esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
Finally, several methodological considerations have to be made. Although the work presented here was a study of the usability and feasibility of the application, only professional input was considered. In this area, clinicians' expertise is considered to be more important than users' perspectives [48], since in the neurorehabilitation field, the physiotherapist has better knowledge about how to implement an effective rehabilitation programme. On top of that, they are involved in the direct delivery of the rehabilitation process. Nevertheless, future studies with patients with ULD are needed to corroborate that the proposed VR application contributes to the physical improvement of the upper extremity and manages to motivate and maintain adherence to the rehabilitation treatment. Such a study could be made possible by the real-time, remote VR setup presented, which reduces the cost of deploying such a solution at the patients' homes. This is aligned with the literature's suggestion [46] to provide feasible solutions for telerehabilitation, allowing the patients to benefit from social care and home-based rehabilitation [35].
###### Abstract
More recently, several publications have faced the problem with a comprehensive modelling approach [4; 6; 22; 23; 24; 25; 26; 27; 28]. In general, musculoskeletal models treat the bones as rigid bodies and represent the viscoelastic tissues as a combination of spring-like elements [10; 28; 29; 30; 31; 32] with non-linear characteristics. These are dramatic simplifications, but from another point of view, they offer the possibility to simulate complex dynamic situations without excessive requirements of computational resources and time. The implementation of a musculoskeletal model of the knee joint is not so straightforward, since many details are to be defined that can strongly affect the results. First, the anatomical structure has to be designed, and this can be done based on medical images (MRI); second, the mechanical properties of the soft tissues, ligaments and cartilage have to be quantified; then, the kinematic conditions, the external loads (ground reactions and inertia forces and moments) and the muscle forces applied to the system have to be determined. Finally, the model has to be animated by the input data so that the internal loads (ligaments and contact forces) are computed based on the dynamic equilibrium principles. In the present work, the procedure has been implemented starting from a previously developed model [29], which has been improved in terms of number and position of the ligaments, adaptation to natural gait and determination of muscle forces. The objective of this work was to analyze the ligament tensions during walking and to understand how they are affected by muscle contractions. The problem is of clinical relevance for many situations in which the knee ligaments are injured, or they must be sacrificed because of knee joint arthroplasty. Our findings could help identify compensatory attitudes to be adopted to prevent ligament overloads or identify which muscles should be used to compensate for surgical removal of a ligament. Knowing the effect of muscle contractions on ligament tensions may suggest proper rehabilitation programs aimed at training appropriate muscle control.
## 3 Materials and Methods
### Radial Basis Function  
The RBFNN is a type of FFNN [35]. It has a simple structure comprising three layers, including a single hidden layer [36]. Its concise training and rapid convergence enable it to approximate any non-linear function [14]. The RBFNN is known for improved prediction efficiency and more stable results [14]. Furthermore, RBFNN frequently demonstrates superior training speeds compared to back-propagation networks [8]. The Gaussian function is considered the basis function of the RBFNN. The representation of the RBFNN output is described in Equation (3):  
\[y(x)=\sum_{i=1}^{M}w_{i}e^{\left(\frac{-(||x-c_{i}||)^{2}}{2\sigma^{2}}\right)}, \tag{3}\]  
where the input, output, center, width, and number of basis functions centered at \(c_{i}\) are denoted by \(x\), \(y(x)\), \(c_{i}\), \(\sigma\), and \(M\), respectively. Similarly, weights are denoted by \(w_{i}\).
## 3 Results and Discussion
### Molecular Docking
#### 3.3.4 Telomerase Reverse Transcriptase
Moreover, Mol5's tail displayed flexibility, allowing it to adjust near Asp254 and five other residues (Asp251, Asn369, Asn192, Arg253, and Ala255), providing a significant advantage over Mol3. The stabilization of Mol5 primarily relied on multiple hydrogen bonds, including three carbon-hydrogen bonds, five conventional hydrogen bonds, and a Pi-donor hydrogen bond, in addition to hydrophobic interactions. This combination suggested an irreversible blocking effect on the TERT binding site. In contrast, Mol3 exhibited a preference for binding with two key residues, Gln308 and Asp254, to block TERT, with a predominant reliance on conventional hydrogen bonds. Notably, it formed a Pi-Pi T-shaped interaction with Tyr256, which caused it to interact unfavorably with Asp251, affecting its PLPchem score slightly. However, its phenoxazine ring successfully blocked the binding site horizontally, where the necessary phosphorylation process occurs. Figure 4: The 3D structure shows the most favorable TERT complexes after docking, with the inhibitors (Mol3 and Mol5) displayed in stick form and colored using default atom colors.
# Strength Characteristics of Iron Tailings Blended Soil as a Road Base Material
## 3 Results and Discussion  
### Unconfined Compressive Strength  
#### 3.1.1 Influence of Iron Tailing Sand Content  
Figure 2 shows the variation of UCS of ITBS with 5% cement and 2% curing agent after 7 d depending on the content of ITS. It can be seen from the figure that, at higher contents of ITS, the strength is firstly observed to increase and then decreases afterward. It is noteworthy that the strength is recorded to be the highest when the ratio of tailing sand to soil (ITS: soil) is 50:50. This can be attributed to the fact that when the content of ITS is less and the content of soil is more, the material strength is mainly derived from the stacking of internal particles and the friction between the surfaces, whereas the effective cohesion between the particles is less. The activity of ITS increases with the increase of ITS. The generated C-S-H gel material is combined with aggregates of soil particles which can increase the cohesion by efficaciously binding the individual particles and achieve the purpose of filling material internal clearance. However, with the increase of tailings content, the gap formed by tailings sand is not filled with sufficient amount of clay and as a result the strength is decreased.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 4 WSN Applications and the Need for SDN-Based Management
### Environmental Applications  
A popular environmental application of WSNs is based on the measurement of physical parameters such as temperature, vibration, sound, chemicals and gases. This has led to application in scenarios such as the agriculture industry for irrigation or animal monitoring, water industry for development of smart water grids to enable efficient distribution and control of potable water [11] and in disaster relief applications for monitoring and fighting forest fires and also in earthquake detection and rescue operations. Other applications include environmental control of pollutants and marine monitoring requiring long term unmanned WSN operations. However, such applications may require deployment of sensors over very large areas, harsh environments or even hard to reach regions which may result in dependence on sensor energy supply which affects network lifetime and the ability to alternate tasks upon change of sensing requirement or upon node failure [65]. There is therefore a need for effective topology, energy and maintenance management of which integration of SDN introduces to the application scenarios. SDN-based management would allow for a centralized network view of essential parameters suitable for large-scale applications, where energy is crucial SDN techniques provide effective resource allocation through software duty-cycling and by enabling easier use of traffic engineering, congestion control, load balancing [66] and mobility management. SDN also provides an effective interface for sensor re-programmability necessary for function alternation without requiring additional hardware such as FPGAs [9].
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### Particle Filter
#### 3.4.1 Particle Initialization  
An initial set of \(m\) particles is generated \(P_{0}=\{\mathbf{X}_{0}^{(i)}|i=1,2,\ldots,m\}\), where \(\mathbf{X}_{0}^{(i)}~{}=~{}[x_{0}^{(i)}~{}y_{0}^{(i)}]^{\mathrm{T}}\) is the position of the \(i\)th particle. The positions of the \(m\) particles are obtained by BLE-based positioning discussed in Section 3.2 plus the measurement noise. The noise follows a Gaussian distribution with a mean of 0 and a standard deviation of the positioning error. An equal weight of \(w_{0}^{(i)}=1/m\) is assigned to each particle.
## 5 Discussions
### Emerging Security Risks with the Expansion of IoE  
The rapid scaling of IoE systems has revealed that traditional security approaches are no longer sufficient [14; 38; 172]. In particular:  
* Centralized security models create a SPOF for large-scale IoE networks, significantly weakening overall security [10; 140; 161].
* Digital identity and authorization issues compromise the reliability of device authentication and access control in IoE environments [40; 82; 83; 84; 160].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Security Technology** & **Advantages** & **Challenges** & **References** \\ \hline Blockchain-Based Security & Decentralized security, immutability, transparency & High transaction costs, scalability limitations & [73; 152; 155] \\ \hline Edge AI for Security & Real-time threat detection, local data processing & Vulnerable to adversarial AI and model poisoning & [16; 29; 30; 64] \\ \hline Quantum-Resilient & Long-term security, resistant to quantum attacks & High computational demand, limited IoE deployment & [52; 70; 127; 132] \\ \hline FL for Privacy & Decentralized AI training, preserves user privacy & Security risks in model sharing, communication cost & [144; 169; 170] \\ \hline \hline \end{tabular}
\end{table}
Table 19: Advantages and challenges of IoE security technologies.  
Quantum computing-based attacks, while currently theoretical, pose a serious long-term threat to the integrity of IoE security infrastructures [66; 67; 139].
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 4 Experimental Realization of JES and Nano-TES  
The nano-TES and JES are experimentally realized and tested thanks to two different device architectures: an auxiliary device and the proper nano-sensor. On the one hand, the auxiliary device was employed to determine the superconducting energy gap and the thermal properties of the material composing the one-dimensional active region. On the other hand, the measurements performed on the proper nanosensor provided dependence of the \(R(T)\) characteristics on the bias current.
## 2 Methodology
### Climate Change Scenarios
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multirow{2}{*}{**City**} & \multicolumn{5}{c}{**Scenario**} \\ \cline{3-8}  & & **A1B** & **A2** & **B1** & **RCP4.5** & **RCP8.5** \\ \hline \multirow{6}{*}{Avg Daily Max (\(\Delta^{\circ}\)C)} & Barcelona & 1.11 & 1.04 & 0.56 & 1.95 & 3.03 \\ \cline{2-8}  & Rome & 0.66 & 0.58 & 0.21 & 2.60 & 3.12 \\ \cline{2-8}  & Berlin & 0.94 & 0.85 & 0.54 & 1.89 & 2.40 \\ \cline{2-8}  & Bern & 0.72 & 0.56 & 0.22 & 1.86 & 3.06 \\ \cline{2-8}  & Vienna & 0.64 & 0.65 & 0.36 & 2.18 & 3.23 \\ \cline{2-8}  & Mean & 0.81 & 0.74 & 0.38 & 2.10 & 2.97 \\ \hline \multirow{6}{*}{Avg Daily Min (\(\Delta^{\circ}\)C)} & Barcelona & 1.12 & 1.04 & 0.47 & 1.86 & 2.81 \\ \cline{2-8}  & Rome & 0.62 & 0.47 & 0.13 & 2.51 & 2.92 \\ \cline{2-8}  & Berlin & 0.95 & 0.77 & 0.60 & 1.29 & 1.80 \\ \cline{2-8}  & Bern & 0.64 & 0.53 & 0.13 & 1.12 & 2.38 \\ \cline{2-8}  & Vienna & 0.72 & 0.45 & 0.22 & 1.84 & 2.35 \\ \cline{2-8}  & Mean & 0.81 & 0.65 & 0.31 & 1.72 & 2.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Increase of average daily maximum and minimum outdoor temperatures in the different future scenarios with respect to the current scenario, with an uncertainty limit of \(\pm\)5%.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 3 Ground Testing
### Estimation of L/D  
The LLT analysis coupled with the 2D airfoil data also allowed estimation of the L/D of each configuration as a function of speed. The L/D vs. speed is depicted in Figure 12 below for each main configuration assuming leveled flight and an MTOM of 4 kg.  
As could be expected, the lowest camber configuration #1 showed the best aerodynamic efficiency for moderate to higher speeds. More cambered configurations showed improvements relatively to configuration #1 only when the AOA became high at lower speeds. They also reduced the stall speed relative to configuration 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**U (m/s)** & \begin{tabular}{c} **RBM \#1** \\ **(N\(\cdot\)m)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)RBM \#2** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)RBM \#3** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)RBM \#4** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **\(\Delta\)RBM \#5** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **\(\Delta\)RBM \#6** \\ **(\%)** \\ \end{tabular} \\ \hline
12 & 12.47 & \(-5.8\) & \(-8.6\) & \(-13.2\) & \(-15.6\) & \(-12.8\) \\
16 & 12.04 & \(-12.5\) & \(-18.2\) & \(-26.5\) & \(-30.6\) & \(-25.2\) \\
20 & 11.22 & \(-20.3\) & \(-28.6\) & \(-43.0\) & \(-48.6\) & \(-39.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RBM differences to configuration #1 at different speeds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CI** & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** & **\#6** \\ \hline U = 12 m/s & 0.0062 & 0.0074 & 0.0148 & 0.0134 & 0.0119 & 0.0119 \\ U = 16 m/s & 0.0059 & 0.0085 & 0.0163 & 0.0144 & 0.0126 & 0.0129 \\ U = 20 m/s & 0.0034 & 0.0093 &Once again, the intense negative twist variation with a root camber increase penalized the efficiency of cambered configurations at moderately low speeds when compared to configuration #1. The expectation of a more gradual morphing strategy for maximum efficiency within the flight speed envelope was not fulfilled with the current prototype. Nevertheless, it could be noticed that there are more than two configurations showing the best performance within this flight speed envelope, with configuration #6 being the most efficient at low speeds, configuration #5 being the most efficient at 13 m/s, and configuration #1 being the most efficient at speeds of 13.5 m/s and above.
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 5 Model Predictive Control Strategies for the EWMS
### Short Term
#### 5.1.2 Pump and Irrigation Controller (EMS)
The volume of water in the water tank \(V_{tank}\) and the state of the battery energy \(SoE\) are expressed as:  
\[V_{tank}(k+j+1) =V_{tank}(k+j)+Rec_{pump}(k+j)\cdot\Delta t\cdot\rho_{rec}-I(k+j) \cdot T_{s}\cdot\rho_{r}, \tag{28}\] \[SoE(k+j+1) =SoE(k+j)-\frac{\Delta t\cdot P_{bat}(k+j)}{1000}, \tag{29}\]  
where \(\rho_{rec}\) is the water volume that the pump recharges per second, and \(P_{bat}(k)\) is the power that the battery provides (positive value) or receives (negative value) from the system at each instant \(k\). The value \(P_{bat}(k)\) is estimated with the following equations:  
\[P_{bat}(k) =\left\{ \begin{matrix}\eta_{char}P_{dem}(k)&P_{dem}(k)<0\\ \frac{P_{dem}(k)}{\eta_{dis}}&\sim\end{matrix} \right. \tag{30}\] \[P_{dem}(k) =\frac{P_{pump}Rec_{pump}(k)}{\eta_{inv}}-P_{Solar}(k) \tag{31}\]  
where \(P_{pump}(k)\) and \(P_{Solar}(k)\) are the power required to operate the pump and the power provided by the solar panel at instant \(k\), respectively. Additionally, in Equations (30) and (31), \(\eta_{char}\) and \(\eta_{dis}\) are the charge and discharge efficiencies of the battery, respectively, which influence the relationship between \(P_{bat}\) and \(P_{dem}\). Finally, \(\eta_{inv}\) is the efficiency value associated with the power inverter. In addition to the objectives considered by the controller in the objective function (23), there were some additional conditions that the MPC had to meet. Specifically, there were some desired limits associated with the water tank level and the state of the battery energy, which were included as the following constraints in the optimization problem:  
\[V_{tank_{min}}\leq V_{tank}(k+j)\leq V_{tank_{max}}, \tag{32}\] \[SoE_{min}\leq SoE(k+j)\leq SoE_{max}. \tag{33}\]  
Additionally, a programmable constraint can be included for the activation of the irrigation valve, represented by the following inequality:  
\[sign(I(k+j))\leq Rest_{irrigation}(k+j), \tag{34}\]where \(Rest_{irrigation}\) can be set to 0 when the system needs to stop irrigation. For example, in this work, \(Rest_{irrigation}\) was 0 for any instant at which solar irradiance \(Ir>100\) Wm\({}^{-2}\) or \(T_{gr}>30\)\({}^{\circ}\)C.  
At this point, the control strategy design, which was focused on optimizing energy consumption in the greenhouse system, was complete. Now, the irrigation objective could be established to control the quantity of energy needed by the system. However, the reference values for irrigation are flexible and can be changed according to the medium-term requirements of a given greenhouse. A medium-term water management system was integrated into the previous controllers to address these requirements, as described below.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 9 Open Issues and Discussion
The use of this method [180] would create many CCI between APs, which may add more challenges when applying the LB algorithm [66; 79; 82; 84; 94] HO management [66], and interference management and coordination [117]. The second solution is more suitable for outdoor places, such as fields,  
Figure 9: (**a**) Hybrid LiFi/Wi-Fi network, (**b**) reflected signal NLOS, (**c**) blocked signals by objects, and (**d**) connected LiFi LOS signals. open spaces, and streets, to provide a more prominent service area, a wide attocell, and a stronger optical signal that contributes to the optical gain [101]. However, this solution is inconvenient for indoor areas, where following international lighting standards is essential [210; 211] because it may hurt the human eyes and lead to discomfort. Blockage and shadowing cause the non-uniform inclusion of the LiFi cells to further constrain the system throughput in the attocell [150]. LiFi service area, HO, and blockage are severe issues in LiFi networks. The causes and effects based on multiple criteria, such as blockage, shadowing, overhead area, interference, and network resources, are presented in this section. Note that blockage is different from shadowing. Specifically, blockage means the signals of a particular network are entirely blocked, thereby leading to the DoS in the stand-alone network and possibly to VHO in a hybrid network. Meanwhile, shadowing means less optical gain is received and may lead to HHO or VHO. When users cross the boundaries of a LiFi attocell service area, a HO and fluctuations in the network resources are triggered. In this analysis, the CCI is considered when interference occurs in two LiFi APs. In Table 7, all the causes and effects of these factors on the LiFi and hybrid LiFi/WiFi networks in multiple scenarios are presented: (i) 1 LiFi AP, (ii) 2 LiFi APs, (iii) 1 LiFi and 1 WiFi AP, and (iv) 2 LiFi and 1 WiFi APs. In this analysis, users' connection is assumed to be initially established on LiFi AP, where 1 means the connection is stable, and 0 means the user is disconnected from the AP. Lack of resources represents the low data rate received from the AP.
# Thermal Stability and Entropy Generation Analysis for
## 3 Entropy Generation Analysis  
The local volumetric rate of entropy generation for a viscous incompressible third-grade fluid in the presence of porous media is expressed as  
\[E_{G}=\frac{k}{T_{0}^{2}}\bigg{(}\frac{d\bar{T}}{d\bar{y}}\bigg{)}^{2}+\frac{ \bar{\mu}(T)}{T_{0}}\bigg{(}\frac{d\bar{u}}{d\bar{y}}\bigg{)}^{2}\bigg{(}1+ \frac{2\beta_{3}}{\bar{\mu}(T)}\bigg{(}\frac{d\bar{u}}{d\bar{y}}\bigg{)}^{2} \bigg{)}+\frac{\bar{\mu}(T)\bar{u}^{2}}{KT_{0}}, \tag{9}\]where the first term is the heat transfer irreversibility, the second term is the entropy generation due to third-grade fluid viscous dissipation, and the last term is the irreversibility due to the presence of porous media. Using the non-dimensional parameters and variables in (5), we obtain the equation for the non-dimensional entropy generation rate as  
\[N_{s}=\theta^{\prime 2}+\frac{\lambda\gamma}{\epsilon}e^{-\alpha\theta}\Big{[}f^{ \prime 2}(1+2\Omega e^{\alpha\theta}f^{\prime 2})+\delta f^{2}\Big{]}. \tag{10}\]  
The non-dimensional entropy generation number, \(N_{s}=\frac{E_{G}a^{2}E^{2}}{KR^{2}T_{d}^{2}}\), is the ratio of the volumetric entropy generation rate to the characteristic entropy generation rate. Equation (10) can be expressed alternatively as  
\[\begin{gathered} N_{s}=N_{h}+N_{f},\\ N_{h}=\theta^{\prime 2},\\ N_{f}=\frac{\lambda\gamma}{\epsilon}e^{-\alpha\theta}\Big{[}f^{ \prime 2}(1+2\Omega e^{\alpha\theta}f^{\prime 2})+\delta f^{2}\Big{]},\end{gathered} \tag{11}\]  
where \(N_{h}\) is the entropy generation due to heat transfer irreversibility and \(N_{f}\) is the entropy generation due to the combined fluid viscous dissipation and the porosity of the porous medium. The Bejan number \((Be)\) is used to evaluate the irreversibility distribution and is defined as  
\[Be=\frac{N_{h}}{N_{h}+N_{f}}. \tag{12}\]  
According to Bejan [18], the Bejan number varies from 0 to 1, where \(Be=0\) tells us that the irreversibility due to fluid viscous dissipation and porosity dominate, whereas \(Be=\frac{1}{2}\) indicates that the irreversibility due to fluid viscous dissipation and porosity is equal to the irreversibility due to heat transfer in the entropy production and \(Be=1\) reveals that the irreversibility due to heat transfer dominates.
## 4 Application to Power Electronics
### Specifications in Terms of the Optimization Problem  
#### 4.2.1 Optimization Inputs  
The optimization inputs in terms of the problem are summarized in Table 1. They deal with the geometrical or physical aspects, to allow us to compute most of the inductance-related geometrical parameters and the resistance detailed in Figure 11, excluding _R1_, _R2_, _R3_, _L1_, _L2_, and _L3_, and the 'Load' resistor, which are fixed parameters.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Name of the Optimization Input and Description**} & \multicolumn{2}{c}{**Bounds**} \\ \cline{2-3}  & **Min** & **Max** \\ \hline a1: Coefficient in regard to primary and first secondary coupling matrices & 0.01 & 2.1706 \\ a2: Coefficient in regard to primary and second secondary coupling matrices & 0.01 & 2.1706 \\ kd: Ratio between the winding width and the magnetic core width of the autotransformer & 0.032 & 3.63 \\  & np: Number of primary turns for the autotransformer & 10 & 45 \\ kd: Ratio between the winding width and the magnetic core width of the interphase inductance & 0.02 & 9 \\ ni: Number of primary turns for each interphase inductance & 7 & 203 \\ L17: Filter inductance & \(10^{-6}\) & \(10^{-4}\) \\ C: Filter capacitance & \(6\times 10^{-6}\) & \(10^{-3}\) \\ kd: Ratio between the winding width and the magnetic core width of the filter inductance & 0.0013 & 52 \\ nf: Number of filter inductance turns & 1 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inputs in regard to the optimization problem.