esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 2 Problem Formulation  
Let \(W\subset\mathbb{R}^{2}\) denote the compact and connected workspace of the robot. Within \(W\) exists a finite set of inner obstacles \(W_{j},j\in[1,...,M]\). The workspace is considered static and the only dynamic elements are the robots themselves. The boundaries of the outer and inner curves are denoted as \(\partial W_{0}\) and \(\partial W_{j}\) and the entirety of the workspace's boundaries can be referred to as \(\partial W\) (see Figure 1).  
Now, consider a group of \(N\) robots, where the position of each robot is described by the state vector \(p_{i}(t)=[x_{i}(t),y_{i}(t)]^{T}\in\mathbb{R}^{2},i\in[1,...,N]\), representing the Cartesian coordinates in the workspace. Each robot has a sensing region \(r_{i},i\in[1,...,N]\) that denotes a disk with a radius equal to maximum distance that can be perceived as unoccupied by the robot's sensors. The explored region of each robot at time \(t\) is noted as \(\varepsilon_{i}(t)=(r_{i}\cap W)\cup\varepsilon_{i}(t-T)\subseteq W\), where \(T\) is the period that the explored region is updated and the initial explored region is \(\varepsilon_{i}(0)=r_{i}\cap W\). The boundaries of the explored region \(\partial\varepsilon_{i}\) can be a part of the workspace boundaries and denoted as \(\partial\varepsilon_{i_{O}}\subseteq\partial W\) or belong to the workspace and denoted as \(\partial\varepsilon_{i_{f}}\subset W\). The latter boundaries will henceforth be referred to as frontiers. The total explored region at any given moment \(t\) can be defined as follows:  
\[\mathcal{E}(t)=\bigcup_{i=1}^{N}\varepsilon_{i}(t). \tag{1}\]  
Problem: Our goal is to create a controller that plans the path of each robot so that \(\mathcal{E}(t_{f})=W\) with \(t_{f}<\infty\), using the information provided by each of the robots in an efficient manner, while ensuring that each robots' trajectory remains safe, i.e., \(p_{i}(t)\in W\) for all \(t\geq 0\).
## 1 Introduction
### Problem Statement and Significance  
Identification of factors that affect the adoption of cloud computing technologies in implementation of HIS has received insufficient attention in the academic literature, and hence there is paucity of studies with systematic analysis and rigor. This study aims to fulfill those gaps through its proposed model of study. It proposes a framework for study of the primary drivers and challenges in the aforementioned dimensions and presents a model. This model proposed has face validity, content validity, and nomological validity and may be confirmed through an empirical analysis. The model and subsequent analysis may provide a better understanding of the challenges and possible solutions in the adoption of cloud computing in the healthcare sector and more specifically in relation to HIS.  
The results of this study are targeted towards executive management of health organizations, especially senior clinical management positions like CTOs, CIOs, and IT managers so that they can make an informed decision about cloud computing adoption. The study elaborates on how HIS functionality can be enhanced through the of cloud computing. Improvements in the operational excellence and financial performance of healthcare organizations using cloud computing technologies that overcome the limitations of in-place legacy information systems are also examined. This study will serve as an aid for future researchers and adopters through its detailed examination of drivers and challenges that are pertinent to the adoption of cloud computing technologies for HIS and will also help in managerial decision making.  
The paper is structured as follows. Section 2 provides an overview of the HIS. Section 3 provides a background of cloud computing and its benefits. Section 4 provides a detailed treatment of the dimensions of financial performance and cost; IT operational excellence and DevOps; and security, governance, and compliance, and associated drivers and challenges pertinent to each of these dimensions. Based on the discussions, a set of hypotheses are proposed with respect to each driver and challenge and its effect on the adoption of cloud computing in HIS. Section 5 presents a confirmatory structural equation model along with single item measures for each of the challenges and drivers and readiness of adoption of cloud computing for HIS. It also outlines some gaps in cloud computing research that are also pertinent to cloud-based HIS, which need further attention and may be taken up by other researchers or the industry. Section 6 gives details on the proposed use of the model for an academic study in Palestine and India. Finally, Section 7 provides conclusions and possible avenues for future research.
## 2 Industry 4.0 Background  
The birth of the fourth industrial revolution, namely Industry 4.0, is assumed to coincide with the Industrie 4.0 initiative [13]. Industrie 4.0 is a strategic initiative included by the German government as part of its High-Tech Strategy 2020 Action Plan, according to which the Internet-based technologies are used to improve all industrial processes, from manufacturing to distribution, from sales to post-sales. In the same period, similar initiatives were undertaken by other countries, such as Internet + in China [11] and Industrial Internet in the USA [3]. All of them moved from the consideration that new paradigms such as Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data, which were already being used to turn \"normal\" environments into smart environments (e.g., Smart Homes, Smart Cities), could be integrated with industry-related processes to enhance their performance.  
Intelligence had been already introduced in industries thanks to embedded systems, i.e., systems made of devices where sensors and actuators are embedded and connected to a control unit via field buses [8]. However, traditional embedded systems are conceived as stand-alone systems. The technologies introduced by Industry 4.0 enable communication and cooperation among devices and stand-alone systems, so that a higher level of intelligence can be provided to industrial processes [8; 14]. More specifically, there are two ways in which communication can provide intelligence to objects: (i) less capable objects can leverage the resources of smarter objects, whether they are located nearby or remotely (e.g., cloud computing). Accordingly, less powerful objects typical of the industrial scenario, such as sensors and RFID (Radio Frequency Identification) tags, can cooperate with other more powerful objects to perform smarter applications; (ii) communication can be used by objects that are already smart, such as smartphones, to \"augment\" their intelligence thanks to information collected by distributed objects, whether it is objective, coming from measures collected by sensors, or subjective, provided by humans. This is the concept on the basis of crowdsensing and crowdsourcing, where big amounts of data gathered by multiple nodes are processed to get more complex information.  
According to these considerations, seamless ubiquitous Internet access, communication between intelligent machines and advanced analytics methods are to be considered the pillars of Industry 4.0 revolution [3], fostered by the IIoT [4; 5] and CPPS [6] paradigms. These components will be analysed in the following.
## 3 Methodology
### Models  
This section provides a justification for the models selected for evaluation concerning the drone detection problem utilizing thermal imaging. The selected models are as follows:  
* **YOLO**: YOLO is chosen because of its ability to perform real-time detection, which is crucial for applications where speed is essential. Additionally, its architecture is well documented and optimized to work with low-resolution images, which is necessary for accurate drone detection in thermal images. It is important to note that, according to most experts, YOLO is considered one of the best image detectors available. Starting from the best-performing models at the CVPR 2023 conference [51] that used YOLOv5 and one of the best-performing models at the same conference [63], which is based on an ensemble model with YOLO-v8, working with this type of model can be considered essential in this type of detection problem. However, recent advances have further improved the performance of YOLO systems with the YOLO-v9 model, which incorporates innovations such as Programmable Gradient Information (PGI) to solve the problem of losing data during information propagation. Therefore, studying the new YOLO model in the context of drone thermal imaging helps to extend the state of the art of drone detection in thermal imaging. This model has different versions [t, s, m, c, e], which have the same input size but with an increasing number of parameters, which influences the detection accuracy but affects the processing speed. For this work, we consider the largest YOLOV9 model corresponding to e, YOLO-v9-e.  
Figure 1: Data distribution by video type [31].  
* **GELAN**: Since it is a model used in YOLOv9, the goal is to compare its performance with YOLO and evaluate whether its simpler architecture provides improvements in drone detection.
* **DETR**: Although it has not yet been implemented for infrared images, it is considered relevant to include a transformer architecture in a detection comparison, given its ability to achieve more precise detection in situations where spatial and contextual relationships are complex, such as thermal images with multiple heat sources. It is part of the Detectron2 group [64], which is also considered one of the best sets of object detection models.
* **ViTDet**: We are interested in determining whether there is a difference between the transformer-based models applied to this task. Like DETR, it is part of Detectron2, making it a good model for comparison.
## 1 Introduction  
While technologies for collecting data are becoming better thanks to sensor technologies, higher processing power, and storage capacities, mining and other industries have found a lot of creative ways to use them. Digital camera systems are now a practical and effective instrument for monitoring mining activities since their cost has reduced drastically in recent decades. Although security and surveillance are their primary applications, cameras have many more potential uses other than observation. Computer vision algorithms looking at images taken by cameras positioned over the mining site may extract basic knowledge about equipment movement, operational conditions, traffic patterns, and infrastructure state. Cameras on equipment paths may gather data for tracking vehicle position, speed, and activity. This research allows one to assess speed rule adherence, traffic density, and road conditions. This information can offer important operational insights and help to increase the success of decision-making processes. With the declining cost of CCTV camera technology, these cameras have increasingly been utilized in mines as a safety measure. Although CCTV cameras provide a lot of information, past studies on how this information can be utilized for mine management have been limited. This study is a bid to fill a gap via an exploration of the contribution of computer vision, especially through the construction of a YOLO model, in providing input parameters for truck dispatching algorithms in real time in the context of an operational mine. This application illustrates, via the optimization of material flow and operational performance enhancement, the application of computer vision technology in a mining environment.
# Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
## 5 Results Analysis Using Synthetic Models
### Varying the TTL  
The number of hosts was set to 195 and the buffer size was fixed at 10 Mb. The TTL is varied from 30 to 240 min; and the impact of this variation on the message delivery ratio, average latency, and number of forwarded messages, were studied. The results are given in Figures 8-10.  
In Figure 8, it can be observed that the delivery ratio increases as the TTL of messages is increased and the pace of increase for the EECSG scheme is more pronounced. This is due to the fact that in the  
Figure 6: Delivery ratio vs. buffer size.  
Figure 7: Average latency vs. buffer size.  
EECSG design, a node gets more time to find a suitable relay node to forward the message. In terms of delivery ratio performance, it is found that EECSG is about 9.28% better than CGOPP and 28.59% better than GSAF.  
In Figure 9, it can be observed that as the TTL of messages is increased, the average latency increases as well, and this increase is less pronounced for the EECSG protocol. It is also observed that in terms of latency performance, EECSG is about 7.21% better than CGOPP and 18.83% better than GSAF. Furthermore, the average latency decreases as the delivery ratio increases and from Figure 8 it can be observed that when the TTL increases the delivery ratio increases.  
In Figure 10, as the TTL of messages is increased, the number of forwarded messages increases. This is due to the fact that the more the TTL time for a message is, the longer the time it takes to find a suitable relay node to forward the message. It is also observed that in terms of performance w.r.t. the number of forwarded messages, EECSG is about 4% better than CGOPP and 9.16% better than GSAF.  
Figure 8: Delivery ratio vs. Time-to-Live (TTL).  
Figure 9: Average latency vs. TTL.
## 3 Routing algorithms based on sensor position
### Grid-based routing
\(IN\) chooses other working nodes by WNSP (Working Nodes Selected Packets) as follows: (1) Supposing the coordinates of \(IN\), \(N\), is \((X_{N},Y_{N})\), \(N\) chooses nodes whose abscissa are between \((X_{N}-\Delta)\) and \((X_{N}+\Delta)\) from next-hop table (\(NT\)); (2) \(N\) chooses the node which has maximum \(|Y\)-\(Y_{N}|\) as working node from \(NT\), constructs WNSP and sends out; (3) Receiving the WNSP, only the node whose ID is included in the WNSP will deal with WNSP; (4) The procedure completes until to find a node near the other edge in the grid as shown in Figure.22: \(N\)-\(N\)-\(N\)-\(O\)-\(P\)-\(Q\), and \(N\)-\(R\), where \(\Delta\) is variable and can be adjusted according to the number of \(IN\) on the curve. Similar to the basic grid idea, there are other algorithms with different routing graph. GPSR (Greedy Perimeter Stateless Routing) is geographic routing based on greedy strategy, which finds neighbors closer to the destination and forwards the packet to the neighbor closest to the destination [54]. GPSR uses Right-Hand Rule to traverse the edges of a void. GPSR picks the next anticlockwise edge. Shown as Figure.23, \(x\) receives a packet from \(y\), and forwards it to its first neighbor counterclockwise about itself, \(z\). Finally, the routing path forms the polygon, shown as Figure.24. In Figure.24, \(D\) is the destination and \(x\) is the node where the packet enters perimeter mode. Solid arrows denote the forwarding hops. Grid-based routing algorithms are similar to hierachical routing strategy. The structure of grid provides some advantages to design the effective routing algorithms. Table 2 compares GLS, TTDD and GBRA according to different metrics. The results in the table are gotten by comparing the algorithms belonging to grid-based routing. It is noted that grid-based routing algorithms is suitable to deal with the mobility problem.
# Genetic Programming to Remove Impulse Noise in Color Images
## 1 Introduction
### Outline and Contribution  
This paper proposes a novel adaptive filter to remove impulse noise in color images. The contribution of this work is twofold. First, this is the first filter to remove noise in color images by exploiting the correlation among the image channels through the genetic programming paradigm. The filter consists of a two-stage solution comprising detection and correction processes. The detection stage decomposes the input color image into its red-green-blue channels; then, it uses a binary classification model to identify which channel values for each image pixel are perturbed by noise. The correction stage modifies each pixel identified as noisy according to its perturbed channels and neighborhood. Second, the detection stage of the proposed filter is an interpretable model that performs very well under different conditions by considering three impulse noise models: salt and pepper, uniform, and correlated. Experimental results show that the filter is the second closest to an equilibrium point in terms of the performance balance, outperformed only by a filter using a black-box deep learning-based approach.  
The remainder of this paper is organized as follows. Section 2 presents the related works on color image denoising methods for impulse noise. Section 3 introduces some preliminary concepts for the problem of color image denoising. Section 4 describes the proposed adaptive filter and the evolutionary process used for its training. Section 5 shows the experimental results obtained from a comparison of the proposed filter with other methods for filtering. Finally, Section 6 presents some concluding remarks.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 3 Interactive Model and Architecture of IoV  
IoV mainly focuses on the integration of human and vehicle which is an extension of human abilities. It is a network model, service model and behavior model of the human-vehicle interaction system which is highly different from the wireless mobile network [28]. IoV applications can be comprehensively characterized into two different ways, safety and non-safety. For example, non-safety applications incorporate vehicle sharing, gaming, infotainment, and map download. Safety applications are, for example, route, remote telematics, indicative, traffic proficiency, co-usable message move, post-crash warning, upgrading traffic well-being, participate to support different vehicles, and ongoing traffic.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scheme** & **Overhead** & **Source Auth.** & **Modification** & **Privacy** & **Loc. Track** & **ID Disclosure** & **Traceability** \\ \hline
[8] & High & ✓ & ✓ & ✓ & ✗ & ✓ & ✗ \\
[18] & High & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\
[19] & Low & ✓ & ✓ & ✓ & ✗ & ✗ & ✓ \\
[20] & ✗ & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\
[21] & Low & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
[22] & Low & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
[23] & High & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ \\
[24] & Medium & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ \\
[25] & Medium & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of authentication-based schemes.
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 5 Proposed Method
### Model Selection  
Training an end-to-end object separation model requires all image-level, object-level, and pixel-level annotation for each data entry. Yet, this kind of dataset is non-existent in the field of X-ray security images. Choosing to train an end-to-end model with a limited dataset has the downside of throwing away the data with incomplete annotations. Instead, we created a threat detection pipeline wherein the localization of the object instances and the prediction of segmentation masks are separated into two different tasks handled by two different models. This way, all the samples in the dataset with only image-level and object-level annotations are used to train the first model, i.e., the object detection model, and the limited samples with additional pixel-level annotations are used to train the second model, i.e., the segmentation model. Combining these models in one pipeline results in an object separation model that can localize each object instance and delineate all the pixels belonging to that instance while also maximizing the utilization of the available annotations. Consequently, selecting the appropriate models to use for the two tasks are conducted using the experiments discussed in the following subsections.
## 3 Results
To better understand the effects of direct plasma treatment on the inflammatory profile of our collagen breast cancer microtissues, supernatants were collected 24 h after plasma treatment, and 13 different cytokines and chemokines were quantitatively assessed in these samples. Significantly increased levels were found for interferon (IFN)-\(\alpha\)2, interleukin (IL)-17A, IL18, and IL33. Despite the significant increases, the absolute changes were of these targets were modest, however. For IFN-\(\gamma\), IL-6, IL-6, and MCP-1, significantly decreased levels were determined, which was especially pronounced for IL-6 and IL-8. These data suggested that  
Figure 3: Gas plasma-treated human breast cancer tissue. (**a**) representative images of human patient-derived breast cancer tissue colonies in collagen 120 h after seeding and being either untreated or exposed to direct or indirect plasma treatment; (**b**) quantification of colonies with plasma treatment at day 2 and show mean and standard error of 7–10 patients, statistical analysis was performed using two-way ANOVA with (*) being \(p<0.05\) and (**) being \(p<0.01\). Scale bar is 250 \(\upmu\)m.  
Figure 2: Scheme of sample preparation and treatment. Human patient-derived breast cancer tissue was surgically removed and dissected ex vivo. Subsequently, the material was filtered several times to achieve microtissues with a size \(<100\)\(\upmu\)m and \(>\)40 \(\upmu\)m, prior to mixing with collagen. The patient-derived cancer tissue-collagen-matrix was cultured in microtiter plates and exposed to the gas plasma jet in a direct or indirect fashion. direct plasma treatment reduced cell viability and the expression of several cytokines and chemokines known to modulate the inflammatory profile of the TME critically. Figure 4: Microscopy and supernatant analysis. (**a**) representative fluorescence microscopy images of human patient-derived breast cancer tissue 24h after the second plasma treatment followed by staining with acridine orange (AO) and propidium iodide (PI); (**b**) quantification of dead cells; (**c**) representative fluorescence microscopy images of thin-sectioned patient-derived breast cancer tissues and staining with DAPI and anti-activated caspase 3 or anti E-cadherin antibodies; (**d**,**e**) quantification of caspase 3 positive cells (**d**) and E-cadherin expression (**e**) in untreated and direct or indirect plasma-treated human patient-derived breast cancer tissues; data (**b**,**d**,**e**) are mean and standard deviation of at least ten patients per group; (**f**) multiplex chemokine/cytokine analysis of supernatants from untreated and direct plasma-treated samples showing absolute levels in pg/mL of 13 targets. Statistical analysis was performed using one-way ANOVA (**b**,**d**,**e**) or \(t\)-tests (**f**), all with (*) being \(p<0.05\), (**) being \(p<0.01\), and (***) being \(p<0.001\). Scale bars are 200 μm (AO/PI and E-cadherin) and 100 μm (DAPI and caspase 3). c = control, \(p=\) plasma.