esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The measurement and analysis of the interaction between Bessel vortex electromagnetic (EM) and several standard targets are presented in this paper. With the aid of the angular spectrum expansion (ASE) method and physics optics (PO) theorem, scattering results on the plates (metal and dielectric) and a sphere could be derived. Furthermore, plane near-field scanning and near-far field conversion methods were implemented to compare the theoretical radar cross section (RCS). In the experiment, the quasi Bessel vortex wave was generated by a holographic metasurface antenna, and the whole measurement was performed in an anechoic chamber. The results of both the theory and measurement show that the scattered fields of the plate and sphere still had characteristics of the vortex EM wave, and the scientificity and accuracy of the measured RCS were verified. Our work involved a vortex scattering experiment in the microwave frequency band, which provides strong support for the application of vortex waves in radar detection and target recognition.  
near-far field conversion; angular spectrum expansion (ASE); physics optics (PO); Bessel vortex wave; scattering; electromagnetic (EM); radar cross section (RCS) Article
# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 4 Experiments and Analysis
### Heading Estimation Based on Motion States  
The experiment about heading estimation was carried out in the same position scenario as shown in Figure 9.  
In Figure 10, the red star refers to the start point and endpoint of the trajectory. The dark green line represents the trajectory and the arrow represents the direction. The current motion state is indicated on each trajectory which includes walking and running. The static state of the pedestrian is contained inside the red ellipse. The pedestrian walked along the dark green trajectory with different motion states.  
Then the heading is estimated by different methods as shown in the following Figure 11.  
Figure 10: Trajectory and motion state of heading estimation.  
Four methods were utilized to estimate the heading angle. In Figure 11, the blue line refers to the heading angle estimated by the electronic compass while the magenta line, red line, and green line were represented as gyroscope-based, Mahony, and improved Mahony based on motion state methods, respectively. The black line denoted the true heading. From Figure 10, the accuracy of the heading angle estimated by the electronic compass was poor and suffered severe fluctuation, but it would be no drift for a long time. In contrast, the heading estimated by gyroscope did not have severe fluctuation but would suffer huge drift problems that would distort the heading.  
For the MCF method, the control parameters \(K_{p}\) and \(K_{i}\) were given two values of 0.001 and 0.000001, respectively. The MCF performed well in the early state but would suffer little drift for a long time without the adaptive parameter adjustment based on the motion state. From the partially enlarged view of the last trajectory, the heading estimated by the improved Mahony based on the motion state was the most accurate, which fluctuated around the true angle and had no cumulative error. Finally, a diagram of different motion states of experiment trajectory is shown in Figure 12 which contained a semi-transparent layer of heading estimation for comparison. It could be concluded that the heading estimation is closely related to the motion state. When stationary, the heading estimation was relatively stable when walking or moving, and the heading would fluctuate.  
Figure 11: Heading angle estimated based on the four methods and partially enlarged view.
## 4 Results
### Results of Analyses Investigating the Effects of Three Factors
#### 4.3.4 Analysis of the Triplet Camera Rotation, Contrast Ratio, and Device Used
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Rotation, Contrast Ratio, Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.60785 & 0.01946 & 31.238 & \(<\)\(2\times 10^{-16}\) \\ INC\_R & 0.16645 & 0.03855 & 4.317 & \(1.58\times 10^{-5}\) \\ INC\_C & 0.13403 & 0.04789 & 2.799 & 0.005131 \\  & 0.01252 & 0.05517 & 0.227 & 0.820459 \\ INC\_R and Gear VR & \(-\)0.05694 & 0.07853 & \(-\)0.725 & 0.468439 \\ INC\_R and INC\_C & \(-\)0.17166 & 0.06188 & \(-\)2.774 & 0.005539 \\ INC\_C and Gear VR & \(-\)0.34893 & 0.13729 & \(-\)2.542 & 0.011033 \\ INC\_R and INC\_C and Gear VR & \(0.56915\) & 0.15483 & 3.676 & 0.000237 \\ \hline \hline \end{tabular}
\end{table} 
Table 36: Results of the logistic regression analysis of the model with the variables camera rotation, contrast ratio and the device used, allowing interactions all variables. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Rotation, Contrast Ratio, Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.60785 & 0.01946 & 31.238 & \(<\)\(2\times 10^{-16}\) \\ NO\_R, NO\_C, Gear VR & 0.01252 & 0.05517 & 0.227 & 0.82046 \\ NO\_R, INC\_C, desktop display & 0.13403 & 0.04789 & 2.799 & 0.00513 \\ NO\_R, INC\_C, Gear VR & \(-\)0.20239 & 0.11945 & \(-\)1.694 & 0.09020 \\ INC\_R, NO\_C, desktop display & 0.16645 & 0.03855 & 4.317 & \(1.58\times 10^{-5}\) \\ INC\_R, NO\_C, Gear VR & 0.12203 & 0.04894 & 2.494 & 0.01265 \\ INC\_R, INC\_C, desktop display & 0.12882 & 0.02841 & 4.534 & \(5.78\times 10^{-6}\) \\ INC\_R, INC\_C, Gear VR & 0.30462 & 0.04418 & 6.895 & \(5.37\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 35: Logistic regression analysis results of effects of the variables camera rotation, contrast ratio, and device used.
## 4 Discussion
### Performance of the OLCI K\({}_{d}\)(490) Algorithm
#### 4.1.2 Comparison with Existing Algorithms Using the OLCI-Derived \(R_{\text{rs}}\)
Although algal bloom-covered areas were mostly masked off in research on the estimation of \(K_{\rm d}\)[61], it was difficult to completely remove these areas in practice; in addition, the algal particle concentrations in the waters surrounding the algal bloom-covered areas were still high. Therefore, the dual band ratio algorithm was more suitable for eutrophic lakes with a wide distribution of algae. Figure 10: (**a**) The OLCI RGB image, (**b**) the single band ratio algorithm-derived \(K_{\rm d}(490)\), and (**c**) the dual band ratio algorithm-derived \(K_{\rm d}(490)\) from the OLCI for 29 April 2017. The semi-analytical algorithm is a semi-analytical model for \(K_{\mathrm{d}}\) that was established by solving \(a\) and \(b_{\mathrm{b}}\) based on a numerical solution of the radiative transfer model [4; 18]. [PERSON] et al. used a multiband quasi-analytical algorithm (QAA) to derive the absorption and backscattering coefficients of water from remote sensing data [62] and then determine \(K_{\mathrm{d}}\)(490) based on its relationships with the absorption and backscattering coefficients. However, the QAA had a low accuracy (R\({}^{2}\) = 0.43, RMSE = 2.86 m\({}^{-1}\), RMSE\({}_{\mathrm{rel}}\) = 47.55%, MNB = \(-\)45.25% and NRMS = 14.79%). This was mainly because the empirical steps involved in this algorithm were unsuitable for type II inland waters with complex optical properties [23], resulting in large errors in the estimation of the inherent optical properties that were eventually passed to the \(K_{\mathrm{d}}\)(490) estimate. As a result, the estimation accuracy of the QAA was lower than that of the empirical algorithm. In contrast, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm had a high estimation accuracy and is therefore suitable for estimating \(K_{\mathrm{d}}\)(490) for waters of turbid inland lakes based on remote sensing data.
## 3 Hardware and System
### System Overview
#### 3.2.1 Sensing Unit  
This unit includes an important number of nodes that act simultaneously as sensors and routers. In this system, end-devices are considered as reduced function devices (RFD) and are programmed to activate the sensor, read the physical measurement and transmit it toward the base station. On the other hand, the routers are considered as full function devices (FFD). They are programmed, in addition to the task performed by end-nodes, to receive data from other nodes and route these toward the control unit.
## 3 Results
### Comparative Analysis  
#### 3.2.1 Feature Extraction Results Under Different Descriptors  
This study employed a controlled experimental design to systematically compare the proposed feature descriptor with state-of-the-art methods including SAR-SIFT, I-SAR-SIFT, and PSO-SIFT. While the feature detection phase consistently utilized the proposed detector, distinct algorithms were implemented for descriptor construction during the feature description phase. The experimental dataset comprised two representative scenes extracted from GF-3 and Sentinel-1 satellite imagery. Quantitative evaluation was conducted using the Correct Match Rate (CMR) metric, defined as the ratio of the Number of Correct Matches (NCM) to the Total Corresponding Matches (TCM), where initial matches were obtained through the NNDR strategy and subsequently validated using the RANSAC  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **Image Date** & **Image Scenes** & \
\begin{tabular}{c} **Image Size** \\ **(Pixels)** \\ \end{tabular} &
\begin{tabular}{c} **Resolution** \\ **(m)** \\ \end{tabular} & **Region** \\ \hline GF-3 & 21 April 2018 & 9 & 28,744 \(\times\) 26,550 & 2.25 and 4.77 & Taihu \\ Sentinel-1 & 20 March 2020 & 15 & 17,144 \(\times\) 13,112 & 20 and 20 & Hunan \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image parameter.  
algorithm. Detailed quantitative comparisons are provided in Table 2. For GF-3 data processing, the proposed method achieved both the highest CMR value and a significantly greater number of final matches compared to benchmark methods. When processing Sentinel-1 data under comparable feature point quantities, the proposed method maintained superior CMR performance among STSU-SAR-SIFT, SAR-SIFT, I-SAR-SIFT, and PSO-SIFT approaches. This superiority originates from the enhanced feature discriminability of descriptors constructed through the multi-scale analysis framework and adaptive neighborhood partitioning strategy, which significantly improves matching uniqueness.
###### Abstract  
Efficient monitoring of concrete pouring operations is critical for ensuring compliance with construction regulations and maintaining structural quality. However, traditional monitoring methods face limitations such as overlapping objects, environmental similarities, and detection errors caused by ambiguous boundaries. This study proposes an Ambient Detection-based Monitoring Framework that enhances object detection by incorporating contextual relationships between objects in complex construction environments. The framework employs the You Only Look Once version 11 (YOLOv11) algorithm, addressing issues of boundary ambiguity and misrecognition through relational analysis. Key components including Distance Relationship (\(\mathbf{D_{R}}\)), Attribute Relationship (\(A_{R}\)), and Spatial Relationship (\(S_{R}\)) allow the system to quantitatively evaluate contextual associations and improve detection accuracy. Experimental validation using 232 test images demonstrated a 12.07% improvement in detection accuracy and a 71% reduction in false positives compared with baseline YOLOv11. By automating the monitoring process, the proposed framework not only improves efficiency but also enhances construction quality, demonstrating its adaptability to diverse construction scenarios.  
Keywords:concrete pouring monitoring; object detection; YOLOv11 algorithm; ambient detection +
Footnote †: journal: Applied sciences
# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 1 Introduction
(iii) Full-scale bridge testing with the implementation of MEMS accelerometers with NB-IoT technology under daily transportation is considered. The third experiment is intended to demonstrate the applicability of the proposed method on a bridge.
# Hybrid-Attention Network for RGB-D Salient Object Detection
## 4 Results and Discussion  
### Comparison with State-of-Art Methods  
We compared the proposed method with seven state-of-the-art methods: ACSD [31], CDCP [33], DCMC [34], DF [17], MBP [21], PDNet [18], and SFP [35]. Table 1 and Figure 2 show that the proposed HANet outperforms the other evaluated methods. Figure 3 shows various saliency maps obtained from each method in typical scenarios. In the first and second rows, the closest objects are non-salient and have the highest pixel intensities. For the comparison methods, the two images are misjudged due to the depth principle error. In contrast, HANet can correctly detect the salient objects by using the information in the hybrid attention map.  
To further demonstrate the effectiveness of HANet, we conducted an ablation study by removing the RGB attention map. The results are shown in the 12 th column of Figure 3, where the miscalculation due to the depth principle error appears. On the third and fourth rows, we show the saliency obtained from HANet in scenes with multiple and large salient objects, confirming the effectiveness of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Datasets** & **Criteria** & **ACSD** & **CDCP** & **SFP** & **DCMC** & **DF** & **MBP** & **PDNet** & **Ours** \\ \hline \multirow{4}{*}{NJUD} & AUC & 0.923 & 0.822 & 0.871 & 0.926 & 0.928 & 0.703 & 0.952 & 0.964 \\  & MeanF & 0.551 & 0.572 & 0.482 & 0.601 & 0.654 & 0.479 & 0.719 & 0.834 \\  & MaxF & 0.733 & 0.594 & 0.655 & 0.740 & 0.782 & 0.557 & 0.796 & 0.866 \\  & MAE & 0.190 & 0.204 & 0.202 & 0.154 & 0.154 & 0.207 & 0.129 & 0.065 \\ \hline \multirow{4}{*}{NLPR} & AUC & 0.837 & 0.895 & 0.864 & 0.931 & 0.841 & 0.781 & 0.957 & 0.982 \\  & MeanF & 0.461 & 0.600 & 0.426 & 0.590 & 0.660 & 0.547 & 0.610 & 0.827 \\ \cline{1-1}  & MaxF & 0.615 & 0.654 & 0.562 & 0.703 & 0.745 & 0.598 & 0.720 & 0.869 \\ \cline{1-1}  & MAE & 0.156 & 0.126 & 0.180 & 0.120 & 0.112 & 0.117 & 0.119 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Saliency Detection Performance of Different Methods on the Testing set of the NJUD and NLPR Datasets.
## 3 Results and Discussion
### Perforation Diameter Analysis  
#### 3.2.1 Impact Process on Velocity Vector of Reactive Fragment and MIS.STRESS of Steel Plate  
(1) Impact process on velocity vector of reactive fragment  
As depicted in Figure 8, the SPH particle velocity vectors of the reactive fragment were all in the direction of impact prior to the onset of impact perforation. An SPH particle velocity vector with radial expansion was gradually formed along the steel plate during the initial stage of impact perforation. In the lateral corner area of the reactive fragment during impact perforation, an SPH particle velocity vector with the opposite impact direction was formed. These SPH particle velocity components caused the steel plate to generate a backsplash fragment cloud and an ellipsoidal forward-fragment cloud. The reactive fragment then underwent shock-induced chemical reaction processes to release energy throughout the impact process. As a result, a variety of physical characteristics of the steel plate and reactive fragments, as well as the impact process, influenced the specific perforation diameter of the steel plate. It was determined that the diameter of the reactive fragments was invariably less than the perforation diameter of the steel plate.  
(2) Impact process on MIS.STRESS of steel plate  
As shown in Figure 9, a cylindrical reactive fragment projectile collided with a first layer of steel plate when it was moving at a speed of 1.5 km/s. In the impact process, the MIS.STRESS was the primary reason for plastic deformation in the area immediately surrounding the perforation of the steel plate. Stress spread throughout the perforation of the steel plate in a radial direction. The steel plate material experienced material fracture failure, perforation, and fracture in the direction of impact when the MIS.STRESS was greater than the dynamic tensile strength of the material. The reflection of the shock compression wave off the back surface of the steel plate and the generation of a tensile  
Figure 8: The size and direction of the velocity vector of the reactive fragment SPH particles during the impact perforation process.  
sparse wave in the impact direction reduced the dynamic yield strength of the steel plate. Additionally, the temperature rise and weakening caused by the impact encouraged the steel plate's fracture.  
During the impact process, the energetic fragments broke apart, forming a velocity vector that spread outward and even formed a reverse velocity vector, which resulted in the perforation of the steel plate being larger than the diameter of the energetic fragments. In addition, by continuously forming, the MIS.STRESS wave spreads toward the surrounding area.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 7 Conclusions and Future Perspectives  
This paper reviews the main methods of traffic flow prediction in the construction of intelligent transportation systems. In our previous article, we introduced three major types of traffic flow prediction methods: statistics-based, machine learning-based, and deep learning-based methods. After discussing the principles, advantages, limitations, and applications of these methods in intelligent transportation systems, we found that, with the rapid development of big data and artificial intelligence technologies, deep learning models have shown obvious advantages in traffic flow prediction, especially when dealing with large-scale, nonlinear, and high-dimensional data, and their prediction accuracy and generalization abilities are significantly better than those of traditional methods [41]. These models play a vital role in the development of ITSs, not only improving the efficiency and
# Theranostic Nanoplatforms of Thiolated Reduced Graphene Oxide Nanosheets and Gold Nanoparticles
## 2 Materials and Methods  
### Chemicals  
Graphene oxide (chemically exfoliated) in aqueous dispersion (0.4 wt.%) was purchased from Graphene Inc., US. Sodium hydroxide (NaOH, \(\geq\)97%, pellet), hydrochloric acid (HCl, 37% in H\({}_{2}\)O), Thiourea (\(\geq\)99%), hydrobromic acid (HBr, 48% volume ratio in H\({}_{2}\)O), ascorbic acid, gold(III) chloride trihydrate, cetyl-trimethylammonium bromide (CTAB), trisodium citrated dihydrate and silver nitrate (AgNO\({}_{3}\)) were purchased from Sigma-Aldrich (St. Louis, MO, USA). For all experiments, ultrapure water was used (18.2 mO\(\cdot\)cm a 25 \({}^{\circ}\)C, total organic carbon (TOC) lower than 5 parts per billion (ppb), UltrapureMillipore(r) Water Type) and the glassware was cleaned with aqua regia (HCl: HNO\({}_{3}\) 1:3 volume ratio) and rinsed with ultrapure water before each use. For cellular experiments, Dulbecco's Modified Eagle Medium (DMEM)-F12, penicillin-streptomycin, L-glutamine,fetal bovine serum (FBS), dimethyl sulfoxide (DMSO), Dulbecco's phosphate-buffered saline (PBS), paraformaldehyde and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) were purchased from Sigma-Aldrich.
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
## 5 Conclusions  
Compared to APIT, the conventional Bat method is more accurate. The suggested Bat with SA approach achieves better results than the traditional APIT and DV-HOP methods in terms of convergence, calculation rate, and success rate. The simulations' findings show that when network area scalability rises, computation time falls. Additionally, compared to the conventional localization process, position accuracy and latency are enhanced. The association between the nodes is more firmly located than in the APIT algorithm. The simulation also shows the consistency of the proposed approach. Thus, the results of the entire system are enhanced, and the WSN is considerably optimizedwiththe help of DV-HOP and APIT with Bat-SA method. The APIT with Bat-SA localization approach outperforms the DV-HOP algorithms in a comparison of their efficacy. Future work on this subject may concentrate on multi-objective programming and the creation of novel optimization methods for localization challenges.  
Conceptualization, T.S.L., K.B.R. and S.S.; methodology, T.S.L., K.B.R. and S.S.; software, T.S.L., K.B.R. and S.S.; validation, T.S.L., K.B.R. and S.S.; formal analysis, T.S.L., K.B.R. and S.S.; investigation, T.S.L., K.B.R. and S.S.; resources, T.S.L., K.B.R. and S.S.; data curation, T.S.L., K.B.R. and S.S.; writing--original draft preparation, T.S.L., K.B.R. and S.S.; writing--review and editing, T.S.L., K.B.R. and S.S.; visualization, T.S.L., K.B.R. and S.S.; supervision, T.S.L., K.B.R. and S.S.; project administration, T.S.L., K.B.R. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 14: Shift vs. latency between existing and proposed method.  
Figure 15: Shift vs. latency betweenproposed and existing DV-HOPs.
## 4 Results and Discussion
### Model Validation  
To confirm the developed thermodynamic models in the present study, model validation is implemented, and the results of the current work are compared with those reported in the literature to determine their degree of accuracy. Table 3 illustrates the validation of the current thermodynamic models with previously published data in [28; 29; 30] for the HDH, ORC, and DCS subsystems. In Table 3, a good agreement is observed between the results obtained in the current work and those reported previously in the literature.
## 4 Data Diversity of UAV
### Overview of UAV Dataset Uses
#### 4.1.3 Agriculture  
UAV datasets help in monitoring crop health, assessing irrigation needs, and detecting pest infestations. Multispectral and hyperspectral imaging from UAVs enable detailed analysis of vegetation indices and soil properties.
## 3 Key Modules
### Doppler Estimation and Compensation  
Due to the water flows and the relative motion between the transmitters and receivers, the system needs to correct the Doppler effect on the received signal. The replicated PN sequences are placed at the beginning, middle, and end of the data frame, respectively. When the signal is affected by Doppler, the receiver signal length will be changed. Through the correlation calculation of the PN sequence at the receiver, the relative positions of the correlation peaks are detected to estimate the Doppler [20].  
From the data frame structure, three different maximum values can be obtained in the search process, and the signal length influenced by Doppler can be estimated by the position of the first and the last maximum value. Since the PN sequence is affected by Doppler, the good autocorrelation property of the PN sequence is severely damaged. Therefore, the Doppler estimation should not use the cross-correlation calculation of the local PN sequence. On the contrary, using sliding autocorrelation between the received signal and the first PN sequence that intercepts from the received signal, three distinct correlation peaks can be obtained. Both the length of the signal at the receiver \(\overline{L}\) and the length of the signal at the transmitter \(L\) can be calculated by the relative position of correlation peaks, respectively.  
Assume that the Doppler factor is \(\alpha\), by comparing \(L\) with \(\overline{L}\), the receiver infers how the received signal has been compressed or extended by the UWA channel.  
\[\overline{L}=(1+\alpha)L \tag{11}\]  
Figure 4: Data frame format of the UWAOSDM/PN.  
The receiver uses the estimated Doppler factor \(\alpha\) to resample the received signal, so that the compression or extension caused by the Doppler can be compensated.