esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Context and Major Findings  
In the beginning of this research, we raised the hypothesis that fraudulent shops can be detected using a relatively small number of features that can be easily obtained from third-party sources, external APIs, or fake shop source code directly. The idea was to avoid complex analysis and computations for feature extraction and keep the number of features as small as possible. This would allow us to apply this method in practical applications, where memory and processing capabilities are restricted, e.g., creating mobile security applications. We tried to find the smallest possible number of features, yet still achieving very high detection accuracy. For this purpose, we evaluated all possible combinations of features (from the initial set of 18 features), using seven popular classifiers. The initial set of 18 features was constructed in such a way that all features can be directly extracted from the URL and the source code of the online shop and the APIs of third parties using simple text analysis techniques.  
As presented in Figure 2, even a small number of proposed features was enough to achieve high classification accuracy. The accuracy of 0.9342 was achieved with only four of the most significant features. Seven of the most significant features ensured a classification accuracy of 0.9605, while the best accuracy of 0.9693 was achieved using 13 and 15 features. Therefore, our research allowed us to identify the small set of the most significant features, which allows one to detect fake shops with high accuracy, avoiding complex, time-consuming computations and analysis of web shop content, such as, for example, comparing shop prices with other known legitimate e-shops [38].
## 4 Results and Discussion
### Effect of the Hartmann Number (Ha)  
Next, the flow regime in the case of different Hartmann numbers was investigated, related with the intensity of the applied magnetic field. The volume fraction was set to \(\varphi=0.03\), the Rayleigh number \(\text{Ra}=5\times 10^{8}\), and the porosity to \(\varepsilon=0.7\).  
After the onset of the magnetic field, notable variations in the stream function, the microrotation, and the temperature contours were observed. The anticlockwise rotating vortex formations gradually disappeared, and two secondary rotating cells (having low stream function values) appeared initially along the diagonal, shifting horizontally and disappearing as the Hartmann number increased (Figure 6). Boundary layers (Hartmann layers) were established on the vertical walls of the cavity. Microrotation contours formed a vortex that moved toward the center of the cavity as the Hartmann number increased. Initially, the isotherms swirled according to the flow field, and as the Hartmann number increased, they became parallel to the vertical walls. The vorticity formed two vortices at the vertical walls for high values of the Hartmann number.  
Figure 5: Average Nusselt number variation with porosity for two characteristic Hartmann numbers. \(Ra=5\times 10^{8};\varphi=0.01\).  
A decrease in the heat transfer was observed, along with an increase in the intensity of the magnetic field, through the variation of the \(Nu_{\rm{ave}}\) along the hot wall (Figure 7). The average Nusselt number decreased with the increase in the Hartmann number. The decrease of the average Nusselt number was more evident for elevated values of the Rayleigh number.  
Figure 6: Stream function (\(\Psi\)), temperature (\(\theta\) ), microrotation (\(N\) ), and vorticity (\(\Omega\) ) contours for a range of Hartmann numbers.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
###### Abstract  
As renewable energy is increasingly integrated into our electricity supply, it becomes more challenging to ensure reliability and security due to the intermittent nature of these resources. With the electrification of buildings and technological advancements across various aspects of their operations, the building sector is expected to play a key role in reducing emissions while supporting the needs of the grid. Buildings and the loads they house can provide grid resources via demand flexibility, shifting, and shedding electric load, as necessary. This key resource has received increased attention from researchers, building operators, electric utilities, policymakers, and system operators as a tool to improve power grid reliability and reduce system costs. Before increasing reliance on demand flexibility, however, a better understanding of its availability is needed to inform planning efforts. This paper includes a review of the literature on current methods and data used to model the available flexibility of power delivered to customers. This review also summarizes how demand flexibility is defined and quantified to help inform future studies in this field. The results of this review illustrate the diversity found within this field of research and the innovation that researchers are employing to solve this complex problem.  
commercial demand; demand flexibility; demand response; demand side management; distribution grid; energy flexibility; industrial demand; residential demand; power grid operation +
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in the form of demand-side management of energy use. Throughout this paper, the term \"demand\" is used to denote energy demand via consumers connected to the power grid. Definitions of the various terms used across the studies analyzed are discussed further in Section 3.1.  
Historically, studies have focused on the operational viability of this resource for various applications or end-uses. More recently, stakeholders such as building managers, electric utilities, policymakers, and system operators have become more interested in quantifying the availability of demand flexibility for various planning activities [4]. Through an increased understanding of the availability of demand flexibility across the grid, operators can improve DR dispatch, streamline planning practices, and reduce the operational costs of the grid. This interest has led to an emerging field of research focused on quantifying the flexibility potential of buildings. As illustrated by this review, these studies have employed a variety of techniques across different sectors, with little standardization and consistency. These techniques will be summarized here.
## 3 Results
### Ultrasound Pre-Processing Techniques  
Pre-processing techniques aimed at improving image readability have been extensively explored to address imaging artifacts inherent in US, which often lead to inaccurate prostate segmentations such as shadows (Figure 3a) and low contrast between the prostate and surrounding structures (Figure 3b,c). Dark areas, known as acoustic shadows, can arise from structures with high echogenicity, such as bones or calcifications. To tackle this, [PERSON] et al. (2022) [39] introduced two novel mechanisms to help networks handle shadow regions at both the image and feature levels. Shadow artifacts were artificially added to training images to enhance the diversity of shadows in the dataset, serving as an innovative data augmentation strategy to increase network robustness on prostate images with shadow artifacts. The authors simulated artifacts by extracting shadow regions from other images using soft thresholding and created augmented training data by fusing these shadow artifacts with other images. Furthermore, shadow features were subtracted from the feature maps, creating a feature space that ignores shadow regions, similar to a systematic drop-out layer. This approach encourages the model to learn the prostate boundary using the remaining shadow-free features. When these methods were added to a traditional U-Net architecture, the results showed an increase in the Dice Similarity Coefficient (DSC) by 1.29% and a reduction in Hausdorff Distance (HD) by 0.33 mm.  
[PERSON] et al. (2019) [29] explored the use of 3D Mean, Median, and Gaussian filters to enhance feature extraction of the prostate boundary in their DL architecture. However, this pre-processing step had no significant effect on DSC, Precision, or Recall, and it slightly degraded HD performance. This indicates that the use of these particular filters is not effective for improving segmentation results in this design.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 5 Current Problems and Discussion
### The Treatment of Atmospheric Effects  
The radiance received by a sensor is affected by atmospheric conditions, azimuth information of the sun/sensor, and so on. In our wavelengths of interest (600-800 nm), the main absorbers are oxygen (O\({}_{2}\)) and water vapor (H\({}_{2}\)O) [47; 127]. With respect to the atmospheric effects, scattering is the primary factor that should be considered in the SIF retrieval approaches. In the Fraunhofer lines, SIF will adequately fill the Fraunhofer well and scattering also contributes to the Fraunhofer well. Many studies about the effects of scattering on the SIF retrieval method have been conducted [61; 128; 129; 130; 131; 132], and conclude that Raman scattering should be considered in the space-sack SIF retrieval method [47].  
In SIF retrieval methods, two retrieval windows are used, which are oxygen absorption bands and Fraunhofer lines. In the oxygen absorption bands, Raman scattering, surface pressure, albedo, and so on may produce errors in the algorithms [37]. SIF retrieval methods usually exploit statistical techniques to calculate the atmospheric effects in the SIF retrieval process, such as PCA [37] and SVD [34]. Fluorescence-free regions, such as deserts, Greenland, Antarctica, and so on, are used for training to estimate the influence of atmospheric effects. The types and number of the trained data must be as representative as possible. By training many fluorescence-free datasets, the computed atmospheric condition can be made more reliable. The proper selection of fluorescence-free regions is critical [34].  
In the Fraunhofer lines, the atmospheric effects are assumed to be very small, and can be neglected [33]. Based on this assumption, the SIF retrieval methods in the Fraunhofer lines are developed. Yet until now, how the neglection of atmospheric effects in Fraunhofer lines give the effects on SIF is not clear. Contrasting with the received radiance, the fluorescence radiance is relatively weak (approximately 1-3% are strong). Inappropriate treatment of the atmospheric effects can affect the fluorescence retrieval results.
## 1 Introduction
The co-estimation of SOC and capacity using a dual filter requires discussion in terms of various aspects: the types of battery models, the types of filters used to estimate states and parameters in the model, the specific settings of the filter, and the degree of battery degradation. Firstly, Thevenin models are most widely adopted, such as first-order (IRC) [31], second-order (2 RC) [32; 33], and fractional second-order [34; 35] models. While extended Kalman filters (EKFs) are commonly employed, variants such as unscented Kalman filters (UKFs) [30] or adaptive extended Kalman filters (AEKFs) [27] are sometimes used. In some cases [31; 35], the filters operate at different time intervals to account for the distinct characteristics of state and parameter estimations. The filter must be initially set, but the values of error, process noise, and measurement noise covariances are not often specified [33; 35]. Aged batteries are required for the filter to yield estimation results, and their degradation levels should preferably be wide and regular--for example, test data prepared at uniform intervals of 5% across the entire range of 30% in capacity fade--but unfortunately, they are not [27; 28; 31; 33; 35]. In addition, despite using Kalman filtering, estimation results are often provided without confidence intervals. The summarized discussion from the aforementioned studies is presented in Table 1. Tracking performance is typically evaluated by deliberately initializing the filter with incorrect state and parameter values, then measuring the convergence time and steady-state error. Each study claimssuperiority based solely on the steady-state error, generally measured by root mean square error (RMSE). However, in prior studies, filters have differed in structure, initialization, and covariance settings, and test data vary in battery degradation level and load profile. Such result-oriented information is insufficient to comprehensively assess the performance and validity of the previously reported estimators and to clearly understand them. Consequently, despite the abundance of prior studies, they are not readily applicable to practical BMS developments. With these drawbacks in mind, our methodology for co-estimating the SOC and capacity is presented in this paper, with an emphasis on validating the developed estimators against abundant test data. Test data are crucial for identifying model parameters and validating state estimates. In this study, we extensively use as many as 30 datasets. These datasets are generated from three load profiles typically applied to modern plug-in hybrid electric vehicles (PHEVs). Nearly 30% capacity degradation is realized in the datasets, which is relatively evenly distributed across ten levels. An enhanced self-correcting (ESC) model, the augmented Thevenin model with a hysteresis element, is applied to a DEKF. This filter updates parameters in the model, ensuring that the model maintains its accuracy in estimating states regardless of battery aging.
## 3 Results and Discussion
### PM5 Calculation  
PM5 semiempirical calculations were performed using the WinMopac 2003 program. The molecular models of representative conjugates are shown in Figure 1. The final heats of formation (HOF) for bioconjugates **1**-**10** are presented in Table 2.  
The lowest HOF values for conjugates **1**-**10**, comprising bile acids and sterols, were observed for cholic acid bioconjugates with gramine and caffeine, compounds **7** and **10**, respectively. A reduction in the number of hydroxyl groups in the bile acid skeleton resulted in a corresponding decrease in the HOF value. A comparable correlation was identified in the hydroxyl groups blocked by acetate groups.  
The OAc groups facilitate the formation of intramolecular H-H bonds and stable host-guest complexes. These complexes may be stabilized by H-H bonding or electrostatic interactions resulting from the OAc groups in the bile acid molecule. In the case of sterol derivatives, the lowest HOF values appear for cholestanol derivatives, which is a cons  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compound** & **HOF [kcal/mol]** \\ \hline
**1** & \(-\)67.9848 \\
**2** & \(-\)90.5785 \\
**3** & \(-\)205.2146 \\
**4** & \(-\)227.2060 \\
**5** & \(-\)156.9531 \\
**6** & \(-\)242.8710 \\
**7** & \(-\)327.7994 \\
**8** & \(-\)293.8754 \\
**9** & \(-\)378.4569 \\
**10** & \(-\)462.4638 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Heat of formation (HOF) of bioconjugates **1**-**10** from steroids and alkaloids.  
The spatial arrangement and interaction of the bioconjugates **7** and **10** are shown in Figure 2. The final heat of formation is \(-1655.7660\) kcal/mol for **7** and \(-2335.0571\) kcal/mol for **10**, respectively. The molecules of bioconjugates are arranged to form a specific kind of channel through which ions or neutral molecules can be transferred. Compensation charges occur only through intermolecular electrostatic interaction. This is a very good confirmation of the conclusion that interactions reduce HOF.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 2 Additive Manufacturing Technology for Metal Mirrors  
Additive manufacturing (AM), more commonly known as three-dimensional (3D) printing, is a rapid prototyping technology. It is a craft of manufacturing parts layer-upon-layer based on a digital model file. The specific process creates 3D models using computer-aided design (CAD) software and then slices and designs support through 3D-printing software. Finally, all the information is sent to a 3D printer, which stacks two-dimensional (2D) slices until the product is manufactured (Figure 5) [25].  
AM is a manufacturing method of \"bottom-up\" accumulation compared with the conventional machining mode. This makes it possible to manufacture novel geometries, complex structures, and customized parts limited by conventional processing methods [26]. AM offers some advantages, such as material savings, design freedom, parts almost in their final shape, design freedom, and reduction of time to market. It has been used wildly in aerospace, biomedicine, and automotive parts manufacturing.
# Notes on Bus User Assignment Problem Using Section Network Representation Method
## 3 Problem Formulation
### Demand Model  
Bus network demand is the outcome of the present transit supply and the transport activity systems, where complicated interactions between the two, along with the socioeconomic characteristics of users, result in journeys (i.e., \(d_{w}\)). Note that there is a need to define a subscript to identify the vehicle type since the bus is the sole means investigated in this study. The group of O/D pairings that are not zero \(W=\{w\triangleq(o,d),w\subseteq\mathrm{V}\times\mathrm{V}|d_{w}>0\}\).  
\[d_{w}(SEC,T)=\sum_{w}\sum_{d}O^{u}(o/z,h)D^{u}(d/O^{u},z,h)\;\;p^{u}(m/O^{u},D^ {u},z,h) \tag{3}\]  
The demand function in Equation (3) calculates the overall demand amount \((d_{w})\), which is a result of the socioeconomic (SEC) features and the current transportation system (\(T\)). \(O^{u}\) represents the total trip number made by passengers of category (\(u\)) and trip aim (\(z\)) originating from \(o\), whereas \(D^{u}\) is the total attraction for passengers of category (\(u\)) and trip aim (\(z\)). \(p^{u}\) is the distribution share that is dependent on origin, destination, trip aim, reference time (\(h\)), and vehicle (\(m\)).  
It is worth noting that as strategic bus network design is the concern of most BNDP studies, it is reasonable to evaluate the inflexible demand type with regard to sufficient reference time (i.e., one hour), the destination, and transit mode (only) to establish static traffic equilibrium in the transit network. Demand elasticity only appeared in the route choice evaluation. It is hypothesized that mixed en-route/pre-trip behavior results from a series of choices taken at various nodes in the network. It relates to the fundamental CLP in which each \(d_{w}\) is allocated a starting station (s) and an ending station (\(r\)).
## 4 Methods
### Implementation of Triangulation Methodology  
_Geostatistical Analysis (Interpolation) of Triangulation Methodology Results_  
The primary geodetic data processing, analyzed above, resulted in the estimation of the east and north velocity component of each GNSS station, as well as in the corresponding errors of these components. The triangulation methodology, implemented widely by various authors [98; 99; 100; 101; 102; 103], was considered as the most suitable between different methodologies (least square methodology, grid-based methodologies, etc.) in order to process these datasets. The principle of the triangulation methodology is the velocity components' combination of three GNSS stations, and their errors, leading to the total velocity calculation. In particular, these stations form a triangle, while each of them is considered as a triangle vertex. Then, the triangle is examined before and after the deformation process [57; 104; 105]. The undeformed triangle is represented by an inscribed inner circle, while the corresponding one is deformed by an ellipse, respectively [104; 105]. The inner circle and ellipse axes' combination lead to the strain parameters' extraction. It should be noted that the procedure mentioned above is based on mechanics principals (more details about the triangulation methodology are provided in the Supplementary Materials). As has already been mentioned, the wider Strymon basin area is surrounded by 16 permanent GNSS stations--a fact that leads to a large number of potential stations combinations. However, the total number of triangles that were eventually constructed and considered for the deformation estimation meet the following criteria: (i) the vast majority of triangles are equilateral in order to be affected equally by the deformation pattern of the three combined GNSS stations and (ii) the smallest possible distance between the GNSS stations is considered in order to efficiently observe the crustal deformation related to specific tectonic structures while significant interaction between the combined stations is documented at the same time [103]. Therefore, 216 triangles were constructed, represented by the corresponding triangle centroids. For all these centroids, a series of strain parameters was estimated, including ('GPS triangular calculator' software; [106]): (i) Maximum (MaHE) and Minimum (MiHE) Horizontal Extension, (ii) Velocity (V), (iii) Maximum Shear Strain (MaxSS), and (iv) Area Strain (AS). Beyond the point spatial distribution of these parameters, a geostatistical process was performed in order to determine the grid pattern deformation. The kriging interpolation methodology (linear approach) was considered to be the most reliable geostatistical methodology, as it smoothly distributes the parameter values within the study area while emphasizing the extreme value concentrations as well as the neutral-transition areas, which are vital for the tectonic interpretation, as they are associated with tectonic regime changes.