esa-sceva/satcom-chunk-collection huggingface dataset:
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
## 1 Introduction
The effects also depend on the characteristics of the person, such as age, gender, and health condition [26; 27]. WBV has been widely studied, and the evidence agrees on the pros and the cons of its application in patients with stroke [32; 33; 34; 35; 36; 37; 38; 39; 40]. The application of FMV for patients with stroke has been less widely studied [28; 29]. Only one review specifically focused on FMV in stroke [28]. The authors summarized eight studies and concluded that FMV showed some evidence in reducing hemiplegic upper extremity spasticity in patients with stroke, and additional randomized controlled trials were needed to study the effects on FMV on spasticity in individuals with stroke [28]. Multiple studies have been conducted on the use of FMV for stroke rehabilitation in upper and lower limb impairments. These studies showed some improvements in functionality and reduction of muscle spasticity. However, there is a lack of consensus regarding its clinical application. The other gaps include the lack of protocol (frequency and amplitude of vibration, number of days and duration of intervention and overall study, etc. ), standardized outcome measures, and recommended vibration devices. The purpose of this review was to focus on the current FMV devices in use, the vibration parameters applied, and protocols of FMV therapy and outcome measurements in post-stroke rehabilitation.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 6 Comparative Study
### Deep Learning-Based VCS Algorithms
#### 6.2.2 Qualitative Comparison  
Different VCS approaches, together with their specific advantages and limitations, are summarized in Tables 3 and 4 to compare their qualitative performances that should be taken into consideration while implementing the network for a particular application.  
Figure 16: Performance comparison based on SSIM obtained by several VCS reconstruction algorithms on 6 grayscale benchmark datasets.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Classification Type** & **Category** & **Traditional/DL** & **Algorithm’s Class** & **Examples** & **Advantages** & **Limitations** \\ \hline Sampling strategy & Temporal VCS & Traditional & GMM based & & Parallel processing can be used, good quality & Too computationally slow, slow reconstruction process, use only the temporal domain to compress the video & Too computationally slow, slow reconstruction process, video \\ \hline  & & TV based & GAP-TV [44] & & & \\ \hline  & & & Deep fully connected network for VCS [45], DCAN [47], E2E-CNN [48] & & & \\ \hline  & Spatial VCS & Traditional & Reweighted residual sparsity & VCS-RRS [52] & Good performances, flexibility & use only the spatial domain to compress the video, Low scalability \\ \hline  & & Extended architectures of SPC & FPA-CS [55], LiSens [56] & High spatial resolution, flexibility & Expensive \\ \hline  & & DL & RNN based & CSVideoNet [53], SDA-CS [33] & & \\ \hline  & & CNN based & ReconNet [34] & & \\ \hline  & Spatio-temporal VCS & Traditional & & & Sample the temporal and spatial dimension simultaneously & Huge computational cost \\ \hline  & & & & & \\ \hline  & & TV based & 3D-Wavelet and 3D-Noiselet approach & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & DL & CNN based & [58; 60; 61; 62] & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different algorithms for video compressive sensing (Part 1).
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 2 Materials and Methods  
### Site Description, Soil Characterization and Evaluation of As Bioavailability  
As described in the first part of this work [50] (which also provides details on the methodologies used), the contaminated site under investigation is located in Southern Italy and includes two different areas: the first (contaminated in various ways) once housed a chemical plant, while the second is a vegetated area that presents only arsenic contamination, about 1000 m from the former chemical plant (external area). In this second work, attention was focused only on the external area. Using a small excavator, soil samples were taken from sections 0.3 to 0.6 m, 0.6 to 1.0 m and 1.0 to 1.6 m (the presence of a layer of rocks prevented us from reaching sampling depths greater than 1.6 m). Unlike the individual study of each sample discussed in the first work, in this case, the different soil samples were gathered and thoroughly mixed to obtain a single sample which was then analyzed to quantify the total and potentially bioavailable arsenic content. This was carried out to bring the experimentation closer to the conditions of a future field test, which is usually not performed with several meter-by-meter planting approaches but by preparing the entire area for planting in the same way. However, it should be reiterated that mesoccosm tests remain small-scale tests compared to field tests.  
Since the bioavailability tests previously detailed and performed in [50] had shown that arsenic subjected to the remediation treatment was difficult to mobilize, adsorption of arsenic to the iron oxides/hydroxides present in the soil was hypothesized. Therefore, in addition to the most widely used chemical additive (0.05 M and 1 M KH\({}_{2}\)PO\({}_{4}\)), other solutions were tested, demonstrating, in particular, the effectiveness of 0.2 M potassium hydrogen oxalate (KHC\({}_{2}\)O\({}_{4}\)). In an attempt to further improve the extractive efficacy, the use of ascorbic acid (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)), alone or in combination with oxalate, was investigated in this work to identify the best possible combination of the two reagents capable of maximizing the bioavailability of arsenic. All tests were carried out in triplicate, considering a soil: extracting solution ratio of 1:25 and a contact time of 4 h.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 4 Discussion
Strategies to enhance signal reliability across varying environmental conditions will be investigated in the near future.
# Mechanical Behaviors and Numerical Simulation Analysis of a New Isolation System
## 2 Design of the Isolation System
### Design of the Test Bearing  
This experiment uses a micro-controlled hydroelectric servo compression shear tester and a combination of two isolation systems and an intermediate steel plate. The arrangement is a double shear combination, with the isolation system located in the middle, as shown in Figure 3.  
Figure 1: Physical diagram of the seismic isolation device.  
Figure 2: Individual components.  
This experiment studied the variation law of the friction coefficient of the composite seismic isolation bearing under different variables. The calculation of the friction coefficient was carried out using the following formula:  
\[F=\mu\ N\]  
To facilitate the determination of the tested friction coefficient, a pair of composite isolation bearings with two friction surfaces was used. The assembly of the isolation bearing is shown in Figure 4. Therefore, the friction coefficient of the composite isolation bearing is given by the test results obtained from this setup.  
\[\mu=F_{1}/2N_{1} \tag{1}\]  
where \(\mu\) is the friction coefficient of the composite seismic isolation bearing.  
The upper connecting plate has a diameter of 250 mm and a thickness of 20 mm. Its lower surface is reserved by five bolt holes of diameter 20 mm and depth 7.5 mm. The middle column has a diameter of 54 mm and a total height of 92 mm, with a bolt column on the upper surface of 20 mm diameter and 7 mm depth, and two cylindrical grooves of diameter 45 mm and depth 32 mm, and diameter 32 mm and depth 43 mm on the lower surface. The lower connecting plate has a diameter of 250 mm and a thickness of 10 mm, with five cylindrical grooves of diameter 54 mm and depth 5 mm on its upper surface, and five cylindrical grooves of diameter 45 mm and depth 5 mm on its lower surface. The two cylindrical axes of the upper and lower grooves are aligned. Bearings will be equipped with different numbers of balls and shims according to the designed working conditions. To ensure the stability of the bearing, the number of balls is set to 1, 4, and 5. The materials of the parts of the bearings are different; the upper and lower connecting plates and intermediate columns are made of 45# steel; the shims, springs, and friction plates are made of 65 mm spring steel; the universal ball is made of turned heavy duty universal ball SP-25; the internal ball material is bearing steel; and the friction material is selected as PTFE.
## 3 Computational Model
### Solution Procedure  
In the present simulation, the governing equations are solved with the Evaporation-Condensation model in the three-dimensional domain using the commercial package Ansys Fluent [34]. Additionally, a quick discretization and first-order upwind scheme are used for all conservation equations to save computational time. For more accurate results, the third-order MUSCL discretization scheme is used. The coupled scheme is used for pressure-velocity coupling and the pressure is calculated with the presto scheme, as it is recommended for two-phase complex flow. The coupled algorithm solves the momentum and pressure-based continuity equations together which makes the solution robust and more efficient over the segregated solution scheme. The gravitational acceleration is set equal to \(-9.81\) m/s\({}^{2}\) in the vertical direction to account for the buoyancy effect with the density gradient. To accomplish robust convergence, various pseudo time values are tested for the fluid and solid zones of the computational domain. The selected pseudo time scale factors for fluid and solid zones are 0.7 and 1, respectively. The pseudo-transient fashion is recommended for the complex two-phase flow, which has high mass transfer through simulation [29]. A grid independence study is performed to grantee the computational results and the generated mesh is shown in Figure 3. The total number of the used tetrahedral mesh is 534,136. The mesh is refined near the tube wall and y+ is approximately equal to 1. Moreover, enhanced wall treatment is used for a smooth transition between the boundary layers. The enhanced wall treatment is recommended in the case of heat transfer simulation and it needs fine mesh in the viscous sublayer.  
Figure 3: The computational mesh of the inverted U-tube.  
Boundary conditions specify the flow and thermal variables on the physical domain boundaries. Figure 2 shows the boundary conditions utilized in the present study. The mass flow inlet applied to each phase and mixture temperature are specified at the flow inlets. The pressure outlet boundary condition is specified as a constant value and is equal to zero-gauge pressure at the pipe outlet. The isolated wall of the duct has been set as hydraulically smooth walls with non-slip boundary conditions.
# Bioremediation of Persistent Organic Pollutant--Oxybenzene with _Pleurotus diamor_
## 2 Materials and Methods
### Sample Preparation for Analysis  
The fungal biomass in powdered form underwent extraction with 125 mL of methanol in a 49 kHz ultrasonic bath (Sonic-2, Polsonic, Warszawa, Poland) for 20 min. After each extraction cycle, the mixture was filtered via paper filter and funnel, with the methanol extract collected in a crystallizer. This procedure was replicated nine times per sample. Pooled extracts from all cycles were air-dried at ambient temperature (23 \(\pm\) 2 \({}^{\circ}\)C), yielding an average of 1.2 g extract per 4 g starting biomass. Both dried extracts and dehydrated culture media were redissolved in HPLC-grade methanol, then passed through 0.22 \(\upmu\)m PTFE membrane filters (ChemLand, Poland) into sterilized glass vials (Witko, Poland). While the media samples were analyzed undiluted, the biomass-derived extracts required 10-fold methanol dilution prior to RP-HPLC/UPLC analysis owing to elevated oxybenzene levels.
## 5 Proposed Classification
### Classification of RL-Based Routing Schemes with Regard to Learning Model  
According to our classification, RL-based approaches are classified in two classes (i.e., model-based and free-model) with regard to learning model. Table 4 compares these classes.  
* **Model-based RL routing scheme:** In this approach, the agent learns a model of the environment based on experience to forecast the value function. See Figure 5. An advantage of these schemes is data efficiency. This means that these methods can learn a more accurate estimation of value function with few interactions with the environment. Moreover, they are flexible against sudden changes in the environment and can be well adapted to these changes. For example, if a road section is blocked in VANET, model-based routing protocols will quickly adapt to this change in the network. However, these methods have high computational costs and are not appropriate for time-sensitive applications. This proves that model-based reinforcement learning methods are efficient when sufficient computational resources are available. However, these methods have a poor performance for large-scale applications such as VANETs where the state space is very big [37; 38].
* **Free-model RL-based routing approaches:** In this class, the agent directly estimates the value function in accordance with the obtained experiences so that these methods do not build any network model. Figure 6 represents a free-model reinforcement learning system. The main advantage of the free-model routing methods is that they are efficient in terms of computing. Therefore, they are more suitable for large-scale and time-sensitive applications in VANET. However, they have two important weaknesses: they need more experience to achieve an optimal response compared to model-based approaches and are less flexible against the sudden network changes [39].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Routing Scheme** & **Advantages** & **Disadvantages** \\ \hline Model-based RL routing method & Data efficiency, high flexibility against sudden changes in VANETs & High computational complexity, inappropriate for large scale networks, inappropriate for time-sensitive applications \\ \hline Free-model RL-based routing method & Low computational complexity, suitable for large-scale networks and time-sensitive applications & Low flexibility against sudden changes in the network environment, need more experiences to achieve an optimal response \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model-based and free-model routing methods.  
Figure 5: A model-based RL system.  
Figure 6: A free-model reinforcement learning system.
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## 1 Introduction
Whether wind disturbance or other external disturbance, their effects are always reflected by acceleration information. Thus, acceleration can be used to estimate the external disturbance of the UAV [24], and we call this method the acceleration feedback method for disturbance rejection control. In our previous work [25], the acceleration feedback method was applied to unmanned helicopter disturbance rejection control in simulations. In this paper, a separate control strategy was used in the aerial manipulator system in which the UAV and the manipulator were controlled separately. In the UAV controller, the system center of mass (CoM) offset was used to compensate partial disturbance of the robotic arm. In the manipulator controller, the inverse kinematics of the aerial manipulator system was used to eliminate effects of the kinematic coupling between the UAV and the manipulator. Our previous work [8] showed that this control strategy works well in the target grasping tasks. This paper pays more attention to the steady flight of the aerial manipulator in strong wind, and the acceleration feedback control method was applied into aerial manipulator control. The main contributions of this article are as follows:  
* An acceleration feedback enhanced robust \(H\infty\) controller of UAV was designed, which can give the aerial manipulator the ability to reject the wind disturbance. The proof of bounded input bounded output) stability of the UAV under the \(H\infty\) controller is also presented. * Successful grasping of an object using an aerial manipulator in a strong wind was performed. To the best of our knowledge, this is the first time an aerial manipulator system has completed this task in strong wind. The rest of this paper is organized as follows. In Section 2, kinematics and dynamics of the aerial manipulator system are introduced. After that, the control scheme of the aerial manipulator system and acceleration feedback enhanced robust \(H\infty\) controller are given in Section 3. Then, the composition of the aerial manipulator system and the experiments of aerial grasping in the wind are presented in Section 4. Finally, conclusions are presented in Section 5.
## 4 Comparison of Light-Tube Optical Properties under Arbitrary and Homogenous Skies  
Simple models of homogeneous skies are still preferred, most likely because of easy implementation and rapid computations. However, the computers are now fast and inexpensive, so there is no significant reason for insisting upon tools with low-accuracy levels. Accurate models of heterogeneous cloud fields are necessary because such conditions occur over 50% of the globe at any time. Inhomogeneous sky situations dominate in many regions. A demand to use the models of homogeneous skies in such situations can result in inaccurate predictions of interior illuminance due to differences between the real and approximated sky luminance fields. It is our intention to inspect the range of errors induced in this approximation. To do this, we compare the exact computations based on the UMRP (Unified Model of Radiative Patterns) of heterogeneous cloud fields [27] against a well-known all-weather model of homogeneous skies [19]. Note that [PERSON]'s model is commonly used in many applications and is designed to predict the sky luminance from the global and direct irradiance data and dew point temperature. The latter is calculated from humidity and real temperature, while the insolation conditions are determined from the solar zenith angle \(Z_{\mathrm{S}}\), [PERSON]'s sky clearness \(\varepsilon\), and [PERSON]'s sky brightness \(\Delta\). The UMRP computes the spectral sky radiance for arbitrary cloud coverage using aerosol and cloud parameters as inputs. The conversion from radiance to luminance is possible through the average spectral sensitivity of the human visual perception of brightness, i.e., the V(\(\lambda\)) function. The luminance distributions determined from [PERSON]'s all-weather model and UMRP are linked with the HOLIGILM tool in order to model the light field below a light guide length of H = 1.5 m, radius r = 0.26 m and internal specular reflectance \(\rho\) = 0.934. We avoid a diffusing cover to better understand the work plane illuminance data obtained from numerical runs assuming various sky conditions. The transparency of a hemispherical top dome (0.92) is fixed for all the model computations.  
Figure 5: Sky luminance distribution in kcd.m\({}^{-2}\) (left plots) and work plane illuminance in klx (right plots) for clouds clumped about the azimuth angle at 0\({}^{\circ}\) (**a**) and 180\({}^{\circ}\) (**b**) with an offset of 2.0 km. Solar altitude is 20\({}^{\circ}\), and the cloud fraction is 0.3. Room dimensions are in meters (see plots on right side of the figure).
## 1 Introduction  
In human visual perception, contrast has a significant influence on the quality of an image. Contrast enhancement is frequently referred to as one of the most important issues in image processing. A poorly-illuminated environment can significantly affect the contrast ratio, producing an unexpected image. Contrast is created by the difference in luminance reflected from two adjacent surfaces. Several studies have given particular attention to this subject.  
Local contrast stretching is an enhancement method performed on an image for locally adjusting each picture element value in order to improve the visualization of structures in both the darkest and lightest portions of an image at the same time. Local contrast stretching is performed by sliding windows across the image and adjusting the center elements [1].  
Global contrast stretching is simple and fast, but its contrast-enhancement power is relatively low. Local contrast stretching, on the other hand, can more effectively enhance overall contrast, but the complexity of computation required is very high due to its fully overlapped sub-blocks [2]. The global contrast stretching method is simple and powerful, but it cannot adapt to the local brightness features of the input image because it uses only global information over the whole image. This fact limits the contrast-stretching ratio in some parts of the image, and causes significant contrast losses in the background and other small regions.  
To overcome this limitation, this study proposes an Eight-Scale Adaptive Inverse Hyperbolic Tangent (8 SAIHT) method. This technique consists of two steps: a sub-scale step and a contrast enhancement step. The sub-scale step is applied to the image for sub-band processing. In the contrast enhancement step, the Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm is applied for contrast enhancement, and to bring out hidden details. The new value of remapped pixel is based on an AIHT map function. Test results indicate that the proposed method could provide better image contrast than conventional enhancement methods in terms of visual looks and image details.
## 3 Challenges for Designing MAC Protocols in UWSNs  
UWSNs have their specific properties and structural restrictions such as, service restrictions, that consist of a restricted expanse of control, short range of communication, limited bandwidth, and restricted storage per sensor node [51]. The MAC protocols enable synchronized transmission between the sensor Rx node and Tx anchor node over a standard channel, regardless of the medium used for communication in UWSNs [46]. The underwater environment presents new problems that are needed to be considered during the development of the MAC protocol compared to the MAC intention for global networks [9; 13; 16; 17]. Issues affecting the underwater communication are discussed in the sections ahead.