esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Simulation Setup  
We simulated and verified our proposed HO scheme and the dual MRNs network model using LTE-sim [15]. LTE-sim is an open source framework for simulating LTE networks. It contains several aspects of LTE networks, such as single and multi-cell environments, user mobility at different speeds and existing hard HO procedures. For simulation, it has new call blocking and acceptance processes to control QoS-based call admission. A connection manager application modifies and implements MRN time control RSS of the serving cell. We compared the performances of the proposed scheme and the existing HO of LTE-sim via simulations.  
We created a simulation network topology comprising 19 macro cells in LTE-sim, as shown in Figure 1. The lines in the figure represent two fixed mobility patterns of trains with dual MRNs. The difference between these lines lies in the number of the cell through which they pass. Table 1 lists the parameters used in the simulation.
## 2 The Class of Systems  
Consider the class of linear Single-Input Single-Output (SISO) system with input/output delay given with  
\[\frac{Y(s)}{U(s)}=G_{\rm s}(s)G_{u}(s)e^{-\tau{\rm s}}=G(s)e^{-\tau{\rm s}}, \tag{1}\]  
where  
\[G_{\rm s}(s)=\frac{1}{\prod_{i=1}^{p}(s+b_{i})}. \tag{2}\]  
Let us define the product as \(G_{\rm s}(s)=G_{\rm s}(s)G_{stb}(s)\) with \(G_{\rm s}(s)=\frac{1}{\prod_{i=2}^{p}(s+b_{i})}\), and \(G_{stb}(s)=\frac{1}{s+b_{1}}\). \(G_{u}(s)=\frac{b}{s-a}\) with \(b_{1}\geq b_{i},\forall i\
eq 1\). \(U(s)\) and \(Y(s)\) are the input and output signals respectively, \(r>0\) is the constant known delay, \(a>0\) is the position of the unstable pole, \(b_{i}>0\), for \(i=1,2,\cdots,p\); \(b>0\) is the gain of the system and \(G(s)\) is the delay-free transfer function. Note that in system (1) only real poles are considered.  
A traditional control strategy based on a unitary feedback output of the form,  
\[U(s)=[R(s)-Y(s)]Q(s), \tag{3}\]  
produces a closed-loop system given by,  
\[\frac{Y(s)}{R(s)}=\frac{Q(s)G(s)\mathrm{e}^{-\tau s}}{1+Q(s)G(s)\mathrm{e}^{- \tau s}}, \tag{4}\]  
where the term \(\mathrm{e}^{-\tau s}\) located in the denominator of the transfer function in Equation (4) leads to a system with an infinite number of poles and, therefore, the stability analysis is complicated to carry out, and the design of the controller should be carefully analyzed.
## 1 Introduction
### Organization of the Paper  
The rest of this paper is organized as follows. Section 2 introduces the MISO-OE systems with time-delays and describes the identification problem. Section 3 presents an auxiliary model-basis pursuit de-noising iterative (AM-BPDNI) algorithm. Section 4 provides a simulation example to show the effectiveness of the AM-BPDNI algorithm. Finally, some summaries are given in Section 5.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 1 Introduction
### Point Cloud  
Reprojecting the points in 3D using the intrinsic parameters in a pinhole model, as shown in Equation (1), with no distortion is carried out according to Equations (5)-(7). For an RGB-D device, \(Z\) is the depth information extracted from the depth frame, \(x\) is the column index and \(y\) is the row index. In these equations, a point is a 3D structure with \((x,y,z)\) data representing a point in the camera's coordinate frame.  
\[point.z=Z; \tag{5}\]  
\[point.x=((X-cx)/fx)*Z; \tag{6}\]  
\[point.y=((X-cy)/fty)*Z; \tag{7}\]  
The origin of the coordinate system for each point cloud is one sensor of the camera, in this case, the left infrared sensor. To reconstruct the scene from multiple cameras or perspectives, the camera's positionin the scene, i.e., the global coordinate system, must be known.  
The global coordinate system's origin is chosen according to the task. It can be the optical centre of one of the camera's sensors, for example. In this work, the chosen origin was the base coordinate system of an ABB IRB 4600 robot [15].
## 2 Structure and Control Criteria  
The structure of the joint control strategy of wind storage systems is shown in Figure 1. First, the regulation power is calculated based on the data of the actual output value of wind turbines \(P_{A}\), the planned output value \(P_{P}\), and the energy storage state of charge (\(SOC\)). Then, the dead zone is set to judge whether the energy storage is acting or not. The dead zone is defined as a predefined threshold range (e.g., \(\pm 5\%\) of the nominal power) where the energy storage system remains inactive. Conversely, the storage system is activated only when the control deviation (e.g., power imbalance) exceeds the thresholds. The exact threshold can be adjusted based on the grid requirements or optimization objectives to balance response frequency and equipment lifespan. When it is judged that the energy storage output is needed, the system will output the regulation power of the energy storage \(P_{R}\).  
To ensure the safe and efficient integration of large-scale wind power into grid operation, it is stipulated that the active power regulation capacity of grid-connected wind power should meet the requirements of the grid management of new energy field stations. Moreover, to improve the competitiveness of wind power, it is necessary to raise the assessment requirements of the wind power generation program to the same level as conventional units. Taking China Southern Power Grid as an example, it is stipulated that the assessment is carried out every 15 min, and the power deviation rate of conventional grid-connected units should not exceed \(\pm 2.5\%\)[29]. The specific requirements are shown in Table 1.  
Figure 1: The basic process of wind storage joint control.  
Table 1 shows that the China Southern Power Grid has made high demands on the regulation accuracy and response speed of wind farms' active output in addition to the need for wind farms to have substantial voltage and frequency adaptability. Accordingly, this paper proposes an evaluation criterion for wind power active power output deviation as follows:  
The average value of the control deviation per minute in each assessment period is expressed using \(K1\), which is defined by the following formula:  
\[K1=\frac{1}{n}\sum_{i=1}^{n}\frac{|P_{pi}-P_{Ai}|}{C_{cap}} \tag{1}\]  
where \(C_{cap}\) is the starting capacity of the wind farm, \(P_{pi}\) and \(P_{Ai}\) are the planned and actual values of the wind farm at sampling point \(i\), and \(n\) is the number of sampling points.  
The \(K1\) standard is one assessment point per minute and 1440 assessment points throughout the day. The qualified standard is \(K1\leq 2.5\%\).
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 4 Methodology  
To properly evaluate the proposed state estimator, measurement data must first be prepared together with the test system model. Moreover, the conventional WLS-SE should serve as the control method or the score to beat. Further, the proposed neural networks should be trained separately without using transfer learning to ensure learning capability. Finally, the WLS-SE's input measurement data should also be the inputs to the other state estimators to ensure input-output consistency.
# Design of Tunable Broadband Graphene-Based Metasurface with Amplitude-Phase Modulation
## 2 Theoretical Analysis
### Graphene-Based Metasurface Coding  
In 2014, Professor [PERSON] proposed the notion of digital coding metasurface [25]. The metasurface units are binary processed using the numbers 0 and 1 to represent different amplitude or phase responses to electromagnetic waves. Generally, the coding metasurface is composed of N \(\times\) M units with the same size. When a plane wave is incident vertically on a 1-bit reflective coded metasurface located in the Fresnel zone of the antenna [26], special far-field radiation direction maps can be formed by adjusting the phase and amplitude of the array units. The radius R of the Fresnel zone is described by Equation (7), where L represents the largest size of the antenna aperture, \(\lambda\) is the wavelength corresponding to the operating frequency.  
\[0.62\sqrt{\
icefrac{{1}}{{\lambda}}}\leq R\leq 2\
icefrac{{1}}{{\lambda}} \tag{7}\]  
Inspired by [27], the unit in the middle of the metasurface is taken as the origin and coordinates are defined as \(nm\), \(-nm\), and so on when expanding in different directions on both sides of the origin unit. The formula for far-field scattering is shown in Equation (8) according to the relevant conventional phased array antenna theory [28] and generalized Snell's law [29].  
\[E(\theta,\varphi)=f(\theta,\varphi)\left[\sum\
olimits_{x=0}^{M_{2}}\sum \
olimits_{y=0}^{N_{2}}I_{mn}exp\;j(k_{0}d_{n}sin\theta+\varphi_{-mn})+\sum \
olimits_{x=-M/2}^{-1}\sum\
olimits_{\varphi=-N/2}^{-1}I_{-mn}exp\;j(k_{0}d_ {n}sin\theta+\varphi_{-mn})\right] \tag{8}\]  
where \(\theta\) and \(\varphi\) are the pitch and azimuth angles of the scattered field, \(f(\theta,\varphi)\) is the far-field of the unit, \(k_{0}\) is the propagation constant, and \(I_{mn}\) and \(\varphi_{mn}\) are the amplitude and phase of the excitation current in the unit located at the coordinates \((x_{m},y_{n})\), respectively. Similarly, \(I_{-mn}\) and \(\varphi_{-mn}\) are the amplitude and phase of the excitation current located at coordinates \((-x_{m},-y_{n})\), respectively. The desired beam state is obtained when the coding units on the digital metasurface are arranged with reasonable planning.
# Wettability of CNW/ITO Micro Structure for Modification of Surface Hydrophilicity
## 3 Results and Discussion
of downward \(\mathrm{P_{L}}\) and \(\mathrm{P_{i}}\) (Figure 6d (right)). Secondly, the sputtered indium tin oxide nano-particles can reduce the air area between the carbon nanowalls (pillars), which induces hydrophilicity shown by causing weakened repellency than hydrophobicity in interaction with water droplets (Figure 6e (right)). In the componential aspect, the high hydrophilicity was determined with an increase of OH bond and other chemical bonds related with the oxygen on the ITO surface. As shown in Figure 6f, the oxygen of the ITO surface induces a hydrogen bond with the hydrogen composition in the water droplet. This is consistent with the expected result by X-ray photoelectron spectra. Other wettability tests were carried out in order to evaluate the stability over time of the wetting properties of all samples and the wettability of the CNW/ITO micro-structure versus different solvents. Thus, the values of the contact angles are constant for all specimens (Figure 7a). The wetting behavior of the micro-structure with ITO sputtered for 45 min versus 4-typed organic solvents (acetone, methanol, isopropyl alcohol (IPA), and ethanol) showed outstanding wettability (\(\mathrm{CAs}<10^{\circ}\)) (Figure 7b). Figure 6: Wettabiliy of pristine-CNW and CNW/ITO micro-structure and theoretical principles and schematic of wettability: (**a**) images of the water droplet’s shape and the corresponding water contact angles values for all specimens; (**b**) optical photographs of the water droplet on the surface of all specimens; (**c**) schematic of surface energy activation area by plasma-ion movement; (**d**) theory of Laplace pressure (\(\mathrm{P_{L}}\)) and wettability; (**e**) theory of hydrophobicity (pristine- CNW) and hydrophilicity (CNW/ITO micro-structure); (**f**) componential principle of hydrophilicity of CNW/ITO micro-structure.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 5 Conclusions  
In this paper, a novel clustering protocol, named IEE-LEACH, is proposed to reduce energy consumption and improve the lifetime of WSNs. Compared with the existing routing protocols, the threshold of the proposed IEE-LEACH protocol introduces four parameters: the initial energy of nodes, residual energy of nodes, total energy of the network and average energy of all nodes. This mechanism can improve the robustness of the network and extend the network lifetime. In addition, the proposed protocol can optimize the number of CHs and their distributions, which can effectively reduce the energy consumption. Furthermore, to decrease the energy consumption, we consider that the nodes closer to the BS do not participate in cluster formation. Moreover, the proposed protocol employs single hop, multi-hop, and hybrid communications instead of a single communication mode in data transmission. Therefore, the proposed approach decreases the overall communication cost and significantly improves the network lifetime. The simulation results demonstrate that the proposed IEE-LEACH protocol has a better energy consumption distribution and is more reliable and energy-efficient than some existing protocols.  
The algorithms proposed in this paper have been conceived by [PERSON], [PERSON], [PERSON] and [PERSON]; designed the experiments, [PERSON], [PERSON], [PERSON] and M.J.; performed the experiments and analyzed the results, [PERSON], [PERSON] and [PERSON]; wrote the paper, [PERSON], [PERSON] and [PERSON].  
The authors would like to acknowledge the National Natural Science Foundation of China for its support of this research. This work was supported by the National Science Foundation of China under Grant Nos. 61761033 and 61461036, and in part by the Natural Science Foundation of Neimongol Autonomous Region of China under Grant No. 2019 MS06033.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSNs & Wireless sensor networks \\ LEACH & Low-energy adaptive clustering hierarchy \\ CHs & Cluster heads \\ IEE-LEACH & Improved energy-efficient LEACH \\ BS & Base station \\ SNs & Sensor nodes \\ SEECH & Scalable energy-efficient clustering hierarchy \\ LA-MHR & Learning automata-based multilevel heterogeneous routing \\ LEACH-M & LEACH-Mobile \\ O-LEACH & Optical LEACH \\ DFS & Distributed fiber sensors \\ SEEN & Stable energy efficient network \\ LEACH-XMP & LEACH-eV extended Message-Passing \\ TDMA & Time division multiple access \\ CSMA & Carrier-sense multiple access \\ LEACH-C & LEACH-Centralized \\ EE-LEACH & Energy-efficient LEACH \\ \end{tabular}
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 1 Introduction
In this study, we collected data from multiple data sources including TROPOMI NO2 products, EPA eGRID ground monitoring network data, and NASA MERRA weather data. The training data used TROPOMI and MERRA variables as inputs and EPA emission data as outputs. It covered more than five thousand power plants across the United States. However, due to the relatively coarse spatial resolution of remote sensing data at present (TROPOMI has 7 \(\times\) 3.5 km before August 2019 and a higher resolution of 3.5 \(\times\) 5.5 km currently), the NO2 reflected in the remote sensing data possibly did not come exclusively from power plants, especially in urban areas. To exclude impacts from other emission sources, we initially chose power plants located in rural regions for initial tests. The results showed that ML was capable of correctly detecting changing trends in NO2 emission by power plants. They confirmed that ML has much promise in identifying a reliable value range for specific ground emission sources, simply based on remotely sensed daily data (if the remote sensing data has no gaps, such as clouds). To improve the stability of AI models, we performed a series of testing experiments to verify the model's usability on sites located in various backgrounds, such as rural and urban settings, and tested how the model could perform on multiple sites instead of a single site. The model presented in this paper was compared to other types of models to ensure its accuracy; we found that LSTM outperformed most ML models with the same given training data. The significance of each input variable was also assessed and refined to improve the ML models. It was found that the day of the year and TROPOMI were the two most significantly correlated variables which means that the combination of date and TROPOMI reflects the highs and lows of power plant NO2 emissions. The significance of the relationship between weather variables and emissions was not obvious in this case and we think that it might be caused by the low temporal resolution of MERRA. In future work, we will look for higher resolution weather products to replace MERRA and expect to see increases in the correlation significance.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 1 Introduction
### Importance of Chaotic Time Series Forecasting  
Forecasting is an approach for creating predictions to determine the direction of future trends using historical data and current trend analysis as inputs [8]. Forecasting is the most significant optimization concept related to energy savings, material savings, increasing efficiency, making appropriate and suitable accurate decisions [8; 9]. On the other hand, chaos theory is an essential part of nonlinear science, developed in the 1970s [10]. Chaos is a long-term non-periodic behavior in a predictable system with a high sensitivity to initial conditions. It shows the order and regularity hidden behind disorganized and complex occurrences. This tendency permeates and promotes many subjects. As a result, chaos research has access to a solution. In the meantime, chaos theory applications are becoming increasingly popular. They are significantly used in diverse scientific applications such as wind farms [11], PV systems, oil and gas [12; 13], hydrological systems [14], etc. A brief description of the need for chaotic time series forecasting in each of these applications is explained below.
## 3 Proposed Method
### Automatic ROI Extraction  
Because of the limitation of the input image size, we aim to extract small ROI patches automatically. That is, we extract ROI patches that include landmark \(i(i=1,2,3...19)\). To extract the ROI automatically, we use registration to locate a coarse location of the landmark. Next, we extract the ROI patch centered on the coarse location. Thus, the landmark location we aim to detect will be included in the ROI patch.  
In this process, the test image is registered to training images, which have annotated landmarks. We consider the test images as moving images and the training images as reference images. The type of transform that we used is translation transform. The optimizer is gradient descent, which we used to update the locations of the moving images. We apply the intensity-based registration method and calculate the mean squared error (MSE) between the reference image and moving image.  
After registration, we copy the landmark locations of the reference images (training images) to moving images (test images). We consider the landmark location of a reference image as the center of the ROI patch, extract a 512 \(\times\) 512 resolution patch image on the moving image, and then resize it to a 256 \(\times\) 256 resolution patch for the test. The extracted images will be treated as input to our trained convolutional neural network (CNN); thus, we can detect the corresponding landmark in the patch image. The registration results are presented in Figure 5.  
Since people's heads vary in shape, randomly choosing one image among the training images as a reference image is not sufficient, and the landmark to be detected will not be included in the ROI. To avoid this situation, we iterate all the training images, which means we perform registration 150 times for one test image (the total number of training images is 150). Then, as the reference image, we choose the training image that has the smallest square error with test images. This enables us to find the closest training sample to the test images. For computational time, different references and moving images vary a lot. The shortest only took a few seconds for one registration, while the longest could take more than one minute. In all, the average time for registering one image to all training samples is around twenty minutes. The procedure is presented in Figure 6.  
Figure 4: The overview of our two-step method.
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 2 Dual-Window Superpixel Data Augmentation (DWS)
### Superpixel-Based Patch Extraction  
Usually, in hyperspectral classification using CNNs, a sliding window of a certain size is applied [23] to extract spatial-spectral information from the image \(I\). The contents of this window \(P\), also called patch, are then fed to the network.  
The proposed scheme replaces the sliding window with the extraction of patches based on superpixel information in order to reduce the computational cost. These superpixels are obtained by applying simple linear iterative clustering (SLIC) [24], a low-complexity superpixel segmentation method commonly used in computer vision, to the image \(I\). Figure 1 shows the result of applying SLIC to the Salinas hyperspectral scene. Strong adherence of the superpixel boundaries to the edges of the image can be observed.  
Figure 2 shows the first stages of DWS, related to the acquisition of the patches, prior to the data augmentation itself. The chosen patch size in this example is \(25\times 25\times B\), \(B\) being the number of bands in the image \(I\). After the application of SLIC to \(I\), each of the resulting superpixels is considered a sample, and a patch is extracted from its center. This reduces the number of processed patches from \(W\times H\), \(W\) and \(H\) being the spatial dimensions of the image, to a much smaller number of superpixels.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 5 Scientific Applications and Evaluation
### Heat Transfer Problem in Climate Projection
#### 5.2.3 Resource Estimation in a Fault-Tolerant Scenario  
Most of the observations from Figures 7 and 8 and Table 8 for both problem sizes are analogous to the findings in Section 5.1.4, including the numerical values of the fitted-line coefficients related to runtime, logical cycles, and the number of \(T\) states. The significant influence brought by deeper data-loading modules for the five-point problem is parallel shifts on longer runtime, more logical cycles, more \(T\) states, and more strict requirements on the logical qubit error rate and \(T\) state error rate. More data-loading qubits do not affect the growth speed of the logical cycle and the number of \(T\) states. Due to the limitation of computational time in Azure Quantum cloud service, we cannot collect more data points to understand this correlation better. However, from a theoretical perspective, this is expected because the QPE costs of HHL circuits are the same with the same \(n_{c}\) in the power flow and heat transfer problems.  
Figure 6: The errors of the solution states in log base 10 (labeled as \"\(\ket{x}\)\"), \(\parallel\ket{x}-\ket{x}_{HHL}\parallel_{2}\), and the errors of the solution vectors in log base 10 (labeled as \"\(\vec{x}\)\"), \(\parallel\vec{x}-\vec{x}_{HHL}\parallel_{2}\), are presented as functions of \(n_{c}\) for two different numbers of grid points. The symbols \(\vec{x}\) and \(\ket{x}\), respectively, refer to the solution vector and the normalized solution vector.  
Figure 7: The runtime in seconds as a function of the number of clock qubits in QPE under the qubit parameter set (**a**) (ns, \(10^{-4}\)) and (**b**) (\(\upmu\upmu,\ 10^{-4}\)). The estimated circuits are HHL circuits for the heat transfer problem.