esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 4 Control System
### Decoupling Method Using Wrench-Current Transformation Matrix  
The proposed ten-coil planar maglev system was a MIMO overactuated system with ten coil current inputs and 5-DOF motion outputs. The yaw rotation around the \(z\) axis could not be controlled because of the axial symmetry of the disc magnet. Therefore, the torque about the \(z\) axis was not considered in the control design. From the Lorentz force law in the previous section, the forces and torques were proportional to the coil currents \(\mathcal{I}\). Therefore, the force and torque equations as a function of coil currents could be written as  
\[\begin{bmatrix}\mathcal{F}_{w}\\ \mathcal{T}_{w}\end{bmatrix}_{5\times 1} =\mathcal{K}_{(x,y,z,\psi,\theta)}\,\mathcal{I}\] \[=\begin{bmatrix}f_{x_{1}}&\dots&f_{x_{k}}&\dots&f_{x_{10}}\\ f_{y_{1}}&\dots&f_{y_{k}}&\dots&f_{y_{10}}\\ f_{z_{1}}&\dots&f_{z_{k}}&\dots&f_{z_{10}}\\ t_{x_{1}}&\dots&t_{x_{k}}&\dots&t_{x_{10}}\\ t_{y_{1}}&\dots&t_{y_{k}}&\dots&t_{y_{10}}\end{bmatrix}\cdot\begin{bmatrix}i_{ 1}\\ \vdots\\ i_{k}\\ \vdots\\ i_{10}\end{bmatrix} \tag{7}\]  
where \(\mathcal{K}\) is the commutation matrix. It represents the relationship between the coils and the forces and torques on the magnet at specific pose \((x,y,z,\psi,\theta)\) when each coil is supplied with a 1 A current.  
In the control system, the total wrench (forces and torques) command \(W_{total}\) from the 5-DOF PID controller and feed-forward controller was decoupled and transformed to coil current command \(\mathcal{I}_{cmd}\) by  
\[\mathcal{I}_{cmd}=\mathcal{K}^{+}W_{total} \tag{8}\]  
where the wrench-current transformation matrix or wrench matrix \(\mathcal{K}^{+}\) is an inverse of the commutation matrix. Since the commutation matrix is usually a nonsquare matrix, the Moore-Penrose pseudoinverse can be computed instead, expressed by  
\[\mathcal{K}^{+}=\mathcal{K}^{\mathsf{T}}(\mathcal{K}\mathcal{K}^{\mathsf{T}}) ^{-1}. \tag{9}\]  
Next, the implementation of the wrench-current transformation matrix for decoupling using the lookup table is presented.
## 3 System Model
### System Configuration  
To configure the edge computing environment, we considered three machines, corresponding to the client, edge, and cloud, as shown in Table 1. Machine 1, with the best computing power, was used as the cloud server, and machine 3, with the lowest computing power, was used as the client device. Also, machine 2, with moderate computing power, was used as the edge device. Ubuntu version 20.04 (Ubuntu, London, UK) was used along with the Python implementation of the TensorFlow object detection API [31]. When an object detection service was requested from an AI service client module to an AI service server module, REST and gRPC were used as the communication protocols.
# Impacts of Heat and Drought on Gross Primary Productivity in China
## 4 Results and Analysis
### Comprehensive Impact of Drought and Heat on GPP  
The conditional ENAG probability of each pixel under different drought levels and heat intensities was calculated by constructing a 3D joint distribution of the annual GPP anomaly, SPI/SPEI, and Tmax. The results are shown in Figures 7 and 8.  
The spatial distribution of ENAG probabilities under conditions of extreme drought and severe heat (Figures 7a and 8a) is more similar to the distribution of ENAG probabilities under extreme drought (Figure 4a,b) than to the distribution of ENAG probabilities under severe heat (Figure 6a). Overall, when heat and drought occur simultaneously, the ENAG probabilities are higher and the number of pixels with high ENAG probabilities increases.  
When the drought level and heat intensity weaken, the ENAG probability in each vegetation region decreases. In the SPI scenario, the maximum ENAG probability was reduced from 90.5% (under simultaneous extreme drought and severe heat conditions) to 33.3% (under simultaneous mild drought and mild heat conditions), and in the SPI scenario, the maximum ENAG probability is reduced from 84.3% (under simultaneous extreme drought and severe heat conditions) to 32.9% (under simultaneous mild drought and mild heat conditions). Generally, the ENAG probability decreased as the drought level decreased, and the higher the drought level was, the less the ENAG probability changed with a change in the heat intensity. The difference in the average ENAG probability range between severe and mild heat among the eight vegetation regions under SPI-derived (SPEI-derived) extreme drought conditions was 5.99% (9.22%); however, there was no discernible difference in the ENAG probability distributions. Under SPI-derived (SPEI-derived) mild drought conditions (Figures 7 and 8j-l), the difference in the average ENAG probability range between severe and mild heat of the eight vegetation regions was 16.01% (15.89%), and the ENAG probability distribution showed an apparent difference with a decrease in the heat intensity.  
Figure 7: Spatial distributions of ENAG probabilities under different drought levels and different heat intensities. From left to right, the heat intensities decrease from severe to mild, and from top to bottom, the drought levels decrease from extreme to mild (Note: the pixels displayed in white have the same meaning as Figure 4).
## 5 Experimental Results
### Main Models and Hyperparameters  
We begin by detailing the primary hyperparameters utilized in various components of our framework. We used the KNN, Logistic Regression (LR), XGBoost, and Naive Bayes (NB) algorithms in the evaluation process. Also, we used k-fold cross-validation with 10 folds for better evaluation. The parameters for the MBHO were selected as follows: maximum iterations (\(M=25\)), a population size that refers to a set of potential attack paths (\(N=10\)), a mutation rate (\(mr=0.5\)), and a weight factor (\(w_{f}=0.001\)). For the binary CS [37], we had a set population size \(N=30\), step size \(=1.5\), lambda \(=2.5\), worst-nest probability \(=0.2\), delta \(=1.5\), \(mr=0.5\), and \(M=25\). All tests were conducted using the Java language (JDK 17) on a machine with an Intel(r) Core(tm) i7-8750H CPU @ 2.20 GHz (12 CPUs) (Intel, Santa Clara, CA, USA) and 32,768 MB of RAM. The tools and libraries we used in this study are as follows: Java Development Kit 17 (JDK 17) 17.0.4+11-LTS-179, Integrated Development Environment (IDE), Intellij ultimate version 2021.2.4, Oracle Machine Learning Library, Tribuo 4.1.0, and XChart Library 3.8.0. Tribuo is a general-purpose, open-source ML library written in Java that can be used for deep learning and natural language processing applications as well [134]. Furthermore, we used the Apache Common Math 3 API for correlation calculation.
## 6 Conclusions  
In this study, we compared the operating performance of the WTES system of the existing heat storage system and the ATES system using a simulation model and clarified the efficient operation method of the ATES system. This study uses three aquifers: pumping wells, thermal storage wells, and reducing wells. The initial temperature is 19.1 \({}^{\circ}\)C groundwater from the surrounding area. The findings obtained in this study are summarized below.  
1. In the ATES system, if the same operation method as the WTES system is used, the heat storage efficiency will decrease, and the energy consumption will increase.
2. When using an ATES system, the cold and hot heat input to the aquifer diffuses quickly and the pumping temperature changes quickly, it is suitable to introduce a pre-cooling and pre-heating coil. That allows heat to be used even when the water temperature approaches the underground temperature.
3. The aquifer heat storage system was used for the pre-cooling coil, and the cooling power consumption was reduced by 20%. If a pre-cooling/pre-heating coil is introduced and energy saving is important, heat is stored at a low temperature close to the geothermal temperature, and the operation that also uses the groundwater heat is performed. In addition, if the reduction of peak power in the daytime is important, it is appropriate to operate so that the heat stored in the pre-cooling/pre-heating coil is used up as much as possible.
4. In cooling operation, ATES-C1-7 was the lowest at COP 2.4 and ATES-C2-14 was the highest at COP 3.7. In heating operation, ATES-H1-45 was the lowest at COP1.2, and in other cases, it was about the same at COP2.4-2.8.
5. In terms of energy efficiency, the heating operation ATES-H1-45 had a low energy efficiency of 4.1 for EER and 3.9 for SEER. In other cases, the energy efficiency was 8.2-12.4 for EER and 8.7-15.3 for SEER.  
All authors contributed to the study conception and design; writing--original draft preparation, J.O.; writing--review and editing, J.O., H.K. and D.S.; supervision, D.S.; project administration, D.S.; methodology, M.N.; formal analysis, M.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We wish to thank Shikoku Electric Power Co., Inc. and Yonden Consultants Co., Ltd. for their cooperation in advancing this research.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 3 QR\(-\)LoRa Design
### Denoising Algorithm  
Due to the randomness, dynamics and multipath of the channel, the envelope of the QR\(-\)LoRa chirp received in the practical propagation environment suffers severe fast fading, and will be contaminated by noise, shown as in Figure 7a. Correspondingly, the difference operation result will present multiple peaks instead of only one perfect peak at the amplitude encoded position, shown as in Figure 7b. There are two problems with the practical envelope compared with the perfect envelope: (1) The perfect linear characteristic  
Figure 5: The result of QR\(-\)LoRa chirp frequency demodulation.  
Figure 6: The result of the differential operation.  
disappears; instead, there are nonlinear distortions that occur with the envelope. (2) The envelope is no longer smooth and presents rapid disturbance. The mentioned distortion and rapid disturbance enable the difference result to exhibit false peaks at multiple sampling points, as shown in Figure 7b. Even worse, some false peaks are stronger than the expected peak. As a result, the expected peak cannot be detected.  
To this end, we propose an envelope denoising algorithm. The main idea is based on the following observations: in the amplitude demodulation, the position of the expected peak in \(|\text{diff}[A(n)]|\) should be consistent with the amplitude jump point, while at other positions the variation in the envelope sequence \(A(n)\) should be as stable as possible. Through observing, the mentioned distortion and rapid disturbance do not change the position of the expected peak, but only cause \(|\text{diff}[A(n)]|\) to produce extensive false peaks. Therefore, the goal of the denoising algorithm is to preserve the expected peak and suppress the extensive false peaks. Thus, we give the basic workflow of the envelope denoising algorithm as follows.  
Each point in \(A(n)\) is compared with the previous one in turn. If the \(n^{th}\) sampling point is smaller than the \((n+1)^{th}\) sampling point, the value of the \(n^{th}\) point is replaced with the average value of these two sampling points. Otherwise, it keeps its own value. The pseudo code of the denoising algorithm is presented in Algorithm 1. By means of raising valleys, the fluctuation on the envelope is eliminated; meanwhile, the maximum descent gradient is completely preserved. An example of Algorithm 1 is shown in Figure 8.  
Figure 7: QR\(-\)LoRa chirp envelope received in practical environment and its difference operation result.  
```
Input:\(A(n)\), \(count\gets 1\) Output:\(B(n)\) while\(count\)\(\textless=15\) %set times fori\(\gets 1\) to n if\(A(i)\)\(<\)\(A(i\)+1\) \(B(i)\leftarrow[A(i)\)+\(A(i\)+1)\(]/2\); else \(B(i)\gets A(i)\); end end if \(count\gets count+1\) end
```  
**Algorithm 1** Envelope Denoising Algorithm
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 5 The Ship Motion Characteristics Caused by Landslide Surges
### The Effect of Landslide Volume on the Ship's Suaying Characteristics  
This section is used on the physical model test to study the effect of the landslide surge generated, after the landslide body enters the water, on the ship's transverse motion characteristics.  
It can be seen in Figure 12 that the ship was in a static equilibrium position before the landslide entered the water. When the landslide body entered the water, it was accompanied by a landslide-induced surge. The surge gravity wave spread to the ship's side, and the ship experienced significant transverse movement displacement. When the landslide-induced surge interacted with the ship, the ship's transverse displacement decreased gradually depending on its own restoring moment. When the landslide-induced surge again acted upon the ship's side structure, the ship's transverse displacement continued to increase and further deviated from the original static equilibrium position. With the gradual decrease of landslide-induced surge energy, the increment of yaw motion gradually decreased, and the amplitude of the ship heave increased successively. When the landslide surge acted upon the hull, the ship's transverse displacement increased to about 30 cm. During the test, it was observed that the interaction between landslide surges and ships led to a complex vortex flow field around the ship, especially in the middle of the ship. The interaction between landslide-induced surges and ships had the greatest impact strength near the water level.  
The landslide body entered the water at high speed and generated a landslide-induced surge with a certain amount of energy. The larger the landslide volume, the larger the landslide-induced surge energy, and the larger the amplitude of the transverse motion. The smaller the landslide volume, the smaller the landslide-induced surge energy, and the smaller the influence of the surge formation on the ship's heave. During a voyage, a ship navigator should pay attention to the sudden transverse load so as to reasonably control the distance between ships and prevent collisions between the ship and the shore and between the ship and the dock.  
Figure 12: Sway motion of the ship by different volumes of landslide.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 3 Rician Beamformer
As the ultrasonic image is of logarithmic nature, to obtain a more convenient depiction, abscissa-axis is also logarithmic. Looking at the RiB(1%) results, the imposed penalty to low signal to noise ratio (SNR) points can be appreciated. One of the main advantages of the proposed method is local contrast ratio preservation in a pseudo-lineal way, while it also mitigates the speckle effect, especially in cavities and low reflection zones (Sections 5.3 and 5.4). The pseudo-lineal ratio between the analysis area and the resulting contrast depends on \(\kappa\). By means of this ratio, beamformer expansion effect can be tuned, allowing its use on other applications such as NDT. As a counterpart, calibration process using standard phantoms is required, evaluating different \(\kappa\) values on testing cysts with fixed reflectivities, until converging to the expected phantom values, see Section 5.2. Starting with the typical ultrasonic imaging model, where convolutive noise is present [11], adapted to TFM:  
\[r_{\mathrm{DAS}}(x,z)\approx\mathrm{T}^{-1}\left[\left(\mathrm{T}\left[h(x,z )\right](\rho,\
u)\right)*n(\rho,\
u)\right](x,z)+n_{a}(x,z) \tag{12}\]  
Figure 2: \(Z_{p}\) random variable behaviour in presence of a point source. **(a)** PSF-DAS and **(b)** probability density function, standardised with respect to the maximum value **(c)**\(Z_{p}\) fitting to (10). where \(r_{\text{DAS}}(x,z)\) is the resulting image analytic form, T is the Cartesian to prolate spheroidal coordinates transform, \(h(x,z)\) is the tissue response, \(n(\rho,\
u)\) is the convolutive noise kernel and \(n_{a}(x,y)\) is the set-up thermo-electric noise. The same aberration type holds in the resulting image using RiB, as long as \(\kappa\ll 1\):  
\[r_{\text{RIB}}(x,z)\approx\text{T}^{-1}\left[\left(\text{T}\left[h(x,z) \right](\rho,\
u)\right)\ast\tilde{n}(\rho,\
u)\right](x,z)+\tilde{n}_{a}(x,z) \tag{13}\]  
Nevertheless, one may argue that kernel memory for RiB, \(\tilde{n}(\rho,\
u)\), is lower than \(n(\rho,\
u)\), specially along \(\
u\) dimension, alleviating speckle effect. On the other hand, additive noises, \(n_{a}(x,z)\) and \(\tilde{n}_{a}(x,z)\), are seemingly the same. This new ultrasonic imaging model is advantageous for most of despeckle techniques proposed on the last decade [12]. Despeckle tasks have to trade-off between analysis window size and image data loss [41]. A smaller acoustic noise kernel memory allows a smaller filtering window resulting in better image quality. This is specially noted on nonlinear and diffusion filtering.
# Flocking of Multi-Agent System with Nonlinear Dynamics via Distributed Event-Triggered Control
## 2 Preliminaries and Problems Formulation
### Design of Action Function  
Define \(\phi(\cdot)\) as a bounded pairwise action function, which is given by  
\[\phi\left(z\right)=\left\{\begin{array}{ll}\alpha_{1}\left(z-\underline{d} \right)e^{\frac{\left(z-\underline{d}\right)^{2}}{p_{1}}},&z\in(r_{0}, \underline{d})\\ \alpha_{2}\left(z-\bar{d}\right)e^{\frac{\left(z-\underline{d}\right)^{2}}{ p_{2}}},&z\in(\bar{d},r]\\ 0,&\text{otherwise},\end{array}\right. \tag{3}\]  
and the pairwise bounded potential function \(\psi(z):\mathbb{R}_{\geq 0}\to\mathbb{R}_{\geq 0}\) is defined correspondingly as  
\[\psi(z)=\left\{\begin{array}{ll}\int_{\bar{d}}^{z}\phi(s)ds,z\in(0, \underline{d})\\ 0,z\in[\underline{d},\bar{d}]\\ f_{\bar{d}}^{z}\phi(s)ds,z\in(\bar{d},\infty),\end{array}\right. \tag{4}\]  
where the parameters \(\alpha_{1},\beta_{1},\alpha_{2},\beta_{2}>0\) are chosen properly, such that \(\psi(r_{0})\geq Q^{*},\psi(r)\geq Q^{*}\) (see Figure 2), and  
\[Q^{*}=Q_{0}+\left\{\frac{N(N-1)}{2}-m_{0}\right\}\psi(r-\tau), \tag{5}\]  
where \(m_{0}\) is the number of edges in graph \(\mathcal{G}(0)\), \(Q_{0}>0\) is associated with the initial states of all agents, which will be defined later.  
Definition (3) indicates that the action function \(\phi(z)<0\) when \(z\in(r_{0},\underline{d})\), \(\phi(z)>0\) when \(z\in(\bar{d},r]\) and \(\phi(z)=0\) otherwise. The potential function \(\psi(z)\) is the integral of \(\phi\) from \(\underline{d}\) to \(z\) when \(z\in(0,\underline{d})\), and from \(\bar{d}\) to \(z\) when \(z\in(\bar{d},\infty)\), which implies that \(\psi(z)\) is positive. Moreover, it decreases with the increase of \(z\) when \(z\in(0,\underline{d})\) and increases with the increase of \(z\) when \(z\in(\bar{d},\infty)\). Obviously, the potential function \(\psi(z)\) reaches its minimum value \(0\) when \(z\in[\underline{d},\bar{d}]\). Condition (5) states that the potential function will be sufficiently large when the distance between two agents' approaches \(r_{0}\) or \(r\), which will be used to ensure that collisions are avoided and edges among agents are preserved.  
Figure 2: The potential function \(\psi(z)\).
# Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings
## 4 Solving Methods
### Branch and Bound  
Branch and Bound (B&B) [19] is an exact method using lower and upper bounds to skip parts of the solution space that cannot contain the optimal solution. Our implementation is based on a priority queue \(\mathcal{Q}\) of nodes. Each queue element \(\
u\) contains a partial solution (\(\
u_{\pi}\)), the machine on which a current algorithm branch is executing (\(\
u_{\mathrm{L}}\)), a set \(\
u_{\mathrm{N}}\) of jobs not yet scheduled on this machine, and the lower bound \(LB(\
u)\).  
The branching method is similar to the one used for the Job Shop Scheduling Problem [42]: first, we consider all jobs on the current permutation level before proceeding to the next one, avoiding having to process the same node twice. If \(\
u_{\mathrm{L}}=m\) and \(\
u_{\mathrm{N}}=\varnothing\), then \(\
u\) is a leaf element (\(\
u_{\pi}\) contains a full solution), and we compute makespan \(C_{max}\). If the resulting value is better than the best-known solution so far, then we update the current upper bound UB. The initial UB is generated by an NEH-like [21] greedy heuristic. The lower bound LB for node is computed as follows:  
\[LB(\
u)=\max_{i\in\mathcal{M}}\left(PRV+\sum_{j\in SET}(\hat{r}_{i}+p_{i,j})+ \sum_{k=i+1}^{m}\min_{j\in\mathcal{J}}p_{k,j}\right), \tag{17}\]  
where:  
\[PRV=\begin{cases}C_{i,\Omega_{i}(\
u_{\pi})}&\text{if }\Omega_{i}(\
u_{\pi})>0, \\ C_{i-1,1}&\text{if }\Omega_{i}(\
u_{\pi})=0\wedge i>1,\\ 0&\text{if }\Omega_{i}(\
u_{\pi})=0\wedge i=1,\end{cases}SET=\begin{cases} \varnothing&\text{if }i<\
u_{\mathrm{L}},\\ \
u_{\mathrm{N}}&\text{if }i=\
u_{\mathrm{L}},\\ \mathcal{J}&\text{otherwise}.\end{cases} \tag{18}\]  
and \(\Omega_{i}(\
u_{\pi})\) is the last job scheduled on machine \(i\) in partial solution \(\
u_{\pi}\). The algorithm stops either when \(Q\) becomes empty or the best node on \(Q\) is worse than current UB.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for GIFT Block Cipher
#### 3.2.2 Permutaiton of GIFT Block Cipher  
After the Sbox operation, the permutation of Table 4 is performed. Similar to the permutation of PRESENT block cipher, bit position changes can be made using Swap gates and are not counted as quantum resources. Therefore, by relabeling the qubits, the permutation of GIFT block cipher can be done without quantum resources.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Airborne Remote Sensing Sensors
Low-resolution \\  & water body temperature, ground temperatures, flood extent, changes in humidity & 2. Can be used at night & 2. Low-precision \\  & extent, changes in humidity & 2. Can be used at night & 3. Vulnerability to environmental interference \\ \hline Passive Infrared Sensors & moving objects, thermal radiation & 1. Usable at night or in low visibility & 1. Monitor within a small range 2. No exact distance information \\  & thermal radiation & 2. Monitor animals/vehicles & 3. No detailed image information \\ \hline Ultrasonic Sensors & water level, object distance & 1. High-precision & 1. Affected by weather \\  & water level, object distance & 2. Low costs & 2. Limited installation location \\  & & 3. Direct measurement & 3. Monitor within a small range \\ \hline Passive Microwave & atmospheric humidity, soil & 1. Suitable for all-weather monitoring & 1. Low-resolution 2. Unsuitable for measuring small 2. Can penetrate clouds and some vegetation & 1. Low-resolution 2. Unsuitable for measuring small 2. Can penetrate clouds and some vegetation & 1. Low-resolution 2. Unsuitable for measuring small 2. Can penetrate clouds and some vegetation & 1. Low-resolution 2. Unsuitable for measuring small 2. Can be used in extreme weather 3. Unaffected by 3. Not well representative of 3. Small-scale terrain features 4. Fast data acquisition & 1. High-resolution 2. Out of the 3. No. Not well representative of 3. Small-scale terrain features 4. Affected by vegetation cover \\  & water levels, vegetation structure, flood depths, surface elevations, terrain changes geomorphological changes & 1. High-precision 2. Suitable for detecting small changes & 1. Complexity of data processing 2. Multiple data calibrations required \\  & water levels, vegetation structure, flood depths, surface elevations, terrain changes geomorphological changes & 1. Unified system deployment 2. Unsuitable for detecting small changes & 1. High costs 2. Multiple data collections 2. Complex data processing 2. High spatial resolution & 1. High costs 2. Complex data processing 2. Multiple data collections 2.