esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Concept
### Basic Kinematics and Kinetics  
A four-bar linkage system with an active element of a rotary actuator and a passive element of a linear spring was proposed for KARE-1 (Figure 2). Three linkages (\(\overline{\text{HK}}\), \(\overline{\text{KA}}\), and \(\overline{\text{HG}}\)) are of equal lengths, \(L\). The knee joint angle \(\theta\) is one between \(\overline{\text{HK}}\) and \(\overline{\text{KA}}\). A passive joint angle \(\gamma\) is one between \(\overline{\text{KA}}\) and \(\overline{\text{GA}}\). The remaining linkage (\(\overline{\text{GA}}\)) has a slide section along which joint G moves. When the slide moves, the length of  
upper linkage parts. It is assumed that \(W\) passes the center of the upper linkage \(\overline{\text{HK}}\) since the upper body usually bends forward when sitting down to keep stability.  
\[\tau_{g}=\frac{w}{2}L\ \sin\!\left(\frac{\theta}{2}\right) \tag{4}\]  
The body assistance ratio \(\Re\) is defined as the \(\tau_{e}\) divided by the \(\tau_{g}\):  
\[\Re=\tau_{e}/\tau_{g} \tag{5}\]  
Plugging in Equations (3) and (4) into (5) gives:  
\[L_{\text{s}}={F_{s}}^{-1}\!\left(\frac{W\sin\!\left(\frac{\theta}{2}\right)}{2 \ \sin\!\left(\gamma\right)}\ \Re\right) \tag{6}\]  
Plugging in Equation (6) into (2) gives a nonlinear equation in terms of \(\gamma,\theta\), and \(\Re\).  
\[\gamma=\frac{\pi}{2}-cos^{-1}\!\left(\frac{\left\{{F_{s}}^{-1}\!\left(\frac{W \sin\!\left(\frac{\theta}{2}\right)}{2\ \sin\!\left(\gamma\right)}\ \Re\right)\right\}^{2}+4L^{2}\cos^{2}\!\left(\frac{\theta}{2}\right)-L^{2}}{4 \left\{{F_{s}}^{-1}\!\left(\frac{W\sin\!\left(\frac{\theta}{2}\right)}{2\ \sin\!\left(\gamma\right)}\ \Re\right)\right\}\!L\cos\!\left(\frac{\theta}{2}\right)}\right) \tag{7}\]  
Then, unknown \(\gamma\) is solved in terms of \(\theta\) and \(\Re\) numerically, even if not explicitly. Then, \(L_{\text{s}}\) and \(\alpha\) are accordingly solved when plugging \(\gamma\) into Equations (6) and (2).  
An active torque \(\tau_{a}\) at joint H, needed to keep the static balance with the spring and gravity forces, is defined as follows:  
\[\tau_{a}=L\sin\!\left(\theta-\alpha-\gamma\right)F_{\text{s}}(L_{\text{s}}) \tag{8}\]  
Once again, \(\tau_{a}\) can be expressed in terms of \(\theta\) and \(\Re\). The \(\tau_{a}\) is produced by an actuator and thus is a unique element that consumes energy. It means that the behavior of \(\tau_{a}\) would determine the energy consumption of the proposed body support.
## 4 Simulation Results and Discussions
### Impact of the \(\delta\) Parameter on the BER Metric  
In this Subsection, we analyze the results of the proposed SS-ELM for different values of the \(\delta\) parameter. As mentioned in Section 3, the \(\delta\) parameter is oriented to decrease the time-variant channel effects in the system performance. To this end, it relates to the number of pilot subcarriers employed in the learning phase of each time slot.  
In Figures 12 and 13, it can be seen the BER resulted from the SS-ELM scheme against \(E_{b}/N_{0}\) for diverse \(\delta\) parameters by considering configurations 1 and 2, respectively. The LS results are also displayed as a reference BER curve. In the following Section, our proposal will be carefully compared with the bench-marking estimators/equalizers. There is a direct relation between the value of the parameter \(\delta\) and the BE performance. We can also see that, for system configuration 1, there is a clear improvement, with values of \(\delta\) near to 12; this enhancement is not present for configuration 2. Therefore, it is possible to say that the impact of the parameter \(\delta\) in the performance of the systems is higher for a rougher system setting, such as the configuration 2. Finally, there is a clear performance improvement for the close interval between 7 and 12 of the parameter \(\delta\) for both of the evaluated configurations. In conclusion, the best overall results are obtained with a SS-ELM characterized by \(\delta=16\), by relaxing the \(E_{b}/N_{0}\) requirement with respect to the LS technique.
# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
## 2 Materials and Methods  
### Experimental Protocol  
The data previously collected [21] from 36 participants (age: 23.1 \(\pm\) 2.9 years, mass: 68.8 \(\pm\) 13.2 kg, height: 173.6 \(\pm\) 9.9 cm, BMI: 22.66 \(\pm\) 2.9, 17 females and 19 males) with no history of upper limb diseases was used for this study. The research protocol was approved by the University of Montreal Ethics Committee (n\({}^{\circ}\) 15-016-CERES-P) and all participants provided their informed consent prior to the experimentation. The experimental protocol fully described by [PERSON] et al. [21] and [PERSON] et al. [22] is briefly summarized.  
The participants performed an overhead box lifting task. We limited our study to the right upper limb, assuming that both upper limbs moved symmetrically with regard to the sagittal plane [23]. In line with the kinematic model developed by [PERSON] et al. [24], 43 reflective skin markers were placed on the thorax and the right upper limb. The kinematics were recorded using an 18 camera ViconTM motion analysis system (Oxford Metrics Ltd., Oxford, UK). Muscle activity was recorded for ten muscles, with bipolar surface EMG electrodes positioned over the anterior, median and posterior deltoids, biceps, triceps, pectoralis major, latissimus dorsi, upper and lower trapezius and Serratus anterior. Indwelling electrodes were inserted into the supraspinatus, infraspinatus and subscapularis. EMG signals were collected at 2000 Hz. They were processed using a second-order bandpass filter, with cut-off frequencies fixed at 20 and 425 Hz, then full-wave rectified. Envelopes were extracted using a 5 Hz low-pass filter. A series of 10 submaximal, and maximal, voluntary contractions (MVC) were performed to validate the electrodes position, and normalize the EMG envelopes, respectively [25]. The forces and moments between the hand and the box (\(8\times 35\times 50\) cm) were measured using a six-dimensional handle force sensor described in previous studies [26]. The box (6 or 12 kg) was lifted from a shelf at hip level and positioned on another shelf at eye level. Each participant performed three trials per load in random order with 30 s rest periods.
## 3 Verification with Numerical Solutions
### Numerical Modeling
track system has the smallest and most uniform mesh; the mesh spacing increases gradually toward the outer boundaries. In Figure 6a, which has the lowest mesh density, the smallest node spacing is 3.6 mm; the total numbers of nodes and elements are 43,865 and 39,530, respectively. In Figure 6b, the intermediate mesh density case, the smallest node spacing is 2.4 mm; the total numbers of nodes and elements are 96,131 and 88,684, respectively. In Figure 6c, the highest mesh density case, the smallest node spacing is 1.8 mm; the total numbers of nodes and elements are 162,893 and 152,324, respectively.
## 4 Experimental
### Simulation Experiments with MAC Protocols and the Wake-Up Modem
#### 4.3.1 Without Acknowledgment  
Figure 9(a) depicts the number of packets correctly received by the sink (normalized to the theoretical maximum). It can be seen that ALOHA-CS achieves the maximum throughput, followed by FAMA x2,  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**MODE** & **Wake-up** & **Modem** \\ \hline TX mode & 120 mW & 120 mW \\ RX mode & \(8.1\,\mu W\) & 24 mW \\ IDLE mode & \(8.1\,\mu W\) & 24 mW \\ SLEEP mode & - & \(3\,\mu W\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Underwater modem energy consumption.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Number of nodes** & **10** & **50** & **100** \\ \hline MACA & 7.5 & 26.5 & 40 \\ MACA Learn & 2.5 & 15 & 32.5 \\ FAMA & 1.5 & 7 & 11.5 \\ FAMA x2 & 2 & 8.5 & 15 \\ ALOHA-CS & 4 & 17 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Backoff time used in the simulations (seconds).  
which is exactly as productive as FAMA, but sends two data packets instead of one after the RTS/CTS exchange. In this scenario, ALOHA-CS outperforms all the other MAC protocols, because it does not have the control packet overhead, while the collision overhead is small, due to the short packet length. The following section shows that when the transmission speed is reduced by two, packet length doubles and that the FAMA x2 protocol then offers higher throughput.  
The MACA learn protocol is exactly the same protocol as the implemented MACA protocol, but uses the wake-up tone capability instead of the selective mode. In this way, all nodes can learn about the on-going transmissions and avoid collisions. As can be seen from the results, we do not achieve better results using this mechanism, due to the higher transmission delay.  
Another conclusion extracted from this figure is that the T-LOHI protocol is very density-dependent, and it may be infeasible to use it in high density networks.  
Depending on the application requirements, delay could be another important factor for further study. Results in Figure 9(b) show that ALOHA-CS has the lowest delay followed by MACA and FAMA x2. In turn, due to their operation, the two T-LOHI flavors show long delays--with the exception of FAMA when using 100 nodes.  
When looking at the power consumption in Figure 9(c), it can be seen that the wake-up tone use of the MACA learn and T-LOHI tone protocols greatly increases the energy consumption of the nodes, since they wake for almost every transmission. It can also be seen that ALOHA-CS has a huge energy  
Figure 9: Results without acknowledgment. (**a**) Normalized received packets; (**b**) delay; (**c**) energy per data packet—node; (**d**) energy per data packet—sink.  
consumption per each correctly received packet and that energy consumption overhead reduces when using FAMA x2 instead of FAMA.  
Focusing on the sink energy consumption as depicted in Figure 9(d), protocols that send a CTS packet back to the source node have high energy consumptions--the lowest being FAMA x2. Protocols that do not employ CTS packets have the lowest energy consumption.
## 2 Void Classification
### Solder Joint Inspection Methods  
Visual inspection methods are not sufficient because the solder joints are covered by the connected components, and, therefore, more sophisticated tools must be used for the inspection.  
X-ray inspection utilizes high-frequency electromagnetic radiation generated using an X-ray tube. These rays are emitted as a fanned beam from a point source, penetrate the specimen, and are captured on the opposite side using an X-ray detector. As the X-ray photons pass through the material, they interact with its internal structure. This technique leverages the penetrating ability of X-rays to identify issues such as an insufficient solder, poor soldering, solder shortages, and other defects in the solder joints.  
When defects like porosity, slag inclusion, or incomplete soldering are present, they appear as bright spots or lines in the affected areas. Visually, these defects can be identified by their brightness compared to the surrounding background in the inspection image. From an image-processing perspective, defects are detected based on variations in pixel gray values within the image.  
The visibility of these features depends on the material's density and atomic number: higher values increase the likelihood of X-ray interaction. The X-ray detector captures all unabsorbed photons in a planar resolution, converting the specimen's volumetric information into a two-dimensional (2D) image.  
SAM utilizes high-frequency acoustic waves, typically ranging from 15 to 300 MHz, to examine both the surface and internal structure of materials. A transducer generates afocused acoustic wave, which is directed at the specimen submerged in water, serving as the coupling medium.  
As the acoustic wave penetrates the specimen, it interacts with the material in several ways: it may be partially absorbed, scattered by fine internal structures, or reflected at interfaces between different materials. These interactions depend on variations in acoustic impedance, which are influenced by the material's density and the speed of sound within it.  
Reflections caused by an interface vertical to the wave propagation direction are reflected straight back, and the transducer records the echo time resolved, holding several interface reflections with different signal propagation delays, also known as time of flight, which is a key feature in SAM. Unlike X-ray imaging, SAM is capable of detecting internal defects such as cracks and delamination. These defects represent separations within the material that cause strong acoustic reflections due to the abrupt transition from solid material to air, gas, or a vacuum--even when the gap is extremely small. This sensitivity allows for SAM to identify flaws that may go undetected when using X-ray methods.
## 4 Cognitive Radio Combined with WBAN
### Tier-2 WBAN Model
#### 4.2.3 Cognitive Radio Controller  
For all the MDs, the CRC acts as a master and a central player in allocating the transmission opportunities. It has two distinctive channels that can transmit and receive data simultaneously. The CRC perform two major tasks: first, the CR instructs the client to control its transmission power based on the EMI measurement level to use the underutilized spectrum; secondly, to find the  
Figure 4: Three-Tier WBAN architecture.  
transmission parameters (e.g., SU transmission power) and dynamically adjust it so that the CRC gathers information from the inventory register [22]. The CRC users are of two types and assigned a licensed frequency for IP-BNC and an unlicensed frequency for OP-BNC, which is independent. When the IP-BNC is idle, the CRC will assign these bands to the variable OP-BNC (increasing during peak working hours). In the proposed EFPOC here, the CRC will allocate the idle licensed channels to the increased OP-BNC, avoid the frequency deficiency, and allow the OP-BNC to use a secure communication channel.
## 2 Key Security Challenges in Traditional Supply Chain Management
### Data Integrity Risks  
Data integrity refers to the capability of maintaining the accuracy, completeness, and consistency of information within a supply chain. Traditional centralized data management systems may contain vulnerabilities that allow unauthorized individuals to alter or manipulate this information. Such vulnerabilities can lead to critical supply chain data being compromised by malicious actors or unforeseen technical issues. For example, in the pharmaceutical industry, the incorrect recording of vital data, such as dosage and manufacturing dates, has the potential to cause severe health consequences. In contrast, blockchain-based systems significantly prevent data manipulation by encrypting each transaction and storing it through distributed ledger technology. Consequently, while protecting all data within the supply chain, any unauthorized changes can be immediately detected, enabling swift preventive measures.
## 3 Results
### Scattering in 2-D Plane Strain Models
#### 3.1.3 Non-Symmetric Rectangular Defect  
The final case examines defects with straight edges (Figure 1d), which are considered to mimic pitting corrosion in the 2-D plane. As shown in Figure 9, the reflection amplitude increases compared to previous cases, reaching up to 0.3 for the deepest defects. The difference in amplitudes between the models increases by up to 17.5%, though the Mindlin model maintains high accuracy for shallow defects. The main reason for this increasing difference is that the Mindlin model does not account for the mode conversion from \(A_{0}\) to \(S_{0}\) modes, which occurs with sharper edge defects [41].  
Figure 8: Transmission from non-symmetric smooth defect: (**a**) transmission coefficients from FEM model (FEM), (**b**) transmission coefficients from Mindlin model (FDM), (**c**) relative difference between the reflection coefficients.  
Figure 9: Reflection from non-symmetric rectangular defect: (**a**) reflection coefficients from FEM model (FEM), (**b**) reflection coefficients from Mindlin model (FDM), (**c**) relative difference between the reflection coefficients.  
The transmission intensity for this defect type is similar to previous cases, decreasing as the defect becomes deeper (Figure 10). The best agreement between models is observed for shallow defects, with the relative error being larger for deep defects. Overall, the Mindlin model demonstrates reasonable accuracy in predicting scattering for relative defect depths up to 0.3.
## 2 LTE
### LTE Initial Attach for UE  
The \"initial attach for UE\" process is a case of the first access to the network by the user subscribing to the LTE network using UE [15, 16, 17]. LTE Initial attach process is shown in Figure 2.  
Figure 1: LTE network.  
Figure 2: Initial attach process.  
The \"initial state after radio link synchronization\" process is one in which UE selects eNB and synchronizes a wireless link. The \"ECM connection establishment\" process is a NAS layer, that is a process of transmitting IMSI to request MME for net access. Through the relevant process, the RRC connection and S1 signaling connection are established.  
The \"authentication\" process is a mutual authentication procedure between UE and MME using EPS-AKA,and the \"NAS security setup\" process is a key setting process to safely transmit NAS messages between UE and MME [18, 19].  
The \"location update\" process is the one that receives personal profile information from HSS after registering the location, and the \"EPS session establishment\" process is the one that allocates network resources, so that the users can be provided with the services [20, 21].
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
## 4 Performance Evaluation
### Message Delivery Ratio  
Figure 11 compares the message delivery ratio for the RDSP system and UF scheme. It shows that the message delivery ration for both systems decreases as the number of relays increased across the path. To elaborate, the packet delivery ratio for path-4 having 8 relays is less than that of path-1 having 7 relays. Similarly, the packet delivery ration of path-1 is less than that of path-2 having 6 relays. Likewise, the packet delivery ratio of path-2 is less than that of path-3 having 5 relays. This  
Figure 10: End-to-end and round-trip delays.  
was because when the number of relays increases across a particular path, the frequent contention and collisions of messages among neighboring relays also increases which eventually reduces packet delivery ratio. However, RDSP system achieved around 8% higher message delivery ratio than UF scheme across all the four paths particularly at high relay densities because it avoids exchange of routing tables thus reduces collisions among neighboring relays and hence achieves higher message delivery ratio. On the other hand, UF scheme utilizes periodic exchange of entire routing tables that leads to frequent contention and collisions among neighboring relays which eventually reduces message delivery ratio.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 2 The Rural Paradigm Shift  
Under the perspective of granting better connectivity standards in the countryside, it is adequate to set forth the differences among several best-fitted technologies in order to find an optimal solution. The first approach is a suitable onset to focus on mobile network connections optimized for rural populations and self-steady links for IoT terminals, whether involving new communication tendencies such as Device-to-Device communications (D2D) or even 5G [8].  
Outdoor and indoor environments require the above aims to lift specific responses within rural population needs. The outdoor schemes consider current traffic estimation of the mobile network by algorithmic focusing since it may provide a proper breakdown for determining the cells' coverage capacity [9]. With respect to indoor environments, achieving an extended coverage based on ad hoc Networks by lower frequency bands involving repeaters would be suitable [10]. Nonetheless, other approaches will be analyzed in Sections 2.1 and 2.2 that cope with issues for both cases.
## 3 Results and Discussion
### Analysis of the Wind Power Density  
The observed wind power density was measured as 31.52 W/m\({}^{2}\). In Table 14, the wind power density values for the different distributions, i.e., GAM, GEV, GUM, IGA, RAY, and WEI, ranged from 19.95 W/m\({}^{2}\) for GUM to 33.78 W/m\({}^{2}\) for IGA. Despite this range, all distributions fell into wind power class 1 (poor), indicating insufficient wind energy potential for large-scale electricity generation. When compared with the observed wind power density, measured at 31.52 W/m\({}^{2}\), the results were consistent, especially with the RAY distribution, which estimated a wind power density of 31.43 W/m\({}^{2}\). These similarities between the observed and modeled values further confirm that the site has limited suitability for wind energy development, as all values indicate poor wind power potential.  
It is, therefore, recommended that small-scale wind turbines for standalone applications be deployed at this site [24]. Notably, previous wind resource assessments in the Eastern Cape Province also suggested small-scale wind turbines for wind speeds measured at heights up to 30 m above ground level [1,7,34]. Additionally, greater wind energy can be captured by utilizing wind turbines with larger rotor diameters or by increasing the hub height [34]. On the other hand, augmentation systems (diffusers and concentrators) that enhance the wind speed at the rotor can be employed to enclose small-scale wind turbines, allowing them to operate effectively in the area under study [4,65].
## 3 Results and Discussion
### Thermodynamic Studies  
Thermodynamic studies can present insight into the nature of the adsorption process. These are evaluated from the concepts of enthalpy (\(\Delta\)H\({}^{\circ}\)), Gibbs free energy (\(\Delta\)G\({}^{\circ}\)), and entropy (\(\Delta\)S\({}^{\circ}\)). The procedure for calculating these parameters is given in Table S4 (Supplementary Materials). The values of \(\Delta\)G\({}^{\circ}\) (kJ/mol) were found to be \(-\)20.5, \(-\)21.1, and \(-\)21.8, at 25, 35, and 45 \({}^{\circ}\)C, respectively (from the plot of ln K\({}_{\rm c}\) versus 1/T, shown in Figure S6, Supplementary Materials), which confirmed the spontaneity of the adsorption process. The values of \(\Delta\)H\({}^{\circ}\) (J/mol) and \(\Delta\)S\({}^{\circ}\) (J/mol K) were found to be 18.7 and 68.67, respectively. The positive values of \(\Delta\)H\({}^{\circ}\) reconfirmed that the adsorptive removal of MB onto nZVISLP is an endothermic adsorption process. A positive value of \(\Delta\)S\({}^{\circ}\) indicates the presence of high randomness at the interface of adsorbent/adsorbate while at the adsorption equilibrium.