esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The parking service is classified as an information collection system deployment and service dissemination tool, based on the information exchanged by means of the IoT. The authors of [36] reveal that the parking service needs lots of computing to complete the whole procedure. Thus, a central solution based on cloud is proposed. The always best connected and best served paradigm is proposed to develop a general middleware for an intelligent vehicle parking system based on cloud [37]. These works focus on the drivers' side and always include competition for one parking space. An IoT-cloud car parking management system is developed based on location. The scheme aims to help administrative officers in locating parking violations quickly [38]. For the ease of algorithm validation for IoT-based traffic service, some simulators are developed. An integrated road traffic-network-cloud simulator for connected car services is designed, which links cloud simulation, road traffic, and network to display both the practical states of vehicles [39]. Moreover, a full-stack, cloud-based test bed is developed for connected car service experimentation. Heterogeneous sensors and vehicular communication and edge servers are integrated based on open application programming interfaces using microservices philosophy [40]. The service oriented ITS application has been accelerated by utilizing the connected car as a mobile IoT resource, together with service computing, edge computing, and 5G [31; 41]. Distance headway, number of stops and speed are collected by connected vehicles and an integrated CVT-AI method is used to estimate the level of service on freeways [42]. It exposes that the accuracy of estimation is associated with the penetration rate of connected vehicles. Moreover, a comprehensive review gives a summary of road traffic density estimation methods based on vehicular ad hoc networks [43]. The infrastructure-based mechanisms mentioned in the paper belong to an IoT-based solution. As service platforms for smart cities, the deployment of connected cars and edge devices has been studied in many works [33; 34]. In this paper, a general framework is proposed based on IoT in a mixed traffic scenario. All devices are connected through networks. By using the computing capacity of the IOT device, a variety of traffic information are sensed. Especially, the cars are not only traffic participants, but also act as a mobile traffic status service provider. The main contributions are summarized below:  
1. A paradigm of service computing about traffic application in IoT-based scenario is firstly proposed. A case for queuing length estimation at traffic intersections is studied. 2. The connected cars provide computing service based on the shockwave model with considering the upstream intersection flow change in a cooperative manner. 3. A queue length estimation is realized with the aid of connected traffic cameras and edge server. A deep neural network is utilized to classify the queuing cars in the intersection.
## 5 Optimization of WPT Coupler  
### Multi-Objective Problem  
Regarding the optimization design problem of the WPT coupler in this paper, increasing the distance between the turns reduces the ECLI, but this leads to a reduction in mutual inductance. Therefore, reducing ECLI and increasing mutual inductance have contradictory requirements for the coil distance; they cannot obtain high performance simultaneously. In addition, the thickness of the magnetic core and the distance between the ferrite core and the coil also affect the performance of the coupler.  
The outer diameter of the transmitter and receiver depends on the size of the coupler, which is 250 mm and 200 mm, respectively. In order to simplify the problem, the number of turns on the primary side and secondary side is limited to 10 and 8, respectively. The distance between each turn of the coil is not equal; it depends on the choice of algorithm. The multi-objective optimization problem can be expressed as:  
\[\left\{\begin{array}{l}\min\,F(X)=[-M,ECLI]\\ s.t.\,1\leq t_{i}\leq 6,\,\,\mathrm{i}=1,2,3\cdots 9\\ 1\leq r_{j}\leq 6,\,\mathrm{j}=1,2,3\cdots 7\\ 1\leq d_{l,cf}\leq 5\\ 1\leq d_{r,cf}\leq 5\\ 1\leq d_{l_{r},f}\leq 5\\ 1\leq d_{r,f}\leq 5\end{array}\right. \tag{21}\]  
where \(M\) is the mutual inductance between the transmitter and receiver.
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 3 Test Results
### Ductility  
To study the effect of steel-bar stiffeners on the ductility of columns under axial pressure load, ductility coefficients of the columns were calculated in this paper. The formula of ductility coefficient is shown in Equation (2):  
\[DI=\Delta_{max}/\Delta_{y} \tag{2}\]  
In Equation (2), \(DI\) is the ductility coefficient; \(\Delta_{max}\) is the maximum axial displacement of the column top, which was assumed to be taken when the load dropped to 85% of the ultimate load in this paper; \(\Delta_{y}\) is the yielding displacement of the column top, which was assumed to be taken when the load increased to 75% of the ultimate load in this paper [26].  
According to Equation (2), the comparison of the ductility of all column specimens except C1 is shown in Table 5.  
It can be seen from Table 5 that the ductility of the column with steel-bar stiffeners was lower than the column (C2) without any steel-bar stiffeners. In the columns (C3-C6) with stiffeners, the values of \(DI\) ranged from 2.01 to 3.20, while the value of the \(DI\) of C2 was 3.43. It proved that steel-bar  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Column Number** & \(\Delta_{y}\)**/mm** & \(\Delta_{max}\)**/mm** & **DI** \\ \hline C2 & 5.1 & 17.5 & 3.43 \\ C3 & 7.2 & 14.5 & 2.01 \\ C4 & 5.0 & 10.5 & 2.10 \\ C5 & 4.4 & 14.1 & 3.20 \\ C6 & 3.5 & 8.1 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of ductility of columns.  
stiffeners could decrease the ductility of these columns. The reason is that the steel bars significantly increased the bearing capacity of the columns, but when these steel bars yielded, bearing capacity rapidly decreased. Then, ductility decreased too.  
In the columns with steel-bar stiffeners inside the tube (C3 and C4), the difference in steel-bar diameter made little difference in column ductility. The ductility of this kind of columns was lower than in the columns with stiffeners outside the tube because the constraint from the core concrete and steel tube lowered the deformation capacity of the steel bars.  
In the columns with steel-bar stiffeners placed outside the tube (C5 and C6), the _DI_ value of C5 was 3.20, while that of C6 was 2.31. It proved that a higher cross-section area ratio of steel to column can lead to lower deformation capacity for the column, while a lower steel-section area ratio can result in higher deformation capacity.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 2 Related Work
Inspired by the effect of node geometry and node depth adjustment, we firstly propose using node depth adjustment to improve tracking accuracy. This paper considers a dynamical node depth adjustment scheme that moves nodes to the optimal depth for tracking the target. We derive the relationship between node depth and FIM, and utilize the relationship as a metric to determine movement of modes. Thus, the key problem is converted to how to find the optimal depth under the framework of optimization problem. In [26], [PERSON] et al. proposed an optimal coordination strategy for sensor motion to maximize the tracking accuracy and the search space was reduced to improve efficiency. A harmony search based deployment algorithm was proposed by [PERSON] et al. that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost in [27]. In this paper, we utilized the harmony search algorithm to solve the optimization problem of node depth. The harmony search is modified to improve searching performance according to target position.
###### Abstract  
This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the strict needs of enhanced Mobile BroadBand (eMBB) applications. We incorporated a Sub-Optimal Low-Latency (SOLL) technique to enhance the critical check node processing stage inherent to the MS algorithm. This technique efficiently computes the two minimum values, rendering the architecture well-suited for specific Ultra-Reliable Low-Latency Communication (URLLC) scenarios. We design the decoder to be reconfigurable, enabling efficient operation across all expansion factors. We rigorously validate the decoder's effectiveness through meticulous bit-error-rate (BER) performance evaluations using Hardware Description Language (HDL) co-simulation. This co-simulation utilizes a well-established suite of tools encompassing MATLAB/Simulink for system modeling and Vivado, a prominent FPGA design suite, for hardware representation. With 380,737 Look-Up Tables (LUTs) and 32,898 registers, the decoder's implementation on a Virtex-7 XC/VX980T FPGA platform by AMD/Xilinx shows good hardware utilization. The architecture attains a robust operating frequency of 304.5 MHz and a normalized throughput of 49.5 Gbps, marking a 36% enhancement compared to the state-of-the-art. This advancement propels decoding capabilities to meet the demands of high-speed data processing.  
5G new-radio standard; wireless communication; protograph codes; quasi-cyclic LDPC codes; min-sum; layered decoding; co-design; FPGA implementation +
Footnote †: journal: _technologies_  
Article
## 3 CLARA WebGIS Products
### Digital Surface Model and Building Height Rasters  
The DSM raster has a resolution of 0.5 \(\times\) 0.5 m and covers an area ranging from 632,450.2 m E to 638,868.7 m E and from 4,500,872.2 m N to 4,506,039.2 m N (Coordinate Reference System CRS: EPSG:32633\(-\)WGS 84/UT, zone 33N\(-\)Projected). The elevations, expressed in meters, are orthometric heights referred to EGM2008 geoid model. Among the 121'879'530 pixels of the raster, 1'921'658 (1.58%) have no height information; these void pixels are indeed located within the urban area, near and among the buildings, and in the deepest valleys of the rocky ravine, where the occlusions and shadows have a negative impact on the photogrammetric matching process. By clicking on any point of the layer, it is possible to visualize the height value of each pixel inside the raster (Figure 13).  
Figure 12: (A) Screenshot of soil-building resonance levels overlapping the geological map in the urban area of Matera. The insets show two examples: a (red-colored) building having ‘F1 blg Hz’ and ‘F2 blg Hz’ values included in the interpolated soil frequency range (100% probability of resonance occurrence), and a (green-colored) building with a probability of resonance effect equal to 0%, along with the related pop-up windows; (B) attribute table of soil-building resonance level layer.  
The building height layer is a masked raster in which only the pixels corresponding to the buildings contain the height (expressed in meters) of the considered building with respect to the ground (Figure 14). It is important to highlight that this height is different from the eave height, which is the facade height (Figure 14). The building height raster covers the same area of the DSM raster and is characterized by the same resolution (0.5 m) and CRS.
# Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
## 3 Results and Discussion
### Comparison with POD
Taking case 3 as an example, Figure 15 displays the reconstructed flow field with different numbers of leading POD modes, as compared with the predicted results of the CNN-DCNN model. It could be found that to have a comparable level of resemblance to the original (CFD) flow field as CNN-DCNN model prediction, POD reconstruction requires at least 20 to 30 leading modes for the pressure field, and about 50 for the velocity fields. This observation is confirmed by the comparison of accuracies of different level POD reconstructions summarized in Table 8: The reconstructed field could not be as accurate as CNN-DCNN until more than about 50 POD modes are retained, beyond this value, reconstruction is more accurate but the improvement is quite limited as the accuracy is already approaching 100%. Besides, it is worth pointing out that POD by itself is typically working with a dataset that is already available (for modal decomposition and identification [52; 53]) rather than a prediction tool such as the proposed CNN-DCNN model, which after trained, is expected to directly provide flow field data that is not necessarily pre-existed. Consequently, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Relative Error** & **Pressure Field (\%)** & **Velocity Field (\%)** \\ \hline Case 1 & 0.59 & 0.12 \\ Case 2 & 0.44 & 0.16 \\ Case 3 & 0.29 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Global error of eigenvalue of the CNN-DCNN model for cases in Figure 14.  
as predicted flow fields correlate closely to the leading POD modes that contain almost the entire energy (eigenvalues) and dynamic information (eigen functions), it is reasonable to be optimistic in terms of the capability of the CNN-DCNN model to retain at least part of the intrinsic information of the flow dynamics. Figure 14: POD eigenvalues calculated from the true and predicted flow fields CFD and CNN-DCNN model prediction: (**a**) case 1: NACA9712, AOA = 0\({}^{\circ}\), \(Re\) = 3 \(\times\) 10\({}^{6}\); (**b**) case 2: NACA2412, AOA = 5\({}^{\circ}\), \(Re\) = 5\(\times\) 10\({}^{6}\); (**c**) case 3: NACA6212, AOA = 3\({}^{\circ}\), \(Re\) = 7 \(\times\) 10\({}^{6}\).
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 3 Theory and Methods
### Spaceborne GNSS-R Features
#### 3.1.2 Features Derived from DDM
The distribution of these six features versus the OSISAF SIT maps is illustrated in Figure 7.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
###### Abstract
These studies mainly focused on how to use traffic pricing as an incentive to influence user behavior and achieve better performance in terms of load balancing [18; 19; 20] or load offloading [21; 22]. These pull-type protocols proposed in [18; 19; 20; 21; 22], however, do not involve the procedure of content recommendation. Thus, we classify them as the \"conventional pull-type protocols\". The typical signaling procedure of conventional pull-type protocols is illustrated in Figure 1b. To overcome the drawbacks of proactive caching, this paper introduces a novel pull-type joint content recommendation and delivery design for personalized content-retrieval services. The contributions of our paper are as follows. First, an interactive, cross-layer-based joint content recommendation and delivery protocol is proposed. Second, based on the proposed protocol, optimal and low-complexity heuristic algorithms are given to jointly optimize content recommendation and radio resource allocation. Third, several theoretical bounds are derived to characterize the performance of the proposed system in a simple scenario. Fourth, the performance of the proposed algorithms is thoroughly compared via simulation in complex scenarios with realistic parameters. Simulation results show that the proposed system can achieve a good balance between maximizing user interest and minimizing transmission outage probability. The remainder of this article is organized as follows. Section 2 introduces the system model and formulates the problem of joint content recommendation and delivery. The optimal and heuristic algorithms to solve the problem are proposed in Sections 3 and 4, respectively. A novel performance evaluation framework is introduced in Section 5, followed by derivations of several theoretical performance bounds in Section 6. Section 7 presents simulation results. Finally, conclusions are drawn in Section 8.
## 2 Material and Method
### Batch Adsorption Experiments  
In the present investigation, all experiments were carried in a batch mode. In the adsorption isotherm experiments, 10 mL of 2.5 mg/L NP was introduced into a centrifuge tube containing different concentrations (0.5-10 mg/L) of adsorbate. The tube was, then, tightly closed with a Teflon-lined screw cap and shaken at 150 rpm at 25 \(\pm\) 1 \({}^{\circ}\)C over 24 h to reach equilibrium. To study the pH dependency of the adsorption, pH (2-10) of the mixture was adjusted by adding 0.1 M HCl/NaOH solution. Once equilibrium was reached, the adsorbent (i.e., MWCNTs) was separated from the solution using a 0.2-\(\upmu\)m membrane filter, and the residual NP concentration was analyzed. For the case of kinetic study, samples were collected after 0.5, 1, 2, 4, 7, 8, 10, 12 and 14 h of reaction, and residual NP concentration of each sample was analyzed. All the experiments were performed three times to determine averages and standard deviations; in fact, the calculated c.v. for each set of experiments was within \(\pm\)3%.
# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 3 Methodology  
Sen2 Cor (version 2.4.0) was used to generate S2C-L2A products from the L1C products using default Sen2 Cor parameters with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM, version 4.1). An examination of metadata files indicates that both ESA-L2A and S2C-L2A had identical processing parameters including adjacency effect correction, no correction for bidirectional reflectance distribution function (BRDF) effects, and a rugged terrain correction for slopes in excess of 7 deg. Table 2 describes each BOA reflectance band evaluated here.  
The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Enhanced Vegetation Index (EVI) were computed from Sentinel-2 spectral bands (Table 2) using BOA reflectance products gridded to 20 m resolution according to Equations (1)-(3), respectively. Vegetation biophysical parameters (LAI, LAIxCab, LAIxCw, fAPAR and fcover) were derived at 20 m resolution from each L2A product using the ESA-SL2P [26] integrated in the Sentinel-2 SNAP toolbox:  
\[NDVI=\frac{\text{Band 8A }-\text{ Band 4}}{\text{Band 8A }+\text{ Band 4}}, \tag{1}\]  
\[NDWI=\frac{\text{Band 8A }-\text{ Band 12}}{\text{Band 8A }+\text{ Band 12}}, \tag{2}\]  
\[EVI=\frac{2.5\text{ (Band 8A }-\text{ Band 4)}}{\text{Band 8A }+\text{ 6.Band 4 }+\text{ 7.5.Band 2 }+1}. \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Site** & **Sentinel-2 granule ID** & **Acquisition Date (Day of Year)** & **Local Overpass Time (hh:mm)** \\ \hline Barrax & T30 SWJ & 18 June 2017 (169) & 10:56 \\ Barrax & T30 SWJ & 28 July 2017 (208) & 10:56 \\ Barrax & T30 SWJ & 17 August 2017 (227) & 10:56 \\ Barrax & T30 SWJ & 16 October 2017 (286) & 11:00 \\ Barrax & T30 SWJ & 26 October 2017 (295) & 11:01 \\ La Crau & T31 TGJ & 19 June 2017 (164) & 10:30 \\ La Crau & T31 TGJ & 18 August 2017 (223) & 10:30 \\ La Crau & T31 TGJ & 7 September 2017 (242) & 10:30 \\ La Crau & T31 TGJ & 7 October 2017 (271) & 10:30 \\ La Crau & T31 TGJ & 17 October 2017 (280) & 10:30 \\ Harth & T32 TLT & 10 April 2017 (088) & 10:30 \\ Harth & T32 TLT & 30 April 2017 (107) & 10:30 \\ Harth & T32 TLT & 10 May 2017 (116) & 10:30 \\ Harth & T32 TLT & 19 June 2017 (155) & 10:30 \\ Harth & T32 TLT & 18 August 2017 (214) & 10:30 \\ \hline \end{tabular}
\end{table}
Table 1: ESA Sentinel-2 Products.  
A 3 \(\times\) 3 pixel (60 m \(\times\) 60 m) moving average filter was applied to all products prior to intercomparison to minimize uncertainties due to product gridding. Intercomparisons were performed only for clear sky pixels with the exception of SLC, where all pixels were sampled. Validation and intercomparisons were summarized using scatter plots and by tabulating \(R^{2}\), RMSE and bias statistics for matching retrievals over a given site. For brevity, geophysical quantities derived from a given Level 2 product are described using the same prefix as the product. For example, the AOT from the ESA-L2A product is described as ESA-AOT.
# Optimal Design of Aperiodic Reconfigurable Antenna Array Suitable for Broadcasting Applications
###### Abstract  
An aperiodic reconfigurable microstrip antenna array is designed to serve as a DVB-T base station antenna operating in a single broadcasting channel. The antenna array is optimized at 698 MHz (center frequency of DVB-T channel 49) to concurrently achieve a particular radiation pattern shaping with high forward gain, main lobe tilting and null filling inside the service area, as well as low sidelobe level outside the service area, and low standing wave ratio at the inputs of all the array elements. To concurrently satisfy all the above requirements, both the geometry dimensions and the array feeding weights (amplitudes and phases) are optimized, thus leading to a complex multi-variable and multi-objective problem. The problem is solved by applying a recently developed particle swarm optimization (PSO) improved variant, called PSO with velocity mutation, in conjunction with the CST software package, which is employed by the PSOvm every time a full-wave analysis is required. Furthermore, all the optimization methods found in the CST environment are compared with the PSOvm. The results show that the PSOvm is capable of producing an antenna array geometry, which is closer to the predefined requirements than the geometries derived by the rest of the optimizers, in the least amount of computational time.  
antenna array; aperiodic array; full wave analysis; particle swarm optimization; reconfigurable antenna arrays +
Footnote †: journal: Electronics Electronics