esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussions: The Influence and Benefits of PPGIS  
### Promoting Inclusiveness  
PPGIS views GIS as a form of visible communication that has the ability to present environmental information and to encourage grassroots participation [10; 22; 23]. The impact of PPGIS on inclusiveness means to clarify and expand the following: who are included, who are empowered through the process, ownership of the product, and the type and degree of participation [36]. In this study, it was the local communities that noticed and raised the issue of hydrological variation and worked with the academic team to introduce PPGIS to explore the wetland situation. Apart from introducing PPGIS and analyzing related data in the beginning, WRCEA also participated in the planning and implementation of substratum elevation monitoring and they held a workshop (platform meeting) to invite stakeholders to jointly interpret the monitoring results. With the assistance of both the county government and the academic team, WRCEA later led a monitoring project on the wildlife refuge, sponsored by the program for Wetlands of National Importance in Taiwan in 2010.  
\"At that time, we first illustrated the wetland area in Google Earth; then, we discussed the best method for monitoring it with the academic team.\" (LC02)  
\"After we complete the first trial, we will use PPGIS to explain our ideas to the other community development associations. Also, we will find some volunteers to help with the monitoring.\" (LC05)  
\"The monitoring data displayed in the 3-D environment plays a very important role in our proposal writing and funding. The officials can clearly understand what we want to do, as well as the capabilities of the community.\" (LC02)  
Apart from WRCEA's participation in the whole monitoring process, PPGIS also played a role in information dissemination in the platform meeting to help understand the implementation and possible benefits of the entire project. In this case, the public sector provided recognition and administrative support for the monitoring project and they provided the required measurement equipment. More importantly, many community members were willing to serve as volunteers for elevation measurement. This process determined how the local conservation group and community members played key roles throughout the monitoring and subsequent management.  
\"Right at the start, the community said they wanted to take a measurement of the substratum elevation and we were shocked. However, using PPGIS to understand the community's method of implementation, we could better know how to coordinate.\" (FB03)  
\"After this kind (PPGIS) of explanation, everyone could better understand, and it also helped to set up the needs of the academic team in the refuge. After several rounds of monitoring, we could go in and finish these facilities ourselves.\" (LC05)  
\"Through PPGIS, we could quickly grasp the silt distribution, and it is easier to reach a consensus with the community.... After everyone's thinking is aligned, things can move forward more smoothly.\" (LC08)
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Throughput Performance
#### 7.3.2 Average UE Throughput  
Figure 10 shows the average downlink and uplink throughputs of each of the 40 UEs for both algorithms. This result shows that the average UE throughput for the proposed algorithm is generally higher. For example, the 3 GPP scheme has 17 UEs with an uplink data rate of below 1 MBps compared to only 9 UEs for the proposed scheme, i.e., a 47% reduction in the number of low throughput users. Similarly, the 3 GPP scheme has 16 UEs with a downlink data rate of below 1 MBps compared to only 8 UEs for the proposed scheme, i.e., a 50% reduction in the number of low throughput users. This finding is generally consistent with the total throughput result in Section 7.3.1.  
Figure 9: Total throughput.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 7 Conclusions  
Taking one online physical lighting control system as a reference, this study designs and trains one lighting neural network model to simulate and realize the corresponding functions that the physical system has accomplished. Unlike many existing lighting control systems or neural network models in which only inside luminance is adjusted or trained, this model takes into consideration both inside luminance and color temperature by means of feeding them into output layer parameters, this simulation matches the physical VCT lighting control system with a high degree of fitness, that is, inside color temperature achieves a 100% recognition rate and inside luminance conforms to unary linear regression with smoother fitting curve. Therefore, this model has wider practicability and applicability, which is of great help to enhance the reliability of this VCT lighting control system. Because this network model is able to work alone or work in parallel with the physically deployed TLCS in cross-check mode, the whole system will definitely gain higher reliability than before.  
To some extent, this study proves the technical feasibility of a lighting neural network to replace the physical lighting control system, which is an effective exploration to improve the level of intelligence of a tunnel lighting control system. With the long-term operation of the physical tunnel lighting system, it is certainly necessary to use the large amount of lighting data collected at the tunnel site to continuously calibrate or optimize this network model, enhance its robustness and prevent the over-fitting problem of this lighting neural network. Additionally, due to its attribute of continuous learning and strong scalability as a software system, this model has the incomparable advantages that a physical lighting control system lacks, providing a new idea to improve the reliability of the lighting control system.  
The authors confirm the contributions to the paper as follows: study conception and design, B.S. and J.H.; data collection, B.S. and J.H.; analysis and interpretation of results, B.S., J.Z. and R.W.; draft manuscript preparation, B.S., R.W. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the scientific research project of Fujian Expressway Science & Technology Innovation Research Institute Co., Ltd. and partly by the traffic scientific research project of Department of Transport of Shaanxi Province (No. 21-02X).  
Not applicable.  
Data Availability Statement: Data generated in this study are available upon request.  
The authors would like to thank all of the participants for attending the experiments.  
The authors declare no conflict of interest.
# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
## 4 Results of the Experiment and Discussion
### Robot Path Planning Based on TSDBO Algorithm
#### 4.5.2 Constraints and Single-Objective Fitness Functions  
In order to guarantee the feasibility of the path planning, a fitting function that fulfills the constraints must be created after the pertinent environmental data have been set up. The fitting function is linked to the following three requirements:  
1. Path continuity. In path planning, the robot cannot appear to walk backwards. That is to say, if the robot starts from the starting point and its position at a certain point is (X1, Y1), then its next position should be (X2, Y2) and the condition X2 \(>\) X1 or Y2 \(>\) Y1 must be satisfied.
2. Limitations of obstacles and boundary conditions. In the path planning result, the robot's moving path cannot appear to cross the boundary, and the search result cannot go beyond the boundary or pass through the area where obstacles exist in the grid.
3. Shortest path. If conditions (1) and (2) are satisfied, the path that retains the shortest path should be selected as the optimal path in robot path planning. In summary, the single-objective fitness function can be described by Equation (13). \[\cos\mathfrak{t}i=\sum_{j=1}^{D-1}\sqrt{\left(xj+1+xj\right)^{2}+\left(yj+1+yj \right)^{2}}\] (13)
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Image Acquisition Module Calibration Experiment
#### 4.2.2 Conversion Coefficient Calibration Experiment  
The unit of spot center coordinates in the image coordinate system is pixel, which cannot represent the true displacement directly [38]. A sketch map of the conversion coefficient calibration experiment is shown in Figure 16. The laser was placed on the micro motion platform and pointed to the fixed image acquisition module, thereby allowing it to move with the micro motion platform.  
To restore the true displacement of the micro motion platform, the horizontal coordinate conversion coefficient \(\alpha\) and the vertical coordinate conversion coefficient \(\beta\) needed to be determined [39]. The calculation formulas are shown in Equation (11):  
\[\left\{\begin{array}{l}\Delta x=\alpha\times\Delta X\\ \Delta y=\beta\times\Delta Y\end{array}\right.. \tag{11}\]  
In Equation (11), (\(\Delta X\), \(\Delta Y\)) represents the displacement of the laser spot in the image coordinate system in pixels, and (\(\Delta x\), \(\Delta y\)) represents the real displacement in the world coordinate system. The \(x\)-axis conversion coefficient calibration experimental data are shown in Table 1.  
The \(x\)-axis conversion coefficient can be obtained with Equation (11), and the arithmetic mean value formula is shown as Equation (12):  
\[\alpha=\frac{\sum_{i=1}^{7}\frac{\Delta x_{i}}{\Delta X_{i}}}{7}=0.1454. \tag{12}\]  
The calibration process of the \(y\)-axis conversion coefficient \(\beta\) was the same as \(\alpha\); that is, \(\beta=0.1454\). The relationship between the positions of the filter imaging screen and the HD industrial camera is invariant. Once the conversion coefficients \(\alpha\) and \(\beta\) are determined, they are invariant and only need to be calibrated once.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 2 Antenna Design Methodology  
### Antenna Design Stages  
Figure 1 depicts the four steps to design the final optimal antenna shape. First, a rectangular monopole antenna (ANT I) with CPW feeds is developed, which resonates at a 30 GHz resonant frequency, as shown in Figure 1b. In design step II (ANT-II), the proposed rectangular monopole antenna is truncated to resonate at 28 GHz, with an impedance bandwidth of 26-31 GHz. To generate the additional 60 GHz frequency band, a U-shaped slot is introduced in the ANT II, as illustrated in Figure 1a. The addition of the U-shape in the proposed antenna design yielded into two frequency bands, in which one band is enough to span the needed frequency range for 5G communication systems. In the final step (ANT IV), a C-shaped slot is created in the upper edge of the radiator to resonate the proposed antenna at a 38 GHz frequency band. The stub loading approach is not only used  
for enhancing the impedance bandwidth, but also improves the impedance matching at the target frequency [22; 23; 24]. The resonating frequencies are then optimized in certain band spectrums. Figure 1b shows the return loss of the proposed antenna during the design steps, and resonates at 28, 38, and 60 GHz frequency bands.  
Here is a breakdown of the design process for the triband mm-wave antenna:  
The basic antenna design consists of a 50-ohm CPW feedline and a printed rectangular monopole (ANT I in Figure 2a). The effective dielectric constant, by neglecting the thickness of the conductor [17], is given in (1). This CPW configuration, seen in Figure 1, has ground on both sides of the feed strip and no back conductor. Using Equations (1)-(7), the essential parameters of the antenna with CPW ground plane can easily be calculated.  
\[Z_{o}=\frac{30\pi K^{\prime}(\mathrm{k})}{\sqrt{\varepsilon_{re}}\cdot K(k)} \tag{1}\]  
\[K^{\prime}(\mathrm{k})=K\big{(}k^{\prime}\big{)} \tag{2}\]  
\[k^{\prime}=\sqrt{1-k^{2}} \tag{3}\]  
\[k=\frac{wf}{wf+2(g)} \tag{4}\]  
\[\varepsilon_{re}=\frac{\varepsilon_{r}+1}{2}\;[P+Q] \tag{5}\]  
Figure 1: (**a**) Proposed design steps of the antenna. (**b**) Antenna return loss comparisons at various phases of design.  
here,  
\[P=\tan\hbar\bigg{\{}1.785\log\left(\frac{hs}{g}\right)+1.75\bigg{\}} \tag{6}\]  
\[Q=\frac{\text{K}(\text{g})}{\textit{l}s}0.04-0.7\text{k}+0.01(1-0.1\epsilon_{r}) (0.25+\text{k}) \tag{7}\]  
where \(K(k)\) denotes the first sort of elliptical integral and \(K(K)\) its complement. The feed strip width (_wf_) is 0.26 mm, and the gap (_g_) between the feed strip and the ground is 0.03 mm, with _l_s being the substrate thickness.
## 3 Results, Analysis, and Simulations
### Link Budget, Coverage, and Capacity Calculation Results  
In our setup, we use MIMO antennas of 64 \(\times\) 64 Tx-Rx for coverage and capacity and a modulation order of 4 for the NLOS scenario and 8 for the LOS scenario. In addition, we use one carrier component, with numerology of 1 and 2 for 3.5 GHz/28 GHz, respectively. For more details about these configurations, the reader can refer to Table 5. In Table 6, we show the results of the Link Budget, coverage, and capacity planning for 3.5 GHz/28 GHz in LOS/NLOS scenarios.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.7 Obfuscation Techniques  
In smart cities, CCTV cameras are often used for traffic management, public safety and crime prevention. However, the footage captured by these cameras often contains sensitive information about individuals, such as their faces and license plate numbers.  
To anonymize this data, it is possible to use an anonymization technique called \"obfuscation\", which aims to conceal sensitive information by making it difficult to read or understand. Other obfuscation techniques include masking, which replaces sensitive information with a fixed value, and generalization, which replaces sensitive information with a less specific value.  
Docbyte's Real-time Automated Anonymization uses a combination of image processing and computer vision algorithms to automatically detect and anonymize sensitive information in video streams, such as faces and license plates. The anonymization technique used in this technology is called \"blurring\" or \"pixelation\", which is a method of obscuring sensitive information by replacing it with a blurred or pixelated version. This technique makes it difficult to identify specific individuals or objects while still allowing the overall scene to be visible.  
This technology could be applied in the context of smart cities to enhance protection of the privacy of individuals captured on CCTV cameras.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result
### Result
#### 5.5.2 Comparison Results
The alternative hypothesis to the null hypothesis is as follows:  
**H1a, H2a, H3a, H4a, H5a,** and H6a**: There is a significant difference between the proposed model and DNNLoc for Tomcat, Aspect), Birt, Eclipse UI, JDT, and SWT. **H7a, H8, H9a, H10a, H11a,** and H12a**: There is a significant difference between the proposed model and LR for Tomcat, Aspect), Birt, Eclipse UI, JDT, and SWT. **H13a, H14a, H15a, H16a, H17a,** and H18a**: There is a significant difference between the proposed model and the BL for Tomcat, Aspect), Birt, Eclipse UI, JDT, and SWT. **H19, H20, H21a, H22a, H23a,** and H24a**: There is a significant difference between the proposed model and NB for Tomcat, Aspect), Birt, Eclipse UI, JDT, and SWT. Table 4 presents the statistical test results, listing the \(p\)-values obtained for each hypothesis. A threshold of 0.05 was used to determine statistical significance. If the \(p\)-value was less than 0.05, the null hypothesis was rejected, and the alternative hypothesis was accepted, indicating that the proposed model performed significantly differently from the baseline. The statistical results show that for all hypotheses, the \(p\)-values were significantly lower than 0.05, confirming the statistical significance of the proposed model's performance improvements. For instance, H10 (Tomcat vs. DNNLoc) had a \(p\)-value of 1.11 \(\times\) 10\({}^{-05}\) (0.0000111), which is well below the significance threshold, leading to the rejection of the null hypothesis. Similar trends were observed across all other comparisons. The Wilcoxon signed-rank test was applied in a single case (H150) where normality was not satisfied, yielding a \(p\)-value of 0.0156, still below the significance threshold, confirming that the proposed model performed significantly better than BL for that specific case. Overall, these results validate that the proposed LSTM-Attention-based bug localization method significantly outperforms the baseline models (DNNLoc, LR, BL, and NB) across all evaluated software projects. The statistical significance of the improvements highlights the robustness and reliability of the proposed approach in accurately localizing buggy source code files.
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Multimedia Content Protection Techniques
#### 2.6.3 Digital Watermarking  
Unlike multimedia encryption, digital watermarking provides posterior protection when the multimedia content is decrypted by the authorized users. It imperceptibly alters the original content (host signal) by hiding the identification information (watermark) in it. This information can later prove ownership, and verify the authenticity of the carrier signal. A digital watermarking system generally includes two stages: watermark embedding and extracting. In the embedding algorithm, a watermark is embedded into the host signal to produce a watermarked signal, while in the extraction algorithm, the watermark is extracted from the manipulated/modified signal. If the signal was not modified during transmission, then the watermark is still present and it can be extracted. The watermark detection can only verify ownership, whereas watermark extraction can prove ownership. A secret key is used during the embedding and the extraction processes in order to prevent illegal access to the watermark.  
Each of these following properties must be taken into consideration when applying a certain watermarking technique [33]:  
* Imperceptibility: The perceptual similarity between the original and the watermarked versions of the digital content. The embedded watermark must not introduce distortion, which can cause quality degradation.  
* Robustness: The ability to detect the watermark after common signal processing operations (such as cropping, compression or additive noise). A watermark must be robust enough to withstand all kinds of signal processing operations (at least below some distortion threshold). Depending on the robustness against attacks, digital watermarking can be categorized as robust, fragile and semi-fragile.
* Capacity: The number of bits a watermark encodes within a unit of time (or space in the case of still images).
* Security: The ability to resist intentional and/or malicious attacks. A watermarking algorithm must be secure in the sense that an attacker must not be able to detect the existence of embedded data or extract/remove it. Watermark information should only be accessible to the authorized parties.  
Digital watermarking has been widely and successfully applied across a wide range of applications, such as copyright protection, transaction tracking, content authentication, broadcast monitoring, copy control, device control and legacy enhancement. The majority of the watermarking schemes proposed in recent years across the above mentioned range of applications focuses on producing image and video watermarked data, and very few focus on audio content.
# A Proposal for Rapid Assessment of Long-Distance Oil and Gas Pipelines After Earthquakes
###### Abstract  
A substantial proportion of long-distance oil and gas pipelines in China traverse active faults and high-risk areas characterised by intricate topographic and geological environments. These pipelines are susceptible to a range of safety concerns, exacerbated by the increasing frequency of strong earthquakes in recent years. To address this issue, a comprehensive risk investigation framework has been proposed for long-distance oil and gas pipelines following seismic events. This initiative aims to ensure the safety of pipeline transportation. In this paper, the elements of pipeline safety evaluation under the influence ofoseismic hazards are first organized, followed by a construction of post-earthquake pipeline safety rapid assessment theoretical framework based on the seismic geological disaster risk evaluation system. Each method in the system is then introduced one by one. Unlike existing studies that predominantly focus on localized fault activity or static risk assessment, our framework introduces three key innovations: (1) a hierarchical integration of multi-source monitoring data (SCADA, UAV-AI, and numerous monitoring devices) into a unified GIS platform, overcoming the fragmentation of existing systems; (2) a dynamic four-step evaluation process (susceptibility \(\rightarrow\) hazard \(\rightarrow\) risk \(\rightarrow\) safety) that incorporates both pre-earthquake geological conditions and post-earthquake real-time triggers (e.g., PGA, rainfall); (3) a novel risk matrix mechanism for pipeline safety, which dynamically updates risk levels based on field monitoring data rather than relying solely on probabilistic models. This study provides a novel theoretical framework for assessing the safety of pipelines after earthquakes, which can provide a timely basis for pipeline management decisions and reduce the potential damage to pipelines caused by earthquakes. It is important to note that this framework is still in a preliminary stage and needs to be continuously deepened and optimised.  
long-distance oil and gas pipeline; earthquake geohazard; safety assessment; GIS; multi-source monitoring data +
Footnote †: journal: Applied sciences  
Article