esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Stereo Matching Algorithms  
Tests presented in this paper considered four stereo matching algorithms considered in Middlebury and KITTI rankings. These are the following algorithms: Efficient Large-scale Stereo Matching (ELAS) [39], Stereo Semi-Global Block Matching (StereoSGBM) [40; 41], Graph Cut with Expansion Moves [42] and MC-CNN [12]. Open-source implementations of these algorithms were used in the experiments.  
ELAS was designed for real-time processing of high-resolution images from stereo cameras [39]. The algorithm is intended for use with autonomous cars. StereoSGBM is the main algorithm provided with the OpenCV library which is one of the most important programming libraries in the field of image processing [41]. The GC Expansion algorithm was also included in experiments because the previous research on EBCA showed that adapting this algorithm to EBCA results in obtaining the best quality of disparity maps. The implementation of GC Expansion is provided by the Middlebury Stereo Evaluation project [9]. GC Expansion is a kind of a stereo matching algorithm which takes advantage of global optimization using Markov Random Fields (MRF) [43].  
There are also stereo matching algorithms derived from deep learning technology which proved to be successful in solving a variety of algorithmic problems [44]. [PERSON] and [PERSON] prepared an influential paper describing their stereo matching algorithm called MC-CNN which takes advantage of convolutional neural networks (CNN) [12]. They trained the network using stereo images and ground truth provided by the Middlebury Stereo Vision project and the KITTI benchmark. These authors used two kinds of network architecture. There was a fast network particularly dedicated for obtaining results in real-time and an accurate network focused on maximizing the quality of results. Each of these  
Figure 1: A sample set of images taken by a four camera rig (two stereo cameras placed side by side) used in ETH3D Benchmark, (**a**) the left image from the left stereo camera, (**b**) the right image from the left stereo camera, (**c**) the left image from the right stereo camera, (**d**) the right image from the right stereo camera  
networks was trained with three different input data sets. These were training data sets from images released in the KITTI benchmark in 2012, another set was based on images from the KITTI set released in 2015 and the third data set was based on images available in Middlebury project. As a result [PERSON] and [PERSON] obtained six trained networks marked as: KITTI 2012 fast, KITTI 2012 accurate, KITTI 2015 fast, KITTI 2015 accurate, Middlebury fast and Middlebury accurate. Moreover, their algorithm was released under open-source license making it possible for other researchers to rerun their experiments.
# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
## 3 Fuzzy DEMATEL for Object-Oriented Metrics
### Metrics of Cause and Effect Matrix  
As presented in Table 8, the value of (D + R) represents the importance of the metric in the rule detection. The higher the value of (D + R), the more important the metric. Therefore, it should be included in the rule generation process. The value of (D \(-\) R) classifies the metrics into cause-and-effect metrics. D represents the sum of the rows and R represents the sum of the columns.  
Using the Formulae (4) and (5), let TMR = (_TMRij_), _i, j_ \(\in\){1,2,...,n}.  
\[D_{i}=\sum_{j=1}^{n}TMRij \tag{4}\]  
\[R_{i}=\sum_{i=1}^{n}TMRij \tag{5}\]  
In Figure 2, we present the causal diagram. The horizontal axis represents (D + R) and the vertical axis represents (D \(-\) R). If we consider the values (D + R), it appears that some metrics are more important than others. We can split the set of metrics into three main groups, labeled Low Importance, Important, and High importance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Cl1** & **C2** & **C3** & **C9** & **C12** & **C13** & **C14** & **C15** & **C16** \\ \hline \hline
**Cl:NC** & 0.0586 & 0.1392 & 0.1517 & 0.1943 & & 0.1699 & 0.1535 & 0.1194 & 0.2011 & 0.1762 \\ \hline
**C2:PS** & 0.0433 & 0.0289 & 0.047 & 0.0602 & & 0.0543 & 0.0418 & 0.0902 & 0.1176 & 0.0473 \\ \hline
**C3:NOA** & 0.0537 & 0.0554 & 0.0619 & 0.1616 & & 0.1247 & 0.149 & 0.0364 & 0.1146 & 0.1088 \\ \hline
**C4:NOM** & 0.0804 & 0.078 & 0.1013 & 0.1725 & & 0.1575 & 0.1559 & 0.0567 & 0.1482 & 0.1369 \\ \hline
**C14:NOPM** & 0.0346 & 0.0969 & 0.0447 & 0.0565 & & & & & & \\ \hline
**C15:FUC** & 0.0909 & 0.1018 & 0.0411 & 0.0702 & & & & & & \\ \hline
**C16:NMR** & 0.0456 & 0.0476 & 0.0698 & 0.1492 & & & & & & \\ \hline \end{tabular}
\end{table}
Table 7: The total-relation matrix.  
\begin{table}
\begin{tabular}{c|c c c c} \hline  & **D** & **R** & **D\(\bullet\)R** & **D\(\!=\!\)R** \\ \hline
**Cl:NC** & 2.395 & 0.918 & 3.313 & 1.477 \\ \hline
**C2:PS** & 0.817 & 1.064 & 1.881 & \(-\)0.247 \\ \hline
**C3:NOA** & 1.699 & 1.376 & 3.074 & 0.323 \\ \hline
**C4:NOM** & 1.888 & 1.897 & 3.785 & \(-\)0.008 \\ \hline
**C5:NOD** & 1.066 & 0.877 & 1.943 & 0.190 \\ \hline
**C6:NODD** & 1.077 & 0.820 & 1.897 & 0.257 \\ \hline
**C7:NMSC** & 1.294 & 1.769 & 3.062 & \(-\)0.475 \\ \hline
**C8:NOC** & 1.893 & 1.907 & 3.800 & \(-\)0.014 \\ \hline
**C9:NCC** & 1.628 & 1.967 & 3.595 & \(-\)0.339 \\ \hline
**C10:ATTED** & 1.666 & 1.954 & 3.620 & \(-\)0.288 \\ \hline
**C11:NOP** & 0.920 & 0.577 &
###### Abstract  
As the interest toward communication-based vehicle safety applications is increasing, the development of secure wireless communication techniques has become an important research area. In this context, the article addresses issues that are related to the use of the visible light communication (VLC) technology in vehicular applications. Thus, it provides an extensive presentation concerning the main challenges and issues that are associated to vehicular VLC applications and of some of the existing VLC solutions. Moreover, the article presents the aspects related to the design and intensive experimental evaluation of a new automotive VLC system. The experimental evaluation performed in indoor and outdoor conditions shows that the proposed system can achieve communication distances up to 50 m and bit error ratio (BER) lower than \(10^{-6}\), while being exposed to optical and weather perturbations. This article provides important evidence concerning the snowfall effect on middle to long range outdoor VLC, as the proposed VLC system was also evaluated in snowfall conditions. Accordingly, the experimental evaluation showed that snowfall and heavy gust could increase bit error rate by up to 10,000 times. Even so, this article provides encouraging evidence that VLC systems will soon be able to reliably support V2X communications.  
accident prevention; automotive applications; intelligent lighting systems; optical noise; vehicle safety; vehicular communications; visible light communication +
Footnote †: journal: Sensors
# Three-Dimensional, Time-Dependent Analysis of High- and Low-\(Q\) Free-Electron Laser Oscillators
## 2 Numerical Formulation
### Analytical Models for Static Magnetic Fields
\begin{split}\mathbf{B}_{u}(\mathbf{x})=B_{u}(z)\Big{[}\bigg{(} \sin(k_{u}z+\phi)-\frac{\cos(k_{u}z+\phi)}{k_{u}B_{u}(z)}\frac{dB_{u}}{dz} \bigg{)}\cosh(k_{u}x)\hat{\mathbf{e}}_{x}+\\ \bigg{(}\sin(k_{u}z)-\frac{\cos(k_{u}z)}{k_{u}B_{u}(z)}\frac{dB_{u }}{dz}\bigg{)}\cosh(k_{u}y)\hat{\mathbf{e}}_{y}+\\ (\sinh(k_{u}x)\cos(k_{u}z+\phi)+\sinh(k_{u}y)\cos(k_{u}z))\hat{ \mathbf{e}}_{z}\Big{]},\end{split}  \tag{22}\]  
where \(\phi\), \(0\leq\phi\leq\pi\), represents the phase shift between the two linear undulators and as before, \(B_{u}(z)\) is given by Equation (18). This model is valid near the axis of symmetry. The ellipticity, \(u_{e}\) of the APPLE-II undulator is given by  
\[u_{e}=\frac{1-|\cos(\phi)|}{1+|\cos(\phi)|}. \tag{23}\]We note that the APPLE-II undulator configured as linear undulator has \(u_{e}=0\), while \(u_{e}=1\) for a helical configuration. The remaining static magnetic field models are used to describe quadrupole magnets, using  
\[\mathbf{B}_{Q}(z)=B_{Q0}(z)\big{(}y\hat{\mathbf{e}}_{x}+x\hat{\mathbf{e}}_{y} \big{)}, \tag{24}\]  
where \(B_{Q0}(z)\) is the constant field gradient over some range \(z_{1}\leq z\leq z_{2}\), and dipole magnets using a field model that is described by a constant field oriented perpendicularly to the axis of symmetry over some range \(z_{3}\leq z\leq z_{4}\). Both, the field models for quadrupole and dipole magnets, have hard-edge field transitions and are curl- and divergence-free over the range where these are defined.
# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 2 Related Work  
The UCP method is the latest and the most widespread method for estimating the effort and costs involved in the realization of software products. The most significant advantage of this method is that the lowest values of relative error in estimation are obtained--between 20% and 35%. The best result achieved by this method is an error value of about 10% [22]. Many researchers [17; 23; 24; 25] have combined this method with other parametric models and models of artificial intelligence. In a previous study [26], the UCP method was used for the estimation of size and effort for mobile applications. Android mobile applications were considered as a case study, and modified UCP was also proposed. The authors of [14] proposed a framework for UCP-based techniques to promote reusability in the development of software applications. The results showed that the framework met five quality attributes, and that it can be used in the early stages of software development. In [27], a systematic review of studies with the best practices in terms of use case point (UCP) and expert judgment-based effort estimation techniques was given. The authors of [28] presented the results of four different models that include the UCP method and Neuro-Fuzzy logic. It was concluded that the Neuro-fuzzy logic model using revised use case points and modified environmental gives the best fitting accuracy at an early stage compared to other models. In another relevant study [29], the authors compared the benefits of statistical analyses of effort estimation approaches for seven real-world software development projects. In addition, they contrasted a conventional Use Case points method with iUCP, an HCI (Human-centric)-enhanced model. Furthermore, they proposed an enhancement of the original iUCP effort estimation formula.  
The critical decisions that defined the new, improved model within the UCP approach were as follows:  
* Examination of the influence of two linearly dependent input values (UUCP and AUCP) on the change in the MMRE value;
* Comparative analysis of two different architectures of artificial neural networks and the obtained results;
* Division of the used dataset to a scale of 70:30, i.e., 70 projects from the selected dataset were used for the training process, while 30 were used for the testing process;* Finding the most efficient methods of encoding and decoding input values, such as the fuzzification method;
* The requirement of a minimum number of performed experiments;
* Testing and validation on other datasets.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.6 Footstep Detection in Homes  
In [116], [PERSON] et al. proposed a new approach to passive footstep detection in home conditions, which could support the development of assistive technologies, especially when monitoring the mobility of the elderly in their natural environment. They focused on using a deep learning model--a recurrent neural network with an attention mechanism--to detect footsteps in complex, noisy sounds typical of home environments. The model was trained on synthetic data representing realistic acoustic scenes, and its effectiveness was assessed based on PSDS indicators, comparing it with systems previously described in the literature. The proposed solution achieved the best PSDS result among the compared methods, similar to the results of universal systems for detecting indoor sounds in the DCASE competition. The system was designed to detect and track footsteps, making it a promising tool for supporting remote healthcare and independent living for seniors.
## 4 Model Design and Implementation
### Lightweight Neural Network Model  
To improve the feature extraction capability and robustness of the model and focus more on the information of practical feature areas in the image, this article designed the EMBConv module and EFused-MBConv module concerning the EfficientNetV2 model structure. The structures of the EMBConv module and EFused-MBConv module are shown in Figure 5.  
As shown in Figure 5, this article integrates the lightweight attention mechanism into the EMBConv module and EFused-MBCon module in the lightweight neural network model structure. The EfficientNetv2-S network model is the lightest model among the EfficientNetv2 network models. However, problems like large computational workload, long training time, and giant model space during training still need to be solved. Considering the application scenarios of distributed intelligent edge computing for disease risk prediction to allow model training and inference to be completed at the edge, this article refers to the backbone network structure of the EfficientNetv2-S network model and designs the Linge model. Because EMB-Conv has a smaller expansion ratio than EFused-MBConv, more minor expansion has less memory access overhead. Therefore, in the structure of the lightweight neural network model Linge designed in this article, EFused-MBConv is used in the Stage1 and Stage2 stages, and the EMBConv module is used in the Stage3, Stage4, Stage5, and Stage6 stages. This adjustment increases the size of the model's receptive field,  
Figure 5: Structure of EMBConv module and EFused-MBConv module.  
reduces the depth and complexity of the model, and further improves the lightweight of the model. The structure of the new lightweight neural network model is shown in Table 2.  
Table 2, ECA0.25 indicates that the number of nodes in the first fully connected layer in the ECA module is 1/4 of the number of feature matrix channels input to the EMBConv module. The new lightweight neural network model reduces memory access overhead and improves feature extraction capabilities and model robustness.
## 2 Experiments  
### Sample Growth and Fabrication  
The epitaxial layers were grown on \(c\)-plane sapphire and silicon(111) substrates using metal-organic chemical vapor deposition. Figure 1 shows schematic diagrams of the layer structures grown on the sapphire and Si(111) substrates. For the growth on silicon substrates, many interlayers were employed between silicon and n-GaN to overcome the lattice and the thermal mismatches and to improve the efficiency of the LED on silicon. The GaN template on Si consisted of an AlN seed layer, an AlGaN buffer layer, a SiN masking layer, an undoped GaN coalescence layer, an AlN interlayer, and an n-doped GaN layer. The crystal qualities were compared using X-ray diffractometry, and threading dislocation density (TDD) measurements. The full widths at half maximum of the X-ray omega rocking curves were 350 and 320 arcsec for GaN/Si and GaN/sapphire, respectively. The TDDs of the GaN layers on sapphire and silicon were estimated to be \(3.8\times 10^{8}\) and \(5.3\times 10^{8}\) cm\({}^{-2}\), respectively. The X-ray and TDD data imply that the crystallinity of the GaN films on silicon is slightly worse than that of the films grown on sapphire. Here, we label the LED on sapphire and the LED on silicon as Sample A and Sample B, respectively.  
Next, InGaN/GaN LED epitaxial layers were grown on each template of sapphire and silicon. The layer structures on the two templates are identical. The layers were composed of an n-GaN layer, 20 pairs of InGaN/GaN short-period superlattice layers for strain relaxation, five periods of In\({}_{0.015}\)Ga\({}_{0.085}\)N/GaN MQW layers, an AlGaN electron blocking layer, and a p-GaN layer. The thicknesses of the InGaN QW and the GaN barrier are 3 and 8 nm, respectively. After the epitaxial growth, LED chips were fabricated as vertical current injection structures with dimensions of 1 mm \(\times\) 1 mm. The fabricated LED chips were then encapsulated with epoxy resin and packaged as a surface-mount device. The packaged LED samples were soldered on a thick copper block and the temperature was controlled by a thermo-electric cooler (TEC).
## 1 Introduction
CARLA (Car Learning to Act) being one of the prominent simulators, provides rich features and environment, however, it still fails on a few fronts such as not being able to fully capture the complexity of the urban environment. Moreover, it mainly relies on manual code, and multiple conditional statements, and provides no pre-defined way to do things automatically based on the dynamic simulation environment. Therefore, there is a dire need to extend the off-the-shelf CARLA simulator with more sophisticated settings that can capture the required dynamics and address the challenges of a higher level of autonomous driving. The novelty of this research lies in contributing to introducing the complex dynamic environment through probabilistic graphical models (PGM) in the classical CARLA. The motivation for using PGM is that it is a robust framework for encoding probability distributions for this complex domain by computing the joint distributions over numerous random variables that interact with one another. Furthermore, the experimental environment is carefully designed, developed, and implemented by capturing the relevant dynamics of specialized use cases of complex and urban driving. Some of the main contributions of this research are:  
* Modeling the complex urban environment based on a state-of-art probabilistic graphical model which is suitable for capturing the dynamics specific to urban and higher-level autonomous driving;
* Design and development of an extension of CARLA referred to as CARLA+ by integrating the PGM framework. Instead of manually catering to each condition, it provides a unified framework to automate the behavior of dynamic environments leveraging PGMs;
* Experimentation and validation of the proposed CARLA+ extension. The paper is structured into six sections. Section 2 provides the background information to comprehend the contents of this paper. Section 3 presents an overview of state-of-the-art simulators and relevant studies. Section 4 elaborates on the design and development of the proposed CARLA+ extension. Section 5 discusses the experimental setup and different settings of experiments to validate the CARLA+ solution. Finally, Section 6 elucidates the conclusion of the study.
## 4 Results
### Off-Body Access Point Network Architecture  
Figures 11 and 12 present results from multiple 37 minutes real-time experiments for varying number of antennas at the AP. Each experiment was conducted as described in Section 3.3. The accumulated energy data was processed to evaluate improvement ratios of EpB and NL for \(M=100\)_vs._\(M=0\) as described in Section 4.1.3.  
Figure 11: Improvement in Network Lifetime for \(M=100\)_vs._\(M=0\) and varying number of antennas at the off-body access point. The straight horizontal lines depict the mean value.  
Figure 10: Effect of \(M\) on improvement in Network Lifetime. Note that highest improvement is obtained for \(10<M<110\).  
Figure 11 presents the NL ratio results. It is evident that NL was improved for the vast majority of runs and for any number of antennas at the AP. The NL was increased by an average of 25% for a single antenna AP, which is lower than the improvement for an on-body AP (40%). We attribute this to the fact that a link from body to off-body AP is much more likely to have a higher cost than on-body links due to body shadowing and distance of the link. This means that the off-body link would impose the dominant cost in the path cost calculation. Since the experiment was performed in a dynamic scenario (movement of subject) the node from which a link to AP is best varied all the time. Optimizing for total path cost (reference system) meant changing the node from which data is transmitted to AP. This resulted in a balancing of the energy consumption across the nodes and is in-fact a type of spatial diversity where the diversity branches are from nodes to off-body AP. Improvement in NL decreased further when number of antennas was increased to 22%, 20% and 16% for 2, 3 and 4 antennas respectively. We attribute this decrease to the increase in spatial diversity gain obtained by the use of multiple antennas at the AP. Each additional antenna introduced an additional set of 5 diversity branches from body to AP.  
Figure 12 presents the EpB ratio for the same data. As expected EpB is higher for \(M=100\) since more energy is used to increase NL. On average across all instances and number of antennas, a negligible 0.025 dB more energy was used when \(M=100\). This result implies that using a network architecture with a multiple antenna off-body AP can increase wearability by avoiding the need to carry the AP on the body and improving NL at practically no cost to nodes' battery size.
## 2 Materials and Methods  
### Macrophytes and microalgae  
The study used two species of microalgae and one macrophyte (see Supplementary Material), all isolated from different aquatic environments in southwestern Peru. The microalgae, _Chlorella_ sp. MC18 and _Scenedesmus_ sp. MJ23-R, were isolated from different environments contaminated by industrial effluents and have been described in detail elsewhere [13]. The macrophyte _Lemma minor_ L. MO23 was isolated from the freshwater body 'Ojo del Milagro' (Characto, Arequipa, Peru; southern latitude 16\({}^{\circ}\)28\({}^{\prime}\)8\({}^{\prime\prime}\), northern longitude 71\({}^{\circ}\)27\({}^{\prime\prime}\)). _Lemma minor_ L. MO23 was identified at the Center Herbarium Arequipense of the National University of Saint Augustin (Arequipa, Peru) through morphological and taxonomic analysis at the species level previously described [14; 15; 16]. Briefly, morphological observations of _Lemma minor_ (also known as duckweed) were conducted using optical stereoscopy (Motic SMZ-171, Hong Kong, China) equipped with a digital camera (Motic(r), MGT101, Hong Kong, China) and software MoticamBTU 3.0. Stereoscopic visions revealed dark green fronts with an oval and asymmetrical shape, featuring a rounded from apex, a hyaline from edge at the base, and an aerenchyma with an apex extended upwards. The morphometric analysis indicated the following values for the front: 3.50 mm length, 1.95 mm width, 0.50 mm thickness, three contiguous fronds, 21.50 mm\({}^{2}\) area, and 1.80 length to width ratio; a distance between the base of the frond and the root insertion of 6.15 mm and two roots. These species are deposited in the private culture collection of the Biochemistry and Molecular Biology Laboratory, located within the facilities at the National University of Saint Augustin.  
Microalgal inocula were maintained in aerated flasks at an aeration rate of 0.25 vvmin. _Lemma minor_ was maintained in 4 L parallelepiped-shaped photobioreactors (PBR), which have been described elsewhere for the culture of both microalgae [13], at the same temperature. Illumination was provided by an overhead array of 18 W cool daylight LED tube lights (Philips Lighting, Eindhoven, The Netherlands), resulting in an average irradiance of 60 umol photons m\({}^{-2}\) s\({}^{-1}\) at the surface of the culture flasks and PBR, following a 12:12 h light-dark cycle at a temperature of 24 \(\pm\) 2 \({}^{\circ}\)C. The growth medium for both microalgae consisted of BG11 medium [17], while half-strength Huther's (HSH) medium [18] was used for _Lemma minor_. These media were prepared using distilled water and sterilized by autoclaving at 121 \({}^{\circ}\)C for 15 min.