esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
## 1 Introduction
Smart vests provide vibration alerts using haptic or tactile sensors [23; 24]. Recently, a smart glass-based PWS was developed using BLE technology [25]. Bluetooth beacons were installed on the equipment, and the pedestrian worker wore smart glasses. Smart glasses measured the RSSI of the BLE signal and provided a proximity warning when the RSSI was above a certain threshold. However, existing wearable PWSs provide one-way proximity warnings to pedestrian workers only. Therefore, it is necessary to develop new PWSs that can provide proximity warnings in both directions, to the equipment operator and the pedestrian worker simultaneously. Recently, smart helmet technology has been widely used by bicycle and motorcycle users [26]. A smart helmet is a technology that monitors a user's health, surrounding environment, and equipment status. In addition, it collects real-time data using wireless sensors and communication technologies. The main functions of the smart helmet include hazard-risk recognition, vehicle condition monitoring, and convenience. The smart helmet immediately determines the user's health condition [27], requests rescue [28], and provides a risk alert [29]. In addition, it provides motorcycle speed and fuel level information to the user in real-time [30] and has convenient functions such as navigation, music playing, and device connection [31]. Efforts are being made to increase workplace safety and convenience by applying smart helmet technology to various fields, such as construction [32], manufacturing [33], and petrochemical work [34]. In the mining industry, many research cases have been presented to implement worker health monitoring, hazard awareness, and wireless communication [35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. However, a smart helmet-based PWS using BLE technology has not yet been developed to provide bidirectional proximity warnings to equipment operators and pedestrian workers in underground mines. The purpose of this study is to develop a smart helmet-based PWS that can provide simultaneous proximity warnings to both equipment operators and pedestrians in underground mines. Bluetooth beacons are installed on mining equipment such as dump trucks, excavators, and loaders, and pedestrian workers wear the smart helmet-based PWS. Whenthe equipment approaches the pedestrian worker, the smart helmet recognizes the BLE signal and emits an LED light. This study analyzed the BLE signal detection distance of the smart helmet for two conditions in the underground mine. One was the Tx power condition of the Bluetooth beacon and another was the facing angle condition between the smart helmet and the Bluetooth beacon. The NASA task load index (NASA-TLX) [45] was analyzed to evaluate the subjective workload felt by the equipment driver and the pedestrian worker for PWSs based on smartphones, smart glasses, and smart helmets.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 1 Introduction  
Visual-based geolocalization is an important research area of computer vision. By extracting features from images or videos, and comparing them with the feature databases, the location of the observation can be determined. Different types of visual geolocation methods have been proposed for different environments [1]. For example, in urban areas, image and 3D point cloud are generally used for locating. However, since the obvious characteristics are relatively sparse in the natural environments, it is difficult to directly adopt the geolocation method for urban areas.  
In areas with no Global Navigation Satellite System signal, or in outer space with no positioning satellite, locating with skyline is an economic and important method for navigation [2]. For example, by extracting the skyline information from a scene with mountain [3; 4; 5] and comparing it with the skyline generated from Digital Elevation Model (DEM) data, the geolocation of the image location can be determined. Locating with skyline has thus become an active research area, which has important applications in unmanned vehicles [6; 7; 8; 9], unmanned aerial vehicles [10; 11; 12; 13; 14; 15], and other fields.  
There are some differences between a photo skyline and a DEM skyline. Any snow cover and vegetation on a mountain interferes with the extraction of skyline features and degrades the contour details of the mountain. Furthermore, some DEM datasets generated from satellite-based data have low resolution and poor precision, which affect the matching of its skyline with a photo skyline. Furthermore, existing methods of locating skyline have been used in an environment where the skyline is far away from mountains and deserts, and the area has few vegetation [16]. However, research has shown that vegetation in hilly areas has a great impact on the feature extraction of the skyline, destroying the original contour details [17; 18]. Furthermore, dense hills make the distance between theskyline and where the photo was taken relatively close (about 1 km), resulting in bad performance of existing geolocalization algorithms.  
The contributions of this paper are as follows. First, a new method of generating skyline descriptor named enhanced angle chain code is proposed. Second, a new skyline feature named lapel point is proposed. As far as we know, we are the first to propose such a new feature using the intersection of skyline and ridge line. Third, a new matching method incorporating skyline distance heatmap and skyline pyramid is proposed for locating hilly areas.  
The rest of this paper is organized as follows. Section 2 reviews the commonly used skyline description and matching methods. Section 3 introduces the causes of skyline geolocation error in hilly areas. Section 4 presents the proposed new features and new matching method for geolocalization. Section 5 presents experimental results on images of hilly areas captured in the suburb of Changsha, China. Finally, Section 6 presents the conclusions and provides some directions for future research.
## 2 Related Works and Problem Statement  
### Graph Theory  
In a directed graph \(\mathcal{G}\), for a system containing \(N\) UUSVs, the information interaction model can be represented by the graph \(\mathcal{G}=\{\mathcal{V},\mathcal{E},A\}\), where \(\mathcal{V}=\{v_{1},\cdots v_{N}\}\) represents the set of UUSVs, \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) represents the set of edges between \(N\) UUSVs. If \((i,j)\in\mathcal{E}\), it means that UUSV \(i\) and UUSV \(j\) are adjacent (the neighbors of UUSV \(i\) are represented by \(N_{i}\)); that is, UUSV \(j\) is within the communication radius of UUSV \(i\) and can receive the signal sent by UUSV \(i\). \(A=\left[a_{ij}\right]\in\mathbb{R}^{N\times N}\) represents the adjacency matrix of the directed graph \(\mathcal{G}\). Generally speaking, there are no self-loops in the graph \(\mathcal{G}\); that is, edges \((i,i)\) cannot appear. All the diagonal elements \(a_{ii}=0\) in \(A\). \(a_{ij}>0\) indicates that UUSV \(i\) can send information to UUSV \(j\) through the edge \((i,j)\); \(a_{ij}=0\) indicates that there is no path between UUSV \(i\) and \(j\).  
The value of element \(A\) in the adjacency matrix of directed graph \(\mathcal{G}\) is as follows:  
\[a_{ij}=\left\{\begin{array}{ll}1&(i,j)\in\mathcal{E},i\
eq j\\ 0&\text{other}\end{array}\right. \tag{1}\]  
The Laplace matrix \(\mathcal{L}\triangleq\left[l_{ij}\right]\in\mathbb{R}^{N\times N}\) of a directed graph \(\mathcal{G}\) can be obtained from the adjacency matrix:  
\[i_{j}=\left\{\begin{array}{ll}\sum\limits_{j\in N_{i}}a_{ij},i=j\\ -a_{ij},i\
eq j\end{array}\right. \tag{2}\]  
**Lemma 1**[21].: _If the interaction graph \(\mathcal{G}\) contains at least one directed spanningtree, then its corresponding Laplacian matrix \(\mathcal{L}\) is positive definite._
## 2 System Model
For an even more compact notation, we now stack the \(P\times 1\) CFR vector \(\mathbf{h}(k)\) over all sub-carriers to obtain the \(KP\times 1\) total CFR vector \(\mathbf{H}\) as follows:  
\[\mathbf{H}=[\ldots,\mathbf{h}(k)^{T},\ldots]^{T}=\mathbf{A}(\mathbf{\theta}) \mathbf{D}(\mathbf{\tau})\mathbf{\gamma}, \tag{6}\]  
where \(\mathbf{A}(\mathbf{\theta})\) is the \(KP\times KQ\) steering matrix defined as:  
\[\mathbf{A}(\mathbf{\theta}){=}\,\text{diag}\{\bar{\mathbf{A}}(\mathbf{\theta}),\bar{ \mathbf{A}}(\mathbf{\theta}),\ldots,\bar{\mathbf{A}}(\mathbf{\theta})\},\;(K\text{ times}) \tag{7}\]  
and \(\mathbf{D}(\mathbf{\tau})\) is the \(KQ\times Q\) TDs matrix defined as:  
\[\mathbf{D}(\mathbf{\tau})\triangleq\big{[}\mathbf{D}_{1}(\mathbf{\tau})^{T}\mathbf{D} _{2}(\mathbf{\tau})^{T}\cdots\mathbf{D}_{K}(\mathbf{\tau})^{T}\big{]}^{T}. \tag{8}\]  
Hence, at pilot period transmissions where sub-carriers are not modulated (i.e., \(s_{k}=1\)), after we stack both \(\mathbf{x}(k)\) and \(\mathbf{n}(k)\) in the same way we did to transform \(\mathbf{h}(k)\) into \(\mathbf{H}\), we obtain:  
\[\mathbf{X}=\mathbf{H}+\mathbf{N}. \tag{9}\]
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
###### Abstract  
With the increasingly obvious trend of satellite communication and network integration, especially the emergence of inter satellite links, the interconnection between space nodes has become the development trend and inevitable requirement of future space communication. However, problems, such as long communication distance, large transmission delay and loss, limited network resources and frequent switching of transmission links, limit the ability of spatial information transmission. Firstly, based on the idea of ring planning and design, this paper uses the joint design of beamforming between nodes to design the physical layer network coding in the relay to realize the reliable transmission of information. Secondly, according to the diversity of relay signal space resources and network node information exchange, a joint design method of relay compression matrix and node precoding vector is studied, which breaks through the existing configuration constraints. In this scheme, the computational complexity is reduced by compressing and precoding the matrix to ensure reliable decoding while obtaining spatial alignment gain and degrees of freedom. Simulated and real data results demonstrate the superiority and effectiveness of the proposed method.  
MIMO; signal space; degree of freedom; physical network coding Article
# External Beam IBA Measurements for Cultural Heritage
## 7 Conclusions  
External beam IBA techniques are still essential and important for compositional studies in cultural heritage because they can provide information about elemental surface and in-depth distribution in a non-invasive, non-damaging, and non-deliberatively destructing way. However, IBA techniques are based on the use of a large-footprint instrumentation, a particle accelerator, thus the impact of laboratory-based analytical techniques in those applications, such as cultural heritage, where the vast majority of the world cultural heritage is immovable could diminish in favour of increasingly more performing portable or transportable energy-dispersive X-ray fluorescence (ED-XRF) systems for elemental analysis and imaging of cultural heritage objects [49]. Nonetheless, ED-XRF is particularly sensitive for mid and high-Z alone, while IBA techniques demonstrate a high sensitivity to elements from all the periodic table, in particular, light elements that are not easily measurable and quantifiable using many analytical methods, allowing single measurement problems to be solved in ways that are otherwise difficult or impossible for other analytical techniques. Moreover, until now, any portable and/or transportable equipment does not offer the same performances as IBA with respect to the detection limits. In this regard, the development of smaller, compact, and transportable accelerators that will allow in situ IBA of cultural heritage materials, such as MACHINA, the Movable Accelerator for Cultural Heritage In situ Non-destructive Analysis [50], jointly developed by INFN, CERN, and the Opificio delle Piette Dure (OPD), an Italian institute for the conservation of works of art, will pave the way to IBA measurements performed directly inside museums and conservation centres, to go where no accelerator has gone before.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The support of the scientific and technical staff at INFN LABEC ion beam laboratory is warmly acknowledged. The author wishes to thank the many colleagues and friends engaged in interesting discussions on the analysis of cultural heritage objects with ion beam techniques, during conferences, meetings, workshops, and visits. Special thanks to [PERSON] from the University of Surrey for his invaluable support in using the DataFurnace code.  
The author declares no conflict of interest.
# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 2 Background
Techniques using this approach are listed below; _Gamma-Gamma Logging_[18]: This technique uses the emission and propagation of gamma rays through the pile to evaluate the concrete's density and porosity, helping to identify defects or discontinuities. Gamma-Gamma Logging (GGL) is conducted by moving transmitter and receiver probes vertically within one or two access tubes. Unlike acoustic methods, GGL is unaffected by environmental vibrations or electrical interference. It also enables full circumferential testing around the tube and can detect multiple anomalies that might be missed by other techniques. _Neutron Logging_[35]: In neutron logging, neutrons are emitted into the pile structure and their interactions with surrounding atoms are recorded as they slow down. The method is particularly sensitive to moisture, making it susceptible to environmental influences. As a result, the data often undergoes further processing to enhance accuracy. The existing non-destructive pile integrity testing methods are compared in terms of their approach, requirements, application areas, cost, accuracy, and time in the Supplementary Materials (Table S1). The methods are divided into three groups in terms of their requirements:  
* _Surface methods (\"S\")_ assess pile integrity from the exposed pile head or surface;
* _Broclude methods (\"B\")_ involve testing from within or adjacent to boreholes drilled near the pile;
* _Access tube methods (\"A\")_ require pre-installed tubes in the pile. According to the cost, the methods are classified as low (\"L\"), moderate (\"M\"), and high (\"H\"). According to the accuracy, the listed methods can take the values of moderate (\"M\"), high (\"H\"), and very high (\"VH\"). In terms of the time spent for implementation, the methods are classified as slow (\"S\"), moderate (\"M\"), and fast (\"F\"). While traditional non-destructive testing methods provide valuable insights into pile integrity, their reliance on manual interpretation introduces potential limitations in terms of accuracy, efficiency, and scalability. Recent progress in machine learning, notably with convolutional neural networks (CNNs), presents effective solutions for automating the interpretation of reflectograms and enhancing the detection of defects. The following section details the methodology adopted in this study, which leverages deep learning techniques and transfer learning to address the challenges associated with pile defect detection and improve the reliability of low-strain pile integrity testing (LSPIT) results. Other non-destructive methods, such as thermal imaging or radiometric techniques, although valuable in broader contexts, are less directly applicable to reflectogram analysis and thus are briefly mentioned without extensive elaboration. The limitations inherent in traditional methods highlight the need for advanced machine learning techniques capable of automatically interpreting complex reflectogram patterns, motivating the transition towards convolutional neural network-based approaches detailed in Section 3.
# A Model for Working Environment Monitoring in Smart Manufacturing
## 3 Model of a System for Monitoring Working Environments in Smart Manufacturing  
In this section the architecture of the proposed system is presented. Figure 1 shows the architecture of the system designed in accordance with a five-layered IoT architecture. Figure 1 also shows the components of the system and their deployment in the architecture layers. The model assumes that different sensor nodes utilizing different connecting technologies are included in this model. The sensor node type and the connecting technology depend on the purpose of the node and its placement. The purpose of the node is defined by the set of physical parameters that should be monitored by the node and the other specific functions, such as possible alarms, display board notifications, etc. Moreover, the location of the sensor node placement affects the connectivity technology used, e.g., wireless technology for sensors placed on moving objects, difficult to reach places, and locations with poor wired infrastructure. Locations with a highly developed wire architecture and with demands for a high data rate will have Ethernet connection. Due to the variety of potential wireless and wired technologies, one of the goals of the process of building the model is the flexibility in selecting components. This flexibility assumes the configuration of the sensor node can be adjustable and easy to change following the specific design requirements, deployment scenarios, and eventual technology trends.  
Based on the previous discussion, we choose to build this model around open-source hardware platforms, such as Arduino/Genuino and similar platforms. The model originated from a student project created for the purpose of an international challenge in building a prototype system for industrial appliances. The model proposed in this section is upgraded with the inclusion of the MQTT protocol.
## 4 Methods
### Baseline Detector
#### Architecture:  
The U-Net consisted of two parts, the encoder and decoder structure, based on the downsample and upsample blocks. In the encoder part, each downsample block consisted of a convolutional layer with a kernel size of 3 and a max pooling layer with a kernel size of  
Figure 4: Example of the masks used as the GT for training the U-Net. On the left is the original image, in the middle the mask, and on the right a visualization of the mask overlaying the original. Green marks symbolize left and right text borders; red marks symbolize the baseline of the text line.  
2. The decoder part was symmetric to the encoder part and used upsample blocks, which consisted of a transposed convolutional layer and a convolutional layer. The transposed convolutional layer had a kernel and stride size of 2, to upscale the feature map. The number of filters in the convolution and deconvolution layers were 16, 32, 64, 128, 256, and 512 and 512, 256, 128, 64, 32, and 16, respectively. In addition to that, we added skip-connections between each upsample and downsample block. The last deconvolutional layer was used to generate the prediction of the 3 classes (baseline, baseline border, and background)  
**Postprocessing:**  
The result of the U-Net is a probability map. Each pixel in the map is labeled as the symbol class with the highest probability (excluding background). Afterwards, we generated two binarized images from the probability map: (1) with only the recognized pixels of the baselines and (2) with only the recognized pixels of the boundaries of the baselines. Since the output of the neural network can have gaps in the lines, we cannot simply use the connected components (CCs) as our text lines, but must first cluster them.  
For this, we used the DBSCAN algorithm with a custom distance matrix to cluster the connected components of the baseline image. The distance between each two connected components was encoded in the matrix and calculated as follows:  
1. If the angle between two CCs was too large, we set the value in the distance matrix to a high value, because such CCs are not clustered later. The degree value we used depended on the pixel distance between the two CCs. We used \(5^{\circ}\) for CCs with a pixel distance greater than 30, otherwise \(15^{\circ}\). For very close CC, whose distance was less than 5 pixels away, a value of \(25^{\circ}\) was chosen.
2. With the previous step, only CCs that lied horizontally on a line were then considered. We then checked whether a baseline border between the CCs under consideration had been recognized by the neural network. If this was the case, the value was also set high in the distance matrix. Otherwise, we stored the pixel distance in the distance matrix.  
Finally, we applied the DBSCAN algorithm, which clusters the CCs into text lines, which are then simplified to obtain a polyline for each cluster.
### Paper Organization and Research Methodology  
To collect data for this paper we used major five search libraries including IEEE Xplore, ScienceDirect, Wiley Online Library, SpringerLink, and MDPI. The overall data collected from these libraries are summarized in Figure 2.  
We searched these databases by using the keywords'smart grid', 'energy management', 'DSM', 'demand response management', 'optimization objectives of DSM', 'PAPR', 'DERs, and 'pricing tariff'. The systematic search work was done in three steps. In the first step, we searched IEEE Xplore for the mentioned keywords where we obtained 270 research papers including journals and conferences. As we mentioned earlier, our focus was on the last decade; therefore, in the second step, we filtered for the last decade and also excluded the low quality journals and conferences which resulted in 150 papers. In the third step, we filtered the download list by studying the titles and the abstracts which excluded another 43 articles. Similarly, we searched ScienceDirect, Wiley Online Library, SpringerLink, and MDPI simultaneously by using the mentioned keywords and obtained 75, 23, 18,  
Figure 2: Database-wise review comparison.  
and 15 research articles simultaneously. After step two, the given list reduced to 47, 13, 10 and 15 articles. Then, through studying the article titles and abstracts, the download lists reduced to 25, 6, 4, and 8. Besides the above libraries, we also searched the Scopus database, US Department of Energy and European Union electricity market to collect various energy consumption reports which yielded 22 papers. The year-wise published papers are summarized in Figure 3.  
The rest of the paper is structured as follows. The general layout of DSM, the classifications of DSM and their roles in cost minimization are discussed in Section 2. Section 3 elucidates the use of DERs and their integration into the main grid. Moreover, this section also explains how DERs can help a given DSM scheme to achieve various performance metrics. The future research directions are outlined in Section 4. The paper is concluded in Section 5.  
The words consumer and end-user are used interchangeably throughout this survey.
# Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
## 5 Volume Rendering and Geobody Extractions
In addition, volume rendering and geobody extraction provide more realistic subsurface features with some uncertainties based on data resolution and the workflow that was implemented in the study. Nevertheless, geological understanding of the studied area as well as integration with other subsurface datasets (e.g., wells, gravity, magnetic, etc.) are needed for obtaining more geologic interpretations (higher levels of confidence in interpretations) and low uncertainty on the subsurface model.