esa-sceva/satcom-chunk-collection huggingface dataset:
# Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
## 1 Introduction
Both the Rao and Wald tests have been applied in different signal detection scenarios [32; 33; 34]. In Ref. [35], the subspace Wald and GLRT detectors are proposed to deal with the signal mismatch problem in MIMO radar. The resulting Wald test is more robust than the GLRT. Considering that some training data may contain jamming, adaptive GLRT, Rao, and Wald tests are designed in noise, clutter, and noise cover pulse jamming in [36]. Persymmetric Wald and GLRT detectors were designed to detect spread spectrum signals in noise in [37]. The proposed Wald test was found to share the same decision rule with the proposed GLRT. To detect point-like targets in a complex Gaussian environment, modified Durbin, Wald, and Rao detectors were designed in [38] by replacing the exact Fisher information matrix (FIM) with its estimator. To detect radar targets in non-Gaussian and nonhomogeneous sea clutter, persymmetric Wald, Rao, and GLRT detectors are derived in [39]. However, as far as the authors know, no work has discussed the design of adaptive Rao and Wald detectors for FSR. In this paper, adaptive moving target detection for FSR in complex Gaussian noise is discussed. The contributions of the paper are three-fold. (1) Adaptive detectors which can realize the direct signal suppression and target detection simultaneously are designed for the FSR by resorting to the Rao and Wald design criteria. Rather than using the imaginary and real parts of the complex parameter to derive the real parameter test statistics, four adaptive target detectors are proposed by regarding the complex parameter as a whole. (2) The equivalence of GLRT, Rao, and Wald detectors based on FSR for a finite amount of data is demonstrated through both theoretical analysis and Monte Carlo simulations when the secondary data are available and not available. (3) Our simulation results show that the proposed Rao and Wald detectors outperform their counterpart and that the detection performance of the proposed Rao and Wald detectors increases when the observation time and the number of the secondary data increase are provided. We have arranged the rest of this paper as follows. Adaptive Rao and Wald detectors for the FSR were designed for when the secondary data are available and not available in Sections 2 and 3. The equivalence of the Wald test and Rao test to the GLRT is proved in Section 4. The detection performance analysis of the derived adaptive detectors is shown in Section 5. Some conclusions are given in Section 6.
## 6 Simulation and Evaluations
### Simulation Results  
#### 6.3.1 Snapshots of Simulations  
Figure 7 shows four snapshots for one experiment in the simulation. The MASS is represented by the small red circle. The VOs are represented by the cones. The MASS starts from (0, 0) and finally reaches (1000, 1000) using the greedy speed scheduling metric. The unit is kilometers. We can see that the MASS can successfully avoid collisions with other ships, even when it is closely surrounded by other mobile ships.  
Collisions happen only when the way-blocking metric reaches one, such that all ways of escape have been blocked by other ships. The density of surrounding ships, the velocity of other ships, and the tolerable maximum way-blocking metric all affect the probability of collisions happening. We evaluated the impacts of these parameters on the probability of collisions happening. Note that in the ideal case when the other ships are sailing at a constant velocity, the dynamic programming method can avoid collisions since the optimal trajectory is selected under the collision avoidance condition. However, the surrounding ships may change their sailing patterns. In the simulation, we added 1% noise to the surrounding ships' velocities and directions in each time interval. This noise may cause collisions of the MASS even in the DP method. Therefore, we also evaluated the collision probabilities of MASS in the DP method. Under each parameter setting, the experiments were run 100 times to evaluate the probability of collisions happening. In these experiments, the maximum velocity of the MASS was 100 km/h, and the maximum acceleration was also 100 km/h2.
## 2 Current-Distortion Problem in Totem-Pole Bridgeless PFC Converter  
A totem-pole bridgeless PFC converter is widely used in high-power applications owing to its high-power efficiency. The topology is shown in Figure 1, where \(L\) is the inductor, \(C_{o}\) is the output capacitor, \(R\) is the load resistor, \(v_{in}\) is the AC input voltage, and \(v_{o}\) is the output voltage. SiC MOSFETs \(Q_{1}\) and \(Q_{2}\) are adopted as the main power switch, to reduce the switching losses. \(S_{1}\) and \(S_{2}\) are adopted to realize synchronous rectification, which further improves the power efficiency. The PF of the converter is calculated using  
\[\mathrm{PF}=\frac{P}{S}, \tag{1}\]  
where \(P\) is the active power and \(S\) is the apparent power. The total harmonic distortion is calculated using  
\[\mathrm{THD}=\frac{\sqrt{\mathrm{I}_{2}{}^{2}+\mathrm{I}_{3}{}^{2}+\ldots+ \mathrm{I}_{N}{}^{2}}}{\mathrm{I}_{1}}, \tag{2}\]  
where \(I_{1}\) represents the root mean square (RMS) value of the fundamental frequency component of the input current. \(I_{2},I_{3},\ldots,I_{n}\) represent the RMS values of the harmonic frequency components of the input current, from the second harmonic to the \(n^{\mathrm{th}}\) harmonic.  
Average current mode control is widely used in totem-pole PFC converters, owing to its advantages of fixed switching frequency and insensitivity to sampling noise. A control diagram of traditional average current control is shown in Figure 2. Due to the limitation of the current loop bandwidth and input filter capacitor, the phase of the inductor current will lead the input voltage, which degrades the PF and THD. The following provides a detailed theoretical analysis from the perspective of input admittance of the PFC converter.
## 3 Probabilistic Evaluation of ALSC Based on BASPCE
### PRPF Calculation Method Based on BASPCE  
In order to conduct the probabilistic evaluation of ALSC, the PRPF method based on BASPCE is proposed. First, it is necessary to construct the BASPCE model of deterministic RPF (DRPF). As illustrated in Figure 1a, DRPF consists of multiple deterministic power flow (DPF) calculations. It is worth noting that the BASPCE model constructed is for DRPF, not for DPF. As shown in Figure 1b. Once the BASPCE model of DRPF is obtained, it can be used to perform PRPF to conduct the probabilistic evaluation of ALSC. Figure 1b illustrates three processes, which are in order.(1) obtaining ED samples,(2)constructing the BASPCE model, (3)performing PRPF using the BASPCE model. The detailed procedure is outlined as follows:  
**Procedure 1: PRPF Calculation Method Based on BASPCE**  
1. Read the parameters of random inputs, including the dimension \(m\), distribution types, and parameters;  
2. Generate \(m\times N_{\text{ED}}\) sampling matrix \(\mathbf{X}_{\text{ED}}\), during which process, corresponding \(m\times N_{\text{ED}}\) independent standard Gaussian distributed matrix \(\mathbf{U}_{\text{ED}}\) and \(m\times N_{\text{ED}}\) correlated standard Gaussian distributed matrix \(\mathbf{Z}_{\text{ED}}\), as shown in Section 2.2;  
3. Execute a batch of DRPFs with \(\mathbf{X}_{\text{ED}}\) and obtain the output load increasing multiple matrix \(\lambda_{\text{ED}}=\left[\lambda_{1},\ \lambda_{2},\ldots,\lambda_{N_{\text{ED}}}\right]\);  
4. Construct the BASPCE model with \(\mathbf{U}_{\text{ED}}\) and \(\lambda_{\text{ED}}\);  
5. Generate a \(m\times N_{\text{MCS}}\) sampling matrix \(\mathbf{X}_{\text{MCS}}\) of the random inputs, during which process, corresponding \(\mathbf{U}_{\text{MCS}}\) and \(\mathbf{Z}_{\text{MCS}}\);  
6. Execute sampling matrix \(\mathbf{U}_{\text{MCS}}\) through the constructed BASPCE model;  
7. Get the results of the probabilistic evaluation of ALSC \(\lambda_{\text{MCS}}\).
## 3 Methods
### Random Forest (RF)  
RF has been a prevalent ML algorithm for a wide range of fields for classification, regression and other complicated problems. As one of the ensemble learning methods, RF grows a multitude of decision trees as base learners, and combines these trees together to obtain a better performance by averaging the predictions [71; 72]. Each tree grows independently with training samples obtained using bootstrap sampling from the original data. Then, m variables out of M input variables are chosen, after which the best of m is used for splitting the node (note that m \(\ll\) M). The final prediction comes from the averaging predictions of each independent tree. This kind of technique is referred to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Description** & **GridSearch Values** & **Searching Results** \\ \hline alpha & \
\begin{tabular}{c} Value added to the diagonal of the kernel matrix \\ during fitting: larger values correspond to increased \\ noise level in the observations; this can also prevent \\ a potential numerical issue during fitting, by \\ ensuring that the calculated values form a positive \\ definite matrix. \\ \end{tabular} & 1e-2, 1e-1, 1 \\ \hline n_restarts\_optimizer &
\begin{tabular}{c} The number of restarts of the optimizer for finding \\ the kernel’s parameters which maximize the \\ log-marginal likelihood; the first run of the optimizer \\ is performed from the kernel’s initial parameters, the \\ remaining ones (if any) from thetas sampled \\ log-uniform randomly from the space of allowed \\ thetas-values. \\ \end{tabular} & 0, 1, 2, 4, 8, 10, 12, 16, 20, 32, 64 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter settings to determine the optimal hyperparameters for the Gaussian process regression (GPR) model.  
as bagging. RF models can provide feature importance estimates, which enables insight into feature selection processes.  
Key hyperparameters include n_estimators, max_depth, min_samples_split and min_samples_leaf. Hyperparameter tuning results using GridSearch and 5-fold cross-validation are displayed in Table 7. The remainder of the RF model hyperparameters are set as defaults.
## 1 Introduction
The structural configuration studied in this paper is intended to utilize the superior strength and energy absorption capacity in tension of the strengthening material. By means of the localized application of the strengthening layer at the tensile side, a solution is achieved with smaller costs than those requiring massive material use like R-HPFRCC or R-UHFFRC (which in addition are not applicable to strengthen existing structures). Furthermore, the strengthening material has a more moderate fiber amount included (2% by volume, still able to achieve strain-hardening in tension) than common UHPFRC, thereby contributing to moderate economic and environmental impact. Drop-weight impact tests were carried out on strengthened beam specimens without stirrups. Two configurations were studied for the strengthening layer, as well as a reference series of conventional RC beams tested for comparison.
## 3 Proposed MISAO
### Results Comparisons and Analysis of CEC2017 Benchmark Functions  
This subsection compares MISAO with seven original highly cited algorithms, namely Particle Swarm Optimization (PSO) [37], Differential Evolution (DE) [38], Genetic Algorithm (GA) [39], Gray Wolf Optimizer (GWO) [40], Whale Optimization Algorithm (WOA) [41], Rime optimization algorithm (RIME) [42], and Subtraction-Average-Basic Optimizer (SABO) [43]. This comparison aims to highlight the algorithm's superiority and rationality. This part aims to validate the optimization performance of MISAO and showcase its innovation and superiority by comparing it with high-performance upgraded methods. The algorithms being compared are the latest optimization algorithms developed within the past five years. These include the moth-flame optimization based on quantum behavioral simulated annealing algorithm (QSMFO) [44], ACO with Cauchy mutation and greedy Levy mutation (CLACO) [45], Gaussian quasi-bone mutation-augmented SMA (GBSMA) [46], and Drosophila Optimizer with Multiple Swarm Outposts Mechanism (MOFOA) [47].  
The test set utilized for comparison was IEEE CEC 2017, as specified in Table 1. The collection comprises single-peak, multi-peak, hybrid, and composite functions to comprehensively assess the performance of each technique across various optimization situations. The swarm intelligence algorithms were initialized with a population size of 30. The maximum number of evaluations was set at 300,000. Additionally, 30 distinct comparison experiments were conducted to verify the experimental fairness.  
\begin{table}
\begin{tabular}{c c c} \hline  & **The Function Name** & \(f_{\text{min}}\) \\ \hline \multirow{3}{*}{Unimodal Functions} & F1 & Shifted and Rotated Bent Cigar Function & 100 \\  & F2 & Shifted and Rotated Sum of Different Power Function & 200 \\  & F3 & Shifted and Rotated Zakharov Function & 300 \\ \hline \end{tabular}
\end{table}
Table 1: Details of the IEEE CEC2017.  
Figure 3: Flow chart of the proposed MISAO.
## 2 Methodology
### Presumptions for the Successful Integration of Green Hydrogen and Biomethane Production Processes  
In this section, we will examine the main presumptions for the successful integration of green hydrogen and biomethane technologies in municipal WWTPs. We will review the general principles and provide specific data for a real municipal wastewater treatment plant.  
The case study is based on data from the WWTP of Kaunas city (Lithuania). Kaunas had a population of 303,000 at the beginning of 2024. The amount of wastewater entering WWTP, including industrial users, is 392,000 population equivalent.  
We have identified seven key presumptions for the successful integration of green hydrogen and biomethane production processes in municipal WWTP:  
* Utilization possibility of all products from electrolysis in WWTP;
* Availability of green electricity for the production of green hydrogen in terms of quantity and time;
* Availability of external infrastructure;
* Energy and products storage requirements and possibilities of various types of energy;
* Use of renewable energy carriers produced in WWTP for WWTP needs;
* Sufficiency of incoming waste streams for the efficient functioning of WWTP as an energy system;
* Technology readiness level of key processes.  
Further, there is a concise description of each of them.
# Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency
## 2 Materials and Methods
### Electrical and Geometrical Parameters  
For the computational experiment, the classical model of a conical levitation inductor featuring one turn of coil rewound, was adopted. The shape of the inductor is based on the actual application of the inductor hence it lacks symmetry. Furthermore, the geometry of the inductor is compatible with the power source available to researchers which is important due to future validation.  
The top and bottom of most wires are connected to a power source. The second notable part of the coil is located just under the top wire, and it contains an inflexion within the contour, which allows it to change the current direction. The cross-section of the 3D model considered in the paper is presented in Figure 2.  
The exact parameters of the model are:  
* the total height of coil (\(h_{\rm C}\))--0.06 m
* the coil wire radius (\(r_{W}\))--0.003 m,
* the vertical distance between neighborhood wire--0.002 m
* the batch radius (\(r_{\rm B}\))--0.0025 m,
* the weight of the batch--0.000176715 kg,
* the radius of bottom wire (\(r_{\rm C}\))--0.01 m,
* coil is made of copper,
* batch is made of aluminum,
* electrical conductivity of copper--\(6\cdot 10^{7}\) S/m,
* electrical conductivity of aluminum--\(3\cdot 10^{7}\) S/m,
* the current--100 A.  
As the research are in the preliminary stage, the batch made of aluminum instead of titanium was assumed due to its low melting temperature and small weight. It is more suitable for the multivariate analysis of the electromagnetic levitation system, as it is easier to achieve both the levitation and melting. It is also a common practice shown in other research [28]. As mentioned, other parameters were variable. To be precise:  
* the batch position (\(z_{B}\)) (Figure 2) was within the set {0.01, 0.015, 0.02,0.025, 0.03, 0.035, 0.04} m above bottom wire,
* the half apex angle (\(\alpha\)) (Figure 2) was within the set { \(\{\frac{611}{32},\frac{511}{32},\frac{411}{32},\frac{311}{32},\frac{211}{32}\}\),
* the current frequency (\(f\)) was within the set {5, 10, 15, 20, 25, 30, 35} kHz.  
Figure 2: The cross-section of the inductor.
## 2 Materials and Methods
### Data Source  
#### 2.2.1 GPP and Land Use Data  
This study employed both the GOSIF GPP dataset and the MODIS GPP dataset, spanning the years 2000 to 2022. The GOSIF GPP dataset was generated at a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) and an 8-day temporal resolution, derived from the global SIF product obtained from the Orbiting Carbon Observatory-2 (OCO-2) and the established linear relationship between SIF and GPP [32; 33]. This dataset is particularly valuable for examining photosynthetic activity, carbon cycling, agricultural productivity, and ecosystem responses to climatic variations and disturbances. Additionally, it provides critical insights for ecosystem management and serves as a reference for terrestrial biosphere and Earth system modeling efforts [34; 35]. The MOD17A2H Version 6.1 GPP product, distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, was accessed through the Google Earth Engine platform. Grounded in the radiation use efficiency framework, this product facilitates the modeling of coupled energy, carbon, and water fluxes alongside plant biogeochemical cycles within terrestrial ecosystems. The MOD17A2H V6.1 GPP dataset represents an 8-day cumulative composite with a spatial resolution of 500 m, and the data were acquired in Tagged Image File Format (TIFF).  
This research employs the 2000 China Land Cover Map, sourced from the National Tibetan Plateau Data Center (NTPDC). This dataset is derived through the integration of multiple data sources using evidence theory, including China's 1:100,000 land use data from 2000, vegetation classifications from the 1:100,000 China Vegetation Atlas, the 1:100,000 China Glacier Map, the 1:1,000,000 China Wetland Marsh Map, and the MODIS 2001 Land Cover Product (MOD12Q1). The fusion of these datasets produces land cover information for China at a spatial resolution of 1 km for the year 2000 [36]. The resulting land cover dataset preserves the overall accuracy of the original land use data  
Figure 1: Overview of the study area.  
while incorporating detailed vegetation type and phenological information from the China Vegetation Atlas. Additionally, it updates wetland delineations and integrates the most recent glacier inventory data, thereby establishing a more comprehensive and standardized classification framework. The dataset was obtained in TIFF.
## 2 Materials and Methods  
### Fabrication of Flexible Microelectrode Arrays  
We fabricated a flexible microelectrode array for sEMG recording on a 4-inch silicon (Si) wafer using the semiconductor fabrication process shown in Figure 1A. First, a 10 \(\upmu\)m thick Parylene C layer was deposited on a 4-inch Si wafer using a Parylene coater. Then, gold (Au) and titanium (Ti) metal electrodes (Ti/Au = 10/200 \(\upmu\)m) were patterned using photolithography and a subsequent lift-off process. A second layer of Parylene C (10 \(\upmu\)m) was then deposited to encapsulate the entire wafer. The first and second Parylene C layers were patterned using photolithography and oxygen (O\({}_{2}\)) plasma dry etching. During the etching process, the electrode sites and pads were exposed for electrical connections between the skin, electrode array, and sEMG measurement equipment. Finally, the fabricated devices were released from the silicon wafer. Figure 1B shows several microelectrode arrays fabricated on a 4-inch wafer. Additional information regarding the fabrication process can be found in our previous papers [13; 14; 15].  
Figure 1: Fabrication process and images of the microelectrode arrays. (**A**) Cross-sectional view of the microelectrode fabrication process. (**B**) Image of the fabricated electrode arrays in a 4-inch wafer. (**C**) Two types of electrode arrays with different electrode sizes: Electrode 1 with an area of 17,671 \(\upmu\)m\({}^{2}\); Electrode 2 with an area of 59,325 \(\upmu\)m\({}^{2}\) (WE: working electrode; CE: counter electrode).  
Figure 1C shows the two types of electrode arrays with different electrode sizes, which are inserted in the zero-insertion force (ZIF) connectors on printed circuit boards (PCB) for connection to the recording system.  
Electrode 1 has circle-shaped electrode sites of diameter 150 \(\upmu\)m (area: 17,671 \(\upmu\)m\({}^{2}\)), and Electrode 2 has a combination of half-ellipse and rectangle-shaped sites with a height of 300 \(\upmu\)m and a width of 230 \(\upmu\)m (area: 59,325 \(\upmu\)m\({}^{2}\)). Different shapes and sizes of the electrodes could thus be selected for different sEMG recordings, depending on the target muscle. It should be noted that the slightly different shape could influence the recording results, although it is known that the electrode size has an integrative effect on the sEMG signal. For measuring the sEMG signal in this study, we mainly used Electrode 2, which showed a higher SNR and signal amplitude. We also compared these two electrodes to evaluate the effects of the electrode sizes, which will be discussed later.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 2 Materials and Methods
### Algorithm Description and Analysis
#### 2.2.1 Traditional Cross-Correlation Algorithm  
In traditional target detection, R-D processing is usually adopted. This is realized by using the two-dimensional correlation between the reference signal and the echo signal as follows:  
\[\chi[\lambda,f]=\sum_{n=1}^{N}s_{sur}[n]s_{ref}^{*}[n-\lambda]e^{-j2\pi f \frac{n}{f_{s}}} \tag{4}\]  
when \(\lambda=\lambda_{m}\) and \(f=f_{m}\), the correlation between the reference signal and the echo signal reaches the maximum value. The \(m\)-th target obtains the main lobe peak value corresponding to \([\lambda_{m},f_{dm}]\). Then, the CFAR algorithm is used to detect the target on the R-D plane obtained by the above formula and the parameters of the target are obtained at the same time.