esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Meta-heuristic approaches for the design of SNs in chemical plants are presented and compared in this work. Within the framework of TS, an ad-hoc PR technique is implemented to provide better intensification and diversification capabilities to TS. Also methods based on SS and PBIL meta-heuristics are proposed, and a parallel procedure is implemented for PBIL. The performance of these strategies for solving test cases from the literature is analyzed.  
In relation to local-search procedures, the incorporation of the PR technique within the framework of TS does not provide advantages for solving the SNDP with respect to the procedure SO-TS presented in a previous work.  
Regarding population-based algorithms, PBIL presents a better overall performance. The mean value of the objective function and the coefficient of variation are lower than the corresponding ones to SS for all cases. Also the technique based on SS is more sensitive to the quality and diversity of the initial set of solutions than the design method based on the PBIL algorithm.  
The comparison of SO-TS and PBIL strategies indicate that the first one strongly depends on the quality of the initial solution. If a good starting point is provided it produces a high quality solution with a low computational effort. In contrast, PBIL is more robust. It is capable of making replicas of the best solutions starting from lower quality initial points at the expense of an increment of the  
\begin{table}
\begin{tabular}{|c|r|r|r|r|} \hline Case & SO-TS & PR-TS & SS (AVG) & PBIL (AVG) \\ \hline
1 & 717 & 1,130 & 3,795 & 16,540 \\ \hline
2 & 781 & 1,063 & 4,771 & 16,674 \\ \hline
3 & 39 & 56 & 2,631 & 15,622 \\ \hline
4 & 110 & 413 & 1,928 & 7,326 \\ \hline
5 & 8,298 & 10,535 & 129,369 & 253,364 \\ \hline
6 & 42 & 56 & 1,442 & 6,882 \\ \hline
7 & 260 & 600 & 2,112 & 9,621 \\ \hline \end{tabular}
\end{table}
Table 4: Number of calls to the evaluation function.  
\begin{table}
\begin{tabular}{|l|r|r|r|r|} \hline  & SO-TS & PR-TS & SS & PBIL \\ \hline Min & 50,846 & 54,974 & 50,847 & 51,124 \\ \hline Max & 290,572 & 290,296 & 290,295 & 55,312 \\ \hline Mean & 197,612 & 265,620 & 66,711 & 51,989 \\ \hline Deviation & 116,792 & 73,913 & 52,803 & 1,375 \\ \hline CV & 59.10\% & 27.83\% & 79.15\% & 2.64\% \\ \hline \end{tabular}
\end{table}
Table 5: Case Study 1 Solutions using Random Initialization.  
computational time. As PBIL algorithms can be naturally run in parallel, the total elapsed time can be reduced for a given number of calls. In this case, data transfer among parallel processors should only be performed for the crossover of probability vectors. Furthermore it should be noted that solution improvement methods based on local search enhance significantly the performance of population-based strategies.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 2 Derivation of the Formula for Laminated T beam
### Centroid Position in 2D Theoretical Analysis  
A laminated T-beam was divided into two laminated plates: the web and the flange. The formulas for the web and flange were derived based on the beam theory and composite laminated ABD matrix, respectively. The basic assumptions used in the 2D composite beam theory are as follows: (1) The deflection of the beam is smaller than the length of the beam. Consequently, the slope of the deflection curve is small, and the square of the slope is significantly lesser than one. Therefore, it can be ignored. (2) The section originally perpendicular to the central axis of the beam remains flat after bending and is perpendicular to the central axis after bending.  
In the analysis process, the flanges of the T-beams were divided into narrow and wide flanges. In a narrow flange, the beam width was assumed to be approximately equal to the beam height. Geometrically, this type of flange can be regarded as a beam structure. When the beam structure is subjected to a lateral load, strain occurs laterally. Therefore, the moment generated laterally can be disregarded. In a wide flange, the beam width was assumed to be significantly higher than the beam height. Geometrically, this type of flange can be considered as a thin plate where a lateral moment exists because the thin-plate structure cannot deform laterally after it is subjected to a lateral load. As the carbon fiber-composite laminate beams were combined layer-by-layer by arranging in different directions, resulting in a difference between symmetric and non-symmetric fibers according to the fiber direction. Stress analysis for symmetric and non-symmetric structures was included in the analytical solution.
# Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
## 4 Proposed Algorithm for Fault Diagnosis in Rolling-Element Bearing  
According to Figure 1, the fuzzy ARX-Laguerre method was used to model the bearing in normal and abnormal conditions. The next step focuses on the designed advanced fuzzy sliding mode observation technique that comprises three main blocks: (a) apply SMO for fault estimation, (b) improve the robustness and chattering attenuation based on the high-order super-twisting (advance) SMO (ASMO), and (c) improve the accuracy of the fault estimation technique, i.e., the T-S fuzzy algorithm applied to the ASMO.
# CMAG: A Mission to Study and Monitor the Inner Corona Magnetic Field
## 1 Introduction
### Observing the Inner Solar Corona
The CMAG mission aims to overcome the aforementioned limitations by observing the inner corona from 1.02 R\({}_{\odot}\) to 2.5 R\({}_{\odot}\) with a consistent spatial resolution of 2.5\({}^{\prime\prime}\), a required spectral resolution of less than 40 pm, a cadence of one minute, and a polarimetric sensitivity of 10\({}^{-3}\) in the polarization continuum in nominal operation. Achieving these ambitious goals is only feasible through space-based observations and the utilization of an externally occulted coronagraph. Figure 1 represents the squared field of view of CMAG superimposed on a simulated K-corona [30] brightness image derived from a magnetohydrodynamic model (Predictive Science Inc. [[https://www.predsci.com/portal/home.php](https://www.predsci.com/portal/home.php)]([https://www.predsci.com/portal/home.php](https://www.predsci.com/portal/home.php)), accessed on 1 June 2023). The CMAG mission holds the potential to revolutionize our understanding of the inner corona by providing direct and precise measurements of the magnetic field and shed light on the processes underlying coronal dynamics, solar flares, and CMEs.
## 3 Results
### Quantitative Analysis  
Since detection methods based on low-rank sparse decomposition typically minimize the background, the standard deviation becomes almost negligible, resulting in Signal-to-Clutter Ratio Gain (SCRG) and Background Suppression Factor (BSF) values of Inf, as shown in Table 3. Our method better suppresses the background in ommatidia imaging regions, while a higher Contrast Gain (CG) value indicates that our algorithm enhances contrast. Therefore, the proposed algorithm demonstrates strong performance in background suppression.  
As illustrated in Figure 10 and Table 3, across the five image sequences, our detection algorithm achieves more comprehensive target detection in each ommatidium at a given false alarm rate, exhibiting a higher detection rate.  
In addition to comparing with traditional model-based methods, we also evaluate the performance of our approach against several representative deep learning-based small target detection models or networks, including SSD [38], YOLOv5 [39], ILNet [40], and MSHNet [41]. These methods are trained and tested on our compound eye infrared dataset, with a training-to-testing split ratio of 9:1. In Figure 11, the comparison helps to further validate the effectiveness and generalizability of our method under the same experimental conditions.  
Although the current deep learning-based small target detection methods included in our comparison demonstrate limited performance on our infrared compound eye dataset, we believe that developing deep learning approaches specifically tailored for compound eye infrared imaging is a promising and meaningful direction.  
In future work, we plan to construct a more diverse and task-specific dataset to better support data-driven learning. Additionally, we aim to explore novel neural network architectures that are well suited to the unique spatial and angular structure of compound eye images, with the goal of enhancing detection performance under complex scenarios.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Node
#### 4.1.2 Node Parameters  
Each emulation node has corresponding physical parameters which are used to describe real network nodes in simulation scenarios. Physical parameters contain communication settings, location parameters, and service configurations. The web front-end in the control plane provides interactive interfaces for researchers to set these parameters. Corresponding settings are stored in a database.  
The communication settings reflect the communication characteristics of the network node, e.g., effective isotropic radiated power (EIRP), G/T, modulation, communication frequency, and etc. According to the different placements of the network node in SN, the location parameters should be discussed separately. For space-based nodes, the location parameters contain eccentricity, period, inclination, right ascension of the ascending node, argument of perigee, and true anomaly. For terrestrial-based nodes, the location parameters refer to longitude and latitude. Through communication settings and location parameters, the calculation of connection relationships and link characteristics between nodes could be gained. The details of calculation principles would be demonstrated in Section 4.2. Service configurations are applied to initialize the function of the emulation node, involving protocol selection, data transmission models, node type selection, etc.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
###### Abstract  
Building footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, building and road maps are manually generated by remote sensing experts or land surveying, occasionally assisted by semi-automatic tools. In the last decade, deep learning-based approaches have demonstrated their capabilities to extract these elements automatically and accurately from remote sensing imagery. The building footprint and road network detection problem can be considered a multi-class semantic segmentation task, that is, a single model performs a pixel-wise classification on multiple classes, optimizing the overall performance. However, depending on the spatial resolution of the imagery used, both classes may coexist within the same pixel, drastically reducing their separability. In this regard, binary decomposition techniques, which have been widely studied in the machine learning literature, are proved useful for addressing multi-class problems. Accordingly, the multi-class problem can be split into multiple binary semantic segmentation sub-problems, specializing different models for each class. Nevertheless, in these cases, an aggregation step is required to obtain the final output labels. Additionally, other novel approaches, such as multi-task learning, may come in handy to further increase the performance of the binary semantic segmentation models. Since there is no certainty as to which strategy should be carried out to accurately tackle a multi-class remote sensing semantic segmentation problem, this paper performs an in-depth study to shed light on the issue. For this purpose, open-access Sentinel-1 and Sentinel-2 imagery (at 10 m) are considered for extracting buildings and roads, making use of the well-known U-Net convolutional neural network. It is worth stressing that building and road classes may coexist within the same pixel when working at such a low spatial resolution, setting a challenging problem scheme. Accordingly, a robust experimental study is developed to assess the benefits of the decomposition strategies and their combination with a multi-task learning scheme. The obtained results demonstrate that decomposing the considered multi-class remote sensing semantic segmentation problem into multiple binary ones using a One-vs.-All binary decomposition technique leads to better results than the standard direct multi-class approach. Additionally, the benefits of using a multi-task learning scheme for pushing the performance of binary segmentation models are also shown.  
Sentinel-1; Sentinel-2; remote sensing; building detection; road detection; deep learning; convolutional neural networks; multi-class semantic segmentation; binary semantic segmentation; multi-task semantic segmentation
## 2 Materials and Methods
### Plasma Device and Properties  
Ambient air reactive oxygen/nitrogen species (RONS) were generated using GA, TC and DBD plasmas in a semi-closure frame, as shown in Figure 1.  
The GA and TC plasmas were individually driven by a 20 W RF self-resonance power supply. The driver can supply a sinusoidal AC output with a peak voltage (Vp) of 6-10 kV at a frequency of 700-900 kHz. Meanwhile, the DBD plasma was driven by a continuous 18 W AC high-voltage source of 7.6 kVpp with a repetition rate of 18.2 kHz. The planar mesh-powered electrode was configured on an alumina ceramic sheet of a 1.0 mm thickness with lateral dimensions of 5.3 cm \(\times\) 11.3 cm.  
Thus, the ambient air molecules (N\({}_{2}\) and O\({}_{2}\)) were ionized and dissociated by these strong electric fields, which induced the RONS with high-energy electrons in the plasma phase. The ionization and dissociation equation could be found in the reaction below (Equation (1)). However, the direct electron impact dissociation of N\({}_{2}\) is much slower than that of O\({}_{2}\), and so the major intermediate product is the atomic oxygen O'. The electron collision with N\({}_{2}\) leads preferentially to the formation of various excited N\({}_{2}\)* molecules, while the generation of RONS follows in the plasma propagation phase (Equations (2)-(7)) [24]. In humid air, electron  
Figure 1: The 3-in-1 nonthermal atmospheric pressure plasma device. (**a**) Overall structure of the 3-in-1 plasma device, (**b**) gliding arc (GA) plasma, (**c**) tesla coil (TC) plasma, (**d**) dielectric barrier discharge (DBD) plasma and (**e**) optical emission spectra of the 3 plasma devices, GA, TC and DBD, in the range from 200 to 450 nm. Red arrows indicate the plasmas generated from different plasma dispensers.  
impact can also produce atomic oxygen (O) and OH through dissociative excitation (Equation (8)). Both O and OH are transient chemical species that have very strong oxidative properties compared to neutral oxidant molecules such as ozone.  
\[\mathrm{e}^{-}+\mathrm{N}_{2}\rightarrow\mathrm{e}^{-}+\mathrm{N}_{2}{}^{*}\, \mathrm{(N}_{2}\,\mathrm{(C)}\,\,\mathrm{or}\,\,\mathrm{N}_{2}\,\mathrm{(B))}, \tag{1}\]  
\[\mathrm{N}_{2}{}^{*}+\mathrm{O}_{2}\rightarrow\mathrm{N}_{2}+\mathrm{O}^{-}+ \mathrm{O}^{-}, \tag{2}\]  
\[\mathrm{O}^{-}+\mathrm{O}_{2}+\mathrm{M}\rightarrow\mathrm{O}_{3}+\mathrm{M}, \tag{3}\]  
\[\mathrm{O}^{-}+\mathrm{O}_{2}\rightarrow\mathrm{NO}+\mathrm{O}^{-}, \tag{4}\]  
\[\mathrm{O}^{-}+\mathrm{N}_{2}\rightarrow\mathrm{NO}+\mathrm{N}^{-}, \tag{5}\]  
\[\mathrm{NO}+\mathrm{O}^{-}+\mathrm{M}\rightarrow\mathrm{NO}_{2}+\mathrm{M}, \tag{6}\]  
\[\mathrm{O}_{3}+\mathrm{NO}\rightarrow\mathrm{O}_{2}+\mathrm{NO}_{2}, \tag{7}\]  
\[\mathrm{e}^{-}+\mathrm{H}_{2}\mathrm{O}\rightarrow\mathrm{e}^{-}+\mathrm{H}+ \mathrm{OH}. \tag{8}\]
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 4 Background Technologies  
### Blockchain and Ethereum  
Blockchain is a distributed ledger technology that allows for the secure, transparent, and tamper-proof recording of transactions. Validators, or miners, confirm transactions by solving complex mathematical problems, with the specific validation method depending on the blockchain (e.g., proof-of-work or proof-of-stake). Each transaction is timestamped and cryptographically linked to the previous block, forming an unalterable chain of records. This immutable characteristic ensures that blockchain records are distributed, shared, and maintained across a peer-to-peer network, making them highly secure and reliable.  
Ethereum is a well-established, open-source decentralized blockchain platform that enables the creation and deployment of smart contracts and decentralized applications (DApps). Developers have the flexibility to establish their own rules for executing transactions, including custom transaction formats and state transition functions. The platform incorporates key blockchain principles such as a decentralized database, transparency, security, and efficiency, ensuring that all stored information is safeguarded against deletion and tampering by malicious actors.  
Ethereum smart contracts are autonomous programs that execute transactions when certain conditions are fulfilled. While these contracts can be coded in low-level, stack-based bytecode referred to as EVM code, the primary method involves using high-level languages like Solidity. Solidity-written smart contracts are compiled to EVM code for execution on the Ethereum Virtual Machine. Ethereum also supports popular languages such as C, Java, Python, etc.  
Smart contracts come with several advantages compared to traditional computer programs, including autonomy, as their execution is overseen by the network; trust, validated through consensus among nodes; data security, as the application's data are permanently stored in the blockchain; and transparency, as the code and storage of smart contracts are publicly available. Smart contracts also help manage access rights and enforce agreements, ensuring that other contracts cannot make unauthorized changes. Aligning seamlessly with our proposed system, smart contracts are crucial for following a set of predefined business rules that trigger different actions based on specific conditions. For example, if trust levels reach certain thresholds or when specific requirements are met, smart contracts enable the execution of corresponding actions. Smart contracts also provide a read-only mode for accessing data and checking smart contract status without altering the blockchain. This feature is cost-free and aids in constructing a transparent system accessible to the public.
# Environmental-Based Speed Recommendation for Future Smart Cars +
## 3 Proposed Approach  
The goal of this paper is to combine, integrate and evaluate multiple information sources for modern vehicles. The proposed approach utilizes the increased connectivity of modern cars and combines it with on-vehicle data. Specifically, weather- and route-based information sources are merged with real-time vehicle data in order to obtain an estimation of the status of the road. As a use case, the proposed approach focuses on providing a recommended speed to the driver, based on the weather status estimation and extracted information throughout the car's route.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 3 Test Setup
### Models  
Initially, the equations that define the behavior of the manipulator and its sensors are written in the most general form possible. This is done with the intent of keeping the models reusable for different manipulator configurations. However, the assumption that all joints are revolute is made because it keeps the model simpler, and it is a constraint that the implemented manipulator follows. Before describing the models, the used nomenclature is presented. Regarding the coordinate frames, two are used per joint. For joint number \(i\) (counting from the base), the two frames are \(i\) and \(i-\). Both have their origins in the rotation axis of the joint and have the \(z\) axis aligned with it. However, frame \(i\) is fixed to the link directly after the joint, whereas frame \(i-\) is fixed to the link directly before the joint. The frames in which the sensors take measurements can be rotated to align with the frames of the adjacent joints [1].  
Some consequences of this nomenclature are that the transformation matrices \(\overset{i-}{i-}T\) are constant and there is no translation in the transformations \(\overset{i}{i-}T\). This implies that all the necessary information required to determine the pose of the manipulator is described by the rotation matrices \(\overset{i}{i-}R\) given by:  
\[\overset{i}{i-}R=R_{\theta_{i}}=\begin{bmatrix}\cos\theta_{i}&-\sin\theta_{i} &0\\ \sin\theta_{i}&\cos\theta_{i}&0\\ 0&0&1\end{bmatrix} \tag{25}\]  
The argument can be extended to the velocity and acceleration of each joint. All the information needed to do so is the rotation matrix, angular velocity, and angular acceleration between the two joints. In the two following sections, two models are built based on this nomenclature. The sensors are also modeled according to each of the state choices.  
Figure 3: Graphical user interface of the PC application.
## 4 Results and Discussion
### Evaluation of Experimental Results  
Experiments were conducted to determine the optimal hyperparameters of the proposed S-ECNN model, which was built with Tensorflow 1.15.4 in Python based on Anaconda Spyder. The training set was further divided into two subsets, where one was for the training, and the other served for the validation. We optimized the structure parameters of 1D-CNN and the optimizer of the whole model, and the optimal structure parameters have been listed in Table 3. The model's accuracy curves and loss curves for the training set and validation set are shown in Figure 9. As the epoch increases, the training accuracy has been improved, but the validation accuracy increases first and then decreases. The training loss decreases, but the validation loss decreases first and then increases. It shows that with the increase of epochs, the model has over-fitting. Therefore, we retain the model when the epoch is 21 before over-fitting.