esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Simulation of Forced Vibration
### Reliability Verification  
The semi-closed box deck of the Second Jiaojiang Bridge lacks aerodynamic data to verify grid reliability. Therefore, in this paper, the aerodynamic forces data are measured by wind tunnel test, and compared with the aerodynamic forces results obtained by numerical simulation. The wind tunnel test was conducted in the second test section of the wind tunnel (see Figure 6), a closed-circuit wind tunnel located in the Northeast Forestry University, China.  
The test section has width and height of 0.8 m and 1 m, respectively. The wind speed can be adjusted from 5 m/s to 70.5 m/s. The quality of the flow field is very stable. The flow is nearly laminar with a uniform velocity profile at the inlet, and the turbulence intensity of the empty wind tunnel is less than 0.5% on average.  
The scale ratio between section model and actual bridge is 1:80, which is consistent with the section size in the numerical wind tunnel. The model has a streamwise width, \(B\), of 0.532 m, a height, \(D\), of 0.044 m, and a span, \(L\), of 0.82 m. The blockage ratio of the model at 0\({}^{\circ}\) wind attack angle is 4.5%, which meets the requirements of wind tunnel test that the blocking rate is less than 5%. Two identical end plates (0.72 m wide and 0.12 m high) are installed on both sides of the section model to prevent end effect and enable a close to bi-dimensional flow field. The section model placed in the wind tunnel is shown in Figure 7. The static forces test is completed by a six-component balance, as shown in Figure 8. The section model is rigidly connected with the force measuring balance. The balance is placed on a rigid base that can adjust the height, and the two are also rigidly connected. The bottom of the model is raised a sufficient distance from the wind tunnel wall to ensure uniform flow field, avoiding the influence of wind speed profile as far as possible.  
Figure 6: Partial view of wind tunnel.  
Three groups of tests for measuring aerodynamic coefficients of semi closed box girder sections were carried out to prevent accidental errors. The sampling time of each group is set to 60 s and the sampling frequency is set to 1000 Hz. The average of three experimental results is taken as a final result, as shown in Table 1. The errors of drag coefficient and lift coefficient are about 5%, proving the reliability of the numerical simulation grid in this paper.
## 6 Advanced Considerations: Environmental Factors in Underwater VLC
### Combined Effects of Environmental Factors  
Turbidity, salinity, temperature, and pressure together create a very complex environment for VLC systems underwater. Ref. [55] insists that understanding such variables will be crucial in performance prediction for diffusion networks using VLC technology. Ref. [8] proposes a versatile path loss model considering changes in turbidity, salinity, temperature, and pressure to enhance the accuracy of performance predictions. That means that [56] demonstrates that, among other issues, all these factors together would cause further signal attenuation and reduced communication ranges at higher bit error rates.  
The interplay of these factors is especially evident in vertical VLC systems, whose turbidity, salinity, and temperature drastically change with depth, leading to complex fading. Refs. [60; 62] model the vertical underwater link as a cascaded fading channel, where the fading coefficients associated with different water layers are affected by varying environmental conditions at different depths. Ref. [63] also points out that temperature and salinity variations can result in turbulence-induced fading, which significantly impacts the average symbol error probability (ASEP) and overall system performance.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 7 Conclusions  
This paper proposes a novel approach, namely, Federated Optimisation (FedOpt) that is able to simultaneously decrease the communication cost and increase the privacy in federated learning settings. In particular, we design a Sparse Compression Algorithm (SCA) for communication efficiency and integrates the additively homomorphic encryption with differential privacy in order to prevent data from being leaked. Compared to the existing approaches, the proposed FedOpt compresses the upstream and downstream communication and reduces the communication overhead. In general, FedOpt is advantageous especially in the network where communication is costly or bandwidth is constrained as it achieve the targeted accuracy within fewer amounts of communication bits. Furthermore, the proposed FedOpt is able to mitigate the security threats for both the local users and the cloud server. In addition, the proposed FedOpt is completely non-interactive which provides higher levels of privacy at the aggregation phase, even when the adversaries collude with honest-users. The experimental evaluation on both MNIST and CIFAR-10 datasets proves that the proposed FedOpt outperforms the state-of-the-art approaches in terms of accuracy, efficiency and privacy. In the future, we will consider the virtualisation of this work through docker to make it useful in real-life environments. In addition, we plan to investigate further approaches for communication efficiency and privacy preservation while maintaining robustness in federated learning, especially with complex neural networks and high-dimensional datasets for diverse learning tasks and their models.  
All the authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by NITech Frontier Institute.  
The authors declare no conflict of interest.  
The following abbreviations and notations are widely used in this manuscript, while the arithmetic operations and their notations are to be understood element-wise:  
\begin{tabular}{l l} FedOpt & Federated Optimisation \\ AI & Artificial Intelligence \\ FL & Federated Learning \\ DL & Deep Learning \\ SGD & Stochastic Gradient Descent \\ DNN & Deep Neural Network \\ SCA & Sparse Compression Algorithm \\ \(\epsilon\)-\(DP\) & Privacy Budget on Differential Privacy \\ II & users \\ \(\delta\) & Secret Key \\ \(\epsilon\) & Expected Loss \\ \(\omega\) & Initial Parameters \\ \(\omega_{o}\) & Global Parameters \\ \(\
u\) & Parameter Vector \\ \end{tabular}
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
###### Abstract  
This review explores the advances in developing adsorbent materials for solid-phase microextraction (SPME), focusing on nanoparticles, nanocomposites, and nanoporous structures. Nanoparticles, including those of metals (e.g., gold, silver), metal oxides (e.g., TiO\({}_{2}\), ZnO), and carbon-based materials (e.g., carbon nanotubes, graphene), offer enhanced surface area, improved extraction efficiency, and increased selectivity compared to traditional coatings. Nanocomposites, such as those combining metal oxides with polymers or carbon-based materials, exhibit synergistic properties, further improving extraction performance. Nanoporous materials, including metal-organic frameworks (MOFs) and ordered mesoporous carbons, provide high surface area and tunable pore structures, enabling selective adsorption of analytes. These advanced materials have been successfully applied to various analytes, including volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), pesticides, and heavy metals, demonstrating improved sensitivity, selectivity, and reproducibility compared to conventional SPME fibers. The incorporation of nanomaterials has significantly expanded the scope and applicability of SPME, enabling the analysis of trace-level analytes in complex matrices. This review highlights the significant potential of nanomaterials in revolutionizing SPME technology, offering new possibilities for sensitive and selective analysis in environmental monitoring, food safety, and other critical applications.  
n  
n nanomaterials; solid-phase microextraction (SPME); porous materials; nanoporous carbons; gas chromatography coupled SPME; SPME fiber coatings; selective adsorption; environmental monitoring; trace analysis
# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 4 Experiments
### Evaluation Metrics on Pascal VOC 2007 Test Set  
The mean average precision (mAP) and the frames per second (FPS) commonly used in the field of target detection were used to compare the detection performance of different methods.  
(1) Mean average precision (mAP): The mean AP represents the average value of all category AP, and the AP computes the average value of the precision over the interval from recall = 0 to recall = 1, which can be formulated as:  
\[AP=\int_{0}^{1}p(r)dr \tag{3}\]  
where, \(p\) is the value of precision and \(r\) denotes the value of recall. The precision indicator can be seen as a measure of exactness or fidelity, and the recall indicator is a measure of completeness. The precision and recall indicators were formulated as follows:  
\[precision=\frac{TP}{(\mathrm{TP}+\mathrm{FP})},recall=\frac{TP}{TP+FN} \tag{4}\]  
where, TP, FP, and FN represent the number of true-positive, false-positive, and false-negative respectively.  
(2) Frames per second (FPS): In order to calculate the computing time of the proposed method and compare it with the existing techniques, we used the same method to calculate the detection efficiency of each method. Specifically, we set the batch size to 1, took the sum of the feature extraction time and the predicted time of 4952 images, and divided by 4952 to calculate the detection time of a single image.
## 4 Experimental Measurements
### Spray Drift Measurements  
Figure 10 shows the measurements of two spray clouds generated by an air-assisted sprayer operating in an area without vegetation. These tests were conducted on 22 July 2014, in the ETSEA campus of the Universitat de Lleida, in Lleida (Catalonia, Spain). During these tests, the sprayer was kept in a static position at a distance of 90 m from the lidar system. Figure 10a shows the result of averaging 100 pulses (_PRF_ = 2 Hz, observation time of 50 s) during which the sprayer was kept in continuous operation. The signal obtained shows two peaks which correspond to the two emission sides of the sprayer. Figure 10b shows a particular type of plot called _range-time intensity_ (RTI). These were created from various consecutive measurements, in this case one each second, and show the evolution in time and distance of the cloud. The gradation of colours corresponds to the intensity of the received signal. In this test, spray measurement was performed from its initiation to its conclusion, with detection also of the residual drift in the air once the spraying had terminated.  
Figure 11 shows the typical set-up of a lidar system during a spray drift study. In this test, the spray drift was generated by an air-assisted sprayer treating a vineyard (background), while the lidar system is placed at a distance of 80 m away (foreground). The laser was pointed perpendicularly to the orchard and above it, to prevent any signal distortion by trees. The backscattered signal due to the interaction with the spray drift cloud is detected by the optoelectronic receiver and through the digitizer is sent to the PC so that the signal is displayed in real-time.  
Figure 10: Detection of pesticide clouds generated by a cross-flow air-assisted sprayer. The sprayer was in a static position and located in a place with no crop. (**a**) Range profile of time-averaged lidar signal (100 laser pulses); (**b**) RTI plot of another pesticide cloud.  
The tests shown in Figure 12 were conducted on 24 July 2014, in an apple orchard attached to the ETSEA campus. The air-assisted sprayer was positioned in one of the orchard inter-rows while the laser beam was aimed at the neighbouring inter-row, parallel to it. The aim of the tests was to detect the fraction of spray able to get past the vegetation or, in other words, the spray drift. The drift generated during the application is shown in Figure 12a until the cloud signal dies out. In this test, the sprayer was kept in a static position so that the small variations in distance correspond to movement of the cloud caused by air currents. Figure 12b is similar to Figure 12a except that the sprayer was moved along the inter-row at constant speed as would take place in a real application. It can be seen how the drift cloud moves away as the sprayer advances along the inter-row.  
Figure 11: Lidar system deployed in the field during a spray drift measurement.  
Figure 12: Detection of the pesticide spray drift in an apple orchard. **(a)** RTI plot corresponding to the sprayer in a static position; **(b)** RTI plot with the sprayer in movement with a constant speed.
## 6 Conclusions  
In this paper, a novel method for locating and identifying pipeline reflectors is proposed based on guided-wave circumferential scanning and phase characteristics. The method is a combination of two different techniques. The first technique determines the circumferential and axial positions of the reflectors by circumferential scanning, leading to an enhanced 2D scanned binary image constructed from a series of image-processing methods used to improve the resolution and detection accuracy. The second technique utilizes the one-to-one phase characteristics between the incident wave and reflection waves to identify the change and type of reflector. Then, the predictions are updated to the enhanced 2D scanned binary images and marked with different colours. An identification index named the  
Figure 15: Analysis results based on the proposed method: (**a**) a sample pipe with two reflectors; (**b**) original 2D scanned image (**Up**), enhanced 2D scanned image (**Middle**) and smoothed 2D scanned image (**Down**); (**c**) enhanced 2D scanned binary image; (**d**) distance curves \(\Psi_{1}\) (**Up**) and \(\Psi_{2}\) (**Down**); (**e**) updated judgment results to the enhanced 2D scanned binary image with different colours.  
reliable index for the character of the reflector (_RICR_) was proposed to evaluate the reliability of the predicted results. FE software ABAQUS/Explicit was used to build 3D solid and finite element models of the sample pipes. Numerical signals with different SNRs and FE simulation signals with different models were successfully performed to validate the proposed method.  
The proposed method and conclusions will offer guidance for further applications, and the findings presented in this paper can serve as a foundation. For future studies on this topic, other factors such as reflectors of different materials and geometric parameters as well as multi-modes of the guide wave will be investigated. Although numerical simulations have validated the proposed method, experimental validation will be conducted in future work.  
Conceptualization, F.L.; Data curation, W.L.; Formal analysis, X.C.; Funding acquisition, Z.T., F.L., Y.Z. and P.Z.; Methodology, Z.T.; Project administration, Z.T.; Software, Y.Z. & P.Z.; Writing--original draft, W.L.; Writing--review & editing, W.L. & Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant number 2018 YFC0809000), the National Natural Science Foundation of China (grant number 51875511) and the Technique Plans of Zhejiang Province (grant number 2019C03112) and China Postdoctoral Science Foundation (grant number 2019M662039).  
The authors declare no conflict of interest.
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 3 Probe Test and Validation
### Isolated Voltage Probe as an Oscilloscope Probe  
The verification of the operation of the system as an oscilloscope probe was carried out in three different situations: measuring the signal generated by a Ward Leonard system configured as an electrical power generator; monitoring a controlled three-phase rectifier; and lastly, monitoring a 200 V with a 50 Hz fundamental harmonic plus a series of additional harmonics generated within the limits indicated in the EN 50160 standard.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 3 Preliminaries and Model Definitions  
The necessary preliminaries utilized in this paper are described with the intention of facilitating the reader's understanding on the proposed method, which includes the definitions of bilinear pairing, the DBDH problem and hash function. Thereafter, the notations, system model and network assumptions are respectively introduced.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 3 Results
### Mapping Distinct Spatiotemporal Patterns
Notably, the central Jabal Al Qara grasslands emerge as phenologically distinct and not a simple mixture of the eastern and western grassland endmembers. Jabal Al Qamar (Panel C) phenology also shows east-west differentiation. The forests are clearly broken into eastern and western zones, divided by a sharp gradient. Spatial mixing in Jabal Al Qamar is likely minimized by the rugged topography that physically divides the range. While the Nejd also shows east-west variation in space, the temporal patterns of each endmember region (shown below) are so similar to each other, and contain so little signal, that we do not conclude any significant phenological distinction between the two regions. Figure 3: Temporal affinity maps and endmember regions for Jabl Al Qara and Jabl Al Qamar. Forest vegetation phenology in Jabl Al Qara (**A**) is largely divided between distinct east and west regions with some mixing in the center of the range. grasslands in Jabl Al Qara (**B**) exist on a continuum with higher maximum greenness and slower senescence in the west than the east. Forest phenology in Jabl Al Qamar (**C**) is also divided by longitude into two distinct clusters, with minimal mixing in the central portion of the range. While both Nejd EMs in Jabl Al Qara are essentially null, enough statistical difference in patterns of cloud cover exists to separate them into two distinct groups. All colors are stretched linearly from 0% to 100% affinity. EM locations are outlined in gray.
## 2 Materials and Methods
### iPRV Procedure  
The iPRV applied the main decomposition method EMD of the Hilbert-Huang Transform (HHT) to calculate the instantaneous pulse rate of PPG [11]. IMF was decomposed by EMD from PPG. The IMF should satisfy two conditions: (1) the number of local minimum and local maximum must be equal to the number of zero-crossings or differ at most by one, and (2) the value of average envelop must be approximately equal to zero at any point. However, the mode mixing problem, which means that different frequency band components are decomposed into the same IMF, exists in IMFs if the signal is by EMD. Therefore, the complete ensemble EMD (CEEMD) was proposed to solve the problem [23]. Before introducing CEEMD, EMD should be described first. The first step of EMD is to calculate the upper envelope and the lower envelope by using extremum points of the raw signal. Next, compute the average envelope using the upper envelope and lower envelope. Third, subtract the average envelope from the raw signal as the output signal. Finally, check if the output signal is IMF or not. If not, let the output signal be a raw signal to redo the above steps. If the output signal is IMF, output the IMF, and using the raw signal subtract the IMF to redo the above steps until the residual is mono-component. For CEEMD, the raw signal is added and subtracted by the same white noise before performing EMD. Therefore, there are two types of raw signal: raw signal with adding white noise and raw signal with subtracting white noise. Decompose these two raw signals into IMFs. To decrease the influence of white noise, repeat the above steps with different white noise many times (50 times in this study). Finally, average all corresponding output IMFs to ensemble IMFs. According to [PERSON]'s theorem, before computing the instantaneous frequency by the Hilbert transform or direct quadrature method, the oscillation of the source data should be a sinusoidal signal. That is why we decomposed PPG by EMD into IMFs. Additionally, according to [PERSON]'s theorem, the source data should be without amplitude modulation for instantaneous frequency calculation by Hilbert transform or the direct quadrature method. Therefore, in this study, the NDQ was applied to calculate the instantaneous frequency [13]. After the modulation of the amplitude was removed, the source data canbe considered as a cosine function. Therefore, we can use the direct quadrature method to calculate the instantaneous phase using the arctangent. The instantaneous frequency is obtained from the derivative of the instantaneous phase. For comparison with the RRi or PPi series, the inverse of instantaneous frequency called the instantaneous pulse rate series (iPR) was computed for further processing.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 3 Methods  
### Technical Approach  
This study presents an advanced tomato ripeness detection framework named YOLO-PGC. The proposed framework is based on an improved YOLOv11 architecture designed specifically for tackling challenges such as occlusion, complex lighting, and variable viewing angles in real-world agricultural environments. The detection pipeline involves several critical stages tailored to the unique characteristics of tomato ripeness detection, which are outlined below.  
Data Acquisition: High-precision imaging devices, including a handheld smartphone equipped with a stabilization gimbal, are used to capture a large dataset of tomato images at various ripeness stages in a commercial tomato plantation. This ensures a diverse dataset covering different illumination conditions, occlusions caused by overlapping tomatoes, and varying backgrounds. The variety of data improves the model's generalization capabilities, making it robust against real-world environmental challenges.  
Data Preprocessing: The acquired images undergo systematic preprocessing, including data augmentation techniques (e.g., rotation, scaling, and flipping), normalization, and noise reduction operations. These steps are specifically designed to enhance image quality, mitigate the effects of environmental noise such as soil and leaf interference, and create a more consistent dataset. Preprocessing ensures that the model can effectively handle variations in tomato appearance caused by different lighting, shading, and occlusion conditions, thereby optimizing the data for model training.  
Detection and Localization: After preprocessing, the enhanced YOLO-PGC model processes images by simultaneously detecting and localizing tomatoes at various ripeness stages. The model is designed to address the challenges of occlusion and complex backgrounds, allowing for accurate detection even when tomatoes are partially hidden or surrounded by cluttered scenes. By leveraging advanced features such as dynamic convolution and multiscale fusion, YOLO-PGC significantly improves detection accuracy and localization precision, especially in challenging conditions such as strong shadows, low light, or overlapping tomatoes.  
The proposed YOLO-PGC architecture substantially upgrades the baseline YOLOv11 framework by introducing key innovations aimed at enhancing performance in agricultural settings. The model is purposefully designed to tackle the specific challenges of tomato ripeness detection and excels at maintaining high detection accuracy and precise localization under diverse illumination conditions, occlusion scenarios, and environmental factors. This systematic approach enables efficient and reliable ripeness evaluation in support of the intelligent agricultural monitoring and decision-making systems that are essential for contemporary precision agriculture.