esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] explained how to use MTs with stroke patients, chronic obstructive pulmonary disease (COPD) patients, cardiac patients, and the elderly in general, both in hospitals and in the private homes of patients [16]. Moreover, the playful games enabled by the MITs motivate patients stay engaged in the games and forget about their surrounding environments, e.g., rehabilitation centers [17]. There are a variety of studies regarding the different measurements of physiological signals. Postural stability is most often characterized with measures based on displacements of the center of pressure (COP) measured with a force platform [18], with specific COP displacements, namely, anterior-posterior (AP) and medial-lateral (ML) displacement, mostly being used to characterize COP stabilograms. The linear method is usually used as the most typical analysis, but the physiological signals from the human body are dynamic, irregular, and non-linear. Therefore, some previous studies regarding gait and balance performance have used patients' COP results as a factor to evaluate the complexity index (CI) of multiscale entropy (MSE). The area under the MSE curve defines CI as \(\sum\limits_{i=1}^{\text{sm}}\text{SampEn(i)}\), where \"sn\" is the scale number. The COP signal shows the maximum difference when m = 2 and r = 0.15 [19]. However, data channels can only be considered separately when using the MSE method, and that is only appropriate in the event that the multivariate signals are not dependent on one another and are not statistically correlation with each other [20]. Relatedly, a published study regarding the MMSE (multivariate multiscale entropy) method provided validation of the method for illustrated benchmark signals, as well as for a simultaneous analysis of the ML and AP components of the postural sway dynamics of both elderly and young subjects [19]. Therefore, the aims of this study were to investigate the effects of a 12-week rehabilitation exercise training program on the postural stability of stroke patients, and to verify the sensitivity of the MMSE method for analyzing physiological signals of postural sway.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 4 Resilience Assessment and Metrics
### Relationship of Characteristics and States of Power System Resilience  
The system state of resilient and traditional systems is described by the variation of system performance \(Q_{(t)}\) over time \(t\), as shown in Figure 6. The curve divides time into several sub-intervals to illustrate the system performance and corresponding characteristics at each time point, to capture the spatiotemporal correlations of the events on the power system [6; 9; 10; 11; 12; 14; 18].  
* Pre-disruption (Anticipation): Before the occurrence of a disruptive event, the system performance remains in a normal state. By analyzing anticipation, the spatial and temporal impacts of events are identified. Certain response strategies are organized to prepare for disruptive events, in order to enhance the resilience of the system.
* Post-disruption (Resistance, Withstanding, Absorption, Adaptation, and Recovery): \(t_{1}\) represents the start time of the disruptive event. In the case of a rapid decline in system performance, the system makes every effort to resist, withstand, and absorb the impacts caused by the event, in order to avoid potential cascading effects and ensure operational continuity. At this time, the robustness of the system comes into play, minimizing the impact of disruptive events on the power system and mitigating the degradation of system performance. At time \(t_{2}\), the system performance reaches the lowest level \(Q_{min}\). The adaptive measures begin to take effect, and the system operators need to develop recovery strategies to adjust and respond to the losses  
Figure 4: Conceptual resilience trapezoid curve [71].  
Figure 5: The resilience curve and its related outage and restore processes [72].  
caused by the disruptive event. At time \(t_{3}\), the system enters the recovery state. The system takes immediate recovery measures to restore power and repair the damaged infrastructure, aiming to bring the system back to its normal state.
* Post-recovery (Learning): \(t_{4}\) is the time when a resilient system recovers its performance to normal levels \(Q_{0}\). Lessons are learned from past experience. The impact of the disruption on the system operations and performance is analyzed, and improvements are made based on them so as to better handle future events.
# Technological Development and Application of Photo and Video Theodolites
## 2 Technological Development
### Development until 1940  
Photo theodolites have been known for more than 150 years in photogrammetry. The main credit for the introduction of photogrammetry goes to [PERSON], an officer in the Engineer Corps of the French Army. He is known as the \"Father of Photogrammetry\" as he constructed the first usable cameras for terrestrial work in 1851 [17]. He generated the first maps by means of photographs which he took from a balloon. His remaining research focused on terrestrial photogrammetry, designing the first photo theodolite which consisted of a camera and theodolite [18]. The end of 19 th century was significant for the development of terrestrial photogrammetry, which was applied in many mapping projects. In 1889, the Italian army developed the photo theodolite (Figure 2, left). [PERSON], in 1899, wrote his dissertation on the \"fundamental geometry of photogrammetry\", in which he solved the problem of determining the position of two camera stations, i.e., stereopairs, independently of terrain measurements from four points identified on both photographs, as well as the methods of relative and absolute orientation. He also developed his own photo theodolite (Figure 2, middle). German [PERSON], with the construction of his photo theodolite (Figure 2, right), designed the first modern stereo comparator which used the principles of stereophotogrammetry and \(x\)-\(y\) coordinate scales [17; 18]. At first, a photogrammetric camera was combined with components such as an aiming device, pitch circle, level, and tripod [19].  
The Wild company in 1922 constructed the first commercial photo theodolite, Wild P30 (Figure 3, left) [20]. The instrument was a combination of a camera and theodolite and, as such, was unique in the world. Wild's phototheodolite was the most accurate over the next three decades. The magnification of the telescope was 28\(\times\), and the direction reading with an optical micrometer was 1\({}^{\prime\prime}\). The focal length of the camera lens was 165 mm. The camera and the theodolite weighed 27.5 kg, while the weight of the complete measuring equipment with all the necessary aids was about 65 kg. The instrument was applicable to all large and small geodetic tasks. The instrument's reliability during field work in the most difficult conditions, which are common during surveys in high mountains, was one of its main advantages. Three years later, Wild introduced a new model of photo theodolite called the Wild FT9 (Figure 3, right). The mechanical construction of the theodolite and the camera simplified the determination of the camera's orientation for photogrammetric postprocessing.
## 2 Research Method
### Improved IDW Interpolation Based on 3D Search Neighborhoods
#### 2.3.2 Improved IDW Interpolation  
This study applied linear interpolation by referring to the IDW function of Shepard [43]. The principle of IDW interpolation is to calculate the unknown values using the neighboring observation value and weight after determining the neighborhoods from the unsampled location. Here, the weight is set in inverse proportion to the power of the distance (Equation (4)).  
\[\lambda_{i}=\frac{\sum_{j=1}^{n}\frac{1}{d_{ij}^{2}}\lambda_{j}}{\sum_{j=1}^{ n}\frac{1}{d_{ij}^{2}}} \tag{4}\]where \(\lambda_{i}\) is the \(i\)th unknown value, \(n\) is the number of neighborhoods, \(\lambda_{j}\) is the \(j\)th known value, \(d_{ij}\) is distance from the location with \(i\)th unknown value to the location with \(j\)th known value and \(p\) is the power (\(p>0\), generally 2).  
The distance is evaluated by the Euclidean distance in 3D vector space (Equation 5)).  
\[d(a,b)=\sqrt{\sum_{i=1}^{n}(a_{i}-b_{i})^{2}} \tag{5}\]  
where \(d\) is distance, \(a_{i}\), \(b_{i}\) are point locations to calculate the distance between two points.  
The final neighborhood is determined to be the neighborhood included in the search distance, or the N-number of neighborhoods closest to the unsampled location is used in IDW interpolation. However, since the nearest neighbors in the 3D observation data are densely populated in a vertical direction and can be mostly formed in a vertical direction, an elevation exaggeration factor is set to adjust the distance scale in a vertical and horizontal directions and is used as the search distance.  
When the search distance is given as the fixed parameter, two search distances separated by horizontal and vertical directions are required, which are used as the explicit radius of the spheroid according to Section 2.3.1. Here, if no neighbor exists within the spheroid, 'NoData' is returned.  
In contrast, when determining the number of neighbors as the fixed parameter, the nearest neighbor from the estimated unsampled location is set and the interpolation is performed. In case the set neighbor is located too far away, the maximum search distance to the vertical and horizontal directions can be limited. In addition, if the number of neighbors given in the neighborhood count is not satisfactory, only the neighbors within the search distance should be used, and if no neighbor exists, \"NoData\" is returned.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 2 Deep Aiding Architecture  
The proposed architecture is illustrated in Figure 2. It combines navigation and tracking by feeding back observable predictions to the tracking control loops such that code and carrier NCO registers are steered according to the navigation solution [10]. For the LEO case, part of the dynamic stress originates from the satellites' motion. INS/GNSS integration is reported in the block diagram for completeness; however, in the scope of this paper, INS is not strictly required and, therefore, will not be developed further. The navigation solution is essential for prediction calculation and can be supplied either by the embedded positioning engine or, alternatively, from an external host through proprietary sentences over UART. Both these configurations are supported by the TeseoV platform and reported in Figure 2.
# Virtualized MME Design for IoT Support in 5G Systems
###### Abstract  
Cellular systems are recently being considered an option to provide support to the Internet of Things (IoT). To enable this support, the 3 rd Generation Partnership Project (3 GPP) has introduced new procedures specifically targeted for cellular IoT. With one of these procedures, the transmissions of small and infrequent data packets from/to the devices are encapsulated in signaling messages and sent through the control plane. However, these transmissions from/to a massive number of devices may imply a major increase of the processing load on the control plane entities of the network and in particular on the Mobility Management Entity (MME). In this paper, we propose two designs of an MME based on Network Function Virtualization (NFV) that aim at facilitating the IoT support. The first proposed design partially separates the processing resources dedicated to each traffic class. The second design includes traffic shaping to control the traffic of each class. We consider three classes: Mobile Broadband (MBB), low latency Machine to Machine communications (IM2M) and delay-tolerant M2M communications. Our proposals enable reducing the processing resources and, therefore, the cost. Additionally, results show that the proposed designs lessen the impact between classes, so they ease the compliance of the delay requirements of MBB and IM2M communications.  
Keywords:NFV; virtualization; 5G; LTE; M2M; IoT; traffic peaks +
Footnote †: journal: Sensors
## 3 Simulation and Analysis
### Experimental Simulation and Analysis
#### 3.3.2 Map 2  
Figure 8 displays the outcomes of the three RRT algorithms applied in Map 2. It can be seen from the figure that in the complex roadway environment, the roadway selected by  
Figure 7: The path-planning results of Map 1. (**a**) Traditional RRT algorithm; (**b**) EP-RRT* algorithm; (**c**) FGA-RRT and PRM fusion algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Path-Planning Length** & **Inflection Points** & **Rate of Success** \\ \hline Traditional RRT & 80.59 & 16.3 & 63\% \\ EP-RRT* & 71.52 & 11.5 & 78\% \\ FGA-RRT and PRM fusion & 67.72 & 3.2 & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results of Map 1 planning efficiency.  
the traditional RRT algorithm can obtain the optimal path, but its path is more tortuous, resulting in the longest path length. Because the goal of the traditional RRT algorithm is to find a feasible path, regardless of the degree of path twists and turns, the generated path is not further optimized. The EP-RRT* algorithm optimizes the initial planned path, but the effect is not good, and there are still redundant paths. In contrast to the initial pair of algorithms, this paper's algorithm combines FGA-RRT and PRM to scatter points on the generated feasible path, traverse and optimize, find the shortest path, and find the ultimate path with the fewest inflection points and minimal path length.  
Table 4 represents the comparative data for the route planning of the three algorithms in Map 2. The table illustrates that, relative to the conventional RRT algorithm and EP-RRT* algorithm, this paper's enhanced algorithm cuts down path length optimization by 16.5% and 11.91%, respectively. Regarding turning points, this paper's refined algorithm shows reductions of 75.35% and 72.22%, respectively, in comparison to the other two algorithms. Regarding planning success rates, this paper's improved algorithm shows an increase of 30.52% and 10.52%, respectively, in comparison to the other two algorithms. In terms of planning success rate, the improved algorithm in this paper increases by 30.52% and 10.52%, respectively, compared with the other two algorithms. According to the comprehensive data, the fusion with the PRM algorithm can shorten the redundant path and make the generated path have fewer turning points. In contrast to the RRT and EP-RRT* algorithms, the path length has been optimized.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Path-Planning Length** & **Inflection Points** & **Rate of Success** \\ \hline Traditional RRT & 79.68 & 14.2 & 66\% \\ EP-RRT* & 75.53 & 12.6 & 85\% \\ FGA-RRT and PRM fusion & 66.53 & 3.5 & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results of Map 2 planning efficiency.  
Figure 8: The path-planning results of Map 2. (**a**) Traditional RRT algorithm; (**b**) EP-RRT* algorithm; (**c**) FGA-RRT and PRM fusion algorithm.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 3 Notations and Preliminaries
### Recommendations  
The server notifies the clients about the type of heterogeneity present in their data and recommends strategies to address the heterogeneity. The recommendations send to clients are denoted as \(R_{Q}\), which indicates quantity skew, \(R_{l}\) means label distribution skew, and \(R_{i}\) means image skew. Data augmentation and clustering are the strategies recommended based on the type of data heterogeneity associated with client data. The recommendations based on JSD and SSIM are shown in Equations (9) and (10).  
\[R_{Q},R_{l}=\begin{cases}\text{JSD}_{d}<0.1&0-\text{No quantity Skew, No Label distribution Skew}\\ \text{JSD}_{d}>Threshold&1-R_{Q},R_{l}\end{cases} \tag{9}\]  
\[R_{i}=\begin{cases}Threshold<\text{SSIM}>0.95&0-\text{No image skew}\\ \text{SSIM}<Threshold&1-R_{i}\end{cases} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline
**SSIM Range** & **Similarity Description** \\ \hline SSIM \textless{} 0.5 & Poor similarity \\ \hline \(0.5\leq\text{SSIM}<\text{Threshold}\) & Moderate similarity \\ \hline Threshold \(\leq\text{SSIM}<0.95\) & Good similarity \\ \hline SSIM \(\geq 0.95\) & Excellent similarity \\ \hline \end{tabular}
\end{table}
Table 3: SSIM similarity range.
## 5 Conclusions  
In this work, a redesign of the leg assembly for RoboREHAB was presented that aims to improve the joint movements of the knee and hip joints that can be achieved by traditional leg assemblies. SolidWorks motion analysis and Simscape analysis demonstrated the feasibility of the redesigned leg assembly. Reinforcement learning was applied to a Simscape model that utilized the redesigned leg assembly. The policy that the network learned while using the new leg design produced angular-position histories and path trajectories that closely reflect the results from the mocap data when compared to the original two-segmented RoboREHAB leg assembly. Results of the validation process showed that, based on the shape and size of the reproduced trajectories, a smooth and more natural gait was learned by the reinforcement-learning algorithm using the three-segmented Simscape model. The learned trajectories, while still improvable, will serve as a foundational reference to diagnose and evaluate normal gait deviations for patients. Therefore, the key result is that a redesigned RoboREHAB, with a three-segmented linkage provides a framework that is better for rehabilitating and recreating human joint movements used in robotic rehabilitation applications. This key finding opens the door to monitor patient progress objectively by direct comparison of the patient's current gait and the natural human gait, allowing for specialized treatment of the muscles or neural connections responsible for the abnormal gait. The learned trajectories could be improved by using variable-length segments for the leg assembly or incorporating the z-direction motion from the motion-capture data to train the reinforcement-learning agent.  
Conceptualization, J.A., C.-H.G. and Y.T.W.; data curation, J.A.; formal analysis, J.A.; investigation, J.A. and A.Y.; methodology, J.A., C.-H.G. and A.Y.; project administration, C.-H.G. and A.Y.; resources, C.-H.G. and Y.T.W.; software, J.A.; supervision, C.-H.G.; validation, C.-H.G. and A.Y.; writing--original draft preparation, J.A.; writing--review and editing, C.-H.G., A.Y. and Y.T.W. All authors have read and agreed to the published version of the manuscript.  
This project has been partially supported by the Capstone Senior Design teams in the Department of Mechanical Engineering at the University of Texas at Tyler since 2019. The authors thank their contributions to building a prototype of the RoboREHAB (robotic walking training device).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System
## 3 Structure and Control Configuration of the Proposed System
### SOC of the Battery  
The State of Charge (SOC) acts as a significant function in the battery-powered systems and it also improves the performance of the battery. Therefore, precise calculation of the SOC can not only preserve the battery, but also prevents over-discharge, enhances the battery life and accuracy of the system by saving energy. The battery SOC is measured by the count coulomb method [21]. The coulomb counting approach estimates the battery discharging current and integrates the discharging current over time to evaluate the SOC. The initial battery SOC (\(SOC_{b}\)) is fixed at 70%. The \(SOC_{b}\) is determined by the resulting equation,  
\[SOC_{b}=SOC_{in}-\frac{1}{3600C_{N}}\int i_{b}dt \tag{13}\]  
where \(SOC_{in}\), \(C_{N}\), and \(i_{b}\) are the battery initial SOC, nominal capacitance, and battery current, respectively.
## 3 Simulation Experiments and Analysis
### Experimental Setup
However, the disadvantage of the RTPK-SFOA is that it requires more computational resources and has a longer computation time than the other algorithms. Figure 2: Schematic diagram of the iteration process. To prove that the improved algorithm proposed in this paper performs well in different experimental scenarios, the location and size of the task area are modified, and a new task scenario 2 is constructed for testing (the relevant information concerning the USVs remains unchanged). Experimental Scenario 2: The task area properties are reset (The specific attributes are shown in Table 4). Various algorithms are run to obtain algorithmic assignment results (Table A3 (located in Appendix A)), and a schematic diagram of the iteration process is shown in Figure 4.  
According to Figure 4, the PK-SFOA outperforms the SFOA, and the final fitness of the PK-SFOA is greater than that of the SFOA because the PK-SFOA introduces an a priori knowledge set. Since the RTPK-SFOA introduces a set of prior knowledge and a set of candidate prior knowledge, which reduces the probability of the algorithm falling into a local optimal solution, the RTPK-SFOA converges faster and has the highest adaptability. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(task_{x}\)**(\(m\))** & \(task_{y}\)**(\(m\))** & \(task_{area}\)**(\(m^{2}\))** \\ \hline Task 1 & 1000 & 1500 & 10,000,000 \\ Task 2 & 2500 & 1500 & 10,000,000 \\ Task 3 & 4000 & 1500 & 10,000,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Task scenario attributes. Figure 3: Data analysis bar graphs. (**a**) Mean values; (**b**) Std values; (**c**) best values; (**d**) worst values. Due to the random nature of the intelligent algorithms, to verify the average performance of the algorithms, this paper runs each algorithm 30 times in Scenario 2 and statistically analyses their final results. The results are shown in Figure 5.  
Figure 4: Schematic diagram of the iteration process. Figure 5: Data analysis bar graphs. (**a**) Mean values; (**b**) Std values; (**c**) best values; (**d**) worst values. After changing the task scenario, it can be seen from Figure 5a,b that compared with other algorithms, the RTPK-SFOA has a smaller standard deviation, the highest average adaptation, and better performance. Figure 5b-d show that although the best adaptations of the WPA [10] and BWO [11] are better than those of the RTPK-SFOA proposed in this paper, their worst adaptations are lower, and their standard deviations are larger, which indicates that these algorithms are less stable and cannot guarantee the acquisition of the optimal solution in a short period. The experimental part of this paper simulates real task scenarios and proves that the RTPK-SFOA has good performance and stability. However, compared with other algorithms, the RTPK-SFOA needs more parameters to be defined and requires more computational resources. In real-world applications, if the CPU performance at the control centre is inferior, extended durations might be needed to obtain allocation results.