esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The course of load forecasting is laborious for the sake of various affecting factors in nature. In this paper, an integration scheme of multiple methods is proposed for probabilistic forecasting on short-term electric load demand. To boost the efficiency of probabilistic load forecasting, several improvements are exploited within the integration scheme. Firstly, a cooperation of WPT and correlation analysis is applied to remove data noise. Secondly, the IWELM with a computation reducing switch algorithm is presented for load forecasting. Thirdly, the CIs of forecasting results are produced by the Gaussian process. Finally, the superiority of the proposed model is validated by actual load data from GEFcom2014, and the corresponding criteria are designed for comparison. The results show that the proposed integration scheme is more flexible, reliable and effective than the other available methods.  
**Author Contributions:** [PERSON] proposed the scheme and drafted the paper. [PERSON] prepared the data and contributed analysis methods. [PERSON] performed the simulation and modeling. [PERSON] analyzed the results and modified the paper.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) under grant 61801518 and the Hubei Provincial Natural Science Foundation of China under grant 2017 CFB661.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{\(\tau=50\%\)} & \multicolumn{4}{c}{\(\tau=90\%\)} \\ \cline{2-9}  & **Model 1** & **Model 2** & **Model 3** & **Proposed** & **Model 1** & **Model 2** & **Model 3** & **Proposed Model** \\ \hline station 1 & 0.067 & 0.072 & 0.077 & **0.058** & 0.268 & 0.159 & 0.154 & **0.114** \\ station 2 & 0.064 & 0.068 & 0.072 & **0.046** & 0.241 & 0.166 & 0.147 & **0.184** \\ station 3 & 0.071 & 0.081 & 0.104 & **0.062** & 0.301 & **0.162** & 0.194 & 0.171 \\ station 4 & 0.066 & 0.076 & 0.081 & **0.049** & 0.255 & 0.184 & 0.139 & **0.108** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the methods under 50%/90% coverage.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{\(\tau=99\%\)} & \multicolumn{4}{c}{\(\tau=100\%\)} \\ \cline{2-9}  & **Model 1** & **Model 2** & **Model 3** & **Proposed** & **Model 1** & **Model 2** & **Model 3** & **Proposed Model** \\ \hline station 1 & 0.343 & 0.252 & 0.251 & **0.216** & 0.344 & 0.292 & 0.305 & **0.285** \\ station 2 & 0.321 & 0.267 & 0.260 & **0.202** & 0.324 & **0.289** & 0.342 & 0.301 \\ station 3 & 0.372 & **0.271** & 0.299 & 0.277 & 0.377 & **0.331** & 0.384 & 0.364 \\ station 4 & 0.315 & 0.263 & 0.249 & **0.220** & 0.315 & 0.301 & 0.317 & **0.297** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the methods under 99%/100% coverage.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 3 Control Strategy of the Converter
### Current Control Strategy with Active Damping  
Passive damping cannot be used in the converter because of large loss. Adding active damping is an effective method to increase damping without additional loss in the control system.
## 4 Classification of Routing Protocols  
During last decade, researchers have proposed different types of routing protocols. In this section, we provide a general overview of the existing routing algorithms, which may be classified as QoS-aware routing protocols, temperature-aware routing protocols, cluster-based routing protocols, postural-movement-based routing protocols and cross-layered routing protocols, as shown in Figure 2.  
Figure 2: Classification of routing protocols for WBSNs.
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 1 Introduction  
Land contaminated with trace elements is a common issue in many parts of the world, which can influence human health and ecosystems [1, 2, 3, 4]. Mapping trace element content in urban and agricultural soils revealed contaminated areas in many regions [3, 4, 5]. In the last decades, geographical information systems (GIS) have become an outstanding and helpful tools for mapping trace element content in various environmental materials and for analyzing spatial data [6, 7, 8, 9, 10]. Moreover, GIS have become integral tools to address natural hazard phenomena [11, 12, 13, 14, 15, 16, 17, 18, 19], as well as to estimate suitable sites for land use planning [20, 21, 22, 23, 24, 25]. These current geospatial technologies are beneficial to demonstrate soil properties [26], environmental parameters and spatial visualization of chemical compounds [27, 28, 29, 30, 31]. Therefore, GIS are essential tools in the spatial analysis of element distribution.  
Geochemical surveys are performed for the identification and delineation of a geochemical anomaly that would aid in locating an element contamination source. Geochemical and spatial analysis methods evidencing relationships of element distribution were instrumental in confirming element geochemical interpretation [27, 28, 32]. Many researchers reported a high concentration of toxic elements in water, soil and sediment [27, 32, 33, 34, 35, 36, 37, 38, 39, 40].  
Land suitability evaluation is essential for planning a sustainable agricultural system [41, 42, 43]. Evaluation of land suitability aims to identify the limiting factors for specificland use. Land use requirements are described, among others, by the absence of a toxic level of elements in the soil. Many plants, vegetables and fruit trees can absorb Cd and Co as they are grown in contaminated soil [44; 45; 46]. Consuming plants, vegetables or fruits grown in contaminated soil may pose a risk for human health.  
Moreover, Cd and Co are just as toxic to plants as they are to humans and animals [44]. On the other hand, many plants can accumulate Cd and Co and consequently can be used for phytoremediation to extract and detoxify these elements from soil [47]. In this study, information about potentially toxic element content is considered for land suitability in the Ioannina basin. More specifically, the approach of land suitability assessment applied in this study is based on the soil contamination with Cd and Co. Various types of anthropogenic activities are recorded in the Ioannina basin, while their impact on soil and water resources is not evaluated until now. Moreover, distribution and delivery of weathering material enriched in potentially toxic elements from the Ioannina basin to receiving Pamvoti's lake may severely affect the lake water and sediments.  
The aims of this study were: (a) to record the cadmium (Cd) and cobalt (Co) concentration in soil of the Ioannina basin; and (b) to assess the land suitability for residential and agricultural use.
## 2 Problem Formulation  
Consider the model of marine vehicle moving horizontally given in Figure 1.  
For the position and orientation description, the North-East-Down (NED) frame is applied, namely \(\eta=[x,y,\psi]^{T}\). The velocities in the body frame are defined by \(\
u=[u,v,r]^{T}\) which means that the surge velocity, the sway velocity, and the yaw velocities are taken into account. The equations of motion for a marine vehicle moving horizontally are as follows [4; 5]:  
\[\dot{\eta}=R(\psi)\
u, \tag{1}\] \[M\dot{v}+C(\
u)\
u+D(\
u)\
u=\tau+f_{E}, \tag{2}\]  
where \(R(\psi)\) is the rotation matrix, \(M\) is the symmetric inertia matrix which contains additional mass (the matrix is positive definite), \(C(\
u)\) is the matrix of Coriolis and centripetal terms, and \(D(\
u)\) is the damping matrix. Moreover, \(\tau=[\tau_{u},0,\tau_{r}]^{T}\), where \(\tau_{u}\) is the thruster force and \(\tau_{r}\) is the yaw torque, \(f_{E}\) means the vector of external disturbances. The corresponding matrices and vectors are of the form:  
\[R(\psi)=\left[\begin{array}{ccc}\cos\psi&-\sin\psi&0\\ \sin\psi&\cos\psi&0\\ 0&0&1\end{array}\right],\quad\ M=\left[\begin{array}{ccc}m_{11}&0&0\\ 0&m_{22}&m_{23}\\ 0&m_{23}&m_{33}\end{array}\right],\] \[C(\
u)=\left[\begin{array}{ccc}0&0&c_{13}\\ 0&0&c_{23}\\ -c_{13}&-c_{23}&0\end{array}\right],\quad\ D(\
u)=\left[\begin{array}{ccc}d_ {11}&0&0\\ 0&d_{22}&d_{23}\\ 0&d_{32}&d_{33}\end{array}\right],\quad\ f_{E}=\left[\begin{array}{c}f_{Eu} \\ f_{Ev}\\ f_{Er}\end{array}\right] \tag{3}\]  
Figure 1: Marine vehicle model sketch.  
with: \(m_{11}=m-X_{\dot{u}}\), \(m_{22}=m-Y_{\dot{v}}\), \(m_{23}=m_{32}=mx_{g}-Y_{r}\), \(m_{33}=J_{z}-N_{r}\), \(c_{13}=-m_{22}v-m_{23}r\), \(c_{23}=m_{11}u\), \(d_{11}=X_{u}+X_{|u|}u|\), \(d_{22}=Y_{v}+Y_{|v|v|}|v|+Y_{|r|v|}r\), \(d_{23}=Y_{r}+Y_{|v|r|}|v|+Y_{|r|r|}r|\), \(d_{32}=N_{v}+N_{|v|v|}|v|+N_{|r|v|}|r|\), and \(d_{33}=N_{r}+N_{|v|r|}|v|+N_{|r|r|}|r\).
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## Appendix A  
**Proof of Theorem 1.** Denote \(N_{m}=2^{n}\) as the mother code length, \(G(n)=\left[\begin{array}{cc}1&0\\ 1&1\end{array}\right]^{\otimes n}\) as the generator matrix, \(g_{n,i}\) as the \(i\)-th row of \(G(n)\), and \(1_{n}^{i}\) as the indices of ones in \(g_{n,i}\).  
Considering the case of \(\mathcal{A}^{\prime}=\{1,\ldots,N_{m}\}\), when \(n=1\), there is \(G(1)=\left[\begin{array}{cc}1&0\\ 1&1\end{array}\right]\). Obviously, if \(j\in 1_{1}^{i}\), there is \(1_{1}^{j}\subseteq 1_{1}^{i}\). Suppose that the relation holds when \(n=r>1\) that is, if \(j\in 1_{r}^{i}\), then \(1_{r}^{j}\subseteq 1_{r}^{i}\).  
When \(n=r+1\), the generator matrix can be represented by  
\[G(r+1)=\left[\begin{array}{cc}\mathbf{G}(r)&0\\ \mathbf{G}(r)&\mathbf{G}(r)\end{array}\right]\text{.} \tag{1}\]  
From (1), if \(1\leq i\leq 2^{r}\), there is \(1_{r+1}^{i}=1_{r}^{i}\). So, if \(j\in 1_{r+1}^{i}\), the relation \(1_{r+1}^{j}\subseteq 1_{r+1}^{i}\) holds. If \(2^{r}+1\leq i\leq 2^{r+1}\), the \(1_{r+1}^{i}\) can be expressed as \(1_{r+1}^{i}=1_{r}^{i-2^{r}}\cup\left\{j+2^{r}\middle|j\in 1_{r}^{i-2^{r}}\right\}\). If \(j\in 1_{r+1}^{i-2^{r}}\), we can obtain the relations \(1_{r+1}^{j}=1_{r}^{j}\subseteq 1_{r}^{i-2^{r}}\subset 1_{r+1}^{i}\) and \(1_{r+1}^{i+2^{r}}=1_{r+1}^{j}\cup\left\{j^{r}+2^{r}\middle|j^{r}\in 1_{r}^{i-2^{r}} \right\}\subseteq 1_{r+1}^{i}\). Thus, the relation \(1_{r+1}^{j}\subseteq 1_{r+1}^{i}\) holds if \(j\in 1_{r+1}^{i}\). Here, we have proved that theorem 1 holds for generating matrix \(G(n)\).  
The indices of information with assistant bits (including CRC bits and parity check bits) is represent by \(\mathcal{A}^{\prime}\), and the indices of frozen bits is \(\mathcal{A}^{\prime}_{c}\). Denote \(\mathcal{A}^{\prime}_{i}\) as the \(i\)-th element of \(\mathcal{A}^{\prime}\), \(g_{i}\) as the \(i\)-th row of \(G_{\mathcal{A}^{\prime}\mathcal{A}}\), and \(1^{i}\) as the indices of ones in \(g_{i}\). We can get the result \(1^{i}=\left\{1_{n}^{\mathcal{A}^{\prime}_{i}}\middle|k\middle|k\in\mathcal{A}^{ \prime}_{c}\right\}\). According to the previous analysis of the generation matrix \(G(n)\), the relation \(1_{n}^{\mathcal{A}^{\prime}_{i}}\subseteq 1_{n}^{\mathcal{A}^{\prime}_{i}}\) can be obtained. Thus, if \(j\in 1^{i}\), the relation \(1^{j}\subseteq 1^{i}\) holds.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Ablation Study  
This study focuses on RSID and optimizes the dehazing model by reducing the number of channels in each layer. Typically, for models with the same network architecture, their size and computational cost are positively correlated with dehazing performance. To investigate this further, a study was conducted to evaluate the impact of different Channel Preservation Rates (CPRs) on the dehazing performance of the model. As shown in Table 4, a detailed performance comparison of dehazing models trained with different CPRs was presented. The original teacher model has 1.53 M Params. For further optimization, the experiments were conducted by compressing the model using CPRs of 1/2, 1/3, 1/4, and 1/5. Significant degradation in dehazing performance is not observed when the parameter count is decreased to 0.06 M. Specifically, when CPR was set to 1/5, the PSNR and SSIM reached 23.9271 and 0.9105, respectively, which were nearly comparable to those of the original model (25.1759 and 0.9223). At this time, the Params and FLOPs of the student model are 0.06 M and 0.44 G, respectively, which is a significant optimization compared  
Figure 7: Visual comparisons of dehazing performance across various methods on the RRSD300 dataset. (**a**) Hazy Image; (**b**) DCP [11]; (**c**) AOD-Net [46]; (**d**) Light-DehazeNet [48]; (**e**) Dehaze-Former [28]; (**f**) SLP [45]; (**g**) DCRD-Net [50]; (**h**) FCTF-Net [29]; (**i**) SCSNet (Teacher Network) [37]; (**j**) DVKT (ours).  
to the 1.53 M parameters and 10.79 GFLOPs of the teacher model. Overall, our method demonstrates a strong knowledge distillation capability in the field of dehazing and can generate an efficient model that is easy to deploy and apply on edge computing devices and smart terminals.  
Additionally, ablation experiments were conducted to evaluate the effects of different knowledge transfer settings, and the results are presented in Table 5. When no transfer strategy was employed and 1/5-SCSNet was trained directly, the performance was moderate. The incorporation of the Intra-KT strategy led to a significant enhancement in model performance, with PSNR and SSIM metrics increasing to 22.3718 dB and 0.9051, respectively. This indicates that intra-layer knowledge transfer can effectively transfer knowledge from the teacher network to the student network. When only the Inter-KT transfer strategy was used, the model's PSNR, SSIM, and LPIPS metrics were 21.5281 dB, 0.8937, and 0.0768, respectively. Compared to the results without any transfer strategy, this also showed substantial improvement, suggesting that the proposed Inter-KT transfer module can effectively transfer cross-layer knowledge from the teacher network to the student network. The optimal dehazing performance was achieved when both Intra-KT and Inter-KT were combined. This is because the two proposed transfer modules facilitate knowledge transfer from complementary perspectives, demonstrating the superiority of the proposed approach.
###### Abstract  
Superconducting (SC) radio-frequency quadrupoles (RFQs) have exhibited outstanding performance in transmitting and accelerating high-current continuous-wave (CW) ion beams. They can complete beam acceleration at a much higher gradient and with much lower power consumption compared with normal conducting (NC) RFQs. In this study, we introduce a novel SC RFQ scheme operating at 162.5 MHz to accelerate 10 mA proton beams from 30 keV to 2.5 MeV. It will be used as a crucial component for a neutron source dedicated to Boron Neutron Capture Therapy (BNCT) and neutron imaging projects. For efficient transmission of proton beams, we selected a relatively high inter-vane voltage of 240 kV, and the beam dynamics design yielded satisfactory results. Subsequently, RF design and multi-physics analysis were carried out to validate the reliability of the design. A 30-centimeter-long cavity was specifically designed for the vertical test and allowed for a thorough evaluation of the performance of the SC RFQ after post-treatments. Additionally, the tuning design of the 30 cm cavity was also carried out.  
superconducting RFQ beam dynamics; post treatment; neutron source +
Footnote †: journal: Applied Sciences
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 8 Smart Pavements in Smart Intersections
These components gather real-time data on traffic patterns, vehicle behaviors, and weather conditions, which are crucial for adjusting traffic signals on time and enabling communication between vehicles and the infrastructure (known as V2I communication). The system facilitates rapid decision-making with minimal delays by deploying sensor networks and digital replicas of environments (digital twins) while addressing common urban challenges, such as traffic congestion and safety concerns. Additionally, temperature and humidity sensors enable predictive maintenance algorithms that effectively prolong infrastructure lifespan and meet sustainability targets. By blending infrastructure with AI-driven analysis, this system illustrates how intelligent intersections can reduce carbon emissions, promote eco-friendly transportation options, and serve as a foundation for resilient smart cities. This collaboration is essential for pioneering transportation research and provides practical insights into data-centric strategies for urban mobility.
# Urban Free-Space Optical Network Optimization
## 4 Results
### Simulation Framework
#### 4.1.2 Constructing the Visibility Graph Based on Urban Data  
The second part of the simulation tests the algorithm on real urban data. Using real urban information modeled as a graph assists in statistical analysis of the city graphs and improvement of the GA. Figure 14 presents an example of such a visibility graph based on urban data of Manhattan Island, from which we took approximately 2000 buildings and 2500 edges; this map is based on OSM.  
Figure 13: Three examples of random red–blue visibility graphs. Graph (**a**) has 100 nodes and 0.3 edge density. Graph (**b**) has 50 nodes and 0.5 edge density. Graph (**c**) has only 20 nodes, which is useful for quickly testing the algorithms.
## 4 System Application Experimentation and Analysis
### Experimentation on the Effectiveness of Data Estimation Using the Self-decision and Filling Method  
#### 4.3.1 Experimental Methods  
Real datasets acquired by the system was tested. A section of consecutive data was chosen. It was assumed that a certain datum was missing, and it was estimated using the self-decision and filling method. Then 100 groups of data were chosen for the evaluation test. The estimated value was compared with the true value to verify the effectiveness of the estimation algorithm. Estimation was performed for temperature, humidity, CO\({}_{2}\) concentration and NH\({}_{3}\) concentration. Node A was taken as an example, and it was assumed that data missing occurred to Node A. As generally the probability of simultaneous data missing for multiple nodes is small, it was accepted that no data missing occurred to Nodes B and C.  
Figure 5: Operation interface of the field monitoring subsystem.  
Figure 6: Real time monitoring page.  
As can be seen in Figures 7-10 and Table 1, the self-decision and filling method had lower maximum absolute errors and smaller RMSEs computed using Equation (3) and thus had better estimation effects than any other single estimation algorithm. The test results showed that the self-decision and filling method had better estimation accuracy and met the estimation requirements of temperature, humidity, CO\({}_{2}\) concentration and NH\({}_{3}\) concentration in henhouses.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Software
#### 3.3.3 Range Migration Algorithm (RMA)  
Prominent SAR algorithms include the Range Doppler Algorithm (RDA), Chirp Scaling Algorithm (CSA) and Range Migration Algorithm (RMA). The RMA is selected as the most appropriate SAR algorithm for InSAR applications as it preserves the phase information across large integration angles [41].  
Figure 14 presents a block diagram that illustrates the RMA processing stages. The Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) are converters from the time domain to the 2-dimensional frequency domain and back. Frequency domain processing is computationally efficient compared to time domain algorithms [42]. The Stolt interpolation warps the data using a 1-dimensional variable to balance the range curvature of the target field. The azimuth compression is essentially a matched filter, whereby a reference function is multiplied with the signal spectrum to focus the spread data to a zero Doppler point. Resultantly, the Stolt interpolation and azimuth compression compresses the signal energy in both the azimuth and range directions, with the full-resolution image visible following the IFFT.  
The RMA produces Single Look Complex (SLC) images from FMCW waveforms for a moving platform. The algorithm inputs include:  
* Raw radar data: the unfocussed in-phase and quadrature (I/Q) data collected by the E312 in a matrix format; the rows and columns correspond to the downrange and cross-range directions, respectively.
* Waveform definition: including sample rate (Hz), LFM sweep time (\(\upmu\)s), bandwidth (Hz), direction and interval, output format and number of samples and sweeps. These parameters are derived from the Signal Source block in GRC.
* Platform velocity: drone flight speed, which is typically 1 m/s, derived from the automated flight path data in DJI GS Pro and verified through the fixed GNSS data in Emlid Studio.
* Operating frequency: radar centre frequency of 5.4 GHz, derived from the UHD: USRP Sink and Source blocks in GRC.
* Range distance: distance between the radar antennas and the beam centre on the ground, derived from Equation (1).  
Figure 14: RMA block diagram.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 2 Materials and Methods  
### Selecting Features Using Genetic Algorithms  
Feature selection is a procedure in machine learning and data mining that aims to select the most representative or relevant subset of features from the original dataset, achieving dimensionality reduction and improving model performance.  
The objective of feature selection is to enhance the performance of machine learning models by decreasing the number of features, thereby mitigating overfitting, reducing computing complexity, and enhancing model interpretability. Feature selection encompasses several strategies, such as filter methods, wrapper methods, and embedding methods. Filter methods are used to choose features based on their statistical characteristics, such as their correlation with the target variable or their information gain. Wrapper approaches employ  
Figure 1: Abstract view of the proposed methodology.  
a dedicated machine learning algorithm to assess the significance of features by training and assessing the model using various subsets of features. Embedded approaches integrate feature selection into the model training process by utilizing regularization techniques such as Lasso and Ridge regression. Genetic algorithms are a form of optimization method that can be employed to pick features. Genetic algorithms involve the evolution of a population of potential feature subsets across numerous generations by genetic operations such as selection, crossover, and mutation. The fitness of each subset of features is assessed using a fitness function, which usually quantifies the performance of the model when using that subset of features. Genetic algorithms can systematically seek for an ideal subset of attributes that maximizes the performance of a model by iteratively evolving the population. The variance metric has been used to evaluate the quality of selected features.