esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 7 Experimental Procedure
### Datasets
#### 7.1.4 German Credit Dataset  
The German Credit Dataset [36] originates from a credit risk assessment study conducted by a German bank. It comprises 1000 instances, each representing an individual credit applicant, and includes 20 attributes such as age, sex, job, housing status, credit amount, loan duration, purpose, and credit history. The primary objective is binary classification--predicting whether an applicant is a good (low-risk) or bad (high-risk) credit customer. The class distribution includes 300 instances labeled as bad credit (0) and 700 as good credit (1). Featuring a combination of categorical and numerical variables, the dataset is widely utilized for benchmarking classification algorithms and exploring fairness in credit scoring. The sensitive attribute age is encoded as young (0) with 149 instances and older (1) with 851 instances.
# Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification
## 4 Experimental Results
### Synthetic Data
#### 4.1.1 CP Based Data  
The motivation for these experiments lies in the fact that if a tensor has a unique CP decomposition, the factor matrices are essentially unique and contain important features of the tensor. We interpret tensors from the same class as having the same factor matrices, with noise added to each factor matrix and to the resulting tensor for every sample in the same class. For class 1, we choose a rank \(r_{1}\), and we generate 3 matrices \(A\), \(B\), and \(C\) with \(r_{1}\) columns and random normal entries that we keep fixed. Each tensor in class 1 is constructed by adding noise to \(A\), \(B\), and \(C\), computing the outer products of the columns of \(A\), \(B\), and \(C\), and adding noise again:  
\[\mathcal{X}_{i}=[A+\eta\widetilde{A}_{i},B+\eta\widetilde{B}_{i},C+\eta \widetilde{C}_{i}]+\rho\widetilde{D}_{i}.\]  
here, \(\widetilde{A}_{i},\widetilde{B}_{i},\widetilde{C}_{i},\widetilde{D}_{i}\) are random matrices whose entries are independent and have a standard normal distribution.  
For class 2, we generate factor matrices \(F\), \(G\) and \(H\) with \(r_{2}\) columns and random normal entries, and we construct tensors as before:  
\[\mathcal{Y}_{i}=[F+\eta\widetilde{F}_{i},G+\eta\widetilde{G}_{i},H+\eta \widetilde{H}_{i}]+\rho\widetilde{E}_{i}.\]Tables 1 and 2 report average area under the receiver operating characteristic curve (AUC) over 20 runs, where 40 samples were used for training and 200 for testing. For KNMDA, SL-action was used in all modes, with imposed regularization with parameter \(\epsilon=1\) and a maximum of 10 iterations. For DATER, DATEReig, and CMDA, we projected onto the space \(\mathbb{R}^{3\times 3\times 3}\) (i.e., we chose the reduced dimension to be 3), which gave the best performance among all tensor spaces of equal size in each mode. We do not report the results of manifold-based MDA methods from [3], since they performed worse than the reported MDA methods.
## 2 Experiments
### Preparation and Characterization of Polymer
#### 2.1.3 Preparation of Polymer  
(1) Figure 1 shows the design idea for the new polymer. Polyacrylamide is severely hydrolyzed in high-temperature and salinity reservoirs, and the more severe the hydrolysis, the easier it is for precipitation to occur from the solution. Furthermore, the solution viscosity retention is low. In order to obtain the desired temperature and salt resistance performance in the polyacrylamide, the polymer was designed according to the molecular design principle [39].  
1. Matching of initiations and polymerization conditions. The molecular weights of polymers obtained using different initiation systems and polymerization conditions are very different, so it was necessary to screen suitable initiations and polymerization conditions. 2. Addition of temperature-resistance and salinity-tolerance monomers. The stronger the rigidity of polymer main chains with the same molecular weight, the larger the hydrodynamic size and the higher the stability. AMPS can effectively improve rigidity. The sulfonic group in AMPS can effectively improve temperature resistance and salinity tolerance. NVP can inhibit the hydrolysis of amides. The copolymerization of NVP and AM can obviously inhibit the hydrolysis of amides in polymer molecules and results in obvious salinity tolerance. 3. Addition of hydrophobic monomers. The hydrophobic association forms a reversible physical cross-linking network structure that improves the temperature resistance and salinity tolerance. 2. After determining the polymerization method and initiation system, various functional monomers were copolymerized using AM, and a new type of polymer was synthesized under suitable reaction conditions [40]. In the process of the experiment, each item had to be accurate. The preparation steps were as follows: 1. Quantitative AM, AMPS, a hydrophobic monomer and an appropriate amount of deionized water were added to the reaction bottle, which was stirred to dissolve the mixture; 2. The pH value of the system was adjusted to the desired range with NaOH solution and the reaction temperature set for 40 \({}^{\circ}\)C; 3. An appropriate amount of initiator (the initiator accounted for 0.03% of the total mass of the monomer) was added over 10 min through N\({}_{2}\) to remove dissolved O\({}_{2}\) from the water, and the temperature was recorded; 4. The gel-like elastic target product was obtained by reacting at 40 \({}^{\circ}\)C for 6-8 h. The polymer samples were obtained by chopping, drying, grinding and sieving. Figure 2 shows the molecular structure diagram for the target polymer; 5. Using an appropriate number of samples, a Fourier infrared spectrometer was used to scan in the range of wave numbers 4000-400 cm\({}^{-1}\) (resolution 0.01 cm\({}^{-1}\)) and the infrared spectrum of the sample was obtained.  
Figure 1: Diagram of the polymer design.
# Ultra-Precision Cutting Mechanism of KDP Crystal in Microplastic Region via Heating Assistance
## 1 Introduction
After the ultraprecision fly cutting of KDP crystals, considering the tool's thermal deformation, they demonstrated that the tool's thermal deformation affected the surface roughness and waviness of the KDP crystals. In addition, it increased the surface slope, which affected surface precision and thus the optical performance of the KDP crystals. Therefore, the surface quality of KDP crystals can be improved by optimizing the UPSDFC process. In addition, the surface morphology is affected by the temperature change in the processed surface. Thermal-assisted machining has been widely used in difficult-to-machine materials such as high-temperature alloys, but there is little research exploring whether thermal-assisted machining is helpful for the brittle surface plastic transformation of optical crystal materials and the improvements in machining surface accuracy. There are also few studies on the effect of the temperature field on the surface quality properties of the KDP crystals during processing, and the material removal mechanism is unclear. To reveal the mechanism of the ultraprecision cutting of KDP and improve the surface processing quality properties, a heat-assisted machining method was developed based on the theory of ductile/brittle transitions. The plastic region range was expanded, and the machining quality was improved by controlling the surface-temperature field of the material. The ductile/brittle transition depths of the KDP crystals were determined using nanoindentation experiments, and the mechanism of the ductile/brittle transitions of the KDP crystals was analyzed. The effects of temperature on the ductile-/brittle-transition depth and the surface morphology of KDP crystals were studied using nanoscratch experiments, and thermal field assistance was explored. The feasibility of the heating-assisted processing method was explored, and the material-removal mechanism under heat was revealed. Finally, the global machining of KDP crystals was conducted on a fly cutting machine. The effectiveness of the heating-assisted machining method in improving the surface quality of KDP crystal was verified.
## 3 Methodology  
### Overview of LinearTGAN  
Figure 2 illustrates the architecture of LinearTGAN, comprising three key components: the degradation, the generator, and the discriminator model. The degradation process generates synthetic LR images from HR-scanned images to simulate input conditions. The generator model reconstructs SR images from LR inputs by learning a mapping function through feature extraction and reconstruction. Finally, the discriminator evaluates the SR-generated images, distinguishing between real HR images and those produced by the generator, thereby facilitating adversarial learning to enhance image quality and realism. The subsequent sections provide a detailed analysis of these components.
## 6 Simulation
### Particles Settlement and Collision  
To demonstrate the feasibility of IMB-DEM-LBM for particle collision in liquid, the settlement of two particles in the same path is simulated. When two departed particles are realized in the container center line, the particles' initial velocity is zero and both of them start to move due to gravity. The upper particle is in the wake of the lower particle, and is accelerated to catch up with the lower particle [30]. They will then kiss together and settle together as a whole. Little fluctuation in the flow may make them depart from each other. The total progress is called \"drafting, kissing, and tumbling\" or DKT, in which simulation of this phenomenon can be done by different techniques such as FEM [31,32], IB-LBM [12]. \"Drafting, kissing and tumbling\" is a random phenomenon, which occurs in the settlement and collision of particles. In experiments, the time interval of the kissing progress depends on when the random disturbance happens. Unfortunately, the experiment cannot catch how and when the disturbance occurs. Hence, the purpose of the simulation is only to show that these phenomena can be replicated or depicted by the method presented in this study.  
For saving computational power, the two particles' physical initial velocity is \(-0.09\) m/s. The initial velocity allows particles to reach balance velocity with less time. Physical domain is \(0.25\times 1\) mm, gravity is \(g=9.8\) m/s\({}^{2}\), particle radius is \(r=2.5\times 10^{-5}\) m, fluid density is \(\rho_{P}=2000\) kg/m\({}^{3}\), and particles mass is \(mass_{particle}=8\times 10^{-5}\) kg. The distance of two particles' center is \(0.125\times 10^{-5}\) m. The collision time is \(\tau=1\), and the lattice domain size is \(200\times 800\). The particles' position and velocity profile is shown in Figure 15a-h for the corresponding time steps at t = 0, 40, 80, 130, 190, 230, 260, 300.  
In Figure 15a, the two particles begin to settle together, and the particles get closer and closer in Figure 15b,c. In Figure 15d, the two particles kiss each other, and begin to drift together. In Figure 15d,e, the two particles adhere together. In Figure 15f,h, little fluctuation makes the particles depart from each other. The particles' motion in Figure 14 satisfies \"drafting, kissing, and tumbling\" phenomenon, which occurred in the experiment conducted by [PERSON] [33].  
Figure 14: Single particle settlement velocity with time step.  
Figure 15: Two particles settling, drafting, kissing, and tumbling.  
Figure 16 shows the x and y velocity of the two settling particles. In Figure 16b, the whole progress can be divided into three time periods. At timestep 0-75, the upper particle chases the lower one, and at timestep 75-190, the two particles drift together and collide repeatedly. At timestep 190-300, the two particles depart from each other. In drift progress, gravity potential is converted into x momentum, as shown in Figure 16a, and when two particles depart from each other, the x coordinate momentum is exchanged to the fluid around the particles, so all the particles' x velocity decreases, as shown in Figure 16a.
# A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period
## 3 Methods
### The Self-Supervised Learning-Based Identification Model for Unknown Internet Traffic
#### 3.2.2 The Clustering-Based Identification Method  
After we have established the JigClu-based pre-training model and generated the pre-trained feature matrix of Internet traffic, we use the traditional clustering method to realize the final identification of Internet traffic. By loading the feature pre-training model and connecting an unsupervised clustering model after the output, Internet traffic from the same type could be identified to the same cluster.  
To the best of our knowledge, the clustering method is to divide datasets into different classes or clusters according to selected features. Its purpose is to make data in the same cluster as similar as possible and data in different clusters as different as possible.  
In our method, we adopt K-means to realize further traffic identification. Among all clustering methods, K-means is an efficient approach that has been widely used in various classification and identification tasks. It calculates the distance between data samples and each cluster center and assigns them to the nearest one. Afterward, the algorithm needs to recalculate the center of each cluster and redistribute each data sample until the termination condition is satisfied.  
Herein, since the features obtained by the pre-training model are continuous, we choose the Euclidean distance to calculate the similarity between data samples, which is shown as in Equation (6).  
\[dist(x,y)=\sqrt{\sum_{i=1}^{n}{(x_{i}-y_{i})}^{2}}=\|x-y\|_{2} \tag{6}\]  
where \(x\) and \(y\) are pieces of data in the dataset, and \(x_{i}\) and \(y_{i}\) are features pre-trained via the JigClu method.  
The purpose of K-means is to find the best solutions that can minimize the square error of the datasets, which is:  
\[E=\sum_{s=1}^{k}{\sum_{x\in C_{s}}{(x-\overline{X_{s}})}^{2}} \tag{7}\]  
where \(\overline{X_{s}}\) is the average vector of the cluster of \(C_{s}\). In other words, it is the center of cluster \(C_{s}\), and is defined as in Equation (8). \(|C_{s}|\) refers to the number of data samples in the cluster.  
\[\overline{X_{s}}=\frac{1}{|C_{s}|}\sum_{x\in C_{s}}x \tag{8}\]
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 2 Materials and Methods
### Methodology
#### 2.2.2 Matching SEVIRI and A-Train Observations  
The A-Train satellites regularly overfly the SEVIRI/MSG observation disk. AVAC-S generates the matchup data of the SEVIRI and A-Train products on the basis of the CPR overpass, which is assumed to be the best-suited instrument for validating SEVIRI cloud parameters. The matching method we used in this study is the same as the one [PERSON] et al. [3] used. However, we did not use parallax correction function in AVAC-S for the SEVIRI viewing zenith angle as in [PERSON] et al. [3]; instead we used parallax-corrected CTH products when generating matchup datasets.
# Dynamic Response of Copper Plates Subjected to Underwater Impulsive Loading
## 2 Experimental Configuration
### Underwater Pressure Peak and Impulse Estimation  
The free-field incident UNDEX pulse in the fluid can be idealized as an exponential pressure attenuation:  
\[p=p_{0}e^{-t/t_{0}} \tag{1}\]  
where \(p_{0}\) is the initial peak pressure and \(t_{0}\) is a characteristic decay time [10]. The free-field momentum (impulse/area) can be given by  
\[I_{0}=\int_{0}^{\infty}pdt=p_{0}t_{0} \tag{2}\]  
In the FSI setup, the peak pressure \(p_{mx}\) and impulse of the shock wave \(I_{mx}\) is given by  
\[p_{mx}=\frac{sf}{s+f}V_{0}\bigg{(}\frac{D}{D_{x}}\bigg{)}^{2}, \tag{3}\]  
\[I_{mx}=\sum_{0}^{\infty}p_{mx}\bigg{[}\frac{s-f}{s+f}\bigg{]}^{n}\Delta t, \tag{4}\]  
where \(f\) and \(s\) are the acoustic impedance values of the fluid and solid, \(V_{0}\) is the flyer impact velocity, and \(D\) and \(D_{x}\) are the diameters of the tube at the impact and pressure prediction locations, respectively. \(n\) is the number of wave reverberations in the flyer and \(\Delta t\) is the time required for the elastic longitudinal wave to twice traverse the flyer plate.  
In this investigation, the diameters of impact locations, the tube at location \(A\) and location \(B\) are 66 mm, 106 mm, and 142 mm, respectively. The acoustic impedance values of solids and fluids are \(40.82\times 10^{6}\) kg/(s\(\cdot\)m\({}^{2}\)) and \(1.46\times 10^{6}\) kg/(s\(\cdot\)m\({}^{2}\)). Therefore, the peak pressure values at the two pressure transducer locations \(A\) and \(B\) can be simplified as follows:  
\[p_{A}=0.5464~{}V_{0}, \tag{5}\]  
\[p_{B}=0.3027~{}V_{0}, \tag{6}\]
## 6 Evaluation, Benefits, and Limitations of GA-Based Routing
### Limitations and Challenges in GA-Based Routing  
While GA-based routing offers significant advantages for optimizing message routing in EnFVs, it also comes with certain limitations and challenges that need to be addressed. Understanding these limitations is crucial for researchers and practitioners to develop more robust and effective solutions. One of the primary challenges is the computational complexity associated with GAs [68, 78]. As the problem size and the number of objectives increase, the computational time required to find optimal or near-optimal solutions may become prohibitively high. The exploration of the solution space through the population of chromosomes and the application of selection, crossover, and mutation operators necessitate substantial computational resources. Researchers should focus on developing efficient algorithms and parallel computing techniques to mitigate this challenge [1]. Another limitation is the difficulty in determining appropriate parameter settings for the GA. Parameters such as population size, mutation rate, and crossover rate significantly impact the algorithm's performance and convergence. Identifying the optimal parameter values is a non-trivial task, as they often depend on the specific characteristics of the routing problem and the objectives involved. Researchers must conduct thorough experimentation and sensitivity analysis to determine suitable parameter settings for different scenarios [68].  
Furthermore, GA-based routing may struggle with finding a balance between exploration and exploitation of the solution space. Premature convergence, where the algorithm becomes trapped in a suboptimal solution, is a common issue. The selection of appropriate selection, crossover, and mutation operators, as well as the management of diversity within the population, are crucial for addressing this challenge. Novel techniques, such as adaptive operators and diversity preservation mechanisms, should be explored to enhance the algorithm's exploration capabilities [79]. Additionally, the inclusion of real-time data and dynamic updates in GA-based routing introduces additional complexities. The incorporation of real-time factors, such as traffic conditions, charging station availability, or flight restrictions, requires efficient data integration and decision-making mechanisms. Dynamic updates further exacerbate the computational complexity and necessitate algorithms capable of adapting the routes in real-time. Research efforts should focus on developing algorithms that can handle dynamic environments and efficiently incorporate real-time data [1; 68].  
Addressing these limitations and challenges will pave the way for more effective and efficient GA-based routing solutions in the context of EnFVs. Future research should focus on developing advanced algorithms, incorporating advanced optimization techniques, and integrating emerging technologies to overcome these challenges and unlock the full potential of GA-based routing.
# Influence of Concrete Strength Class on the Long-Term Static and Dynamic Elastic Moduli of Concrete
###### Abstract
The influence of different admixtures on the long-term durability and strength characteristics were investigated and reported in a previous study [9]. There is still a lot of missing information regarding the long-term variation in the mechanical and elastic properties of concrete, with time being the single most important obstacle to gathering the knowledge at a faster pace. The present paper contributes to filling this information gap in terms of the strength and dynamic elastic properties of concrete. The latter characterization offers insightful information concerning the vibration damping characteristics of a material. The dynamic modulus of elasticity may be used to assess the material damping ratio and, by means of empirical conversion equations, to determine in a non-destructive manner the static modulus of elasticity (Young's modulus), a key property used during the design stage of a structure. This paper presents the results obtained from laboratory tests on the long-term (6 years) characterization of concrete from the point of view of the dynamic shear and longitudinal moduli of elasticity, dynamic Poisson's ratio, static modulus of elasticity, and compressive and tensile splitting strengths. The main parameters of the research were the concrete age, which was either 28 days [10] or 6 years, and the concrete strength class (three different strength classes were considered).
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 5 Conclusions
**Abbreviations**  
The following abbreviations are used in this manuscript:  
ANN Artificial Neural Network  
AR Auto-regressive  
AR-LSTM Auto-regressive Long Short-Term Memory  
ARIMA Auto-regressive Integrated Moving Average  
ARMA Auto-regressive Moving Average  
BAT Battery  
BPTT Back-Propagation Through Time  
CNN Convolutional Neural Network  
CV Cross-validation  
DLC Direct Load Control  
EL Electrolyzer  
EM Energy Management  
ESS Energy Storage System  
FC Fuel Cell  
FT Fuel Tank  
GHI Global Horizontal Irradiance  
GR Grid  
GRU Gated Recurrent Unit  
IoT Internet of Things  
LSTM Long Short-Term Memory  
MAE Mean Absolute Error  
MG Microgrid  
MSE Mean Squared Error  
MILP Mixed Integer Linear Programming  
ML Machine Learning  
MPC Model Predictive Control  
NARX Nonlinear Auto-regressive with exogenous input  
NMG Networked Microgrid  
RNN Recurrent Neural Network  
RES Renewable Energy Source  
S-ANC Switched Auto-regressive Neural Control  
S-MPC Switched Model Predictive Control  
SVM Support Vector Machine  
QP Quadratic Programming  
WT Water Tank  
\(\eta_{ch}^{l}\) Charging efficiency of accumulator \(l\)  
\(\eta_{dis}^{l}\) Discharging efficiency of accumulator \(l\)  
\(F\) controller model  
\(H\) constraint  
\(J\) objective function  
\(P_{m}^{max}\) Maximum values of power flows, 5 kW  
\(PV\) Photovoltaic  
\(PV_{GR}\) or \(P_{1}\) Power flow from PV to grid  
\(PV_{LD}\) or \(P_{2}\) Power flow from PV to load  
\(GR_{LD}\) or \(P_{3}\) Power flow from grid to load  
\(PV_{BAT}\) or \(P_{4}\) Power flow from PV to battery  
\(BAT_{LD}\) or \(P_{5}\) Power flow from battery to load  
\(FC_{BAT}\) or \(P_{6}\) Power flow from fuel cell to battery  
\(BAT_{EL}\) or \(P_{7}\) Power flow from battery to electrolyzer  
\(EL_{FT}\) or \(P_{8}\) Hydrogen flow from electrolyzer to fuel tank