esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Grasping Modes and Working Principle of the VGS Robot Finger  
The proposed hand has multiple grasping modes, the detail grasping process of different grasping modes are shown in Figure 1. Under the linear-parallel pinching grasping mode, the distal phalanx moves along a straight line and there is no displacement in the vertical direction. When the first phalanx touches objects firstly, the finger will work in the self-adaptive grasping mode that is after the first phalanx reaches the objects, the distal phalanx will continue to rotate until both phalanxes touch objects.  
The shift between the different grasping modes is achieved by using the variable geometrical structure mechanism, and the detail working principle of the finger mechanism is shown in Figure 2.  
As shown in Figure 2, under the linear-parallel pinching mode, point \(E\) and \(G\) are at the same position and the second phalanx will move along a straight line. Under the self-adaptive grasping mode, when the first phalanx cannot rotate anymore, the elastic component becomes longer, and then point \(E\) and \(G\) will be apart from each other. The geometrical structure of the VGS finger will change and the distal phalanx begins to rotate until it reaches objects. How the elastic component changes its length during the two different grasping modes is shown in Figure 3.  
Figure 1: Linear-parallel pinching and self-adaptive grasping modes.  
Figure 2: The working principle of the finger mechanism.  
Figure 3 shows how the elastic component changes its length from the linear-parallel pinching mode to the self-adaptive grasping mode. Actually, the shift between the two different grasping modes is executed automatically. During a grasping process, when the two phalanxes do not reach objects, the finger will work in the linear-parallel pinching mode; if the first phalanx reaches objects firstly, the finger will work under the self-adaptive grasping mode; on the other hand, if the distal phalanx touches objects firstly, the finger will work under the linear-parallel mode. The automatic shift between different grasping modes makes the VGS finger achieve a mechanism intelligence and can help reduce the complexity of the control system.
## 3 Materials and Methods
### Materials
#### 3.1.3 Network Layer  
Bluetooth Wireless Communications Stage  
The _Network Layer_ enables the pH sensor to connect to other devices such as gateways (e.g., in the form of smartphones or personal computers) or even other sensors. Such connectivity is characteristic of _Industry 4.0_ applications. In the instrument described in this work, a Bluetooth radio version 2.0 (Linvor, Jinan city, Shandong, China, LV-BC-2.0) enables wireless transmission of pH data, and serves as the _Network Layer_. The radio, implemented in a breadboard (Olimex, Plovdiv, Bulgaria, BLE HC-06) for ease of integration, is a Class 2 device, and exhibits a maximum communication range of 10 m [39]. This range is sufficient for isolated VF applications. However, for distributed VF applications or, more generally, other _Industry 4.0_ applications, a Class 1 Bluetooth device can be employed to yield a  
Figure 3: The signal conditioning stage is constructed by implementing an offset section, followed by a detection/amplification stage. The sensor is coupled using an array of precision op-amps in buffer and non-inverting amplifier configurations to ensure adequate performance.  
range of 100 m. In fact, for conventional farming, the Bluetooth technology enables a very desirable feature: _Mesh Networks_[40]. In such a topology, a rather large area (e.g., 1000 m\({}^{2}\)) can be covered with a handful of Bluetooth radios. Any gateway, functioning as part of the _Application Layer_, which supports Bluetooth communications, as specified in this section, can interface with the instrumentation, potentially minimizing deployment challenges encountered in global _Industry 4.0_ applications.
## 2 Time-History Records  
Since the Institute of Engineering Mechanics at the China Earthquake Administration released the first digital time-history record in 1978, China has established a national seismic network, regional seismic networks, and a large-scale earthquake emergency mobile network. With the seismic network expanding, time-history records steadily accumulate, enabling the establishment of an _EM_ suitable for China region.  
[PERSON] [32] examined over 300 earthquake events and identified a minimum magnitude threshold of \(M_{L}\)4.0 for earthquake-induced landslides. Below this threshold, seismic waves generally trigger sliding only on highly sensitive slopes, while events with magnitudes above 6 can induce large-scale landslides. Supported by the National Earthquake Data Center of China ([[http://data.earthquake.cn](http://data.earthquake.cn)]([http://data.earthquake.cn](http://data.earthquake.cn)), accessed on 10 September 2024) and the PEER Ground Motion Database ([[https://ngawest2.berkeley.edu](https://ngawest2.berkeley.edu)]([https://ngawest2.berkeley.edu](https://ngawest2.berkeley.edu)), accessed on 10 September 2024), this study collected 312 strong-motion records from 19 earthquakes in China between 2007 and 2021, with magnitudes ranging from \(M_{S}\)5.2 to \(M_{S}\)8.0. In the _SSA_ of slopes, the horizontal inertial force often predominates, so the horizontal component of the acceleration time-history records was selected. Using Fourier transform and baseline correction, a database was created to establish _EM_ for the permanent displacement of earthquake-induced slopes in China. The database's distributions of magnitude (\(M_{S}\)), peak ground acceleration (_PGA_), Arias intensity (\(I_{a}\)), and duration (\(T\)) are illustrated in Figure 1.
## 3 Dataset and Benchmark
### Mapping (3D Reconstruction)  
Our subsequent goal is to obtain the mapping benchmark by expanding upon our established localization. As illustrated in Figure 3, this paper introduces two distinct RGB-D reconstruction processes based on different data association methods. Figure 3a shows a RGB-D reconstruction with loop closure, relying on ORB-SLAM3. In contrast, Figure 3b illustrates RGB-D reconstruction based on ground truth, preventing additional loop closure as acquiring ground truth incorporates loopback correction. To optimize the effect of mapping and reduce the amount of calculation, we use three filters when constructing the map. (i) Pass-through filters are employed to filter data along a specific dimension efficiently. It can remove errors caused by the inherent characteristics of the system and the imaging principle. (ii) Voxel filters are utilized to reduce point sampling and mitigate bias. (iii) Statistical outlier removal filters serve the purpose of eliminating outliers.  
Figure 2: Hardware and coordinate system.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 3 Methodology
### Aspect Sentiment Classification Structure
#### 3.2.2 Local Context Focus
\[bdist=max(D) \tag{7}\]  
\[SRD=bdist*\alpha \tag{8}\]  
\[ASRD=max\{SRD,STHD\} \tag{9}\]  
where \(D\) is the set of shortest distances from each word to the aspect word, bdist is the maximum value in \(D\), \(\alpha\) is the hyperparameter to regulate the size of \(SRD\), and \(STHD\) denotes the minimum threshold to prevent the calculated \(SRD\) from being too small, resulting in the local context not containing the sentiment words. The second method is to determine the local context by the length of the text, where \(n\) is the input text length. The formula for the second calculation method is shown below. \[num=max\{n*\beta,Snum\} \tag{10}\]  
where \(\beta\) is a hyperparameter to set the number of local context words, Snum denotes the minimum number and serves a similar purpose as STHD to prevent the calculated threshold from being too small, resulting in too little information being contained in the local context. After obtaining the shortest distance set \(D\) from each word to the aspect word, the top num words that are closer to the aspect word are selected as local contexts. After determining the local context, we refer to the context-feature dynamic mask strategy proposed by [PERSON] et al. [4] to mask the non-local context and preserve the local context semantics. Suppose the set of local context words is \(LCW\), the input text \(S=\{w_{i}|i\in[1,n]\}\), and the local context is \(V^{I}\). \[v_{i}^{m}=\left\{ \begin{array}{ll}O,&w_{i}\in LCW\\ I,&w_{i}\
otin LCW\end{array} \right. \tag{11}\]  
\[M=[v_{1}^{m},v_{2}^{m},\ldots,v_{n}^{m}] \tag{12}\]  
\[V^{CDM}=V^{I}\bigodot M \tag{13}\]  
where \(O\) and \(I\) denote the zero and one vectors, respectively, and \(v_{i}^{m}\) denotes the masking vector, which determines whether each word \(m_{i}\) belongs to a local context word according to the adaptive semantic relative distance calculation. In this study, only the CDM strategy is used instead of the CDW strategy. Because the CDW strategy will indirectly introduce the influence of other irrelevant words. To avoid this part of the impact, only the CDM strategy is used. Meanwhile, to avoid information loss, global contextual sequence features are used to supplement the information.
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 2 Materials and Methods
### Tradeoff Process  
The intensity of SCMP is calculated using Equation (15) after obtaining the intensity \(I_{up}^{low}\) via Equation (6).  
\[I_{scmp}(k)=\frac{I_{up}^{low}(k)}{PAN^{low}(k)}PAN^{high}(k), \tag{15}\]  
where \(k\) indicates the pixel position. The low-resolution PAN image, \(PAN^{low}\), is obtained using Equation (16).  
\[PAN^{low}=I_{up}^{low}+c_{1}MS_{up,nir}^{low}-c_{2}MS_{up,hu}^{low}-c_{3}MS_{up, gm}^{low}-c_{4}MS_{up,rel}^{low}, \tag{16}\]  
where \(I_{up}^{low},MS_{up,nir}^{low},MS_{up,hu}^{low},MS_{up,gm}^{low}\), and \(MS_{up,rel}^{low}\) are the intensities of the low-resolution RGB image, NIR, blue, green, and red, respectively, and these are upsampled to the same sizes as those of the PAN image.  
If the PAN image is corrected using the intensity \(I_{scmp}\) obtained via SCMP, there will be little loss of spatial information. Since the intensity correction is performed appropriately on the image, the spectral distortion when using SCMP is smaller than that of the other component substitution methods. Figure 2 shows the intensity of the original RGB image, the intensity image generated by SCMP, and the intensity image obtained by CS of Nihonmatsu and Yokohama images. From this figure, it can be seen that the intensity obtained by CS had less spatial information than SCMP. In order to increase the quality of the intensity reproduced by CS using SCMP, the intensities obtained by the CS and the SCMP were combined linearly using the tradeoff parameter \(\tau\). In the tradeoff process, the high-resolution intensity images were obtained by SCMP and CS, and these were linearly combined by Equation (17).  
\[I^{high}=\tau I_{sr}+(1-\tau)I_{scmp}\;s.t.\;|\;\tau|\leq 1 \tag{17}\]  
Figure 2: Intensity of RGB images of (**a**) Nihonmatsu, (**b**) Yokohama. (i) Intensity of the original image, (ii) intensity of SCMP, (iii) intensity obtained via compressed sensing.
## 3 Results and Discussion
### Robustness and Method Control
#### 3.4.1 Amines Extraction  
The FFD for the robustness screening was based on the \(3^{3-1}\)-level factors model, with the temperature (33, 35, and 37 \({}^{\circ}\)C), NaOH solution (14, 15, and 16 M), and time (28, 30, and 32 min) being evaluated. The selection of the desirability values was based on the TPA and IP (%RSD) results at the optimum point; therefore, 1% was defined as desirable (\(7.04\times 10^{8}\) and 1.68), 0.5% as acceptable (\(6.12\times 10^{8}\) and 6.32), and 0% as unacceptable (\(5.20\times 10^{8}\) and 10.97). Figure 3a presents the desirability plot for the evaluated conditions. The highest desirability index was reached within the studied range. Table S10 (Supplementary Material) describes the analysis of the agreement between the observed and the predicted values for robustness verification. According to the results, all the observed values are within the range of the predicted values at the 95% confidence interval.
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 3 Results and Discussion
### Testing the Biochar Efficiency as Tartrazine Adsorbent from Synthetic Aqueous Solutions
#### 3.2.2 The Influence of the Temperature of the Solution on the Adsorption Process  
In order to investigate the influence of the temperature on the adsorption process, 5 mL tartrazine solution of 5 mg L\({}^{-1}\) was mixed with 5 mg of biochar and stirred at 400 rpm for 20 min at temperatures ranging from 25 to 45 \({}^{\circ}\)C. The results depicted in Figure 5 highlight a clear increase in removal efficiency with a temperature of up to about 35 \({}^{\circ}\)C, followed by a decrease in the efficiency at higher temperatures. The efficiency increases along the mentioned temperature range from about 49% at 25 \({}^{\circ}\)C to above 88% at around 35 \({}^{\circ}\)C and then decreases with the temperature.  
According to the literature, chemisorption increases with an increase in temperature at first as the requirement of the activation energy for the reaction is fulfilled by an increase in temperature. Then, it decreases due to the exothermic nature of adsorption.  
In our case, the pseudo-second-order kinetic model is applicable for the removal of tartrazine on biochar, suggesting that chemisorption is the process that controls the adsorption rate.  
For economic reasons and taking into account the fact that, on a large scale, temperatures higher than ambient temperature are difficult to obtain, for the subsequent experiments, a temperature of 30 \({}^{\circ}\)C was chosen.  
Figure 4: Point zero charge of biochar.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 8 Simulation Results
### Performance of the System in Mode 1 and Mode 2
As a result of the scheduling at \(t_{i+1}\), ten UAVs will change layer in mode 1 while six UAVs will change layer in mode 2 to avoid collision with other UAVs. To show the trajectories of the UAVs that will change layer clearer, the trajectories of UAVs that will change layer at \(t_{i+1}\) in mode 1 are shown in Figure 16(a), while the trajectories of the UAVs that will change layer at \(t_{i+1}\) in mode 2 are shown in Figure 16(b). Figure 14: Trajectories of the UAVs scheduled at \(t_{i}\) in (**a**) mode 1 and (**b**) mode 2. One UAV is represented by one color and UAVs coming from the same way have the same color. From Figure 17, we can see that lesser number of UAVs will change layer in mode 2 at \(t_{i+1}\). This is because the trajectories of the UAVs that will change layer at \(t_{i}\), as shown in Figure 15b, allow more UAVs scheduled at \(t_{i+1}\) to travel in the intersection without needing to change layers. The travel time in the intersection of a UAV increases as the number of times it changes layer increases. Consequently, the total time in the system of the UAVs increases as the number of UAVs that change layers increases. Therefore, the total time spent in the intersection of the UAVs scheduled at \(t_{i+1}\) using mode 1 is longer than mode 2 since more UAVs will change layer in mode 1. Since the objective of the scheduling is to minimize the total time spent in the system of the UAVs being scheduled, UAVs can change layer anywhere in the intersection in mode 1 as long as the total travel time is minimized. However, it can be observed that it is better for a UAV to change layer at the start and end of its path in the intersection because this gives higher chance for other UAVs that will be scheduled at a later scheduling time to travel in the intersection without changing layer, thereby, decreasing the total travel time of the UAVs in the next scheduling time. Therefore, instead of only minimizing the total travel time of the UAVs being scheduled, minimizing the possible conflicts of their trajectories  
Figure 16: Trajectories of the UAVs scheduled at \(t_{i+1}\) in (**a**) mode 1 and (**b**) mode 2. One UAV is represented by one color and UAVs coming from the same way have the same color. Figure 15: Trajectories of the UAVs scheduled at \(t_{i}\) that will change layer in (**a**) mode 1 and (**b**) mode 2. One UAV is represented by one color and UAVs coming from the same way have the same color. with UAVs that will be scheduled at a later scheduling time must also be considered. In conclusion, it is better to schedule the UAVs using mode 2 compared to mode 1 since mode 2 improves the complexity of the scheduling and utilizes the intersection more efficiently.
# Safety and Energy Implications of Setback Control in Operating Rooms during Unoccupied Periods
## 3 Results and Discussion
### Annual Estimations  
To provide a global view of the prospective annual savings, the analysis described in Section 2.3 is extrapolated for a yearly basis. The Typical Meteorological Year data of the hospital location is considered for the outdoor air operating conditions, while the set supply airflow, temperature, and relative humidity correspond to the design conditions given in Table 2. For the unoccupied periods, the less favorable supply airflow registered during the tests of 1500 m\({}^{3}\)/h is considered.  
To simplify the prediction of the non-occupied periods, an emergency OR is considered, which is also used during the weekends. Use periods from 7:00 to 15:00 are considered for the estimations. A ventilation setback is established at 1500 m\({}^{3}\)/h to evaluate the less-favorable scenario. The temperature setback is adjusted to the set points given in Table 2.  
The expected savings through the setback operation account for 15,884 kWh of natural gas and 5498 kWh of electricity, corresponding to 75% and 69% energy savings, respectively, which results in a 5664 kgCO\({}_{2}\) reduction in the emissions. These predicted energy savings correspond to the expected figures from the literature [16; 17].  
Since the actual use of the operating rooms is less, these calculations will be conservative. This is justified regarding the use periods registered for the 15 ORs of general use (Table 7). A typical operating schedule is from 8:00 to 15:00, though, usually, at 14:00, all ORs are already no longer being used. On the contrary, the obstetrics and the burns ORs are used on demand and, thus, present unusual occupation; while the obstetrics OR is usually occupied during larger periods than the general ORs, the burns OR is rarely used.  
If the results are extrapolated from the total of 18 ORs in the hospital, the annual energy savings would account for 1.12% of the total natural gas and 0.64% of electric energy consumed at the hospital during 2021, which would involve more than 9400 EUR and 10,490 EUR, respectively, as well as a reduction in the carbon emissions of more than 100 tons of CO\({}_{2}\). Since the real use periods of the ORs are shorter, the actual energy savings are expected to be more than these estimations.  
An investment to equip each OR requires 1682 EUR: 625 EUR for the exhaust fan frequency converter and 200 EUR for the backdraft shutter to enable a pressure-independent exhaust duct, 70 EUR for the occupancy sensor, 212 EUR and 340 EUR for the airflow and the differential pressure sensors, respectively, 60 EUR for minor materials, and 175 EUR for labor costs. For 18 ORs, this implies a total investment of 30,276 EUR, which would be returned in less than two years. Moreover, provided that most ORs are already equipped with most of these systems, the actual investment would be a minor cost.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Plasma Irradiation
#### 2.7.2 Active Oxygen O\({}_{2}^{*}\)
The changes of the Raman and PL spectra indicated that oxygen plasma can lower the MoS\({}_{2}\) crystal symmetry and increase the lattice distortion, which can be attributed to the defects that may benefit MoS\({}_{2}\) as electrochemical catalyst. Figure 24: SEM images of CVD-grown MoS\({}_{2}\) mono-layers with oxygen plasma treatments for 0 s, 10 s, 20 s, and 30 s oxygen plasma exposure [129]. Both the density and width of the cracks increased with exposure time. Reprinted with permission from Reference [129]. Copyright © 2016 American Chemical Society. [PERSON] et al. [130] treated MoS\({}_{2}\) few-layers (15 layers with approximately 12 nm thickness) in oxygen plasma for 3 min. The plasma was generated by flowing air past an electrode supplied with 20 W of RF power at 200 mTorr. The PL efficiency of the few-layers was increased after the plasma treatments. [PERSON] et al. [131] grew triangular MoS\({}_{2}\) mono-layers on SiO\({}_{2}\)/Si substrates by CVD technique. The MoS\({}_{2}\) mono-layers were then subjected to plasma-oxygen treatment for times ranging from 10 s to 120 s. Ultra-high pure oxygen gas (99.9999%) was activated by a RF plasma cell, and the working pressure was \(1.3\times 10^{-3}\) Pa under an ultra-high pure oxygen gas flow of 2 sccm. Optical properties of the mono-layers, such as PL and Raman scattering, changed with treatment time. Figure 25a shows optical images of the pristine MoS\({}_{2}\) mono-layer and these MoS\({}_{2}\) mono-layers treated with oxygen plasma for 10 s, 30 s, 60 s and 120 s. Figure 25b shows integrated PL intensity mapping images obtained from each of the mono-layers. The integrated PL intensity decreased with the oxygen plasma treatment time. Figure 25c shows the peak position of the main PL peak. The main peak position of the PL spectra gradually red-shifted from 674 nm to 692 nm with an increasing oxygen plasma treatment duration. It was suggested that shallow defect states were generated by the oxygen plasma treatments.
# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 2 Materials and Methods
### Objective Function and Model Training
The second anti-interference classification task training optimizes in the opposite direction through two kinds of losses, namely, classification and regression, so that the representation learning module can extract classification features while containing fewer features that can reflect the difference information of the sites. In this way, after two training and alternating iteration optimizations, the ability of the regression component to identify the site difference information is gradually enhanced, and the site difference information contained in the classification features extracted by the representation learning module is gradually reduced. In this way, the confounding effect brought by site heterogeneity will be weakened in ASD classification, and anti-interference classification will finally be realized.