esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### Healthcare Benefits
#### 3.2.2 Communication Protocols  
The IoMT devices presented in Section 3.2.1 use various communication protocols. Among these are device-to-device communication (D2D), machine-to-machine communication (M2M), and human-to-machine communication (H2M). M2M allows communication without the need for human intervention, unlike H2M communication, in which a human-machine interaction is required. D2D is an optimal alternative for device-to-device communication in situations where cellular communication technology is not available. Figure 11 shows the classification based on the types of communication used by these systems.  
The classification of the type of communication protocol used in smart health systems is based on that presented by [PERSON] et al. [14], which focuses on the technologies and techniques required in a smart healthcare network.  
Figure 11 shows that the majority of smart healthcare systems (59.09%, \(n=26\)) use device-to-device (D2D) communication. This is due to the fact that large parts of the systems and architectures dedicated to intelligent healthcare are made up of wireless devices (body sensors and wearable devices) that communicate with each other and share information.  
Note that 27.28% (\(n=12\)) of the systems use human-machine communication (H2M). This technology is used in robotic telesurgery systems, in which there is interaction between a surgeon and a robotic arm, which through a human system interface (HSI), can remotely manipulate the surgical robot.  
Only 11.36% (\(n=5\)) of the studies presented made use of D2D and M2M communication. Some studies propose systems that require these two types of communication. According to [25], D2D and M2M communications in conjunction with intelligent medical devices generate significant improvements in various health scenarios.  
Like D2D, M2M communication in IoMT devices plays an important role in the development of smart healthcare systems, as it enables multiple interconnections with smart devices [17]. However, only 2.27% (\(n=1\)) of the works presented by the authors exclusively used this type of communication.  
Figure 11: Communication protocols.
# Experimental Study on the Effect of Different Parameters on Rotor Blade Icing in a Cold Chamber
## 3 Experimental Results and Analysis
### Effect of Blade Material on Ice Shape  
In experiments, different materials are often used to make rotor blades. In this paper, the influence of blade materials on the ice shape was studied. In order to ensure the same icing conditions, we installed two blades of steel and composite material on the rotating mechanism, which were cross-distributed, as shown in Figure 14. The experiment was carried out under case 9. The icing situation of rotor blade is shown in Figure 15, and the comparison of ice shape is given in Figure 16. It can be seen that the icing of rotor blades of different materials was similar. For the whole rotor blade, there was more ice at the tip and less at the root. The overflow liquid water was frozen into ice at the blade tip, and its shape was similar. The ice shapes on the cross-section were almost the same, and the ice thickness and icing range were not different, which indicated that the influence of blade material on icing was relatively small. This was because the blade material had a certain influence on the ice accretion at the initial time, but the ice accretion in the subsequent time gradually increased on the basis of the ice accretion at the initial time. The influence of material on icing process was gradually weakened. Therefore, the material properties may have had little effect on the ice shape. However, the chord length was small and the Mach number of the blade tip was low in this experiment, and thus the liquid water flow may not have been significant. More large-scale experiments can be carried out to verify the conjecture in this paper.  
Figure 12: Ice shapes under different blade numbers: (**a**) case 1, four blades; (**b**) case 8, two blades.  
Figure 13: Comparison of ice shapes under different blade numbers.
## 5 Conclusions  
In this paper, a new approximation method was developed to estimate the lateral behavior and maximum inter-story drift ratio demands of multistory buildings subjected to earthquake ground motion. A closed-form solution was proposed for a shear-flexural beam model with base rotation (\(\theta\)). The main contributions of the research are as follows:  
* The study presented closed-form solutions for displacement, inter-story drift ratio, and modal participation factors as functions of \(\alpha\) and \(\theta\), where \(\theta\) is the story drift of the first storey.
* The lateral stiffness ratio (\(\alpha\)) was suggested through a simple equation, depending on the Blume coefficient.
* The \(\alpha\) equation proposed in this study is simpler and more sensitive than the approaches in the existing literature.
* The proposed approach in this paper improves the existing literature by offering a new perspective on the approximate method and a comprehensive investigation of building behavior.
* Dynamic analyses were conducted using the novel continuous shear-flexural beam model and the SAP2000 program to examine the behavior of buildings in a broader framework. Various framed building models with distinct characteristics and different numbers of stories were considered, and the results were presented comparatively using graphical representations. Compared to the existing approaches in the literature, the model proposed in this study can predict the building behavior profiles along the height and the maximum inter-story drift ratio, a crucial engineering parameter, with very close error rates.
* The proposed novel beam model can be used for dynamic analysis, preliminary design, and assessment of buildings under earthquake effects.
* The utilization of the continuous model in the dynamic analyses of low-story building models was achieved with the proposed model in this study.  
In conclusion, the proposed model in this study not only offers a more profound understanding of building dynamics but also introduces a promising tool for future investigations in the field.  
Conceptualization, H.C. and H.T.T.; methodology, H.C. and H.T.T.; software, H.C. and H.T.T.; validation, H.C. and H.T.T.; investigation, H.C., H.T.T. and H.C.; resources, H.C. and H.T.T.; data curation, H.C. and H.T.T.; writing - original draft, H.C. and H.T.T.; writing - review & editing, H.C., H.T.T. and H.C.; visualization, H.C., H.T.T. and H.C.; supervision, H.C., H.T.T. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Comparison with Experimental Data  
50,000 Monte Carlo simulations were carried out to compute 10% and 90% percentiles and means of chloride concentrations at time \(t\) = 388 days at different measurement depths (5 mm, 15 mm, 35 mm, 35 mm). A comparison was made between the [PERSON]'s model (Equation (2)) with the values of Table 2, the proposed Bayesian approach with Equation (4) and the identified values (Table 5), and the experimental measurement values (Figure 9) for all loading cases. We note in Figure 9 that the 10% and 90% percentiles of the two models remain very close for all loading cases, and all points of the data fall within the 10% and 90% percentile ranges of both approaches. This indicates that the model used is complex enough to represent this experiment. Moreover, both percentiles are closer to the data for the larger depths (x = 25 or 35 mm). This is due to the fact that the model does not take into account the effects of the convection zone for depths near the surface of the concrete. This result indicates that the addition of a new random variable (\(\alpha\)) did not significantly modify the percentiles in comparison to the [PERSON]'s model. This means that the proposed approach was able to distinguish between material and concrete cracking related effects and uncertainties in a comprehensive way.  
Figures 10-12, compare the a posteriori histograms of the child nodes \(C(x,t)\) obtained from the proposed Bayesian approach with Equation (4) and the identified values (Table 5), and those obtained from Monte Carlo simulations with Equation (2). We can see that for all the loading cases, the probability densities follow the same distributions. There is a slight difference between the densities of the BN and those of the Monte Carlo simulations, which changes with the depth and depending on the loading case. For all cases, this difference is small for the depths of x = 5 mm and 15 mm; as the depth increases, the difference between the densities more visible. In the case of static loading and for all depths, we notice that the difference decreases compared to the unloaded case; which shows that the values of the parameters \(C_{s}\) and \(D\) are optimized after the integration of the loaded test data. However, for cyclic loading, it increases slightly compared to the other two cases; which may be due to the relative uncertainties to the complex process of opening and closing of cracks during loading cycles. Therefore, we can conclude that the methodology allows these parameters to be updated taking into account uncertainties associated with the model and measurement methods.  
Figure 9: _Cont._Figure 10: Distribution of chloride profiles with Equation (2) and Equation (4) for the unloaded case.  
Figure 9: Comparison of chloride content into concrete at measurement depths.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 2 Improved A* Algorithm  
### The Principle of A* Algorithm  
The search flow chart of the A* algorithm is shown in Figure 1. First, create a list of paths to store the path from the starting point to the target point. During the search process, maintain an open list and a closed list to store the nodes to be extended and the extended nodes. Each time, the node with the smallest total cost in the open list is selected as the current node of the search and moved into the closed list. Then, determine whether there are obstacles in the current node. If there are no obstacles, update the open list and the closed list. And judge whether the current node is the target point, if so, the feasible path is searched out.  
Global path planning is conducted based on a complete understanding of the environment. Accordingly, this paper acquired the three-dimensional point cloud data of an underground roadway at a working face of China Shanxi Zijin Mining Co., Ltd., as shown in Figure 2. The color map encodes point-cloud elevation, transitioning from red at the lowest points through orange, yellow, green, cyan, blue, to magenta at the highest points.  
Due to the large volume of raw point cloud data and the difficulty of directly visualizing the internal structure of the roadway, the dataset was processed by filtering, down-sampling, and planarization, yielding the planarized point cloud map shown in Figure 3. As illustrated in Figure 3, the underground roadway map exhibits a complex and intricate structure, characterized by numerous branching passages and frequent turns. Additionally, the underground roadways are generally narrow, averaging around 4 m in width, which significantly increases the risk of collisions and poses considerable challenges to safe navigation. Subsequently, the processed point cloud was further converted into a  
Figure 1: A* algorithm search flow chart.  
Figure 2: Three-dimensional point cloud map of the underground roadways.  
grid map. Additional impassable obstacles were then introduced at the map boundaries and along interfaces with unexplored regions, ensuring complete closure of the underground roadway. This effectively prevents the planned paths from traversing unknown areas. Finally, the classic A* algorithm is employed for path planning, and the resulting path is shown in Figure 4.  
The black grid in the figure is an obstacle, the mobile robot cannot walk, the blank area is a passable area, the yellow is marked as an extended node, and the green plus yellow is marked as a search node. It can be seen that the width of the roadway is different. The starting point and the end point can be randomly marked in the blank area of the figure.  
It can be seen from the magnified view of Figure 4 that the classical A* algorithm has problems such as the path passing through the obstacle corner point, the path approaching the edge, and the inflection point not being smooth in the roadway grid map. This chapter improves the above three problems.  
Figure 4: Original A* path planning diagram.  
Figure 3: Point cloud map after roadway descent sampling.
## 6 Research Areas
### Multi-Robot Collaboration  
Researchers have used multiple robots, such as UAV-UAV or UAV-UGV pairs for better navigation. In the UAV-UGV pair, the UGV performs the actual inspection while the UAV provides navigational support by being its eye from a higher vantage point [33, 34, 131]. Construction sites are highly cluttered, and it becomes difficult for a ground-based robot to navigate the site due to limited visibility [33]. To address this challenge, a collaborative system of a UAV and a UGV can be used for localization and path planning [33, 34]. The UAV provides an eagle-eyed view of the construction site and creates a point cloud with 2D photogrammetry which is then used by the UGV for navigation [34, 131]. However, in indoor spaces, the presence of a large number of obstacles limits the effectiveness of this approach without a suitable anti-collision system in the UAV [242].  
Another approach is to use the UAV for the main inspection and the UGV as a docking station for recharging the battery of the UAV [243]. This is useful in long-duration missions since the UAV needs to be lightweight and can be equipped with a smaller battery with a limited runtime. The UGV with a larger battery accompanies the UAV and provides periodic power boosts [243].  
The UAV-UAV approach is used for navigation and localization in Global Navigation Satellite System (GNSS)-challenged environments [244]. This is achieved by coordinating the movements of two UAVs in such a way that one of them is in a location where GNSS is available, while the other calculates its absolute position based on its relative position from the first UAV [244].  
[PERSON] et al. [208] developed an algorithm for the efficient allocation of tasks and path-finding for multiple robots while keeping path obstructions and the single-charge run capacity of robots as constrained. Genetic and k-means algorithms in conjunction with the Travelling Salesman Problem (TSP) also allow finding the optimum solution to the multi-robot path planning and task allocation problem [245, 246]. Multi-robot collaboration is important for the efficient use of these tools while avoiding clashes.
## 4 Statistical Testing of ENMRA Algorithm
### System Model
#### 5.2.5 Statistical QoS Guarantees  
The statistical QoS to reaffirm end-to-end data traffic reliably in MI-USNs was analyzed. The reliability of the upsurged transmission was particularly crucial in nearly all underground sensing applications with lower packet loss [44]. The bounded delay was also significant in the setting of timing constraints and real-time monitoring [59]. The communications link \(i-j\) is given for the packet delay and link reliability with the aim of distributed functionality between sensors.
## 3 Analysis of the Influencing Factors in Topology Optimization
### Volume Fraction  
Before performing TO, it is necessary to define the structure's volume fraction (\(\overline{v}\)), which is the ratio of the volume after TO to the original volume, as illustrated in the following formula:  
\[\overline{v}=\frac{v_{to}}{v_{0}}\times 100\% \tag{9}\]  
where \(v_{to}\) denotes the volume after TO and \(v_{0}\) signifies the original volume. The smaller the \(\overline{v}\) level, the greater the weight reduction.  
With other parameters held at default values, this study analyzes the impact of varying volume fractions (\(\overline{v}\)) on the TO structure configuration. The results are presented in Table 1. According to Table 1, it can be observed that for hexahedral meshes, the TO results at various volume fraction values are constrained by the structural geometry. As the volume fraction lessens, more material is removed; in the case of a volume fraction equal to 10%, only the non-design regions remain. In contrast, when the volume fraction is set as 60%, 70%, or 80%, the optimization results demonstrate relatively optimal structural configurations, but undesirable features, such as isolated bodies and disconnected branches, still affect structural performance. Furthermore, for a volume fraction of 60%, the minimum optimized volume is still 15.85% larger than the original unoptimized volume and cannot achieve the weight reduction goal. Compared to hexahedral meshes, the tetrahedral meshes exhibit a substantial reduction in isolated bodies and disconnected branches in the TO results. Meanwhile, the optimal structural configuration is obtained in the case of \(\overline{v}\) = 60%, but it still fails to achieve the weight reduction objective.  
Figure 3: Boundary conditions and load settings of gooseneck chain.  
In summary, the volume fraction substantially affects the TO structure configuration; however, regardless of whether hexahedral or tetrahedral meshes are utilized, the current settings for \(\overline{v}\) have not resulted in an ideal structure that meets the weight reduction goal. This is primarily due to the involvement of the mesh size and its related parameters in the TO procedure, and the default mesh size does not usually satisfy the optimization requirements. Therefore, further analysis of the influence of the mesh size on the structure configuration is required. In addition, it is necessary to check whether the mesh sizes determined by the static mesh sensitivity analysis are suitable for the TO. For the \(\overline{v}\) setting, this study refers to the experimental validation results from Ref. [43] and utilizes the ratio of the finite element model volume to the original model volume as the default input value of \(\overline{v}\), i.e., 41.75%.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 4 Simulation
### System Total Noise Simulation  
To validate the comprehensive noise source model and self-noise model proposed in this study, as well as to serve as a detailed supplement to the overall experimental results presented later. We extended the simulation by including the influence of 1/f noise in addition to the band-limited Gaussian white noise. The 1/f noise predominantly affects the frequency range from \(10^{-4}\) Hz to 1 Hz. Figure 11 presents the PSD of the EIN for each source, where the solid lines represent theoretical values, and the scatter plots in the same color show the simulation results. As illustrated, the simulation results are in close agreement with the theoretical predictions across all frequency ranges, thereby validating the effectiveness of the simulation and theoretical models.  
Further analysis reveals that, for the CS60 seismometer, different noise sources dominate across specific frequency bands. Below 0.008 Hz, the differential amplifier and integrator contribute most significantly to the overall noise. This observation slightly differs from [PERSON]'s findings [23], which identified the integrator noise as the dominant source. Above the 0.008 Hz frequency band, our results on the dominant noise sources are largely consistent with [PERSON]'s conclusions. This difference may arise from subtle differences in the modeling approach or parameter assumptions betwe  
Figure 11: EIN from different noise sources: Solid lines denote the theoretical values, while corresponding simulation results are represented with symbols such as rectangles, circles, triangles, and diamonds in matching colors.
## 2 Materials and Methods
### Feature Extraction  
For FB models, feature extraction was used to reduce the initial set of 92 features from the seven biophysiological signal modalities (Table 1) to a reduced feature set that excluded less relevant features. This feature selection process aimed to identify the most informative and discriminative variables that could effectively predict anxiety levels.  
First, after the features were calculated for each of the modalities, a Spearman correlation matrix was computed to understand the relationship between the features and label. A heatmap of the correlation matrix was then created to help with quick visualization of the results (Figure 2). Based on the correlation analysis, important features were selected and prioritized for model training. Second, the selection criteria involved setting a threshold for correlation coefficients. This step aimed to streamline the analysis and focus on the most impactful variables. This was carried out in a two-step process. The first was to look at the features of the same modality that were too closely correlated with each other. Features of the same signal with correlation higher than 0.95 were eliminated, where the feature with a higher correlation to anxiety was kept. The second step was looking at the feature correlation with anxiety label and eliminating the ones with less than 0.00009. This reduced the feature space from 92 features down to 54.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ACC** & Mean, stdv, min, max, abs integral of each x, y, & Stdv: Standard deviation, Min: minimum value, Max: maximum value, Net: Magnitude or length. \\  & z-axis and of norm. Peak freq of each axis & Peak freq: highest freq domain component \\ \hline
**BVP** & Mean, stdv, min, max, Peak freq & bpm: beat per min; ibi: interbeat interval, sdnn: stdv of ibi, \\
**ECG** & Mean, stdv, min, max. bpm, ibi, sdnn, sdsd, rmssd, rmssd: rms of ibi, pnn20, \% of \\  & pnn20, pnn50 & successive beats with more than 20 ms diff, \\ \hline
**EDA** & Mean, stdv, min, max of each signal, SCL, and SLR. & SCL: Skin Conductance Level, SLR: Skin Conductance \\  & Slope, and drange & Responses, drange: dynamic range. \\ \hline
**EMG** & Mean, stdv, min, max, drange, abs integral & \\ \hline
**RESP** & Mean, stdv, of each signal, inhalation, and & i/e: inhalation to exhalation ratio. Resp rate: \\  & exhalation. i/e ratio, and resp rate & respiration rate. \\ \hline
**TEMP** & Mean, std, min, max, drange, slope & \\ \hline \hline \end{tabular} Note: ACC = acceleration; BVP = blood volume pulse; ECG = electrocardiography; EDA = electrocardiogram activity; EMG = electromyography; RESP = respiration rate; TEMP = skin temperature.  
\end{table}
Table 1: Features from each physiological signal.  
Figure 2: Spearman correlation between all features, including labels. Features of some modalities were too closely correlated with each other to warrant inclusion. For example, for EDA features, EDA mean, min, and max are almost identical, unlike EDA std. In that case, only one of the three features was kept.