esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.3.2 Open Issues and Future Trends
Most artificial intelligence algorithms for wireless communication network physical layers are still in the simulation stage. [PERSON] et al. [101] pointed out how to overcome ML and DL technique-related issues; researchers must be assisted in training their models on common measurable data and accurately assessing the performance of different algorithms, and authentic datasets from real communication systems or prototype platforms in real physical surroundings must be made available to all researchers. Figure 9: Dataset types used in selected papers. Furthermore, the presence of adversaries makes it even more challenging to learn and characterize RF signals because of the unreliability of the underlying data. In **(S2)**, the authors pointed out that most traditional ML techniques are susceptible to malicious attacks. The susceptibility increases once the attacker knows the features used by the learning algorithm. The attacker becomes smart enough to mislead the learning process. With this knowledge, adversaries can use a generative model to generate signals to spoof the transmission of known transmitters. This renders moot the traditional learning algorithms in wireless channels. Comparing machine learning and deep learning algorithms in the context of PLA remains an open issue for further research. However, with the potential of improved data augmentation techniques, we recommend delving deeper into the data to explore hidden features that can help detection and authentication models perform even better.
# A Titanium Alloy Defect Detection Method Based on Optical-Acoustic Image Fusion
## 6 Conclusions  
This work describes an optical-acoustic image fusion method used to identify both surface defects and inner defects in titanium alloys. In terms of optical detection, the Ti-YOLO algorithm was developed by enhancing the backbone, neck, and detection head of the YOLOv10n model. The Ti-YOLO algorithm is both efficient and lightweight, achieving an accuracy of 99.0% with a processing speed exceeding 160 FPS. Regarding acoustic detection, C-scan technology was employed to acquire detection data. Furthermore, we successfully resolved the issue of spatial registration between optical and acoustic images. The experimental results demonstrate that the proposed fusion detection method performs effectively and offers a promising approach for practical detection engineering. In the future, it is essential to develop optical-acoustic defect detection methods to minimize actual errors. On the one hand, greater emphasis should be placed on utilizing more advanced equipment. On the other hand, improvements in signal processing programs are necessary to meet the demands of emerging detection tasks.  
Conceptualization, Y.Z. and M.W.; methodology, Y.Z.; software, M.W.; validation, M.W., Y.H. and G.Z.; formal analysis, Y.H.; investigation, M.W.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, M.W.; writing--review and editing, M.W.; visualization, M.W.; supervision, Y.Z.; project administration, M.W.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 20: The decision-level test report.  
**Funding:** The financial support for this research work was provided in part by the Natural Science Foundation of China under Award No. 52275524, the Shandong Provincial Natural Science Foundation under Award No. ZR2024 MF082, and major scientific and technological innovation projects in Shandong Province under Grant No. 2022 ZZLGX04.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to the privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Digital Signage Systems' Design and IoT Technologies Comparison
### Comparison of IoT Technologies
#### 3.1.4 Satellite Communications  
Satellite communications are gaining interest in industrial and other IoT applications, where other types of communications are difficult to deploy. We refer to IoT situations, where we have to collect data from sensors or RFID, and control actuators with messages. In many industrial and cruise-ship scenarios, a large number of actuators and sensors are distributed over a wide area, and sometimes they are located in remote areas where there are no networks, and therefore the use of satellite systems becomes of paramount importance. In our case scenario, to use satellite communications, we should first consider two enabling factors: the first one is the interoperability between actuators and sensors with the satellite systems, and the second is the support of IPv4 and IPv6 over satellite. Moreover, radio resource management algorithms and the deployment of heterogeneous network interoperability are required to improve the efficiency of IoT over satellite [27]. Furthermore, we should consider the time delay that occurs while a signal is transmitted by the satellite through a transmission link, considering other timing group delays (TGD) [28]. Thus, we cannot use satellite communications in our implementation.
## 4 Discussion
### Comparison of Products  
Three products, FROM-GLC-seg, Forest2010, and ESA GlobCover, were selected for comparison with our results, and the validation accuracy is shown in Table 6. FROM-GLC-seg [40] and Forest2010 [41] are needleaf and broadleaf forest products for 2010, and the method is RF with a spatial resolution of 30 m. Forest2010 is the product for China, and OA is 72.7%. ESA GlobCover2009_V2.3_Global is a 2009 product with the RF method and a spatial resolution of 300 m [42]. All three products cover Huize County (the study area of this paper), and Landsat TM 2010 images were selected for comparison. The validation data are from the forest management inventory.  
In forest and non-forest identification, the differences in OA, PA, and UA of each product are relatively small, but the differences in Kappa coefficients are large, indicating that the consistency effect of product spatial distribution is poor. In the identification of needleleaf and broadleaf forests, OA is close, but the differences between the remaining three metrics are obvious, which is still mainly due to the lower identification of broadleaf forest in the case of class imbalance. The proposed method outperforms the existingproducts in various evaluation metrics, with better generalization capabilities. These findings imply that the proposed model can effectively identify the forest types in a large area.
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Illumination Correction  
In this study, automated multi-scale Retinex was used for illumination correction. Retinex is a theoretical model of vision ([PERSON], 1986) that has had a significant impact on adjusting unevenly illuminated color images. According to the Retinex theory, an image  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Location** & **Stage** & **Sampling Data** & **Weather Condition** \\ \hline \multirow{8}{*}{Experiment 1} & & Tillering & 11 May 2019 & Sunny (not cloudy) \\  & & & 11 May 2020 & Partly cloudy \\  & & Elongation & 23 May 2019 & Sunny (not cloudy) \\  & & & 20 May 2020 & Sunny (not cloudy) \\  & & & 04 June 2019 & Partly cloudy \\
2019–2020 & Gao’an & Booting & 28 May 2020 & Sunny (not cloudy) \\  & & Heading & 11 June 2019 & Sunny (not cloudy) \\  & & & 09 June 2019 & Partly cloudy \\  & & Grouting & 22 June 2019 & Partly cloudy \\ \hline \multirow{8}{*}{Experiment 2} & & Tillering & 15 May 2019 & Sunny (not cloudy) \\  & & Elongation & 22 May 2019 & Partly cloudy \\ \cline{1-1}  & & Booting & 03 June 2019 & Partly cloudy \\ \cline{1-1}  & & Heading & 11 June 2019 & Partly cloudy \\ \cline{1-1}  & & Grouting & 24 June 2019 & Overcast \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sampling dates and weather conditions of early rice in the fields.  
\(S(x,y)\) can be expressed as the product of an illumination image \(L(x,y)\) and a reflectance image \(R(x,y)\), as demonstrated in Equation (3).  
\[S(x,y)=L(x,y)\times R(x,y) \tag{3}\]  
A single-scale Retinex (SSR) image is obtained through a logarithmic transformation:  
\[\ln R(x,y)=\ln S(x,y)-\ln[F(x,y)/S(x,y)] \tag{4}\]  
where \(F(x,y)\) is a Gaussian surround function explicitly given by Equation (5):  
\[F(x,y)=\lambda\ exp\Big{[}-\Big{(}x^{2}+y^{2}\Big{)}/c^{2}\Big{]} \tag{5}\]  
the constant \(c\) is the scale parameter; \(\lambda\) is a constant matrix, and its values are determined by Equation (6).  
\[\iint F(x,y)dxdy=1 \tag{6}\]  
The anti-logarithmic transformation of \(\ln R(x,y)\) back to the real domain yields the corrected image.  
A multi-scale Retinex (MSR) is obtained by taking the weighted sum of the SSR on multiple scales of an image using Equation (7):  
\[S_{i}(x,y)=L_{i}(x,y)\times R_{i}(x,y) \tag{7}\]  
transforming to the logarithmic domain,  
\[\ln R(x,y)=\sum_{k}^{K}\omega_{k}\Big{\{}\ln S(x,y)-\ln\Big{[}F(x,y)\bigotimes S (x,y)\Big{]}\Big{\}} \tag{8}\]  
where \(K\) is the number of Gaussian surround functions and \(\omega_{k}\) is the weight of the \(k\)-th SSR; \(\otimes\) represents multiplication by elements.  
Automatic multi-scale Retinex correction starts by applying bilateral filtering to an input-enhanced image for noise reduction. Then, the image is transformed to the logarithmic domain for MSR enhancement. The resulting image is then automatically color-corrected using a dynamic range adjustment. Finally, the image is transformed back to the spatial domain to obtain a final enhanced image. The study by [PERSON] et al. [23] contains the specific steps and parameter settings.
## 6 Summary  
The purpose of this review paper has been to state a comprehensive suitable definition of a metasurface and identify its salient features as well as highlight some of the numerous advantages which can be achieved through the use of metasurfaces. The sub-wavelength nature of building blocks of metasurfaces allows them to be defined using homogeneous boundary conditions. Another important characteristic of metasurfaces is the phase discontinuity which occurs when an electromagnetic wave goes through a metasurface. Both these behaviours can be successfully described by using generalised refraction and reflection laws. A superior feature of metasurfaces is its low-loss behaviour (as demonstrated particularly by [PERSON]'s metasurfaces) in stark contrast to metamaterials. Thus, metasurfaces allow us to utilise the exotic properties of metamaterials like engineered refraction and reflection without inducing manufacturing complexity and losses associated with metamaterials. Metasurfaces also offer spatial filters with a stable frequency response with respect to the incidence angle.  
Although considerable successful work has been done on metasurfaces in the microwave regime, the inclusion of metasurfaces in the design for future communication and imaging devices is still an area with immense potential for research and growth. Bandwidth is a non-negotiable key requirement for most RF applications therefore, future challenges will include designing metasurfaces with a stable phase and frequency response over a wide band. Another important aspect would be switch-able or active metasurfaces which are cost-effective and relatively simple to fabricate. The addition of switching components can introduce non-linearities in the metasurface which would merit a sound theoretical investigation.  
The lead author of this paper is [PERSON]. Additional support was provided by [PERSON] and [PERSON].  
This research was funded by EPSRC, grant number EP/S030301/1, \"Anisotropic Microwave/Terahertz Metamaterials for Satellite Applications (ANISAT)\".  
The authors declare no conflict of interest.
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 3 Methodology
### Feature Fusion Framework  
The feature fusion structure adopted by the traditional YOLOv7 integrates principles from both the feature pyramid network (FPN) [34] and the path aggregation network (PANnet) [35]. Top-down feature fusion transfers rich semantic information from the high-level layers to the low-level layers by up-sampling. Meanwhile, bottom-up feature fusion transfers rich geometric information from the low level to the high level by down-sampling. This bidirectional feature fusion structure has demonstrated promising results in the feature fusion of conventional ground images. However, UAV images pose a greater challenge due to the abundance of small objects and the distribution of multi-scale objects.  
The BiFPN framework is anticipated to address the above challenges [36]. Compared with the traditional PANet feature fusion structure of the YOLOv7 algorithm, BiFPN not only includes up-sampling and down-sampling but also introduces a novel cross-level feature fusion connection, which is a more advanced and effective feature methodology. At the same time, the learnable weights will be allocated to each fused feature, enabling the adjustment of the contributions from various features and enhancing the utilization of crucial feature layers. According to [36], the weighted fusion method is used in this work, and the feature fusion results \(O_{n}\) can be expressed as follows:  
\[O_{n}=\sum\limits_{i=0}^{2}\frac{\lambda_{n}^{i}}{\sum\limits_{j=0}^{2}\lambda _{n}^{j}+\varepsilon}\cdot I_{i}\,,\ n\in\{3,4\} \tag{1}\]  
where \(n\) represents different nodes of the BiFPN-like structure. \(I_{i}\) and \(\lambda_{n}^{i}\) denote the input at level \(i\) and the learnable weight parameter of level \(i\), respectively. Moreover, \(\varepsilon\) represents a small constant employed to maintain the stability of the formula.  
As shown in Figure 5, inspired by the traditional BiFPN structure, a BiFPN-like feature fusion structure is used in our proposed YOLOv7-UAV algorithm. We employ the extracted features after 4 and 8 times down-sampling to execute a weighted cross-level connection. The more advanced connection method compared to PANnet can better fuse high-level semantic information and low-level geometric information, thereby enhancing the accuracy of object detection in UAV images. In addition, the structure of edge nodes from C2 is retained, as low-level geometric feature information is highly advantageous for recognizing small objects.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Prediction Head** & **Feature Graph** & **Anchor Size** \\ \hline P2 & \(160\times 160\) & (3,4) (3,8) (7,6) \\ P3 & \(80\times 80\) & (6,13) (14,8) (12,17) \\ P4 & \(40\times 40\) & (27,14) (21,29) (48,38) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anchor box size setting in YOLOv7-UAV.  
Figure 5: The BiFPN-like structure of YOLOv7-UAV.
## 5 Numerical Experiments
### Sensitivity Analysis
#### 5.5.1 Effect of the Number of Depots and Empty Containers  
Due to the random generation of depot and customer locations, the results of a single experiment exhibit a high degree of uncertainty. To reduce the impact of this uncertainty, for each selected instance, 10 experimental scenarios with varying request vertices are randomly generated while ensuring that the total number of requests is fixed. Notably, the locations of depots are also randomly distributed, but remain fixed for the 10 experimental scenarios. The results for instances 38 and 46 are each presented in an individual 3D surface plot, as shown in Figure 6.  
Figure 5: Convergence curves of the LNS and GA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instance** & **Minimum** & **Maximum** & **Average** & **Difference (\%)** \\ \hline
25 & 1343.1 & 1348.6 & 1346.2 & 0.41 \\
30 & 1520.7 & 1526.4 & 1522.9 & 0.37 \\
35 & 1898.2 & 1905.8 & 1902.4 & 0.40 \\
40 & 2166.5 & 2171.3 & 2169.8 & 0.22 \\
45 & 5463.9 & 5470.2 & 5466.1 & 0.12 \\
50 & 15,652.0 & 15,667.2 & 15,659.3 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical summary of the objective values from the ten repeats.  
It is important to note that as the number of depots increases, the benefit of providing an adequate supply of empty containers initially becomes evident. However, when the number of depots reaches a sufficiently high level, the marginal impact of further increasing the number of empty containers on the objective value diminishes. Under the same number of empty containers, increasing the number of depots can significantly reduce the optimal objective value. However, once the number of depots reaches a certain threshold, e.g., five depots when there are 13 empty containers in Figure 5(a), its effect on the objective value gradually diminishes. This result indicates that an increase in the number of depots can lead to a more decentralized transport network, which may enhance the flexibility and the efficiency of truck dispatching and empty container repositioning, thereby reducing the total transportation costs. Furthermore, under the same number of depots, the more empty containers that can be supplied, the more options for truck dispatching become available, thereby helping to reduce the objective value. These observations highlight the importance of both depot placement and empty container availability in optimizing transportation networks, suggesting that an optimal balance between the number of depots and empty containers is crucial for minimizing transportation costs.
# Optimal Order of Time-Domain Adaptive Filter for Anti-Jamming Navigation Receiver
## 6 Conclusions  
The optimal time-domain adaptive anti-jamming filter based on the Kaiser window method is proposed by analyzing the effect of the adaptive filter order on interference suppression performance and hardware occupancy of navigation receivers. With no need for detailed information of navigation interference, but only the interference bandwidth and JSR, the filter designed by the Kaiser window can be used to obtain the optimal order to meet the anti-jamming requirements, and the calculated optimal filter order is corrected according to the test data. The algorithm is simplified by the FIR filter design method, without knowing the specific information of interferences, and the convergence process of the adaptive algorithm is avoided, the time cost significantly reduced, the accuracy of the optimal order selection ensured. Through simulation analysis and practical tests, the following conclusions have been obtained:  
1. A higher power interference scenery requires a larger optimal filter order to meet the time-domain adaptive anti-jamming requirements.
2. A more expansive bandwidth interference scenery requires a larger optimal filter order to meet the time-domain adaptive anti-jamming requirements.
3. The time-domain adaptive filter can meet navigation receivers' 3 dB\(\cdot\)Hz anti-jamming requirement in simulations and 8 dB\(\cdot\)Hz requirement in practical tests with the optimal filter order.  
[PERSON] performed the theoretical study, conducted the experiments, processed the data and wrote the manuscript. [PERSON] designed the system, provided research suggestions and revised the manuscript together with [PERSON] and [PERSON] helped in performing the experiments. [PERSON] and [PERSON] provided the experiment equipment and suggestions for the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.62003354).  
Figure 11: Effect of the filter order on narrowband interference suppression performance.
## 6 Results and Discussion
### Analogy Test
#### 6.1.2 FastText Models
Overall, our work achieved high scores for the Word2 Vec and FastText models with 86% and 90% accuracy, respectively. This achievement outperforms the work of [PERSON] et al. [9], where their best score was around 60% accuracy. Additionally, we applied the same test using the Aravec model [11], which achieved an accuracy of 79%. Table 6 presents the overall results compared with previous Arabic word embedding models. Table 7 presents the best scores of Sentiment analysis. Figure 7: FastText models analogy test results.
###### Abstract  
The potential application of multiferroic materials in new electronic devices attracts more and more attention from people either in an academic field or industry. This paper reports that M-type lanthanum-doped barium ferrite (La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\)) demonstrates full antiferroelectric (AFE) and excellent magnetoelectric coupling effects at room temperature, while its AFE phase displays a zero macroscopic net polarization. The dramatic change in the dielectric constant near the Curie temperature far below room temperature represents the transition from ferroelectrics (FE) to antiferroelectrics. The fully separated double electric polarization hysteresis (\(P\)-\(E\)) loops confirmed its AFE performance. Its \(E_{F}\) and \(E_{A}\) are located at 1100 kV/cm and 850 kV/cm, respectively. The large \(M\)-\(H\) loop showed a strong magnetic property simultaneously. The UV-Vis-NIR optical spectrum revealed that La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) is also a semiconductor, whose direct bandgap energy (\(E_{g}\)) was determined to be 1.753 eV. Meanwhile, La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) showed strong ME coupling and a GMR effect. A 1.1 T magnetic field reduced its resistance by 110% at 30 kHz. The multiple functions combined in one phase would create new options for high energy storage capacitors, microactuators, pyroelectric safety sensors, cooling devices, and pulsed power generators and so on, as well as great opportunities for generating new electronic devices with active magnetoelectric coupling effects.  
La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\); multiferroics; antiferroelectrics; magnetism; giant magnetoresistance +
Footnote †: journal: Applied Physics Letters  
Acticle Full Antiferroelectric Performance and GMR Effect in Multiferroic La\({}_{\bf 0.75}\)Ba\({}_{\bf 0.25}\)Fe\({}_{\bf 12}\)O\({}_{\bf 19}\) Ceramic