esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Attack Strategies
### Eavesdropping and Man-in-the-Middle Attacks
#### 7.5.2 Potential Damages and Consequences  
As federated learning becomes increasingly popular for its decentralized approach, concerns about eavesdropping and man-in-the-middle (MitM) attacks have gained significance. FL allows multiple edge devices to collaboratively learn a shared model while keeping the data local, but this distributed setup introduces vulnerabilities. Eavesdropping in FL involves the unauthorized interception of communications between client nodes and the central server. While raw data are not shared, model weights, gradients, and updates are transmitted, which an eavesdropper could exploit to infer information about the original training data. Over time, a persistent eavesdropper could develop a shadow model, approximating a client's data distribution.  
MitM attacks are even more severe. An attacker could intercept, alter, and forward communications between clients and the server, manipulating gradients or model up dates. This could poison the global model, causing it to degrade or behave unpredictably, potentially leading to global model drift. Such attacks can also undermine aggregation mechanisms like Federated Averaging, skewing the model in unintended directions, especially when differential privacy is involved. These attacks pose significant risks to data privacy, potentially revealing sensitive information like personal identifiable information (PII). Moreover, MitM attacks can disrupt consensus protocols in federated settings, leading to delays or halting the learning process. To protect FL systems, advanced cryptographic techniques, secure aggregation methods, and robust consensus protocols are essential [105].
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix B State-of-the-Art Anomaly Detection Models  
### Density-Based Models  
#### b.1.1 Density-Based Spatial Clustering with Noise (DBSCAN)  
DBSCAN [39; 40] is an unsupervised clustering methodology that groups together comparable observations based on a similarity metric. Clusters are high-density regions and are defined by the \(\varepsilon\)-neighbourhood of observations and by _MinPts_, the minimum number of points required to be in a radius of \(\varepsilon\) from an observation to form a dense region. An anomaly is any observation that neither has no _MinPts_ in its \(\varepsilon\)-neighbourhood nor appears in the \(\varepsilon\)-neighbourhood of other observations.
## 6 Current Applications of DTs in Transportation Infrastructure
### Infrastructure Maintenance
By integrating detailed inspection datasets into building information modeling (BIM) and digital twin (DT) platforms, engineers can continuously update virtual models with geometric, material, and environmental data. This synergy enhances the fidelity of structural condition assessments by enabling both local (defect-level) and global (system-level) performance analyses. Furthermore, incorporating NDT-derived data--such as crack width progression and steel reinforcement corrosion--into finite element or machine learning models within DT environments enables more accurate predictions of load-bearing capacity and structural risk [114]. Consequently, maintenance plans can be adjusted based on the actual state of deterioration rather than fixed schedules, thereby reducing costs and minimizing unexpected downtime. Dynamically updated NDT data not only enhance predictive maintenance strategies but also prevent catastrophic failures through proactive interventions, extending service life and ensuring bridge infrastructure safety.
## 3 Attacks on 5G NSA
### Active Attacks
#### 3.1.1 Radio Jamming  
Radio jamming is a technique of performing an active attack where the adversary disturbs the communication between a legitimate sender and a legitimate receiver by increasing the noise on the wireless channel. Depending upon the frequencies jammed by the adversary, this type of attack can be used to deny all services to a UE or may force it to downgrade to a lower generation of mobile networks [7]. An attack performed using this technique is not persistent because the system self-recovers when the attacker stops performing the attack. Further, it is challenging for an attacker not to be detected.  
The attacks based on this technique are usually performed using either constant jamming or smart jamming approaches. In a constant jamming approach, the attacker jams the complete communication bandwidth over time and disturbs the communication of all victims. A smart jamming approach is targeted at specific control information, such as the Physical Broadcast Channel (PBCH) or Physical Random Access Channel (PRACH) [19]. Although smart jamming is more efficient than constant jamming, it requires the attacker to be in sync with the cell to obtain the position of control information.
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 4 Experimental Verification and Analysis
### Experimental Results and Analysis
#### 4.3.2 Results and Analysis on PU Dataset  
The fault size categories of the samples in the PU dataset are fewer than those in the CWRU dataset, but their fault categories are diverse, and the real bearing faults in this domain are closer. The fault recognition accuracy of the CDAN and four comparison methods on the PU dataset is shown in Figure 9. The average training time for the CDAN was 14.43 s, while the average training time for Models 1 to 4 was 36.89 s, 31.64 s, 23.08 s, and 18.90 s, respectively. On the PU dataset, the average accuracy of fault diagnosis for the comparison methods was 82.32%, 84.88%, 84.56%, and 85.69% respectively. The CDAN achieved an average fault diagnosis accuracy of 89.78%, which is more than 4% higher than Model 1, Model 2, Model 3, and Model 4. Model 3 used an extractor to map features from both laboratory and real fault samples, while the model proposed in this paper separately set different feature extractors for mapping laboratory and real fault samples. The CDAN achieved an average fault diagnosis accuracy 5.22% higher than Model 3, indicating that a model with two feature extractors performs significantly better in recognition performance than a single feature extractor.  
In order to explore the details of CDAN in diagnosing unlabeled bearing faults, the confusion matrix of fault identification results under A\(\rightarrow\)B operating conditions is shown in Figure 10.  
Figure 8: Feature clustering diagram of Model 3.  
Figure 9: Experimental results on the PU dataset.  
It is clear that the CDAN can effectively detect the health status of bearings across four different cross-domain scenarios. The comparative results of the experiment show that the CDAN has good recognition ability for unlabeled bearing faults. Multiple sets of experimental data are superior to the compared methods, indicating that the CDAN has better generalization performance and stability. The feature visualization results using the CDAN method under experimental conditions A\(\rightarrow\)B are shown in Figure 11, while Figure 12 displays the feature visualization results using the Model 3 method under experimental conditions A\(\rightarrow\)B. A comparison between Figures 11 and 12 clearly shows that the clustering of samples from the same fault category in Figure 11 is more compact, with larger distances between different fault categories, indicating better performance compared to Figure 12. This demonstrates that the proposed model's fault recognition performance surpasses that of Model 3.  
Figure 11: Feature clustering diagram of CDAN on the PU dataset.  
Figure 10: Confusion matrix of CDAN under condition A\(\rightarrow\)B.
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 6 Ablation Studies
### Discussion  
The results from our ablation studies vividly illustrate the importance of each component in the MaskedSkipUNet architecture. In particular, the incorporation of MaskedConv3D layers, both in the latent space and as skip connections, proves crucial for optimising anomaly detection. While several methods mask the image directly and gauge the reconstruction error based on this altered input, our results highlight the advantage of masking at the convolution layer instead. This approach not only retains the original image data but also compels the model to develop a deeper understanding and sensitivity to anomalies, evident from our superior performance metrics.  
Figure 9 visually compares the performance of each model variant on a frame from the Ped2 dataset, containing normal pedestrians and an anomalous cyclist. The AutoEncoder struggles to reconstruct both the cyclist and pedestrians, resulting in blurry outputs. The UNet achieves high-fidelity reconstructions for all subjects, including the anomaly, thereby failing to highlight it. Our proposed MaskedSkipUNet maintains sharp reconstructions of pedestrians while degrading the reconstruction of the cyclist, successfully isolating the anomaly. Other variants like the Conv3D SkipUNet and E/D Masks MaskedSkipUNet show partial effectiveness. Notably, as the number of MaskedConv3D layers increases from one to three, and ultimately to our final model with four, reconstructions of normal subjects improve, while the anomaly remains less visible, confirming the value of multiple masked layers.
## 4 Performance Analysis
### Engineering Applications  
The new power frequency Magnetic Anomaly Detection Technology proposed in this paper has a wide application potential. It mainly relies on the power frequency field generated by the worldwide 50/60 Hz power grid. It is characterized by low-frequency, strong energy, and cross-media propagation ability. The 50/60 Hz power frequency field signal was detected with the electric field sensors used by low-earth orbit satellites of different countries. The specific detection conditions are shown in Table 3.  
In summary, the electric field data of the power frequency background field over the ocean can be detected by low-earth orbit satellites. Ground, water, and underwater targets generate power frequency amplitudes and phase disturbances that are stronger than the background field, and their features differ greatly from the background field. Therefore, it is feasible to detect targets by low-earth orbit satellites with power frequency electromagnetic waves.  
The power frequency electromagnetic field signal spreads over a long distance, affects a wide range, and crosses media. It can be applied in the following major fields, namely detection of flight targets, land targets, marine targets, and unexploded ordnances, as well as mineral exploration, as shown in Figure 12:  
Figure 11: Extracting wavelet energy ridge and geomagnetic calibration. Among them, (**a**) is the time-frequency ridge image extracted on the basis of Figure 10c, and (**b**) is the interference caused by the object measured by the fluxgate sensor in the geomagnetic environment. The change time of the two is consistent, which means that our scheme effectively detects the power frequency magnetic anomaly signal caused by the target.  
\begin{table}
\begin{tabular}{c c c} \hline
**Satellite Model** & **Detection Point** & **Detection Intensity** \\ \hline Russia Chibis-M Satellite & Over the Pacific Ocean & 1.67 uV/m \\ USA C/NOFS Satellite & Over the South China Sea & 90 uV/m \\ \hline \end{tabular}
\end{table}
Table 3: Satellite power frequency detection in various countries.
## 2 Doppler Power Spectrum (DPS) and Coherence Time (CT) as Radio Channel Metrics
### Coherence Time Models  
The shift Doppler represents a temporal change of Doppler spread due to receiver spatial displacement. Additionally, temporal variations on the Doppler spread are also related to the radio channel coherence time (CT).  
In order to describe this behavior, the mathematical models proposed by IEEE P802.11, in [19,23], V. Va, in [22], and Rappaport, in [24, 25, 26, 27], are the most relevant. However, the V. Va model is the only one that considers the effects of the antenna radiation beam pattern over CT metrics. In this way, this apart describes (and analyzes) only this CT model.  
In particular, the V. Va model establishes the CT expression from the correlation function of the channel and the user spatial displacement, when the user is located in a scattering ring, concerning a base station.  
The CT metric can be described from the pointing angle \(\mu_{r}\) and the antenna beamwidth \(\theta\) in the receiver. Depending on the value of the pointing angle, two different cases can be analyzed, as proposed in [22].  
**Case 1:** when the angle \(\mu_{r}\) is small,  
\[T_{c}(\theta)=\sqrt{\frac{\frac{1}{R^{4}}-1}{(2\pi f_{D})^{2}\theta^{4}+\frac{1}{ 2\theta^{2}R^{4}}\Big{(}\frac{\mu_{D}\sin\mu_{r}}{D_{r,\lambda}}\Big{)}^{2}}} \tag{5}\]  
where \(R\) is the correlation coefficient, it is found when the correlation function of the channel depends on the coherence time (\(|R_{h}(T_{c})|=R\)), \(D_{r,\lambda}\) denotes the scattering radius normalized by the carrier wavelength, and \(\theta\) is the beamwidth in the receiver.  
**Case 2:** when the angle \(\mu_{r}\) is not small,  
\[T_{c}(\theta)=\sqrt{\frac{1-(1+\theta^{2}\log R)^{2}}{\frac{1}{4}(1+\theta^{2 }\log R)\Big{(}\frac{f_{D}\sin\mu_{r}}{D_{r,\lambda}}\Big{)}^{2}+(2\pi f_{D})^{ 2}\theta^{4}}} \tag{6}\]  
The difference between expressions (5) and (6) is due to the behavior of the CT when \(\mu_{r}\) is small, since, in this case, the sensibility of the approximation is higher than when \(\mu_{r}\) is not small. These variations can be seen as the state change in the receiver movement in very small-time lapses.
## 3 Research Methodology
### Role of Service Broker Policies  
The SBP contributes to one or more (publicly or privately) cloud services on account of one or more users of that product through three main jobs: aggregation, incorporation, and configuration brokerage [22]. The cloud broker framework helps suppliers of software to collaborate with cloud vendors of organizations such as Verizon, Google, and Amazon.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Policy** & & **Pros and Cons** & \\ \hline \multirow{6}{*}{Proximity-Based Routing} & \(\bullet\) & It is one of the most straightforward methods for simulating a cloud environment [1]. & The difficulty with this method is that some virtualized data centers become overwhelmed while others remain underutilized [1]. \\  & \(\bullet\) & This policy is known as the data center with the shortest path [14]. & \(\bullet\) & It redirects the demands to other service brokers if there are no matching results between the user’s demands [11]. \\ \hline \multirow{6}{*}{Optimized Routing Efficiency} & \(\bullet\) & Routing based on proximity to services selects the quickest path from the data center to user requests, which may cause the nearby data center to become overburdened because the channel bandwidth is really limited [11]. \\  & \(\bullet\) & This policy gives the end-user the fastest response time possible at the time of query [14]. & \(\bullet\) & The optimum path is chosen by performance-optimized routing, which is dependent on the latency network and data center workload. As a result, time is wasted [11]. \\ \hline \multirow{6}{*}{Dynamic Reconfiguring-Based Routing} & \(\bullet\) & The service broker is in charge of delivering scalability to the cloud system’s applications [1]. & \(\bullet\) & There is no support for diverse cloud service models, and there is no desirable support for QoS features with these service broker techniques [11]. \\  & \(\bullet\) & The service broker adjusts the number of virtual machines in the data centers dynamically [14]. & \(\bullet\) & Achieves a time of execution of 0 s [11]. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pros and cons of service broker policies in cloud computing environments.  
Rather than recurringly billing middle customers for services offered by a cloud service, associates may provide guidance, conversion facilities, and hourly guidance, says informal platforms Vice President of Comcast Company, [PERSON] [19]. Cloud service brokers are cloud computing operations that include [11].
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 3 Experimental Setup of A-RoF Based DAS
### 5G/6G RAUs and Postprocessing  
At the 5G RAU, the 5G signal is photo detected by the PIN photodiode (R402 PIN \(\sim\)8 GHz) and then amplified by an in-built trans-impedance amplifier. A low-pass filter (LPF) is used to filter out the THz signal for the 5G RAU. The 5G-RAU signal detected by the optical transceiver is reshaped and retimed in the electrical-driven mixer-based phase-locked loop clock receiver equipment, being further used to convert the IF signal to a 5G signal in the desired frequency band.  
At the 6G-RAU, the optical signal passes through an OBPF, which has the bandwidth and filter-edge roll-off equivalent to \(\sim\)1.5 nm and \(\sim\)12 dB/GHz. The output of the OBPF is combined with the output of the laser, i.e., LO operating at 1315 nm followed by its detection by the UTC-PD. The 6G-RAU includes an optical LO operating at \(\lambda_{2}\) and a UTC-PD for optical heterodyne mixing. An electrical mixer driven by LO can be used to frequency up-convert a signal from the IF to the required band. Note that the additional OBPF and LPF were used to filter any unwanted frequencies from the 5G and 6G RAUs.  
In this proof of concept, IF-over-Fibre link experiments were conducted, where the 5G signal at the IF band was transported in an analog waveform.
## 6 Conclusions  
Currently, the ZPP method is the most widely used method for rainfall detection in X-band marine radar images. However, the ZPP method has many disadvantages, such as difficult threshold selection, low detection accuracy and random interference of wave echo in the detection area. Therefore, many new methods of rainfall detection have been being proposed by researchers.  
In this article, the RZE method for rainfall detection in the occlusion area of the X-band marine radar images is proposed. The RZE method is a combination parameter method, which determines the detection threshold by statistical analysis of a large number of radar image data. Additionally, when the rainfall detection area is defined in the wave area of the radar image, the RZE method also completes the task of rainfall detection. In addition, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. The radar images detected as rainfall are determined by the RZE method, and the retrieval result is compared with the actual rainfall intensity measured by the rain gauge to verify the effectiveness of the proposed method. The experimental results show that the RZE method not only has better rainfall detection performance compared to the ZPP method, but also can effectively retrieve rainfall intensity level from X-band marine radar images.  
Although the RZE method can effectively detect rainfall, it should be noted that the detection accuracy of the proposed method should be further investigated and improved  
Figure 16: Rainfall intensity level retrieval results. (**a**) The RZE substituted into fitting function; (**b**) Rainfall intensity retrieved by fitting function.  
when the detection area is defined in the wave area of the radar image. In addition, when the RZE method is used to retrieve rainfall intensity level, the relationship between the raindrop size and the radar echo is not considered. Moreover, the error exists in the verification of the retrieved rainfall intensity results in this paper, since the rainfall measurement methods of the rain gauge and the radar are different. In the future, the data collected from different sea areas and other radar systems should be used to further verify the applicability of the proposed method.  
Conceptualization, Z.L. and L.S.; methodology, L.S.; software, L.S.; validation, Z.L., L.S. and Y.Z.; formal analysis, L.S.; resources, Z.L.; writing--original draft preparation, L.S.; writing--review and editing, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province of China, grant number BK20180988, the National Natural Science Foundation of China, grant number 41906154.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information
## 2 Related Works
### Corrosion Segmentation  
To detect corrosion and segmentation, traditional image-processing and machine-learning methods are widely used: wavelet domain analysis combined with support vector machine (SVM) [12], threshold segmentation methods [13], color space-based analysis [14] and texture analysis [15]. These are simple and customized for specific applications, but they rely heavily on manual feature extraction and are difficult to generalize.  
Because of its rapid development in recent years, deep learning performs prominently in many classical vision tasks. Its great advantage is that it learns features automatically.  
[PERSON] et al. [16] used a three-layered feedforward neural network for corrosion detection on ships. [PERSON] et al. [17] proposed a channel attention-based metallic corrosion detection (CAMCD) method. Squeeze-and-Excitation (SE) [18] attention improved corrosion detection performance in ResNet [19] networks. [PERSON] et al. [20] proposed a method based on Faster R-CNN, which accurately found the corrosion area on a coated metal plate. [PERSON] et al. [21] used a cascading Mask R-CNN network combined with transfer learning and a cable inspection robot to solve automatically the accuracy and location problems in a stay-cable surface inspection. The proposed method reached the best IoU (0.743) and F1-Score (85.1%) among the classic canny algorithm and mainstream segmentation networks. [PERSON] et al. [22] built a corrosion dataset for the segmentation and performance evaluation of the pyramid scene parsing network (PSPNet) [11] and Mask R-CNN [23] for semantic segmentation and instant segmentation on this dataset, respectively. The authors also developed a two-stage data augmentation scheme that has been empirically shown to reduce overfitting significantly and improve, for instance, segmentation performance. The above two papers both used the Mask R-CNN framework [23], which combines Faster R-CNN [24] with FCN [8] to be an effective framework for corrosion segmentation. These methods confirmed the great potential of deep-learning models for corrosion segmentation. However, most of them are focused on specific applications and ignore the essential characteristics of general corrosion images, which makes implementing an algorithm for all corrosion image segmentation difficult. Because a corrosion image has no fixed pattern due to its randomness, its main features are distributed at different depths of the network. Thus, both spatial and semantic levels of contextual information need to be learned to reflect better the corrosion and multi-scale features that need to be incorporated so that the feature representation capability can be augmented.
## 2 Materials and Methods
### Characterization  
The crystallinity of the sample was determined using X-ray diffraction (XRD) with Cu K\(\alpha\) radiation at a wavelength of 0.15406 A (Siemens X-ray Diffractometer D5000, Munich, Germany). The surface morphology of the samples was observed using a field emission scanning electron microscope (FESEM) (JEOL JSM-7600F, Tokyo, Japan) and high-resolution transmission electron microscopy (HRTEM) (JEOL JEM-2100F, Japan). The optical properties of the samples were examined using a UV-Vis-NIR spectrophotometer (UV-3101 PC Shidmadzu, Kyoto, Japan). Details of the chemical composition and binding energy were obtained with an X-ray photoelectron spectrometer (Fourier Kratos Analytical Axis Ultra DLD/2009, Manchester, UK).