esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Dataset Selection  
When exploring the datasets provided by GPS spoofing detection studies based on machine learning, we observe that the SATUAV dataset introduced in [30] requires additional hardware modifications, most notably a camera mounted on the UAV. However, our approach focuses on standalone ML solutions that operate independently, without relying on any external onboard devices. On the other hand, the log file dataset from [16] demands high computational resources when training the model during UAV missions due to both the high dimensionality of the sensor logs and the overhead of real-time processing. Therefore, the dataset from [31] remains our preferred choice [37] as it provides characteristics of GPS signals collected from real flights. This dataset captures signal anomalies at early stages, even before the signals reach the PVT (position, velocity, time) block of the GPS receiver, enabling earlier detection of spoofing attacks. Furthermore, the dataset includes simulations of three common types of GPS spoofing attacks: simplistic, intermediate, and sophisticated. Identifying these attack types provides critical insight for designing and deploying effective post-detection countermeasures. However, this dataset presents a challenge that consists of handling timeseries features. The primary function of a GPS receiver is to collect satellite signals when available, which causes varying time intervals between the collected signals. Consequently, it is essential to apply appropriate preprocessing techniques to handle timestamp irregularity so that ML models can perform classification correctly.  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline
**Axis** & **Paper** & **Methodology** & **Limitation** \\ \hline \multirow{8}{*}{Machine learning} & [30] & Convolutional neural network (CNN) & Requires camera onboard. \\ \cline{2-3}  & [16] & Extreme gradient boosting (XGBoost) and genetic algorithm & High-dimensionality dataset, model training overhead. \\ \cline{2-3}  & [31] & nu-SVM \\ \cline{1-1}  & [32] & Extreme gradient boosting (XGBoost) \\ \cline{1-1}  & [33] & Stacking ensemble approach: KNN, NB, DT, RF, LR & \\ \cline{1-1}  & [34] & MOD and WMOD ensemble approach: SVM, NB, DT, KNN, LDA, RF, ANN, LR, EN, AdaBoost & \\ \cline{1-1}  & [35] & CNN: U-net \\ \cline{1-1}  & [36] & Stacking ensemble approach: SVM-CNN \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
In the following section, we outline the main stages carried out to construct the GPS spoofing dataset for UAVs:
## 3 Results
### Relationship between SPAD Readings and Vegetation Indices (VIs)  
Single VI-based best-fit models were assessed and provided in Table 5. The relationship between the simulated ground-measured SPAD readings and studied VIs was found to be significant with GNDVI (R\({}^{2}\) = 0.78; \(p\) < 0.001), while a weak relationship was noticed with GLI (R\({}^{2}\) = 0.09; \(p\) < 0.05). The best-fit models are observed with the peak stage of carrot growth at 60 DAP to 70 DAP. The correlation was low with all the studied VI earlier than 45 DAP, while, during the later stage (i.e., 90 DAP onwards), the results expressed a moderate correlation with RDVI and SIPI (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **NDVI** & **GNDVI** & **RDVI** & **SIPI** & **GLI** \\ \hline SPAD-30 & 0.14 NS & 0.26 NS & 0.09 NS & 0.49 NS & 0.24 \\ SPAD-45 & 0.38 * & 0.30 NS & 0.12 NS & 0.45 * & 0.09 NS \\ SPAD-60 & 0.68 ** & 0.67 ** & 0.48 ** & 0.51 * & 0.12 NS \\ SPAD-75 & 0.74 ** & 0.78 ** & 0.64 * & 0.46 * & 0.16 \\ SPAD-90 & 0.52 * & 0.54 * & 0.55 * & 0.37 * & 0.17 NS \\ SPAD-105 & 0.42 ** & 0.23 * & 0.17 NS & 0.41 NS & 0.10 NS \\ SPAD-120 & 0.37 * & 0.31 * & 0.15 NS & 0.29 & 0.14 NS \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of coefficients of determination analysis between ground-measured SPAD readings and vegetation indices from Sentinel-2 data.  
Figure 5: Leaf nitrogen concentration (%) vs. absolute SPAD values across the carrot growth stages.
## 5 Discussion  
### Methodological Validation and Application as a Reference for Other Multicultural Regions  
Current research on cultural integration zones primarily focuses on areas such as the Shaanxi-Gansu-Sichuan region, the Jiangxi-Fujian-Guangdong region, and the northwestern Yunnan cultural integration area. However, delineation remains ambiguous for regions like the Liaoning-Hebei border and the Jiangxi-Hunan border, which require further exploration. By selecting the representative Jiangxi-Anhui cultural intermingling area for the study of traditional villages and their residential cultural landscapes, this research contributes to enhancing the understanding of multicultural intermixing zones. Methodologically, the current study predominantly employs qualitative approaches, resulting in a lack of objectivity in both the processes and outcomes of cultural zoning. The integration of quantitative and qualitative methods, specifically through \"principal component analysis and cluster analysis\", represents a significant advancement in the exploration and validation of the methodology for cultural landscape zoning in traditional villages and residential areas within the Jiangxi-Anhui junction. This approach not only serves as a methodological validation but also offers a more objective and comprehensive zoning framework, which will encourage and facilitate the in-depth study of traditional village cultural landscape zoning in other culturally intermingling regions.
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 3 Challenges and Research Gap
### Challenges in DFU Classification  
The automatic classification of DFU has several challenges including:  
* Lack of training data due to costly and time-consuming data collecting and labeling by experts.
* Patient's ethnicity.
* Low contrast between target objects and background.
* Various image qualities (different capturing devices).
* Heterogeneous and complex shapes.
* Lack of a robust and effective deep learning model to differentiate between DFU classes.
###### Abstract  
Path planning for intelligent semi-trailers encounters numerous challenges in complex traffic conditions. Serious consequences, such as vehicle rollover, may occur when the traffic conditions change. Therefore, it is vital to consider both the surrounding dynamic traffic conditions and the vehicle's roll stability during the lane-changing process of intelligent semi-trailers. We propose an innovative path-planning method tailored for intelligent semi-trailers. This path-planning method is designed for semi-trailers on straight-road alignments. Firstly, we employ a fuzzy inference system to process information about surrounding traffic, make lane-changing decisions, and determine the starting point. Secondly, the lane-changing path is generated using a B-spline curve. Subsequently, we apply a particle swarm optimization algorithm to enhance the B-spline curve. Thirdly, we utilize a Transformer model to analyze the nonlinear relationships among information about surrounding traffic, vehicle information, and the roll stability of the intelligent semi-trailer. We establish the roll stability boundary for the vehicle. Finally, we design a multi-objective cost function to select the optimal path. The simulation results demonstrate that the proposed method dynamically adapts the planned path to variations in driving parameters, ensuring trackability while reducing the steering angle, lateral acceleration, and yaw rate. This approach meets the roll stability requirements of intelligent semi-trailers, significantly enhances their stability during lane changing, and provides robust support for safe and efficient operation.  
intelligent semi-trailer; path planning; roll stability; fuzzy inference system; B-spline curve; particle swarm optimization; Transformer; adaptability to traffic conditions 2020 IEEEexample: accepted: 18 January 2025  
19 February 2025  
20 February 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON] A Semi-Trailer Path Planning Method Considering the Surrounding Traffic Conditions and Vehicle Roll Stability. _Appl. Sci._**2025**, 15, 2383. [[https://doi.org/10.3390/appl502353](https://doi.org/10.3390/appl502353)]([https://doi.org/10.3390/appl502353](https://doi.org/10.3390/appl502353))  
**Copyright:** 02 2025 by the authors.  
Liconses MDPT, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 1 Introduction  
Electromagnetic induction imaging (EMI) allows mapping of the electromagnetic properties of an object. It is not limited to two-dimensional maps and, when combined with appropriate inversion algorithms, it can produce full three-dimensional images. Precisely to highlight such tomographic capabilities, it is often referred to as magnetic induction tomography (MIT) [1].  
Potential applications of EMI cover a wide range of fields, from industrial monitoring to security screening and biomedical imaging. However, severe technical limitations, mostly due to the sensor, have so far limited the use of EMI to the detection of cracks and corrosion in metallic structures, a procedure usually termed eddy-current testing (ECT).  
This article is aimed at reviewing the motivations which led to the development of EMI with atomic magnetometers, the key enabling role they played in overcoming important limitations of EMI, and the progress so far. Perspectives for future developments are discussed.  
This work is organised as follows. In Section 2, the basic principles of the technique are reviewed. Section 3 discusses potential application areas for EMI and highlights the limitations of conventional inductive coils for these applications. Section 4 reviews the first demonstration of EMI with atomic magnetometers, and then covers in detail progress achieved in different application areas using the atomic sensors. Section 5 summarises the state-of-the-art of EMI with atomic magnetometers and identifies paths for future developments.
# The Low-Loss Spot Size Converter for Alignment with Cleaved Single Mode Fiber
## 2 Design of Materials and Methods  
The scheme of the SSC is shown in Figure 1. The SSC consists of the ion-implanted GRIN waveguide and the 2-D inversely-tapered waveguide. The GRIN waveguide compresses the mode field with 8-10 \(\upmu\)m MFD of the cleaved SMF into a small mode field with 3-4 \(\upmu\)m MFD, both horizontally and vertically. As shown in Figure 1, closely following the GRIN waveguide is the 2-D inversely-tapered waveguide, which could convert the small mode field with 3-4 \(\upmu\)m MFD into the fundamental mode of the Si waveguide adiabatically. The 2-D inversely-tapered waveguide is located above the buried oxygen (BOX) layer. Details about the design will be explained in the following sections.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 3 Results
### Cropped Land Model Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class** & **Maize** & **Beans** & **Cassava** & **Bananas** & **OtherVeg** & **Trees** & **NonVeg** & **\% Accuracy** \\ \hline Maize & 52 & 1 & 1 & 2 & 0 & 1 & 0 & \(91\pm 4\) \\ Beans & 1 & 16 & 2 & 0 & 0 & 0 & 0 & \(84\pm 8\) \\ Cassava & 1 & 1 & 12 & 1 & 1 & 0 & 2 & \(67\pm 11\) \\ Bananas & 2 & 0 & 1 & 11 & 0 & 1 & 0 & \(73\pm 11\) \\ OtherVeg & 1 & 4 & 2 & 1 & 21 & 1 & 0 & \(72\pm 8\) \\ Trees & 4 & 0 & 1 & 0 & 1 & 53 & 0 & \(90\pm 4\) \\ NonVeg & 0 & 5 & 1 & 0 & 0 & 0 & 16 & \(73\pm 9\) \\ Total Pts & 61 & 27 & 20 & 15 & 23 & 56 & 18 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the random forest (RF) model; a 95% confidence interval is provided for the accuracy assessment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Class** & **Model Area (ha)** & **SAS Category** & **2019A SAS (ha)** & **Difference** \\ \hline
1. Maize & 222,570 & Maize & 215,159 & 3\% \\
2. Beans & 319,548 & Beans & 299,443 & 7\% \\
3. Cassava & 322,060 & Cassava & 195,135 & 65\% \\
4. Bananas & 137,784 & Bananas & 253,996 & \(-\)46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of areas under cultivation by selected crops predicted by the model to the cultivated area reported in the 2019A SAS.
## 3 Parameter Analysis
### Influence of the Slope Angle of Reef Bottom Layer on Site Responses
#### 3.1.2 Influence on the Responses of Reef Slope and Seabed  
Figure 6 shows the distribution of horizontal peak acceleration on the reef island surface and seabed. The influence of the slope angle \(\theta\) of the bottom layer is mainly reflected in the impact on the peak acceleration on the slope surface of the reef island. For small slope angle (\(\theta=30^{\circ}\)) and low- to medium-frequency input ground motions (Kobe and Darfield), the horizontal peak acceleration on the slope surface is already close to the peak acceleration at the reef platform, while for high-frequency input ground motion (Iwate), the peak acceleration at different locations on the slope surface has a small difference.  
Figure 6: Distribution of horizontal peak acceleration on the reef island surface and seabed (\(L=500\) m).  
Figure 7 demonstrates the distribution of vertical peak accelerations on the reef island surface and seabed. It can be seen that as the slope angle of the bottom layer increases, the vertical peak acceleration on the slope surface becomes closer to the free-field result. When the slope angle \(\theta=65^{\circ}\), it is almost consistent with the vertical peak ground motion of the free field.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 6 Conclusions  
We have proposed a novel STDM watermarking based on visual saliency-based JND model, which gives us an improved way to model the HVS in the watermarking algorithms. According to the psychologists research, visual saliency plays an important role in the JND map analyses. In this regard, the spatial CSF, luminance adaption effect and visual saliency effect are introduced to calculate the slacks at the watermark embedder and detector. In order to extract the saliency information, a simple but more effective VS model is introduced and the visual attention effect is investigated for the JND model. Then a comprehensive JND model is defined with the combinations of the VS feature. Finally, by using the new JND model, a novel perceptual STDM watermarking scheme with a better trade-off between robustness and fidelity is presented. Experiments determine that our proposal produces powerful resistance against common attacks compared to other STDM watermarking algorithms.
###### Abstract  
Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4 DVar) assimilation method (referred to as POD-4 DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4 DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4 DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.  
POD-4 DEnVar; satellite radiance; radar radial velocity and reflectivity; rainfall forecast +
Footnote †: journal: Applied Sciences
###### Abstract  
To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation.  
empirical mode decomposition; variational mode decomposition; wavelet thresholding method; machining error decomposition; error compensation  
15 September 2024  
Revised: 2 October 2024  
Accepted: 6 October 2024  
Published: 7 October 2024
## 3 System Architecture  
### Computing Power Network  
With the widespread deployment of edge servers and RSUs, it has become more convenient and accessible for connected cars to utilize these vast distributed computing resources. Edge computing has witnessed remarkable advancements in recent years, enabling users to access a wide range of applications and services on their mobile devices [22]. However, the computing power of a single vehicle or node is very limited, and for compute-intensive tasks, this can lead to increased computational loads and prolonged task processing time. Moreover, the lack of an effective collaborative mechanism between the edge nodes and cloud computing nodes results in low efficiency and a high task allocation error rate. How to more efficiently utilize these computing resources has become an urgent problem to solve. Consequently, the CPN [23] has emerged, connecting distributed edge and terminal nodes to form a much more powerful network and allocating resources through a unified scheduling algorithm. The research presented in this paper is set within the scenario of a CPN, utilizing scheduling algorithms to enable image segmentation tasks to better acquire computing resources.  
The CPN consists of three layers: cloud, edge, and terminal. The terminal devices are at the bottom layer, closest to the users, and are primarily responsible for image sensor data collection and preprocessing. Edge servers are in the middle edge layer, situated between the cloud and the terminal layers, and are responsible for processing the computational tasks uploaded from terminal devices. Cloud servers are in the top layer. They are responsible for model training and distributing models to edge servers, enabling each edge server to handle image segmentation tasks. When necessary, they also process edge computing tasks. Edge servers are close to the vehicles at the terminal layer, resulting in small transmission delays, but their storage capacity and computing power are less than those of the cloud layer. Cloud servers have strong computing capabilities and large storage capacities, enabling them to efficiently handle computing tasks. However, cloud servers are far from the terminal layer, leading to significant data transmission delays [24]. The three-tier architecture of the CPN is shown in Figure 1.  
Figure 1: CPN architecture.