esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Evaluation of TBM Pilot Tunnels to Rear Blasting Using the Protection Shield
## 4 Numerical Analysis Results
### Pilot Tunnel Underground Settlement  
Regardless of the installation of a protection shield, the amount of settlement in the pilot tunnel (point A) during tunnel blasting did not exceed the standard value (10 mm) (Figure 10). The settlement decreased by 65.22% and 60.37% without and with a protection shield, respectively, when the separation distance between the blasting point and protection shield increased from 2 m to 4 m. Further, it decreased by around 93% and over 99% when the separation distance increased from 8 m and 20 m (Table 4). Accordingly, a separation distance of at least 8 m between the blasting point and protection shield was considered safe regardless of the installation of a protection shield. However, the installation of the protection shield was expected to affect the crown settlement of the tunnel as the separation distance between the blasting point and protection shield tended to decrease by up to 34.02% when the protection shield was installed (i.e., when the separation distance between the blasting point and the protection shield was 2 m).  
Figure 8: Definition of plastic zone.  
Figure 9: Plastic zone according to separation distance between the blasting point and the protection shield: (**a**) Case 1-1, (**b**) Case 1-2, (**c**) Case 1-3, and (**d**) Case 1-4.
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
## 1 Introduction  
Dual-view display provides different images for viewers in different directions. It can meet the needs of different viewers at the same time, and it saves a lot of space and cost. It is increasingly commercialized, especially for car navigation, home entertainment and business applications. For example, when a dual-view display is used in an automobile, it can represent navigation information for the driver and entertainment information for the passenger simultaneously. Some dual-view liquid crystal displays have been proposed [1; 2], but they can only display 2D images. For automobile applications, optical see-through capacity is needed because the driver needs to see the road while watching the navigation information.  
Recently, 3D displays, including holography display [3; 4; 5], integral imaging display [6; 7; 8; 9], volumetric display [10; 11] and so on, have received extensive attention. Integral imaging is considered to be one of the most promising display technologies. It has the advantages of full parallax [12] and continuous viewpoint [13], and it supplies various physiological depth cues, so viewers can freely change the accommodation and convergence without feeling any visual discomfort [14; 15]. Integral imaging has a small viewing angle, and the viewing zone is periodically repeated. Therefore, it is suitable for dual-view or multi-view displays. By using some kinds of multiplexing methods, it can present different 3D images in different directions, and meet the needs of different viewers. In recent years, our team proposed several ways to implement dual-view 3D displays [16; 17; 18]. We proposed a dual-view integral imaging 3D display [16], but the viewing angle is only half of the conventional one. In order to increase the viewing angle of the dual-view 3D display, we proposed a dual-view integral imaging 3D display using polarizer parallax barriers [17], but the resolution of each 3D image is reduced to half of the conventional one. In order to solve these issues, Seoul University proposeda projection-type dual-view 3D display based on integral imaging [19], but the display system is bulky. Then we proposed a dual-view integral imaging 3D display using an orthogonal polarizer array and polarization switcher [18], but the brightness is not high.  
In this paper, we propose a dual-view integral imaging 3D display system based on a MHOE. The viewing angle and resolution of 3D images in the dual viewing zones are almost same as the conventional one. The dual-view 3D display system can provide different 3D images in different zones via controlling the projection beams. In Section 2, the fabrication and reconstruction principles of the MHOE are analyzed, and the projection part of the dual-view 3D display system is designed. In Section 3, the different 3D images are displayed in different viewing zones, and the imaging characteristics of the MHOE are analyzed. The principle of the proposed system is verified by the display experiments.
# Leather Defect Detection Based on Improved YOLOv8 Model
## 5 Experiments and Results
### Other Discussions  
1. In introducing EMA, this paper explores two approaches to incorporating EMA within the C2f module. The first approach is to place the EMA before the first bottleneck within the C2f module, referred to as Model A. The second approach is to place the EMA before the final convolution within the C2f module, referred to as Model B. The two methods of incorporating the EMA produced different results. Without adding other modules, the mAP50 of model A was 0.961 higher than that of model B, which was 0.956, while the parameter and GFLOPS of model A were 8.4M and 3.05, which were lower than those of model B, which were 9.3 and 3.09, respectively. These indicate that the EMA joining approach represented by model A is better than model B in terms of performance, computational complexity, etc. Therefore, this study adopts the EMA joining approach for model A, the model with the EMA incorporation method.
2. To validate the generalization capability of the model, we additionally collected 200 images of leather defects that were not included in the original leather defect dataset. On this small dataset, the original YOLOv8n defect detection achieved an mAP of 0.915. In this paper, the YOLOv8-AGE for leather defect detection achieved an mAP of 0.937, indicating that the algorithm possesses good detection capabilities and generalization performance.
3. In comparison with existing solutions, our enhanced model demonstrates several advantages. Relative to the vision transformer (ViT)-based leather defect detection method proposed by [PERSON] et al., our approach offers a significantly reduced number of parameters and computational complexity while maintaining comparable accuracy. This makes our model more efficient and less resource-intensive. Furthermore, when contrasted with the robust leather defect detection method based on trainable guided attention proposed by [PERSON] et al., our study presents a model with a lower parameter count and leverages the simplicity of deployment associated with the YOLO series, which is crucial for practical industrial implementation.
## 3 Results
### Global TEC Responses  
With the IGS Ionosphere Map Exchange Format (IONEX) data, combined with the IGS Ionosphere Combination Center from JPL IONEX products [40; 41], the global TEC responses were further analyzed. Figure 4a showed the global TEC variation for the American sector (longitude: 70\({}^{\circ}\) W), the European-African sector (longitude: 20\({}^{\circ}\) E), and the Asia-Australian sector (longitude: 130\({}^{\circ}\) E), respectively. It turns out that the European-African sector suffered from the event the most, keeping long TEC enhancements in EIA [42], which is consistent with previous results [43]. The increase of the TEC around the EIA regions in the European sector lasted for three days. The distortion of the equatorial crest was observed on the main phase of the storm and its recovery phase, however, the clear equatorial crest appeared again after 25 June. Similar characteristics were observed in Figure 4b, demonstrating the variation of TEC differences. Background TEC was calculated using the most geomagnetic quiet days' value in the same month. The TEC enhancement in the Asian-Australian sector was dominant on 23 June, with an obvious southward expansion of the EIA region. The TEC decrease was found in the American high latitudes on 23 June, indicating the TEC inhibitions during the geomagnetic convection.  
Figure 3: The magnetic perturbations from the high latitude local observations.
# Resilient Consensus Control for Multi-Agent Systems: A Comparative Survey
## 2 Preliminaries and Consensus Problem for MASs
### Consensus Problem for MASs  
The consensus control problem of MASs has attracted tremendous attention from researchers in the past decades. In general, suppose that the MAS consists of \(N\) agents with first-order integrator continuous-time dynamics described by  
\[\dot{x}_{i}(t)=u_{i}(t),\ i=1,\ldots,\ N, \tag{1}\]  
of which \(x_{i}(t)\in\mathbb{R}^{n}\) and \(u_{i}(t)\in\mathbb{R}^{n}\) denote, respectively, the state and control input of agent \(i\) at time \(t\).  
The consensus problem for agents described by (1) can be divided into the leaderless consensus problem and the leader-following consensus problem, according to the theoretical frameworks.  
Definition 1: The leaderless consensus problem is to construct a controller for a MAS given by (1) such that  
\[\lim_{t\to\infty}\|z_{i}(t)-z_{j}(t)\|=0,\ j\in\mathcal{N}_{i},\ i=\ 1,\ldots,\ N, \tag{2}\]  
where \(z_{i}(t)\in\mathbb{R}^{m}\) and \(z_{j}(t)\in\mathbb{R}^{m}\) denote the state or output of agent \(i\) and \(j\), respectively; \(\mathcal{N}_{i}\) represents the neighbor set of the \(i\)-th agent.  
Definition 2: The leader-following consensus problem is to construct a controller for a MAS given by (1) such that  
\[\lim_{t\to\infty}\|z_{i}(t)-z_{0}(t)\|=0,\ i=\ 1,\ldots,\ N, \tag{3}\]  
where \(z_{i}(t)\in\mathbb{R}^{m}\) and \(z_{0}(t)\in\mathbb{R}^{m}\) denote the state or output of agent \(i\) and the leader, respectively.  
Moreover, the first-order discrete-time MAS is composed of \(N\) agents with dynamics given by  
\[\dot{x}_{i}(k+1)=u_{i}(k),\ i=1,\ldots,\ N, \tag{4}\]  
of which \(x_{i}(k)\in\mathbb{R}^{n}\) and \(u_{i}(k)\in\mathbb{R}^{n}\) denote, respectively, the state and control input of agent \(i\) at time point \(k\in\mathbb{N}\). The above definitions can be correspondingly extended to the MAS given by (4).  
Recently, the consensus problem for MAS with linearized or nonlinear dynamics have been studied in [23; 24; 25].
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 1 Introduction
[PERSON] and [PERSON] (2022) [21] used carbonized hemp seeds for the simultaneous adsorption of Cr(VI) and Cd(II). [PERSON] et al. (2023) [22] conducted experiments on the adsorption of Pb(II), MB and ibuprofen using hemp seeds deposited with MnO/CuO and MnO/ZnO. Methylene blue (MB) has been extensively used in important applications, including in the textile and dyeing industries; wastewater from these industries contains high concentrations of MB dye. Similarly, metal ions (Cd(II) and Pb(II)) are used as mordant agents in the dyeing process [23]. Several studies have reported on the simultaneous removal of Cd(II) and MB; [PERSON] et al. (2022) [23] used orange peel waste to remove Cd(II) and MB from water, while [PERSON] et al. (2021) [24] used porous carbon from Albizia lebbeck pods for the adsorption of Cd(II) and MB. The adsorption of Cd(II) and MB with membranes has been reported by [PERSON] et al. (2011) [25]. [PERSON] et al. (2019) [26] prepared xanthate-modified baker's yeast to remove Cd(II) and MB from an aqueous solution. Hemp seeds are consumed as food and are also used as a medicine for controlling cholesterol, cardiovascular and gastrointestinal problems, as well as other diseases [27]. A few studies have been conducted using hemp as an adsorbent. However, carbonized hemp seeds have not been used as cadmium and methylene blue adsorbents. The objective of this study was to use pristine hemp seeds (PHSs) and carbonized hemp seeds at 500 \({}^{\circ}\)C (CHS-500) and 700 \({}^{\circ}\)C (CHS-700) to adsorb methylene blue and cadmium from water. We hypothesize that cadmium and methylene blue adsorption will be higher using carbon-based hemp seeds. Furthermore, the materials' batch adsorption, equilibrium, thermodynamics, kinetics and isotherms were assessed.
# Topological Navigation and Localization in
###### Abstract
(See an example in Figure 2). In addition, a localization system, specifically designed to be used with the topological navigation system and its associated topological map, is also proposed. An important aspect to be addressed when working with heavy-duty machinery, such as the LHDs, is the way in which automation systems are developed and tested. In order to address this important issue, we use a development process for autonomous systems for mining equipment, which is comprised of the following four-stages: (i) development using specific simulation tools, which are fed with real data from mining environments, (ii) development using scale-models of the actual machines, (iii) validation and testing using real machines in test-fields, and (iv) validation and testing using real machines in actual mine operations. This development process was used for achieving autonomous navigation of LHDs. The proposed topological navigation and location systems were validated using a commercial LHD, during several months at a medium-scale sub-level stoping mine, located in the Coquimbo Region of Chile. Figure 1: LHD and sensors for the Autonomous Navigation System. The main contributions of this paper are the following:  
* A topological navigation system for mining vehicles operating in tunnels, which can be used in any tunnel network by any articulated vehicle. * A novel localization system specifically designed for tunnel-like environments, which estimates the vehicle's global and local pose using the topological map. * A development and testing methodology that can be used in the development process of heavy-duty machinery. * Full-scale navigation experiments using a commercial LHD on a productive level of a sublevel stoping mine. This paper is organized as follows: First, related work on autonomous LHD navigation is presented in Section 2. The proposed topological navigation system for LHD is described in Section 3. Then, in Section 4, the development and testing methodology is described, and in Section 5, validation results achieved in an actual mine are presented. Finally, conclusions of this work are drawn in Section 6.
# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 3 Methodology
### Parcel Object Detection Algorithm  
According to the depth of the network and the width of the feature map, YOLOv5 can be divided into four models: YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x: S, M, L, and X, representing Small, Medium, Large, and Xlarge, respectively. It means that the model's overall structure remains constant as shown in Figure 3, but the size and complexity of each model are scaled [22]. YOLOv5s has the fastest processing speed and YOLOv5x has the highest detection accuracy among these models [23]. To find the applicable optimal algorithms for an unmanned parcel storage system, we trained and tested the four scales of YOLOv5. Table 3 shows the basic calculation amount and speed of YOLOv5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Size (Pixels)** & **Test Dataset** & **mAP (0.5)** & **FPS** & **GPU** \\ \hline YOLOv1 & 448 \(\times\) 448 & VOC2007 & 63.4 & 45 & Titan X \\ \hline YOLOv2 & 544 \(\times\) 544 & MS COCO & 44.0 & 40 & Titan X \\ \hline YOLOv3 & 608 \(\times\) 608 & MS COCO & 57.9 & 20 & Titan X \\ \hline YOLOv4 & 608 \(\times\) 608 & MS COCO & 65.7 & 62 & Tesla V100 \\ \hline YOLOv5s & 640 \(\times\) 640 & MS COCO & 56 & & Tesla V100 \\ \hline YOLOv5m & 640 \(\times\) 640 & MS COCO & 63.9 & & Tesla V100 \\ \hline YOLOv5l & 640 \(\times\) 640 & MS COCO & 67.2 & & Tesla V100 \\ \hline YOLOv5x & 640 \(\times\) 640 & MS COCO & 68.9 & & Tesla V100 \\ \hline YOLOv6s & 640 \(\times\) 640 & MS COCO & 35.9 & & Tesla V100 \\ \hline YOLOv6l & 640 \(\times\) 640 & MS COCO & 52.5 & 550–620 & Tesla V100 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison between deep learning-based object detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **mAP (0.5)** & **mAP (0.5:0.95)** & **Parmas (M)** & **FLOPs@640 (B)** \\ \hline YOLOv5s & 56.0 & 37.2 & 56 & Tesla V100 \\ \hline YOLOv5m & 63.9 & 45.2 & 63.9 & Tesla V100 \\ \hline YOLOv5l & 67.2 & 48.8 & 67.2 & Tesla V100 \\ \hline YOLOv5x & 68.9 & 50.7 & 68.9 & Tesla V100 \\ \hline \end{tabular}
\end{table}
Table 3: Basic Parameters Setting Values of YOLOv5.
## 2 Materials and Methods
### Methods
#### 2.2.6 Histogram of Oriented Gradient (HoG)  
The histogram of oriented gradient (HoG) [56] is a feature descriptor used in image processing and computer vision for object detection and recognition. The technique has become popular due to its robustness and effectiveness in obtaining local shape information while being comparatively unaffected by global variations in position and illumination [57].  
The gradient of an enhanced reconstructed image from the previous steps was computed first. The gradients show the edges and contours of the face in the image. Then, the image was divided into small, connected regions called cells. For each cell, a histogram of gradient orientations was created, where the gradient orientation was binned, and the magnitude of the gradient was used to vote for the corresponding orientation bin. The HoG descriptor included a step where groups of cells, known as blocks, were normalized. This normalization process involved adjusting the histogram values to account for changes in lighting conditions, enhancing the descriptor's invariance to factors such as illumination and contrast. The block normalization was performed by computing the L2 norm of the histogram vector within the block. The final HoG descriptor was a concatenated vector of the normalized histograms from all blocks within the detection window.
# IoT-Enabled Vehicle Speed Monitoring System
## 5 Conclusions and Future Work
### Future Work
#### 5.1.1 Vehicle Collision and Auto Accident Detection and the SOS Service  
Accident detection systems can be used in connection with vehicle speed monitoring systems to identify accidents on highways. This may be enhanced by placing several sensors on the road that can find the car's location and timing. The system may be modified to send vehicle data to rescue centers in order to notify them to the need for first assistance.
## 3 The Proposed Vehicle Dataset
### The Uses of the VehDS-LA Dataset
The proposed VehDS-LA can be used for studies related to ITS in the downtown of the city of Los Angeles, as shown in Figure 17. Our VehDS-LA includes information of vehicle samples in terms of their real-world geographical locations, as well as the vehicles' movement-related information in terms of directions and speeds based on the infrastructure of LA streets. Therefore, studies related to vehicle-to-vehicle, vehicle-to-pedestrian, and vehicle-to-network communications in LA city can utilize the vehicles information stored in our proposed dataset. * **SDN-based vehicular networks studies:*
* Software-defined networking is one of the most recent network architectures that aims to facilitate the network management task and to enhance the utilization of network resources in an efficient way [44; 45]. The architecture of SDN is made up of three components, which are data plane, control plane, and application plane [46]. The data plane comprises network devices that are responsible for forwarding data [47]. The control plane is made up of a set of  
Figure 16: Building a machine learning model. Figure 17: Using the proposed vehicle dataset in ITS studies in LA city. SDN controller(s) to control and manage operations of the whole network [48]. The application plane consists of end user applications that interact with SDN controller(s) to perform specific tasks [49; 50]. Southbound interface is used to perform the communication between the data and control planes based on a standardized protocol [51]. Northbound interface is utilized to establish the communication between the control and application planes [48]. Figure 18 shows the architecture of SDN-based vehicular networks, where vehicle samples of our proposed VehDS-LA can be utilized to construct a vehicular network. The studies that are focused on SDN-based vehicular networks can benefit from our proposed dataset in performing vehicle mobility management and supervision tasks by SDN, where realistic vehicle location coordinates and movement-related information exist.
# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 2 Related Work
The linear product may not be sufficient to capture the non-linear structures of interaction between users and items. That is, the simple and fixed inner product has its limitations, which will cause a limitation to the model. To solve this problem, [PERSON] et al. [7] applied the neural network in the MF model to learn the non-linear interaction between the user and the item according to the non-linear learning ability of the neural network, which greatly improves the performance of recommendation systems of the model. However, GMF only contains the embedding layer and the output layer, which only models the second-order interaction between the user and the item and cannot well capture the high-order interaction which hides richer information. In other words, GMF is a shallow model, which can effectively model the low-level features of interaction between users and items, but it is not sufficient to capture the high-order interaction that contains a lot of richer and more abstractive information, which may limit the performance of recommendation systems of the model. In recent years, more and more works have attempted to combine matrix factorization with deep neural network for recommendation tasks because of the ability of the deep neural network to learn high-level and more abstractive features. The work of [PERSON] [31] combined matrix factorization with deep convolutional neural networks. [PERSON] et al., utilized heterogeneous network embedding and deep learning embedding methods to automatically extract semantic representations from structural knowledge, text knowledge, and visual knowledge in the knowledge base, and then combined them with the matrix factorization model in collaborative filtering to make recommendations [32]. [PERSON] et al. [10] proposed the CDL model, which has good expressive power by performing deep representation learning on content information and collaborative filtering on the rating matrix. [PERSON] et al. [33] proposed an asymmetric neural matrix factorization recommendation system. By adopting the deep neural network, the model could learn the high-order embedding vector representations of users and items from the interaction matrix and then complete the score prediction based on matrix factorization. These methods utilize deep neural networks to learn high-order features of users and items, which endows the model the ability to learn the interaction of high-order abstract features of users and items. However, for the part of collaborative filtering, they still apply MF which combines the feature vectors of users and items by simple linear inner product, it limits the performance of recommendation systems of the model to a certain extent. Therefore, in IFDNAMF recommendation model, by adding hidden layers on the non-linear second-order interaction, IFDNAMF could utilize deep neural networks to model non-linear high-order interaction between users and items, as well as solve the problem that is generated by the simple linear inner product of MF.