esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spearman Rank Correlation  
The whole dataset exposed a mean Spearman Rank Correlation Coefficient of 0.76. The \(p\)-value was below 0.01 for 1019 of the 1048 exercises. A detailed overview of the individual SRCCs, including the standard deviation for the exercises, is visualized in Figure 5.  
The SRCC varied between the tracked angles with a range of \(-0.27\) to 0.99 as mean values per exercise and angle as displayed in Figure 5. When considering the results aggregated per joint angles (Table 3), all negative correlations were observed for the elbow angles (left elbow 0.36, right elbow 0.42) in both iPad views, with the side view performing worse than the frontal view. The shoulder angles exposed a mean SRCC of 0.81 for both shoulders. Knee and hip joints were also tracked with moderate SRCC values (left hip: 0.82, right hip: 0.84, left Knee: 0.75, right knee: 0.81).  
While the SRCCs differed between the exercises, all of them exposed moderate linear correlations with values above 0.5 (Table 4). The Leg Extension Crunch showed a correlation of 0.84. Front Lunge correlated with 0.80, followed by the Single Leg Deadlift with an SRCC of 0.79. The Squat and Side Squat exercises showed a correlation of 0.78. The SRCC of the  
Figure 5: Pivot Table of the average Spearman Rank Correlation Coefficients (SRCC) distributed over the eight exercises and the eight tracked angles, each measured from the two iPad perspectives _Frontal_ and _Side_. The dashed boxes indicate which joints were specifically targeted by the respective exercise. The heatmap visualizes the performance of the individual joints per exercise, with darker green color referring to a higher positive correlation and darker orange color referring to a higher negative correlation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Angle** & **SRCC** \\ \hline leftElbow & 0.36 \\ leftHip & 0.82 \\ leftKnee & 0.75 \\ leftShoulder & 0.81 \\ rightElbow & 0.42 \\ rightHip & 0.84 \\ rightKnee & 0.81 \\ rightShoulder & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The aggregated SRCC values for all joint angles.  
Lateral Arm Raise was 0.68, and the SRCC of the Reverse Fly was 0.67. The Jumping Jacks performed worst with a correlation of 0.60.  
Similar to the wMAE, considering only the relevant joints for the specific exercises positively influenced the SRCCs of all exercises except for the Jumping Jacks, where it remained the same, and the Single Leg Deadlift, where it was reduced by 0.01 (Table 4).  
Comparing the two positions of the iPads, the side view performed slightly better than the frontal view, with SRCCs of 0.80 and 0.73, respectively.  
Similar to the wMAE, the SRCC is relatively consistent across the recorded subject, with values between 0.72 and 0.82.
# Study on Mechanism of Series-Flow of Pollutants between Consecutive Tunnels by Numerical Simulation
## 2 Mathematical Model
### Validation  
In the experiment of [PERSON] et al. [18], the scale model was composed of two consecutive tunnels and an open roadway section. The radius and length of the single tunnel were set as 0.243 m and 4 m, respectively. The tunnel portals were free of mountains. \(u_{\text{up}}\) was set 0.75 m\(\cdot\)s\({}^{-1}\) and \(u_{\text{up}}\)/\(u_{\text{down}}\) ranged from 0.47 to 2.58. The numerical model constructed in this paper was used to simulate the airflow and pollutants dispersion between consecutive roadway tunnels, and the structure size of the numerical model is the same as the [PERSON]'s tunnel with \(\Delta L\) = 2.3 m. The comparison of series-flow ratio \(\varphi_{s}\) between the simulated and experimental values is made in Figure 2. \(\varphi_{s}\) is the series-flow ratio and its expression is \(\varphi_{s}\) = \(Q_{s}\)/\(Q_{\text{up}}\), where \(Q_{s}\) stands for the pollutant quantity carried by the series-flow into the downstream tunnel and \(Q_{\text{up}}\) the total pollutant quantity emitted by the upstream tunnel. It shows that CFD simulated values of series-flow ratio \(\varphi_{s}\) are in good agreement with the experimental data, where the maximum error is not more than 8%.
# Ultrasound in Telemedicine: A Brief Overview
###### Abstract  
The delivery of healthcare from a distance, also known as telemedicine, has evolved over the past 50 years, changing the way healthcare is delivered globally. Its integration into numerous domains has permitted high-quality care that transcends the obstacles of geographic distance, lack of access to health care providers, and cost. Ultrasound is an effective diagnostic tool and its application within telemedicine has advanced substantially in recent years, particularly in high-income settings and low-resource areas. The literature in Pubmed from 1960-2020 was assessed with the keywords \"ultrasound\", \"telemedicine\", \"ultrasound remote\", and \"tele-ultrasound\" to conduct a SWOT analysis (strengths, weaknesses, opportunities, and threats). In addressing strengths and opportunities, we emphasized practical aspects, such as the usefulness of tele-ultrasound and the cost efficiency of it. Furthermore, aspects of medical education in tele-ultrasound were considered. When it came to weaknesses and threats, we focused on issues that may not be solved immediately, and that require careful consideration or further development, such as new software that is not yet available commercially.  
ultrasound; telemedicine; eHealth; tele-ultrasound +
Footnote †: journal: Medical Imaging  
1
Footnote †: journal: Medical Imaging  
1  
+++ ==WARNING: Truncated because of repetitions==
_Applied_  
6  
++  
measure as it eliminates both long transportation times and extra doctor visits. A lack of access to ultrasound doctors, despite technological advancements and mobile devices with high bandwidth that allow for seamless live-image transmission, is a concern in both low-income and high-income countries. Moreover, the main challenge in telemedicine remains an organizational one, knowing how and when to take advantage of the technology.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
###### Abstract  
The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the collocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes.  
trajectory data; HBase; coprocessor; spatiotemporal query +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 10; doi:10.3390/f11010010
## 3 Results
### Adverse Events  
All reported adverse events were minor, transient, and resolved spontaneously. During the treatment with the bimatoprost preparation, adverse events were reported by 5 participants (18.52%). Participants noted the occurrence of closed comedones and inflammation in the eyebrow area, and one participant experienced temporary pruritus after product application. Four (14.81%) women reported adverse events during the application of sweet almond oil, among which the most frequently reported were closed comedones and inflammation in the eyebrow area. The difference in the frequency of adverse events between the study groups was not significant (\(p\) = 0.716).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Survey Item** & **Answer** & **Bimatoprost n (\%)** & **Sweet Almond Oil n (\%)** & \(p\)**-Value** \\ \hline Improvement in eyebrow density & Yes & 19 (70.37) & 8 (29.63) & \\  & No & 8 (29.63) & 19 (70.37) & 0.003 \\ \hline Eyebrow hair elongation & Yes & 16 (59.26) & 7 (25.93) & \\  & No & 11 (40.74) & 20 (74.07) & 0.014 \\ \hline Eyebrow hair thickening & Yes & 9 (33.33) & 10 (37.04) & \\  & No & 18 (66.67) & 17 (62.96) & 0.778 \\ \hline Eyebrow hair hydration & Yes & 16 (59.26) & 24 (88.89) & \\  & No & 11 (40.74) & 1 (3.70) & 0.001 \\ \hline Darkening of eyebrows & Yes & 6 (22.22) & 3 (11.11) & \\  & No & 21 (77.78) & 24 (88.89) & 0.278 \\ \hline Comfort using the preparation & Very comfortable & 12 (44.44) & 12 (44.44) & \\  & Comfortable & 5 (18.52) & 6 (22.22) & \\  & Undecided & 1 (3.70) & 1 (3.70) & 0.996 \\  & Uncomfortable & 3 (11.11) & 3 (11.11) & \\  & Very uncomfortable & 6 (22.22) & 5 (18.52) & \\ \hline Treatment satisfaction & Very satisfied & 6 (22.22) & 3 (11.11) & \\  & Satisfied & 9 (33.33) & 3 (11.11) & \\  & Undecided & 9 (33.33) & 12 (44.44) & \textless{}0.001 \\  & Dissatisfied & 3 (11.11) & 8 (29.63) & \\  & Very dissatisfied & 0 (0.00) & 1 (3.70) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants’ reported outcomes on eyebrow hair after treatment with bimatoprost and sweet almond oil.
## 4 Results and Discussion
### Two Types of Starting Processes under Different Lip Angles
Figure 10 shows the fully-starting flow field (flow field A) and the flow field with a closed separation zone (flow field B) at Ma = 4.3, where the inlet wall are remarked by blue lines, the streamlines are denoted by black lines, and the wave system are represented by red lines. The fully-starting flow field was obtained according to the hysteresis principle by decreasing the incoming Mach number from a high value to 4.3. As shown in Figure 10a, when the inlet is fully-starting, the airflow capture capability is not affected by the internal flow, and the cowl lip shock impinges on the compression wall, forming a small-scale separation bubble. As displayed in Figure 10b, the internal flow also does not affect the flow capture performance. However, a comparatively large-scale separation bubble is formed in the internal contraction section. Meanwhile, the position where the separation zone is located forms an aerodynamic throat, and the separation zone induces complex shock systems that significantly increase the flow loss. Furthermore, the separation bubble scales of the two flow fields vary considerably. Therefore, in view of the flow structure, the flow field with the closed separation zone cannot be considered as a starting flow. Table 1 shows the aerodynamic parameters at the throat of the two flow fields, where \(Ma_{t}\) and \(p_{t}\) represent the mass-average Mach number and static pressure at the throat, and \(p_{\infty}\) is the static pressure of the freestream. Though the two flow fields have identical mass flow ratios, the total pressure recovery coefficient of flow field A is significantly greater than that of flow field B. In addition, the airflow in flow field B obviously decelerates, resulting in a relatively high-pressure rise. For the combustion chamber, when a closed separation zone is formed, the airflow parameters differ significantly from those of the fully-starting state, so the engine may not work normally even though the mass flow meets the requirements. Thus, the flow field B is still deemed to be at the unstarting state. Therefore, for the inlet with \(\delta\) = 10\({}^{\circ}\), the starting Mach number (Ma\({}_{s}\)) is 4.7.
# Nonwoven Textile Waste Added with PCM for Building Applications
## 2 Materials and Methods
### Phase Change Materials  
For the subsequently described investigation Micronal(r) DS 5001 X was chosen, an organic PCM produced by the German company BASF. It is a dry powder PCM, microencapsulated with highly crosslinked polymethylmethacrylate polymer wall, formaldehyde-free. The particle size varies from 0.1 to 0.3 mm, with a bulk density ranging from 250 to 350 kg/m\({}^{3}\). Its 26 \({}^{\circ}\)C melting temperature falls within the range of average operating temperatures that characterize a Mediterranean climate, making the Micronal(r) DS 5001 X suitable for construction applications through direct incorporation into building materials for the purpose of improving interior comfort conditions [22; 23; 24]. Once this melting point is reached, the material inside the microcapsules will maintain a slightly constant temperature for a more or less significant period depending on the amount of PCM used, and this because all the absorbed thermal energy is used to break the chemical bonds and make the phase change happen. This thermal energy is called latent heat of fusion, and for this PCM is about 110 kJ/kg, with an overall storage capacity of 145 kJ/kg in temperature range 10-30 \({}^{\circ}\)C.  
More detailed information about the properties of the Micronal(r) DS 5001 X can be found in [PERSON] et al. [25]. According to these authors this material shows a phase-change temperature between 26.09 \({}^{\circ}\)C and 27.81 \({}^{\circ}\)C, a melting enthalpy between 114.98 kJ/kg and 142.55 kJ/kg and a solidification enthalpy between 117.85 kJ/kg and 137.85 kJ/kg.  
A dynamic Differential Scanning Calorimetry (DSC) analysis (Figure 2) with constant heating rates of 0.5 \({}^{\circ}\)C/min was performed in order to characterize the thermal behavior of the PCM. The analysis was carried out on a 5.5 mg sample of PCM by using a DSC 1 Star System Mettler Toledo device, according to ISO 11357-1 [26].  
Figure 1: Merino wool, pre and post-carding process.  
The first endothermic transformation at \(T=0\)\({}^{\circ}\)C refers to the phase change of the water present within the sample, therefore it is not to be taken into consideration. Data resulting from the second endothermic process, on the other hand, refer exclusively to Micronal(r) DS 5001 X: area is 632.09 mJ, melting enthalpy is 114.93 kJ/kg, with \(T_{onset}=24.72\)\({}^{\circ}\)C, \(T_{peak}=29.19\)\({}^{\circ}\)C, and \(T_{endset}=33.71\)\({}^{\circ}\)C. The melting enthalpy of the PCM (Figure 3) is consistent with that indicated by the manufacturer on the data sheet (110 kJ/kg). On the contrary, the peak temperature can be wrongly compared with the phase change temperature. In fact, \(T_{peak}\) depends on the measurement conditions, including heating speed and mass of the sample used, so it is not an intrinsic value of the material and it is generally unreliable for comparison purposes [25]. Thus, the discrepancy of about 3\({}^{\circ}\)C compared to the nominal value of the melting temperature (26\({}^{\circ}\)C) can be explained in the light of the above differences.
# An Extended Proxy-Based Sliding Mode Control of Pneumatic Muscle Actuators
## 3 Controller Design and Stability Analysis
### Extended Proxy-Based SMC Design  
In this study, we extended the idea of conventional PSMC to a general nonlinear system which is described by the SISO second-order model (6). The main idea of our controller design is to add the model-based compensation to the PID-type virtual coupling controller and propose a new SMCstrategy for controlling the proxy. The comparison between the conventional PSMC and our EPSMC is shown in Figure 2.  
In Figure 2, the masses of object (PMA) and proxy are \(M\) and \(m_{p}\), respectively. \(x,p,x_{d}\) are used to denote the positions of object, proxy, and reference. \(f_{c}\) means the virtual coupling force between the proxy and physical object, and \(f_{SMC}\) is the sliding mode controller for controlling the proxy to approach the reference trajectory. To obtain a stable extended PSMC for the nonlinear system (6), we design the following novel sliding manifolds:  
\[\left\{\begin{array}{l}\sigma_{p}=\dot{x}_{d}-\dot{p}+H\left(x_{d}-p\right)+ I\int\left(x_{d}-p\right)dt\\ \sigma_{x}=\dot{x}_{d}-\dot{x}+H\left(x_{d}-x\right)+I\int\left(x_{d}-x\right)dt \end{array}\right. \tag{10}\]  
Unlike the conventional PSMC, the proposed sliding mode manifolds contain integrals of \(x_{d}-p\) and \(x_{d}-x\), which play important roles in reducing the steady error in the tracking control. Furthermore, we also design new virtual coupling force \(f_{c}\) and sliding mode controller \(f_{SMC}\) as:  
\[\left\{\begin{array}{l}f_{c}=\frac{1}{b}\left[K_{P}\left(p-x\right)-f+\dot{ x}_{d}+H\left(\dot{x}_{d}-\dot{x}\right)+I\left(x_{d}-x\right)\right]\\ f_{SMC}=\Gamma\text{sgn}\left(\sigma_{p}\right)-K_{P}\left(p-x\right)+m_{p} \dot{x}_{d}+m_{p}H\left(\dot{x}_{d}-\dot{p}\right)+m_{p}I\left(x_{d}-p\right)+ \\ \frac{1}{b}\left[K_{P}\left(p-x\right)-f+\dot{x}_{d}+H\left(\dot{x}_{d}-\dot{x }\right)+I\left(x_{d}-x\right)\right]\end{array}\right. \tag{11}\]  
Please note that the proposed virtual coupling force \(f_{c}\) is not a pure PID-type controller. Some terms based on known dynamics are added for the convenience of theoretical analysis. Similar consideration is taken in the design of sliding mode controller \(f_{SMC}\).
## 2 Materials and Methods
### System Features and Selected Use Cases  
The six core components and the communication layers are combined to provide the required functionality addressing one or more of the aspects mentioned in the introduction. In the remaining of this technical note, we focus on three of them to highlight the contributions the V-Cockpit platform brings in this regard by first describing them, then by showing the results we obtained, and finally by discussing their impact and relevance. The three main aspects we selected are (1) the creation of a new cockpit design, (2) the testing and validation of such a design, and (3) the automatic feedback the system provides by gathering and analyzing data collected during experiments. These three aspects are representative steps closing the loop that progresses from the initial conceptual idea about a new design to the collection of the required information to further refine it in a successive iteration.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 3 System Model and Problem Formulation
### Problem Formulation  
Assume that \(\gamma\) denotes the maximum energy difference of the received symbols, \(\gamma=\text{max}\Big{(}\frac{\mu_{1}}{\mu_{0}},\frac{\mu_{0}}{\mu_{1}}\Big{)}\), and \(\gamma\) is brought into Equation (13). Thus the two cases of Equation (13) can be combined as:  
\[P_{e}=\frac{1}{2}Q\Big{(}\sqrt{L}\Big{(}\frac{\gamma}{\gamma-1}\ln\gamma-1 \Big{)}\Big{)}+\frac{1}{2}Q\Big{(}\sqrt{L}\Big{(}1-\frac{\ln\gamma}{\gamma-1} \Big{)}\Big{)} \tag{14}\]  
As shown in Equation (14), the BER of the AmBC system decreases monotonically with \(\gamma\) increasing. This is consistent with the real situation. As \(\gamma\) increases, the gap between \(\mu_{0}\) and \(\mu_{1}\) becomes greater. Thus, the parts of the received signals' conditional probability distributions under \(d=0\) and \(d=1\) are greater. So, the area of overlap between the two probability distribution curves is smaller, which means that the BER is smaller.  
In this section, our goal is to maximize the maximum energy ratio \(\gamma\) by jointly optimizing the active beamforming vector at the reader and the passive phase shift of the RIS, subjected to the RIS phase constraint, as well as the energy limitation of the beamforming vector. Mathematically, this problem can be formulated as follows:  
\[\mathbf{P}\mathbf{I}:\underset{f\ \theta}{\max}\ \gamma \tag{15a}\] \[s.t.|\boldsymbol{\Phi}_{i}|=1,\quad\forall i=1,2,\cdots,N\] (15b) \[\|\boldsymbol{f}\|^{2}=1 \tag{15c}\]  
Since the objective function and constraints are non-convex, \(\mathbf{P}\mathbf{I}\) is a non-convex optimization problem. Generally, the alternating optimization (AO) method is used to solve the fixed weighting coefficient problem, but there is relatively high complexity. Furthermore, for the problem \(\mathbf{P}\mathbf{I}\), it is difficult for AO to handle the weighting coefficient's dynamic change. In this article, a DRL framework is proposed to solve the problem \(\mathbf{P}\mathbf{I}\) effectively.
## 4 Discussion
### Analysis of the Evaluated Metrics for Each Method
#### 4.1.4 Cycles per kWh  
The number of cycles per kilowatt-hour reflects battery degradation due to energy usage. Results indicate that V2 GSmooth has the most favorable behavior, utilizing fewer cycles per unit of delivered energy. This advantage highlights V2 GSmooth as the preferred method for extending the operational life of the storage system, as it imposes less stress on the battery over time.
## 5 Simulation Results
### Fault Detection Results
In Figure 10a, the fault happens at 3 s. It can be seen that the 20% LOE doesn't trigger the fault alarm since its effect to the motion of the quad-rotor is not serious. Hence, it is treated as a case of uncertainty. In Figure 10b, the fault happens at 3 s, which is highlighted by the transparent yellow block. During the 60% LOE of one rotor, the fault alarm is triggered at approximately the 5, 10, and 15 s, which are the time when quad-rotor is flying passing through the wait points and changing the attitude for the next tracking target. Figure 10: _Cont._  
Figure 9: Judgement of fault estimation result. Due to the medium fault level of rotor, some motion with large attitude change of the quad-rotor would trigger the fault alarm. Nonetheless, the judgement of FDI returns to the normal flight successfully within the smooth flight periods. In Figure 10c, the fault happens at 3 s, which has been highlighted by the transparent red block. As one could see, the fault has been detected immediately after the rotor failure, which guarantees the safety flight of the quad-rotor. The result is acceptable, though there are some switching periods during the time the fault is happening. These are the result of a tradeoff between the correction for misjudgment of the FDI, which returns the fault control to the normal one, and the safety concern, which maintains the fault tolerant control. Nonetheless, the switching times are actually quite few that would not affect the safe flight of the quad-rotor. In Figure 10d, the perturbation caused by disturbance is shown within the 1 to 2 s, which is covered by the green block, and the fault happens at 10 s that last until the end of simulation. As discussed before, the fault detection result would be affected by disturbances, which means misjudgment is inevitable. Nevertheless, with the proposed algorithm, the control law would be switched back to the normal one in a short period after the perturbation. Meanwhile, the short period that treated as fault happening would not be regarded as rotor fault, but rather as an external disturbance.