esa-sceva/satcom-chunk-collection huggingface dataset:
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 3 Experimental Results
### Number of Kernel Parameters Comparison among Different Steganography Networks
#### 3.2.2 Imperceptibility of Stego Video Frames  
The imperceptibility of the stego video frames is an important index to measure the steganography algorithm. The better the steganography algorithm, the higher the visual quality of stego video frames, which ensures that the secret information is invisible and not easy to be suspected. It can be evaluated from both qualitative and quantitative perspectives.  
Qualitatively speaking, Figure 8 shows the visual quality of the steganographic video frames of P_F32_2 and P_F16_3. They both have high visual quality and do not appear to carry a hidden message. Then, on the right side of them, we can see their gray residuals obtained by subtracting the cover video frame pixel by pixel. Residual, in a sense, represents the hidden message. The residuals of P_F32_2 and P_F16_3 both reflect the outline of the video frame, where P_F32_2's is much clearer, proving that the stego frames of PyraGAN achieve a good video content adaptation, and P_H16_3 achieves a large reduction in the number of parameters at a small cost in the visual quality of the residual.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **HiDDeN [21]** & **SteganoGAN [22]** & **PyraGAN\_F32\_2** & **PyraGAN\_F16\_3** \\ \hline BPP(bit) & 0.0018 & 1 & 1 & 1 \\ \hline PSNR(dB) & 40.081 & 41.428 & **43.109** & 42.995 \\ \hline ACCU(\%) & 0.793 & 0.993 & **0.994** & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different steganography CNNs.  
Figure 8: The visual quality of steganographic video frame of PyraGAN.  
Quantitatively speaking, PSNR, shown in Equation (8), is used to measure the similarity between two video frames pixel by pixel. First, MSE is calculated according to Equation (2) and the PSNR value is calculated from MSE, where \(A\) represents the peak signal.  
\[PSNR=20\times log_{10}\bigg{(}\frac{A}{\sqrt{MSE}}\bigg{)} \tag{8}\]  
The PSNR of different model is shown in the third row of Table 2. The proposed P_F32_2 achieves the best performance, for which the PSNR value in bold in Table 2 is 2 dB higher than that of SteganoGAN and 3 dB higher than that of HiDDeN, because the CU mask is a good assistant to help the model learn features of the cover video frames. In addition, the attention mechanism focuses on \"which\" features and \"where\" features are important for information hiding. Therefore, it realizes the adaptive information hiding based on the content of the cover video frames. Furthermore, at the expense that the PSNR value of P_F16_3 is 42.995 dB, underlined in Table 2, and only 0.1 dB lower than that of P_F32_2, the model P_F16_3 realizes the reduction of computational complexity greatly. Although P_F16_3 has the minimum parameters, its stego video frames have almost the best quality because its increase of multiscale feature extraction layers additionally boosts the performance of invisibility.
# Exploring the Evolution of Autonomous Vehicle Acceptance through Hands-On Demonstrations
## 4 Results and Discussion
### Current Users Survey Results  
This subsection analyzes the survey results of those passengers who agreed to interact and complete the pre-established route aboard the ATLAS autonomous vehicle for a distance ranging from 1 to 3 Kms in an urban environment. As described in Section 3, the path included roundabouts and crosswalks where different people were waiting to cross. Passengers were seated in the passenger and rear seats and were accompanied by a professional driver (in case any intervention was required) and a technician who provided information on the operation and performance of the vehicle.  
During these demonstrations, a total of 27 people participated in the survey. If we analyze the demographic aspects of the survey, it should be noted that of the total number of respondents, 14 were men and 12 were women, plus one person who preferred not to indicate their gender. Furthermore, the age of the respondents was evenly distributed between 18 and 65 years. Detailed information on this survey can be found in [35].  
Among the main findings of this survey is that most participants believed they had limited knowledge concerning autonomous driving. This was not the case when the study focused on knowledge about the automotive sector; by observing Figure 3, it can be concluded that the percentage of people with medium or superior knowledge on the sector (approximately 82%) is higher than the percentage of those who had medium or superior knowledge regarding autonomous driving (approximately 63%).  
Focusing on the analysis of the results on autonomous vehicles and on the acceptance of this technology from the point of view of people who have been able to test this type of vehicle, the most relevant conclusions are centered on two responses, as shown in Figure 4:  
* First, as shown in the diagram on the right, regarding the question regarding the possibility of using the autonomous vehicle daily if they had access to it, 81.48% of respondents answered that they would use it. This suggests a willingness to adopt this technology and a high predisposition of people to use these vehicles and to get involved in their implementation.
* Despite this positive feedback, the study also demonstrates the need to bring this technology closer to people through demonstrations such as the ones presented in this work, given that nearly 78% of respondents stated that, although they would use the vehicle, they would be somewhat concerned about doing so.  
Figure 3: Current users’ knowledge on autonomous driving and automotive sector.
# Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
## 5 Conclusions  
There is not a unique, established IoT network architecture; therefore, the complexity, number of layers, number of sensors, and use cases of each architecture vary. Nevertheless, it is fundamental that the IoT architecture must support several protocols, maintain interoperability and ensure perpetual monitoring. Within this context, this study presents a suggestion for an IoT edge-to-edge (SEDGE) architecture that can link multiple communication technologies, establish interoperability with other systems and edges while supporting auto-healing actions for monitoring stability.  
The main objective of SEDGE is to integrate all necessary features into an autonomous gateway and make it simple to link any gateway to the BMS systems. To allow future SEDGE expansion and modification to incorporate new technologies and/or protocols, each gateway's network is robustly integrated with several sensor technologies. Moreover, interoperability is provided via an extra semantically enriched layer, providing the capability for the SEDGE to communicate with heterogeneous systems and sources. Moreover, specific healing actions are applied at the edge depending on the type of the error.  
Figure 17: The real and reconstructed or predicted time series of the proposed model with real and predicted anomaly points.  
SEDGE healing actions are of two types, those for which automated actions may be applied (e.g., kill processes) and those for which the end user must take further actions (e.g., the Wi-Fi network is unstable). Furthermore, continuous monitoring of SEDGE is applied, and an AE-LSTM algorithm detects all the problematic situations. The selected algorithm is 99.4% efficient and is more accurate than other tested algorithms. Finally, to ensure that fatal errors will not affect the SEDGE, automated actions are performed if the monitored SEDGE processes exceed the predefined limits.  
Future actions include the integration of new connectivity protocols, such as an M-bus protocol and weather stations that usually communicate with a Wi-Fi protocol. Additionally, the semantics layer will be enriched with more information. Finally, extensive experiments will be made on more SEDGEs, and more auto-healing actions will be provided.  
Writing--original draft, V.-G.V., A.P. and S.A.; Writing--review and editing, A.D., K.K., C.-N.A., S.K., D.I. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The research leading to these results was partially funded by the European Commission \"LC- EEB-07-2020--Smart Operation of Proactive Residential Buildings\"-- PRECEPT H2020 project (Grant agreement ID: 958284).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 3 Methodology
### Conceptual Model and Research Hypotheses  
This research aims to understand how UGC influences the purchase intention (PI) of tourist products and services on the TikTok platform by observing the variables: need for information (NI), perceived credibility (PC), usefulness of information (UI), perceived risk (PR), and social influence (SI), presented in Figure 1, that constituted the research model designated by XIAM (eXtension of the Information Acceptance Model).  
Figure 1: Research Model XIAM. Source: Own elaboration.  
In developing the model, elements of the IACM Model (Information Acceptance Model) were considered, namely, the relationship between the variables of information needs and perceived credibility and the usefulness of information and the relationship between the usefulness of information and the intention to purchase [69]; as well as the UTAUT Model (Unified Theory of Acceptance and Use of Technology) with the incorporation of the social influence variable [70]. The variable perceived risk was also incorporated in relation to purchase intention, based on the models proposed by the authors [PERSON] et al. [71], in which perceived risk is considered a possible determinant of purchase intention, and [PERSON] et al. [72], who found an influential relationship between perceived risk (more significant in financial and social risk) and purchase intention.  
The IACM model introduced by [PERSON] and [PERSON] [69] (p. 50) suggests that to understand the influence of eWOM information on consumers' purchase intention, it is necessary to observe not only the characteristics of eWOM as well as consumer behaviour regarding this type of information. This model is an extension of the IAM model (Information Adoption Model), introduced by [PERSON] and [PERSON] [73], which allows a better understanding of which characteristics of eWOM influence its adoption, with the integration of part of the TRA model (Theory of Reasoned Action), introduced by [PERSON] and [PERSON] [74], referring to consumer behaviour regarding eWOM.  
The UTAUT model developed by [PERSON] et al. [70] (p. 447) seeks to explain which variables lead to the intention and subsequent behaviour of using new technologies, considering four variables as important direct determinants of behavioural intention and usage behaviour: performance expectancy, effort expectancy, social influence, and facilitating conditions.  
Following the proposed model, six research hypotheses were formulated, presented below:  
**H\({}_{\text{1}}\):**: _Social influence positively influences purchase intention_.
**H\({}_{\text{2}}\):**: _Social influence positively influences the usefulness of information_.
**H\({}_{\text{3}}\):**: _The need for information positively influences the usefulness of information_.
**H\({}_{\text{4}}\):**: _Information credibility positively influences information usefulness_.
**H\({}_{\text{5}}\):**: _The usefulness of information positively influences purchase intention_.
**H\({}_{\text{6}}\):**: _Perceived risk influences purchase intention_.
## 4 Result and Discussion
### Analytical Measurement and Visualization after Preprocessing
#### 4.1.1 Naive Bayes Multinomial  
Naive Bayes was used for classification. It assumes that the occurrence of a specific feature is independent of the occurrence of other features. From a prediction perspective, the performance of this model is considered very fast compared to other models. We scraped 148 apps reviews from 14 categories from Google Play Store. There were 40 reviews on one page, we collected a total of 506,259 reviews from Google Play Store applications. We applied the naive Bayes algorithm for classification on that dataset of reviews and found different information on different parameters concerning TF and TF/IDF. We calculated the classification accuracy of the model for each application category and reported the precision, recall and F1 scores. Figure 3 illustrates a visualization bar chart for the naive Bayes algorithm in which series1 indicates the accuracy of the algorithm, series2 indicates the precision, series3 indicates the recall, and series4 indicates the F1 score measurement.
## 4 Special Case of Low Frequency and Low Viscosity
To verify the theoretical results, numerical amplitudes of the scattered surface wave were calculated by applying the uniform tractions (shown in Table 1) with a frequency of 5 kHz on the crack surface in the finite element models. The amplitudes of the scattered surface waves in \(x\) and \(z\) direction were detected at the free surface of the model, where \(x=50\) mm. The numerical and theoretical results are compared in Figure 6. The theoretical results are in good agreement with the numerical results, as shown in Figure 6. In addition, with the increase in crack length, the amplitude of the scattered surface wave increased and the error between the numerical and analytical results increased. The special case of low frequency and low viscosity proved that the reciprocity theorem, together with the virtual wave technique, is feasible to determine the amplitude of the scattered surface wave of viscoelastic materials. Figure 5: Crack-opening displacement produced by: (**a**) normal stress; (**b**) shear stress.
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
###### Abstract  
This paper presents a frequency-reconfigurable dual-band radio frequency (RF) crossover based on quarter-wavelength coupled lines (CLs) and open stubs. Initially, an even-odd-mode analysis was conducted for the design, and closed-form equations were found. Then an advanced design system (ADS) was utilized to support and further optimize the theoretical analysis. Afterwards, high-frequency simulation software (HFSS) was used to simulate the proposed design. The proposed device is printed on a 1.524 mm RO4003C printed-circuit board (\(\epsilon_{r}=3.55\)). The frequency tunability is achieved by employing two varactor diodes connected to the open stubs. When the biasing voltage is altered, the capacitance of the SMV1405 varactor can change from 2.67 pF to 0.63 pF. Accordingly, the two operating frequencies can be continuously tuned from 2.06 GHz to 2.40 GHz and from 5.44 GHz to 5.84 GHz. For the low-frequency range, return loss and isolation are above 15 dB, and the insertion loss is less than 1.1 dB. As for the high-frequency range, the return loss is greater than 20 dB, the isolation is better than 15 dB, and the insertion loss is lower than 1.6 dB. The measurement results agreed well with the simulation results, and the crossover overall size is 45.5 mm \(\times\) 29.4 mm. The proposed device can be utilized for various application areas, such as 5G smartphone applications and satellite communication.  
c 2024 132641 1
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 4 Discussion
### Future Prospects  
Our aggregation approach (averaging SHAP values across stations and aligning on common features) provides a template for future work across distributed sensor networks. Rather than building a separate model for each site or naively merging heterogeneous data, our method offers a principled way to compare and consolidate model behavior across contexts. This type of methodological clarity enhances both trust and scalability. One promising direction for future research is the integration of counterfactual explanations. These methods, which identify the smallest change needed to flip a model's prediction, can help stakeholders understand thresholds and risk boundaries [24]. For instance, if a region is about to cross a pollution threshold, counterfactuals could indicate which variable(s) must change--and by how much--to remain below the limit. This offers a more actionable interpretation than SHAP alone and aligns closely with regulatory use cases.  
In addition, exploring temporal explainability--how the importance of features evolves over time--could deepen our understanding of seasonality and event-based pollution spikes. While our current model uses temporal splits for validation, the SHAP values themselves are static with respect to time. Using time-series SHAP or other longitudinal techniques could uncover recurring patterns and long-term shifts in pollution dynamics.  
Another area for exploration is multimodal data fusion, including possibly satellite imagery, meteorological forecasts, and/or social media signals (X formerly Twitter) [30]. While our current model relies on structured environmental measurement data, many researchers have shown that multimodal models can improve both accuracy and generalizability. Ensuring that these richer models remain interpretable will be a major challenge for the field, but one that is essential if XAI is to remain aligned with its real-world goals [8, 21]. Finally, model governance remains an open issue. Even interpretable models can be misused or misunderstood. Developing human-in-the-loop systems, where domain experts can query, validate, and contest model outputs, would greatly increase trust and robustness, and in this state, XAI applications could be considered mandatory.
## 4 Attitude Control Experiments  
### Uwoc Experiments  
An experiment was conducted in a water tank to verify the performance of the AUV using the developed UWOC system. The turbidity of the water tank was measured using Valeport's HYPERION Turbidity sensor, and the average turbidity value was 0.267 Neph-elometric turbidity units (NTUs). Since water with turbidity below 1 NTU is considered exceptionally clear and potable [28], the absorption and scattering coefficients for the water tank are assumed to correspond to those of pure seawater, as detailed in Table 2. Figure 17 shows the ground installation of the optical communication system, which is placed 800 mm away from the water surface. The experiment was performed on the communication speed, communication range, and the communication error rate of the AUV depending on the ambient light, and the attitude of the AUV was controlled by transmitting the control command through wireless communication.  
Figure 16: Light detection waveform (not clear signal).  
Figure 17: UWOC system installed on the water tank.  
Figure 15: Light detection waveform (clear signal).
## 3 Challenges in Intrusion Detection Systems
### Complexity and Importance of Spatiotemporal Feature Extraction  
In the modern cybersecurity landscape, particularly within the context of the Internet of Things (IoT) and large-scale distributed networks, IDS are confronted with increasingly sophisticated attack methods [27; 28]. These attacks are highly dynamic and frequently exhibit pronounced spatiotemporal characteristics [24]. Traditional intrusion detection methods make it difficult to capture the complex spatiotemporal characteristics of network traffic, which allows attack behaviors to use temporal dependencies and spatial distributions to evade detection [29]. The complex sources of data include the following aspects:  
1. Multidimensionality of Data: Network traffic data typically contain multiple dimensions, including network layer information and time-series data [30]. The interaction of these features increases the difficulty of extracting critical spatiotemporal patterns [31];  
2. Heterogeneous Network Environments: The heterogeneity of different network nodes and devices makes it challenging for a unified feature extraction method to adapt to diverse network environments, thereby affecting detection accuracy [32];  
3. Temporal Dependencies: Many attack behaviors exhibit temporal dependencies, which traditional static feature extraction methods fail to capture, leading to inadequate detection of complex attacks [33].  
The key to improving the robustness and accuracy of IDS is to extract spatiotemporal features [34] effectively. Precise spatiotemporal analysis not only allows the system to maintain stability in the face of complex and evolving attack patterns but also significantly improves the detection of Advanced Persistent Threats (APT) and zero-day attacks [35; 36]. To ensure sustained effectiveness in dynamic network environments, IDS must accurately capture temporal dependencies in network traffic [30].
## 5 Impact of Color Conflicts Caused by Late Arrivals, Node Mobility or Topology Changes  
### Ideal Functioning  
For a given stable topology, if the coloring process has been done according the assumptions and the rules introduced in Section 3.4, the expected behavior of node \(N_{i}\) is the following:  
Let us assume that \(N_{i}\) gets the color \(k\), it has to know the list of its \(J+1\) 1-hop neighbors \(N_{ni0},N_{ni1},\cdots N_{niJ}\) and the color of each of them \(C_{ni0},C_{ni1},\cdots C_{niJ}\). \(N_{i}\) has to be awake during all the slots of color \(k\) but also during the slots of color \(C_{mix}\) of each of its 1-hop neighbors.  
When it has to send a data frame, \(N_{i}\) can only transmit it to one of its 1-hop neighbors \(N_{mix}\) during the slot of color \(k\). When it receives a data frame, it is a frame sent by one of its 1-hop neighbors \(N_{niy}\) having the color \(C_{niy}\). When it receives an acknowledgement, it has been sent by one of its 1-hop neighbors \(N_{niz}\) having the color \(C_{niz}\).
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 3 Hyperspectral Anomaly Detection Based on the Channel Attention Mechanism and LRX  
In this paper, a hyperspectral anomaly detection algorithm based on the channel attention mechanism and LRX is proposed, as illustrated in Figure 1. The algorithm follows a two-step process: first, it obtains the reconstructed image using the auto-encode network with the channel attention mechanism, and then uses the LRX algorithm for anomaly detection on the reconstructed image. The auto-encode network based on the channel attention mechanism consists of two sub-networks: the channel attention network and the auto-encoder network. The channel attention network is designed to extract the effective bands of HSIs as input in order to focus on the separability of the spectral bands. Meanwhile, the auto-encode network performs encoding and decoding operations on the HSIs, incorporating the channel attention mechanism. This process can effectively reduce the noise and can generate the representative reconstructed image that accurately captures the characteristics of the land covers. Finally, the LRX algorithm is utilized to the representative reconstructed image to better detect anomalies.