esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Solutions to Optimization Problems
### Heuristics Algorithms  
Metaheuristic optimization methods, such as evolutionary algorithms including genetic algorithms (GA), particle swarm optimization (PSO), quantum PSO (QPSO), and simulated annealing (SA), are popular techniques in the engineering field [165; 166; 167]. These methods are useful for solving optimization problems that are highly nonlinear, nonconvex, and complex. They can also be applied when the system's mathematical model is unclear or too complex to develop.  
PSO and SA have been used in a hybrid algorithm for RISeUAV deployment and RIS elements allocation [70]. The authors of [75; 168] have used the PSO technique as the benchmark to evaluate their proposed RISeUAV path planning method. Further, GA has been used to solve subproblems of RISeUAV path planning [80]. To tackle the computational complexity of the multilateral optimization problem the blocked coordinate descent (BCD) alternating optimization approach is used. The optimization problem is regarded as mixed integer nonlinear programming (MINP), and different objectives are solved using a combination of heuristic and gradient-based techniques. A combination of binary and continuous GA is used to solve the UAV beamforming and RIS ON/OFF and phase shift optimization problem.  
Although heuristic optimization methods are easy to formulate and solve, they suffer from the risk of being trapped in sub-optimal solutions and require extremely long runtimes. Additionally, these methods lack learning capabilities, making them unsuitable for real-time optimization. As a result, machine learning (especially deep reinforcement learning, or DRL) techniques have gained popularity in recent years.
## 3 Data and Methodology  
### Time-Series Analysis  
The basic idea of Small Baseline Subsets (SBAS) is to set a spatiotemporal baseline threshold, select high-quality interferograms to form one or more short baseline subsets, simulate terrain phases using DEM data according to the radar conformation equation, and then remove them from the radar interferograms. By utilizing the different characteristics of atmospheric delay and deformation in the time and space domains, the atmospheric delay phase is distinguished from the residual phase. For decorrelation and noise phase, multi-view methods are used to remove them. Finally, singular value decomposition (SVD) is used to estimate the deformation rate, and the average deformation rate is integrated in the time domain to obtain the value of historical deformation [19; 20]. Firstly, assuming there are N + 1 SAR images of the same region obtained at time t0, t1,..., tN. Assuming that each image can interfere with at least one other image, this means that each short baseline subset is composed of at least two images. Based on the above assumptions, the number of generated interferograms is M, and it can be inferred that M satisfies the following inequality (assuming \(N\) is odd):  
\[\frac{N+1}{2}\leq\text{M}\leq\text{M}(\frac{N+1}{2}) \tag{1}\]  
Assuming that the kth interferogram is generated from two SAR images obtained at time t\({}_{\text{A}}\) and t\({}_{\text{B}}\), the terrain phase has been removed, and assuming t\({}_{\text{B}}>\) t\({}_{\text{A}}\), in the azimuth distance pixel coordinate system (x, y), the interference phase of k at (x, y) can be expressed as  
\[\delta\Phi_{\text{k}}(\text{x, y})=\varphi(\text{t}_{\text{B}},\,\text{x, y})-\varphi(\text{t}_{\text{A}},\,\text{x, y})\ \approx\ \frac{4\pi}{\lambda}[\text{d}(\text{t}_{\text{B}},\,\text{x, y})-\text{d}(\text{t}_{\text{A}},\,\text{x, y})] \tag{2}\]  
In the formula, \(\lambda\) is the radar wavelength; D (t\({}_{\text{B}}\), x, y) and d (t\({}_{\text{A}}\), x, y) are the cumulative deformation values of the line of sight (LOS) relative to the reference time t\({}_{\text{0}}\) at time t\({}_{\text{B}}\) and t\({}_{\text{A}}\), respectively. Therefore, d (t\({}_{\text{0}}\), x, y) = 0.
# Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
## 6 Experimental Evaluation  
### Data Description  
To demonstrate the effectiveness of the proposed algorithm with the optimized parameter values, obtained as described in the parameter analysis section, we used two sets of data from a real single open-channel river in Canada and a real river network in China.  
**Case 1:**_A Real Single Open-Channel River_.  
The Kootenai River originates in British Columbia, Canada, and flows through Montana, Idaho, USA. A 24-km-long section of the river was used for this study, as shown in Figure 4. There are three hydrological stations on the river: Bonners Ferry, Tribal Hatchery, and Klockmann Ranch, which are located upstream, midstream, and downstream of the river, respectively. The measured water volume flow at Tribal Hatchery was used as the boundary condition for the upstream entrance (as shown in Figure 5a), and the measured water level at Klockmann Ranch (as shown in Figure 5b) was used as the boundary condition for the downstream exit, whereas Bonners Ferry was used as the test site. The cross-sectional information and water level and flow information used in the tests were obtained from the official website of the American Geographic Society. Table 6 shows the basic information of the selected 24-km-long section of the Kootenai river.  
**Case 2:**_A Real River Network_.  
Located in the southeast of Zhejiang Province, China, the Le Liu Hong plain river network covers an area of 325 km\({}^{2}\). It is divided into three river network systems--Hongqiao, Lecheng, and Liushi--according to its topography and geomorphology, which are controlled by control gates and are independent of each other. Each of the three river network water systems has a water level station, Hongqiao station, Lecheng station, and Liushi station, as shown in Figure 6. The hydrodynamic model of the river network of Le Liu Hong plain includes 398 rivers, 304 nodes, and a total of 1962 cross-sections. In this study, we used the 2019 Lekima typhoon storm flood data for data assimilation testing.
## 3 Materials and Methods
### AIS Data Preparation and Analysis
#### 3.2.2 Ship Type Statistics and Analysis  
Because different types of ships have different behavior characteristics, it is necessary to conduct ship type analysis to determine whether the types of classified ships are representative. Therefore, this paper adopts the database self-matching method to statistically analyze the AIS data. The AIS message 5 contains static information such as ship types. Different types of ships have unique type codes [37]. The first and second digits of ship type codes represent the ship type and supplementary information, respectively. Figure 2 shows the distribution of the AIS data ship types.  
According to the definition of the \"ship and cargo type\" attribute field of message 5, AIS data define each type of ship as a unique code [37]. The codes of passenger ships, cargo ships, tanker ships, fishing ships, and tug ships are \"60-69\", \"70-79\", \"80-89\", \"30\", and \"52\", as shown in Table 2. The ship category code \"9\" refers to other types of ships, and code \"0\" has no specific type definition. These two types of ships are not considered in spaceborne AIS data analysis. Figure 2a,b show the distribution of ship types by ship category code and the top 20 ship types with the largest number of messages by ship subcategory code. It can be observed that cargo ships, tanker ships, fishing ships, passenger ships, and tug ships are the top five types of ships with the largest proportion. These ships are representative ships sailing at sea and can provide massive sample data for training classification models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellites** & **Message Types** & **Fields Selected** \\ \hline \multirow{4}{*}{HY-1C/HY-2B} & Message 1 (dynamic information) & \multirow{2}{*}{MMIS, Time, Time Stamp, Longitude, Latitude, SOG, A, B, C, D, Type} \\  & Message 2 (dynamic information) & \\ \cline{1-1}  & Message 3 (dynamic information) & \\ \cline{1-1}  & Message 5 (Static information) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction to the AIS data used.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 3 Simulation Results
### Integration of Thermal Via with I/O Pads on PCB
The increase in copper plating thickness from 0.01 mm to 0.02 mm resulted in a relatively small increase in the maximum temperature for all three QFN packages. This phenomenon is attributed to the reduced heat capacity of the copper plating layer when the thickness of the copper plating is decreased. Even in these scenarios, the high-lead QFN package maintains the lowest maximum temperature. Based on the data presented in this section, it can be concluded that, under variations in thermal via diameter and copper plating thickness, the high-lead QFN package exhibits superior heat dissipation performance.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 2 Materials and Methods
### Nationwide Flood Mapping Framework  
#### 2.2.1 National Disaster Monitoring: Scheme A  
In response to the need for national flood monitoring, the goal has been set to develop a novel flood monitoring system based on a new flood monitoring framework and index. After data acquisition, an advanced global flood-mapping algorithm was applied to perform surface water change detection, specifically focusing on dynamic flood mapping based on national disaster monitoring in Figure 1. The conceptual framework of Figure 1a, Scheme A, shows that national land monitoring is necessary for both regular preparedness and disaster emergency response to support operational activities, especially emergency planning and action using multiple satellite data. At the same time, numerical models can be combined for flood reproduction and prediction to support the enhancement of disaster response operations using hydrological simulation and flood forecasting models in national and international river basins.
## 4 Traditional MPPT Techniques
### Lookup Table-Based Algorithm  
The Lookup Table (LT) MPPT algorithm relies on precomputed data stored in a lookup table to optimize the power output of PV systems. This approach involves creating a table that maps different environmental conditions, such as irradiance and temperature, to the corresponding optimal voltage or current settings for maximum power extraction. During operations, the system uses this table to determine the most efficient operating point based on real-time measurements [50]. One of the main advantages of the LT algorithm is its fast response time, as it eliminates the need for complex real-time computations by using predefined data. However, its primary disadvantage is that it requires accurate and extensive data collection to build the lookup table, and its performance is limited by the comprehensiveness and accuracy of these data. If the environmental conditions differ greatly from those used to build the table, the algorithm might not adjust properly to conditions that fall outside of its range, thereby resulting in inefficiencies [51]. Figure 8 shows the flowchart of the LT MPPT.  
Figure 8: Flowchart of the LT MPPT.  
Figure 7: Flowchart of the CV MPPT.
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 3 Metasurfaces for OAM Generation
### Continuously Shaped Metasurfaces  
The scatterers in the previous discussion provide discrete levels of abrupt phase shift by employing the geometric phase concept. In this section, we review several prototypes with continuous or quasi-continuous phase levels [87; 88; 96]. They generate OAM with high purity.  
In Figure 10a, a metasurface is composed of annular apertures with smoothly changed widths. The phase shift comes not only from the geometric phase due to the varying aperture orientation but also from the plasmon retardation phase which is modulated by the aperture width. With the contribution of the two phases, arbitrary OAM orders can be generated. Under the incidence of circularly polarized wave, according to (9), the radially polarized component acquires a geometric phase of \(e^{i\phi}\) and the cross-circularly polarized one has a geometric phase of \(e^{2 ib}\). To fabricate the metasurface sample, a 2-nm-thick Cr film and a 450-nm-thick silver film were subsequently deposited on a quartz substrate using magnetron sputtering. The annular apertures are then milled on the silver/Cr film through focused-iron beam lithography. Figure 10b shows the a pattern constructed from catenary-shaped atoms. The inclination angle for a single catenary gradually varies a total of _pi_ from one end of the catenary to the other end. The induced geometric phase doubles the value of inclination value for the cross-circularly polarized output wave. By arranging the catenaries accordingly, OAM beams can be produced. Since this geometric phase does not depend on frequency, the design has a broadband response. The fabrication process of the catenaries is similar to that of the annular apertures in Figure 10a, except that a 120-nm-thick Au film was deposited instead of the 450-nm-thick silver film. The direction of the grating grooves in Figure 10c is designed to satisfy \(\theta(r,\phi)=l\phi/2\). It is illuminated by a RCP wave and the transmitted LCP wave carries an OAM of order \(l\). The gratings were created by etching of a 500-\(\upmu\)m-thick GaAs wafer through electron cyclotron resonance source (BCl\({}_{3}\)) to a depth of 2.5-\(\upmu\)m.  
Figure 9: Schematic structure of metamaterials for generating OAM-carrying vector beams and the spatial distributions of phase and polarization of generated OAM-carrying vector beams (\(\sigma=-1\), right circularly polarized (RCP) input beam). Reproduced with permission from [94], Copyright The Optical Society, 2013.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
###### Abstract  
It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several random A-scan echoes (i.e., single channel detection echo sequence) to accurately predict the wavelet of any scene. The layered detection scenes with objects buried in different region are set for the 3D Finite-Difference Time-Domain simulator to generate radar echoes as a dataset. Additionally, the simulation echoes of different scenes are used to test the performance of the neural network. Multiple experiments indicate that the trained network can directly predict the wavelets quickly and accurately, although the simulation environment becomes quite different. Moreover, the measured data collected by the Qingdao Radio Research Institute radar and the unmanned aerial vehicle ground penetrating radar are used for test. The predicted wavelets can perfectly offset the original data. Therefore, the presented LSTM network can effectively predict the wavelets and their tailing oscillations for different detection scenes. The LSTM network has obvious advantages compared with other wavelet extraction methods in practical engineering.  
ground penetrating radar; long short-term memory network; wavelet extraction Article  
A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar  
2022126457. [[https://doi.org/10.3390/app1236457](https://doi.org/10.3390/app1236457)]([https://doi.org/10.3390/app1236457](https://doi.org/10.3390/app1236457))
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & **Mean** & **SD** & \
\begin{tabular}{c} **Mean Difference** \\ **(No\_PU-PU)** \\ \end{tabular} & **SE** & **t Statistic** & \(p\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Signal intensity** \\ \end{tabular} } & \multirow{2}{*}{RA} & No\_PU & 76.15 & 30.91 & \multirow{2}{*}{63.7} & \multirow{2}{*}{6.17} & \multirow{2}{*}{10.33} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\ \cline{3-3} \cline{5-8}  & & PU & 12.41 & 10.67 & & & & \\ \cline{2-8}  & \multirow{2}{*}{SF} & No\_PU & 180.02 & 28.35 & \multirow{2}{*}{73.8} & \multirow{2}{*}{5.21} & \multirow{2}{*}{14.15} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\ \cline{3-3} \cline{5-8}  & & PU & 106.26 & 39.72 & & & & \\ \cline{2-8}  & \multirow{2}{*}{DF} & No\_PU & 154.90 & 43.02 & \multirow{2}{*}{82.2} & \multirow{2}{*}{7.75} & \multirow{2}{*}{10.60} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\ \cline{3-3} \cline{5-8}  & & PU & 72.72 & & & & & \\ \cline{2-8}  & \multirow{2}{*}{BG} & No\_PU & 53.40 & 33.08 & \multirow{2}{*}{46.6} & \multirow{2}{*}{6.43} & \multirow{2}{*}{7.25} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\ \cline{3-3} \cline{5-8}  & & PU & 6.75 & 5.84 & & & & \\ \hline \end{tabular} \({}^{*}\)_p_ < 0.05. Abbreviations: BG, background; DF, deep fascia; RA, rectus abdominis; SD, standard deviation; SE, standard error; SF, superficial fascia.  
\end{table} 
Table 2: Comparison of the signal intensity for No_PU and PU. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & **Mean** & **SD** & \
\begin{tabular}{c} **Mean Difference** \\ **(No\_PU-PU)** \\ \end{tabular} & **SE** & **t Statistic** & \(p\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **SNR** \\ \end{tabular} } & \multirow{2}{*}{RA} & No\_PU & 13.18 & 5.21 & \multirow{2}{*}{\(-\)35.70} & \multirow{2}{*}{8.51} & \multirow{2}{*}{\(-\)4.20} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\ \cline{3-3} \cline{5-8}  & & PU & 5.82 & 3.52 & & & & \\ \cline{2-8}  & \multirow{2}{*}{SF} & No\_PU & 31.37 & 10.73 & & & & \\ \cline{2-8}  & \multirow{2}{*}{PU} & PU & 67.07 & 43.87 & & & & \\ \cline{2-8}  & \multirow{2}{*}{DF} & No\_PU & 25.64 & 6.67 & & & & \\ \cline{2-8}  & & PU & 37.54 & 22.23 & \multirow{2}{*}{\(-\)11.91} & \multirow{2}{*}{4.33} & \multirow{2}{*}{\(-\)2.75} & \multirow{2}{*}{0.013 \({}^{*}\)} \\ \hline \end{tabular} \({}^{*}\)_p_ < 0.05. Abbreviations: DE, deep fascia; RA, rectus abdominis; SD, standard deviation; SE, standard error; SF, superficial fascia; SNR, signal-to-noise ratio.  
\end{table} 
Table 3: Comparison of the SNRs for No_PU and PU.
## 1 Introduction
Numerically, the eigenvalues from the stability analysis indicated that the positive upper branch eigenvalues were physically stable compared with the negative values of the lower branch eigenvalues. Experimentally, the stability of nanofluids significantly indicated that the performance of working fluids and the preparation of nanofluids in order to maintain the thermophysical properties were very important [26]. Looking at the literature on MHD flow due to heat source/sink and thermal radiation, it is mentioned that the flow due to an exponentially shrinking boundary is still a relatively under-explored area. The investigation of boundary layer flow and heat transfer past an exponentially shrinking surface has gained more attention, as it finds important applications in many industries and manufacturing processes. Driven by the above reviews, the present paper was devoted to determining numerical solutions to MHD boundary layer flow and heat transfer of hybrid nanofluid over a permeable exponentially shrinking surface with a magnetic field, heat sink/source, and thermal radiation effect. This study also considered water as a base fluid, and carbon nanotubes (CNTs) including single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs) as nanoparticles, and copper (Cu) was considered as a hybrid nanoparticle. The results were obtained for several physical parameters, and they are also presented in tabular and graphical format. The present results obtained were compared with previously published results, indicating good agreement.
## RESTful M2M Gateway for Remote Wireless Monitoring for District Central Heating Networks  
**[PERSON] * and [PERSON]**  
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts & Telecommunications, Beijing 100876, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-6119-8026; Fax: +86-10-6119-8034.  
External Editor: [PERSON]  
_Received: 17 July 2014; in revised form: 2 October 2014 / Accepted: 6 November 2014 / Published: 27 November 2014_  
**Abstract:** In recent years, the increased interest in energy conservation and environmental protection, combined with the development of modern communication and computer technology, has resulted in the replacement of distributed heating by central heating in urban areas. This paper proposes a Representational State Transfer (REST) Machine-to-Machine (M2M) gateway for wireless remote monitoring for a district central heating network. In particular, we focus on the resource-oriented RESTful M2M gateway architecture, and present an uniform devices abstraction approach based on Open Service Gateway Initiative (OSGi) technology, and implement the resource mapping mechanism between resource address mapping mechanism between RESTful resources and the physical sensor devices, and present the buffer queue combined with polling method to implement the data scheduling and Quality of Service (QoS) guarantee, and also give the RESTful M2M gateway open service Application Programming Interface (API) set. The performance has been measured and analyzed. Finally, the conclusions and future work are presented.  
**Keywords:** RESTful M2M gateway; Wireless remote monitoring; Programmable logic controller; district heating
## 4 Case Study: Application to Missouri Crash Dataset
### Dataset Overview and Application  
The dataset comprises 6400 police-reported crash records from the Missouri State Highway Patrol, covering incidents from 2019 to 2020 [43]. Each record includes structured variables--such as crash severity, light condition, and construction zone (czone)--as well as an accompanying unstructured narrative describing the crash.  
Among these records, 91 were originally labeled as construction zone crashes (czone = 1), while the rest were labeled as non-construction zone crashes (czone = 0). The fine-tuned GPT-3.5-turbo model was applied to this dataset to predict construction zone involvement using both structured features and textual narratives. Each model-generated prediction was compared with the original ozone label. Any mismatch (i.e., predicted \(\
eq\) actual) was flagged as a potential misclassification, which was subsequently submitted for expert validation to assess the model's effectiveness in detecting labeling errors.  
Figure 3: Misclassification detection and expert validation workflow.