esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 RL Control System
### Agent Structure  
The objective of the agent is to simultaneously control the whole platoon of vehicles, as the LQC described in the previous section does. Therefore, the agent receives observations from all vehicles and can communicate actions to the two follower vehicles.  
In particular, the state vector is defined as follows:  
\[s=\begin{pmatrix}t_{h,1}\\ t_{h,2}\\ v_{L}\\ v_{F1}\\ v_{F2}\\ a_{L}\end{pmatrix}, \tag{26}\]  
where:  
* \(t_{h,1}\) and \(t_{h,2}\) are the time headway of follower 1 with respect to the leader and follower 2 with respect to follower 1, respectively;
* \(v_{L}\) and \(a_{L}\) are the velocity and acceleration of the leader;
* \(v_{F1}\) and \(v_{F2}\) are the velocities of follower 1 and follower 2.  
The action vector \(a\) is defined by the normalised torque commands for the two follower vehicles, \(T_{m,F,1}\) and \(T_{m,F,2}\):  
\[a=\begin{pmatrix}T_{m,F,1}\\ T_{m,F,2}\end{pmatrix}. \tag{27}\]  
Each normalised torque can range from \(-1\) to \(1\), with \(-1\) representing the maximum breaking torque and \(1\) representing the maximum traction torque.  
The design of the reward function for the RL controller plays a similar role to the definition of the cost function for the LQC controller that was described in Section 3.2, and a proper definition must be developed to meet the safety and comfort requirements.  
In contrast with the LQC cost function, which must be quadratic, there is no restriction on the structure of the reward function. However, to quantitatively compare the performance of the two methods, we decided to express two reward terms, \(r_{h,1}\) and \(r_{h,2}\) as a quadratic function of the difference between a desired time headway and the current time headway for the two vehicles. The reward was then set to the average of these two terms:  
\[\begin{array}{l}r_{h,1}=1-w_{1}(t_{h,des}-t_{h,1})^{2},\\ r_{h,2}=1-w_{2}(t_{h,des}-t_{h,2})^{2},\end{array} \tag{28}\]\[r=\frac{r_{t_{h,1}}+r_{t_{h,2}}}{2}, \tag{29}\]  
where \(t_{h,des}\) is the desired time headway and \(w_{1}\), \(w_{2}\) are tuneable weights. Since \(w_{1}\) and \(w_{2}\) determine how strictly the agent attempts to enforce the desired time headway of the two followers, in agreement with the LQC approach (where the corresponding elements on the main diagonal of Q and R matrices are the same), they are both set equal to 1.  
To guarantee safety and no collision between the vehicles, the following actions are taken if the time headway becomes null:  
* The reward is set to the maximum negative value (\(-10\));
* The corresponding training iteration has been stopped.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 6 Implementation Description
### Forward Kinematics (FK-HMD and FK-HSD)
In the FK-HMD module the \(\theta_{1}^{MD}[F32](n)\), \(\theta_{2}^{MD}[F32](n)\) and \(\theta_{3}^{MD}[F32](n)\) input signals are received through the \(\mathbf{b}(n)\) array (Equation (43) in Section 5), then all calculation are performed in parallel resulting in the \(\mathbf{c}(n)\) array (Equation (44) in Section 5) with the \(x^{HMD}[F32](n)\), \(y^{HMD}[F32](n)\) and \(z^{HMD}[F32](n)\) signals as shown in Figure 4. For the FK-HSD module the \(\theta_{1}^{MD}[F32](n)\), \(\theta_{2}^{MD}[F32](n)\) and \(\theta_{3}^{SD}[F32](n)\) input signals enter the module via the \(\theta^{SD}(n)\) array (Equation (49) in Section 5) and after performing all parallel computations, the resulting signals \(x^{ENV}(n)\), \(y^{ENV}(n)\) and \(z^{ENV}(n)\) are output from the module via the \(\mathbf{1}(n)\) array (Equation (49) in Section 5).
## 2 Results and Discussion  
### Profiling of Onion and Lemon Volatiles Using SPME/GC-MS and Multivariate Data Analysis  
Three independent biological replicates representing each specimen including fresh and pickled samples were extracted and analyzed under identical conditions. The volatile analysis of lemon samples (Figure 1) revealed for the presence of 31 volatile components, with monoterene hydrocarbons representing the major class at ca. 96.6% and 97.5% in the fresh and pickled lemon, respectively (Table 1). The fresh lemon monoterepenes pool was characterized by a relatively high \(\beta\)-pinene level (33%) versus an enrichment of limonene and \(\delta\)-terpinene in pickled lemon. With regard to oxygenated monoterpenes, \(\alpha/\beta\)-cital (odor key marker) was remarkably reduced in pickled lemon and likely accounted for the lower aroma strength of pickled lemon. Furthermore, total sesquiterpene hydrocarbons were found to be slightly higher in fresh lemon with (\(E\))-\(\alpha\)-bergamotene as major component compared with a relative increase of (\(E/Z\))-\(\alpha\)-farnesene and \(\beta\)-bisabolene in pickled lemon. SPME/GC-MS volatiles analysis of fresh and pickled \(A\). _cepa_ onion bulbs detected the presence of 20 volatile components belonging to both sulphur and non-sulphur compounds (Figure 1). Sulphur volatiles were dominant with 96.6% and 98.1% in pickled and fresh samples, respectively. Several of the volatile components either disappeared or were reduced in pickled onion, as shown in Table 2. Moreover, significant increases in the levels of allyl methyl trisulfide, 3,5-diethyl-1,2,4-trithiolane, and 2-hexyl-5-methyl-3(2\(H\))-furanone were detected in pickled onion. Such chemical change in volatiles makeup due to pickling is likely to affect its organoleptic properties such as taste and aroma. Figure 1: Solid-phase microextraction (SPME)/GC-MS chromatogram of headspace volatiles collected from fresh and pickled onion bulb and lemon fruit.
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 5 Conclusions  
The specific conclusions are as follows:  
(1) From 2001 to 2020, the climate in the study area was mainly dry to wet. Vegetation showed carbon and water use efficiency of 0.656 and 1.351 g C\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), respectively. CUE decreased from west to east, whereas WUE increased from southwest to northeast. The CUE of different vegetation types was GRA (0.681) > MF (0.616) > WSA (0.595) > DBF (0.584) > CRO (0.582) > SA (0.567) > DNF (0.541), while WUE was DNF > MF > WSA > DBF > SA > CRO > GRA.  
(2) From 2001 to 2020, CUE in Inner Mongolia declined by 0.1%-year\({}^{-1}\), whereas WUE fell by 0.008 g C\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) but was not significant. Except for the high-altitude mountainous areas in western Inner Mongolia and the eastern margin, CUE increased. CUE declined in only 10.58% of the areas, mostly around the Mawususu Sands and Hulun Lake. WUE fell in 56.79% of the areas, with 7.63% dropping considerably, mostly in the northeastern typical grasslands of Xilinguole League.  
(3) The negative correlation between the drought index (DSI) and CUE was the largest, accounting for 97.96% of the watershed area, and the areas passing the significance test accounted for 71.6%. The correlation coefficients of WUE and DSI were spatially opposite to the correlations between CUE and DSI. The drought index (DSI) and WUE had the highest positive association and the largest negative correlation for 54.21% of the vegetation cover area. The CUE and WUE of Inner Mongolian vegetation types were adversely linked with the DSI except in GRA. Drought in Inner Mongolia mostly influenced the CUE of different plant types, which had a higher negative correlation than WUE.  
Methodology, G.C.; writing--original draft, S.W, C.Z. and Y.W.; writing--review and editing, T.L. All authors have read and agreed to the published version of the manuscript. This study was jointly funded by the Natural Science Foundation of Inner Mongolia (2021 ZD12), the Inner Mongolian Applied Technology Research and Development Fund Project (2020 GG0020), Inner Mongolia Autonomous Region's Key Research and Development and Achievement Transformation Plan Project (2022 YFHH0100), the Yinshanbeilt Grassland Eco-Hydrology National Observation and Research Station, and China's Institute of Water Resources and Hydropower Research, Beijing 100038, China (YSS202114). Not applicable. The authors are grateful to the editors and the anonymous reviewers for their insightful comments and helpful suggestions. The authors declare no conflict of interest.
## 5 Conclusions
The reason is that, at equal conditions of feature visibility and imaging definition, the mapping accuracy achievable strongly depends on the operators' skills and expertise. [PERSON] and [PERSON] [6] have recently reviewed this aspect specifically based on published exercises. However, even in situations when operators are fully trained, manual examination remains a time-consuming task and is therefore not sustainable over large areas or for analysis of long time series. It is beyond the scope of this paper to demonstrate how automated processing for the detection and extraction of features should be designed and deployed. It is instead worth spending a few words on the possible technical requirements and recommendations that future research could take into account to develop such automated processing tools. In particular, the preparation of training data that are required to develop an effective machine learning approach appears to be a critical step. The experience gained by the community on different geographic locations is now being translated into reference catalogues of disturbance/threat/damage types and associated databases of documented incidents (see the example presented in [5]). These are repositories from which it is possible to derive categories of simple, distinctive and frequently ubiquitous features and to define, for example, the labelled training data necessary to instruct supervised learning for the automatic recognition of patterns due to a certain process relevant for heritage conservation. Training data that are built according to the above-mentioned criteria (and specifically the distinctiveness of the pattern from other unrelated objects) should help to reduce the occurrence of false positive errors and mismatching. Recent publications presenting the first attempts of automation on feature detection in VHR multi-spectral satellite images have addressed this technical requirement by proposing algorithms searching for repeated featural motifs and building upon existing methods of supervised classification and unsupervised localization [34]. Although these methods have been applied at VHR level, they outline one of the possible directions along which to develop methods suited for HR time series such as those provided by Sentinel-2. Sentinel-2A/B scenes were sourced as open data from the European Space Agency (ESA) Copernicus Open Access Hub. The VHR images (c 2018 DigitalGlobe and CNES/Airbus) were analysed in Google Earth Pro. ASTER GDEM is a product of METI and NASA and was made available by USGS via the LP DAAC Global Data Explorer. [PERSON] and [PERSON] conceived, designed, and performed the experiments; analysed the data; and wrote the paper. The authors declare no conflicts of interest.
## 1 Introduction  
Short fibres, especially steel fibres, can be used for the reinforcement of concrete to increase ductility, toughness, resistance to cracking and tensile strength (see, e.g., [1, 2, 3, 4, 5, 6]). As a consequence, it is possible to reduce or even avoid the use of traditional reinforcements. The mechanical properties of steel fibre reinforced concrete (SFRCs) are highly dependent on the fibre content and the fibre distribution and orientation inside the concrete elements (see, e.g., [1, 7, 8]). Areas with a very low number of fibres or fibres that are not oriented in the direction of the tensile tension lead to a higher risk of cracking and can cause the failure of the whole structure (see, e.g., [9, 10]). For this purpose, during the casting of SFRC, significant quality control measures, e.g., the regular testing of the amount of steel fibres by wash out procedures, need to be applied. Assuming an optimum concrete composition, SFRC typically possesses a longer service life and thus, a lesser need for maintenance. This makes it an interesting material for the repair of reinforced concrete structures (see, e.g., [4, 11]). Nevertheless, in the case of SFRC application, the real fibre content and, especially, the fibre distribution are not well known and so, the use of significant safety factors is necessary during the design stage (see, e.g., [12, 13, 14, 15]).  
These problems show the need for a test setup that is easy to apply, ideally non-destructive and can be used for rehabilitation processes to determine the fibre parameters, for example (see, e.g., ([16, 17, 18, 19]). Possible methods that were used in different studies are based on computer tomography (CT), microscopic analysis of sectional images or inductivity measurements (see, e.g., [10, 17, 18, 20, 21, 22, 23, 24, 25, 26]). The main problems with CT analysis are its high cost and the limited size of specimens. Furthermore, it is not possible to use this technique in situ on construction sites. The same arguments can be found for cross sectional image analysis, whereas this technique is even less accurate than CT scanning. Inductive techniques are easier to use but are also limited to small specimen sizes.  
A technique that is already used for structural concrete monitoring to identify and locate steel rebars is the electric resistivity measurement (see, e.g., [27; 28; 29; 30; 31; 32]. Based on an easy-to-use test setup, which was already tested in past studies conducted by the authors [33], a new application test based on the analysis of cylindrical specimens is proposed in this research paper. In the first part of the study, the test setup is adjusted and a numerical model is generated to simulate the flow of the alternating current, which enables the comparison and calculation of the resistivity of the different specimen directions that are possible. With help of the proposed setup, cylindrical specimens with fibre contents of up to 80 kg/m\({}^{3}\) are analysed and correlations between the electrical resistivity and the fibre content and fibre orientation are developed to enable the use of drilling core samples in the second part [34].
# Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations
## 3 Results  
### Data Description and Processing Strategy  
The BDS-3 and Galileo quad-frequency data were collected on a 104 km baseline and a 126 km baseline from 0:00 to 24:00 UTC, the day of year (DOY) 130, 2020, using a pair of Trimble receivers in Xi'an, China, with a sampling interval of 10 s. A total of 8640 epochs were collected, and 26 BDS-3 and 22 Galileo satellites were tracked. The number of BDS-3 and Galileo satellites, with RDOP values as a function of epochs, is shown in Figure 1.  
It can be seen from Figure 1a that the number of tracked BDS-3 and Galileo satellites ranged from 9 to 15 and was 12.7 on average during experiment 1. The minimum number of tracked satellites is 9 around the 4750 th epoch and the 5600 th epoch, during which RDOP values are greater than 3. However, the reliability and stability of the long-baseline posi  
Figure 1: The number of BDS-3 and Galileo satellites and RDOP values as function of epochs: (**a**) experiment 1; (**b**) experiment 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multirow{3}{*}{\
\begin{tabular}{c} **Combination** \\ **Schemes** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **Wavelength/m** \\ **BDS-3 (Left)** \\ \end{tabular} } & \multicolumn{6}{c}{**Satellite Systems**} \\ \cline{3-11}  & & \multicolumn{3}{c}{**BDS-3**} & \multicolumn{6}{c}{**Galileo**} \\ \cline{3-11}  & & \multicolumn{3}{c}{**Galileo (Right)**} & \(i_{1}\) & \(j_{1}\) & \(k_{1}\) & \(m_{1}\) & \(i_{2}\) & \(j_{2}\) & \(k_{2}\) & \(m_{2}\) \\ \hline
1 & EWL & 20.93 & 9.77 & 1 & \(-1\) & 0 & 0 & \(-1\) & 1 & 0 \\  & Second EWL & 3.26 & 4.19 & 0 & 0 & 1 & \(-1\) & 0 & 0 & \(-1\) & 1 \\  & WL & 0.78 & 0.75 & 0 & 1 & 0 & \(-1\) & 1 & \(-1\) & 0 & 0 \\
2 & EWL & 20.93 & 9.77 & 1 & \(-1\) & 0 & 0 & \(-1\) & 1 & 0 \\  & WL & 0.78 & 0.75 & 0 & 1 & 0 & \(-1\) & 1 & \(-1\) & 0 & 0 \\
3 & EWL & 3.26 & 2.93 & 0 & 0 & 1 & \(-1\) & 0 & \(-1\) & 0 & 1 \\  & WL & 0.78 & 0.75 & 0 & 1 & 0 & \(-1\) & 1 & \(-1\) & 0 & 0 \\
4 & EWL & 20.93 & 4.19 & 1 & \(-1\) & 0 & 0 & 0 & \(-1\) & 1 \\  & WL & 0.98 & 0.81 & 1 & 0 & \(-1\) & 0 & 1 & 0 & \(-1\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficients of the proposed combination schemes in BDS-3 and Galileo.  
tioning performance is not affected because of a sufficient number of satellites. During the entire observation span, the average RODP value is about 1.85. In experiment 2, the number of BDS-3 and Galileo satellites is less than that in experiment 1, and the corresponding RODP value is larger. The average value of the observable RODP is about 2.61.  
The specific processing strategies for long-baseline data are shown in Table 6:
## 4 Numerical Results
This shows that DEIM and GPOD techniques can significantly speed-up the simulation time of POD approach with small trade-off on accuracy. Notice that POD-DEIM approach provides the fastest speedup, but it has slightly less accuracy than the others. As a result, the POD-GPOD approach is shown to balance between the accuracy and the computational speedup. In the next two numerical experiments, we will consider Allen-Cahn equation with parameter variation for different initial conditions and boundary conditions.
## 4 Performance Evaluation  
The experimental simulation was conducted under the IEEE 802.15.4 physical (PHY)/MAC layer environment using the MATLAB simulator to evaluate the performance of MCSS. The simulation results of MCSS were compared to those of the legacy TSCH MAC [26] with a single slotframe and TMSS [31] to verify the superiority of MCSS. We describe the simulation configuration in the following subsections and discuss the simulation results in detail.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 3 A Force-Based Digital Thimble
### Force-Based Target Selection  
In terms of pointing, the Digital Thimble mirrors the functionality of a traditional mouse. Users apply extra force on the surface to activate the cursor, then slide their index finger while keeping the thimble's cap in contact with the surface. The optical sensor detects the movements and adjusts the cursor's position accordingly. The translation of finger movements into cursor movements is facilitated through the Unity3D mouse input API. The device uses contact force for target selection, with two different approaches developed for this purpose:  
1. **On-press**: Users navigate the cursor to the target and then exert extra pressure to select the target.
2. **On-release**: Users navigate the cursor to the target while maintaining extra pressure and then either release the extra pressure or lift their finger to confirm selection.  
The system detects extra pressure when users' contact force exceeds 400 g. This threshold was carefully chosen through multiple lab trials to maintain a balance between detection accuracy and user comfort. It is set at a level that is high enough to avoid accidental activations yet low enough to ensure a comfortable user experience.  
The system provides visual feedback to inform users of its _active_ (ready for cursor positioning) and _inactive_ status. The cursor is initially black but turns green when it is ready for positioning. Specifically, with the on-press selection method, the cursor turns green when the system senses the finger's presence on a surface. With the on-release method, the cursor turns green when the system detects extra pressure applied to a surface. The use of green to indicate the active state was chosen due to its universal association with the \"go\" signal, helping users intuitively understand the system's current operational mode [62].
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 2 Unit Cell Modeling and Design
### Unit Cell Design  
Guided by the EC model of the unit cell, the configuration of the proposed MSRRA unit cell is shown in Figure 3. It is a multi-layer structure; the periodic of the unit cells is 4 mm. On the top layer, two microstrip patches with 0.035 mm thickness make up the unit cell scatterer. The RTS880 LZ (\(\alpha\)r = 2.2 and tan\(\delta\) = 0.0009) substrate medium with 0.508 mm thickness makes up the second layer of the unit cell. Below the RTS880 LZ substrate medium, a metal plane with 0.035 mm thickness acts as the ground plane which helps to improve the reflection efficiency of the MSRRA. The FR-4 (\(\alpha\)r = 4.4 and tan\(\delta\) = 0.018) substrate medium with 1.016 mm thickness makes up the second substrate layer, which mainly strengthens the whole unit cell structure. Between the ground plane and FR-4 substrate medium, an FR-4 prepreg layer with 0.2 mm thickness is used to bond the upper and lower structure together, as shown in Figure 3b. The bottom layer of the MSRRA unit cell contains the DC-bias network circuits, which provide DC bias for the varactor diode. All the unit cell structure parameters, which have been iterated and optimized, guided by the EC model, are summarized in Table 1. A varactor is placed between the microstrips patches, adding variable capacitance \(C_{\varphi}\) to the unit cell. The dynamic range of the capacitance \(C_{\varphi}\) is \(C_{\varphi max}\) = 0.22 pF to \(C_{\varphi min}\) = 0.033 pF for 0-16 V reverse bias voltage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline P & 4 mm & t & 0.035 mm \\ \hline W & 1 mm & h1 & 0.508 mm \\ \hline L & 2.2 mm & h2 & 0.2 mm \\ \hline g & 0.8 mm & h3 & 1.016 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the proposed MSRRA unit cell.