esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 6 Results and Discussion
The fixed distance between the alert and Emergency Belt can be made dynamic by considering different vessels and their deceleration capacities. The range of this distance highly impacts the time bridges get for evacuation and opening. On the other hand, the amount of time a movable bridge needs to evacuate bridge vehicles and open depends on the bridge length, bridge positioning, and complexity of different transportation modes that link to the bridge. Therefore, the distance between Alert Belt to an Emergency Belt at each side of the bridge could also be calculated based on these parameters. Future research into determining the dynamic distance between the Alert Belt and Emergency Belt for different movable bridges and parameters would be highly useful. Furthermore, this approach should be validated and improved using more case studies, as Fort MadisonSwing Bridge was the only bridge used in this study. Other movable bridges apart from the swing bridges will also be considered in future studies to understand the feasibility of the system with more focused parameterization using the designed software system and practical implementation wherever possible. The braking distance of the ship depends on the initial speed of the ship and the load of the ship. If the initial speed is higher, it takes more time to stop and tends to move further due to inertia even after stopping the ship. According to this paper, a ship speed limit of 11.5 km/h (around 6 knots) has been suggested to be maintained. High loading, tank ships and other water vessels may have a larger braking distance after the Emergency Belt. If the braking distance exceeds the distance limit, a collision may occur between ships and bridges even after the ship is stopped. Such cases may need a broader space to maintain an assured braking distance. In this research, the maximum default DSRC 300 m range has been suggested to be maintained affordable and handy with a conventional default sensor rather than high ranged and costly equipment. Further research can be performed considering the shipload factor to maintain safety assurance for braking distance. Lastly, it is also important to examine the impact of a longer-range DSRC on movable bridge emergency safety. Ideally, this examination would be in the form of a simulation-based scenario, which is intended to be described in a future research analysis on the concept.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 4 Optimization Algorithm of Tension Distribution  
As mentioned above, the cable-driven parallel robot is driven by eight cables, but the movement of this robot only includes translation and rotation in three directions. As a result, this parallel robot is a redundant robot. Due to its redundancy, there are infinite sets of tension distributions when the motion state of the robot is known. In order to obtain the optimal tension distribution, an optimization method is addressed for the tension distribution in this section.
## 3 Results and Discussion
### Image Acquisition  
Molten pool images were captured using the proposed vision system during mild-steel GMAW. Images were captured using bandpass filters for wavelength bands with high and low relative intensities. The FWHM was 10 nm for all bandpass filters used in the experiment. Subsequently, when a filter was selected for the wavelength band suitable for monitoring, the conditions and ND filter that minimized the interference of the arc plasma were selected. Figure 8 shows the captured images. When bandpass filters of 420 nm and 508 nm with high relative intensities were used, radiation from the arc plasma made it impossible to observe the molten pool. When filters for wavelengths with low relative intensities were used, the molten pool was observed. At 660 nm, the relative intensity was low. However, the red color emitted from the molten pool reacted strongly because of the high quantum efficiency of the image sensor in Figure 4, causing the interference of arc plasma and image saturation.  
At the NIR wavelengths of 752, 808, and 830 nm, the interference of the arc plasma was relatively low, and the molten pool was observed. Among them, the 830 nm wavelength exhibited the lowest interference. In addition, the edges of the molten pool and base metal were clear. Next, images were captured by combining the 830 nm band-pass and ND filters [OD0.5 (transmission: 32%), OD1.0 (transmission: 10%), and OD2.0 (transmission: 1%)] to reduce image noise, and Figure 9 shows the corresponding images. As the OD increases, the saturation area decreases; however, the sensitivity of the images decreases.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 3 A Festival Mobility Model
### The Sonar Festival Dataset
#### 3.1.3 Analysis  
In order to understand the mobility of people during a festival, it is crucial to perform an analysis of the current dataset. The conclusions of this analysis will provide us with more in-depth knowledge about the mobility patterns and will help us later replicate this behavior with a synthetic mobility model. The following histograms will offer some insights into the Sonar festival dataset. For this analysis, only data from the first day of the festival will be discussed, but the conclusions hold for the other two days.  
Figure 2a displays the distribution of nodes across all six locations at the festival venue. The most popular location is 3, with about 20,000 nodes being detected during the day. Although the number of people attending the first day of the festival was 9616 after blacklisting some nodes, a person can show up multiple times at a certain location throughout the day, which explains why approximately 20,000 nodes were present at location 3. Locations 4 and 6 displayed a similar level of popularity, reaching figures of slightly under 10,000 and 7500, respectively. The least popular locations were 5, 1, and 2.  
Figure 2b shows the number of attendees during every hour of the first day of the festival. The number of people attending the festival steadily increases, starting with 2:00 p.m. until 5:00 p.m., when there is a slight drop. It can be assumed that the main event started at 7:00 p.m., given the fact that the number of attendees reached a peak at that time. The chart follows a downward trend after 8:00 p.m., with the least number of people being recorded after 10:00 p.m.  
Figure 1: Outlier values for day 1 of the Sonar dataset.
## 3 Results and Discussion
### Fingerprint of Lilium from Different Producing Areas
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline retention time & 5.057 & 7.595 & 8.551 & 9.582 & 9.996 & 11.316 & 13.608 & 14.134 & 14.666 \\ S1 & 553.160 & 720.003 & 2493.508 & 16,684.236 & 745.833 & 1123.478 & 19,941.123 & 1491.304 & 459.358 \\ S2 & 352.577 & 619.850 & 1385.452 & 8930.486 & 772.572 & 999.958 & 12,704.896 & 948.117 & 232.146 \\ S3 & 76.810 & 137.818 & 236.915 & 1655.764 & 134.458 & 172.626 & 2185.241 & 181.494 & 45.762 \\ S4 & 1867.483 & 1194.929 & 3040.669 & 18,721.670 & 721.511 & 2111.509 & 28,837.820 & 1466.589 & 1005.349 \\ S5 & 450.022 & 559.284 & 1810.930 & 11,021.721 & 568.574 & 1124.389 & 15,685.887 & 1128.031 & 708.934 \\ S6 & 1632.156 & 1606.044 & 2780.719 & 18,779.398 & 1011.487 & 2117.609 & 25,357.936 & 1882.969 & 918.524 \\ S7 & 175.348 & 23.990 & 471.366 & 4771.725 & 828.921 & 41.486 & 2720.517 & 40.642 & 43.021 \\ S8 & 838.258 & 59.536 & 680.910 & 7264.089 & 543.151 & 104.057 & 3082.765 & 1739.468 & 120.907 \\ S9 & 291.285 & 272.558 & 1993.438 & 18,746.029 & 1995.966 & 101.064 & 7211.860 & 73.377 & 76.055 \\ S10 & 3018.340 & 1225.741 & 1132.852 & 14,390.272 & 693.518 & 622.854 & 6689.991 & 634.910 & 6259.253 \\ R & 925.544 & 641.975 & 1602.676 & 12,096.539 & 801.599 & 851.903 & 12,441.804 & 958.690 & 986.931 \\ relative area & 102.83 & 85.34 & 61.38 & 52.01 & 59.68 & 94.02 & 78.12 & 72.68 & 191.31 \\ RSD & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The relative area and retention time of the common peaks of ten batches of Lilium. Figure 2: The control graph R of Lilium. SPSS 27.0 software was used to standardize the relative peak areas of the common peaks of ten batches of Lilium fingerprints. Custer analysis was carried out using the inter-group connection method, where the inter-sample distance was calculated by square Euclidean distance. The results are shown in Figure 3. When a clustering distance of 10 was applied, the data could be grouped into three main categories. The first category included S1, S2, S3, S4, S5, and S6, all of which belong to _Lilium lancifolium_ [PERSON]. The second category comprised S7, S8, and S9, all of which are classified as _Lilium brownii var. Viridulum_ [PERSON]. The third category consisted of S10, identified as _Lilium davidi var. unicdor_ cotton. The HPLC gradient elution method was used to establish the fingerprints of 20 batches of fresh Lilium from _Lilium lancifolium_ [PERSON], _Lilium brownii var. [PERSON]_ [PERSON], and _Lilium regale_ [PERSON]. The fingerprints were divided into two categories by cluster analysis, with a common peak similarity of 0.930-0.997 [24]. The fingerprint of 15 batches of C. dendron samples was established using the UPLC method, and the similarity was found to be above 0.910 [25]. This study compared the similarity of three kinds of Lilium, including _Lilium davidi var. unicdor_ cotton, and further expanded the scope of comparison compared to the previously reported literature.
## 1 Introduction
### Study Methodology and Procedures  
We present a prototype of a firefighting drone that can be used to construct firebreaks to prevent the initial spread of grass fires. This prototype is developed and validated experimentally. As the first step, the existing literature and case studies related to field fire suppression techniques and firebreak construction are reviewed. Continuous fire-extinguishing ball deployment, as well as the relevant case studies and experiments, are investigated. In the second step, focus group interviews (FGI) are conducted with three firefighting experts and three drone specialists to evaluate the feasibility of utilizing drones to continuously deploy fire-extinguishing balls for firebreak construction. In these interviews, the design of the proposed continuous fire-extinguishing ball-deployment module is evaluated. In the third step, a prototype based on the evaluated design is fabricated. In the fourth step, experiments are conducted to assess the accuracy of the fire-extinguishing ball-deployment module and automated flight of a ground control system (GCS)-guided drone equipped with the aforementioned module to predetermined target points. Multiple drone operations are executed, and real-time kinematic (RTK) technology is utilized to field test firebreak construction and evaluate the accuracy of the proposed fire-extinguishing ball-deployment module. These tests validate the feasibility of firebreak construction for containing field fires. The research procedures and methods are summarized in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline Introduction & Review of the literature and case studies related to the use of fire-extinguisher drones and wildfire firebreak construction. \\ \hline
2. FGI Expert Survey & - Phase 1: interviews with experts to ascertain the functionality and potential of using fire-extinguisher drones for firebreak construction. \\
2. FGI Expert Survey & - Phase 2: evaluation and review of the design of fire-extinguisher drones. \\ \hline
3. Equipment Production & Production of a consecutive fire extinguisher deployment module by considering expert opinions. \\ \hline
4. Experimental Validation & - Experimental tests to determine the accuracy of fire extinguisher deployment and autonomous drone flight to target locations. - Verification of the accuracy of firebreak construction and effectiveness of fire suppression using drones. \\ \hline Conclusion & Achievements, limitations, and future research directions. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Study flow.
## 5 Validation: The Use of the Ontology to Extract a Historical Site Component from LiDAR Data
### Semantic Segmentation of LiDAR Point Cloud for Object Extraction  
For the purpose of the extraction of the components of a historical site from LiDAR point clouds presented in the previous section, we chose to use a machine-learning approach. These methods allow more automatic segmentation and extraction of information from the LiDAR data. With the machine learning-based solutions, we attempted to regroup the points belonging to each predefined class of objects in the proposed HBH ontology. This necessitates the training of a classifier on a data set as the first step.  
For this purpose, a feature vector is designed to describe the properties of each point. The feature vector generally consists of quantitative values for each feature. For the task of extracting points belonging to our historical building from point clouds, semantic segmentation based on the feature vectors proposed in [38] is chosen to process the input point clouds presented in the previous section. In this work, among others, features such as (i) \"directional height above\" compares the height difference between a point and its neighbours in eight directions, (ii) \"difference of normal\" is based on a normal estimation which can better distinguish when vegetation and man-made objects are used.  
Then, following semantic segmentation of the point cloud, they were used in a CAD-like segmentation. This approach decomposes historical buildings into their components based on their geometric properties. Each building component is identified as a geometric shape from point clouds. The normal estimation is commonly used to extract the geometric properties such as smoothness of surfaces from point clouds.  
A region-growing algorithm is applied to detect the smoothness of surfaces through predefined criteria (e.g., the proximity, smoothness, and curvature) of growing surfaces by comparing the properties of adjacent points. The smoothness is evaluated by the angle between the normal vectors based on the normal estimation of point clouds. An appropriate smoothness threshold depending on point cloud quality (e.g., point density and noise) can mitigate cases with under-segmentation and over-segmentation results. For deciding the smoothness threshold automatically from point clouds, [PERSON]'s method [39; 40] was chosen to help to mitigate the interference of edge points during the growing regions. We ran the region-growing segmentation algorithm continually on the segmentation results from the previous step to decrease under-segmentation results for the case that two planar surfaces connect with a gentle angle. Then RANSAC (RANdom SAmple Consensus) algorithm [41] is used to estimate the geometric parameters of primitives representing building components.
## 2 Materials and Methods
### Data Input of AquaCrop Model and Analysis
#### 2.4.1 Climate Data  
A set of climate data such as rainfall, temperature, humidity, wind speed and sunshine were obtained from the local Huaykhamin weather station and input into the daily temporal model. The data were collected during the rainy season (24 June to 31 December 2013). The average rainfall was 1281.6 mm, the average temperature was 27.3 \({}^{\circ}\)C, average relative humidity was 79.45%, average wind speed (2 m above the surface) was 0.73 m/s and the average duration of sunshine per day was 6.64 h. The daily reference evapotranspiration (ETo) was computed using the full set of data based on the FAO Penman-Monteith method [56] with the help of ETo calculator software [31; 57] and the climate data of the study area as an input. Thus, the maximum reference crop evapotranspiration (ETo) was calculated as 5.1 mm per day, the minimum as 2.3 mm per day and the mean as 3.96 mm per day.  
The default CO\({}_{2}\) file supplied with the AquaCrop model was applied for crop yield simulation. Since both the studied groups were located within approximately 500 m of each other, the same climate data were applied to both. Evaporation was simulated using crop transpiration and soil evaporation and the daily transpiration was used to derive the daily biomass productivity of the crop [26].
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 3 Methodology
### Fdia Localization Method Based on Ga-Cnn-Lstm  
The architecture of the designed model is depicted in Figure 4 and includes an input layer, a 1D convolutional layer, an LSTM layer, a fully connected layer, and an output layer. The input data are a one-dimensional measurement vector representing the measurement values of n instruments at each time instance. The CNN-LSTM model first utilizes the convolutional operations in the CNN part to extract key spatial features from the input vector. Subsequently, the LSTM network conducts a temporal sequence analysis on these features to identify and learn dynamic patterns and long-term dependencies that evolve over time. This allows the model to more comprehensively understand the operating state of the power system and effectively capture FDIAs. The fully connected layer maps the feature vectors to the final classification results, and the output layer applies a sigmoid activation function to output the probability of each instrument being attacked. We introduced a threshold between the model output and the classification labels. When the output value of the sigmoid function is greater than or equal to 0.5, it is classified as 1, indicating an attack; when the output value is less than 0.5, it is classified as 0, indicating no attack.  
Based on the impact on the model's learning capability, generalization ability, training efficiency, and ability to prevent overfitting, the hyperparameters selected for optimization by the GA included learning rate, number of iterations, batch size, activation function, number of convolutional kernels, kernel size, number of convolutional and LSTM layers, size of the LSTM units, and dropout rate. During the initialization phase of the GA, we randomly generated a set of hyperparameter combinations to form the initial population.  
Figure 4: CNN-LSTM model.  
Figure 3: Flow chart of the genetic algorithm.  
Subsequently, using a fitness function, the performance of each individual was assessed. In the iterative process, the population evolved through the genetic operations of selection, crossover, and mutation, in the hope of discovering a hyperparameter combination with superior performance. After several generations of optimization, when the preset maximum number of iterations was reached, we selected the hyperparameter combination with the highest fitness from the population. Finally, this optimal set of parameters was used to train the CNN-LSTM model, ensuring that the model could achieve excellent performance on the given dataset. Figure 5 shows a detailed flowchart of the genetic algorithm in the process of optimizing the model parameters, further explaining the logic of the optimization steps.
## 5 Results and Discussion  
The software running environment was Intel i7-8700K, 3.7 GHz. The operating system was ubuntu 16.04, and the algorithm was implemented by C++ language. The global path planning phase simply generates the discrete path points and obtains the shortest path points according to the A* algorithm, all of which can be completed in a relatively short time. The local path planning uses the B-spline curve to represent the path. Combined with time, map, curvature, and ocean current constraints, the local path planning problem is modeled as the nonlinear optimization problem with multi-constraint conditions shown in the formula (10). Then, the online learning SHH is adopted to solve this problem. In this section, three scenarios with the different marine environment, including only ocean current information, only dangerous area information and both ocean current information and dangerous area information contained, were simulated to test and analyze the running effectiveness of the model and algorithm. The general metaheuristic algorithms, such as particle swarm optimization (PSO) [27], firefly algorithm (FFA) [17], were used in the path planning problem. The FFA algorithm proved to have better performance compared with other algorithms in [17]. In this paper, the results of online learning SHH were compared with the PSO and FFA.  
The number of B-spline control points was set to 7, where the number of variable control points was set to 3 and the curve order was set to 3. According to the parameter design method of [37], the number of iterations of the SHH algorithm was set to 80 times. The number of individual swarms was set to 100, random long-distance jump and mutation operations \(\lambda_{\text{max}}=40\), random movement \(\lambda_{\text{min}}=20\), and follow-up operations \(\lambda=0.4\). Referring to the paper [27], the inertia \(\omega\), the personal influence \(c_{1}\) and the social influence parameters \(c_{2}\) of PSO were set to 0.7298, 1.496, and 1.496 respectively. According to the reference [17], the number of FFA iterations was set to 80, the number of fireflies was set to 100, the attracting factor \(\gamma_{\text{FFA}}=0.05\), and the random moving factor \(\alpha=3\). The iterations times and the number of individual swarms of PSO, FFA, and SHH were set to the same value to compare the difference in the performance of the three algorithms under the same conditions.  
It is worth noting that in the actual program implementation, the operation set only contained the first 6 operators, and did not contain the last 3 operators. After the inclusion of the runaway operators, the convergence of the algorithm drops sharply, and the results of the convergence cannot occur many times in the test. These two operations were not suitable for solving the optimization problem of the path planning model.