esa-sceva/satcom-chunk-collection huggingface dataset:
# A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information
###### Abstract  
Power assistive devices have been developed in recent years. To detect the wearer's motion, conventional devices require users to wear sensors. However, wearing many sensors increases the wearing time, and usability of the device will become worse. We developed a soft gait assistive suit actuated by pneumatic artificial rubber muscles (PARMs) and proposed its control method. The proposed suit is easy to wear because the attachment unit does not have any electrical sensors that need to be attached to the trainee's body. A target application is forward walking exercise on a treadmill. The control unit detects the pre-swing phase in the gait cycle using the pressure information in the calf back PARMs. After the detection, the suit assists the trainee's leg motion. The assist force is generated by the controlled PARM pressure, and the pressure input time is changed appropriately considering the gait cycle time. We conducted walking experiments; (1) verifies the proposed control method works correctly, and (2) verifies whether the gait assistive suit is effective for decreasing muscular activity. Finally, we confirmed that the accurate phase detection can be achieved by using the proposed control method, and the suit can reduce muscular activity of the trainee's leg.  
Keywords:power assistive device; pneumatic artificial rubber muscle; soft gait assistive suit; gait phase detection +
Footnote †: journal: Applied Sciences
## 2 Overall Technical Parameter Requirements and Architecture Analysis
### Software Configuration  
The software configuration mainly includes the following aspects:  
1. Data collection and processing: preprocessing, time synchronization, and spatial registration operations are performed on the data collected by LiDAR and cameras to facilitate subsequent data fusion and processing.
2. Multi-modal data fusion: by integrating 3D LiDAR detection results with image data, the system achieves complementary data advantages and improves detection accuracy.
3. Obstacle detection and identification: design and implementation of obstacle detection and identification methods based on the information obtained from multi-modal data fusion.
4. Human-machine interaction interface: develop a user-friendly human-machine interaction interface for displaying obstacle detection results and alarm information.
5. System integration and testing: integrate various modules and perform performance testing and validation of the entire system.  
Through the above overall architectural design, an intelligent detection and obstacle avoidance system integrating 3D stereoscopic visualization safety monitoring, real-time high-density point cloud detection, and hierarchical refined risk warning is formed. This  
Figure 2: System architecture.  
system provides high-precision obstacle avoidance feedback and collision risk warning for subsequent robotic arms.
## 2 Materials and Methods
### Experimental Conditions and Setup  
We tested the functionality of the all layers and prototype comprehensively, indoors as well as outdoors. The test lasts from several minutes to a few hours up to days (30 h is the longest after battery charging). In general, six prototypes have been tested by five volunteers including three males and two females aged 26 to 48 years old. Users have been from different occupations ranging from ordinary workers with no technical information on the functionalities of the prototype to scientists in the fields. In every experiment, the user has been asked to wear the prototype on the top side of left-hand with outside-oriented microphone, in a good skin contact and comfortable. Users have been recommended to wear the prototype in a fix location on the left-hand to avoid moving the prototype. Besides, we have strongly asked the subjects to avoid covering the surface and microphone of the prototype. All users have been trained to only switch on/off the prototype, pair BLE, and configure the prototype with the application. Additionally, each user has been asked to avoid interruption of the data transmission unless otherwise necessary. At the end of each experiment, user has reported the technical problems and data interruption, if any. Each user also submitted the written protocol, specifying the activities and switching time between, if applicable. In each test, the prototype was BLE-paired with a smartphone, which starts the bidirectional data transmission. If activated, the sensors have sampling rates of sound level (2 Hz), gas (1 Hz), UV radiation (15 s time interval), air temperature (1 Hz), air humidity (1 Hz), air pressure (1 Hz), skin temperature (15 s time interval), PPG (15 s time interval), gyroscope (50 Hz), magnetometer (50 Hz), and accelerometer (50 Hz). During the tests, the prototype measured a surrounding sound level of 32 to 85 dB, with the resolution of 1 dB. NO\({}_{2}\) and CO are limited to 52 and 1680 ppm with the resolution of 1 and 5 ppm, respectively. This specification for UV index is restricted up to 9 with the resolution of 1. For all tests, we extracted the presented data from the smartphone in Comma-Separated Value (CSV) format. The sampling rates, specified above are constant and have been applied in every experiment. We have not imposed any specific conditions/restrictions to users, unless otherwise it is clearly stated. Hereafter, we will support the functionalities of the prototype with two types of tests: from Sections 3.1-3.5, the experimental tests are focused on the detail description of the tests for each sensor and the respective parameters. In these sections, the general conditions and specifications mentioned in Section 2.4 are applicable. However, if additional conditions have been available due to the nature of experiment(s), will be reported in the that particular subsection. We support the comprehensive functionaries of the prototype, data synchronization, accurate time stamp, and labeling the activities in Section 3.6.
## 6 Performance Evaluation
### Simulation Setup  
Our simulations were based on two sets of queries, _Uniform_ and _Skewed_, with the workspace fixed at 50 km \(\times\) 50 km square. In _Uniform_, spatial query ranges are uniformly placed on the workspace. On the other hand, in _Skewed_, the distribution of spatial query ranges on the workspace follows the _Zipf_ distribution with skew coefficient \(\upalpha=0.8\). Each spatial query range in both _Uniform_ and _Skewed_ is a square. The movements of the moving objects that we generated follow the _random waypoint model_[38], which is one of the most widely used mobility models: each moving object chooses a random point of destination on the workspace and moves to the destination at a constant speed distributed uniformly from 0 to maximum speed, which we set to 50 km/h. Upon reaching the destination, it remains stationary for a certain period of time. When this period expires, the moving object chooses a new destination and repeats the same process during the simulation time steps. The computational capability of each moving object was randomly selected from the range between 25 and 100 spatial query ranges, and thus the threshold value \(\uptheta\) of the BP-tree (used in MQM), QR-tree (used in QRT), BQR-tree (used in BQRT), and GQR-tree was set to 25. For SR, we used the \(64\times 64\) grid indexes for indexing queries and safe regions.  
Each non-spatial attribute \(a\in A\) is assumed to be categorical because numerical attribute \(\dot{a}\) can be replaced with the categorical attribute by discretizing the domain of \(\dot{a}\). The domain of \(a\) is \(32\) and the distribution of each non-spatial attribute value \(o.a\) of each moving object \(o\) follows the _Zipf_ distribution with skew coefficient \(\upalpha=0.8\). Each non-spatial value specified on a subset of \(A\) by each query \(q\) in both _Uniform_ and _Skewed_ follows the same distribution. We list the set of used parameters and their default values (stated in boldface) in the simulations in Table 2. In each simulation, we evaluated the effect of one parameter while the others were fixed at their default values. We ran each simulation for 1000 simulation time steps and measured the average of the CPU-time (in ms) and total number of messages. At each time step, 10% of queries in _Uniform_ and _Skewed_ were set to be updated (_i.e._, reinserted after they are deleted). Note that this update rate is sufficient to study the performances of SR, MQM, QRT, BQRT, and GQRT because these methods focus on dealing with stationary or quasi-stationary queries.
## 2 Analysis of the Working Principle of MOTs  
### Basic Structure  
An MOT, also known as a scattering force trap, uses the scattering force resulting from the combined action of a magnetic field and a laser to trap atoms. Its structure diagram is depicted in Figure 1[39,40].  
The MOT consists of a vacuum chamber containing gaseous atoms (represented by gray circles), two anti-Helmholtz coils (shown as blue ellipses), and six elliptically polarized beams (indicated by yellow arrows). The coils are symmetrically distributed along the z-axis on both sides of the vacuum chamber, with the distance between the coils equal to the radius. Both coils carry the same magnitude of current \(I\), but in opposite directions,  
Figure 1: Diagram of a typical MOT.  
creating a gradient magnetic field (illustrated by black curved arrows) that starts at zero at the coordinate origin and increases linearly along the axis. The six beams are directed into the vacuum chamber along six axes, with two opposite beams polarized in opposite directions. The combination of the magnetic field and the light field from the six beams creates a potential trap that traps the atoms. The central region of the magnetic field and light field aligns with the center of the vacuum chamber, where the atoms are trapped.
## 5 Results and Discussion
### Damage Analysis of Interface Structures  
As can be seen from Figure 13, the worst damage of the dam body occurs at the interface between II-CC and RCC, and the inflection point of the dam. Under the action of complex dynamic load, the interface of different materials is a weak link, and the spatial variation of its damage is very obvious, as shown in Figure 14. The solid curves represent the pulse ground motions, and dash curves represent non-pulse ground motions. The damage here is the damage of the dam at the end of the near-fault earthquakes.  
Under the pulse ground motions, the greatest damage occurs in the part (4 to 7 m range) close to the junction of the three materials of the dam, and the maximum is about 0.9. The second peak point of damage curves appears right above an inflection point which is at the lower-right part of the dam, about 30 m from the junction point. Under the non-pulse records, the maximum damage occurs at the interface endpoint, in which the maximum is about 0.9. Although the second peak also appears right above an inflection point which is at the lower-right part of the dam, it is less than 0.3 and obviously smaller than that under pulse records. The differences between the damages caused by the two types of seismic motions are not only the numerical magnitude but also the spatial distribution. In addition, it can be found from the figures that the difference between the damage of the dam under pulse seismic motion records and that under non-pulse seismic motion records goes up with the incidence angle. It can also be inferred that with an increase of earthquake magnitude, the impact of pulse ground motions to the dam increases.  
Figure 15 shows the tensile damage on inflection point D under the input angle of 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), respectively. The red curves show the pulse ground motions, and the blue curves show the non-pulse motions. When the P wave incident angle increases, the damage of the inflection point is more and more serious. In addition, the maximum of tensile damage under pulse ground motions with the incident angles of 45\({}^{\circ}\), 60\({}^{\circ}\), and 75\({}^{\circ}\) are 2.96, 2.22, and 2.26 times higher than that under non-pulse ground motions with the incident angles of 45\({}^{\circ}\), 60\({}^{\circ}\), and 75\({}^{\circ}\), respectively.  
Figure 14: The spatial variation of dam body tensile damage.
## 5 Rheological Properties of the Control and xGNP Modified Asphalt Models  
### Viscosity Measurement Method and Results  
Dynamic shear viscosity is an important parameter of fluids to measure the resistance to gradual deformation induced by shear stress. If the shear speed caused by the external force is appropriate, the fluid particles move parallel to the particles sheared. The speed varies linearly from the sheared layers to different layers. The resistance between these layers is caused by friction. The formula to calculate viscosity is shown in Equation (5).  
\[\eta=\frac{Fy}{Au} \tag{5}\]  
where \(F\) is the applied force; \(A\) is the area of the plate; \(\eta\) is the dynamic shear viscosity; and \(u\)/\(y\) is the shear gradient.  
During the construction of asphalt pavement, the viscosity determines the mixing and compaction temperatures, which relates to the pump ability, mix ability and workability of asphalt. Based on the American Society for Testing and Materials (ASTM) D4402, the Brookfield DV-II plus viscometer (Figure 5a) was selected to test the viscosity of asphalt in the laboratory. The viscosity test results are shown in Figure 5b.  
Figure 5: Laboratory results of viscosity of the control and xGNP modified asphalt binders. (**a**) Brookfield DV-II plus viscometer for testing viscosity of asphalt; (**b**) viscosities of the control (PG 58-28) and xGNP modified asphalt binders.  
From the test data, the viscosities of the xGNP modified asphalt binder (light blue line, Figure 5b) are higher than those of the control asphalt binder (red line). It indicates that multi-layer xGNP particles increase the viscosity of the modified asphalt binder. In addition, the viscosity decreases with the increase in temperatures of the asphalt binder. Exponential trends were also observed in the test data. The MD simulation for viscosity of the asphalt binder model was discussed in the following Section 5.2.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
### Seaweed's Ability to Absorb Heavy Metals  
Compared to lower-level organisms in the aquatic food chain, it is said that seaweeds can contain heavy metal concentrations that are \"thousands of times higher\" [14]. Studies have shown that metal concentrations in seaweed species were higher than the concentrations in ambient sea water [15]. Seaweeds' unique qualities in their cell walls allow for the biochemical process of retaining particulate metals. Polysaccharides and proteins in the cell wall contain anionic carboxyl, sulfate, and phosphate groups, which act as binding sites to absorb heavy metal particles. Thus, the makeup of the cell walls is the reason why seaweed contains both concentrations of trace minerals essential for the human diet and has a significant absorption capacity for heavy metals several times higher than those found in surrounding seawater [16].  
While many materials have biosorption capabilities, seaweed is unique in its high heavy metal absorption capacity due to its inability to regulate how many trace metals it absorbs [17; 18; 19; 20; 21]. Heavy metal concentrations in seaweed also do not undergo short-term fluctuations, which is useful in reliably monitoring ecosystem health. Compared to green species, brown seaweed species are considered better at absorbing heavy metals [22].
###### Abstract  
The aim of this work was to determine the bioactive compounds concentration in herbs from organic and conventional production. In 2017 and 2018, herbs of four species, including basil (_Ocimum basilicum_ L.), bear's garlic (_Allium usinum_ L.), marjoram (_Origanum majorana_ L.), and oregano (_Origanum vulgar_ L.), were examined. The concentrations of polyphenols, phenolic acids, flavonoids, carotenoids, and chlorophylls were measured. Next, separation and identification of the individual bioactive compounds were completed. The obtained results show that organic herbs contained significantly higher concentrations of total polyphenols, flavonoids, and phenolic acids compared to conventional herbs in both investigation years. On the other hand, conventional herbs contained significantly higher concentrations of chlorophylls and carotenoids, including beta-carotene.  
organic herbs; conventional herbs; polyphenols; flavonoids; carotenoids; chlorophylls +
Footnote †: journal: Applied Sciences
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 1 Introduction
At present, the methods of investigating the vibration of the track structure include field testing, FEM, numerical methods, etc. However, these methods are mainly focused on the instantaneous vibration responses in the time domain. Although many researchers and scholars have attached importance to the vibration responses in the frequency domain, the widely used indices, like the acceleration level, mobility, etc., are single evaluation indices. Further, the track-bridge system is a typical stratified structure from top to bottom, so its vibration mainly manifests as the transfer, storage, and consumption of energy in each component. To study the vibration characteristics of the track-bridge system, an energy method must be utilized. The energy method on the basis of power flow theory involves the amplitude and phase information of the force and velocity, which is not a single evaluation index. Its energy indices can be employed to evaluate the vibration damping performance. [PERSON] and [PERSON] [12] and [PERSON] and [PERSON] [13] made contributions to the conceptual development of conducting power flow analysis of vibration isolation systems. Furthermore, research in many areas, like marine engineering, mechanical engineering, aerospace engineering, etc., has previously been conducted to study vibration characteristics based on this method. Therefore, the power flow method has potential applicability to investigating the vibration characteristics of the track-bridge system. Bridges have been widely used in the HSR system because of their higher stability, but the induced vibration problem urgently needs to be solved. However, few researchers and scholars have carried out studies on the vibration energy of the ballastless track on bridges of the HSRs. Accordingly, the energy method based on power flow theory is applied in this work to study the vibration characteristics of the ballastless track on bridges of the HSRs. Further, a numerical model of the vehicle-track-bridge coupled system is established. The CRTS-II track, which has been widely employed in the HSR system in China, is taken into account. By means of the model, the effects of different track conditions and running speeds on the vibration energy are investigated. The rest of this paper is organized as follows: Section 2 presents the methodology of the vehicle-track-bridge coupled system. Section 3 discusses the influences of the different track conditions and running speeds on the vibration energy. Finally, Section 4 presents the conclusions.
## 4 Results and Discussion
### Scanning Electron Microscopy (SEM)  
Scanning electron microscopy (SEM) images of fly ash (FA) and hydrated lime (HL) are presented, respectively, in Figure 9A,B for comparison purposes. The images taken with a magnification of 1000x show an abundance of small particles with large variation in the interparticle spaces indicating the lack of cohesion. Some of these voids are indicated using yellow arrows as examples.  
Figure 10 presents a series of SEM images at different magnifications of a DM sample treated with the highest amount of stabilizing hydrated lime, fly ash, and Portland cement (35/84.5/20). Note that these figures are typical of all treated samples but only one sample is presented for clarity. Panels A and B present two different areas at a magnification of 1000x. These images, when compared to those in Figure 9, reveal that cementitious product envelope smaller particles and bond individual particles together; i.e., the number of voids, such as those indicated by yellow arrows in Figure 9, is significantly reduced. The interactions create a continuous texture, which is less porous and leads to higher strength. Panels C and D show SEM images at magnifications of 2000x and 3000x, respectively. These images reveal that an excess of fly ash material exists that does not participate in a reaction, as highlighted by white arrows. These larger magnification images also show that the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SN**} & **ID** & **Ave CS7** & **Ave CS28** & **Ave Submerged** & **Remaining CS** \\  & **(HL/FA/PC)** & **(kPa)** & **(kPa)** & **CS (kPa)** & **(Submerged/28)** \\ \hline
1 & 15/34.5/10 & 4059 & 5219 & 2125 & 41 \\
2 & 25/57.5/10 & 5664 & 7171 & 3540 & 49 \\
3 & 35/80.5/10 & 6424 & 8257 & 4416 & 53 \\
4 & 15/34.5/15 & 5933 & 6972 & 3036 & 44 \\
5 & 25/57.5/15 & 6490 & 8857 & 4789 & 54 \\
6 & 35/80.5/15 & 7460 & 8888 & 5106 & 57 \\
7 & 15/34.5/20 & 7824 & 9381 & 5865 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Strength measurements for SDMB.  
Figure 8: UCS of all stabilized samples.  
cemetitious reactions commence on the surface of fly ash particles forming a layer of hydration products on the surfaces, which leads to higher enhancement of the properties of stabilized DM.