esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 1 Introduction  
The most important activity in the software development process is the assessment of effort, which includes assessing the time and money required for the software project to be successfully completed. Project development time is critical, both for project clients and project implementers. The amount of money required for investment in a project influences whether the project will be started or not, and whether it will end within the set framework. Software companies use a variety of software tools and services to meet customer requirements. Many methods measure the size of software, its complexity, and the time needed to build it. All methods can be divided according to whether they are parametric or nonparametric [1, 2, 3]. Within this division, there are several different approaches, three of which are the most commonly used:  
1. An approach based on analyzing the number of source code lines and estimating the effort required to implement the project. The most commonly used model of this approach is the Constructive Cost Model (COCOMO2000) [4, 5].
2. An approach based on the analysis of functional points to estimate the magnitude of the functionality of the software being developed. Within this approach, two models were initially distinguished: IFUG (created by the International Function Point Users Group) [6] and Mk II (Mark II) [7]. Subsequently, within the IFUG model, the following were developed: messaging messaging messaging (created by the Netherlands Software Metrics Association) [8], IFUG 4.1, and COSMIC FP (the COmmon Software Measurement International Consortium function point) [9].
3. An approach based on the analysis of users and use cases for the assessment of effort. Within this approach, the most commonly used models are: COBRA (COnstraint-Based Reconstruction and Analysis) [10] and UCP (Use Case Point Analysis) [11].
## 3 Research Model and Methodology
### Methodology
#### 3.2.3 Data Evaluation  
The gathered data were precisely encoded in order to facilitate statistical analysis employing the Minitab 18 software. In assessing statistically significant differences in each variable, a chi-square (x\({}^{2}\)) test was conducted. This test was specifically employed to determine whether the observed variations in the variables under investigation are of substantial statistical importance. Chi-square tests were emphasized in this study because they are well suited for analyzing categorical data, particularly in assessing associations between variables such as lane-changing behavior and roundabout signalization. While the chi-square test is often categorized as a nonparametric test due to its lack of assumptions about the underlying data distribution, it is technically a parametric test under certain conditions, such as when expected frequencies are sufficiently large. However, given that our data involved categorical variables without an underlying continuous distribution, nonparametric approaches were preferred to ensure robustness, particularly in cases where sample sizes in specific categories were small or unevenly distributed. This approach minimizes the risk of violating parametric assumptions, ensuring the reliability of our statistical findings. The computation of the \(p\)-value was performed to further elucidate the significance of the observed differences. The formula for the chi-square test is provided below.  
\[\chi_{\mathrm{c}}^{2}=\sum\frac{\left(\mathrm{O_{i}-E_{i}}\right)^{2}}{E_{i}}\]  
where  
c = degree of freedom;  
O = observed value(s);  
E = expected value(s).  
To further interpret the strength of associations identified through chi-square tests, Cramer's V was used as a post hoc measure of effect size [41]. Cramer's V provides a standardized metric for understanding the magnitude of relationships between categorical variables, offering additional insight beyond statistical significance. This combination of chi-square analysis and Cramer's V allowed us to assess not only whether relationships existed but also their practical significance within the context of roundabout traffic behavior. The formula for Cramer's V is provided below.  
\[q_{\mathrm{c}}=\sqrt{\frac{\chi_{\mathrm{c}}^{2}}{n(k-1)}}\]  
where  
\(\chi_{\mathrm{c}}^{2}=\) chi-square test statistic;  
n = total sample size;  
k = the smaller of the number of rows or columns in the contingency table.
## 3 Methodology
### Spatial Texture Analysis  
After performing the pre-processing steps, texture measures were applied to each backscattering element (HH, HV, VV and VH). Previous research has shown that texture measures provide vital information from radar imagery [20; 34]. Among several statistical texture methods previously proposed, the gray-level co-occurrence matrix (GLCM) is one of the most powerful for land cover monitoring; thus, the GLCM is used in this study. Texture measures represent the spatial distribution of the gray-level value and its frequency relative to another one for a specific displacement at \((x,y)\) and orientation (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\)). From a sub-image of a given window size, \(l(x,y)\), the GLCM is a matrix \(P\) with size \(Ng\times Ng\) (\(Ng\): the number of gray-levels) whose \(P(i,j)\) element (\(1\leq i\leq Ng\); \(1\leq j\leq Ng\)) contains the number of times a point with gray-level \(g_{i}\) occurs in a set of positions relative (based on the displacement and the angle mentioned before) to another point with gray-level \(g_{i}\)[22]. The texture measures are calculated from the matrix \(P\) as follows:  
\[\text{Angular Second Moment }(\text{ASM})\ =\sum\limits_{i}\sum\limits_{j}\{P(i,j)\}^{2} \tag{2}\]  
\[\text{Contrast }=\sum\limits_{n=0}^{Ng-1}n^{2}\left\{\sum\limits_{i=1}^{Ng} \sum\limits_{\begin{subarray}{c}j=1\\ |i-j|=n\end{subarray}}^{Ng}P(i,j)\right\} \tag{3}\]  
\[\text{Correlation }=\frac{\sum\limits_{i}\sum\limits_{j}{(ij)P(i,j)}-\mu_{x}- \mu_{y}}{\sigma_{x}\sigma_{y}} \tag{4}\]  
\[\text{Homogeneity }=\sum\limits_{i}\sum\limits_{j}\frac{1}{1+(i-j)^{2}}P(i,j) \tag{5}\]  
\[\text{Variance }=\sum\limits_{i}\sum\limits_{j}{(i-\mu)^{2}}P(i,j) \tag{6}\]  
\[\text{Mean }=\sum\limits_{i=2}^{2 Ng}iP_{x+y}(i) \tag{7}\]  
\[\text{Entropy }=-\sum\limits_{i}P\log(P(i,j)) \tag{8}\]  
\[\text{Dissimilarity }=\sum\limits_{i,j=0}^{Ng-1}P_{i,j}(-\ln P_{i,j}) \tag{9}\]  
where \(p(i,j)\) is the \((i,j)\)-th entry in a normalized gray-tone spatial dependence matrix \(P(i,j)/R\); \(R\) is the total sum of \(P\); \(p_{x}(i)=\sum\limits_{j=1}^{Ng}P(i,j)\) is the \(i\)-th entry in the marginal probability matrix obtained by summing the rows of \(p(i,j)\); and \(\mu_{x}\), \(\mu_{y}\), \(\sigma_{x}\) and \(\sigma_{y}\) are the means and standard deviations of \(p_{x}\) and \(p_{y}\).  
In this study, eight textural features at angle \(0^{\circ}\) and distance 1, different window sizes ranging from \(3\times 3\) to \(21\times 21\) and a quantization level of 64 were used to evaluate its performance for classification.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Geospatial and Statistical Data Analysis  
For statistical analysis of the post-processed aquaculture pond objects, we calculated summary statistics and spatial statistics using the pandas library in Python. The post-processed pond vector files include more than 30 features and are large datasets with more than 10 GB data. In order to bypass memory limitations and enable faster data processing, we transferred the datasets for intermediate data storage into the geofeather format ([[https://pypi.org/project/geofeather/](https://pypi.org/project/geofeather/)]([https://pypi.org/project/geofeather/](https://pypi.org/project/geofeather/)), accessed on 29 October 2021), which supports pandas data frames and enables faster import and export of larger geospatial files. To characterize national- and sub-national-level statistics of aquaculture pond objects such as amount, area, shape, and size features, we merged the pond object vector file with the administrative areas at three different administrative levels (GADM0, GADM1, GADM2, see Section 3.2.2).
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 1 Introduction
This is due to the locations, shapes, and background environments of the detected objects exhibiting high degrees of uncertainty. Moreover, dense small objects, such as crowded people, lead to effective individual characteristic information lay in a relatively small area of the image. Thus, to address these issues, researchers have proposed various strategies. For instance, [PERSON] has introduced the SENet attention mechanism into the YOLOv3 model to optimize the loss function [14]. [PERSON] and his team augmented YOLOv7 with a small object detection layer and integrated a collaborative attention mechanism [15]. [PERSON] modified the Darknet network by adding a set of residual blocks at the end, replacing IoU with DIoU, and generating 12 anchor boxes using the K-means algorithm [16]. [PERSON] improved YOLOv3 by introducing depthwise separable convolutions into feature extraction and incorporating focal loss into the loss function [17]. But for these approaches, the selection of attention mechanisms should be suitable and the introduction of complex operations inevitably increases the computational burden of the models. Hence, addressing these challenges, we proposed the EAAnet, which surpasses the mainstream SOTA (State-of-the-Art) models in terms of precision while maintaining a relatively low computational cost. The primary results are illustrated in Figure 1. Based on the YOLOv5 model, improvements were made and experimental comparisons were conducted. The main contributions are as follows:  
1. A supervised learning dataset of COP (Crowd Occlusion Person) was constructed, which selected 9000 labeled images from the WiderPerson [18] dataset. These images were split into train dataset and validation set at a ratio of 9:1. 2. The backbone was optimized by integrating the CBAM [19], enhancing the focus on crucial fine-grained feature information while suppressing unimportant background details. 3. BiFPN [20] was introduced into the neck of the original YOLOv5. The bidirectional connections in BiFPN ensure the integrity of features for each layer, facilitating the construction of a multi-scale feature fusion network. This effectively addresses the transmission and fusion of feature information across different scales, further enhancing the precision of object detection.
# Angular Dependence of Photonic Crystal Coupled to Photovoltaic Solar Cell
## 1 Introduction
Figure 1: _Cont._  
Regarding photonic crystals in the photovoltaic device, performance of the solar cell can be enhanced by matching the photonic crystal with the solar irradiance. Ideally, the characteristics of the photonic crystals adapted to the solar cells are (Figure 1): (a) high reflectivity in a specific spectral range; (b) high transparency in a different spectral range; and (c) a steep edge between both spectral ranges. Each spectral range will be selected depending on the functionalities requested for the photonic crystal (beam splitter, back reflector, reduction of thermal losses, etc.). It is known that the reflectance of the 1 DPC, which is defined by the Bragg peak position, also depends on the incident angle of the incoming irradiance. Although transmittance and reflectance are the most interesting parameters for 1 DPC integration on the solar cell, the angular dependence of this optical material has to be taken into account, and more specifically, the optical coupling performance. Despite previous research works on the design and characterization of 1 DPC integrated with solar cells [26], the majority of them have not paid attention to the angular dependence phenomenon or angular dependence has been characterized under indoor conditions with a solar simulator, without considering the complex outdoor environment, where angle dependence is a key parameter: [PERSON] et al. [27] evaluated the angular dependence but they only considered indoor measurements with a dye solar cell coupled to a 1 DPC as a back reflector; and they were not able to analyze the potential effect of the polarization of the light. They concluded that a proper design of 1 DPC will enhance the photovoltaic performance of the solar cell. Thus, the aim of this work was to analyze the optical coupling performance of a 1 DPC integrated on a crystalline silicon solar cell, as a function of the incidence angle and solar irradiance spectrum dependence, combining indoor and outdoor measurements. 1 DPC was coupled to the front side of the solar cell as a beam splitter. For that purpose, the next sections will describe how to design and manufacture the 1 DPC suitable with the spectral response of the solar cell selected for this analysis. Furthermore, the optical characterization of both devices (solar cell and 1 DPC + solar cell) at indoor and outdoor conditions will be presented.
## 1 Introduction
In some literature, a standard proportional-integral (PI) controller is implemented for voltage regulation purposes, and some others use artificial intelligence (AI) to auto-tune the PI controller [20; 21]. Apart from tuning difficulty under dynamic operating conditions, PI-based DC voltage controllers suffer from a narrow regulation range since they do not have active power tracking capability where voltage regulation is only performed based on instantaneous PV voltage operating points [22]. Constant voltage has also been achieved by adjusting the load value, which is not practical for real applications [23]. Another aspect to consider is the maximum power point tracking (MPPT). Since MPPT requires a DC-DC converter [24], it is impossible to control the same converter for both MPPT and voltage regulation. One method discussed is the use of cascaded converters in which one converter operates for MPPT and the other for regulation [25]. Controlling the modulation index at the inverter stage is also not recommended in modern MLI since it can result in incorrect output with missing levels [15]. Some literature did not consider the variation of irradiance and temperature [26]. The perturb and observe MPPT-based voltage controller (POVC), as given in [27], is a better technique to achieve both voltage regulation and MPPT in a standalone PV system since voltage regulation is performed based on targetted power. Thus, active tracking is possible. However, the paper does not include any experimental evaluation. In this paper, the experimental analysis of the POVC is presented. The research is further extended by connecting a boost DC-DC converter stage to an MLI for a standalone PV power system in an effort to study their suitability to be implemented as an alternative power source in rural areas. The POVC is used to regulate the voltage at one of the inverter's inputs. Under full-load conditions, POVC is employed for MPPT. Because there is still a paucity of literature on the ability of reduced component MLI with a high number of output levels to be connected to renewable energy sources, the isolated sources type MLIarchitecture presented in [13] is selected. The simulations are run on MATLAB/Simulink software, and experimental hardware is built to confirm the findings.
## 3 Experiments
### Experiments on Inria Dataset  
The Inria dataset consists of 180 aerial images in total captured over ten different cities with a broader area of 810 km\({}^{2}\) and includes different settlements and landscapes. The size of each image is \(5000\times 5000\) pixels with a spatial resolution of 0.3 m. In Inria, the ground truth is only given for the training set. For the ease of comparison, we split the dataset as mentioned by [PERSON] et al. [63], and [PERSON] et al. [68]. We selected 25 images (the first five images of each of the five cities from the training set) for validation. For the Inria dataset, the training set contains 55, 955 images and the validation set encompasses 9025 images. For the ease of experimentation, the original images were randomly cropped to a size of \(256\times 256\). For data augmentation, we followed the same data augmentation as used in TeranusNet [61], where random vertical and horizontal flips were used in the experiments.  
We pruned TeranusNet and U-Net using the proposed approach. In this study, we reproduced both models and trained them from scratch on the Inria, as well as the AIRS dataset for 30 epochs. These were utilized as baseline models. Consequently, the original accuracy of each network may slightly vary from the reported one in the literature. The importance weights were then applied to each filter by employing different weight initialization strategies. We iteratively pruned the least important filters in each pruning iteration. The pruning process was stopped when the difference between the losses incurred by the unpruned model and the loss incurred by the pruned model was more than the desired tolerance error. To restore the original performance, an additional fine-tuning for 15 epochs was adapted.  
We attempted to intuitively illuminate the significance of different weight initialization strategies, namely random, logistic chaotic map, He, Lecun, and Xavier, via a series of experiments on the Inria dataset. We ran the experiments three times and report the accuracies as \"mean \(\pm\) std\". The pre-defined threshold was set to 1.2-times the mean of the importance weights. Accordingly, the percentage of the filters pruned from TeranusNetand U-Net varied based on the initialization strategy used. Table 1 depicts the pruning performance of TeranusNet and U-Net based on different weight initialization strategies.
# Architectural Patterns for Blockchain Systems and Application Design
## 3 Pattern Identification
### Mapping Components and Characteristics to Software Patterns  
The identified 13 components and their architectural characteristics were mapped to well-known software patterns [15; 41], as shown in Table 1. The rationale for this mapping is as follows:  
* The N-Tier pattern divides an application into multiple layers or tiers. Each tier has a particular responsibility, and communication happens between them through well-defined interfaces. In the context of blockchain systems, the components are mainly on two tiers: on-chain and off-chain. Each tier has its variations of architectures.
* The Peer-to-Peer (P2P) architecture relies on a distributed network of nodes that communicate directly with each other to exchange data [15]. The network component of the blockchain is responsible for facilitating communication and transactions between participants in a decentralized manner. All communication happens through the network, eliminating the need for a centralized server or authority.
* In the Layer architectural pattern, an application is divided into multiple layers, each with a specific function [15]. In the context of blockchain systems, the pattern providesa framework to ensure the separation of concerns, promote reusability, and facilitate testing and debugging.
* The Shared Repository pattern is responsible for storing data and ensuring data consistency across a network [15]. In blockchain systems, the ledger is a distributed database that maintains a record of all transactions across the network. It is not only shared, but is also immutable and replicated across the network. Each validation node has an identical copy of the ledger. The pattern provides a standardized method for maintaining a reliable and secure distributed ledger.
* The Broker pattern uses decentralized mechanisms for secure communication between nodes and actors in a system. It involves components acting as message producers, brokers, and clients [15]. In the context of blockchain systems, every node within the network has the capability to act as a producer or a consumer of messages, determined by whether, for example, it submits transactions or receives blocks from the network.
* The Pipe-Filter pattern represents a processing pipeline in which data flow through a series of filters, with each filter transforming the data in some way [15]. In blockchain systems, it ensures the authenticity of transactions and identifies potential threats. The ledger, consensus, and smart contract stream transactions through a series of filters to extract data and detect suspicious activity.
* The Access Control patterns involve methods such as authentication and authorization to ensure that only authorized individuals have access to the system's resources [42]. The digital wallet, incentive mechanism, and permission management aim to protect blockchain systems from various types of threats. They provide an extra protection layer by offering authentication, encryption, and authorization for users.
# Evacuation Management System for Major Disasters
###### Abstract
Referring to the evacuation routes and the associated pedestrian and vehicular evacuation simulation modules, proposals usually follow different approaches [9], allowing the management of traffic interactions, the enhancement of route flows, and the estimation of the time of the vehicular evacuation stage. Here, we opted for a dynamic traffic-modelling approach for the implementation of the routing and vehicular evacuation considering a discretized Cell Transmission Model (CTM), assuming a relationship between flow and density; see \"Conceptual Model\" section. For the pedestrian modelling, there are many different approaches [24; 25], but a stochastic flow-based approach similar to the previously proposed methodologies [26; 27] was followed.