esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design and Analysis of Algorithm
### Vehicle Control Algorithm  
The control algorithm for CAVs is developed within the MDP environment framework, leveraging the network architecture of the TD3 training algorithm. This algorithm takes the speed, position, signal phase, and timing information obtained from vehicle sensors as input. Through the training process, the algorithm outputs an action strategy to the vehicle controller for optimization of acceleration, ensuring smoother, safer, and more efficient driving behaviors.  
Additionally, as the vehicle approaches intersections, the algorithm undertakes a detailed assessment of traffic signals. Specifically, it evaluates whether the remaining duration of the green light is sufficient for the vehicle to navigate the intersection smoothly and safely. Following this analysis, the algorithm issues precise commands to the vehicle controller, directing it to accelerate or decelerate as necessary. The flow chart of this algorithm is shown in Figure 3.  
The environmental input data involve collecting information on the vehicle's speed (\(v_{t}\)), position (\(x_{t}\)), and signal light status, including the current status of signal lamps (\(\phi_{t}\)) and the remaining duration of the green light (\(g_{t}\)). Through multiple iterations equal to the number of training epochs multiplied by the ratio of sample volume to batch size, the  
Figure 3: Flow chart of the Connected and Automated Vehicles (CAV) control algorithm.  
action strategy with the maximum reward, namely the optimal acceleration, is output to control the vehicle. The algorithm proceeds through the following specific steps:  
1. Initialization: Upon initiation, the environment state (\(S_{t}\)) is reset, and essential road and traffic demand data are transmitted to the vehicle controller. The TD3 algorithm receives state information, including speed, position, and SPaT. It initializes action (\(A_{t}\)) and provides a predicted sequence of actions to the environment.
2. Interaction with the environment: After receiving the action sequence, the environment calculates the reward (\(R_{t}\)) of the vehicle until it approaches the road boundary. Subsequently, the calculated state and reward are transmitted back to the controller. The TD3 algorithm stores these tuples \((S_{t},A_{t},R_{t},S_{t+1})\) in the experience pool, accumulating valuable training samples.
3. Training: Training begins once the replay buffer reaches its capacity, utilizing the stored samples to refine decision-making for vehicle actions. The algorithm continues training until the maximum exploration step is reached, signifying the conclusion of the current episode of training. Upon reaching the maximum number of iterations, the algorithm indicates the attainment of the terminal state.
4. Output: The algorithm outputs a control strategy (\(\pi(a_{t})\)) for driving actions, including uniform speed, acceleration, and deceleration. This strategy is meticulously designed to maximize cumulative rewards (\(R^{*}\)), reflecting the algorithm's learned optimal behavior in response to the dynamic road environment and traffic conditions.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
###### Abstract  
In recent decades, there has been considerable interest in developing underwater remotely operated vehicles (ROVs) due to their vital role in exploring ocean depths to perform missions in various applications, including offshore oil and gas, military and defense, scientific research, and aquaculture. To this end, researchers must consider multiple aspects to develop ROVs, such as general design, power and thrust system, navigation and control, and obstacle avoidance. Accordingly, this paper proposes an integrated framework for designing and implementing an ROV prototype, considering the mechanical, electrical, and software systems. Eventually, image processing was implemented using Python to examine the ROV's capabilities in performing underwater missions. The proposed design employs six thrusters to provide controllability of the ROV in six-degrees-of-freedom (DOF). We coated the track width of the printed circuit board (PCB) with a composite mixture of tin, silver, and gold to resist corrosion and harsh environments, enhance the circuit performance and solderability, and increase its life span. The PCB was designed to sustain 30 A with 10 cm \(\times\) 10 cm dimensions. The image processing results revealed that the proposed ROV could successfully identify the benthic species, follow the desired routes, detect cracks, and analyze obstacles.  
ROV; ocean robotics; underwater detection; marine; image processing; sensors 2023
## 5 Results and Discuss
### Comparison of Defects Categories and Model Structures  
To dig out the potential reasons for the different performances of these three models in our results, the processing differences of the models were analyzed according to the characteristics of the different defects. The feature fusion method in YOLOv5 and YOLOv8 is used for detecting small and dense objects, and mostly B-scan images of defects belong to small objects except for head defects, so that the YOLO series is superior to Faster R-CNN. Unlike the FPN layer of DETR, YOLOv5 adds a bottom-up feature pyramid behind the FPN layer, YOLOv8 also employs the FPN and utilizes anchor-free detection, which significantly accelerates the model's operational speed and achieves enhanced detection performance. With this combination, the FPN layer conveys powerful semantic features from the top-down and the feature pyramid conveys powerful localization features from the bottom-up, aggregating parameters from different backbone layers to different detection layers, which further improve the feature extraction. Furthermore, the mosaic data enhancement method as an improvement (trick) in YOLOv8 referenced the CutMix data enhancement method, which reads four images at a time, then flips, scales and cuts the four images, respectively, and arrange them in four directional positions to form a new image. The method enriches the detection background, solves the problem where the target scale in the datasets is too singular [30], and further enhances the localization capability of the model. However, the bag of specials (BoS) in YOLOv8, like SPP, Spatial Attention Module (SAM), Mish activation functions, and GIOU-loss, improve the detection accuracy of the model but add a little inferential cost; the training speed of YOLOv8 is slightly faster than that of YOLOv5 (4 h vs. 7 h). Figure 10 shows the contents of the four categories of defects in the B-scan images of the damage identified by the YOLOv8 algorithm: (a) rail head defects, (b) rail web, (c) head and bothhole defects, and (d) base defects, identified by the YOLOv8 algorithm in the B-scan images.
## 5 Evaluation
### Individual Makespan Results  
For reference, Tables 8-10 show _makespan_ or the total CPU time of each worker and the largest CPU time difference between the workers and the three algorithms. For _OpenFOAM_, no job was assigned to _PCI-PC4_, because all of the 32 jobs can be executed simultaneously at _PC5_ and _PC6_. The largest CPU time difference by the proposal is smaller than the ones by the others, except for _ColorGame_, _SoccerMatch_, _AnimalTour_, and _MyLibrary_, where in Table 4, the increasing CPU time of _PCI_ is much larger than other workers, and the far smaller number of jobs was assigned. Therefore, the proposal can balance well the job assignments among the workers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Job Type** & **FCFS** & **T-FCFS** & **Proposal** & **LB** \\ \hline BassixAppX1 & 536 & 268 & 221 & 203.04 \\ BassixAppX2 & 470 & 235 & 184 & 178.67 \\ ColorGame & 621 & 276 & 233 & 224.04 \\ SoccerMatch & 828 & 414 & 370 & 356.03 \\ AnimalTour & 666 & 319 & 289 & 262.82 \\ MyLibrary & 520 & 260 & 238 & 227.07 \\ OpenPose & 272 & 272 & 220 & 209.97 \\ OpenFOAM & 1044 & 131 & 131 & 81.55 \\ Total & 4957 & 2175 & 1886 & 1743.19 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum _makespan_ results (s).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Job Type** & **PCI** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** & **Diff.** \\ \hline BassixAppX1 & 536 & 516 & 516 & 510 & 506 & 500 & 36 \\ BassixAppX2 & 470 & 450 & 450 & 440 & 435 & 442 & 35 \\ ColorGame & 621 & 574 & 574 & 570 & 560 & 570 & 61 \\ SoccerMatch & 828 & 770 & 770 & 780 & 792 & 775 & 58 \\ AnimalTour & 638 & 666 & 666 & 650 & 612 & 620 & 54 \\ MyLibrary & 520 & 500 & 500 & 462 & 462 & 480 & 58 \\ OpenPose & 240 & 264 & 264 & 272 & 261 & 252 & 32 \\ OpenFOAM & 840 & 844 & 844 & 1044 & 917 & 981 & 204 \\ \hline \hline \end{tabular}
\end{table}
Table 8: FCFS _makespan_ detail (s).
## 2 Methodology
### Computational Domain and Boundary Conditions  
As shown in Figure 2, the entire calculation domain was 600 m long, 72 m wide, and 36 m high. The train was located in the middle of the width of the computational domain and was lower in the height direction. As described in Section 2.1, the entire computational domain could be divided into three sections in the length direction (shown in Figure 2a). Only the middle section of 200 m in length was filled with a crosswind given by a velocity inlet, and its speed \(V_{w}\) was applied to a constant of 20 m/s. The other outer surfaces of the domain were all pressure outlets to replicate the reality. As shown in Figure 2b, an overset mesh was applied in current simulations to produce a relative motion of the train, bringing many more flexible mesh modifications during motion than the traditional meshing strategy. It had an independent grid distribution and exchanged real-time grid information with the background domain. The nose tip of the train head was 10 m away from the front end of this overset region, and the nose tip of the tail was 15 m away from another end, which ensured the flow development and stable information exchange between the two domains. This overset region had a total length of 102 m, a width of 5 m, and a height of 4.6 m, as shown in Figure 2d. The interfaces between the overset region and the larger domain were set to overset mesh boundary conditions, and the bottoms were set to the wall boundary. The distance from the train surface to the outer surfaces of the overset region allowed enough grid layers to be distributed in the space between the exchange surface and the train surface, making information exchange more reliable. The nose tip of the head of the train was initially located at a position 100 m away from the crosswind region and ends until the nose tip of the tail car reached 100 m out of the crosswind region, and the entire travel distance was 477 m. With a train speed \(V_{t}\) of 300 km/h, the physical time for the simulation was 5.724 s.
## 2 Materials and Methods
### Catalytic Activity Test and Optimization of Transesterification Conditions  
In the transesterification process, the performance of the produced catalysts was evaluated. The reactions were carried out at 338 K for 3 h with a 5 wt.% catalyst amountand a methanol/oil molar ratio of 10:1. The transesterification processes were carried out in a three-neck reaction flask with a magnetic stirrer and a water-cooled condenser. The flask was charged with 100 g of Jatropha oil and then heated in the water bath to the desired temperature. Then, methanol and catalyst were added to the oil, and the reaction mixture was agitated at 600 rpm. Centrifugation was used to separate the biodiesel phase from the catalyst and glycerol after the end of the transesterification process. Finally, rotary evaporation was used to eliminate any residual methanol in the biodiesel phase. To determine the FAME content of the product, the obtained biodiesel product was analyzed on an Agilent 7820 GC by using an internal standard method. The previously mentioned techniques were used to determine the conversion of oil to biodiesel [29]. The content in FAME of the biodiesel sample was determined using gas chromatography (GC, Agilent 7820, FID) following the European regulated procedure EN14103. In the work, biodiesel samples were analyzed by GC to determine the FAME content of the product. The GC was equipped with an HP-1 capillary with dimensions of 30 m \(\times\) 0.32 nm \(\times\) 0.25 \(\upmu\)m. Sample volumes were 1.0 \(\upmu\)L, the carrier gas was nitrogen, and the GC sample was separated in a constant flow mode with a flow rate of 4.0 mL-min\({}^{-1}\). A split injector was used with a split ratio of 40 and a temperature of 553 K. The oven temperature was initially held at 423 K, then elevated to 473 K at a rate of 293 K-min\({}^{-1}\) and further elevated to 503 K at a rate of 274.8 K-min\({}^{-1}\), held for 1 min, and finally raised to 533 K at a rate of 283 K-min\({}^{-1}\). The total run time was 26 min. The internal standard was methyl tridecanoate. To investigate the optimum conditions for the transesterification of Jatropha oil, CaNa/AC was applied to conduct a series of transesterification reactions using various catalyst amounts (1-5 wt.%) and temperatures (323-343 K) at different methanol/oil molar ratios (6:1-14:1) and times (1-5 h). Each transesterification reaction was performed in triplicate, and the average values were reported.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
### Evaluation Multiple Object Tracking Performance
Overall, our method consistently achieved the best results in FN and MOTA across the three experimental videos, demonstrating its adaptability to various video scenarios and its ability to overcome diverse challenges for accurate tracking outcomes. The practical tracking outcomes of the proposed method are showcased, accompanied by an analysis. The results for PETS09-S2L1 are depicted in Figure 9(a). It is evident that our method accurately tracked all objects, even when pedestrians with ID 1 and 2 briefly stopped and temporarily occluded each other during their encounter. Despite this, our tracker maintained the continuous tracking of both pedestrians. Similarly, pedestrian ID 3 navigated around pedestrians with ID 1 and 2 after passing through a streetlight that momentarily obstructed the view. It also experienced occlusion during this process, but tracking seamlessly continued after the occlusion was cleared. The results for PETS09-S2L2 are shown in Figure 9(b). In this video, a significant number of pedestrians walked randomly, and the pedestrian density was higher, leading to more pronounced occlusions among pedestrians. The results show that most of the targets were accurately tracked. Pedestrians located farther away from crowded areas, such as pedestrians with ID 40 and 58, were successfully tracked. Among the pedestrians within  
Figure 9: Tracking results in (**a**) PETS09-S2L1, (**b**) PETS09-S2L2, (**c**) TUD-Crossing. the crowded areas, apart from individual pedestrians who could not be detected owing to extensive occlusion, the majority, including pedestrians with ID 41 and 11, were also effectively tracked. The results for the TUD-Crossing dataset are illustrated in Figure 10c. Although this video featured fewer pedestrians, the use of a frontal view camera angle resulted in relatively larger areas of occlusion among pedestrians. The video depicted multiple pedestrians converging in the same direction, such as pedestrians with ID 2, 3, 4, 5, and 6, sometimes temporarily obscured by pedestrians moving in the opposite direction, such as pedestrian ID 1. From the images, it is evident that these pedestrians' IDs were maintained successfully, even after brief occlusions. Additionally, pedestrian ID 1 continued to be accurately tracked despite the presence of multiple pedestrians in the background.
## 3 Model Description
### Cnn-Lstm  
Because the direction of one-dimensional convolution kernel is stable, data features in time direction can be automatically extracted, therefore, one-dimensional CNN has better predictive performance in time series. Furthermore, the prediction performance of one-dimensional CNN and LSTM hybrid model for greenhouse environmental parameters is still unclear [43; 44; 45].  
In the study, the hybrid model of the one-dimensional CNN and LSTM is designed for the prediction of environmental parameter, including the input layer, two one-dimensional convolutional layers, two Maxpooling layers, the flatten layer, the LSTM layer, the dropout, the fully connected layer and the output layer, the framework of the LSTM prediction model constructed is shown in Figure 7. The one-dimensional convolution layer is located after the input layer, the convolution layer is followed by the Maxpooling layer, two one-dimensional CNN layer were used to extract local features from time series. The input information is output through the flatten layer as the input of the LSTM layer. The flattening layer is used for size transformation to facilitate the output to LSTM layer. The dropout layers were added after the LSTM layer to protect the model from the overfitting. The dense layer has the deeply connected neural network structure. The final layer of the hybrid model is the output layer for implementing the prediction of greenhouse environmental parameters.  
The number of CNN layers neurons, the pool size, and the number of LSTM layers neurons were optimized in the study. These details and parameters also used for the orthogonal experimental design for the CNN-LSTM hybrid model are given in Table 5. The kernel size parameter represents convolution window size in the 1D CNN model. The network architecture has also been trained in different combination hyperparameters, which was also generated using the orthogonal design. The effect of seven factors was studied including the number of first CNN layer neurons (A\({}_{\text{c}}\)), the first pool size (B\({}_{\text{c}}\)), the second CNN layer neurons (C\({}_{\text{c}}\)), the second pool size (D\({}_{\text{c}}\)), the first LSTM layer neurons (E\({}_{\text{c}}\)) and the second LSTM layer neurons (F\({}_{\text{c}}\)). The number of neurons that gave the minimum error was determined. Each factor involves three different levers, as shown in Table 5.  
For the CNN-LSTM model, 80% of the data was also used as the training set and 20% as the test set. Each model has been trained on the dataset of 18 combinations of training and test datasets. The CNN-LSTM model was also developed using a tensor flow and Keras library based on Python 3.6. Moreover, with regard to the other network parameters, the following values have been set: batch size = 128, and number of epochs = 500, activation = ReLu, Kernel_initializer = uniform, loss = MSE, validation_split = 0.2.  
Figure 7: The frame of the CNN-LSTM network for regression problems.
## 4 Results and Discussion
### Effect of Construction Condition
The peak value of the vertical displacement of the SSC is 41.14 mm, and negative vertical displacement appears at the straight tube area. The result indicates that the accident causes severe localized uplift of the SSC, which needs to be taken care of before reconstruction. Figure 15 shows the vertical displacement of the SSC in pressurized case. The distribution of vertical displacement of the SSC is similar to that of the unwatered case. However, the peak value drops to 22.45 mm, which is around half of that in unwatered case. Moreover, the maximum negative displacement is 18.96 mm, which is slightly larger than that in the unwatered case. This result verifies that temporary IWP also helps a lot in controlling the uplift of the SSC. Figure 12: Vertical displacement of the stay ring (m): (**a**) unwatered case; (**b**) pressurized case. Figure 14: Vertical displacement of the SSC (unwatered case) (m): (**a**) top view; (**b**) bottom view. Figure 13: Raising deformation of the upper flange. Figure 15: Vertical displacement of the SSC (pressurized case) (m): (**a**) top view; (**b**) bottom view. After the raising deformation accident, it is of great importance to acquire the post-accident stresses of steel structures, which determines whether it is necessary to replace them. Moreover, it also has a lot to do with the post-accident assessment of the structures and the rearrangement of the fixing devices. Figure 16 shows the Mises stress of the SSC in the unwatered case. The stress value is below 257.7 MPa in most area, and the peak value is 385.6 MPa. Meanwhile, it is quite obvious that the stress value at the external load area is reasonably larger. Actually, the SSC has been designed to withstand the bursting IWP of maximum headwater plus water hammer with a conservative allowable stress intensity considered by reference to some design manuals or specifications for steel pencstocks [27; 28; 29]. In this case, the yield strength of the SSC is designed to be 490 MPa. Therefore, the structural strength of the SSC should not be a major concern, but its severe deformation is still in need of repair and assessment. In fact, however, it is worth mentioning that the reconstruction of this SCS was still carried out in an unwatered condition. With overall rearrangement and improved quality control of the concrete construction, the reconstruction went on well and it has been in operation since then.
## 3 KIDE4I: A Generic Semantic-Based Task-Oriented Dialogue System
### Key Element Extraction  
The main function of the key element extraction component, thoroughly described in [18], is to obtain the relevant key elements from a transcribed user voice command that conveys a piece of information. Furthermore, it has been designed so as to learn over time from new interactions by generating training data semiautomatically to implement a supervised key element extraction component in the future.  
Figure 2 shows the architecture for the component. When the command arrives to the key element extraction component, first, its syntactic analysis is obtained through the linguistic tool Freeling [30], in its version 4.0.  
This syntactic representation of the user command serves as input to the next subcomponent, **Foma**[31]. Foma is a software that is able to convert regular expressions--defined in grammars--to finite-state automata, which is used in different natural language applications, such as morphological analysis. In this case, _definitions_ and _rules_[18] need to be defined. On the one hand, definitions--most of which can be reused between use cases--model the different syntactic structures that are of interest for the use case (e.g., a _noun phrase_ can appear in the syntactic tree as a noun--_container_--or as a noun + adjective--_blue container_). On the other hand, according to the necessities of the use case and considering  
Figure 1: KIDE4I architecture.  
Figure 2: Architecture of KIDE4I’s key element extraction component.  
the modelled definitions, a set of rules have been defined (e.g., _targets_ appear as _noun phrases_ in the use case at hand). These rules are used to delimit by tags the parts in the syntactic tree that correspond to the key elements in a given use case.  
Since the syntactic tree does not include the words involved explicitly, but through indexes, a last step in processing is necessary to obtain key elements from user commands. Given a tagged syntactic tree, the **interpreter** subcomponent extracts the words that correspond to the indexes in the tree and classifies them according to the key elements defined for the use case.  
The result of this key element extraction component is the set of key elements extracted from the user command, classified considering the necessities of the use case.  
Once enough data has been obtained to train a supervised model, the Foma subcomponent is to be complemented with a machine-learning-based subcomponent that will tag the user command at word level, using BIO tags, considering the syntactic tree obtained from Freeling [18]. In this case, the interpreter will parse these word-BIO tag pairs to obtain the set of keywords for the command in the format described above.