obtained at the stage of defining the workspace is used to determine the positions of the end-effector inside the workspace, in which the interference of the links occurs. The coverage is divided into many boxes of equal size less than the approximation accuracy.  
For the coordinate of the center of each of the boxes, conditions (14) and (25) are checked. If all the conditions are met, it is entered into a new set. The resulting set will contain boxes that correspond to the workspace without interference.  
Workspace for configuration 1 according to Table 1 after excluding interference areas, as well as interference areas for \(a\) = 450 mm, \(c\) = 200 mm, \(d\) = 150 mm, \(e\) = 230 mm, \(\alpha_{min}\) = \(\beta_{min}\) = \(\gamma_{min}\) = 10\({}^{\circ}\), \(\alpha_{max}\) = \(\beta_{max}\) = \(\gamma_{max}\) = 350\({}^{\circ}\), D\({}_{link}\) = 10 mm are shown in Figure 12. Table 2 shows the change in the volume of the workspace, taking into account the interference, without interference of links, as well as volume reduction for various configurations by taking into account the interference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Configuration No.**} & \multicolumn{2}{c}{**Volume of the Workspace, \(\times 10^{4}\) mm\({}^{3}\)**} & \multirow{2}{*}{**Volume**} \\ \cline{2-2} \cline{4-4}  & **With Interference** & & **Without Interference** \\  & **Areas** & & **Area** \\ \hline
1 & 4822 & 2457 & 49.05 \\
2 & 4285 & 1741 & 59.37 \\
3 & 4285 & 1741 & 59.37 \\
4 & 4705 & 1183 & 74.86 \\
5 & 4285 & 1741 & 59.37 \\
6 & 4705 & 1182 & 74.86 \\
7 & 4705 & 1182 & 74.86 \\
8 & 4449 & 806 & 81.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Volumes of workspaces taking into account link interference.
## 2 The Antenna Pointing Control System
### Proposed Antenna Pointing Control System  
The proposed mechanical antenna pointing control system has mechanical two-axis gimbals mounted on board a fixed-wing UAV, as shown in Figure 3. To relay a wireless circuit, it is necessary to equip two antennas, each driven by a two-axis gimbal, on the fixed-wing UAV. In addition, these antennas are covered with a radome to reduce air re-sistance.  
Realizing the proposed antenna pointing control system requires investigating the command antenna's driving angle, including conditions to block the antenna's direction by the UAV body itself, the required pointing accuracy, and motional interferences be-tween the UAV and the two-axis gimbal.  
As the UAV turns, the direction of the onboard antenna should change, corresponding to the roll angle, heading angle, and the positions of the UAV. If the roll angle increases, depending on the distance between the UAV and the ground station, as well as on the altitude, then the UAV body may block the antenna's direction. Therefore, the antenna's direction angle with respect to body-fixed coordinates is analytically derived by using the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Electrical Scanning** & **Mechanical Scanning** \\ \hline Gain fluctuation & Large & Small \\ Pointing accuracy & Inverse proportional to number of arrays & Constant \\ Weight & Inverse proportional to pointing accuracy & Constant \\ Complexity & Proportional to number of arrays & Constant \\ \hline \hline \end{tabular}
\end{table}
Table 1: Merits and demerits of two scanning method.  
Figure 3: Antenna with two-axis gimbal onboard a UAV.  
Figure 2: Antenna scanning method.  
attitudes and positions of the UAV as parameters, which can clarify the conditions in which the antenna's direction is blocked by the UAV body.  
The required pointing accuracy is critical for the antenna pointing control system. The required accuracy can be determined according to the antenna's size, i.e., antenna pattern, the frequency used, and transmitting power, so as to satisfy the ITU's Recommend-dations and Report [17; 18].  
Another problem peculiar to mechanical scanning antennas is that the drive of the antenna influences the motion of the UAV, thereby resulting in a change in the antenna's direction. Conversely, the motion of the UAV also influences the motion of the antenna--that is, the two-axis gimbal. Those effects can be clarified by analyzing the dynamics of the UAV attitude and the antenna driven by the two-axis gimbal.
## 3 Results
### Results of Oddball Counting Error Rate  
#### 3.3.1 Oddball Counting Error Rate Under Full eHMI Penetration Condition  
The mean and SD of oddball counting error rate across different eHMI information content conditions are presented in Table 6. Based on the results of the Shapiro-Wilk normality test, significant deviations from normality were observed for the single-level medium (\(p\) = 0.028), single-level high (\(p\) = 0.017), three-level low-medium-high risk (\(p\) = 0.006) conditions, suggesting that these data do not follow a normal distribution. As a result, a Friedman test was conducted to compare the differences between the eHMI information content conditions. The Friedman test revealed no statistically significant differences between the eHMI information content conditions (\(\chi^{2}(5)\) = 3.37, \(p\) = 0.643).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**P300** & **Low** & **Medium** & **High** & **Low-Medium** & **Medium-High** & **Low-Medium-High** \\ \hline Mean & 1.10 & 1.37 & 0.97 & 1.03 & 0.33 & 1.47 \\ SD & 1.51 & 1.49 & 1.61 & 0.94 & 1.10 & 1.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean and SD of P300 amplitude across different eHMI information content conditions under partial penetration condition.  
Figure 9: P300 effects across different eHMI information content conditions under partial penetration condition.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 6 Simulation of 5G RAN Deployment and Operation Approaches
The average power consumption of BSs in Tx power scaling mode is assumed to be 80% of the average power consumption in active mode (Table 2). The Tx power scaling mode is analyzed, since it is confirmed in References [4; 5] that scaling of the Tx power of BSs according to the space and time DV variations can additionally give a contribution to the enhancement of the EE of a mobile access networks. Hence, a very conservative assumption related to the reduction of average instant BSs power consumption for 20% on a daily basis in comparison with a power consumption of small BSs constantly operating in the active state at the highest Tx power is used in this analysis. Another MN characteristic that is used in the presented analyses is the fact that the deployment of BSs in cellular RANs is based on ensuring the projected capacity needs for satisfying peak network traffic volumes during a BS exploitation period of approximately 10 years since initial BSs deployment. Besides the fact that such an implementation imposes large initial capital investments for MNO, it also imposes increased operational costs in terms of large monthly energy bills paid by MNO for operating RAN with significantly larger capacities than those needed during most of the 5G RAN equipment lifetime. These larger 5G RAN energy costs consequently result in reduced 5G network EE, which can be improved if an appropriate 5G BSs deployment and operation approach is exploited. Hence, finding an optimal deployment and RRM approach from the EE perspective represents a crucial challenge to MNOs.
# Machine Learning Techniques for Non-Terrestrial Networks
## 5 Machine Learning General Aspects
### Machine Learning Methods Taxonomy
#### 5.1.1 Supervised Learning  
In supervised learning, the training set is a collection of input and expected output pairs. The aim is to learn the relation between input and output spaces. Supervised learning can be further split in two sub-categories: (i) regression and (ii) classification. Regression makes use of sigmoid or linear functions in order to approximate a real value. Conversely, classification has the aim to categorize data samples into one of several classes. In recent years, classification is a sub-field, where the use of neural networks (NNs) has excelled. With the continuous increase in computational resources, deep neural networks (DNNs) proved that this task can be solved with higher accuracy, using large datasets and more complex neural network architectures. Some of the most important techniques in classification are support vector machines (SVMs), decision trees (DTs) and decision tree ensembles, such as random forests (RFs). Concerning NN-based methods, multi-layer feed-forward networks (FNNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often used to solve this task, and more specifically, in network communications. Some examples of supervised learning applications in NTNs are remote sensing, decoding, demodulation, and network traffic control.
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 4 Potential Indicators to Measure Process Modularity and Process Complexity
### Selection of the Indicator to Measure Process Modularity  
Four process modularity indices were considered for the given purposes. The first of them is called Singular Value Modularity Index (_SMI_). Although this measure was originally intended to enumerate the degree of product modularity, it is also applicable to measure process modularity. This index enumerates the degree of system modularity  
Figure 1: Applied methodological framework.  
by employing singular value decomposition on the design structure matrix. For the given purpose the following equation was proposed [49]:  
\[SMI(\Sigma_{DSM})=1-\frac{1}{N\cdot\sigma_{1}}\sum_{i=1}^{N-1}\sigma_{1}(\sigma_ {1}-\sigma_{i+1}) \tag{1}\]  
where \(N\) represents the number of system components; \(\sigma_{i}\) means singular value, where \(i=1,2,\,\ldots,\,N-1\).  
The SMI measure can reach values between zero and one, while values closer to zero mean a minimum degree of modularity, and vice versa.  
The second indicator, the cross-module independence (_CMI_), calculates the ratio of the sum of relations inside all modules to the sum of all relations, and is expressed by the following equation [50]:  
\[CMI=1-\sum_{int=1}^{n}\frac{R_{int}}{T} \tag{2}\]  
where \(n\) stands for the number of modules, while \(int=1,2,\,\ldots,\,n\); \(R\) represents the number of inside connections; and \(T\) is the number of all linkages in a network.  
The third one, the optimal modularity indicator \(Qd\), proposed by [PERSON] and [PERSON] [29] can be also used to identify optimal process structure from a set of alternative ones. This index is quantified by using equation:  
\[Qd=\sum_{s=1}^{n}\biggl{(}\frac{ls}{L}-\frac{ws^{out}\cdot ws^{in}}{L^{2}} \biggr{)} \tag{3}\]  
where \(n\) indicates the number of modules, \(L\) represents the sum of all edges in a network, \(ls\) is the sum of internal edges in module \(s\), \(ws^{out}\) is the number of output edges in module \(s\), and \(ws^{in}\) stands for the number of input edges in module \(s\).  
The last considered indicator, named the optimal modularity indicator \(M(G)\), is aimed to measure the OM of process structures. This indicator is expressed as follows [51]:  
\[M(G)=\frac{n}{\sum(Nj\cdot\ln Nj)} \tag{4}\]  
where \(n\) is the number of modules in a network, \(Nj\) is the number of couplings extracted from columns of related visibility matrix, \(j=1,\,\ldots,\,K\), while \(K\) is the number of columns in a related design matrix.  
Principally, all the mentioned modularity indicators are more or less suitable for exploration of the relationships. However, it is possible to identify relevant differences in their sensitivity to recognize slight topological nuances when comparing similar process structures. These differences were already studied in our previous work [51], where the indicator \(M(G)\) was prioritized against the concurrent ones. Therefore, it will be used for the intended purpose in this study.
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Image Compression
#### 3.4.2 Indirect Modeling  
Unlike direct modeling, which updates the probability based on the last probability predicted, indirect modeling tries to learn the answer based on a similar sequence from the past.  
Indirect modeling is implemented with the use of indirect context maps, which use two-step mapping. An optional run context map is also included, which is used for modeling runs of bits.  
The first mapping is between a context value and a bit history called state. The state is modeled as an 8-bit value with the following meaning: A zero value means the context value was never seen before. States from 1 to 30 map all the possible 4-bit histories. The rest of the states represent bit counts of zero and one or an approximation of the ratio between zeroes and ones if the number of previously seen bits exceeds a count of 16. The states are used as indexes in a state table which contains transitions to the next state depending on the value of the next bit. The states were empirically chosen to try to model non-stationarity and different state maps were proposed in other compression programs [22].  
The states are kept in a hash map implemented as a table with 64-byte entries to fit in a cache line. The entries contain checksums for the context value to prevent collisions and up to seven state values. Since the map expects byte data, at bit 0, 2, and 5, the bucket for a context value is recomputed via a dispersion function. The seven state values can hold information about no bits known (one value), one bit known (two values), and two bits known (four values). At bit zero, only three states are needed and, as an optimization, the next four bytes implement a run map that predicts the last byte seen in the same context value, logarithmically weighted by the length of the run. The hash map implements a \"least frequently used\" eviction policy and a \"priority eviction\" based on the state of the first element in the bucket. States are indexed based on the total number of bits seen, and, therefore, the more information available is favored.  
The next mapping is between the state and one or more probabilities. This is done in a similar manner as in direct modeling by using a state map. For each input, four probabilities are returned, one stretched, one skewed, and two depending on the bit counts of zero and one for that state. The fifth probability out of the indirect context map comes from the run map.  
Unlike stationary maps, more contexts can be added to the indirect map, meaning that they share the same memory space and are identified by an index. Each context has its own state map accessed by the index. Having states modeled as 8-bit values makes them more memory efficient than the 32-bit representation for stationary maps.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 6 Sensor Fusion for Detection
### ROI Fusion  
ROI fusion is adapted from the classical two-stage detection framework [174]. Regions of interest (ROIs) can be considered as a set of object candidates without category information. The fusion architecture can be further divided into cascade fusion and parallel fusion. In cascade fusion, radar detections are directly used for region proposal. Radar points are projected into image view as the candidate locations for anchors. Then, the ROI is determined with the help of visual semantics. In the second stage, each ROI is classified and its position is refined. [PERSON] et al. [175] adopted two techniques to improve the anchor quality. They added offsets to anchors to model the positional imprecision of radar detections. To mitigate the scale ambiguity in the image view, they rescaled the anchor size according to the range measurements. In their following work [176], they directly proposed 3D bounding boxes and then mapped these boxes to the image view. In this way, the rescaling step can be avoided. It is also possible to propose the region on the radar point cloud using visual ROIs. For example, [PERSON] [177] proposed a frustum-based association to generate radar ROI frustums using visual bounding boxes.  
Cascade fusion is particularly well suited for low-resolution radars, where the radar point cloud has a high detection recall, but is very sparse. However, there are two potential problems with the cascade structure. Firstly, the performance is limited by the completeness of the proposed ROIs in the first stage. In other words, if an object is missed, we cannot recover it in the second stage. The second problem is that the cascade structure cannot take advantage of modality redundancy. If the radar sensor is nonfunctional, the whole sensing system will fail. Therefore, it is necessary to introduce a parallel structure to ROI fusion. [PERSON] et al. [176] adopted a two-branch structure for ROI fusion. The radar and visual ROIs are generated independently. Then, the fusion module merges radar ROIs and visual ROIs by taking a set union, while the redundant ROIs are removed through NMS. To enable the adaptive fusion of modalities, [PERSON] et al. [178] proposed a gated region of interest fusion (GRIF) module for ROI fusion. It first predicts a weight for each ROI through a convolutional sigmoid layer. Then, the ROIs from radar and vision are multiplied by their corresponding weights and elementwise added together.
# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
## 5 Experimental Setting  
In the experiments, a space 2 m long, 2 m wide, and 0.6 m high was constructed. The LiDAR was placed in the middle, 30 cm above the ground, as shown in Figure 7. The space was surrounded by water pipes to simulate the rain, and a fog-making machine was used to simulate the heavy smoke. The top of the space was sealed with plastic film, as shown in Figure 8. Three types of weather conditions were simulated: normal weather, heavy smoke, and rainy weather with heavy smoke. The response under the different weather patterns was studied, and the algorithms applied to improve the signal. Under normal conditions, the LiDAR signal was good and undisturbed, as shown in Figure 9. Figure 10 shows a flowchart of the experimental procedure.  
When simulating rainy weather, the size of the raindrops will also affect the LiDAR signal. If the raindrops are too small, the effect on the signal will not be obvious. Therefore, the simulated raindrops were made larger to achieve the required level of interference, as in Figure 11. In the second condition, dense smoke was simulated using the fog-making device, as in Figure 12. It was found that dense smoke has a considerable impact on LiDAR measurement. The denser the smoke, the more the light is dispersed to the surrounding environment. The third type of weather condition was both rain and heavy smoke, as in Figure 13. The measurements show that the LiDAR was more affected under the influence of two types of weather at the same time rather than when exposed to any single weather condition. The LiDAR's measurement capability was significantly reduced. Heavy smoke interfered so much with the signal that there was no signal in the four corners of the space.  
Figure 11: Simulated rainy weather.  
Figure 10: Experimental flowchart.  
The root mean square error (\(RMSE\)) was calculated to find the error value for the operation as follows:  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{n}(\hat{y}_{t}-y_{t})}{n}}, \tag{8}\]  
where \(\hat{y}_{t}\) represents the data under normal weather conditions and \(y_{t}\) is the data under the influence of severe weather conditions, or the data after improvement. The larger the value of the RMES, the greater the deviation from the original data; the smaller the value obtained, the smaller the deviation from the original data. Comparison shows that the error values of the \(x\)-axis and the \(y\)-axis are very small. Only the error value of the \(z\)-axis is relatively large, so only the \(x\)-axis and the \(y\)-axis were compared.