esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result  
### Subjective Stress and Workload Analysis  
#### 4.1.1 Perceived Stress  
The subjective perceived stress results obtained from the participants did not meet the normal distribution due to the low sample numbers; thus, the non-parametric test method through Mann-Whitney U test was used for the age demography and Kruskal-Wallis H test for the participants' experiences. This was conducted to explore whether there were significant differences in demographic characteristics in the groups, with the perceived stress levels divided into low stress, moderate stress, and high stress.  
To examine whether there was a significant difference in perceived stress levels between young and old participants, a Mann-Whitney U test was conducted. The groups were classified based on age into young participants (n = 12) and old participants (n = 12). The results of the Mann-Whitney U test indicated that there was a statistically significant difference in the perceived stress levels between the two age groups (U = 35.50, \(Z=-2.142\), \(p=0.032\)). Specifically, the young participants had a higher mean rank (15.54) compared to the old participants (mean rank = 9.46), suggesting that the young participants generally reported higher levels of perceived stress, as represented in Figure 6.  
Similarly, for the experience demographic, a Kruskal-Wallis H test was performed, with the groups categorized as low experience (n = 3), moderate experience (n = 7), and high experience (n = 14). The test compared perceived stress levels across these three groups and yielded a Chi-square statistic of 5.129 with 2 degrees of freedom and a \(p\)-value of 0.077. Since this \(p\)-value was greater than the conventional significance level of 0.05, we could not conclude a statistically significant difference in perceived stress across the groups. Therefore, no formal post hoc pairwise comparisons were conducted. However, the exploratory pairwise results are presented in Table 5 for descriptive insight only.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 4 Multi-Scale Convolutional Neural Networks for Anchor Bolt Corrosion Recognition
### Multi-Scale Convolutional Neural Network Model Design  
#### 4.2.1 Construction of MS-CNN  
Considering the inherent multi-scale characteristics of complex patterns in measured signals for corrosion diagnosis, this paper proposes an MS-CNN fault diagnosis method for improving accuracy corrosion diagnosis. Although CNN models improve their feature self-learning ability by learning the vibration characteristics in raw signals, the robustness of the model may be poor if it does not consider these complex patterns present at multiple time scales. In this section, a new improved multi-scale procedure based on traditional multi-scale analysis methods is proposed to extract more information at multiple time scales. The structure and parameter settings of the MS-CNN model are shown in Table 2. Two new convolution branches are added in series within the \(3\times 3\) kernel convolution layer, as low-level convolutions have small-size filters. These branches include mid-high-level convolution kernels with \(4\times 4\) and \(5\times 5\) kernels to extract multi-scale signal features from the input. The use of a convolution kernel divided into three different scales for extracting ultrasonic conduction wave characteristics of bolt corrosion may be motivated by the need to capture corrosion conditions at varying scales. In ultrasonic guided wave detection, corrosion typically exhibits characteristics at different scales, including local fine corrosion and large areas of corrosion. Additionally, batch normalization (BN) is added after the convolutions to avoid overfitting and improve the convergence speed of the network. The network structure design is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Number** & **Structure Type** & **Parameter** \\ \hline
1 & Input layer & 762 waveforms \\
2 & Convolutional layer 1 & \(3\times 1\) \\
3 & Convolutional layer 2 & \(4\times 1\) \\
4 & Convolutional layer 3 & \(5\times 1\) \\
5 & Concat layer & — \\
6 & Convolutional layer 4 & \(3\times 1\) \\
7 & Dropout layer & Probability is 0.5 \\
8 & Fully connected layer & — \\
9 & Output layer & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: The structure and parameter settings of the MS-CNN model.  
Figure 8: The framework of the MS-CNN model consists of three pathways that can extract features at different time scales.
## 2 Materials and Methods
### Study Case
#### 2.2.2 Hydrogeology Context  
The geological context has already been described in a previous paper [3] and interpreted by [PERSON] et al. [32]. From top to bottom, it consists of Cenozoic loose sediments overlying Mesozoic Cretaceous marl and chalk, and at depth, there is Paleozoic bedrock consisting of fractured phyllites and quartzites. To summarize, a'shallow aquifer' can be considered to consist of a succession of sand and clayey deposits. This aquifer system overlies an Upper Paleocene clay layer located at a depth of 74 m and of about 10 m thickness [33]. This clay layer lies directly on the Cretaceous layers reduced to 9 m of thickness with marl (above) and chalk (below). Under these Cretaceous formations, the Paleozoic fractured bedrock is considered the 'deep aquifer' system.  
A simplified map showing the model boundaries (see Section 2.3.2, the available piezometers, and the three ATES systems is shown in Figure 2. Two ATES systems (Building 1 and Building 2) (Figure 1) are exploiting the shallow aquifer system with the sandy portions acting as aquifers and hosting the well screens. The third ATES system has wells screened in the deep aquifer made of fractured phyllites and quartzites. The low permeability layers between these two aquifers (i.e., the clay layer of the Upper Paleocene and the Cretaceous marks) separate the shallow aquifer system from this deep aquifer.  
The potentiometric behaviors in these two aquifer systems are different, as shown on the regional potentiometric maps (Figure 2) [33]. Pumping tests performed in the upper aquifer system have shown no measurable influence on the Paleozoic bedrock aquifer system [3].  
Figure 2: Observation wells and regional potentiometric maps in the shallow Cenozoic-Paleocene aquifer system (**left**) and in the deep Paleozoic bedrock aquifer (**right**).
## 2 Open Challenges When SS Meets ISAC
### Hardware and RF Front-End Limitations  
ISAC requires a shared RF front-end for both sensing and communication, which can lead to hardware impairments like phase noise, nonlinearity, and limited dynamic range. Balancing the design between high-fidelity sensing and high-speed communication remains an unresolved issue.
## 2 Methods
### The Design of the Data Compression Algorithm
#### 2.3.2 LZW Algorithm  
The LZW algorithm [20] has to create a dictionary table. An encoding index is created within the dictionary for the first occurrence of each character in the data. Subsequent characters are read and combined with the previous character to form a string. It is then assessed whether this string already exists in the dictionary. If so, it is replaced by its encoding index. If not, a new encoding index is formed within the dictionary. These steps are repeated continuously until all characters in the original string have been read, completing the process as illustrated in Figure 5. As a result, longer repeated sequences within the data can be substituted by shorter codes, thereby achieving data compression.
## 2 Materials and Methods
### Metal Interaction Experiments
#### 2.2.5 Cancer Cell Viability Assay  
CellTiter 96(r) aqueous non-radioactive cell proliferation assay (Promega, Madison, WI, USA) was performed to evaluate the in vitro effects of Fe\({}^{2+}\), Cu\({}^{2+}\) and 4\({}^{\prime}\)-trifluoromethyl derivate (**9**) in breast adenocarcinoma MCF/S0.5 (parental MCF7 cells adapted to a low-sera environment) and MCF7/182R-6 (derived from MCF7, resistant to the antiestrogen fulvestrant) cell lines. The employed method uses the bioreduction of tetrazolium salt of MTS (3-(4,5-dimethylthiazol-2-yl))-5(3-carboxymethoxypphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) into a colored formazan with an absorbance peak at wavelength 490 nm, which took place only in viable cells via mitochondrial metabolism. Experiments were conducted in accordance with the manufacturer guidelines. The cells were treated with the tested compounds at different concentrations (1 nM to 750 \(\upmu\)M), metals (1 nM to 750 \(\upmu\)M) or vehicle (DMSO 0.1%) for 48 h in DMEM/F-12 without phenol red media in 96-well plates. Alternatively, xanthone derivate **9** was pre-incubated with the metals prior to being added to cell culture plates and incubated for 48 h. In these experiments, xanthone concentration was maintained constant (500 \(\upmu\)M) while the metals were tested over a range of concentrations (1 nM to 750 \(\upmu\)M) and vice versa. At the end of the treatment, 20 \(\upmu\)L of MTS reagent was added to each well and incubated for a further 3 h. After this incubation period, absorbance at 490 nm was measured using a plate reader (Tecan, Mannedorf, Switzerland). The results are expressed as the relative cell viability, considering vehicle (DMSO 0.1%) and toxic control (SDS 10%) as 100% and 0% of the response, respectively. All experiments were performed in triplicates and repeated at least three times.
# Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
## 3 The Proposed System
### Placement of Batteries  
After we segmented the warehouse into clusters based on their traffic densities, our next objective was to identify optimal battery placement within each cluster. This is crucial, as strategic placement ensures minimal interruption to the UGV traffic, optimizes the UGV operational times, and enhances the overall efficiency of the warehouse. The problem of the placement of batteries and power stations typically has the following constraints:  
* Proximity to shelves: cells adjacent to shelves are ruled out for battery placement to avoid hindrance during product loading.
* High-traffic zones: Extreme ends of each row, which typically observe high traffic, are not prioritized for battery placement. Placing batteries at these cells could impede UGV operations.
* Distance from high-traffic cell: The ideal battery placement should not be too distant from the cell with the highest traffic within the cluster. This ensures UGVs have quick access to charging, minimizing downtime.  
Mathematically, we represent the scoring function for a cell c within a cluster as  
\[Score(c)=\alpha\times TrafficDesirability(c)-\beta\times ProximityToShelves(c)- \gamma\times DistanceFromHighTrafficCell(c) \tag{2}\]  
where:  
* \(a\), \(\beta\), and \(\gamma\) are weighting constants that help to balance the influence of each factor on the overall score.
* TrafficDesirability(\(c\)) can be represented by an inverted bell curve, often taking the shape of \(1-(\text{Traffic}(c)-\text{mean}(\text{Traffic}))^{2}\).
* ProximityToShelves(\(c\)) can be a binary value (1 if adjacent to a shelf, 0 otherwise).
* DistanceFromHighTrafficCell(\(c\)) is the Manhattan distance from the most trafficked cell within the cluster.  
After calculating the scores for every cell within a cluster, the algorithm picks the cell with the highest score as the battery placement point. This procedure is reiterated for each cluster in the warehouse, ensuring that batteries are optimally located to cater to UGV energy demands without causing operational bottlenecks. It is worth mentioning that the weights \(\alpha\), \(\beta\), and \(\gamma\) can be used to further tune the outcome of the post-processing based on actual warehouse operations and UGV behaviors. This flexibility in adjusting the weightsmakes the proposed scoring system adaptable to various warehouse scenarios. Figure 3 depicts the outcome of the battery placement in a 20-by-20 warehouse environment.
## 3 Research Results
### Improved IDW Interpolation and Mapping
#### 3.3.3. Publishing 3D Web App  
OGC I3S (indexed 3D Scene Layers) Standards were designed to stream SLPK (Scene Layer Package) that contains the most heterogeneous spatial data, such as KML, KMZ, Geodatabase, Multipatch, and Shapefile, friendly to the mobile or web environment based on modern web standards [44]. These standards allow for accessing 3D GIS services on mobile or web browsers without additional plugins and are used as a method to promote user accessibility and service interoperability. Thus, this study used Esri ArcGIS Pro to convert the 3D building model and the voxel data generated above to SLPK, and using ArcGIS Online, it developed an I3S-based 3D Web App (Figure 12). The background map is displayed in 2.5D system for visualization under the subsurface, and the voxel data with seismic liquefaction vulnerability were visualized under the surface to produce realistic scenes. Furthermore, through the filtering of the risk grade categorized above, the regions with liquefaction hazard mapping could be effectively browsed.  
Figure 10: Improved IDW interpolation results: (**a**) Borehole data applied with elevation exaggeration factor; (**b**) NP\(-\)I; (**c**) NP\(-\)II; (**d**) NP\(-\)III; (**e**) FD\(-\)I; (**f**) FD\(-\)II.
## 8 Future Directions
### Ethical and Legal Considerations  
The widespread deployment of AI in communication networks raises significant ethical and legal issues. There is a pressing need for ethical AI frameworks to ensure fairness, transparency, and accountability. Ethical considerations are particularly important when AI systems influence access to resources or manage critical network infrastructure.  
Legal compliance is equally vital, especially concerning data privacy laws like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. As AI-based communication systems gather, store, and analyze personal data, adherence to these regulations is necessary to avoid legal ramifications and maintain user trust.  
Table 17 provides an overview of ethical principles and corresponding regulatory requirements that AI-enabled networks should consider.  
To ensure responsible AI deployment, future research should focus on developing AI governance frameworks for communication networks, addressing ethical guidelines and regulatory standards.
## 2 Related Work  
### Development and Application of Object Detection Algorithms  
With the development of deep learning technologies, object detection algorithms have found widespread applications across various domains. Object detection algorithms can be categorized into two types: two-stage and one-stage algorithms. Two-stage algorithms first generate candidate boxes and then classify these boxes; a typical example of such algorithms is the Faster R-CNN series. One-stage algorithms, including SSD [12] and the YOLO series, do not require complex frameworks, enabling real-time detection.  
To address issues such as complex image backgrounds and severe occlusion, [PERSON] et al. [13] introduced a Target-Aware Attention Module (TAAM) and a Channel Attention Module (CAM) in the YOLOv6 [14] model. The TAAM helps suppress background features, while CAM reduces irrelevant channel features. The modified model enhances the extraction of prohibited items' information in complex backgrounds.  
In the research on tower crane safety management based on computer vision technology, [PERSON] [15] developed an automatic system for the collection, analysis, and early warning of safety distances for tower cranes based on Mask-RCNN [16]. This system utilizes the Mask-RCNN method to recognize video data and extract RGB color from the mask layer, thereby identifying hazardous areas and worker coordinates. These coordinates are then transformed into real-world distances to calculate the safety distance. By incorporating a mask layer, this method allows for the presence of distorted angles between the camera and the target, thus improving the recognition accuracy and applicability. [PERSON] [17] proposed using Mask-RCNN to address the safety hazard caused by mismatched hook and steel ladle ear axes during the hoisting process. Compared to the manual acquisition of the matching status, the proposed method can quickly and accurately determine the hook's matching status. [PERSON] et al. [18] introduced a construction equipment recognition framework based on computer vision (CV) technology, which, using information captured by cameras, automatically estimates the overall posture of construction equipment.  
Compared to existing research, the approach of using object detection methods to detect prohibited items in tower crane operation scenarios, to prevent safety accidents, is highly innovative. This method consumes fewer resources and is more efficient than traditional behavior recognition methods. Furthermore, the improved object detection model achieves a higher mAP than the more advanced YOLOv8 while also maintaining a balance between speed and the number of parameters.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### GAN and LLM-Based Solutions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **GANs (Generative Adversarial Networks)** & **LLMs** \\ \hline Primary Use Cases & Synthetic attack data generation, anomaly simulation, and data augmentation for ILS & Threat log analysis, automated incident response, and policy summarization and retrieval \\ \hline Advantages & Helps train models with limited real attack data, reveals blind spots via adversarial testing & Learns from diverse, unstructured data, enables real-time, context-aware decision support \\ \hline Model Input & Numerical/time-series telemetry, and network traffic data & Text-based logs, configuration files, and incident documentation \\ \hline Challenges & Requires stable training, risk of generating unrealistic or biased samples, and needs task-specific adaptation (e.g., TimeGAN) & High inference cost, risk of hallucinations, and needs domain adaptation and prompt tuning \\ \hline Deployment Consideration Tables & Typically used offline for training IDS or simulators & Needs lightweight variants for edge/real-time use (e.g., quantized or distilled models) \\ \hline Potential Integration & Enhancing IDS through adversarial robustness testing & Assisting operators in decision-making and reporting tasks \\ \hline \hline \end{tabular}
\end{table} 
Table 33: Comparative overview of GAN and LLM applications in smart grid security. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Advantages** & **Limitations** \\ \hline Threat Detection and Analysis & * Analyze vast amounts of data to identify potential threats and anomalies. & * Resource-Intensive: Requires significant computational resources for training and deployment. * Hard Interpreting Interpreting and validating results can be challenging. * Maintenance: Needs regular updates and maintenance. False Positives/Negatives: Risk of incorrect detection, which can undermine effectiveness. * Large Dataset: Training requires large datasets that may include sensitive information. \\ \hline Vulnerability Management & Predictive Analysis: Predict potential vulnerabilities and exploit trends. & * Data Reaches: Improperly secured data can be targeted by attackers. * Misuse Potential: Can be used to generate harmful content like deepfakes. * Ethical Considerations: Ensuring fairness and preventing misuse. * Black Box Nature: Lack of transparency in decision-making processes. * Interpretability: Decision-making processes are not easily interpretable. \\ \hline Data Privacy and Compliance & * Follow Analysis: Ensure adherence to data privacy regulations. & * Accountability: Challenges in regulated industries where understanding decision processes is crucial. \\ \hline \hline \end{tabular}
\end{table} 
Table 32: Advantages and limitations of LLMs for smart grid security.
# Use of Gradient-Based Shadow Detection for Estimating Environmental Illumination Distribution
## 3 Results
Assuming that the two illumination sources are not attached to each other, we employed Gaussian smoothing and non-maximum suppression in a 3 by 3 mask to remove the spurious neighbor matches, as shown in Figure 7b. The final illumination positions were determined using the weighted average of the matching costs in the 3 by 3 mask, of which the center pixel is the local maximum position for the non-maximum suppression. Figure 7: (**a**) Matching score in the longitude and latitude angle domains; (**b**) non-maximum suppression; (**c**) illumination sources for the low threshold. In the experimental results, we found that illumination sources with matching scores <35% relative to the brightest illumination source could be classified as insignificant. However, by using this low threshold value, we may also detect false shadows, meaning that an illumination source casting an indistinct shadow may incorrectly be classified as an important source in the scene. To verify these potentially falsely-detected illumination sources, we calculated the intersection sets of the shadow regions that passed the local feature-based matching. By computing the relative extent to which these shadow areas were darkened according to the inclusion relationships of the candidate shadows, we were able determine the important illumination sources from the passed shadow regions. To evaluate the performance of the proposed method, we estimated the illumination information for two cases: one with two light sources and one with three. Table 1 shows the comparison of the results of the existing method and those of the proposed method. More specifically, the errors of the obtained illumination sources were computed using the average absolute deviation of the measured angles and the computed angles. Table 1 shows that the light directions estimated by the proposed method are closer to the ground truths (measured directions) of the real light sources, as compared to results generated by the method previously published in Reference [4]. Figure 6b shows the input image from Case 1 of Table 1. Figure 8 shows the input images of Cases 2 to 5, and the final AR image of the Utah teapot model in Case 4. Figure 8e,f show the comparison of the effects of the illumination locations obtained by the previous method to those obtained by the proposed method. To generate images that are more naturally rendered, 15 subsampling points per the obtained illumination source were generated. In comparison the real scene (Figure 8c), we can see that the shadows obtained by the previous method are generated falsely.