esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Object Tracking with DeepSORT  
Object tracking is a video processing task that allows the tracking of objects or instances detected within consecutive frames in videos. It is used to develop many real-world applications, such as autonomous driving, video processing, robotics, and human-computer interaction. Probabilistic, feature extraction, and deep learning-based models have been developed for object tracking purposes. In the last layer of the CNN (Convolutional Neural Network), classification and object detection are performed according to a vector representing the object. In the DeepSORT algorithm, each detected object is passed through the neural network to obtain a vector, and two objects are associated using these vectors. An ID number is assigned to each object tracked on a frame [38; 39].  
In this study, objects detected with YOLOv8 were tracked with the DeepSort algorithm. Thus, unique identification numbers were assigned to objects in consecutive frames. Due to the efficiency, flexibility, ease of use, and customizability advantages of YOLO for object detection, YOLOv8 + DeepSORT algorithms were preferred in this study. The reasons why DeepSORT is preferred over other object tracking methods are presented below:  
* High tracking accuracy due to using deep features;
* Not having a high computational cost;
* Providing image tracking, even in densely crowded environments.
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 4 Feature Vector Extraction Based CNN
### Single-Stream Extraction  
Single-stream extraction is the most common type of CNN. Single-stream extraction is a structure that has been used in many CNNs, such as GoogLeNet. Single-stream extraction has a funnel-shaped structure in which layers are stacked and descended.  
The CNN used in this paper was designed based on Res_Block in Figure 4 consisting of three convolution layers, is a method devised in Res Net [25] that uses the residual learning method. Res_Block simplifies the loss function with a shortcut structure between the input and output and is designed to alleviate the gradient vanishing problem that occurs when designing a deep CNN. The CNN used in this paper was designed based on Res_Block in Figure 4. Res_Block was applied to both Single-stream and Dual-stream extraction structures.  
Figure 4 shows the structure of the Single-stream extraction used in this paper. The \(I_{Merge}\) was constructed by stacking Gabor filter image \(N\) channels and gray-scale inspection images in parallel, and this \(I_{Merge}\) is used as an input to CNN. A feature map with 64 channels was extracted through a convolution layer using a 7 \(\times\) 7 kernel and sampled at a size of 112 \(\times\) 112 using max pooling. After that, the feature map was extracted through 4 Res_Blocks, and max pooling was applied between each Res_Block. The numbers in parentheses of Res_Block mean \(C1\), \(C2\), \(C3\) of Res_Block, and the number of repetitions of Res_Block, respectively. The number at the bottom of Res_Block means the width and height of the feature map, which is the intermediate result of the layer. 2048 \(\times\) 1 size feature vector \(V\) Merge is output from the last Res_Block, and when this vector passes through  
Figure 4: Single-stream network structure based on ResNet50.  
the fully connected layer, it is converted to \(O\) as a classification result. The output result \(O\) consists of a confidence score for OK and stain defects.
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 3 Variance Characteristics of Surface Albedo for Essential Land Types and Scales
### Discussion of Albedo Variances Characteristics  
Numerous studies focus on well-known landscapes and regions worldwide as well as on the globe as a whole. First, the cryosphere, which is covered by perennial ice and snow with high albedo, has attracted much attention, including the Arctic and Antarctic, and high-altitude areas, such as the Asian water tower (i.e., Tibetan Plateau) and the Alps. In terms of vegetation regions, temperate and boreal forests and Mediterranean agricultural landscapes have received widespread attention. Deserts and urban landscapes have also been areas of concern. Analysis shows that surface albedo for typical land types generally varies with some common factors, such as surface structure, moisture, impurity contamination, surface inhomogeneity and roughness, surface density, SZA, and topography. In particular, snow- and ice-covered areas with high albedos have attracted much attention, because a significant change in albedo can be seen during the formation and melting periods of snow and ice. Moreover, increasing attention on special materials shows a trend in finer terrestrial monitoring.  
Therefore, a surface albedo dataset with high spatiotemporal resolution and accuracy is demanded for an elaborate Earth survey. First, these typical variance characteristics summarized in this study are promising to be used as prior knowledge for an accurate estimation. In addition, with rapid development in the spatiotemporal resolution of satellite observations and remote sensing from airborne observations, such as research aircraft [153] and unmanned aerial vehicles (UAVs) [154], albedo variances at multiple scales can be investigated and compared.
# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 2 VANETs Applications
### Safety Applications  
Applications under the safety subgroup include time-sensitive applications. If the relevant information is not delivered in time, its usefulness is lost. The quality of service demanded by safety applications is close to real-time. Inefficient safety-related content dissemination could lead to life loss and disabilities. Road safety applications primarily assist drivers in avoiding vehicle collisions and lowering crash fatality ratios [5; 6; 8; 9]. The majority of applications in this category are location- and time-based. These applications have a more localised spatial scope in terms of the extent of a geographical area in which the information is required and considered valuable (referred to as spatial validity of the content) [5; 6; 9]. Some of the safety applications are as follows:  
* Post-crash notification: The spatial validity of this application is 500 m, and the temporal validity is 30 s [5; 6; 9].
* Emergency vehicle warning: The spatial validity of this application is 500 m, and the temporal validity is 10 m [5; 6; 9].
* Dangerous road warning: The spatial validity of this application is 100 m, and the temporal validity is 10 s [5; 6; 9].
# Malicious Powershell Detection Using Graph Convolution Network
## 4 Experimental Results
### Results
#### 4.2.2 Number of Powershell Scripts  
Figure 10 shows the accuracy according to the number of powershell scripts. We provided accuracy when the number of adjacent nodes was zero and two. When it was 0, the average accuracy was 93.25%. When it was 2, the accuracy was, on average, 97.6%. When it was 2, the accuracy increased by 4.35%. This meant that we increased the accuracy using adjacent nodes rather than using an identity matrix.  
Note that because we used 5-fold cross validation, the accuracy did not increase when the data size increased from 250 to 1000. However, when we used adjacent nodes (e.g., top-2), the accuracy increased compared to using an identity matrix (e.g., top-0).
# Free Vibrations and Flutter Analysis of Composite Plates Reinforced with Carbon Nanotubes
## 3 Results and Discussion
### Free Vibration Analysis of CNT/Nanocomposite Plates
CNTs were analyzed. To the author's knowledge, there are no a priori imposed criteria for selecting the CNT volume fraction. The paper by [PERSON] et al. [19] reported that there was no effect of increasing the thickness-to-width ratio on the natural frequencies of nanocomposite plates. The values of the frequencies were at a constant level, but the authors did not address this, and the results were given only for one volume fraction of CNTs (11%). In the current analysis, it was observed that an increase in thickness at a constant width and length has an effect on the increase in eigenfrequencies, and this is a constant increase (Figure 5). For all CNT fractions analyzed, a constant 2.5-fold increase in the fundamental frequency was obtained for a thickness of 5 mm compared to the values for a thickness of 2 mm. In this sense, the present frequency characteristics can be considered similar to those reported in the work of [PERSON] et al. [19]. In general, an increase in plate thickness causes an increase in natural frequencies. Figure 5: Variation of the fundamental frequency of simply supported nanocomposite plate versus the plate aspect ratio for various plate thicknesses (\(l\) = 2 mm, and \(h\) = 5 mm) and volume fractions of CNTs (1%, 3%, and 5%). Figure 6: Variation of the non-dimensional frequency of simply supported square nanocomposite plates (\(a/b\) =1, \(a/h\) = 200) versus the volume fraction of CNTs for the first five modes. This indicates that even much lower-volume fractions of nanotubes in composite plates (almost 10 times lower than in the work of, e.g., [PERSON] et al. [19] or [PERSON] et al. [14]), allow us to obtain similar dynamic properties of the structure. In addition, selected nanotube fractions in the range of 1-5% CNT allow for a uniform distribution of CNT and are likely to form a more stable CNT-polymer system than higher CNT fractions. The most important thing from the current analyses is that low-volume fractions of CNT percentages can effectively increase the natural frequencies of nanocomposite plates.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 4 Performance Evaluation  
To evaluate the effectiveness of VGRQ, MATLAB simulations are performed. This section highlights the performance parameters utilized in the simulations and presents the results and analysis of VGRQ, comparing it with QDVGDD. Table 2 presents the specific values assigned to various parameters utilized in the simulation process.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 3 BIM and 3D Concrete Printing Process
### The Role of Robotics in 3D Concrete Printing  
Despite being considered a traditional industry in terms of technology, the construction sector is developing robotic technology to help in complex or dangerous tasks [39]. In this sense, the use of robotic technology in construction has the advantages of improving the quality of the final product and reducing costs thanks to the optimization of time and resources [40].  
Robots used for 3D concrete printing are commonly equipped with a nozzle, capable of extruding layers of cementitious materials with specific dimensions. As a complement, both the material preparation and a delivery system are required to continuously feed concrete to the nozzle. In turn, software controls both the robot and the material preparation unit [41].  
The purpose of these robots is to reproduce the movements and joints made by the human arm and hand, which allow up to 7 degrees of freedom. This is possible because of the combination of the 5-axis head together with a 6-axis robotic arm. Their greatest advantages are the improvement in efficiency in construction, a reduction in the margins of error, and an increment in terms of work safety [20].
## 1 Introduction
To address the generalization issue of NDPs in complex robot control scenarios, particularly in robot arms with multiple degrees of freedom, we propose a novel RL approach DDPG-DMP that combines DMP-based control policy with the actor-critic RL framework from Deep Deterministic Policy Gradient (DDPG) [32]. Our method seamlessly integrates a DMP controller, whose parameters are determined by the actor networks, into the modern RL's actor-critic structure to generate smooth and efficient robot control trajectories. DDPG-DMP develops an adaptive inverse controller that can be learned through the RL process to flexibly transfer the output trajectories of the DMP controller to various robot control signals while preserving trajectory continuity. This enables the DMP-based policy to generate control action trajectories that differ from the input states. Therefore, the proposed method enjoys a smooth control of various control signals including torque, angle, and end-effector position without requiring human prior knowledge. Evaluated on various robot arm simulation tasks based on torque, angle, and end-joint displacement control, DDPG-DMP achieved significant improvements in learning efficiency and control performance compared to the original DDPG without DMPs and the related work on NDPs. The relationship between DDPG-DMP and other related works is presented in Table 1 where \(\bigcirc\) and \(\times\) indicate the involved and uninvolved terms, N/A indicates the unapplicable term. The contributions of this work can be summarized as follows:  
1. Developing a novel RL approach specific for robot control tasks by integrating DMP-based policy and DDPG's on-policy actor-critic framework. It enhanced the learning capability and the sample efficiency compared to the related work on NDPs [30], which inherits the off-policy characteristics of PPO. 2. Designing an adaptive inverse controller that learns the hidden relationships between DMP outputs and various robot control actions. This overcomes the limitation of NDPs [30], which rely on human prior knowledge for designing inverse controllers. As a result, it significantly expands the applicability of DMPs in the RL domain. 3. Evaluated on various simulated robot arm scenarios, the proposed method achieved superior control performances compared with NDPs and the original DDPG, highlighting the potential of RL with a DMP-based policy in complex robot control applications. The remainder of this paper is organized as follows. The background of NDPs and actor-critic networks is introduced in Section 2. Section 3 details the proposed approach. The experimental results are presented in Section 4. The conclusion is presented in Section 5.
# Use of Smart Technology to Improve Management of Utility Tunnels
## 3 Smart System Design
### Monitoring Layer
#### 3.5.1 Tunnel Monitoring  
Figure 5 illustrates the tunnel monitoring. It includes smart sensors to follow indoor conditions such as temperature, humidity, air quality, smoke, gas emission, and water infiltration. Survey cameras are used to track intrusions in the tunnel. Staff access to the tunnel is controlled using a digital identification system. For each staff member, the system provides information about the identity, contact, and hierarchy, as well as authorized area in the tunnel. Presence in the tunnel is tracked using an internal geo-localization system [23; 24]. At each instant, the system provides identities of staff members in the tunnel, as well as their positions. The system also provides the history of staff presence in the tunnel.  
Tunnel monitoring concerns also the tunnel equipment, such as the tunnel access (open/closed, identity of users), compartmentalization equipment, ventilation system (debit, temperature, etc.), and lighting system (on/off, color).
# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
## 5 Results and Discussion  
This study is based on the simulation of a UAV-based wireless network in an IoT environment. The results show that the proposed strategy is effective in improving network coverage and connectivity while reducing energy consumption and delay. The experiment considers the effect of different beaconing periods and encountering rates on the convergence time of the proposed strategy. The results show that a lower beaconing period and a higher encountering rate lead to faster convergence times. The analysis of the game theory model used in the study reveals that the proposed strategy results in a Nash equilibrium where each UAV maximizes its utility while maintaining network connectivity. The study also evaluates the impact of various parameters, such as the learning rate and the number of UAVs, on the performance of the proposed strategy. The results show that the proposed strategy is scalable and can adapt to different network configurations and scenarios. This section concludes with a discussion of the limitations of the study and the implications of the findings for the design and deployment of UAV-based wireless networks in IoT environments.  
According to the values given in Tables 2 and 3 and by using Equations (8) and (9) and energy costs \(C_{b}=1\) and \(C_{s}=1\), the energy efficiency and the average latency for strategic beaconing are given as follows: in the symmetric case where \(\tau_{1}=\tau_{2}=0.4\), encounter probabilities: \(P_{1}:0.27\) and \(P_{2}=0.27\); energy efficiency values: \(EE_{1}=0.19\) and \(EE_{2}=0.19\). The equilibrium-beaconing strategy exhibits high energy efficiency with a slight decrease in the encounter rate level compared with the continuous-beaconing policy.  
The following (Table 4) gives a qualitative and quantitative comparison with related works and shows how our work gives more details about small-cell networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Scope** & **Methodology** & **Results** & **Contribution** & **Limitations** \\ \hline \multirow{4}{*}{[39]} & Providing insights into the potential of 6G NR-U for wireless communication in UAVs. & Systematic literature review to identify the potential of 6G NR-U for potential of 6G NR-U for wireless communication in UAVs. & 6G NR-U can potentially provide high-bandwidth and low-latency communication in UAVs. & Highlights the challenges and opportunities associated with the deployment of 6G NR-U for communication in UAVs. & Does not provide a comprehensive analysis of the technical aspects of 6G NR-U for wireless communication in UAVs.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
###### Abstract  
Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (\(R^{2}\)) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.  
Keywords:PM2.5 concentration prediction; deep learning; AC-LSTM network; attention mechanism +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results
### Comparative Study
There is always a chance of a failure in such a scheme contingent upon the outer environment and weather conditions; research work is still being pursued to improve such image-processing techniques. In our work, the drone is independent of such outer environmental uncertainties and the system works autonomously with the use of the hill climbing algorithm. In Reference [39], the authors presented a positioning system using a binary distance laser sensor and ultrasonic sensors. The system was composed of a charging station comprising a single transmitting coil and a drone equipped with a single receiving coil. The system worked by detecting the position of the receiving coil and aligning the transmitting coil with it for wireless power transmission. The positioning system took almost 5 s to detect and align with the receiving coil. Using sensors, the system complexity increases; it requires some specific areas for installation of the drone and the charging station. There is the possibility of errors in installing these sensors properly at the proper position so that the alignment between coils is always perfect. In our system, there is no physical sensor that needs to be installed on the drone or charging station. The time it takes to get the best possible solution is 1 to 2 s (min to max, depending on the distance of misalignment). The algorithm used in our system is much faster and much more robust, giving accurate results. Moreover, in Reference [35], there is just a single transmitting coil used to transfer the power. In our case, the charging station is composed of multiple transmitting coils because using multiple transmitting coils gives the possibility of designing large charging stations for big drones. If the drone size is big, it will require more space for landing on the charging station. Using multiple transmitting coils decreases the time requiredto align the coils. Generally, the drone lands at any point on the charging station. In Reference [35], it was made sure that the drone lands in the range of the sensors to initiate the alignment process; however, it would be almost impossible to initiate this process of alignment if the drone landed on the charging station and was out of range of the sensors. For this, in our charging station, multiple arrays of transmitting coils were installed to allow the drone to land freely anywhere on the charging station without the possibility of getting out of range.