esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
###### Abstract  
**Background and Objective:** This study aimed to examine the possibility of using surface electromyography (sEMG) to aid in assessing the correctness of overhead deep squat performance. Electromyography signals were recorded for 20 athletes from the lower (rectus femoris (RF), vastus medialis (VM), biceps femoris (BF), and gluteus (GM)) and upper (deltoid (D), latissimus dorsi (L)) muscles. The sEMG signals were categorized into three groups based on physiotherapists' evaluations of deep squat correctness. **Methods:** The raw sEMG signals were filtering at 10-250 Hz, and then the mean frequency, median frequency, and kurtosis were calculated. Next, the maximum excitation of the muscles expressed in percentage of maximum voluntary contraction (%MVC) and co-activation index (\(CAI\)) were estimated. To determine the muscle excitation level, the pulse interference filter and variance analysis of the sEMG signal derivative were applied. Next, analysis of variance (ANOVA) tests, that is, nonparametric Kruskal-Wallis and post hoc tests, were performed. **Results:** The parameter that most clearly differentiated the groups considered turned out to be %MVC. The statistically significant difference with a large effect size in the excitation of RF & GM (\(p\) = 0.0011) and VM & GM (\(p\) = 0.0002) in group 3, where the correctness of deep squat execution was the highest and ranged from 85% to 92%, was pointed out. With the decrease in the correctness of deep squat performance, an additional statistically significant difference appeared in the excitation of RF & BF and VM & BF for both groups 2 and 1, which was not present in group 3. However, in group 2, with the correctness of the deep squat execution at 62-77%, the statistically significant differences in muscle excitation found in group 3 were preserved, in contrast to group 1, with the lowest 23-54% correctness of the deep squat execution, where the statistical significance of these differences was not confirmed. **Conclusions:** The results indicate that sEMG can differentiate muscle activity and provide additional information for physiotherapists when assessing the correctness of deep squat performance. The proposed analysis can be used to evaluate the correctness of physical exercises when physiotherapist access is limited.  
overhead deep squat; EMG; muscle excitation; %MVC; \(CAI\); median frequency; ANOVA
## 3 Materials and Methods
### Numerical Modeling
#### 3.3.2 Contact Interactions and Friction Coefficient  
The modeling of contact interactions was carried out to ensure the correct transmission of forces between structural components and to maintain numerical stability in finite element simulations. Contact interactions were defined between pairs of surfaces subject to separation and sliding during loading, considering realistic boundary conditions [27; 50; 51; 52].  
The specified contact interactions include between the column flange and the beam head, between the bottom surface of the L-shaped plate and the flanges of the beam and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parts** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** & \
\begin{tabular}{c} **Density** \\ **(kg/m3)** \\ \end{tabular} &
\begin{tabular}{c} **Yield Stress** \\ **(MPa)** \\ \end{tabular} \\ \hline Bolt & 210 & 0.300 & 7850 & 640 \\ Nut & 210 & 0.300 & 7850 & 640 \\ Clamp & 210 & 0.300 & 7850 & 275 \\ Steel/Profiles & 210 & 0.300 & 7850 & 275 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties for bolted joints.  
column head, between the nut surface and frame, between the nut surface and the clamp surface; between the top surface of the beam and the L-shaped plate; between the bottom surface of the beam and the frame; and between the clamps and the flanges of the column and beam, as well as between the clamps and the gusset plates, ensuring that no unrealistic penetration occurs between rigid components. The contact between the bolt shank and the hole wall was modeled as frictionless, assuming free sliding [53; 54].  
This simplification is justified because load transfer occurs predominantly through friction between surfaces compressed by bolt pretension. Additionally, the clamp is not in direct contact with the bolt shank, as the clamping force is applied only to the bolt head and nut, considering the washers. Consequently, lateral constraints had a limited influence on the structural response [11; 27; 54].  
CONTA174 and TARGE170 elements were used to model these interactions, representing surface-to-surface contact and ensuring compatibility between deformable bodies and rigid surfaces. The Augmented Lagrange method was employed as the contact algorithm, providing more excellent numerical stability and allowing iterative updates of contact stiffness at each iteration. This approach ensures the progressive redistribution of forces in the joint region, preventing convergence issues and instabilities in the numerical model [27; 50].  
The friction coefficient (\(\mu\)) between steel surfaces was set to 0.20, as recommended for steel surfaces under similar conditions. This value was chosen to reflect the surface contact properties and optimize the accuracy of the simulation, ensuring consistency with experimental studies [5; 54; 11; 53].
## 2 Measurement Details  
The SPNW for our experiment is fabricated on a SOI wafer, which has a profile of 250 nm-Si, 3 \(\upmu\)m-SiO2, and 500 \(\upmu\)m-Si. The SPNW is designed as a cross-section of A0 = 450 nm \(\times\) 250 nm and has a length of L = 4 mm, and is further covered by a 3-\(\upmu\)m top silicon oxide layer. Furthermore, mode adapters are integrated into the waveguide input and output coupling facets to reduce the coupling loss to less than 3 dB per facet and suppress any facet Fabry-Perot oscillations [31]. Figure 1 shows the scanning electron microscope (SEM) image and 3-D structural detail chart of the SPNW.  
The temporal and spectral profiles of the pulses were measured by the experimental setup with an attojoule-sensitive XFROG as shown in Figure 2. The mode-locked femtosecond pulse fiber laser is customized by CSA IoP, which can output femtosecond pulses with pulse duration FWHM of 150 fs and wavelength FWHM of 60 nm. To measure dynamics of ultra-short femtosecond pulses, we constructed a sensitive second-harmonic XFROG setup based on a BBO crystal (1-mm thickness, provided by CASTECH INC. CHINA) and a Horiba JY FHR1000 spectrometer with a SYMPHONY II UVCCD-1024 \(\times\) 256-BIDD detector, which enabled the minimum detected pulse energy to be down to about 500 aJ, and the spectral resolution to be better than 0.1 nm. There was also a programmable, finely motorized translation stage, THORLABS Z825, which enabled the finely moving controlled retro reflector mirror to provide temporal resolution down to less than 1 fs for the measurement setup.  
Figure 1: The structural diagram of the Si photonic nanowire waveguide (SPNW). (**a**) 3-D structure detail chart with top right 45-degree view. (**b**) Top view SEM of the partial SPNW before overlaying the top silicon oxide layer. (**c**) SEM of the right view after overlaying the top silicon oxide layer.  
The input pulse energy can be finely controlled by a set of half-wave plates and linear polarizers mounted on a motorized rotation stage. In order to finely control the temperature of the SPNW chip for measurement (DUT in Figure 2), the SPNW chip was bonded on a thermoelectric cooler (TEC) driven by a TEC temperature controller (THORLABS TED4015), which can provide a temperature control range of \(-55\) to \(150\)\({}^{\circ}\)C and a temperature control resolution better than 0.002 \({}^{\circ}\)C. The output pulse will also be measured by an optical spectrum analyzer (Anritsu MS96A), which has a measurement wavelength resolution of 0.02 nm. A flip mirror will switch the optical path for OSA and XFROG measurements.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 2 Materials and Methods
### State of the Art
#### 2.2.2 Challenges in the Management of Swarms of Heterogeneous Autonomous Vehicles  
Designing a solution to orchestrate a swarm of heterogeneous vehicles from different vendors is a complex task, mainly due to the lack of off-the-shelf products. Sockets was one of the first solutions used to communicate complex systems, as they were fixed with static IP addresses. However, as complexity increases, it becomes very hard to develop and maintain a socket-based solution because programmers need to manage all of the point-to-point communications. Additionally, in mobility, IP addresses can change as objects move around, so socket-based solutions are not suitable. For this reason, publish/subscribe solutions with a central broker were introduced [11]. In this case, all messages are sent to a central broker and the broker ensures that they reach the right destination. This approach avoids the need for static IPs but introduces a single point of failure because the service is interrupted if the broker fails. Moreover, as all messages go through the central broker, the performance can be slower. To overcome these limitations, other publish/subscribe technologies such as the DDS for real-time systems standard follow a distributed approach where the responsibility of the central broker is split out to all of the various nodes that make up the DDS network, enabling direct peer-to-peer communication without the need for a central message manager. In addition, DDS manages time and space decoupling, allowing late joiners to receive previous messages. Other interesting features of DDS are high-performance, real-time guarantee, scalable data exchanges and QoS policies, all of which are highly suitable for complex systems such as autonomous vehicles. In fact, DDS technology is used as a real-time communication backbone for many military unmanned vehicles [12], European Space Agency [13] and NASA robots [14; 15].  
The use of DDS as a real-time communication middleware has proven to be a valid solution for this type of system since DDS offers mechanisms that simplify the communication process between multiple diverse robots and their command and control systems.
## 3 Results and Discussion
### Vegetation Cover Stability Analysis  
The coefficients of variation in image element values were calculated for long-term remote sensing raster images of vegetation cover change in the Alar Reclamation area over the past 30 years using MATLAB software, and the results are shown in Figure 7 and Table 3, where the distribution of the coefficient of variation is highly spatially autocorrelated with vegetation cover. The mean CV value for Alar Reclamation was 0.40, with a standard deviation of 0.29, and 89.52% of the regional coefficient of variation values ranged from 0 to 0.8, indicating that Reclamation as a whole is in a state of steady growth and change. In total, 23.82% of the area has a CV coefficient value between 0 and 0.2, which is a very stable state. The areas with very stable vegetation cover are mainly the three reservoirs of Shengli, Upstream and Dolang, the Tarim and Aksu rivers, and part of the Taklamakan desert around the eleventh regiment. In total, 43.40% of the areas have CV coefficients between 0.2 and 0.5, which is a relatively stable state, mostly human activity areas mainly on the north and south banks of the Tarim River. In total, 22.30% of the areas have CV coefficients between 0.5 and 0.8, which is a weak state of variation. In total, 10.48% of the areas have CV coefficients between 1 and 2.13, which is a high fluctuation and intense variation stage, mainly in the areas around Duolang Reservoir and Shengli Reservoir, because where water resources are abundant, people reclaim the desert, build new regiments and plant economic crops such as cotton and pepper, which improve the vegetation cover. In summary, the interannual fluctuations in vegetation cover are mainly caused by climate change and human activities.  
Figure 7: Spatial distribution of vegetation cover variation coefficients in the study area in 2020.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Ncharq, SDRT, FOCAR, S&W and ARQ Performance Comparison  
Figure 8 shows the performance of SDRT, SDRT, FOCAR, S&W ARQ and NCHARQ protocol. We consider a practical and dynamic scene to conduct our simulations here. We assume that all nodes in the network can move with water flow at the speed of 0-1 m/s. Each node can move in any direction and the structure of the network will change largely with the flow of times. The node distribution is the same as Section 5.3 at the initial state. From Figure 8a, we can see clearly that our proposed protocol NCHARQ can achieve lower transmission redundancy with the help of nodes cooperation, so the improved system can achieve a low energy consumption. When the number of nodes is small (\(N+1\leq 4\)), the distance between adjacent nodes is very long and the transmission links suffer from large packet loss rate. However, for non-cooperative schemes, the increased number of hops may result in a larger number of transmission redundancies because each node has to send a large number of packets to its adjacent downstream node and the node cannot utilize the information from other nodes in the network. For NCHARQ, with the help of all nodes in the network, the transmission redundancy can be much lower when the number of nodes is sufficient. We can save about 50% or more sent packets compared with other protocols when \(N+1\geq 7\). The redundancy factor \(\alpha\) does not have a pronounced impact on the performance, but \(\alpha=1.2\) is advisable for it achieves the least \(\eta\) in the simulation. Figure 8b shows the average delay \(\phi\) for different protocols. When the number of nodes is small, it will be unlikely for nodes to receive packets from other nodes except for the adjacent one. Thus, the network coding will be almost useless and it may be not advisable to use NCHARQ in such a scene. The packet error rate between adjacent nodes is very large (\(P_{pac}(i,i+1)>0.7\) for \(N+1<4\)) in this situation, and thus it is suggested to add more nodes to the network to achieve high reliability. It can be seen clearly that our proposed protocol NCHARQ with \(\alpha=1.2\) and \(\alpha=1.3\) achieves the lowest average delay compared with SDRT and FOCAR as the number of nodes increases. For wait and stop ARQ, the average delay is too large (\(\phi\geq 30\) s) and is not drawn in the figure. The average delay can be as low as 0.53 s for sending a data packet successfully when \(N+1=5\), which saves at least 50% of the transmission time compared with the other protocols.  
Figure 7: (**a**) the transmission redundancy \(\eta\) for different transmitting sound level \(SL\); (**b**) the average delay \(\phi\) for different transmitting sound level \(SL\).
## 2 Related Works and the Key Contribution of This Paper
### Conventional Stereoscopic Video (CSV) Transmission  
We also consider related works on CSV transmission, where left and right video streams are transmitted with one stream prioritized to ensure 2D service continuity [15].  
This approach is relevant to the current study as it addresses issues related to preserving video quality in the presence of transmission errors. The paper builds on these concepts by applying them to the MVD format, which poses additional challenges due to its higher data requirements and complexity.
## 6 Implementation of the SMC and the MPPT Algorithm  
The practical implementation of any SMC requires several steps:* Limit the switching frequency to avoid MOSFET saturation and reduce switching losses;
* Derive a practical control law for the SMC;
* Calculate both the switching function and the output signal in real-time. Those topics are addressed in the following subsections.
# An IoT-Based System for Efficient Detection of Cotton Pest
## 2 Materials and Methods
### Implementation Scenarios
#### 2.4.3 Scenario 3 Real-Field Environment  
In addition to these experiments, more complex scenarios in the field are considered concerning two aspects. The first was that if this sensor device was out in the field, insects other than cotton could affect it or its values and it could be difficult to understand whether these signals were cotton insects or some other similar bugs affecting the device. In this case, technologies such as machine learning or artificial intelligence can be used as a solution; however, identifying specific insects is outside of the scope of this study. Secondly, along with the cotton pests, signals from other pests could also be received that were not harmful to the crop or were responsible for controlling the growth of cotton pests or were used to destroy them. In such a case, artificial intelligence and signal processing can be used to filter the signals.  
Another difficult scenario might be the responding pattern of the sensor to rain or high humidity. However, this sensor might not work in case of high humidity or rain, but it met all the standards of the outdoor environment and would resume its work after the environmental parameters are back to normal. It may not need to work during the rains because there were no flying insects during the rains. The experiments were conducted in three different environments to observe and test the real-time performance of this prototype and the observation and various results of these experiments showed that the required performance of the device was very reasonable concerning the detection of insects (sensing), communication overhead, and base station communication with field sensors.  
The prototype required very low computational resources compared with other similar-capacity devices. The device consisted of sensors that had a lithium battery which was able to provide power to sensors and a Zigbee device for several days while the base, located in the control station, required minimal resources.
# Indoor PV Modeling Based on the One-Diode Model
## 2 Methodology
### Characteristic Point Translation Technique  
As previously discussed, the parameter-scaling technique is inaccurate at low illuminance levels. Therefore, a new technique for modeling PV behavior under these operating conditions is required. In this study, the translation technique was chosen, since it only requires reference values to obtain the characteristic point values under other operating conditions. The proposed characteristic point translation technique is expressed as follows:  
\[I_{sc}=I_{sc,ref}\Bigg{(}\frac{G}{G_{ref}}\Bigg{)}[1+\alpha_{Isc}(T-T_{ref})] \tag{14}\]  
\[I_{mp}=I_{mp,ref}\Bigg{(}\frac{G}{G_{ref}}\Bigg{)}[1+\alpha_{Isc}(T-T_{ref})] \tag{15}\]  
\[V_{oc}=V_{ocn}+\beta_{Voc}\Big{(}T-T_{ref}\Big{)} \tag{16}\]  
\[V_{mp}=V_{mp,ref}-V_{oc,ref}+V_{ocn}+\beta_{Voc}\Big{(}T-T_{ref}\Big{)} \tag{17}\]  
where \(\alpha_{Isc}\) is the current temperature coefficient, \(\beta_{Voc}\) is the voltage temperature coefficient, \(G\) is the light level, \(V_{ocn}\) is the translated open-circuit voltage at a specific light level, and _ref_ is the reference value.  
The equations for the short-circuit and maximum-power currents show that they are proportional to irradiance and temperature [32; 33; 34; 35; 36]. Furthermore, the advantages of these equations are dependent on the temperature coefficient, which carries dimensions such as amps/\({}^{\circ}\)C or volts/\({}^{\circ}\)C. This means that these coefficients change whenever the manufacturer restructures the series-parallel arrangement or the number of cells in the PV module [37]. The translated open-circuit voltage in Equation (16) is also proposed using the reference characteristic points and can be expressed follows:  
\[V_{ocn}=C_{2}V_{oc,ref}\ln\Bigg{[}1+\frac{1-\frac{G}{G_{ref}}}{C_{1}}\Bigg{]} \tag{18}\]  
\[C_{1}=\Bigg{(}1-\frac{I_{mp,ref}}{I_{sc,ref}}\Bigg{)}e^{\frac{-V_{mp,ref}}{ \zeta^{2}\alpha_{oc,ref}}} \tag{19}\]  
\[C_{2}=\frac{\Big{(}\frac{V_{mp,ref}}{V_{cc,ref}}-1\Big{)}}{\ln\Big{(}1-\frac{I_ {mp,ref}}{I_{sc,ref}}\Big{)}} \tag{20}\]  
It is important to consider the impact of temperature on \(V_{oc}\), \(I_{sc}\), and the filling factor. Therefore, in this study, we use the characteristic point values experimentally obtained at the reference illuminance of 1000 lux at the same temperature. These characteristic point values, which include the \(V_{oc}\), \(I_{sc}\), and the filling factor, capture the effects of the temperature. Nevertheless, modeling at different temperatures is an exciting proposition to be considered in future work.
# Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control
###### Abstract  
The aim of this work is to present an automatic solution to control the surveillance camera merely by the movements of the operator's head. The method uses convolutional neural networks that work in a course-to-fine manner to estimate head orientation in image data. First, the image frame of the operator's head is acquired from the camera on the operator's side of the system. The exact position of a head, given by its bounding box, is estimated by a Multitask Cascaded Convolutional Network. Second, the customized network for a given scenario is used to classify the orientation of the head-on image data. In particular, the dedicated image dataset was collected for training purposes and was given a discrete set of possible orientations in the vertical and horizontal planes. The accuracy of the estimators is higher than 80%, with an average of 4.12 fps of validation time. Finally, the current head orientation data are converted into a control signal for two degrees of freedom surveillance camera mounting. The feedback response time is 1.5 s, which is sufficient for most real-life surveillance applications.  
neural networks; machine learning; image processing Article