esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Biofeedback in Dynamic Biomechanical Gait Training for Chronic Ankle Instability
## 4 Discussion
### Gait Training for CAI  
Gait-training techniques specifically tailored to patients with chronic ankle instability (CAI) remain underexplored in the current literature. While numerous studies have addressed treatments for chronic pathological conditions, many rely on similar techniques and feedback methods, with limited emphasis on dynamic motions. As discussed previously, repeated ankle sprains often result in abnormal gait patterns. A notable prior study developed a destabilization device to induce inversion perturbations during walking [8]. This approach demonstrated an increased activation of the peroneus muscles, highlighting the clinical utility of such devices. However, its utility may be constrained by the focus on motor control strategies [23; 24] that are associated with abnormal neuromechanics in patients with CAI.  
Abnormal movements in patients with CAI often involve supraspinal motor control functioning as a feed-forward mechanism [23; 24; 25], potentially resulting in unstable movement patterns within the supraspinal sensorimotor pathways [1; 23; 24; 26]. Effective gait-training strategies should ideally integrate both internal and external feedback [27], with concurrent feedback being recognized as particularly effective [28]. Previous studies have recommended exploring multimodal feedback approaches, including auditory and visual cues. However, auditory feedback is not always readily accessible to researchers [29].  
This study adopted a concurrent feedback strategy incorporating both auditory and visual feedback to optimize gait training for patients with CAI and reduce the risk of recurrent ankle sprains. The devices used in this study were designed specifically to provide concurrent feedback. We believe that employing targeted strategies in combination with gait-training devices can effectively enhance dorsiflexion and version, thereby offering substantial benefits to individuals with CAI.
## 4 Discussion  
Comparing pure copper sinter and a composite containing 5% tuff, a very clear improvement in properties such as softening point, relative density, and hardness after densification is visible. As the tuff content increases, the conductivity of the composites decreases. This is a result of the reduction in the cross-section of the composite due to the presence of non-conductive tuff particles. In the conducted tests, no diffusion of the tuff components into the copper and its subsequent contamination was found. The purity and thus the conductivity of the matrix remained unchanged. The decrease in conductivity was due to the presence of non-conductive ceramics. The electrical conductivity values of copper-tuff composites correspond to the copper alloys used for resistance welding electrodes. The highest values of the relative density of composites were obtained for the tuff content of 15%. However, these values are at a similar level for composites with 5%, 15%, and 30% tuff and they vary in the range of 94.1 to 96.8. Compared to pure copper sinter, this is an increase in relative density of about 10%.  
Today, welding technology is one of the most widely used in industries such as aerospace, construction, automotive, and others. Technological development also requires new research for the application of these solutions for more demanding purposes, for example, underwater welding [35; 36; 37; 38]. New materials solutions are also needed. Modern techniques such as 3D printing solutions [39] allow for the application of new and more advanced reinforcement in the copper matrix such as graphene admixtures or different nano-additives [40; 41]. Not all of them are possible for application in the welding industry, but they show a modern trend in this area.  
The presented research shows that volcanic tuff is an attractive reinforcing material for copper matrix composites for application in resistance welding electrodes. It could be a successful replacement for traditionally used materials as well as other, more expensive admixtures. It is also worth paying attention to the ecological nature of the proposed filler. In addition to being used in metal composites as reinforcement, it can be easily recycled. Composites reinforced with aluminosilicate sinter can be recycled through the spontaneous flow of liquid metal from the pores of the reinforcement. Recycling of such materials is possible as opposed to composites containing, e.g., graphite. The ceramic tuff particles in the metallurgical process float to the surface which makes it possible to recover practically pure copper [23; 28].
## 2 Materials and Methods
### Methods
#### 2.3.3 Trend Analysis  
Linear regression is a statistical method used to analyze the relationship between two or more variables. The slope is a key parameter in linear regression analysis, indicating the change in the dependent variable for each unit change in the independent variable. On a temporal scale, trends in drought characteristics can be analyzed using linear regression and subsequently fitted to simulate trends for each raster grid in the study area, facilitating the assessment of drought evolution. Therefore, the calculated slope values can reflect the actual change in drought characteristics over a specific period. The specific formula for the slope is as follows:  
\[\text{Slope}=\frac{\text{n}\sum_{i=1}^{n}\left(\text{i}\ \times\ \text{DC}_{i} \right)-\sum_{i=1}^{n}\text{i}\ \times\ \sum_{i=1}^{n}\text{DC}_{i}}{\text{n}\sum_{i=1}^{n}\text{i}^{2}-\left(\sum_{ i=1}^{n}\text{i}\right)^{2}} \tag{4}\]  
where Slope is the slope of the raster regression equation indicating the rate of change of drought characteristics over time, DC\({}_{i}\) represents the average DC value in year i, and n is the length of the study period.  
When Slope \(>0\), it indicates that the DC of the raster point is increasing; when Slope = 0, it indicates that the DC of the raster point remains relatively stable during the study period. When Slope \(<0\), it indicates that the DC of the raster point is decreasing.
## 3 Methodology and Cooperative Game Theory Models
### Mathematical Forms of the Cooperative Game Models
#### 3.1.2 Model II  
For some reasons beyond the individual, sub-group, and grand coalition rationalities, a supreme authority may want all players to participate in the grand coalition and satisfy any unhappy parties with a minimal cost or may want to extract a certain amount of benefit from the grand coalition without violating any of the rationalities listed above. For this purpose, Model II, as defined by Equations (6) and (7), has been developed as a specific version of Model I by replacing Equations (1) and (4) with Equations (5) and (6), respectively. Model II has been developed in order to (i) test for the presence of a core solution and its extent if present, or (ii) measure the minimal external subsidy \(Z\) needed for the grand coalition benefit, \(b_{N}+Z\) if no core solution exists. Model II provides optimal solutions for the \(Z\) and all \(x_{i}\) in this regard.  
While \(x_{i}\) is the optimal player benefits, the optimal \(Z\) may have three different values and meanings: (i) if \(Z=0\), then one unique core solution exists that satisfies all parties and rationalities, (ii) if \(Z>0\), then an infinite number of core solutions exist within the flexibility of deductible \(Z\), and this \(Z\) is the maximum value that can be taxed without violating the core constraints (i.e., players' rationalities), and (iii) if \(Z<0\), then no core solutions exist at all, in this case at least \(\mid\!Z\!\mid\) amount of subsidy (i.e., -tax) is required to reach a core solution [32; 42].
## 3 Calculation of Coal Pillar Width of Gob-Side Entry Driving  
### Study on the Floor Failure Range of Residual Coal Pillars  
Residual coal pillars exist after mining the #1 coal seam. The floor failure depth resulting from the residual coal pillars can directly reveal whether the lower-coal-seam mining is affected. The coal pillar load acts on the floor and causes damage (Figure 4).  
The \(ED\) curve is a logarithmic spiral, and its formula is ([PERSON] et al., 2021 [24]):  
\[r=r_{0}\mathrm{e}^{\alpha\cdot\tan\varphi_{f}} \tag{1}\]  
where \(r\) is the spiral radius with \(O\) as the origin at an angle of \(\alpha\) with \(r_{0}\); \(r_{0}\) is the length of \(OE\); \(\alpha\) is the angle between \(r\) and \(r_{0}\); and \(\varphi_{f}\) is the internal friction angle of rock strata.  
Since \(\angle OFE=\angle EOF=\pi/4+\varphi/2\),  
\[r_{0}=B/2\cos\left(\pi/4+\varphi_{f}/2\right) \tag{2}\]  
where \(B\) is the width of the coal pillar acting on the floor.  
When the floor reaches the maximum yield failure depth, \(\alpha\) can be calculated by:  
\[\alpha=\pi/4+\varphi_{f}/2 \tag{3}\]  
Then the maximum floor failure depth can be expressed as:  
\[H_{\max}=\frac{B\cos\varphi_{f}}{2\cos\left(\frac{\pi}{4}+\frac{\varphi_{f}}{ 2}\right)}\mathrm{e}^{\left(\frac{\pi}{4}+\frac{\varphi_{f}}{2}\right)\tan \varphi_{f}} \tag{4}\]  
Figure 4: Floor failure model.  
Figure 3: Deformation and failure of a gob-side coal roadway. (**a**) Deformation of the roof and rip. (**b**) Roadway floor heave.  
According to Equation (4), the maximum floor failure depth under the influence of residual coal pillars is related to the coal pillar width and floor lithology. Based on the actual geological conditions of the coal and the rock strata, the internal friction angle \(\varphi_{f}\) is 38\({}^{\circ}\) and the width of the coal pillars \(B\) is 10 m. They are substituted into Equation (4): \(H\)max = 27 m, which is larger than the average distance of 19 m between the two coal seams. Therefore, the failure range of residual coal pillars in the upper coal seams has a significant influence on the lower-coal-seam mining and the mining roadway layout.  
Based on the above analysis, the #2 coal seam roadway can be arranged at the position where the residual coal pillar stress is less concentrated on the floor, which is conducive to roadway stability. The influence range of the high floor stress concentration of residual coal pillars in the #1 coal seam \(L_{OG}\) is shown in Figure 4,  
\[L_{OG}=(h_{1}+h_{2})\tan\varphi \tag{5}\]  
where \(h_{1}\) is the thickness between the #1 and #2 coal seams, m, calculated as 19 m; \(h_{2}\) is the #2 coal seam thickness, m, calculated as 3 m; and \(\varphi\) is the propagation angle of stress influence, calculated as 25\({}^{\circ}\).  
Based on the calculation of \(L_{OG}\), that is, at the #1 coal seam floor, the concentrated influence range of the coal pillars is 11 m. With reference to the calculation results, if the #2 coal roadway is arranged over 11 m away from the residual coal pillars of the #1 coal seam, the roadway will be less affected by the residual coal pillars of the #1 coal seam.
# Optical Lensless-Camera Communications Aided by Neural Network
## 5 Conclusions and Future Work  
In this paper, we propose a lensless camera-based communication system model, which provides a model for new designs for OCC systems. To evaluate the indoor and outdoor BER performance of our system when using the proposed decoding algorithm, we set up an experimental platform and performed the simulations. For indoor communications, we analyzed the BER performance of our proposed decoding algorithm when the transmitter was white lighting LED light or blue LED light. For outdoor communications, we analyzed the BER performance of snowy days with different snowflake densities. We concluded that the decoding algorithm using the BP neural network that we  
Figure 15: BER performance using different decoding algorithms indoors (the transmitter is blue LED light): (**a**) the BER using our proposed decoding algorithm and (**b**) the BER using threshold determination method at different thresholds.  
designed can adapt well to the changes of the time-varying environment, guaranteeing the reliable performance of the communication system over error probability.  
In future work, we will consider high-order modulation to increase the data rate. Additionally, we will also propose some effective methods to improve the communication distance of our system.  
Conceptualization, X.C., S.Z. and Y.Z.; Methodology, X.C. and S.Z.; Software, S.Z.; Formal Analysis, X.C., S.Z. and Y.Z.; Investigation, H.S. (Hanyang Shi); Data curation, S.W.; Writing--Original Draft Preparation, S.Z.; Writing--Review & Editing, X.C., S.Z. and H.S. (Hongliang Sun); Visualization, Y.Z.; Project Administration, Y.Z.  
This research was funded by Jilin Scientific and Technological Development Program under Grant 20180101040 JC.  
The authors declare no conflict of interest.
## 4 Discussion
### Comparison with Currently Existing Studies  
Muscle stretch reflex is often expressed in a PD-like form. It is rational to use proportional and derivative components to predict muscle activation levels, as muscle spindle is sensitive to change in muscle fiber length and rate of change. The reflex model used in this paper is similar to that described in [32], in which the muscle activation was predicted by PD algorithm during quiet standing. Since the  
Figure 12: Ankle joint torque prediction results with musculoskeletal model and sEMG.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Muscle** & \(F_{\textbf{MAX}}\) **(N)** & \(\theta_{\textbf{p}}\) **(\({}^{*}\))** & \(L_{CE0}\) **(cm)** & \(V_{CE0}\) **(cm/s)** \\ \hline
**SOL** & 4572.3 & 28.3 & 4.78 & 50 \\
**GAS** & 2225.7 & 12 & 5.78 & 60 \\
**TA 1** & 1148.9 & 9.6 & 6.04 & 60 \\ \hline \hline \end{tabular} \({}^{1}\) The parameters of TA are listed here, because to obtain accurate ankle joint torque, the contribution of TA is needed.  
\end{table}
Table 5: Musculoskeletal model parameters of subject B.  
inputs of the model shown in [32] are the position and velocity of CoM, this model reflects the reaction of CNS to the body's global state. Nevertheless, there is still a strong necessity to reveal the joint-level local response to the disturbance or the external environment, especially in the case of controlling a joint-level exoskeleton device and prostheses. The experimental results of this paper evaluate the possibility of using local ankle joint kinematics information to predict calf muscle activation. Local reflex control can release the burden from a higher-level controller, such as the human brain or a robot central controller. Moreover, the situation in [32] can be regarded as quasi-static, whereas the situation in this paper is dynamic. Our results imply that the stretch reflex control model is suitable, at least in the forepart of upright standing push-recovery movement.  
As acceleration has been suggested to be an important part of muscle activation prediction [27], this study validates the effect of adding an acceleration item into Equation (1) with the same data used in Section 3.3 for the prediction result. To this end, a one-way ANOVA is performed in this study. The results are shown in Figure 13. For all five subjects, the differences between the prediction results in the cases including the acceleration item and those without are insignificant (at a significance level of 0.01). This indicates that the acceleration item may not dominate the forepart of ankle joint torque control under our experimental conditions. The feedback delay between the ankle joint and the vestibular system may be the longest, as the ankle joint is the most distal joint to the vestibular system. This long delay will cause disadvantages in local ankle joint control. Moreover, the experimental study conducted in [27] is based on a cat, whose height differs greatly from that of humans, which may result in acceleration having greater affects than those found in our study.
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 4 Applications  
The rapid spread of the disease has changed lifestyles and increased people's tendency to do things remotely. Under these conditions, the need to connect to communication networks has increased. With the advent of COVID-19, new technologies have tried to solve the crises behind it [41; 42; 43; 44]. Among these technologies, 5G-related services also are useful in this manner. Such efforts can be seen in [45; 46; 47; 48]. Given the new pandemic problems as well as the new technologies mentioned, more works are still needed to use the maximum capacity of these technologies in this regard. We shortly introduce one of the potential applications of D2D service to solve some pandemic problems in hospitals. The increase in the number of patients in hospitals has meant that hospital network resources generally do not meet the need for treatment during the pandemic. Therefore, services that can shift the focus away from cellular and centralized networks will be of particular importance. Suppose, for example, a hospital that receives a large number of patients but has limited network communication infrastructure to transfer patient data to physicians.  
Figure 4: The sum rate as a function of (**a**) the number of potential D2D transmitters, (**b**) the number of co-channel D2D receivers, and (**c**) the D2D search radius.  
Figure 3: Jain’s fairness is computed and compared according to (**a**) the number of potential D2D transmitters, (**b**) the number of co-channel D2D receivers, and (**c**) the D2D search radius.  
D2D service can be one of the options to solve these challenges. Considering the two main goals of this paper, sustainable communication and the transmission of bulky contents, a scenario of joint resource allocation and pairing in a decentralized network based on D2D communications is presented in a typical hospital (Figure 5).  
The hospital scenario shown in Figure 5 is in accordance with the proposed system model in Figure 1 with \(N=2\) and \(K=3\). In the mentioned hospital, physicians are considered as receivers (r1 and r2) and hospital equipment is considered as transmitters (\(t_{ij}\)). In this scenario, due to the large number of users in the hospital network, physicians use the cellular user resources instead of connecting to the network. They use the co-channel D2D links with the cellular user to connect to the nearby medical devices, considering the mentioned resource allocation and pairing protocols. Thus, the resource allocation and pairing mentioned in the system model can be implemented in a typical hospital. As a result, patient data transfer is done optimally and with an acceptable data rate.
## 4 The First-Round Optimal Design of Aerostatic Thrust Bearings
### Optimal Design of the Geometrical Parameters of the Restrictor  
To optimize the orifice diameter, the air film thickness, and the orifice number of the thrust bearing, their impact on the performance of aerostatic thrust bearing is investigated in this section. Except for these three variables, the values of other geometrical parameters of the air bearing are the same as shown in Table 1.  
Figure 4 presents the performance curves of aerostatic thrust bearings with varying orifice diameter \(d\) and air film thickness \(h\). It can be seen that with the increase of the air film thickness, the stiffness curve ascents first and then descends, which has a peak. However, the LCC curves decline, and the VFR curve increases monotonically. Moreover, with the increase of the orifice diameter, the LCC and the VFR of the aerostatic thrust bearing increase dramatically. However, for the stiffness curves, the maximum stiffness declines greatly with the increase of orifice diameter. Moreover, the air film thickness,  
which is corresponding to the peak position of stiffness curves, declines gradually along with the decline of orifice diameter. It indicates that a higher stiffness can be acquired by the combination of smaller orifice diameter and a smaller air film thickness.  
Figure 5 presents the performance curves of the aerostatic thrust bearing with different orifice numbers \(n\). It shows that the LCC and stiffness of aerostatic thrust bearing can be improved by increasing the orifice number. However, it can also cause a dramatic increase in VFR. In addition, the LCC and stiffness only rise slightly when the orifice number increases to 12, while the VFR increases greatly. This means that the performance of aerostatic thrust bearing cannot be enhanced continuously with increasing orifice number.  
According to the above investigation, the combination of smaller orifice diameter and a smaller air film thickness helps improve the stiffness, and increasing the orifice number  
Figure 4: The performance curves of aerostatic thrust bearing with varying \(d\) and \(h\).  
Figure 5: The performance curves of aerostatic thrust bearing with varying orifice number \(n\).  
Figure 3: The finite element method (FEM) model of the air thrust bearing.  
can also improve the stiffness. To improve the performance of aerostatic thrust bearing, the orifice diameter of 0.15 mm, the air film thickness of 13 \(\upmu\)m, and the orifice number of 8 are selected in this section. Compared with the initial design, the LCC is improved by 28.3%, from 781.0 N to 1001.8 N, the stiffness is improved by 44.2%, from 72.7 N/\(\upmu\)m to 104.8 N/\(\upmu\)m, and the VFR is reduced by 27.2%, from 9.2 L/min to 6.7 L/min.