esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Overview of EMG-COP Correlation  
Under stable conditions, the EMG-COP correlation coefficients exhibited a range from small to large (\(|r|\) = 0.1 to 0.8) for both standing feet-apart and standing feet-together positions. Notably, median time delay (\(\tau m\)) alignments were only seen in the sagittal plane for the PL, GM, and SO muscles while standing feet apart. In contrast, the feet-together stance showed median time delay (\(\tau m\)) alignments in both COP directions, although this was limited to the GM and SO muscles.  
For unstable conditions, the EMG-COP correlation coefficients for each participant ranged from small to large (\(|r|\) = 0.1 to 0.8) across both foot positions. During feet-apart standing, median time delay (\(\tau m\)) alignments were found in specific muscles for both COP directions: COPap alignments were seen in the RF, TA, GM, and SO muscles, while COPmlalignments were observed in the GM and SO muscles. When standing with feet together, median time delay (\(\tau m\)) alignments were noted in both COPap and COPml directions across various muscles. Specifically, COPap alignments at the median time delays (\(\tau m\)) were evident in the RF, TA, GM, and SO muscles, while COPml alignments were found in the GM and SO muscles.  
The overviews of individual cross-correlations between seven lower limb EMG signals and COP displacements in the anteroposterior (COrap) and mediolateral (COPml) directions during bipedal standing on stable and unstable surfaces are presented in the feet-apart and feet-together positions, as shown in Figures 2 and 3, respectively.  
Figure 2: An overview of the individual cross-correlations between seven lower limb EMG signals and COPap and COPml displacements during bipedal standing on a stable surface (SS). The data are presented in (**A**) feet-apart and (**B**) feet-together positions. Each participant provided two data points: one from the non-dominant (ND) leg and one from the dominant (DO) leg, calculated from the middle 60 s of EMG and COP data. The color code distinguishes the data for the dominant leg (DO) and non-dominant leg (ND), with different colors used for each. A vertical line denotes the median time delay (\(\tau m\)) for each cross-correlation.
## 2 Preliminaries
### Graph Theory  
Graph theory is helpful to represent the interaction relations of multi-agent systems. A graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) is normally with the sets of vertices \(\mathcal{V}\) and edges \(\mathcal{E}\). The set of vertices \(\mathcal{V}=\{1,2,\ldots,n\}\) is composed of the indices of agents. \(|\mathcal{V}|\) represents the cardinality of the set \(\mathcal{V}\), which satisfies \(|\mathcal{V}|=n\). The set of edges is represented by \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\). If an edge exists between two vertices, the two vertices are called adjacent. In this work, simple and undirected graphs are considered.  
The adjacency matrix of \(\mathcal{G}\) is denoted by \(G^{A}=[a_{ij}]\in R^{n\times n}\), where \(a_{ij}\) represents the connection of nodes \(i\) and \(j\) on a graph. Since the simple graph is considered, we have \(a_{ii}=0\). Since the graph is undirected, we have \(a_{ij}=a_{ji}\) and \(a_{ij}=1\) if \((i,j)\in\mathcal{E}\), otherwise, \(a_{ij}=0\). The degree matrix of \(\mathcal{G}\) is denoted by \(G^{D}=\text{diag}\{\sum_{j=1}^{n}a_{1j},\ldots,\sum_{j=1}^{n}a_{nj}\}\). The neighbor set \(\mathcal{N}_{i}=\{j\in\mathcal{V}:(i,j)\in\mathcal{E}\}\) of agent \(i\), is composed of the indices of the agents \(j\), which has interaction with the agent \(i\). In other words, if \(a_{ij}>0\), then, agent \(j\) is a neighbor of agent \(i\). The number of neighbors of agent \(i\) is equal to \(|\mathcal{N}_{i}|\).  
We define a diagonal matrix \(G^{L}=\text{diag}\{l_{1},\ldots,l_{n}\}\), which indicates the knowledge of reference formation trajectory (RFT) of each agent. If \(l_{i}=1\), agent \(i\) can obtain the RFT (by sensing/detecting), i.e., a leader. Otherwise, if \(l_{i}=0\), agent \(i\) is a follower, for \(i\in\mathcal{V}\). Then, the leader set is defined as \(\mathcal{V}_{L}=\{i\in\mathcal{V}:l_{i}>0\}\). The leader set \(\mathcal{V}_{L}\subset\mathcal{V}\) is a subset of \(\mathcal{V}\), which contains the indices of the leaders. Particularly, all the quadrotors are leaders, when \(\mathcal{V}_{L}=\mathcal{V}\). The indices of the followers are contained in the complementary set of \(\mathcal{V}_{L}\), namely, \(\mathcal{V}-\mathcal{V}_{L}\).
## 3 Results and Discussion  
As mentioned in Section 1, the main focus of this article is the scattering of waves within the high-frequency pass-band of a finite-sized slab of elastic metamaterial comprising a cluster of Swiss cross-shaped building blocks, as shown in Figure 1. The dispersion diagram, projected over the boundaries of the irreducible first Brillouin zone, is presented in Figure 2a, with an emphasis on the high-frequency regime of interest (>2.5 MHz). For a complete overview of the dispersive behaviour of the periodic systems of Swiss crosslike holes in Figure 1, the reader is referred to [19], where the focus is on complete band-gaps.  
Despite the horizontal and vertical extents of the slab in Figure 3 being finite, the dispersive properties of bulk aluminium, and of the infinite periodic microstructured constituent, provide invaluable information for wave scattering phenomena within a finite structure. Previous studies, also using Bloch-Floquet quasi-periodicity to derive dispersions surfaces from which interesting frequencies were identified for finite substructures, have been carried out in several research fields, including civil [39,40] and biomedical [41,42] engineering.
# Retrospective Analysis of Functional Pain among Professional Climbers
## 3 Results
### Incidence of Functional Pain in the Cohort  
All participants in the climbers' group and the non-climbers group experienced functional pain in at least one of the concerned regions. In the climbers' group, the most frequent location of pain was the upper limb (95.11%, \(n=175\)), whereas among the non-climbers group, it was the back (85%, \(n=136\)) (Table 4).  
As demonstrated in Table 5, there were differences in pain occurrence in the intragroup and intergroup analysis performed using chi2. The female climbers often developed pain in the GHJ (73.56%), elbow (44.83%), and fingers (59.77%), whereas the control group females reported the back (C = 76.67%; Th = 73.33%; L-S = 81.11%) and head (88.42%) as the most frequent locations of functional pain. Next, the male climbers usually reported functional pain in the GHJ (46.88%), elbow (40.63%), and fingers (79.17%), while the control group males indicated the cervical spine (47.14%), thoracic spine (41.43%), lumbosacral spine (50.00%), and head (65.71%) as the most common painful locations. In the control group, the women were more likely to develop back pain and headaches than the men (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Part of Body**} & \multirow{2}{*}{**Location of Pain**} & **Climbers** & **Control** \\  & & _n = 183_ & _n = 160_ \\  & & **\% (_n_)** & **\% (_n_)** \\ \hline \multirow{4}{*}{Upper Limb} & GHJ & 59.56 (109) * & 32.50 (52) \\ \cline{2-4}  & Arm & 29.51 (54) & 29.38 (47) \\ \cline{2-4}  & Elbow & 42.62 (78) * & 20.63 (33) \\ \cline{2-4}  & Forearm & 23.50 (43) & 21.88 (35) \\ \cline{2-4}  & Hand & 33.33 (61) & 35.00 (56) \\ \cline{2-4}  & Fingers & 69.95 (128) * & 23.13 (37) \\ \hline \multirow{4}{*}{Spine} & Cervical & 32.79 (60) & 63.75 (102) * \\ \cline{2-4}  & Thoracic & 33.88 (62) & 59.38 (95) * \\ \cline{2-4}  & Lumbosacral & 39.34 (72) & 67.50 (108) * \\ \hline \hline \multicolumn{4}{c}{Head} & 22.40 (41) & 81.25 (130) * \\ \hline \hline \end{tabular}  
* \(p<0.05\) chi2.  
\end{table}
Table 4: Prevalence of functional pain depending on the participation in climbing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Part of Body**} & \multirow{2}{*}{**Location of Functional Pain**} & **Female Climbers** & **Male Climbers** & **Female Control** \\  & & _n = 87 (\%)_ & _n = 96 (\%)_ & _n = 90 (\%)_ & _n = 70 (\%)_ \\ \hline \multirow{4}{*}{Upper Limb} & GHJ & 73.56 (64) * & 46.88 (45) * & 40.00 (36) & 22.86 (16) \\ \cline{2-4}  & Arm & 27.87(24) & 31.25 (30) & 33.33 (30) & 24.29 (17) \\ \cline{2-4}  & Elbow & 44.83(39) * & 40.63 (39) * & 22.22 (20) & 18.57 (13) \\ \cline{2-4}  & Forearm & 25.29(22) & 21.88 (21) & 24.44 (22) & 18.57 (13) \\ \cline{2-4}  & Hand & 31.03(27) & 35.48 (34) & 44.44 (40) ** & 22.86 (16) \\ \cline{2-4}  & Fingers & 59.77 (52) * & 79.17 (76) */\({}^{\sim}\) & 25.56 (23) & 20.00 (14) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prevalence of functional pain depending on gender.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## Preliminaries and Definitions
### Access Tree Structure  
The access tree is used to describe an access structure. Each leaf node in the tree represents an attribute. Each non-leaf node represents a relational function. The relational function can be AND (\(n\) of \(n\)), OR (\(1\) of \(n\)), \(n\) of \(m\) (\(m>n\)) threshold, and so on. As can be seen in Figure 1.  
Our PCAC scheme achieves secret-sharing about \"AND\" door and \"OR\" door, where \"AND\" door is expressed as (\(n\), \(n\)), and \"OR\" door is expressed as (\(1\), \(n\)). Our scheme needs to achieve the transformation from the access tree structure to the LSSS sharing-generating matrix, and this step will be given in Section 4.
# Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers
## 3 Proposed Method  
The proposed method consists of three parts: feature extraction for the interaction between drivers, SVM-based goal estimation, and collision check based on the trajectory prediction. Figure 3 shows the schematic of the proposed method. It is assumed that all vehicles have sensing systems, thus, the position, speed, and acceleration of surrounding vehicles can be obtained. Using this information, the features are extracted. The proposed method considers the interaction between drivers by using the dynamic potential field method. In addition, the distance between the target vehicle and the end of the acceleration lane is extracted as a feature. Using these features, the goal estimation is performed based on SVM (support vector machine) [11]. The goal of lane-changing conducted by the target is output among the four candidates: \(A\), \(B\), \(C\), and \(D\) as shown in Figure 2. After the estimation, the collision check is conducted. If there is a possibility that the target vehicle would collide with the lead or rear vehicle, the \(D\) is determined as the goal even other spaces are estimated in the previous step. Details of each part are described in the following subsections.  
The feature extraction for the interaction, SVM-based goal estimation, and collision check based on the trajectory prediction are novel proposals compared to our previous paper [10]. In addition, the objective and the target situation are different with [10].  
Figure 2: Problem definition: the lane-changing vehicle is defined as the target, and it is represented in red. The preceding vehicle on the mainstream lane is defined as the lead, and the following vehicle on the mainstream is defined as the rear. The two vehicles are indicated in blue. There are four classes as goals for the target vehicle. The \(A\) class indicates the front space of the lead vehicle, the \(B\) class is the space between the lead and rear vehicles. Furthermore, the \(C\) class represents the behind space of the rear vehicle. Lastly, the \(D\) class indicates that the target maintains the current lane.
## 3 Examples of New Empirical Laws from \(y_{2\text{A}}(x)\)
### Continuous Relationship for Laminar Flow to Turbulent in Hydraulics  
The importance of the flow characteristics of water and hydrocarbons is recognised for all transport networks through circular conduits (pipes). The capillary model is the most commonly used approach to understand flow behaviour in the geological porous media of the subsoil. Knowledge of the relations governing these flows through circular channels is a crucial aspect that presents a particularly differentiated behaviour between low-velocity (laminar) and high-velocity (turbulent) regimes. The transition interval between these two flow regimes is neither a strictly continuous step nor discontinuous. In laminar flow, the fluid advances homogeneously along the channel length and width; when the flow is completely turbulent, the fluid becomes homogeneously disordered. Spatial and temporal heterogeneities arise within the transition interval, and different results can be obtained from instantaneous images or punctual measurements. The formulation applied until 2020 relatively disregarded the transition interval between these regimes, particularly its effects on two crucial parameters for hydraulic calculations: (1) the ratio of the axial distribution of the flow velocity inside the pipes to velocity in the axis \(v(r_{\rm D})/v_{\rm max}\) (the velocity law), and (2) the friction factor in smooth pipes\(f_{\rm smooth}\), for which there were no applicable relationships in the transition interval between the two flows. In [PERSON] et al. [4], relationships for both parameters were presented for the velocity law as a function of the turbulence exponent \(\tau\) (Figure 7) and for the friction factor as a function of the Reynolds number \(Re\) (Figure 8). Thus, the previous transition interval ambiguity of the conventional equations was solved, providing expressions that continuously reflect the fluid flow behaviour for any regime.  
The main problem we encountered was that, in general, the characterisation of the transition interval had been underestimated. This contrasts with the fact that the interpolation between laminar and turbulent flow is supported from an experimental point of view in the classical and most recent widespread studies, and its convenience was predicted by well-known authors (referred to in that publication). Although, in this case, the two generated laws show that both phenomena can be represented by a double-asymptote  
Figure 6: Porosity/permeability relationship in granular porous media for \(\O=0\)–\(50\%\)[3].  
dependence, the one shown by the velocity law is particular because it deals with the radial behaviour of the friction attenuation phenomenon as a function of the distance to the wall.
## 4 Developing Prediction Models for the Performance of the Warm Mix Asphalt
### Developing a Prediction Model for the Stiffness Modulus of Warm Mix Asphalt  
Table 4 summarizes the input data of the response surface methodology to develop a prediction model for the stiffness modulus of the warm mix asphalt. Table 4 consists of two factors: the testing temperatures and the production temperatures, while the response is the results obtained from the stiffness modulus test. Table 5 presents the fit summary of the models. As reported in Table 5, the two-factor interaction model (2 FI) adequately fits the data, since the lack of fit is insignificant and the coefficient of determination (R\({}^{2}\)) is very strong. Lack of fit is used to describe the adequacy of the model based on the functional relationship between the independent and the dependent variables. The insignificant lack of fit (\(p\)-value \(>\) 0.05) indicates that the model fits the data well, while the significant lack of fit implies that the model does not accurately fit the data, which in turn requires the addition of new terms or a transformation of the data. Furthermore, the difference between the adjusted and predicted coefficient of determination (R\({}^{2}\)) of the 2 FI model was less than 0.2, which implies that the model fits the data. Table 6 shows the outputs of the ANOVA for the 2 FI model. It can be seen that the model is significant since the \(p\)-value is less than 0.05. Also, the significant \(p\)-value of the factors indicates that the testing temperatures and production temperatures have a significant influence on the stiffness modulus. This reveals that there is a very strong agreement between the factors and the response. The 2 FI model is described in Equation (1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Testing Temperature (\({}^{\circ}\)C)** & **Production Temperature (\({}^{\circ}\)C)** & **Stiffness Modulus (MPa)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Input data of the stiffness modulus model.
# Safety and Security Considerations for Online Laboratory Management Systems
## 2 Research Methodology  
### Taxonomy  
The purpose of this taxonomy is to deliver a systematic classification of the main concepts in OLMS's safety and security. In addition, it gives a historical perspective, standard terminology, identification of seminal papers, and highlights gaps in this field, and it will serve as a foundation for future research.  
A comprehensive feature diagram is used to present the taxonomy (Figure 1) that resulted from the literature analysis. The feature diagram for Safety and Security in OLMS organizes the key elements into a hierarchical structure, ensuring a precise classification of mandatory and optional aspects. At the top, safety and security in OLMS branches into major areas: Remote Laboratories, Computer Safety and Security Evolution, LegalRequirements and Regulations, Security in Remote Laboratories and OLMS, and Proposed Solutions. The Remote Laboratories section includes the first implementations and the different types of remote laboratories. The Computer Safety and Security Evolution sections involve security, safety history, and evolution. The Legal Requirements and Regulation section consists of the safety and health regulations and the governmental agencies, the consumer products acts, prevention by design, privacy regulations, and safety and security standards. The Security in Remote Laboratories and OLMS section entails data security, instrument security, and the classification of security attacks and their consequences. Finally, the Proposed Solutions are divided into general security concerns, security frameworks, access control and authentication, communication security, threats and vulnerabilities, and integrity and monitoring. In the feature diagram, the mandatory elements are marked with a black circle, while optional elements are marked with a white circle, providing flexibility in different regulatory and security contexts. This structured approach helps define a taxonomy for understanding and analyzing OLMS's safety and security concerns.
## 3 Research Method and Process
### Numerical Model Construction and Experiment
#### 3.1.2 Model Type Division  
Different types of numerical models are constructed with the natural slope shape and slope of the surface loess slope as variables. In order to ensure that the relative position of the surface loess slope and the underground goaf in all the models is consistent, the slope length under the coupling of different slope shapes and slopes is set to different lengths. Based on the above variables, a total of 20 different types of numerical models were constructed under the same mining and geological conditions (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Types** & **Slope Shape** & **Natural Slope/(c)** & **Natural Slope Length/(m)** \\ \hline A1 & & 5 & 688.45 \\ A2 & & 15 & 231.82 \\ A3 & straight slope & 25 & 141.97 \\ A4 & & 35 & 104.61 \\ A5 & & 45 & 84.85 \\ \hline B1 & & 5 & 613.05 \\ B2 & & 15 & 201.40 \\ B3 & concave slope & 25 & 117.15 \\ B4 & & 35 & 96.14 \\ B5 & & 45 & 78.88 \\ \hline C1 & & 5 & 722.56 \\ C2 & & 15 & 257.37 \\ C3 & convex slope & 25 & 162.34 \\ C4 & & 35 & 116.97 \\ C5 & & 45 & 98.15 \\ \hline D1 & & 5 & 688.78 \\ D2 & & 15 & 233.27 \\ D3 & composite slope & 25 & 143.33 \\ D4 & & 35 & 106.57 \\ D5 & & 45 & 90.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The model types for the numerical simulation.  
Figure 2: Three-dimensional numerical model of convex slope with slope of 25\({}^{\circ}\).
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 3 Range Extension of the Indoor Standard Device for Large-Scale Length Measurement
### Design and Performance Analysis of Corner Reflectors  
As an optical range-extension component in this paper, the hollow corner reflector is composed of a group of glass prisms with three perpendicular faces, as shown in Figure 3.  
Figure 2: The top view of an indoor standard device for large-scale length measurement.  
Compared to solid corner cubes, hollow corner reflectors offer three distinct advantages. First, the hollow corner reflector uses air as the transmission medium and will not cause transmission optical path differences. Second, the hollow corner reflector can achieve ultra-precise return light parallelism (i.e., the angle between the outgoing beam and the return beam) by precision setting. Last, the manufacturing of hollow corner reflectors with large apertures is relatively effortless. In contrast, solid corner cubes, typically produced through conventional machining, usually have diameters no greater than 50 mm, and their retroreflection parallelism is typically limited to around 3\({}^{\prime\prime}\). The hollow corner reflector required for this work has a diameter of \(\Phi\)100 mm and a return light parallelism of 0.2\({}^{\prime\prime}\). We set up a measuring system, as shown in Figure 4, which is mainly composed of an autocollimator, a reading display platform, and a lifting mechanism. We adjusted the corner reflector and measuring system to ensure levelness, recorded the readings of the corner reflector, and took the maximum value of the measurement result as the measurement result.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Mrd-Mac  
Multi-hop relay directional MAC protocol, which is abbreviated to MRD-MAC, is another directional MAC protocol for a 60 GHz WPAN, and was proposed in [39]. This approach is mostly suitable for multi-gigabit indoor mmWave WPANs equipped with electronically steerable directional antennas. Primarily, it is a combination of the traditional AP-based single-hop and multi-hop protocols in the nodes. The job of the single-hop mechanism is to retain the established mmWave links. Subsequently, with the help of PNC and sequential polling policy, the transmission links take place. The PNC always finds an alternative route to the next node if there is a failure on any particular node. The multi-hop mode is responsible for the relay action with other intermediate nodes. However, the overall system performance degrades owing to the lack of spatial reuse and burst traffic contestation on the single route transmission. The overall process can be divided into five stages, discovery algorithm, the normal mode of operation, trailing control phase, lost node discovery and establishing a relay path, and achievable rates.  
* Advantages: In the above-mentioned protocol, adaptive beamforming antennas are used for improving the directivity in 60 GHz communication. It introduces a cross layer representation, and the design aspects address the blockage vulnerability and  
Figure 4: Deafness problem in directional beamforming.  
Figure 5: Operation of directive CSMA/CA.  
diectivity issues of mmWaves. In terms of obstacle handling and operation in an unobstructed environment, the network throughput remains reasonably consistent.
* Disadvantages: There is a direct relation between the control overhead and the node counts with multi-hop connectivity. For every superframe, the AP of the MRD-MAC protocol needs to check with every terminal whether the relay node is still connected to the current superframe, which increases the overhead.