esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 5 Results and Discussion
### Weights Optimization and Validation
The optimization results from the two intervals show that the genetic algorithm assigns larger weights to the poorer WCPs. Interval 41 corresponds to working conditions where \(|U_{h}(n)|\), \(|U_{v}(n)|\), \(|T_{h}(n)|\) and \(|T_{v}(n)|\) are all medium, and the weights are 0.20, 0.17, 0.32 and 0.31, respectively. In addition, interval 81 corresponds to working conditions where \(|U_{h}(n)|\), \(|U_{v}(n)|\), \(|T_{h}(n)|\) and \(|T_{v}(n)|\) are all poor, and the optimized weights are 0.08, 0.08, 0.46 and 0.38, respectively. The optimization results from intervals 41 and 81 show a preference for adjusting the shield tail gap, and the preference is more obvious when the shield tail gap is poor. The optimization results of the weights are consistent with the expectation, which proves the reliability of the algorithm. The intelligent selection method of segment assembly points obtained by dividing the working conditions into 81 intervals and optimizing the weights by genetic algorithm in each interval is called 81-GA. Other than this, the method 1-GA was obtained by using the genetic algorithm to optimize the weights without partitioning, and based on the experience of shield machine drivers, the empirical method (EM) was established with \(\lambda_{h}:\lambda_{v}:\xi_{h}:\xi_{v}=1:1:2:2\). The above three methods were tested with test data. Figure 14 shows the testing correct rate of the above methods. It can be seen that 1-GA has a correct rate 3.4% higher than EM, which shows that the genetic algorithm optimizes the weights with superiority, and the empirical taking of the weights has certain reasonableness (the accuracy reaches 79.1%). Moreover, 81-GA has a 7.6% higher correct rate than 1-GA, which shows that dividing the working condition into intervals and adaptive change of the weights with the WCPs can make the weights match the working condition better, and the assembly point selection correct rate is higher. The 90.6% correct rate of the test set reflects the high reliability of the 81-GA.
## 6 Extended Evaluation: Mobile Scenario
### Experiment Setup and Methodology  
To facilitate controlled movements of the LED transmitter across different positions, we set up our VLC system on a table top with the receiver in static position. We 3D printed a housing for each of the two LED transmitters to be integrated with a 3-wheel RaspberryPi controlled robot as shown in Figure 5. With a distance of 0.5 m set between the VLC transmitter and receiver, we consider three key positions of the transmitter; A, B and OFOV (out of Field-of-View):  
1. Position A is the point from where the LED transmitter illuminates Pixel 1 area of the shutter. When the robot is in position A, the receiver must be able to identify the signal is on Pixel 1 and only OPEN Pixel 1 (keeping others closed).
2. Position B is the point from where the LED transmitter illuminates Pixel 2 area of the shutter. When the robot is in position B, the receiver must be able to identify the signal is on Pixel 2 and only OPEN Pixel 2 (keeping others closed).
3. Out of Field of Views (OFOV) are the marked positions on the straight line trajectory of the robot where the LED signals are either minimal or out of field-of-view of the receiver lens and photodiode. When the robot is in this position, the receiver must identify there is no active transmission from the LED being received and hence must close all pixels to avoid any noise or interference signals being sampled on the receiver.  
Figure 5: LED transmitters placed on GoPiGo RaspberryPi robots.
# Balanced Team Formation Using Hybrid Graph Convolution Networks and MILP
## 4 Proposed GCN-MILP Model
### MILP Model
#### 4.2.3 Objective Function  
The objective function is to minimize  
\[\alpha\sum_{j=1}^{m}\left|\sum_{i-1}^{n}s_{i}x_{ij}-\frac{1}{m}\sum_{k-1}^{m} \sum_{i=1}^{n}s_{i}x_{ik}\right|+(1-\alpha)\max_{j-1}^{m}\left(\sum_{i-1}^{n}s _{i}x_{ij}\right) \tag{2}\]  
while adhering to the following constraints:  
1. Player assignment: \[\sum_{j=1}^{m}x_{ij}=1,\quad\forall i=1,\ldots,n\] (3)
2. Team size: \[\sum_{i=1}^{n}x_{ij}=p,\quad\forall j=1,\ldots,m\] (4)
3. Binary restriction: \[x_{ij}\in\{0,1\},\quad\forall i=1,\ldots,n,\forall j=1,\ldots,m\] (5)  
where \(noTms\) is the required number of teams, \(noCnd\) is the total number of candidates, \(cEmbed\) denotes the computed candidate embeddings, and \(tAssign\) represents a two-dimensional matrix of assignment of the size (\(noCnd\), \(noTms\)). In the assignment matrix, a cell has a value of one if the candidate is a member of the team or zero if they are not a member. The first constraint represents a validity check that must be satisfied by having each candidate as a member of one team at most. The second constraint verifies that each team has \(p\) members at most.  
We use PuLP [25], which includes a default solver algorithm: CBC (COIN-OR Branch-and-Cut) [26]. PuLP is an open-source solver capable of solving a broad range of LP problems. In addition, PuLP allows for the usage of APIs to enable the usage of other solvers (GUROBI, GLPK, CPLEX, etc.). Figure 3 shows the model's pipeline and the collaboration between the GCN and the MILP solver.  
In practice, the systematic adjustment of weights for constraints can be achieved via several methods: iterative refinement, in which one solves the MILP iteratively and adjusts weights based on the results, or a sensitivity analysis, which uses techniques like Pareto optimization to generate a deeper perspective of conflicting objectives, etc. In our experimental work, we employ the technique of iterative refinement as a baseline to test our model.
## 2 Study Area and Materials
### Data Used in This Study
#### 2.2.3 Model Parameter Data  
The model-assisted parameter data (Table 1) mainly include the Chongqing area administrative division map, latitude and longitude, altitude, land cover data, and digital elevation model (DEM), among others. Land cover data are derived from a LANDSAT-TM image with the aid of MCD12Q1 [21]. MCD12Q1 is from the Earth Observation System/Medium resolution Imaging Spectroradiometer (EOS/MODIS) of the National Aeronautics and Space Administration (NASA), with a spatial resolution of 500 m \(\times\) 500 m from 2001 to 2009. The vegetation biomass data are the statistical of the area of the dominant tree species provided by the National Forest Resources Statistics (1989-1993). According to the relationship between the biomass (B) and the storage volume (V), the total biomass of each dominant tree species and the average biomass of each forest vegetation type were calculated. The results of the study on the biomass of forest, farmland, grassland and desert in China obtained by [PERSON] et al. (1996) were referenced. In addition, the values given by the CEVSA model, and the biomass allocation parameters of each stand refer to the values given by the Biome-BGC model and GLOPEM-CEVSA model that were improved by [PERSON]. The DEM data with a spatial resolution of 1 km were obtained by spatial resampling using 90 m resolution STRM data provided by the Digital Image Network of the Computer Network Information Center of the Chinese Academy of Sciences. After removing outliers, the projection and resampling processes are redefined to be consistent with the rest of the study data presented in this paper. First, the NPP is calculated from GPP minus autotrophic respiration (Ra) based on light energy efficiency model using fPAR, PAR and meteorological data. The relevant parameters of Ra can be obtained from the Biome-BGC ecological process model.
# Mechanical Behavior of Ti6 Al4V Scaffolds Filled with CaSiO\({}_{3}\) for Implant Applications
## 1 Introduction
Nevertheless, in vivo life of the load bearing scaffolds can be increased by improving the interfacial bond of the bone and the metallic scaffolds. To improve the interfacial bonding, Ti6 Al4V scaffolds are filled with vollastonite. Spark plasma sintering (SPS) was used for integrating vollastonite into the cellular Ti6 Al4V structure [26]. Highly porous acicular vollastonite (CaSiO\({}_{3}\)) with micro- or nano-sized pores results in high corrosion resistance and good biocompatibility [27; 28]. Hence, the present study aims to fabricate Ti6 Al4V scaffolds reinforced with bioactive elements (wollastonite) for the proper integration of the implant to the bone via tissue growth. By reviewing the articles, the deficiency of comparison between the mechanical properties of scaffolds, FEA, and an assortment of them regarding the applications is tangible. Five different Ti6 Al4V scaffolds (samples) with identical outside dimensions and similar weight, namely: Rectangular, primitive, lattice, gyroid and honeycomb, were considered. They were chosen based on the desire to provide a continuous surface that is important for the growth/cultivation of viable cells and other aspects of tissue engineering [29]. The rectangular type has vertical available surface, whereas the primitive is made by sequential hollow spheres (proper for fluid permeability and drag delivery). Therefore, scaffolds A and B have vertical flat and spherical areas to flourish the cells. In the case of the lattice type structure, it is possible to specify the size of the cell and the diameter of the strut to figure out the proper cylindrical surface of the metallic rods. It is an intersecting cellular structure that is easy to use during the following of the SPS process, since it can be easily modeled, printed and filled by binder powders, and also allows a larger shrinkage required to produce hard metal-ceramic composites with a low porosity level by SPS [30]. The gyroid-type structure represents irregular continuous curves, and honeycomb considers embedded tubes, which are connected by horizontal washers. A SolidWorks design and Ansys FEA combination were used to generate these structures and to further anticipate the mechanical behavior of the scaffolds subjected to compressive loading. The results from simulations are compared with the experimental compressive results.
## 3 Methodology
### Buck Circuit Model  
The DC-DC buck circuit is widely used in energy harvester systems; therefore, it is important to simulate and evaluate its practical application. The DC-DC buck circuit in Simulink is similar to the LTC 3588-1 chip distributed by Linear Technology, which was used in the application. This chip integrates a full-wave rectifier and a high-efficiency DC-DC buck converter.  
In the simulation, the circuit was integrated into the Simulink environment along with the oscillatory mechanical part, rectification, and DC-DC buck conversion. The ODE45 solver was used to solve differential equations.  
The overall model of the DC-DC buck circuit, the rectified input, and the 1-DOF oscillatory system are depicted in Figure 2. A mass-spring-damper system controls a voltage source that powers the rectification circuit. After rectification, the signal is regulated by a Zener diode and delivered to the DC-DC converter circuit. The simulation was conducted for 4 s, with a sinusoidal frequency sweep from 0 to 50 Hz, to analyze the response of the extraction system.  
Figure 2: General model of the buck DC-DC circuit.
###### Abstract  
The validation of algorithms developed from in situ reflectance to estimate water quality variables has the challenge of atmospheric correction (AC) when applied to satellite images. Estimating water quality variables from satellite images requires an accurate estimation of remote sensing reflectances (Rrs) which vary according to the AC applied. Validation processes for both Rrs and water quality algorithms were carried out, relating the in situ Rrs (convoluted to Sentinel-2-MSI spectral response function) with the satellite Rrs coming from different ACs (C2 RCC, C2X, C2 XC, and Polymer), and also relating the in situ water quality variable data with estimated water quality variable values, applying the water quality algorithms to the Rrs obtained for each AC. Regarding the Rrs validation results, the best ACs tested in this work were C2 XC and Polymer. Regarding the water quality algorithm validation, the best results were also obtained using C2 XC and Polymer Rrs. The results demonstrate the usefulness of the water quality algorithms developed from in situ reflectances since they are not specific to an AC and can be used with any processor.  
remote sensing; C2 RCC; C2X; C2 XC; Polymer; Secchi disk depth; total suspended solids; CDOM; chlorophyll a; mycocyanin Article
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Electro-Optical Module for High-Temperature Operation  
The IOCore micro-transceiver is a building block that can be integrated into different types of electro-optical modules or co-packaged configurations. Figure 16b shows a low-form factor electro-optically pluggable module called the EOB (Electro-Optical Blade), which includes advanced heat dissipation structures on the upper and lower surfaces of the IOCore micro-transceiver, enabling operation at 105 \({}^{\circ}\)C. When used at an ambient temperature of 105 \({}^{\circ}\)C, the junction temperature, Tj, is designed to be 120 \({}^{\circ}\)C or less, which is within the temperature specifications for the ICs and laser.
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 2 Vehicle System Modelling  
In order to develop suitable control methodology, the 4 WS4 WD vehicle must be modelled for controller design and simulation. The model used for this work is a fully dynamic model that consists of three components, vehicle body dynamics to estimate vehicle body movement, drive unit dynamics to model the tire force acting on the drive unit in vehicle coordinate frame and a tire dynamic model to model the force generated by the tire in a wheel coordinate frame. The separation of dynamic model allows force sensors to be incorporated easily and reduces uncertainties by obtaining actual force measurements. In the end, an offset error model is proposed for evaluating the tracking performance and is used in the controller design.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 2 ZoomFFT Algorithm  
### Principle  
The ZoomFFT can be summarized with the following four steps shown in Figure 1[21]: frequency shift, low pass filtering, resampling, and FFT.  
The sequence \(s(n)\) is the input signal with the length of \(N\). And \(S_{D}(k)\) is the corresponding refinement spectrum of sequence \(s(n)\), which can be expressed as [21]  
\[\begin{split} S_{D}(k)&=\mathcal{F}(h_{LP}(nD)* \Re(s(nD)e^{-j\omega_{0}nD})\\ &=\frac{1}{D}H_{LP}(e^{j\frac{2\pi}{D}k})S(e^{(j\frac{2\pi}{D}k- \omega_{0})})\end{split} \tag{1}\]  
where \(h_{LP}\) the impulse response of the low pass filter is used for anti-aliasing. \(\omega_{0}\) is the frequency shift parameter. \(D\) is the refinement factor.  
The number of FFT points is \(N\). The spectral resolution of the Zoom-FFT algorithm is [35]  
\[\Delta f_{N}^{{}^{\prime}}=\frac{f_{s}^{{}^{\prime}}}{N}=\frac{f_{s}/D}{N}= \frac{\Delta f_{N}}{D} \tag{2}\]  
where the sampling frequencies for the FFT and Zoom-FFT algorithms are, respectively, \(f_{s}\) and \(f_{s}^{{}^{\prime}}\), and the spectral resolutions are \(\Delta f_{N}\) and \(\Delta f_{N}^{{}^{\prime}}\). The resolution of the spectrum after refinement is increased D-fold compared with the original range.
# Analysis and Design of a Minimalist Step Climbing Robot
## 3 Design and Modeling
### Locus of the Center of Wheel While Ascending/Descending the Stairs  
The motion of the robot depends on the trajectory of the wheel center on the steps. It is found that the pattern of the trajectory depends upon the radius of wheel as compared to height of the step. This can be categorized into two groups, which are the following: radius of wheel larger than or equal to height of the step and the other group being the radius of wheel smaller than height of the step. In the following section, equations of the corresponding paths followed by the wheels are discussed.  
Figure 3 illustrates the path traced by center of the wheels. The trajectory shown in solid lines represents that of a wheel, for which its radius (\(R\)) is equal to or greater than the height of a single step (\(h_{s}\)), whereas the path in dotted lines represents the trajectory of a wheel for which its radius (\(r\)) is less than \(h_{s}\).  
It can be observed from Figure 3 that the center of the wheel traces a linear horizontal path until the wheel touches the edge of the step. Thereafter, in the case of the wheel  
Figure 3: Locus of the wheel center on steps.  
Figure 2: **(a)** A schematic view of the stair climbing robot; **(b)** Robot as a 5 bar linkage mechanism; **(c)** Top view of the robot.  
possessing a radius larger than or equal to the height of the step, the center of wheel traces a circular path with the edge of the step as the center and radius \(R\). Since the motors of front wheel and rear wheel are attached to the frame, any oscillation of the frame will result in respective changes in the angular velocities of the wheel. Hence, the angular velocities of the wheels are determined by using Equation (7). In the following section, equations of the path traced by the front and rear wheels are discussed.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 3 Plant State Space Models and Their Properties
### Properties of the Discrete-Time State Space Model
#### 3.3.1 Spectrum of \(A^{d}\)  
For the matrix \(A^{d}\), we have the following:  
\[A^{d}=(\mu I+A)(\mu I-A)^{-1}\,,\quad\mu\
ot\in\sigma(A)\,. \tag{29}\]Hence, for \(\lambda\in\mathbb{C}\),  
\[\begin{array}{rcl}\lambda I-A^{d}&=&\lambda(\mu I-A)(\mu I-A)^{-1}-(\mu I+A)( \mu I-A)^{-1}\\ &=&(\mu(\lambda-1)-(\lambda+1)A)(\mu I-A)^{-1}\end{array} \tag{30}\]  
which, for \(\lambda\
eq-1\), leads to the following:  
\[\frac{1}{\lambda+1}(\lambda I-A^{d})(\mu I-A)=\mu\frac{\lambda-1}{\lambda+1} I-A\,. \tag{31}\]  
For the determinants of both sides, we obtain the following:  
\[\frac{1}{\lambda+1}\det(\lambda I-A^{d})\det(\mu I-A)=\det(\mu\frac{\lambda-1 }{\lambda+1}I-A)\,. \tag{32}\]  
As \(\det(\mu I-A)\
eq 0\), then  
\[\det(\lambda I-A^{d})=0\quad\text{if and only if}\quad\det(\mu\frac{\lambda-1 }{\lambda+1}I-A)=0\,. \tag{33}\]  
This means that, if \(-1\
ot\in\sigma(A^{d})\), then the eigenvalues \((\lambda_{I}(A^{d}))_{l=1}^{l=4}\) and \((\mu_{I}(A))_{l=1}^{l=4}\) are related to each other by the following equality:  
\[\mu_{I}(A)=\mu\frac{\lambda_{I}(A^{d})-1}{\lambda_{I}(A^{d})+1}\,,\quad l=1,2,3,4\,; \tag{34}\]  
or, equivalently, if \(\mu_{I}(A)\
eq\mu\) (it is always satisfied by (24)), then  
\[\lambda_{I}(A^{d})=\frac{\mu+\mu_{I}(A)}{\mu-\mu_{I}(A)}\,,\quad l=1,2,3,4\,. \tag{35}\]  
If we introduce the notation \(\mu_{I}(A)=\operatorname{Re}\mu_{I}+j\operatorname{Im}\mu_{I}\), then (35) implies that  
\[|\lambda_{I}(A^{d})|=\sqrt{\frac{(\mu+\operatorname{Re}\mu_{I})^{2}+( \operatorname{Im}\mu_{I})^{2}}{(\mu-\operatorname{Re}\mu_{I})^{2}+( \operatorname{Im}\mu_{I})^{2}}}\,, \tag{36}\]  
which indicates that  
\[\begin{array}{ll}|\lambda_{I}(A^{d})|<1&\text{if and only if}\quad \operatorname{Re}\mu_{I}(A)<0\,,\\ |\lambda_{I}(A^{d})|=1&\text{if and only if}\quad\operatorname{Re}\mu_{I}(A)=0\,,\\ |\lambda_{I}(A^{d})|>1&\text{if and only if}\quad\operatorname{Re}\mu_{I}(A)>0\,. \end{array} \tag{37}\]  
The general meaning of the above results is that the considered transformation preserves the internal stability (or instability) of the plant.
# Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector
## 1 Introduction
(3) Combining multiple terms of measuring point coordinates with environmental causal factors results in a model with too many input variables and too much model complexity [9]. In recent years, with the rapid development of deep learning theory, some scholars are trying to introduce deep learning models with better fitting ability into the field of dam deformation prediction [14; 15], compared with machine learning models and shallow artificial neural networks, deep learning models can fully consider the lag effect of causal factors (i.e., the influence of causal factors at historical moments on the deformation of measuring points). A Convolutional Neural Network (CNN) is one of the most important networks in deep learning with high spatio-temporal feature extraction capability and computational efficiency, which has been widely used in remote sensing image recognition [16; 17], traffic flow prediction [18; 19], intelligent road tunnel investigations [20] and other fields, but less applied in the field of dam deformation prediction. In summary, in order to improve the problems of the multi-point model with fused spatial coordinates and improve the prediction accuracy of the model, this paper proposes a 2D-1D-CNN model with fused spatial feature vectors by combining convolutional neural networks, takes a concrete dam as an example and compares the prediction results of this model with the current commonly used models to verify its effectiveness. Meanwhile, the prediction results of the SVM model are used as samples to explore the differences in prediction performance between the multi-point model with fused spatial coordinates and the multi-point model with fused spatial feature vectors.
# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Impact of AutoML on Business Operations
#### 3.4.6 Supports Sustainability Initiatives  
AutoML optimizes processes and resource usage, reducing the environmental impact of business operations. As organizations increasingly prioritize sustainability, there is a growing need for tools that can help balance economic growth with environmental responsibility. AutoML can contribute to these efforts by enabling businesses to analyze and optimize their energy consumption, supply chain logistics, and waste management practices [68; 69]. For instance, AutoML can be used in the energy sector to develop models that predict energy demand and optimize energy distribution, reducing waste and ensuring that resources are used more efficiently. AutoML can optimize production processes in manufacturing to minimize resource usage and reduce emissions, supporting the industry's shift towards more sustainable practices [70]. In addition, AutoML can help companies monitor and report on their sustainability metrics, providing real-time insights into their environmental performance and helping them meet regulatory requirements. By automating the analysis of complex datasets, AutoML enables businesses to identify opportunities for reducing their carbon footprint and improving their overall sustainability.  
AutoML's ability to process large volumes of data quickly and accurately makes it valuable for tracking progress toward sustainability goals. This helps ensure that initiatives are effective and scalable [23]. As sustainability becomes an increasingly important aspect of corporate strategy, AutoML will continue to play a key role in helping organizations achieve their environmental objectives.