esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation
### Experimental Results
#### 5.2.2 Joint Compliance Control  
The joint compliance of the robotic orthosis in tracking control and impedance control mode in sagittal plane are shown in Figure 9. The torque profile of subjects \(T_{int}^{passive}\) (the black line) and the estimation of the active human torque \(\hat{T}_{h}\) (the green line) are depicted in Figure 10. All these data are also averaged over all subjects for two GCs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gait Parameter** & **Passive Walking (Mean \(\pm\) SD)** & **Active Walking (Mean \(\pm\) SD)** \\ \hline \(MTE_{Hip}\) [degrees] & 4.25 \(\pm\) 1.01 & 5.7 \(\pm\) 3.17 \\ \(RMSTE_{Hip}\) [degrees] & 2.09 \(\pm\) 0.14 & 3.04 \(\pm\) 1.91 \\ \(MTE_{Knee}\) [degrees] & 5.85 \(\pm\) 0.56 & 6.81 \(\pm\) 2.32 \\ \(RMSTE_{Knee}\) [degrees] & 3.09 \(\pm\) 0.70 & 3.16 \(\pm\) 1.26 \\ \(MaxComp_{Hip}\) [rad/Nm] & 2.09 \(\pm\) 0.14 & 4.94 \(\pm\) 1.07 \\ \(MaxComp_{Knee}\) [rad/Nm] & 5.08 \(\pm\) 0.20 & 9.01 \(\pm\) 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum tracking error (MTE), RMSTE and maximum compliance (MaxComp) of hip and knee joint in the experiment.  
Figure 10: The human-robot interactive torque (HRIT) of AIRGAIT robotic orthosis during active and passive modes: (**a**) Hip joint and (**b**) Knee joint.  
Figure 9: Joint sagittal plane compliance of AIRGAIT robot orthosis: (**a**) Hip joint and (**b**) Knee joint.  
It can be observed that in passive mode, the compliance is set at low level such that movement of the lower limb is dominated by the robotic orthosis. When the subject is in active mode, \(T_{int}^{active}\) is outside the bandwidth where the center is the passive one \(T_{int}^{passive}\). Consequently, the compliance controller increases the joint compliance to encourage the contributions of the subjects in training process. For example, \(|T_{int}^{active}|<|T_{int}^{passive}|\) in the first half of GC which means the robotic orthosis reduces the support to the subjects. The unexpected behavior of the system in the range of 60% to 100 % of gait cycle in knee joint may be caused by the healthy subjects whose contribution against the movement of the robotic. However, the adaptation of the compliance demonstrated that the compliance controller is able to provide the assistance based on the effort of the subjects while the tracking controller is still stable to guide the subject limb.
## 2 Experiment
### Film Deposition
#### 2.1.2 Surface Metrology  
The adoption of the AlN seed layer, the Al\({}_{1-x}\)Sc\({}_{x}\)N gradient layer, and pure nitrogen sputtering environment greatly reduced the occurrence of abnormally oriented grains (AOGs). AOGs are a series of wurtzite Al\({}_{1-x}\)Sc\({}_{x}\)N crystals that do not have their c-axis perpendicular to the substrate [49]. They could erupt from the crystalline interface if grown under unfavorable conditions, especially for films with higher Sc concentration [50]. If not suppressed, they may occupy the entire film surface [49; 51; 52], severely degrading device performance [53; 54] and locally slowing the etch rate. To examine the film quality, atomic force microscopy (AFM) scans were conducted using a Bruker Icon AFM, and most of the films measured showed a roughness of <2 nm. Figure 1a shows the surface of an Al\({}_{0.64}\)Sc\({}_{0.36}\)N film within a 20 \(\times\) 20 \(\upmu\)m\({}^{2}\) field. The root mean square (RMS) surface roughness R\({}_{\text{q}}\) is 0.840 nm and R\({}_{\text{a}}\) is 0.641 nm. The film quality is also supported by the rocking curve measurements, which were performed by a Rigaku Smart Lab X-RAY Diffractometer (XRD) with a high resolution Parallel Beam (PB) Ge (220) \(\times\) 2 monochromator (Rigaku Corporation, Tokyo, Japan). The Omega scan data are centered at 18.13 \({}^{\circ}\) with a full width at half maximum (FWHM) of 1.80 \({}^{\circ}\) for a 500 nm film deposited on Si (100) using the same process, indicating that the film is highly \(c\)-axis textured. All films had a similar quality based on the AFM and XRD measurement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Sc Alloy** & **0** & **5** & **10** & **12.5** & **15** & **20** & **25** & **28** & **30** & **32** & **34** & **36** & **38** & **40** & **42** \\
**in Film (\%)** & & & & & & & & & & & & & & & \\ \hline Sc Target Power & 0 & 40 & 80 & 130 & 185 & 300 & 400 & 450 & 510 & 555 & 610 & 655 & 685 & 710 & 770 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scandium concentration vs. Sc target power.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 2 Description of the Algorithm
### Accuracy Analysis
In the practical application of the phase comparison algorithm, the DF accuracy is more sensitive when the azimuth angle is close to \(0^{\circ}\) and \(180^{\circ}\), making it more susceptible to other measurement errors.
## 3 Results  
Twenty-one participants with mild TFA (age = 29.7 \(\pm\) 5 years; weight = 73.8 \(\pm\) 7.5 kg; height = 169.5 \(\pm\) 6.6 cm) and twenty-four healthy subjects (age = 21.2 \(\pm\) 1.7 years; weight = 67.4 \(\pm\) 8.1 kg; height = 166 \(\pm\) 6.8 cm) took part in the study (Table 1). A significant difference was detected between the BMI values of the subjects. In the healthy  
Figure 1: Underfoot WalkinSense 8-point sensor placements. The placement of the sensors under the feet (**a**) and the image of the sensors, each in different colors, was reflected on the screen while recording (**b**). Figure 2: Figure showing the activity and peak pressures of the underfoot sensors, each shown separately, during the stance phase. individuals, the BMI value was recorded as a healthy weight (18.5-24.9) at 24, while in the TFA group, a BMI value of 25.4 was considered overweight (25.0-29.9). The plantar pressure distribution and sensor activation percentage values obtained from the TF amputees and healthy individuals were examined. The sensor activation percentage shows how active the sensor is during the stance phase, and with an increase in the sensor activation percentage, the activity under the sensor indicated under the foot increases. When we look at the results of the activation percentage of the study, the activation percentages for sensors 1, 2, 3, 4, 6, 7, and 8 were significantly different under the sound limb between the TFA and healthy subjects (Table 2). The 1-, 2-, 3-, 4-, and 6-point sensor activation percentages significantly increased, whereas the 7- and 8-point sensor activation percentages decreased in the sound limb TF participants compared with the healthy subjects. In the TF amputees, the percentage of sensory activation in the sound limb under the foot was higher than that of the control group, especially under sensor numbers 1, 2, 3, 4, and 6. This result shows that the contact activation of these sensor regions during the stance phase in walking is higher; i.e., the percentage of contact in the forefoot and midfoot stance phases is higher than in the control group. The sensor activation percentages for sensors 7 and 8 were found to be less than those for the control group. In these sensor regions, the hindfoot region showed less activation during the gait stance phase. This demonstrates that the contact of the first, second, third, fourth, and sixth metatarsal phalanx points is more intense than the other points in the stance phase and that the forefoot of the foot is used more. When the plantar pressure distribution of the foot was examined, as shown in Table 3, it was determined that the pressure in sensors 1, 3, 4, and 6 in the stance phase of the gait was higher in the TF amputee sound limb than in the control group. In addition, the pressure in sensor number 8 decreased. According to this result, the change in pressure distribution under the sound limb foot in the TF amputees was significant in sensors 1
# Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System
## 4 Demonstration HMI Design
### Stage 2--Acceptance  
The goal of the acceptance screen (Figure 5) is to assist the user in determining whether a given event is a false alarm or not. It achieves this primarily by showing video footage of the time of the event alongside a plot showing contact force over time. Red visual indications in the event data table highlight multiple unexpected or abnormal values. Events that are not immediately obvious using these two primary sources of information can be verified through the observation of additional sources of contextual and sensor data from the time of the event. Additionally, moving the cursor over a highlighted value provides a short description of how the data differ from the expected values or where the system is at an increased risk of false alarms. For example, if it is raining, the deflection of light in water droplets on the pantograph can cause false alarms in vision-/light-based detection systems [24].  
Several studies have highlighted issues with multiple and false alarms [14; 41]. Therefore, once a user determines the validity of the event, they can select yes to indicate a false alarm, marking the event as such and returning to the notification screen, or selecting no to continue to the next stage. Furthermore, this helps with learning and feedback for machine learning systems.  
By allowing the user to use their own knowledge of the values expected during normal operation, with the HMI providing additional assistance by highlighting values outside of a predetermined range, the HMI allows less experienced operators to still use the system whilst being 'taught' the range of values that may indicate a real fault.
# A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy
## 3 Propositional Logic Problems
### Sequences of Propositional Formulas  
The following problem is related to a Boolean combination of satisfiability checks on a sequence of propositional formulas. This is a parameterized version of the problem BH\({}_{i}\)-Sat, which is canonical for the different levels of the Boolean Hierarchy (see Section 1). The problem is complete for the class\(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\).  
\begin{tabular}{|l|} \hline BH-Sat(level) \\ _Instance:_ A positive integer \(k\) and a sequence \((\varphi_{1},\ldots,\varphi_{k})\) of propositional formulas. \\ _Parameter:_\(k\). \\ _Question:_ Is it the case that \((\varphi_{1},\ldots,\varphi_{k})\in\mathrm{BH}_{k}\)-Sat? \\ \hline
**Complexity:**\(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete [7; 28; 37]. \\ \hline \end{tabular}  
The above problem is used to show the following lower bound result for \(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete problems. No \(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-hard problem can be decided by an fpt-algorithm that uses only \(O(1)\) many queries to an NP oracle, unless the Polynomial Hierarchy collapses to the third level [18; 37].  
The next \(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete problem is based on the problem \(\mathrm{SAT}\)-\(\mathrm{UNSAT}=\{\,(\varphi_{1},\varphi_{2}):\varphi_{1}\in\mathrm{SAT}, \varphi_{2}\in\mathrm{UNSAT}\,\}\).  
\begin{tabular}{|l|} \hline Bounded-SAT-UNSAT-Disjunction \\ _Instance:_ A family \((\varphi_{i},\varphi^{\prime}_{i})_{i\in[k]}\) of pairs of propositional formulas. \\ _Parameter:_\(k\). \\ _Question:_ Is there some \(\ell\in[k]\) such that \((\varphi_{\ell},\varphi^{\prime}_{\ell})\in\mathrm{SAT}\)-UNSAT? \\ \hline
**Complexity:**\(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete [7; 28; 37]. \\ \hline \end{tabular}
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 2 Background
### Pre-Copy  
The pre-copy algorithm is the most popular algorithm in VM live migration domain. The core thought of the pre-copy algorithm is to minimize the downtime in the migration process through maximizing the memory mirroring synchronization between the source host and the destination host. Figure 1 provides an overview of the pre-copy algorithm architecture. The pre-copy algorithm can be divided into the following two phases:  
1. Iteration phase: In this phase, memory mirroring synchronization between the source host and the destination host will be maximized by iteratively copying the memory data from the source to the destination. In this phase, the source VM still works, so part of memory pages would be modified during the previous transmission turn. These memory pages should be synchronized until the number of the remaining memory pages is less than a threshold or the iteration turn is greater than an iterative threshold.
2. Stop-and-copy phase: In this phase, the source VM will be stopped and the remaining pages will be copied to the destination VM. Then the new VM in the destination host is started.  
The pre-copy algorithm can shorten the downtime significantly in case of a read-intensive VM. However, if the VM has write-intensive workloads, the speed of memory write may be faster than the network transmission speed. As a result, the iteratively copy behavior would just waste bandwidth resources and overrun the total migration time.
## 2 Formulation of the Optimization Problem
#### Genetic Algorithm  
The genetic algorithm is one of the bio-inspired methods--its principle of operation is modeled on the processes of [PERSON]'s natural evolution [8,9]. Genetic algorithms are inspired by the processes of natural evolution and work by the selection, crossing and mutation of genes. The optimization process begins with a random population of initial solutions, which are then evaluated for their suitability to solve the problem. The best solutions are selected for further reproduction, during which genotypes are crossed and mutated, leading to a new generation of solutions. It is an artificial intelligence technique that uses mechanisms inspired by natural selection and genetics to find solutions to optimization problems [10, 11, 12]. The optimization algorithm begins its process by generating an initial population, which then evolves in subsequent iterations (Figure 6) [13,14].  
Each population P(t) consists of a set of chromosomes of a certain number. Each chromosome \(C_{t}^{1}\), in turn, presents a geometric representation to a given structural form of the system.  
\[P(t)=\left[C_{t}^{1},C_{t}^{1},\ldots,C_{t}^{j},\ldots,C_{t}^{N},\right] \tag{5}\]  
where  
t--population index (iterations),  
N--the number of chromosomes in the population,  
Figure 6: Flowchart of the genetic algorithm [10,11,13].  
j--chromosome index,  
C\({}_{t}^{j}\)--J-th chromosome in the population t.  
\[C_{t}^{j}=\left[x_{1}^{j},x_{2}^{j},\ldots,x_{t}^{j},\ldots,x_{n_{r}}^{j}\right] \tag{6}\]  
where  
n--the number of genes in a chromosome,  
x\({}_{i}^{j}\)--I-th gene on the j-th chromosome.  
A new population is created by crossing selected chromosomes and introducing mutations. Mutations lead to the creation of new solutions that can potentially be better than previous ones. The old population is replaced by a new population that was created by the process of crossing and mutation. The process is repeated iteratively until the final condition is reached. The condition for stopping an algorithm can be defined as, for example, achieving an optimal solution, a set number of generations, or no improvement in subsequent generations.
## 3 Adaptive Control of PEMFC Power Supply
### Model of PEMFC  
The control law of SMC is selected in the form of:  
\[u=\frac{1}{2}[1-sign(s)] \tag{7}\]  
where \(s=i_{L}-i_{L,ref}\).  
Then, the derivation of \(s\) can be obtained by:  
\[\dot{s}=-\frac{1}{L}v_{o}+\frac{1}{2}[1-sign(s)]\frac{v_{f}c}{L} \tag{8}\]  
where, in the case of \(s>0\), \(sign(s)=1\), \(\dot{s}=-\frac{1}{L}v_{o}\leq 0\); when \(s<0\), \(sign(s)=-1\), \(\dot{s}=-\frac{v_{o}-v_{f}c}{L}\geq 0\), since the output load voltage of the buck converters is smaller than the input voltage. Therefore, the inductor current control is manipulated by the control law represented by Equation (7) satisfying the Lyapunov stability condition ( \(\dot{V}\leq 0\)). Moreover,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline PEMFC power (\(P_{f}\)) & 100 & W \\ Number of cells (\(N\)) & 20 & - \\ Thickness of the membrane (\(l_{fc}\)) & 0.0178 & cm \\ Area of the membrane (\(A_{fc}\)) & 22.5 & cm\({}^{2}\) \\ Inductor (\(L_{0}\)) & 470 & mH \\ Capacitor (\(C_{0}\)) & 1000 & μF \\ Constant resistance (\(R_{0}\)) & \(2.05\times 10^{-2}\) & \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of DC/DC buck converter and PEMFC system.  
the output voltage regulation can be calculated by the indirect current controller according to the equations in [43].
## 4 Localization-Based vs. Localization-Free Routing Protocols  
Localization-based routing protocols use the two- or three-dimensional position coordinates information of the sensor nodes to construct the routing paths. This information is helpful in constructing the optimal routing paths from the bottom to the surface of the water, which may improve the performance parameters such as propagation delay and other parameters. However, the calculation of the position information of the sensor nodes is challenging as the sensor nodes move with ocean currents. Energy is also consumed in knowing and calculating the position information of the sensor nodes. These challenges compromise the performance of the localization-based routing protocols. These protocols are used in underwater object tracking where knowing the exact position of a target is necessary or any application that requires preciseness in the position calculation of the sensor nodes or the tracked objects.  
Localization-free routing protocols do not require the two- or three-dimensional position coordinates information of the sensor nodes. Instead, these protocols make use of pressure sensors as a measure of the pressure of water on the sensor nodes, which in turn, measures the depth of the sensor nodes. The depth of the sensor nodes is then used to construct the routing paths. This strategy saves the amount of energy and reduces the propagation delay in the calculation of the position of the sensor nodes. These protocols are used when scalability of the network is desired, such as in underwater military operations or when general-purpose monitoring of the sea is the objective.
## 2 Materials and Methods
### Continuity Equation and Navier-Stokes Equation  
In this study, the heat exchange capacity was very small; therefore, the influence of the energy equation on the research results was ignored. The conservation equations of mass and momentum in a fluid medium are as follows:  
\[\frac{\partial}{\partial t}(\alpha_{i}\rho_{i})+\
abla\cdot(\alpha_{i}\rho_{i }v_{i})=0, \tag{1}\]  
where \(\alpha_{i}\) is the volume fraction of each phase (seawater and ice particles), \(v_{i}\) is the velocity of each phase in m/s, \(\rho_{i}\) is the density of each phase in kg/m\({}^{3}\), \(\
abla\) is the Laplace operator and \(i=l\) and \(i=s\) refer to seawater and ice particles, respectively. The Navier-Stokes (n-s) equation of viscous incompressible seawater [30] is as follows:  
\[\frac{\partial}{\partial t}(\alpha_{l}\rho_{l}\overset{\rightarrow}{u}_{l})+ \overset{\rightarrow}{\
abla}\cdot(\alpha_{l}\rho_{l}\overset{\rightarrow}{u }_{l}\overset{\rightarrow}{u}_{l})=-\alpha_{l}\overset{\rightarrow}{\
abla}p +\overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{\tau}_{l}+\alpha_ {l}\rho_{l}\overset{\rightarrow}{g}+\overset{\rightarrow}{M}sl, \tag{2}\]  
\[\frac{\partial}{\partial t}(\alpha_{\mathrm{s}}\rho_{s})+\overset{ \rightarrow}{\
abla}\cdot(\alpha_{\mathrm{s}}\rho_{s}\overset{\rightarrow}{u }\overset{\rightarrow}{s}\overset{\rightarrow}{u}\overset{\rightarrow}{s}) =-\alpha_{s}\overset{\rightarrow}{\
abla}p+\overset{\rightarrow}{\
abla} \cdot\overset{\rightarrow}{\tau}\overset{\rightarrow}{s}+\alpha_{\mathrm{s }}\rho_{s}\overset{\rightarrow}{g}-\alpha_{s}\overset{\rightarrow}{\
abla}p _{s}+\overset{\rightarrow}{M}sl, \tag{3}\]  
\[\overset{\rightarrow}{\tau}l=\alpha_{l}\mu_{l}(\overset{\rightarrow}{ \
abla}\cdot\overset{\rightarrow}{u}\overset{\rightarrow}{+}\overset{ \rightarrow}{\
abla}\cdot\overset{\rightarrow}{u}\overset{\rightarrow}{T})+ \alpha_{l}(\lambda_{l}-\frac{2}{3}\mu_{l})\overset{\rightarrow}{\
abla} \cdot\overset{\rightarrow}{u}\overset{\rightarrow}{I}, \tag{4}\]  
\[\overset{\rightarrow}{\tau}\overset{\rightarrow}{s}=\alpha_{s}\mu_{s}( \overset{\rightarrow}{\
abla}\cdot\overset{\rightarrow}{\mu_{s}}+\overset{ \rightarrow}{\
abla}\cdot\mu_{s}\overset{\rightarrow}{T})+\alpha_{s}( \lambda_{s}-\frac{2}{3}\mu_{s})\overset{\rightarrow}{\
abla}\cdot\overset{ \rightarrow}{\mu_{s}}\overset{\rightarrow}{I}, \tag{5}\]  
where \(g\) is acceleration of gravity in m/s\({}^{2}\),\(\tau\) is the shear stress of the seawater in Pa, \(p\) is the static pressure of the seawater in Pa, \(M\)_sl_ is the interphase force between ice and seawater phases in kg/m-s\({}^{2}\), \(u\) is shear viscosity in kg/m-s, \(\lambda\) is seawater viscosity in kg/m-s and \(I\) is unit tensor.
# Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia
## 1 Introduction  
The transition to sustainable energy is gaining momentum worldwide, with solar energy emerging as a key player in reducing carbon emissions and promoting environmental sustainability. One crucial application of solar energy is powering electric vehicle (EV) infrastructure, particularly charging stations. As EVs become more popular, the availability and distribution of charging stations have become critical to supporting their widespread adoption. Saudi Arabia, which was previously dependent on oil, has started to shift torenewable energy sources (RESs). This change aligns with a national goal to reduce environmental impact and diversify energy sources. The region's abundance of solar energy presents an ideal way to meet energy needs, particularly for supplying electricity for a recently established network of EV charging stations.  
Hail City, located in the north-central region of Saudi Arabia, is a model case study due to its geographic and climatic conditions, which are highly conducive to solar energy utilization. However, despite its potential, the development of solar-powered charging infrastructure in Hail City is still in its nascent stages. Understanding the factors that impact the expansion of such an infrastructure is essential for city planners, policymakers, and investors aiming to promote renewable energy (RE) and support the growth of EV usage. Hail City's urban area has grown significantly with the growth in its population, positioning the city for continued expansion. Urban infrastructure must be re-evaluated in light of this expansion to handle the growing population while minimizing adverse environmental effects, particularly those resulting from transportation. There is a global movement toward electric automobiles to lessen dependency on non-RESs and carbon emissions; with growing interest and investment in EV infrastructure, Hail City reflects this trend.  
This study explored various technological factors influencing the number of solar electric vehicle charging stations (EVCSs) and policy-related aspects. By identifying these factors, this research aimed to provide insights into how Hail City can enhance its infrastructure to meet the increasing demand for sustainable energy solutions in the transportation sector. A solar station was also designed to supply an EVCS with a capacity of 700.56 kWp.