esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of Earthquake Surface Ruptures Using Deep Learning
###### Abstract
In addition, this algorithm can generate pixel-level predictions of cracks in the image. In other words, each pixel in the image is classified as cracked or non-cracked. This study applies the image segmentation algorithm based on deep learning to extract cracks from post-earthquake remote-sensing images. The fully convolutional network (FCN) model [19] is the most commonly used model for crack segmentation. FCN converts the fully connected layer into a convolutional layer on the basis of the VGG network, and an upsampling operation is performed on the feature map by transposed convolution. Then, the feature map is restored to the original size so that each pixel of the image is predicted and the crack image segmentation is realized. [PERSON] et al. [20] applied the U-Net network to concrete crack detection; compared with the FCN network, the U-Net network can achieve higher accuracy through a smaller training set. U-net is an improved image segmentation network based on FCN. The network adopts a symmetric structure of encoding-decoding and uses skip connections to fuse low-level semantic features and high-level semantic features. U-Net greatly improves the segmentation accuracy of the model and is widely used in medical image segmentation [21]. DeepCrack [22] is a new model that utilizes a deep hierarchical neural network for crack segmentation at the pixel level. The DeepCrack model uses the first 13 layers, which correspond to the first 13 layers in VGG-16, but the fully connected layers and fifth pooling layer are removed to achieve meaningful side-output with different scales and decreased memory requirements and computation time. Earthquake surface rupture identification based on deep learning is a challenging issue. 1. Compared with crack identification in roads, houses, and concrete buildings, the background of post-surface earthquake surface rupture images is more complex. The surface ruptures of the Maduo MS 7.4 earthquake are distributed in different geographic regions, such as mountains, grasslands, swamps, alluvial fans, and dunes. 2. The characteristics of post-earthquake surface ruptures have complex shapes and changeable directions, which commonly have oblique, arc-shaped, and cluster-shaped distributions. In addition, post-earthquake surface ruptures can appear in any position and direction, making them difficult to distinguish. This study applies the deep learning semantic segmentation method to segment the remote-sensing images of earthquake surface ruptures. In addition, we create a series of improvements to the VGG16 model [23] based on the characteristics of post-earthquake surface rupture images to improve the extraction accuracy of surface ruptures in the remote-sensing images. Based on the proposed deep learning method, we could automatically identify and draw small and irregular surface ruptures from high-resolution UAV images to achieve the high-efficiency, high-precision, and intelligent detection of post-earthquake surface rupture damage.
## 6 Conclusions  
This paper proposed a delay-optimal approximation algorithm (DOPA) based on the UN cooperation method to solve the problem of the \"near-far\" effect and to improve the delay performance in WPMEC. The DOPA made it possible to offload UN tasks to the edge server for execution, returning the completed tasks as soon as possible, thereby enhancing the UN service experience, particularly for UNs located farther from the server. The computational complexity of the DOPA was relatively high because its solution process involved solving a mixed-integer programming problem that combines time and power allocation. To reduce the computation time of the DOPA, a DLOO framework was proposed, which could provide an efficient solution for predicting the transmission power values of the UNs. Simulation results showed that the proposed scheme outperformed the non-cooperation method, regardless of channel conditions or network size. Furthermore, the DLOO framework achieved high-precision predictions, while effectively reducing the computation time overhead of the DOPA.  
Previously, the DOPA was limited to providing delay optimization solutions for single-AP scenarios and could not be applied to multi-AP scenarios. In multi-AP scenarios, the collaboration between APs to optimize energy harvesting and system delay became more complex. Additionally, generating training data using the DOPA in large-scale networks required a significant amount of computing time, leading to prolonged training time for the DLOO framework. Therefore, in future work, we will optimize the DOPA to reduce its computational time overhead in large-scale scenarios. Additionally, we will investigate delay optimization methods for multi-AP scenarios based on the DOPA. In these scenarios, we will explore delay optimization issues using local computation, balancing data volumes between local computation and remote offloading. If communication energy consumption is too high, tasks could be executed locally.  
M.L.: conceptualization, methodology, supervision, writing--original draft, and writing--review and editing; Z.F.: conceptualization, writing--original draft, methodology, investigation, formal analysis; B.Y.: formal analysis and investigation. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (62102239), the Xi'an Science and Technology Plan Project (22 GXFW0023), and the Natural Science Basic Research Program of Shaanxi (2021 JQ-314).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
Figure 9: Comparison of model solutions times of the DLOO and DOPA.
## 4 Design Procedures and Simulation Results
### Design of Ise Lab's Topology  
For the design of the Ise lab's topology, the same values for the constants \(J\) and \(D_{p}\) that were calculated for synchronverter in Section 4.1 were used. For the implementation of the governor model, a \(K\) value of 0.01 with a time delay \(T_{d}\) of 0.16 s was used. The frequency and ROCOF of the system after a step-increase of 2 kW on the load, with and without the Ise lab's system, is presented in Figure 13a,b, respectively. The dip in the frequency and the ROCOF of the system was reduced with addition of the virtual inertia unit, as expected. The additional inertia from virtual inertia system increased frequency settling time compared to the case without the virtual inertia system. The settling time, however was higher than with the synchronverter. Figure 13c shows the power injected by the inverter during the step-load increase. There is a short transient at 50 s, which was a consequence of numerical oscillation in solving the swing equation. The peak-power injected was similar to that of the synchronverter, but the time taken for the power to return to the steady-state value of 1000 W was much longer, leading to a larger energy usage from the DC side.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 4 Fire Monitoring
### Example Case-Study: 2019 Torrington Fire (New South Wales)  
Between November 2019 and January 2020, uncontrolled bushfires burned through more than three million hectares of New South Wales and Queensland. Activation of the International Charter Space and Major Disasters occurred on 13 November, and at the time of writing, 28 \"wildfire situation\" map products had subsequently been produced by Copernicus EMS using optical satellite datasets including Sentinel-2, SPOT6 and Landsat-8.  
We assess the application of Sentinel-1 SAR to replicate such a map product by selecting a region covered by a Copernicus EMS burned area extent map derived from Landsat-8 imagery (map product 04 TORRINGTON, v1 produced 20191113: [48]) in Torrington, close to the border of Queensland and New South Wales (see location in Figure 2A). A red-green-blue (RGB) change detection image was produced (Figure 5A) by assigning backscatter intensity images acquired prior to (20191025), during the onset of (20191106) and after (20191130) fires in this region to the red, green, and blue channels, respectively. The resulting RGB image (Figure 5A), is compared to the burned area extent obtained from Copernicus EMS (delineated by the red line), with Sentinel-2 imagery displayed for reference in Figure 5B.  
From the composite SAR image, it is possible to infer some information about different burn stages. Red areas indicate backscatter that has decreased since 20191025 (potentially material burned between 20191025-20191106), and in this case, the red region at 151.5\({}^{\circ}\) E in Figure 5A corresponds to the location of burning at or before 20191106 visible in the Sentinel-2 image (Figure 5B middle).  
There are a number of challenges in the interpretation of these data and in implementing operational tools based on SAR for fire mapping, particularly in the absence of ground-truth information. These include the dependency of the backscatter response upon the material burned (i.e., understory vegetation or tree canopy); moisture content; imaging geometry of the sensor; and the SAR wavelength (e.g., [44]), which dictates the depth of the signal's penetration within the vegetation canopy and the sensitivity of the measurements to different sized scatterers (e.g., X-band--leaves; L-band--branches and trunks). Experiments with multiband SAR data (Section 7.1) will therefore strengthen the interpretation of these data, which require further testing to streamline the integration into operational burned area mapping [49].
# An Observation Scheduling System for Radio Telescope Array
## 3 The Overall Architecture of the System
### Intelligent Scheduling Module  
Generally, when observing multiple targets, manual scheduling becomes necessary if the cumulative observation time for all targets exceeds 24 h or if there is an overlap in the sky areas where the targets are located. To make observations more convenient, this module in question calculates the observable duration of each target within the visible sky of the radio telescope array and prioritizes them based on the inverse of their observation durations as weights. The scheduling process begins by allocating the time slot for the target with the highest weight from the preset total observation time, marking this slot as used. Subsequently, the process is repeated for the target with the next highest weight. This continues until all targets are sorted or the available time slots are exhausted. The final step is to visualize the sorted targets. Figure 4 depicts the intelligent scheduling process flow, providing a clear overview of how targets are sequenced efficiently within the constraints of observation time and sky coverage.  
The specific methodology involves extracting all target information from the source file and then utilizing Equation (1) to calculate the elevation variations of all targets across a 24-h period. Given that the antenna at Guizhou Normal University has an elevation restriction, with a minimum limit of \(15^{\circ}\), any elevation values falling below this threshold are omitted from the analysis. The target sources are subsequently sorted in ascending order based on the range of their elevation variations, and their observation priority is assigned according to the inverse of their respective observation durations. The observation time for the highest-priority target is set first, followed by the secondary target, and so on until all targets are scheduled or the allocated observation time is completely used. The sorted schedule is then displayed on the software interface, with time plotted on the horizontal axis and elevation on the vertical axis.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 2 Background and Related Work
### OpenWSN  
OpenWSN is an open-source project that implements the IPv6 over the TSCH mode of IEEE 802.15.4e (6 TiSCH) architecture [10]. The 6 TiSCH network architecture tries to standardize IPv6 on top of the TSCH mode of IEEE 802.15.4e and as such bridge the gap between deterministic industrial networks and traditional IP networks [11]. It aims to provide low latency and high reliability for low-power, critical wireless applications. As such, the OpenWSN firmware provides a complete protocol stack based on IoT standards such as IPv6 over Low-Power Wireless Personal Area Network (6 LoWPAN), Routing Protocol for Low-power and Lossy network (RPL) and Constrained Application Protocol (COAP) [12; 13; 14], as shown in Figure 2. The newest update of the OpenWSN firmware was used when rebuilding and extending the energy model [15]. The hierarchical design of the project makes it relatively easy to port the project to new hardware platforms. Hardware drivers for most common IoT hardware are already available as part of the OpenWSN project itself.  
Figure 1: TSCH schedule example.  
Figure 2: The IPv6 over the TSCH mode of IEEE 802.15.4e (6 TiSCH) architecture stack. RPL, Routing Protocol for Low-power and Lossy network.  
Next to the firmware, useful software such as the OpenVisualizer is also provided. Although the main use of the OpenVisualizer project is to connect the OpenWSN network to the Internet, it also provides the ability to monitor the network. The tool shows the internal state of all the modes that are physically connected to the computer running the OpenVisualizer, e.g., the neighbor table, scheduling table and packet queue. It also has the ability to run simulated modes and to debug the communication with Wireshark [16].
# GenAI-Assisted Database Deployment for Heterogeneous Indigenous-Native Ethnographic Research Data
## 3 Comprehensive Review of Database Type for ERD
### Graph Databases  
Graph databases are another type of NoSQL database characterized by its use of a graph structure for data storage and relational querying. As described by [PERSON] et al. (2013), \"Graph databases represent an ideal way to combine multiple-source data but, to be successful, strategies accounting for inconsistencies and format differences have to be defined to support coherent analysis. Also, the continuously changing nature of crowd-sourced data makes it difficult, for the research community, to compare technological approaches to the  
Figure 1: ERD store in key-value NoSQL database.  
Figure 2: ERD store in document database.  
different tasks that are linked to cultural heritage, from recommendation to management\". They utilized nodes and edges to represent data and the relationships between data. Because graph databases allow for direct connections between data relationships, searches can often be conducted through a single operation. The inherent storage of relational properties within the database enables rapid and intuitive querying of relationships, making graph databases particularly useful for highly interconnected data.  
[PERSON] [14] concluded that graph databases offer significant advantages in data management by effectively representing complex relationships between entities. These characteristics improve data traversal and query performance, surpassing traditional relational databases in navigating complex connections. Their flexible data model adapts to evolving business needs, ensuring scalability for large datasets. For instance, consider a graph database storing ethnographic fieldwork sample data, as shown in Figure 3. The nodes could represent different aspects of the research, such as locations, interviewers and topics, while the edges would represent the relationships between these aspects, such as \"subtopic\", \"make\", or \"verbatim\".  
Graph databases excel at combining data relationships with the data themselves, making them well-suited for applications where data interconnectivity is high and complexity is significant. Compared to traditional relational databases, graph databases are more capable of handling large-scale, unstructured and semi-structured data, and they find applications in scenarios such as social network analysis, fraud detection, network analysis, risk management, intelligent search and recommendation systems.  
A key feature of graph databases is their efficient data querying and analysis capabilities. They employ query languages such as Gremlin [15], SPARQL [16] and Cypher [17] for complex querying and analytical operations such as recursive queries, graph-pattern matching and community detection. These functionalities enable the discovery of implicit relationships within the graph structure and facilitate a deeper understanding and analysis of the data.
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements
### Piezoresistive Sensors  
These kinds of sensors use conductive materials that change their electric properties when they suffer a mechanical deformation. This is called the piezoresistive effect. Piezoresistive sensors are good detecting stress or strain. Therefore, they can be used to detect the movement of a joint in a human body. Usually, these kinds of sensors are formed by a thin film conductor on a silicone elastomer [21]. The small size allows for mounting strain sensors on the human body to detect motion like the bending of an elbow or knee. Sensors that are attached to the human body need to be flexible and comfortable to the wearer. Textile based strain sensors fulfill these requirements. Usually, textile strain sensors use a normal strain sensor embedded in textile, but new materials are conductive yarns or conductive ink. These new materials make a sensor more comfortable and water resistant.  
A very recent work presents a simple and low-cost way to manufacture piezoresistive sensors using a composite of carbon black, a matrix of thermoplastic polyurethane and silver nanoparticles as sensing material [22]. This strain sensor shows more or less 18 times improvement in sensitivity at maximum strain compared with bare carbon black-based sensors. This sensor allows a high accuracy detection of finger bending, elbow flexion and wrist rotation. Sensors with higher sensitivity allow the development of accurate wearable applications to track the movement of the human body. These kinds of applications can be used to track the movement of professional athletes to increase their performance, help a patient with physical rehabilitation and track activities of an elderly person.
## 2 Related Work  
### Wide-Angle and Fisheye Image Distortion Rectification  
Wide-angle and fisheye lenses have been widely adopted in intelligent surveillance, autonomous driving, and smart city applications due to their superior field-of-view coverage capabilities [21]. However, these lenses are prone to introducing significant radial and tangential distortions during image capture [22], which compromise geometric consistency and adversely affect the accuracy of downstream tasks such as object detection, 3D reconstruction, and multi-view image stitching.  
To address this issue, existing approaches can be broadly categorized into two main types: geometry-based models and deep learning-based methods.  
Geometry-based approaches aim to correct distortions by explicitly modeling the inverse transformation of the imaging process. Representative models include the Brown and Lowe 5 distortion model, equidistant and equisolid angle projection models, and interpolation techniques such as TPS [1; 9]. These methods offer good interpretability and computational efficiency but heavily rely on precise camera parameters. As such, their generalization capacity is limited, making them inadequate in complex, dynamic, or non-rigid distortion scenarios.  
In recent years, deep learning-based methods such as RectNet and DeepRecNet have demonstrated outstanding performance by predicting nonlinear deformation fields in an end-to-end manner [6; 23]. These models do not require intrinsic camera parameters and exhibit strong adaptability. However, they often involve complex architectures and high computational loads, which hinder their deployment on resource-constrained edge devices such as the Jetson TX2.  
To alleviate deployment bottlenecks, some studies have incorporated lightweight structures--such as depthwise separable convolutions, channel pruning, and knowledge distillation--to compress model size [24; 25; 26]. Nevertheless, these methods often sacrifice accuracy and lack system-level optimization for real-time performance and energy efficiency, limiting their practical applicability in real-world engineering scenarios.  
In summary, traditional methods remain stable and efficient in controlled environments, while deep learning models offer greater flexibility under complex conditions. However, a unified solution that effectively balances correction accuracy, model compactness, and deployment efficiency remains lacking. To fill this gap, this study proposes a distortion correction model that integrates Transformer-based multi-scale feature extraction, optical flow constraints, and lightweight architectural design. The goal is to develop an all-in-one system that achieves high accuracy, real-time performance, and embedded deployability, thereby addressing the unmet demands of edge computing scenarios in wide-angle image processing.
## 2 Materials and Methods
### Characterization Techniques  
#### 2.3.1 Scanning Electron Microscopy (SEM)  
An SEM JEOL JMS-840A-Oxford ISIS 300 model (Jeol Ltd., Akishima, Japan) was employed for the microscopic observation of the fibers' surface characteristics, as well as to perform a qualitative comparison of their surface roughness. It was also employed for the observation of the morphology of the fractured surfaces of the composites. All studied samples were placed on conductive adhesive tapes, sputter-coated with carbon in order to provide good conductivity to the electron beam, and observed under a 20 kV voltage.