esa-sceva/satcom-chunk-collection huggingface dataset:
# A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System
## 1 Introduction
Further in [16], a variety of assistive service robots are developed for the elderly and vulnerable populations. Such robots address the loneliness of the elderly through communications and interactions. Additionally, they have significant advantages in data collection to assess the health status of the elderly. The humanized design of robots provides a better affinity for humans and can be more easily integrated into the daily life of patients in the home or hospital environments. Furthermore, combined with interactive entertainment, such robots can effectively prevent dementia and the phenomenon of declining ability in daily life activities for rehabilitation training of lower limb limbs for hemiplegic patients. This can also restore the motor function of lower limbs and achieve self-care in daily life and returning to normal life, whereas assistive service robots are not sufficient for these purposes. However, these robots have some limitations. For example, exoskeleton walking rehabilitation robots based on treadmills are bulky and heavy. Furthermore, their dynamic models are more complicated, and because the motors are often installed at joints, it is difficult for patients to control the movements. In this paper, our objective is to address the issue with the complex structure and fixed use of lower limb rehabilitation robots. We propose a human lower limb rehabilitation robot that provides bodyweight balance support and mobility for patients. Our lower limb rehabilitation robot achieves self-weight loss and balance, and mobile patient companion for stroke patients. Moreover, we develop a new interactive system for remote lower limb rehabilitation robots. The designed system interface includes robotic rehabilitation parameters, patient information material, video screens, and database management. The system allows doctors to coach and supervise stroke patients in telerehabilitation exercises. This also enables the medics to analyze rehabilitation data, with the aim of objectively and quantitatively assessing the effectiveness of the patient's rehabilitation. The system consists of a server computer connected to the network for the doctor, and a client computer connected to the rehabilitation robot and the network. The network acts as a data transmission channel between the server computer and the client computer. This paper is organized as follows: In Section 2, the framework of the rehabilitation robot system design for the stroke patients is presented, and the construction method of the rehabilitation robot is introduced. The remote control system and its hardware, software, and telerehabilitation system design are also presented. In Section 3, the experiments with the remote control system for the rehabilitation robot are conducted and the results are discussed. Finally, the conclusions are presented in Section 4.
# _Applied_
## 4 Evaluation and Platforms
### Platforms and Experimental Setup  
The feature extraction is performed using the Python 2.7 package _librosa_ 0.7.2. The classical ML classifiers are implemented using the Python library scikit-learn 0.20.4. The experiments are executed in two different platforms. The experiments performed in a desktop PC are used as a reference. The desktop PC includes a 2.6 GHz Intel Core i7 running Ubuntu O.S. 18.4. Additionally, a RPi 4B+ running Raspbian GNU/Linux 10 is used as representative of an embedded system. One of the objectives of this work is to be able to run the MosAlc on an embedded system maintaining a high accuracy and a low classification time while performing a fair comparison against DL approach.
## 2 Theoretical Background
### Optimization Algorithms  
Optimization algorithms play a crucial role in finding the best solutions to complex problems across various fields. Numerous optimizers are available, each with distinct strengths, weaknesses, and suitability for different types of optimization challenges. When selecting the most appropriate optimization technique, it is essential to consider both the characteristics of the problem and those of the algorithms, as each optimizer is designed for specific scenarios. This careful analysis can lead to more effective and efficient optimization results by ensuring that the chosen method is adapted to the specific requirements of the task. Among these algorithms, heuristic and nature-inspired optimization methods have attracted considerable attention due to their effectiveness and adaptability.  
Nature-inspired optimization algorithms are those that draw inspiration from natural processes. Typically, these algorithms consist of two main stages: _exploration_ and _exploitation_. During the exploration phase, candidates are allowed to freely navigate the search space through random movements. This phase is crucial for identifying promising regions within the search space and usually occurs at the beginning of the iterations [37]. After the exploration phase, the algorithm moves to the exploitation phase, where the previously identified solutions are refined and improved. As a result, the variability of the solutions is generally lower in the exploitation phase than in the exploration phase [38].  
In this paper, we present four PSO-style optimization algorithms [39]. The following subsections outline the steps of these algorithms, which belong to the PSO framework,highlighting their similarities and differences. These PSO-style algorithms operate within a vector space and feature operators that facilitate movement to different points in the search space. In PSO-style optimization algorithms, the population size is denoted by \(N\), and it represents the number of solutions. Furthermore, the maximum number of iterations will be MaxIter.
## 5 Discussion  
### Comparisons of Hydrocarbon Generation  
#### 5.1.1 Hydrocarbon Generation in Different Geological Periods  
In order to intuitively reflect the changes in hydrocarbon generation at different geological times, we quantitatively counted the hydrocarbon generation of the AA' section (Figure 9). The results show that most of the source rocks were in the immature-to-early-mature stage from the Jurassic to the Paleogene (Figure 7), resulting in only a small amount of oil and gas generation, accounting for 10.02% of the total hydrocarbon generation in the whole historical period. Since the Neogene, the source rocks have matured rapidly and began to generate a large amount of oil and gas, showing a gradually increasing trend. Especially in the Pliocene, the proportion of hydrocarbon generation reached 26.61% (Figure 9). The maturity of the source rocks ranged from 0.70% to 2.0% during the Quaternary (Figure 7) and still generated a large amount of oil and gas in this period, accounting for 27.88% of the total hydrocarbons generated (Figure 9). It can also be seen from the above  
Figure 8: Hydrocarbon generation of the AA’ section at different geological times in the eastern Kuqa Depression; the thickness of the model perpendicular to the section direction is fixed at 1 km.  
data that the main hydrocarbon generation periods in the eastern Kuqa Depression are the late Neogene and the Quaternary. During this period, the proportion of oil generation decreased, while the gas generation increased significantly, with gas dominant (Figure 9).
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 4 Results and Discussion
### Full Guidance  
In this approach, UAVs are the only guidance during the evacuation, where each pedestrian follows the nearest UAV to guide them to the nearest exit. The results of AET and mortality for full guidance models are as follows:  
_(a)_ _Average Evacuation Time_  
Figure 5 illustrates the AET results in two different UAV positions: middle and random. The lowest AET was with random UAVs, and where exit doors were opposite--a scenario which slightly outperformed adjacent exit doors. For opposite exit doors, when CD \(\leq\) 0.5, AET was around 7 min, reaching 10 min when CD = 2. Placing UAVs in the middle of the hall with opposite or adjacent exit doors led to a high AET of approximately 11 min when CD = 2. Compared with the no-UAV approach, adopting UAVs randomly in the hall to provide full guidance in the evacuation process gave better AET outcomes.  
Figure 5: Average evacuation time for full guidance model.  
In the full guidance model, since pedestrians follow the UAV to guide them instead of moving directly to the nearest exit, the results show a higher AET compared to analogous situations in the partial guidance approach.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 4 Electric Vehicle Battery Chargers Categories
### On-Board Charging  
On-board charging (OBC) in electric vehicles involves converting external AC power into DC power to charge the vehicle's battery pack. It is a critical process requiring various components like rectifiers and transformers to ensure safe and efficient charging [62]. The OBC, typically integrated into the vehicle's powertrain or near the battery pack, can be built within the vehicle or be part of the charging cable [140], as shown in Figure 5. Common methods include Level 1, standard outlets, and Level 2, with dedicated charging stations offering higher voltage. On-board charging provides convenience, as EVs can be charged at home, work, or other accessible locations. Level 1 charging (120 volts) is cost-effective, while Level 2 (240 volts) provides flexibility without requiring specialized infrastructure [3]. Owners can charge their vehicles wherever suitable outlets or stations are available [141]. For on-board chargers, popular models and manufacturers commonly found in electric vehicles include the Tesla Supercharger, which offers fast-charging capabilities for Tesla owners, the Nissan CHAdeMO which is compatible with the Nissan Leaf for fast charging at CHAdeMO-equipped public charging stations, the BMW i3 equipped with an on-board charger supporting both AC and DC charging for flexibility at various public charging stations, and the Chevrolet Bolt EV with an on-board charger capable of accepting both AC and DC charging for fast-charging at compatible public charging stations. Table 5 details the advantages and disadvantages of on-board and off-board chargers.
# PTG-HEFA Hybrid Refinery as Example of a SynBioPTx Concept--Results of a Feasibility Analysis
## 3 Results and Discussion
### Environmental Assessment  
The GHG balances for the scenarios have been conducted according to the described methodology. Referring to Figure 3, the provision of jatropha-based HEFA-SPK can achieve a GHG reduction of 76% compared to the fossil reference defined in RED. Prerequisite is the exclusive use of electricity from wind and solar systems to cover the process energy demand. The provision of hydrogen via electrolysis is electricity-intensive and the use of grid electricity under the forecasted conditions leads to significantly higher emissions. In particular, the relatively high share of fossil fuels in the German electricity mix prevents higher GHG reductions. Other ways to reduce emissions from the supply of HEFA-SPK, in addition to the use of renewable electricity or a significantly higher share of the same in the electricity mix, is the use of in-process provided naphtha for hydrogen production via steam reforming. Reductions of more than 70% can be achieved here. On the other hand, the use of sustainably grown jatropha, which means jatropha grown mainly from extensive cultivation, can reduce overall GHG emissions.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Scenario** & **Inputs** & & & & **Outputs** & & \\ \hline  & **Feedstock \({}^{\text{a}}\)** & **Electricity \({}^{\text{b}}\)** & **Water \({}^{\text{a}}\)** & **HEFA-SPK \({}^{\text{a}}\)** & **Diesel \({}^{\text{a}}\)** & **Naphtha \({}^{\text{a}}\)** & **Fuel Gas \({}^{\text{a}}\)** \\ \hline
1 (reference) & 500 & 988 & 222 & 227 & 40 & 135 & 26.4 \\
2 (dynamic) & 500 & 996 & 222 & 227 & 40 & 135 & 26.4 \\
3 (stand-alone) & 500 & 999 & 222 & 227 & 40 & 135 & 26.4 \\
4 (natural gas) & 500 & 33 & 254 & 227 & 40 & 135 & 36.2 \\
5 (biomethane) & 500 & 33 & 254 & 227 & 40 & 135 & 36.2 \\
6 (naphtha) & 500 & 32 & 279 & 227 & 40 & 88 & 13.7 \\
7 (rapeseed) & 500 & 1088 & 240 & 237 & 45 & 123 & 27.1 \\
8 (palm) & 500 & 884 & 203 & 249 & 104 & 38 & 25.1 \\
9 (UCO) & 500 & 1096 & 241 & 235 & 43 & 129 & 24.7 \\
10 (diesel) & 500 & 826 & 192 & 60 & 328 & 22 & 14.7 \\ \hline \hline \end{tabular} \({}^{\text{a}}\) kt a\({}^{-1}\); b GWh a\({}^{-1}\).  
\end{table}
Table 4: Most important mass and energy balances for the different scenarios.
## 3 Related Works
### Approximate Algorithm and Heuristics  
In [6], [PERSON] et al. introduce an iterative method called Inner to solve MinTCover by first addressing the Coalesce subproblem. This involves defining key time points for each vertex, called inner points, around which activity intervals are constructed to cover all interactions. The authors provide a 2-approximate solution to Coalesce in linear time by formulating an ILP model, relaxing its integer constraints, and iteratively refining the inner points until convergence. They do not establish an approximation factor for MinTCover but can compute a feasible solution efficiently.  
[PERSON] et al. [10; 18] propose 2 Phases, an \(O(T\log n)\) approximation algorithm. They construct a union graph \(G_{u}\) by aggregating timestamps of temporal edges. The first phase applies a 2-approximate Minimum Vertex Cover (MVC) algorithm [19] to select activity intervals. The second phase, based on a SetCover approximation [20], uses randomized rounding to determine a Minimum Non-Consecutive Timeline Cover, later transformed into a MinTCover solution.  
[PERSON] et al. [11] introduce FastMinTC+, a heuristic method extending an MVC approach. It consists of an initialization phase that selects high-degree vertices for coverage and an iterative refinement phase where low-loss nodes are removed, and new nodes are selected using the Best from Multiple Selection (BMS) heuristic. The process iterates until a minimal timeline with a reduced sum-span is obtained. Their benchmark comparisons show that FastMinTC+ is computationally efficient, consistently outperforming Inner in execution time and proving superior for dense graphs, while 2 Phases, despite its theoretical guarantees, struggles with scalability in large instances.
## 5 Performance Analysis and Results
### Encryption Quality Metrics
#### 5.2.1 Deferential Attack Analysis  
The number of pixels change rate (also known as the NPCR) and unified average changing intensity (also known as the UACI) are two frequently used tests that were used to evaluate the sensitivity of the encrypted image. To strengthen resistance to the differential attack, each small change to the plain image should result in a significant disruption of the cipher image. Consider \(C_{1}\) and \(C_{2}\), two cypher pictures for two planar images \(p_{1}\) and \(p_{2}\), which only differ by one pixel. \(C_{1}(i,j)\) and \(C_{2}(i,j)\) are the gray-scale pixel values of the two images \(C_{1}\) and \(C_{2}\), respectively. The _NPCR_ and _UACI_ are defined as [13]:  
\[NPCR=\frac{\sum_{i=1}^{N}\sum_{j=1}^{M}D(i,j)}{M*N}*100\% \tag{27}\]  
\[UACI=\frac{1}{M*N}\frac{\sum_{i=1}^{N}\sum_{j=1}^{M}|\mathcal{C}_{1}(i,j)-C_{2} (i,j)|}{2^{n}-1}*100 \tag{28}\]  
where \(D(i,j)\) is an identical-sized bipolar array to the cypher picture that is described as:  
\[D\;(i,\;j)=\left\{\begin{aligned} & 1\;if\;C_{1}(i,j)\
eq C_{2}(i,j)\\ & 0\;if\;C_{1}(i,j)=C_{2}(i,j)\end{aligned}\right. \tag{29}\]  
In the simple images, \(p_{i}\) is the value of the initial pixel. Without modifying any other values, it is changed to \(p_{i}=(p_{i}+100)\;mod256\) to obtain a second image and encrypt the two images in order to calculate the NPCR and UACI values of the two encoded images. Tables 10 and 11 display the findings of the NPCR and UACI for the proposed MP-FrFT, FrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images. Tables 10 and 11 shows the \(NPCR\) and \(UACI\) the comparison among the proposed MPFrFT and FrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**Encryption Scheme**} \\ \cline{2-3}  & **MPFrFT** & **FrFT** \\ \hline Cameraman & 99.6661 & 98.6773 \\ \hline Peppers & 99.4720 & 98.3463 \\ \hline Boat & 99.1598 & 99.0762 \\ \hline \hline \end{tabular}
\end{table}
Table 10: \(NPCR\) comparison among the proposed MPFrFT and FrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(E_{b}N_{0}\) (dB) & 2 dB & 8 dB & 8.50 dB \\ \hline \multirow{4}{*}{**Image**} & Decrypted Image & \multirow{4}{*}{\(BER\)} & \multirow{4}{*}{\(0.0340\)} & \multirow{4}{*}{\(4.00\times 10^{-6}\)} & \multirow{4}{*}{\(0\)} \\ \cline{2-3}  & & & & \\ \cline{1-1} \cline{2-3}  & & & & \\ \cline{1-1} \cline{2-3}  & & & & \\ \cline{1-1} \cline{2-3}  & & & & \\ \cline{1-1} \cline{2-3}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._
# Performance Characteristics of an Orthopter-Type Vertical Axis Wind Turbine in Shear Flows
## 2 Experimental Approach
### Experimental Setup for Uniform Flow Case  
Figure 3a shows the experimental setup for the uniform flow case. The experiments were conducted using a closed circuit wind tunnel with an open test section. The size of the cross section of the wind tunnel outlet was 1.25 m \(\times\) 1.25 m. The blockage ratio which is defined as the ratio of the projected rotor's swept area to the wind tunnel outlet area was approximately 18%. The O-VAWT was set in the test section so that the rotor center was at the center of the cross-section of the wind tunnel outlet and 0.850 m downwind of the wind tunnel outlet. Here, we defined the rotor center as the point on the rotational axis of the rotor and at the mid-height of the blades. In addition, we set the origin of the coordinate system at the rotor center, as shown in Figure 2c,d. The rotor was driven by a motor (Mitsubishi Electric, GM-S) and its rotational speed (\(\omega\)) was monitored by using a digital tachometer (Ono Sokki, HT-5500) and controlled by using an inverter (Hitachi, SJ200). The rotor torque was measured by using a torque meter (TEAC, TQ-AR), which was connected to the motor and shaft via couplings. The output signal of the torque meter was converted by a 16-bit analog-to-digital converter with a sampling interval of 0.5\({}^{\circ}\), and 36,000 items (50 revolutions) of data were stored. To measure the reference wind speed \(U_{\infty}\), an ultrasonic anemometer (Kaijo Sonic, DA-650-3 TH and TR-90 AH) was set approximately 2 m upwind of the wind tunnel outlet. The value of \(U_{\infty}\) was kept at 8 m/s. The value of tip speed ratio \(\lambda\), which is defined as:  
\[\lambda=R\omega/U_{\infty}, \tag{3}\]  
was varied from 0.1 to 0.8 with an increment of 0.1.
## 2 Method
### New Backbone: Lightweight Channel Attention Network (LCANet)
The specific structure of LCANet is shown in Table 1. Replacing the backbone with LCANet, although the accuracy of the model slightly decreases, the volume of model becomes smaller.
## 3 System Model of MA-SM  
In order to increase the spectral efficiency, several independent signal symbols are transmitted from a combination of activated antennas in the MA-SM system at a cost of additional radio frequency (RF) chains. Therefore, the MA-SM combines the advantage of low complexity of SM system and the high spectral efficiency of spatial multiplexing (SMux). Let the number of active antennas at each channel use be denoted by \(N_{P}\). The spectral efficiency of MA-SM achieved by the spatial symbols, i.e., the indexes of the available combinations, is given by:  
\[SE_{spa}=\lfloor log_{2}C_{N_{P}}^{N_{T}}\rfloor \tag{4}\]  
where \(C_{N_{P}}^{N_{T}}\) is the set of combinations of length \(N_{P}\) out of the available \(N_{T}\) transmit antennas. The set of all antenna combinations that can be used for transmission is denoted by \(\Gamma=\{\gamma_{1},\cdots,\gamma_{N}\}\). Following thenotation in [20], we define \(\mathbf{\zeta}_{j}\) as a vector of length \(N_{T}\) with binary entries of 0 or 1. The values associated with these vectors are explained in light of the following example. Let \(N_{T}=4\) and \(N_{P}=2\), then:  
\[\gamma_{1}=(1,2) \mathbf{\xi_{1}}=(1,1,0,0)\] \[\gamma_{2}=(1,3) \mathbf{\xi_{2}}=(1,0,1,0)\] \[\gamma_{3}=(1,4) \mathbf{\xi_{3}}=(1,0,0,1)\] \[\gamma_{4}=(2,3) \mathbf{\xi_{4}}=(0,1,1,0)\] \[\gamma_{5}=(2,4) \mathbf{\xi_{5}}=(0,1,0,1)\] \[\gamma_{6}=(3,4) \mathbf{\xi_{6}}=(0,0,1,1)\]  
Because \(N\) should be a power of two, only \(N=4\) combinations are used for transmission. The system equation is then given by:  
\[\mathbf{y}=\frac{1}{\sqrt{N_{P}}}\mathbf{Hs}+\mathbf{n}, \tag{5}\]  
where the vector \(\mathbf{s}\) contains exactly \(N_{P}\) non-zero elements whose locations are determined by \(\gamma\).  
The selection of the antenna combinations used for transmission out of the available ones is based on maximizing the Hamming distance among the \(\mathbf{\zeta}\) vectors. In the sequel, the same set of combinations selected in [20] will be used to conduct a fair comparison and analysis. As a contribution in [20], a rotation angle is associated with each of the used combinations, where all symbols transmitted from a given combination of antennas are rotated with the same angle. Table 1 summarizes these parameters for three configurations considered in [20].  
Based on the above system description, the total spectral efficiency of the MA-SM system is given by:  
\[SE_{ma-sm}=SE_{sig}+SE_{spa}=N_{P}\times q+\lfloor log_{2}C_{N_{P}}^{N_{T}}\rfloor \tag{6}\]
# Supervised Learning Based Peripheral Vision System for
## 3 Proposed Approach
### Mathematical Expression for Loss function  
Suppose that the PCE architecture [22] denoted by function '\(F\)' takes an image '\(x\)' and a binary mask '\(M\)'. The binary mask contains 'one' for masked pixels and 'zero' for the provided pixels to generate plausible content for the masked image \(F(x,M)\). The PCE training consists of the optimization of the two loss functions: a \(L_{1}\) loss and a GAN loss. The \(L_{1}\) loss has its masking in such a way that it has non-zero values only inside the corrupted region. The mathematical expression for \(L_{1}\) loss is given as follows:  
\[L_{L1}=\left\|M\odot(F(x,M)-x)\right\|_{1} \tag{1}\]  
where \(\odot\) is the element-wise multiplication operation.  
The general mathematical expression for PatchGAN [20] is as follows:  
\[L_{GAN}=\min_{G}\max_{D}E_{x\sim Pdata(x)}[\log(D(x,M))+\log(1-D(G(x,M),M))] \tag{2}\]  
where the discriminator '\(D\)' differentiates between the real and fake images, while the generator '\(G\)' tries to befool the discriminator using generated samples. In our case, we do not have any random mask. Finally, the GAN loss [20] is defined as follows:  
\[L_{GAN}=\min_{F}\max_{D}E_{x\sim Pdata(x)}[\log(D(x,M))+\log(1-D(F(x,M),M))] \tag{3}\]  
The PCE architecture utilized the discriminator having same functionality as the global discriminator [20] except that the PCE architecture restores the ground truth pixels before processing the generated image with the discriminator. Thus, the generated region remains consistent with the context. The overall loss is mathematically defined as follows:  
\[L=\lambda L_{L1}+(1-\lambda)L_{GAN} \tag{4}\]  
where \(\lambda\) has the value of 0.999 for all experiments following [21]. We used the PCE architecture [22] with the default settings. PCE network was used for image inpainting using binary mask and the extrapolation problem can be tackled [22], inverting the same mask.