esa-sceva/satcom-chunk-collection huggingface dataset:
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
## 4 Application and Results
### Application of State Residualisation Technique  
The state categorisation described in Section 3.1.2 is applied to the three test cases, with \(\varepsilon_{part}\) selected as 0.75 for the short-circuit events in Cases 1 and 2, and 0.55 for the line-tripping event in Case 3, which results in three different grouping formulas, as shown in Table 2. The principle for choosing \(\varepsilon_{part}\) is not overly critical, but it should be based on the severity of an event, such that the less severe the event in question, the smaller \(\varepsilon_{part}\) should be, since for a less-severe event the states will be more likely to have equal participation in a mode. Hence, choosing a smaller \(\varepsilon_{part}\) for a less-severe event can disjoint the system states and separate them into different groups. In this way, the state variables that are most relevant to an event are grouped into individual groups, while the remainder are grouped into a few larger groups. In general, simply choosing \(\varepsilon_{part}\) as 0.75 for short-circuits, and as 0.55 for line-tripping, leads to satisfactory grouping.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values (pu)** & **Parameters** & **Values (pu)** \\ \hline \(R_{f}\), \(L_{f}\), \(C_{f}\) & 0.005, 0.15, 0.066 & \(k_{pv}\), \(k_{iz}\), \(k_{pi}\), \(k_{li}\) & 0.52, 1.16, 0.74, 1.19 \\ \hline \(m_{p}\), \(m_{q}\) & 0.02, 0.0001, & \(l_{nom}\) & 1 \\ \hline \(\omega_{c}\), \(\omega_{cq}\) & 31.4 rad/s & \(K_{VI}\), \(K_{X/R}\) & 0.67, 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid-forming converter RLC filter and control parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Number of Groups** \\ \hline Case 1 & 23 \\ Case 2 & 21 \\ Case 3 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different groups are formed for different cases, given the topology changes.  
Subsequently, modal state residualisation of (42) in Section 3.1.3 is applied, by choosing the desired size \(n\) as equal to the phasor approximate model. Three reduced models are obtained, which are summarised in Table 3. For example, for Case 1, the variables to be retained are almost the same as for Case 2, except for the remaining GFMs, which are less impacted by the short-circuit. The result is not surprising given that the short-circuit is applied at the same bus, and the topology of each system is very similar. However, for Case 3, as the system topology is more noticeably different from the previous two cases, the variables to be retained are different.  
It is seen that state residualisation helps to identify zones to reduce, while high-level details are retained elsewhere, depending on the simulated event. It is confirmed that those parts of the system that are located far from the simulated event are generally modelled with fewer details than those parts that are located nearby.
# Monitoring Vehicles on Highway by Dual-Channel \(\varphi\)-OTDR
## 6 Conclusions  
In conclusion, the work successfully demonstrates a DC-\(\varphi\)-OTDR to locate the vehicles on the highway. Dual-channel system helps to reduce the probability of unreliable measurement caused by signal fading and noise interference, which is experimentally confirmed over the campus road. In the results, signal fading does happen on one sensing fiber, making the SC-\(\varphi\)-OTDR fail to detect the vibration, but the synthesized signal in the DC-\(\varphi\)-OTDR shows good positioning capability. Additionally, an improved processing method named CDM&V is presented in signal demodulation to improve the performance of the sensor. We compare the DC-\(\varphi\)-OTDR processed by the CDM&V with the DC-\(\varphi\)-OTDR processed by the CDM. The results show that the CDM&V-based DC-\(\varphi\)-OTDR outperforms the CDM-based one in terms of locating the running vehicle with higher SNRs and alleviating the differentiating step dependence of discovering vibration peaks. One important point required to be mentioned is that when the synthesized signal--which combines the accumulated signals from both fiber channels--is used to determine the vibration, the light speed discrepancy between two sensing fibers must be taken into account. For this, we offer a method of achieving position matching between two accumulated differential signals from the dual sensing channels. However, it should be pointed out the good performance of achieving higher SNR and resisting to wrong measurement for the double-channel sensor is at the expense of complexity, relative to the single-channel system. Thus, a trade-off between cost and performance has to be considered before applying the DC-\(\varphi\)-OTDR in real applications.  
[PERSON] and [PERSON] completed this study; [PERSON] participated in the design of the experiments; [PERSON] conceived this study. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant nos. 61665001 and 61965005), the National Science and Technology Major Project (grant no: 2017 ZX02101007-003), the Natural Science Foundation of Guangxi Province (grant no: 2019 GXNSFDA185010), and the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (No. YQ17115). The APC was funded by the National Natural Science Foundation of China (grant no. 61965005). In addition, [PERSON] acknowledges the funding from Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing and Guangxi Experiment Center of Information Science (Guilin University of Electronic Technology), and [PERSON] also acknowledges the funding from Guangxi distinguished expert project.  
The authors declare no conflict of interest.
## 2 Theory of VMD  
### A Brief Introduction of the VMD Algorithm  
VMD can decompose a signal \(x(t)\) into a number of band-limited intrinsic mode functions (BLIMFs) \(u_{k}\) that have the sparsity property in their nature [12; 25], which can be represented as follows:  
\[\min_{\{u_{k}\},\{u_{k}\}}\left\{\sum\limits_{k=1}^{K}\|\;\partial_{t}\Big{[} \Big{(}\delta(t)+\frac{j}{\pi l}\Big{)}*u_{k}(t)\;e^{-\mu_{k}t}\Big{]}\;\|_{2}^ {2}\right\}\!,\text{Subject to }\sum\limits_{k=1}^{K}u_{k}(t)=f(t) \tag{1}\]  
where \(\delta\) denotes the Dirac delta function, \(j^{2}=-1\), \(\|\cdot\|_{2}\) denotes the \(L_{2}\) norm, \(\partial_{t}\) is the derivative with respect to \(t\), \(*\) denotes the convolution operator, and \(\omega_{k}\) is the center frequency. A solution of the minimization Equation (1) can be defined as a saddle point of the augmented Lagrangian that is given below:  
\[\mathcal{L}(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum\limits_{k=1}^{K}\| \;\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi l}\Big{)}*u_{k}(t)\;e^{- \mu_{k}t}\Big{]}\;\|_{2}^{2}+\|\;f(t)-\sum\limits_{k=1}^{K}u_{k}(t)\;\|_{2}^{2 }+\langle\lambda(t),f(t)-\sum\limits_{k=1}^{K}u_{k}(t)\rangle \tag{2}\]  
in which \(\alpha\) denotes the balancing parameter of the data-fidelity constraint. The benefits of the embedded Wiener filtering in the algorithm are that VMD is much more robust to sampling and noise. The mode and the center frequency are both iteratively updated in the frequency domain. Consequently, the temporal mode is obtained as the real part of the inverse Fourier transform of the filtered analytic signal using the determined center frequency and bandwidth.  
The VMD algorithm in detail can be found in [12; 13]. The whole process of the VMD algorithm is shown in Figure 1. The behavior of VMD in the presence of irregular samples, an impulsive response, fractional Gaussian noise, and tone separation has been thoroughly investigated in [14]. Mode aliasing is associated with tone separation to some extent and will be illustrated in the following subsection.  
Figure 1: The flow charts of variational mode decomposition (VMD).
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 4 Architecture of VLC Transceiver Circuits  
The architecture of the proposed VLC transceiver system is presented in Figure 10, and it includes a digital baseband, which was implemented in MATLAB, and an AFE, which was designed in Cadence. The digital baseband in the transmitter aimed to generate a transmitted signal and to encode and modulate this signal by equipping the NN-based FFE, while the baseband in the receiver decoded and demodulated the received signal with the RBF-NN-based equalizer. The AFE in Cadence included a LED driver circuit, a passive equalizer, and a TIA. In addition, the models of the LED, PD, and channels that were built in Section 2 were implemented in Cadence as the VLC link for analyzing the system performance. To verify the design of the proposed system, a co-simulation platform comprising MATLAB and Cadence was utilized.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 3 Methodology  
A comprehensive overview of the methodology is presented in Figure 3. First, the temporal behavior of the Sentinel-1 SAR backscattering coefficients (\(\sigma^{\circ}\)) over agricultural plots was studied to determine the best metrics that identify and discriminate the rice plots. Two methods were tested for mapping the rice areas. The first used the decision tree and the second was based on a Random Forest classifier. For the plots' limit, we used a thematic vector file of land cover produced by INRA in 2011. First, agriculture plots were extracted by masking the urban, water and forest plots. The extracted plots contain the 832 reference plots surveyed for crop types in 2017 (described in Section 2.2). Then, the mean backscattering coefficient was calculated for each plot from each preprocessed S1 image by averaging the \(\sigma^{\circ}\) values in the linear unit of all the pixels within the plot. The mean values of the 832 reference plots were further used for analyzing the temporal behavior of SAR backscattering and generating the suitable metrics used for classification.
## 3 Basic Concepts
### K-Means Clustering  
K-means clustering is a fast and commonly used technique due to its low iteration rates and ease of implementation. The k-means algorithm works by attempting to find the cluster centers (C\({}_{1}\), C\({}_{2}\),..., C\({}_{K}\)) in a way that minimizes the total squared sum of distances between each data point \"X\({}_{i}\)\" and its closest cluster center (C\({}_{i}\)). However, the performance of k-means heavily depends on the initialization of the centers, which is a key issue with this algorithm. The algorithm establishes strong connections between data points and their closest cluster centers, resulting in cluster centers that do not leave the local range of data density. As the LAC algorithm is implemented via k-means, it still suffers from the issue of the random initialization of the centers. Thus, in this research, we optimized the algorithm by using KHM.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 5 Towards a Completely Distributed and Dynamic Approach
### Proposed DRCS Scheme
Nodes choose transmit channels as well as their parents in periodic intervals, called route-update intervals (RUI). The routing and channel selection scheme should ensure that new nodes that are added to the network at any time are able to connect to the network and send information to the sink. In our proposed scheme, this is ensured by sending the beacon messages in different channels in rotation. Figure 8: Conceptual diagram of DRCS. Figure 7: Illustration of dynamic transmit channel selection. Hence, a new node is always able to receive beacons from its neighbors and can connect to the network, irrespective of its initial choice of a default channel.
# Application of RFID to Soil-Erosion Research
## 5 Conclusions  
RFID has been shown to provide insights into the movement of individual particles that go some way to address the call made by Reference [4]. However, weaknesses remain. First the particles are large by the standards of soil particles. The most common size of particle transported by interrill flow is about 100 \(\upmu\)m, Reference [15], whereas those used in this study have a diameter of several millimetres. This issue may not be significant if the relationships among transport distances of particles of different sizes is well understood. However, the evaluation of a transport-distance approach to modelling soil erosion, Reference [16], demonstrated that this is not the case, Reference [17]. Advances in this area are needed if the benefits of RFID are to be exploited. Secondly, unless the particles can be detected remotely, recovery rates may be insufficient. However, remote detection requires an external antenna, resulting in larger particles. Thirdly, the detection range for the latter type of RFID remains quite small. Fourthly, errors in the location of the detected particles will affect the error associated with any estimate of rates of soil erosion that are based upon them. Improvements are needed to achieve larger a detection range before the full potential of RFID for measuring soil erosion can be realized.  
Conceptualisation, A.P. and J.C.; methodology, A.P. and J.C.; formal analysis, A.P. and J.C.; investigation, A.P., J.C., Y.O., N.S.; resources, Y.O. and N.S.; data curation, A.P. and J.C.; writing--original draft preparation, A.P.; writing--review and editing, Y.O. and J.C.; visualisation, A.P. and J.C.; project administration, A.P. and Y.O.; funding acquisition, A.P. and Y.O. This research was funded by The Leverhulme Trust, grant number EM-2013-015. Further support was provided by a collaborative research grant from the Interdisciplinary Project on Environmental Transfer of Radionuclides, University of Tsukuba.  
**Acknowledgments:** We thank the numerous students and technicians from Tsukuba University and the National Research Institute for Earth Science and Disaster Prevention who assisted in the experiments reported in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 3 Results and Discussions
### Groundwater Nitrate Concentration Mapping  
The nitrate concentration in the study area is shown in Figure 4. This concentration was mapped using the data from a study previously conducted in this region [17]. The distribution of data points (wells) that were interpolated to obtain the nitrate concentration values for the study is shown in Figure 1. The highest and lowest nitrate densities recorded were (59.89 mg/L) and (5.04 mg/L), respectively, as presented in Figure 4. The World Health Organization (WHO) has set a threshold value of 50 mg/L for differentiating between polluted and non-polluted water resources [15]. Therefore, some of the study area wells can also be classified as polluted. In some parts of the area, the nitrate concentration is observed to be more than the threshold value of 50 mg/L. Figure 4 shows the nitrate concentration map. Figure 5, produced by the DRASTIC model, resembles Figure 4, as the areas that are more vulnerable to pollution also have greater nitrate concentrations.  
Figure 3: Groundwater contamination vulnerability zones for the study area.
# AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications
## 2 Background and Related Work  
### Popular SDKs for Hand Gestures  
Devices built to support hand tracking usually come with a Software Development Kit (SDK) that provides visualization and simple interactions with virtual objects using hands within immersive virtual environments.  
The Mixed Reality Toolkit [18] (MRTK) is a popular choice for AR and VR applications, as there are several sophisticated devices supported. It allows users to manipulate virtual objects by pinching and other rather simple hand gestures. The MRTK relies mostly on virtual objects such as menus, buttons, and sliders for interaction. This SDK should not be considered as a framework for hand gestures but rather a whole toolkit to enable interaction with virtual objects by using natural input, such as hands or eye gaze.  
The Leap Motion SDK [19] can be used with specific hardware to enable hand-gesture-based interaction in AR, VR, MR, or even desktop-based systems without HMDs. The SDK provides various options for hand gestures, such as pinching or grabbing. Other gestures can be defined, but the SDK as it stands is limited to static gestures.  
Recently, the Oculus portfolio was extended with HMDs that have built-in hand tracking support. The SDK [20] to support this hardware enables hand-based interaction with objects by using simple hand gestures such as pinch, unpinch, and pinch and hold.  
In contrast to these SDKs, the framework proposed in this paper allows quickly and easily designing for arbitrary one-handed gestures. Complex dynamic gestures can be recorded and recognized for usage within an interactive system, which is not possible with the aforementioned SDKs.
# Shared-Custodial Wallet for Multi-Party Crypto-Asset Management
## 2 Background  
### Multi-Party Computation  
MPC is a cryptographic technique developed in the 1980s by [PERSON] [6, 7]. It was initially designed to allow multiple parties to compute functions of their combined inputs without revealing their corresponding inputs to one another or any other party. Consider a scenario in which \(m\) individuals want to calculate the value of a function, \(f(x_{1},x_{2},x_{3},\ldots,x_{m})\), which is an integer function of \(m\) integer variables, \(x_{i}\), with a bounded range. Initially, a person \(P_{i}\) knows the value of \(x_{i}\) but no other values of \(x\). Individuals can calculate the value of \(f\) by communicating with each other without revealing any information about the values of their variables.  
In traditional single-signature wallets, a single key is utilised to enable one authenticated owner to sign and broadcast transactions to the blockchain [8]. Therefore, it must be kept secret and secure at all times [9], which poses a security threat in case the key is compromised. MPC wallets represent a new approach to address the security vulnerabilities of traditional cryptocurrency wallets. Rather than having a single private key, MPC wallets divide the key into \"shards\" or \"key shares\" and distribute them among multiple parties, which are then used to sign the transaction.
## 2 Materials and Methods  
### Lagrangian Particle Tracking Model  
A Lagrangian particle tracking model is employed to simulate the transport of microplastics in the marine environment. This model allows for the displacement of a passive tracer to be tracked as \(dr=Udt\). Here, \(dr\) represents the tracer's displacement in the \(x\), \(y\), and \(z\) directions over time, while \(U=(u,v,w)\) is the particle velocity. If the freezing of particles into ice is considered, particle velocity is calculated as \(U=(u_{ice},v_{ice},0)\). Being in ice, the particles are not allowed to move vertically.  
The freezing process of microplastics in ice is currently uncertain, as we have discussed in our previous work [45]. In order to parameterize it, we relied on the studies [46; 47], which were based on laboratory experiments of freezing MP beads in ice. After 15 days, the laboratory experiment in [46] showed that the percentage of microplastics incorporated into the ice varied from 10 to 50%. Our approach assumes that the daily probability of plastic inclusion into ice is estimated to be 10%, provided that the ice freezes and the local ice area exceeds 15% concentration. The probability of MP release into water during ice melting for each grid point is estimated as \(P=\frac{\Delta V_{i}}{V_{i}}\), where \(V_{i}(x,y,t)\) is the ice volume and \(\Delta V_{i}(x,y,t)\) is the local ice volume change due to melting over time \(t\).  
When the particles are located in the water, the sinking or rising velocity of the particles \(w_{s}\) is added to the vertical ocean velocity \(w_{ocn}\) so that \(u=u_{ocn}\), \(v=v_{ocn}\) and \(w=w_{ocn}+w_{s}\). The ocean and ice state fields are generated by the large-scale ocean model.  
Particle sinking or rising velocity is dependent on its physical properties (density, size, and shape) [48]:  
\[w_{s}(x,y,z,t)=\frac{\
u}{2R}d^{3}\Big{(}38.1+0.93d^{12/7}\Big{)}^{-7/8}, \tag{1}\]  
where  
\[d=2R\Big{(}\frac{g}{\
u^{2}}\frac{\rho_{p}-\rho_{ocn}}{\rho_{ocn}}\Big{)}^{1/ 3}. \tag{2}\]  
Here, \(\rho_{ocn}\) is the three-dimensional seawater density, \(g\) is gravity, \(\rho_{p}\) is the plastic density, \(R\) is the particle radius, and \(\
u\) is the kinematic viscosity.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 3 Beam Prediction Access Scheme
### Network Architecture  
The network architecture for the proposed scheme is presented in Figure 2. It is composed of input sequences, four BRNN-LSTM layers, where each layer is composed of 50 cells (neurons).  
Figure 2: Proposed BRNN-LSTM network architecture. BRNN: bidirectional recurrent neural network; LSTM: Long short-term memory.  
Each LSTM cell in the BRNN-LSTM model is composed of an input \(g_{t}^{in}\), input modulation \(g_{t}^{mod}\), forget \(g_{t}^{f}\) and output \(g_{t}^{out}\) gates that determine information entering the cell state, see Figure 3.  
The output of the last BRNN-LSTM layer is fed as the input of the dense layer (as per Figure 2), which is composed of linear activation function. The output of the dense layer presents the output of the proposed scheme, which is the beam index prediction at time step \(t+1\). The process over which this prediction process is achieved is now presented.
## 2 Basic Theory
### Experimental Modal Analysis
#### 2.2.1 Theoretical Modal Analysis  
For any linear system, its dynamic equation is given by  
\[\mathbf{M}\cdot\ddot{\mathbf{x}}+\mathbf{C}\cdot\dot{\mathbf{x}}+\mathbf{K}\cdot\mathbf{x}=\mathbf{F}(t) \tag{1}\]  
where \(\mathbf{M}\) is the mass matrix; \(\mathbf{C}\) is the damping matrix; \(\mathbf{K}\) represents the stiffness matrix; and \(\mathbf{F}(t)\) is the applied excitation force. For undamped free vibration, the equation can be simplified as follows:  
\[\mathbf{M}\cdot\ddot{\mathbf{x}}+\mathbf{K}\cdot\mathbf{x}~{}=0. \tag{2}\]  
In modal analysis, the structure is assumed to be linear and, therefore, the response is assumed to be harmonic:  
\[\mathbf{x}~{}=~{}\mathbf{\phi}_{i}\cos(\omega_{i}t) \tag{3}\]  
where \(\mathbf{\phi}_{i}\) is the mode shape (eigenvector) and \(\omega_{i}\) is the natural circular frequency of the mode shape \(i\).  
By substituting the values of the response in linear Equation (2), the equation can be obtained as follows:  
\[-\omega_{i}^{2}\mathbf{M}\cdot\mathbf{\phi}_{i}\cos(\omega_{i}t)+\mathbf{K}\cdot\mathbf{\phi} _{i}\cos(\omega_{i}t)=0. \tag{4}\]  
Notice that the solution \(\mathbf{\phi}_{i}\) = 0 is not meaningful, and \(\omega_{i}\) needs to be solved:  
\[\left(-\omega_{i}^{2}\mathbf{M}+\mathbf{K}\right)\cdot\mathbf{\phi}_{i}=0. \tag{5}\]  
For modal analysis, by solving the matrix Equation (5), we can obtain the natural circular frequency and mode shapes of the system.