esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Data Acquisition  
For the experiments, three datasets were taken into account, which have very different characteristics and applications. First, the CIFAR-100 (Canadian Institute for Advanced Research 100) dataset is a widely used dataset in the computer vision community, especially within AI, for evaluating image classification models. It was introduced by [PERSON] et al. [22] in 2009 and is composed of 60,000 color images, each 32 \(\times\) 32 pixels in size and divided into 100 different classes. Each class contained 600 images. In addition, the 100 classes were organized into 20 superclasses, grouping similar categories according to their shared characteristics.  
Unlike its simplified version, CIFAR-10, which contains only 10 categories, CIFAR-100 offers a greater challenge due to its diversity of classes and the similarity between some categories, making it a key benchmark for evaluating DL models in classification tasks.  
The second dataset used is called 38-Cloud, which contains 38 Landsat 8 scene images and their manually extracted pixel-level ground truths for cloud detection. The entire images of these scenes were cropped into multiple 384 \(\times\) 384 patches to be suitable for DL-based semantic segmentation algorithms. There are 8400 patches for training and 9201 patches for testing. Each patch has 4 corresponding spectral channels: red (band 4), green (band 3), blue (band 2), and near-infrared (band 5). Unlike other computer vision images, these channels are not combined. Instead, they are located in their corresponding directories  
Figure 1: Proposed methodology in this study.  
The 38-Cloud dataset was introduced in [23], but it is a modification of the dataset in [24]. All data were prepared at the Laboratory for Robotics Vision (LRV), School of Engineering Science at Simon Fraser University, Burnaby, BC, Canada.  
Finally, the Monkeypox Skin Lesion Dataset (MSLD) was proposed by [PERSON] et al. [25] and specifically designed for the detection and classification of skin lesions caused by monkeypox. This dataset was created to provide an accessible and reliable database for the development and evaluation of DL models in the automated diagnosis of this disease.  
The dataset consists of images of skin lesions categorized into different classes, including cases of monkeypox, as well as images of similar skin diseases, such as smallpox or measles, and healthy skin. The diversity in the images of the MSLD allows us to evaluate the ability of computer vision models to differentiate between visually similar conditions, which is crucial for medical applications based on AI.  
These three datasets illustrate the broad applicability of AI in multiple fields, from general image classification and earth observation to medical diagnosis. The use of these systems in this study highlights the importance of implementing HPC or DS infrastructures, even with low scalability or aging hardware, to meet the growing computational demands in disciplines such as remote sensing, computer vision, and health sciences.
## 1 Introduction
### Related Work  
The study presented in [15] inspired our work and is the closest study in approach to our work. The authors present the spectral clustering algorithm to compensate for the nonlinear deficiencies. The authors compare spectral clustering against DBSCAN and k-means. However, there is no detail on the parameters used for the DBSCAN setup and no information on how the algorithm is affected by varying the nonlinear phase noise and increasing the modulation format. We extended the work to validate the spectral clustering algorithm for different values of nonlinear phase noise and varied the modulation format by 16, 32, and 64 to verify the algorithm's behavior. We performed an approach to operate the algorithm in streaming because the algorithm is a good candidate for real-time applications. We performed a comparison against k-means as the standard reference in clustering algorithms because it has no parameters except k, which is always equal to the constellation size. The comparison with other algorithms, such as DBSCAN, hierarchical, or deep learning [16], whose performance depends on other parameters, is an exciting and complex topic that deserves a unique approach to determine a relationship between the multiple variables addressed in this study. A comparative work between the behavior of different clustering algorithms applied to constellations can be seen in [17]. However, the spectral clustering algorithm is not included in this article, and the nonlinear phase noise presents a distortion in the cluster shape that would make the work difficult for all the cases presented. With a similar approach to the previous work, in [18], we find the most recent work at the time of writing. The authors formally present the spectral clustering algorithm as a coherent receiver with no data preamble. However, the behavior of the spectral clustering algorithm is not explored for different levels of nonlinear phase noise, and tests are performed for the 16-QAM modulation format.  
In [19], the authors developed a k-means clustering phase correction algorithm to address phase deviations in special-shaped 8-QAM constellations for underwater visible light communication. Their experimental results showed that a circular constellation achieved the most significant performance improvement after applying the correction method. The authors demonstrated the power of machine learning algorithms, in this particular case, k-means, to find the relationship in non-standard ways, such as those proposed in geometrical shaping. The algorithm is specific to the particular case of 8-QAM and does not explore nonlinear phase noise.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Infrastructure-Aided Control Plane  
A FANET without terrestrial infrastructure needs to maintain itself in a decentralized manner as conventional ad hoc networks. On the other hand, when a FANET is to be operated with terrestrial networks, it can take advantage of terrestrial infrastructure. To this end, gNBs can help. For example, as an extreme case, the coverage of gNBs can be extended to support all UAVs in a FANET to exchange information in the control plane, as shown in Figure 15, which might be possible as the data rate for the control plane can be relatively low. In this case, any UAV directly communicates with a gNB to exchange control information at a low rate in the control plane that uses a different network from the FANET for the data plane, where data packets are transmitted through UAVs.  
The terrestrial infrastructure-aided control plane can provide a number of advantages, including mobility control of UAVs by gNBs so that topology control and routing can be done in a centralized manner. However, the coverage of gNBs may not be sufficient to communicate with all the UAVs when the density of gNBs is low. In addition, if there are a large number of UAVs, the bandwidth allocated to the control plane should increase, which is often undesirable due to the limited bandwidth. As a result, the terrestrial infrastructure-aided control plane should adapt to given conditions, e.g., the number of UAVs, coverage limit, bandwidth allocation, etc.
## 2 Literature Review  
### Cost Contingency Prediction  
Contingency can be defined as the amount of funds, budget, or time needed in addition to the estimate to reduce the risk of project objective overruns to a level that is acceptable to the organization [17]. This study specifically examines cost contingency, where a portion of the project budget is set aside to cover the unforeseen risks or challenges that may occur during a project. This allocation functions as a financial buffer for unexpected expenses beyond the initial project scope, such as design changes, material cost changes, delays, or unforeseen site conditions [11, 17]. Typically, cost contingency is determined as a percentage of the total project cost that is informed by historical data and adjusted for factors such as project complexity, risk assessments, and client requirements [2]. Previous research has employed both traditional estimation methods and ML approaches to estimate cost contingency.
## 2 Modeling and Research Methods
### Temporally Topological Measures
#### 2.2.3 Clustering Coefficient  
The clustering coefficient measures the possibility of two neighboring nodes being connected, and it is defined as the ratio of the actual edges between them to the number of all possible edges. The clustering coefficient quantifies the strength of intra-cluster connectivity and characterizes the closeness between nodes within a network. In particular, the clustering coefficient \(C(t,v)\) of a given node \(v(t)\) is computed using a function of the number of edges \(n_{e}\) among its neighbors and \(d(t,v)\), which is the degree of node \(v(t)\). The function is defined as  
\[C(t,v)=\left\{\begin{array}{ll}\dfrac{2n_{e}}{d(t,v)(d(t,v)-1)}&\text{if }d(t,v)>1\\ 0&\text{otherwise}\end{array}\right. \tag{4}\]  
Note that, in the above equation, the condition \(d(t,v)>1\), indicates that the node \(v(t)\) is connected to two or more neighboring nodes, while \(\dfrac{d(t,v)(d(t,v)-1)}{2}\) represents the number of potential edges in the network. If the node \(v(t)\) has less than or equal to one neighbor, then its clustering coefficient is equal to zero.  
The average clustering coefficient \(C(t)\) in a network is defined as the mean of all node clustering coefficients. It can be calculated as follows:  
\[C(t)=\dfrac{1}{N}\sum_{i=1}^{n}C(t,i) \tag{5}\]  
The clustering coefficient characterizes the density of a network and the inter-connection of nodes. A high clustering coefficient indicates a dense network with closely connected nodes, while a low clustering coefficient implies a sparser network with less connectivity between nodes.
## 2 Materials and Methods
### The Design of the No-Reference Combined Metrics for RS Images
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SROCC**} & \multicolumn{2}{c}{**SROCC**} \\ \cline{2-6}
**Metric** & **NA Subset** & \
\begin{tabular}{c} **Whole** \\ **TID2013** \\ \end{tabular} & **Metric** & \multicolumn{2}{c}{**NA Subset**} &
\begin{tabular}{c} **Whole** \\ **TID2013** \\ \end{tabular} \\ \hline ARISM [43] & 0.0736 & 0.1449 & IL-NIQE [25] & **0.7221** & **0.4921** \\ ARISMC [43] & 0.0520 & 0.1380 & JINBM [19] & 0.1632 & 0.1412 \\ BIBLE [48] & 0.3331 & 0.2812 & LPC\_SI [53] & 0.3196 & 0.3233 \\ BIQI [38] & 0.4999 & 0.4050 & LPSI [54] & 0.4695 & 0.3949 \\ BLIINDS-2 [39] & 0.4834 & 0.3946 & MLV [55] & 0.1755 & 0.2013 \\ Blur Metric [44] & 0.0393 & 0.0075 & MSGF\_PR [56] & 0.3085 & 0.2437 \\ BPRI [50] & 0.2849 & 0.2289 & NIQE [26] & 0.4451 & 0.3132 \\ PSS [50] & 0.3334 & 0.0218 & PIQE [37] & 0.2473 & 0.1364 \\ LSSs [50] & 0.0880 & 0.1138 & NIQMC [57] & 0.1141 & 0.1129 \\ LSSn [50] & 0.1936 & 0.1675 & NJQA [58] & 0.0233 & 0.0997 \\ BRISQUE [36] & 0.4952 & 0.3673 & NMC [59] & 0.0485 & 0.0542 \\ C-DIIVINE [60] & 0.4773 & 0.3734 & NR\_PWN [21] & 0.0259 & 0.0163 \\ CORNIA [41] & 0.4735 & **0.4352** & OG-IQA [61] & 0.3874 & 0.2761 \\ CPBDM [20] & 0.0948 & 0.1115 & PSI [46] & 0.1677 & 0.0940 \\ DESIQUE [62] & 0.1236 & 0.0691 & QAC [42] & **0.5034** & 0.3723 \\ DIIVINE [40] & 0.4456 & 0.3438 & SDQI [63] & 0.2254 & 0.2244 \\ dipIQ [64] & 0.2532 & 0.1395 & DIQU [65] & 0.2950 & 0.2401 \\ FISH [45] & 0.0219 & 0.0524 & TCLT [60] & 0.3151 & 0.2331 \\ FISH\_BB [45] & 0.1551 & 0.1450 & GM\_LOG [67] & 0.1556 & 0.1089 \\ SISBILM\_SM [51] & 0.4787 & 0.3178 & HOSA [49] & **0.5690** & **0.4705** \\ SISBILM\_SFB [51] & 0.4276 & 0.3363 & SMETRIC [68] & 0.0157 & 0.0969 \\ SISBILM\_WM [51] & 0.3992 & 0.2392 & SSEQ [69] & 0.4659 & 0.3410 \\ SISBILM\_WFB [51] & 0.3789 & 0.2929 & WNJE [47] & 0.3967 & 0.3018 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The absolute SROCC values obtained for the NR elementary metrics (the three best results are marked with bold font).
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 1 Introduction  
Improving the safety of unprotected road users is one of the priorities of European and national road safety programs [1]. The pedestrian, as a vulnerable road traffic participant, is particularly exposed to the risk of an accident, injury, or death. Compared to European countries, Poland has recorded a very low level of safety for many years. Multidirectional corrective actions taken in 2020 improved this state.  
Last year, Poland achieved a significant positive impact in that field, representing a satisfactory average level of safety of vulnerable road users [2]. However, vision zero has still not been achieved [3]. To prevent traffic hazards, road infrastructure inspections and audits were carried out [4]. Numerous research studies are being performed to implement the results of pedestrian safety improvement [5, 6, 7].  
Figure 1 shows a view of pedestrian crossings with an autonomous lighting system containing two LED luminaires, four PV panels, and four batteries.  
Depending on the formal requirements and the size of the pedestrian crossing, the volume of vehicle traffic, and the number of pedestrians, a lighting system containing different numbers of PV panels, batteries, and luminaires, manufactured with LED technology should be designed.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
###### Abstract  
Biocompatible nanomaterials have attracted enormous interest for biomedical applications. Carbonaceous materials, including carbon nanotubes (CNTs), have been widely explored in wound healing and other applications because of their superior physicochemical and potential biomedical properties to the nanoscale level. CNTs-based hydrogels are widely used for wound-healing and antibacterial applications. CNTs-based materials exhibited improved antimicrobial, antibacterial, adhesive, antioxidants, and mechanical properties, which are beneficial for the wound-healing process. This review concisely discussed the preparation of CNTs-based hydrogels and their antibacterial and wound-healing applications. The conductive potential of CNTs and their derivatives is discussed. It has been observed that the conductivity of CNTs is profoundly affected by their structure, temperature, and functionalization. CNTs properties can be easily modified by surface functionalization. CNTs-based composite hydrogels demonstrated superior antibacterial potential to corresponding pure polymer hydrogels. The accelerated wound healing was observed with CNTs-based hydrogels.  
carbon nanotubes (CNTs); conductive hydrogels; antibacterial; wound healing +
Footnote †: journal: Applied Sciences  
17 September 2021  
Accepted: 6 October 2021  
Submitted: 14 October 2021
## 1 Introduction  
Corrosion under insulation (CUI) refers to external corrosion on the surface of piping and/or vessels fabricated from low alloys, carbon manganese steel, or austenitic stainless steel that happens beneath insulation due to the penetration of water. CUI is typically localized corrosion but is difficult to identify because it is hidden under insulation material until it becomes a serious problem, which especially occurs in chemical plants that have been running for an extended time [1].  
The CUI problem in industries increases with years. As had been reported by Exson Mobile Chemical and National Association of Corrosion Engineers (NACE), the utmost incidence of leakages in the refining and chemical industries is due to corrosion under insulation (CUI) and out of 30 facilities, 17 state CUI as the focal challenge in their industries [2]. CUI also has a high implication to financial cost. The United States Congress reported that direct loss and maintenance costs due to corrosion are approaching $276 billion per year, where practically 40% to 60% of maintenance costs are allocated for CUI piping detection and rectification of CUI occurrence [3, 4]. This value would be twice if indirect costs were considered. Likewise, the NACE Corrosion Costs Study stated that corrosion costs in the United States are approaching $1 trillion annually and are estimated to exceed this amount in a couple of years [5]. In addition to important CUI detection methods for prevention and inspections, it is also very important to have a high-accuracy CUI prediction model as a guideline to schedule effective maintenance planning to avoid higher costs due to CUI.  
Over the past few years, prediction modeling has attracted increasing attention and has been considered for applications in several industries, including for cases such as corrosion in piping [6; 7; 8]. The current standards for CUI prediction, such as in API 581, are developed based on experimental and/or numerical works, which are sometimes generalized and contain flaws [9]. At present, numerous prediction models for CUI have been developed using historical field data as data input on each local industry or country [10]. Although this type of data input is well established for enabling the development of different places of corrosion under the insulation model to be used as a local maintenance guide, the data input effects on the available model have not been extensively studied. Some research has been reported without the changing behavior of the corrosion phase, as the historical field data was only collected after decades of operation [11; 12]. This issue affects the practicality and the quality of the prediction model developed.  
Therefore, a substantial study on the type of data input required for developing a prediction model is essential to understand the relative importance of combined data input in the CUI prediction model. In this work, the single data input, from historical field data and experimental data explicitly, and combined data input of both field and experimental data are tested to investigate the effect of type of data input on the CUI prediction model.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 4 Experimental Design
### Experimental Data  
The experimental data include remote sensing metadata and image data. In this research, the Metadata Extraction Tool, which was developed using Java programming language, is used to extract the necessary fields and values of the metadata file in the Landsat8 data package and transform them into corresponding fields in the archiving model. There are about 100 million global metadata records from 2013 to 2017, which can be obtained from the USGS website \"[[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))\". We downloaded the image data from different data centers, and these data include Landsat data, FY data, and Sentinel data. To simplify the expression, the time information and other information after it in the data filename of each data product are replaced with the symbol \"*\". Table 2 presents detailed information of each data product.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Filename** & **Satellite** & **Data Source** & **Data Amount** \\ \hline LT05\_LIGS\_123046.* & Landsat5 & https: & 131 MB \\ LC08\_LIGT\_123046.* & Landsat8 & //earthexplorer.usgs.gov/ & 926 MB \\ \hline FY3A\_MERSI\_GBAL\_L1.* & FY3A & http: & 285 MB \\ FY3B\_MERSI\_GBAL\_L1.* & FY3B & //satellite.nsmc.org.cn/ & 328 MB \\ FY3C\_MERSI\_GBAL\_L1.* & FY3C & portalsite/default.aspx & 444 MB \\ \hline S1A\_IW\_CRDH\_15 DV\_* & Sentinel1 & https: & 872 MB \\ S2A\_MSILC\_* & Sentinel2 & //scihub.coprenicus.eu/ & 522 MB \\ S3A\_OL\_1\_EFR\_2016* & Sentinel3 & & 711 MB \\ S3A\_OL\_1\_EFR\_2017* & Sentinel3 & & 618 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detail information of each image data.  
Figure 4: The storage mechanism of remote sensing image data.
## 2 Background
### Related Work  
The following are the highlights from a comprehensive review reported in [21]: _Pegasus_ is presented in [12] as a tool that enables the development of software that functions through a natural language entry based on a mechanism that resembles the human brain. _Attempto Controlled English_ (ACE) is presented in [22; 23; 24; 25] as a formalized version of the English language that can be transformed into first order logic. _Computer-Processable Language_ (CPL) is presented in [14; 26] as a natural controlled language developed with a naturalistic focus and featuring an embedded unambiguous language called CPL-Lite. _Metafor_ is presented in [27] as a tool that was envisaged to support novice programmers by enabling them to describe a problem in natural language and then returning Python code sketches. _Natural Language Computer_ (NLC) is presented in [4] as a programming language focused on the management of arrays that appear on the screen by using a subset from the English language to resolve the expressiveness problems of the C language. _Macho_ is presented in [28; 29] as a tool that enables the generation of source code based on a simple sentence in English. Presented in [30; 31], _Natural Language Command Interpreter_ (NLCI) is a tool focused on generating executable code from an entry in English and is based on an ontology generated from an API. As already mentioned in the survey, while these languages propose solutions from the perspective of natural language, none provide a programming model that describes the elements required for the design of general-purpose naturalistic programming languages.
## 2 Materials and Methods
### Statistical Analysis  
The data were analyzed using R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria) [32], along with the SNPassoc package (version 2.1-0) [33]. Results for categorical variables were expressed as frequencies and percentages, while continuous variables were presented as means \(\pm\) standard deviation (SD) or medians with interquartile range, depending on their distribution. The Shapiro-Wilk test was used to assess normality, and comparisons were performed using Student's \(t\)-test for normally distributed data or appropriate non-parametric tests for skewed data. The Hardy-Weinberg equilibrium (HWE) was tested using the \(\chi^{2}\) goodness-of-fit test.  
Associations between genotypes and susceptibility to intrauterine fetal demise (IUFD) and recurrent miscarriage (RM) were evaluated using logistic regression to calculate odds ratios (OR) with 95% confidence intervals (CI). The most suitable inheritance models were identified using the Akaike information criterion (AIC). Associations between genotypes and continuous variables such as newborn weight and placenta weight were examined using a univariate general linear model. Both unadjusted and confounder-adjusted statistical results are reported as mean \(\pm\) SD. Statistical significance was set at \(p<\) 0.05.  
Linkage disequilibrium (LD) and haplotype analyses were conducted using Haploview software (version 4.2) [34], which applies the expectation-maximization (EM) algorithm. Statistical power was calculated using the \"genpwr\" package (version 1.0.2) in R [35]. For the sample of 310 women (190 cases with fetal death and 120 controls), assuming an additive model with a case-to-control ratio of 190/310 (0.613), an alpha of 0.05, and a power of 0.80, the odds ratio was estimated at 1.75 based on the minor allele frequencies of the selected SNPs.