esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Self-Consumption Photovoltaic Installations: Profitability Thresholds
## 2 Materials and Methods
### Technical and Economic Valuation  
Finally, this section describes the methodology used to evaluate PV installations with self-consumption, both technically and economically. Thus, as an index of technical valuation of the different facilities, the ratio of annually self-consumed energy versus annual energy needs is proposed.  
For the economic assessment of the self-consumption project the evaluation is proposed as that of a conventional project in which, due to the investment of capital, during the useful life of the project, income is obtained that is materialised as a saving with respect to the situation--normally initial--in which there is no PV plant. In this way, saving is quantified as the economic difference between two situations: the first situation in which all electrical energy is taken from the grid and no self-consumption installation is carried out, and the second situation in which a self-consumption PV installation has been implemented and, therefore, only the energy taken from the grid and the possible compensations are valued. Accordingly, for the economic assessment of PV installations with self-consumption, the following parameters will be analysed:  
* _Net Present Value_, \(NPV:\) accounts for the present value of all economic flows in the useful life of the project according to Equation (26), in which \(F\) is the income of each annual period \(j\) updated to the initial year (Equation (25)), \(M\) is the maintenance expenses calculated in the initial year, \(C_{0}\) is the initial investment \((j=0)\), \(n\) is the useful life of the installation, \(d\) is the discount rate or interest rate required of the investment, \(i\) the inflation rate and \(\Delta p\) the rate of year-on-year increase in energy prices. \[NPV=-C_{0}+\sum_{j=1}^{n}F\frac{(1+\Delta p)^{j}}{(1+d)^{j}}-\sum_{j=1}^{n}M \frac{(1+i)^{j}}{(1+d)^{j}}\] (26)
* _Period of return on investment or \(Pay-back\)_: is an indicator that measures how long the total investment will be recovered at the present value. In this way, it serves to accurately reveal the date on which the initial investment will be covered. When the cash flows are the same every year, the calculation of the Payback will be given by Equation (27).  
\[Pay-back=\frac{C_{0}}{F} \tag{27}\]
# New Chaotic Dynamical System with a Conic-Shaped Equilibrium Located on the Plane Structure
## 2 Mathematical Methods, Discovered Model and Numerical Analysis
### Chaos Localization and Search Algorithm  
Application of the LE concept in the chaos localization procedure can be automatized and has three consequent steps. The first step is the specification of the mathematical model, that is, the set of ordinary differential equations should be explicitly defined. This is a place where additional requirements for dynamical flow can be pronounced, such as the existence of hyperbolic equilibrium and its location in state space. The second step is defining the degree of freedom for upcoming tabularized calculations. These \(n\) parameters must be chosen carefully and should be able to fold the vector field in a different fashion or change the nature of dynamical flow without changing or moving the predefined equilibrium. This means that parameters leading to the unwanted properties of a dynamical system will be excluded from the calculated grid. If such exclusion is not possible, penalization of objective function can do the trick as well. The third step is the repeated calculation of LE in \(n\)-dimensional hyperspace of the chosen free internal system parameters. To reduce time demands, number \(n\) should be kept small. Due to inevitable numerical errors in the LE calculation, sets of parameters leading to the positive largest LE are stored and verified by sequential hand-made numerical verification and visual confirmation of the attractor.  
Values of LE are an independent norm used in the process of calculation, such that the standard distance in Euclidean space has been adopted (supplemented by Gram-Smith orthogonalization). Since the analyzed vector field is polynomial, there are no problems with the discontinuity of gradients. Active calculation of LE begins if the state under inspection is part of the attractor, that is, the transient motion is omitted. Achieved values of LE in each step of the search procedure (using final iteration time \(10^{4}\)) were verified by considering different initial conditions randomly picked in the vicinity of the state space origin, where trajectory is strongly pushed towards chaotic attractor. The value of the largest LE was adopted if it showed closely related values and insensitivity.  
Note that the approach described above represents a time-consuming brute force numerical method and is only allowed thanks to the availability of the computational power of contemporary personal computers and workstations. Since individual calculations of LE for the different set of free parameters are totally independent, the hyperspace of model parameters can be arbitrarily divided into smaller pieces and distributed over multiple computational cores. Thus, parallel processing applied on a chaos location algorithm is logical and represents a huge advantage here. Routine, as discussed above and shown by the results achieved in this work, has been reached by using Matlab and the build-in toolbox of Compute Unified Device Architecture (CUDA) technology, invented and distributed by NVIDIA company.
# The Deep Deterministic Policy Gradient Algorithm Based on RIS Technology in a Coal Mine Tunnel
## 2 System Model
### Coal Mine Channel Modeling and Reflection Characterization Analysis
The SINR of the \(k\)th user is represented as follows:  
\[\beta_{k}=\frac{\sqrt{P_{k}}\Big{|}\mathbf{h}_{nm,k}^{H}\mathbf{\Phi}\mathbf{H}_{nu}\mathbf{\phi}_ {k}\Big{|}^{2}}{\sqrt{P_{n}}\sum\limits_{n.n\
eq k}^{K}\Big{|}\mathbf{h}_{nm,k}^{H} \mathbf{\Phi}H_{nu}\mathbf{\phi}_{n}\Big{|}^{2}+\sigma^{2}+p\cdot\sigma_{\text{imp}}^{2}} \tag{10}\]  
where \(p\) is the probability that \(\mathbf{B}\) equals to 1, assuming that each signal has the same transmission power, i.e., \(P_{t}\). Therefore, the total link rate (in units of \(\text{bps}/\text{Hz}\)) in the system can be expressed as follows:  
\[C=\sum\limits_{k=1}^{K}\log_{2}(1+\beta_{k}) \tag{11}\]  
To more accurately describe the characteristics of the coal mine RIS channel, this paper analyzes, in detail, the relationship between the number of reflections of the signal between the tunnel walls and the path length based on the \"second law of tent\" in geometric optics. As shown in Figure 2, by dividing the propagation path into horizontal and vertical mapping planes, the propagation law of the signal under multiple reflections is clarified. In the figure, the blue dotted line represents the horizontal mapping plane, the green dotted line represents the vertical mapping plane, and the yellow curve represents the propagation path of the signal from the base station to the user after being reflected by the RIS. Assuming that the number of reflections of the signal in the coal mine tunnel is \(L\), the total number of effective paths to the RIS formed by multiple reflections of the signal through the coal mine tunnel walls is as follows:  
Figure 1:0  
According to [PERSON]'s law, the reflection coefficients of vertically polarized waves and horizontally polarized waves can be expressed as follows:  
Figure 1:1  
Figure 1:2 Schematic diagram of the second law of tent. Figure 1:3  
where \(\varepsilon_{r}\) is the relative dielectric constant of the coal mine tunnel wall. Assuming that the roughness distribution of the coal mine tunnel wall follows the Gaussian distribution with a mean of 0 and a variance of \(h\), then \(\rho_{r}\) represents the roughness loss factor. The roughness loss coefficient caused by the rough surface of the coal mine wall is as follows:  
Figure 1:4  
where \(\rho_{r}=\exp[-8(\frac{\pi h\cos\theta_{i}}{\lambda})^{2}]\). Multiple reflections of the signal on the two side walls and the top and bottom plates of the tunnel will cumulatively affect the total reflection coefficient. Assuming that the ray is reflected \(m\) times at the two side walls and \(n\) times at the top and bottom plates, the reflection coefficient at this point can be expressed as follows:  
Figure 1:5  
It can be expressed uniformly:  
Figure 1:6  
where \(R_{k}\) denotes the polarized reflection coefficient corresponding to each reflection.
## 3 Methods  
Motivated by _SkysatStero_, we introduce _Planet4 Stereo_--an automated pipeline using ASP for the stereo photogrammetric processing of multi-temporal PlanetScope images.  
_Planet4 Stereo_ is implemented in Python 3.10, merely requiring the installation of ASP version 3.4.0 and a few Python libraries commonly known for geo-processing, like _Geopandas_, _GDAL_, or _Rasterio_. It was tested with Ubuntu 22-04 LTS running in the Windows Subsystem for Linux (WSL).  
The pipeline is illustrated in Figure 2 following a similar scheme as described in the literature (see Section 2). As an initial step (not illustrated in the figure), the operator should download the requisite input data, i.e., the PSS, a coarse-resolution DEM (hereinafter referred to as 'approximation DEM'), and, if required, glacier mask data.  
This approximation DEM, as outlined in the ASP documentation, facilitates the calculation of the disparity, particularly in topographies characterized by significant inclines [27]. One option is the use of the Copernicus World DEM (GLO-30) with a GSD of 30 m [6]. The pipeline commences with the selection of the potential stereo pairs, whereby the minimum necessary overlap and convergence angle are analyzed. The resulting image block configuration is optimized through the utilization of bundle-block adjustment for bias correction. Even if the images already have similar perspectives, they are orthorectified using the approximation DEM to further improve image correlation in the stereo matching process. In this so-called ortho workflow, the approximation DEM serves as an initial guess when calculating the disparities and subsequently the 3D point cloud. This workflow is highly recommended by ASP with steep terrain as it occurs in glaciological applications. The pairwise-calculated 3D point clouds are aligned to the given approximation DEM to correct for any shifts in georeferencing that may have occurred due to bundle-block adjustment without using GCPs. This is followed by rasterizing all pairwise-calculated point clouds to DEMs. In this step, the ASP option is used to create orthorectified images in the area of the respectively generated DEM. In a later step, all images orthorectified in this way can be used to create an orthomosaic of the entire scene. Additionally, an error map is generated to represent the mean intersection error of the reconstructed 3D surface points within each grid cell. This map facilitates the investigation of potential systematic errors. The pairwise-generated DEMs, error maps, and orthophotos are then combined  
Figure 2: _Planet4 Stereo_ pipeline. In this flow chart, the processes are marked in blue, the input data in red, and the generated data in violet. Data flow is marked by arrows. The modification of data is indicated by the use of a gradient background color.  
into a mosaic to produce the final products: a DEM mosaic, a merged error map, and an orthophoto mosaic covering the ROI.  
The following sections provide a detailed overview of the methodology, with a particular emphasis on the tools utilized within the ASP environment. Tools are denoted using (_brackets and italics_).
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 3 The Proposed Method
### Mechanism MSCNN-SK Network  
The MSCNN-SK (Multi-Scale Convolutional Neural Network with Selective kernel) network proposed in this paper is shown in Figure 2. It is mainly divided into a multi-scale mean difference layer, a feature extraction layer based on the dynamic selection mechanism of the convolution kernel, and a multi-scale information fusion layer.  
The specific workflow of the MSCNN-SK network is described as follows: first, the multi-scale mean difference layer calculates the 0 th order, 1 st order and 2 nd order mean difference sequences, and extracts the characteristics of the average rate of change of the time-domain response signal; secondly, the convolutional layer with the dynamic selection mechanism of the convolution kernel extracts fault characteristic information from the multi-scale mean difference data; then, the extracted multi-scale features are expanded into a one-dimensional vector and input to the two fully connected layers for information fusion and further feature extraction; finally, the fully connected layer obtains the analog circuit diagnosis results.  
Figure 1: Schematic diagram of the convolution kernel dynamic selection mechanism.
# Numerical Study of the Seismic Response of an Instrumented Building with Underground Stories
###### Abstract  
Engineering practitioners do not usually include soil-structure interactions in building design; rather, it is common to model and design foundations as embedded joints with joint-based reactions. In some cases, foundation structures are modeled as rigid bodies, embedding the first story into lower vertical elements. Given that the effects of underground floors on the seismic response are not generally included in current building design provisions, it has been little explored in the literature. This work compares and analyzes models to study the effects of different underground stories modeling approaches using earthquake vibration data recorded for the 16-story Alcazar building office in downtown Vina del Mar (Chile). The modeling expands beyond an embedded first story structure to soil with equivalent springs, representing soil-structure interaction (SSI), with varying rigid soil homogeneity. The building was modeled in a finite element software considering only dead load as a static load case because the structure remained in the framing stage when the monitoring system was operating. The instruments registered 72 aftershocks from the 2010 Maule Earthquake, and this study focused on 11 aftershocks of different hypocenters and magnitudes to collect representative information. The comparisons between empirical records and models in this study showed a better fit between the model and the real vibration data for the models that do consider the SSI using horizontal springs attached to the retaining walls of the underground stories. In addition, it was observed that applying a stiffness reduction factor of 0.7 to all elements in deformation verification models for average-height buildings was suitable to analyze the behavior under small earthquakes; better results are obtained embedding the structure in the foundation level than embedding in the street level; the use of horizontal springs with [PERSON]'s model with traction for the analysis of the structure yields appropriate results; it is necessary to carefully select the spring constants to be used, paying special attention to the vertical springs. Even though the results presented herein indicate that the use of vertical springs to simulate the SSI of the base slab can result in major differences concerning the real response, it is necessary to obtain more data from instrumentation across a wider variety of structures to continue to evaluate better design and modeling practices. Similarly, further analyses, including nonlinear time-history and high-intensity events, are needed to best regulate building design.  
seismic response; underground stories; earthquake vibration recordings; structural modeling; soil-structure interaction; buildings +
Footnote †: journal: Applied Sci. 2021, 11, 3190. [[https://doi.org/10.3390/app11073190](https://doi.org/10.3390/app11073190)]([https://doi.org/10.3390/app11073190](https://doi.org/10.3390/app11073190))
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 3 Non-Cooperative Relative Positioning
### Time-Of-Flight Cameras  
Time-Of-Flight (TOF) or 3D cameras are used in many different fields, including human-machine interaction, industrial automation and robotics. In automotive applications, they are used in driver assistance and safety applications, such as pedestrian recognition or pre-crash detection [28]. Unlike laser scanners, the TOF camera captures the entire scene with one single light pulse. Each camera pixel measures the time delay of modulated infrared light by comparing the phase of the outgoing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Dimensional Resolution** & **Range** & **Azimuth Angle** & **Accuracy** & **Cycle** \\ \hline Quangery M8-1 & 3D & 150 m & 360\({}^{\circ}\) & 0.05 m, -, 0.03\({}^{\circ}\) & 33 ms \\ Ibeo LUX & 2D & 200 m & 110\({}^{\circ}\) & 0.1 m, - 0.125\({}^{\circ}\) & 20 ms \\ Continental SRL1 & 2D & 10 m & 27\({}^{\circ}\) & 0.1 m, 0.5 m/s, - & 10 ms \\ Velodyne HDL-64E S2 & 3D & 120 m & 360\({}^{\circ}\) & 0.02 m, -, 0.09\({}^{\circ}\) & 50 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Laser scanners.  
and the incoming signal [29; 30]. The distance information is captured simultaneously for the entire scene [31].  
When compared to video cameras, the TOF camera's CMOS sensors have currently much lower resolution (64 \(\times\) 8 pixels [31] and around 200 \(\times\) 200 pixels in Table 3). The typical range up to where TOF cameras operate is 10 m and, therefore, suited for pedestrian recognition, parking assistance or object recognition. [PERSON] presents a CMOS sensor for an TOF camera that targets a distance of about 20 m [31]. The field of view of TOF cameras is around 40\({}^{\circ}\).  
A typical value for the ranging accuracy is around 1 cm. Compared to video-based techniques, TOF cameras use a single lens and reach more accurate depth information [28]. When compared to laser scanners, TOF cameras have higher frame update rates ranging from 20 fps [32] to 200 fps [31]. The advantage of TOF cameras is that they do not use mechanical components as laser scanners. TOF camera sensors, like laser scanner sensors, have problems with incident sunlight, which is a great drawback for situation awareness in automotive applications. Additionally, the price of TOF cameras, although decreasing in recent years, is still high for market introduction.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 5 Discussion  
One limitation of the study is that the images were scaled to a resolution of 256 \(\times\) 256 and 512 \(\times\) 512 pixels, which may have limited the classification performance. While this choice of image resolution offered the advantage of reducing the need for computing resources during experimentation, it is possible that the performance of the models could be improved by using images with a higher resolution.  
Furthermore, the classification process was performed on a single image, even though each person in the dataset had at least four images. One possible way of improving the classification process could be to simultaneously classify multiple images of the same person using techniques such as the one presented in [44] in order to improve the accuracy of the classification results.  
Another limitation of the study is that only positive examples were used to generate the synthetic images. This approach was taken in order to balance the dataset and prevent bias towards the negative class. However, a new possibility for experimentation could be to use two classes of images, both for positive and negative examples, in order to explore the performance of the models in a more realistic scenario.
## 3 Sensitivity
The comparison of the maximum \(Sh\) and \(Sd\) of \(T\)(x,0,0,\(t\)) and \(T\)(0,0,0,\(t\)) (Figure 7b,c, respectively) for \(\tau\) = 1 s, reveals that for short bursts the sensitivities to the depth and the height are higher for the timing graph at the origin, \(T\)(0,0,0,\(t\)), as pointed out in Figure 5. This confirms the proposed data selection (\(T\)(x,0,0,\(\tau\)), \(T\)(0,y,0,\(\tau\)), and \(T\)(0,0,0,\(t\))), combining spatial and temporal information, as adequate data for the fast characterization of these heat sources. Moreover, for \(\tau\) = 5 s the sensitivities \(Sd\) and \(Sh\) of \(T\)(x,0,0,\(\tau\)) (Figure 7b) are similar to the sensitivities of \(T\)(0,0,0,\(t\)), (Figure 7c), which still break the degeneracy of \(Sd\) and \(Sh\) in \(T\)(x,y,0,\(\tau\)). As a summary, increasing the duration of the burst increases the sensitivity asymptotically, but at the expense of an eventual degeneracy of \(Sw\) with \(Sd\) and \(Sh\), that does not occur for short bursts. A rule of thumb could be to use the shortest excitation that covers the depth that needs to be proved. It is worth noting that the analysis presented in this section can be extrapolated to other material properties, heat source dimensions, and depths, and excitation durations, provided that the thermal diffusion length associated to the end of the burst, \(\mu_{\tau}\), keeps the same relationship with \(w\), \(h\), and \(d\).
## 7 Conclusions and Future Research Extensions
The scope of the future research for this study may focus on the following aspects: (1) evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms using the realistic operational data, collected from the marine container terminals; (2) compare the developed solution algorithm against the other state-of-art solution algorithms; (3) develop local search heuristics to improve performance of the crossover and mutation operators; (4) evaluate different strategies for altering the crossover and mutation probabilities (e.g., use feedback from the search instead of deployment of the stochastic operators); (5) assess performance of the developed solution algorithm for different termination criteria; (6) apply the proposed self-adaptive Evolutionary Algorithm for the multi-objective berth scheduling problem, where the considered objectives are conflicting in their nature; (7) assess the effects of encoding additional parameters of the self-adaptive Evolutionary Algorithm (e.g., population size, penalty terms for infeasible individuals, selection operator parameters) on its performance; and (8) evaluate performance of the developed self-adaptive Evolutionary Algorithm for different selection mechanisms. [PERSON] formulated a mixed integer linear mathematical model for the discrete dynamic berth scheduling problem with spatial requirements (**BSPSR**), designed the SAEA algorithm, and conducted the numerical experiments throughout this study. [PERSON] assisted with development of the SAEA algorithm and encoding the SAEA algorithm within the MATLAB 2016 environment. Moreover, [PERSON] conducted the parameter tuning analysis for the SAEA, AEA, DPCEA, and EA algorithms. [PERSON] assisted with generating the input data for the numerical experiments and compilation of the manuscript. [PERSON] assisted with a detailed analysis of the SAEA, AEA, DPCEA, and EA outputs throughout the numerical experiments. This research received no external funding. The authors declare no conflict of interest.