esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation
### Evaluation Metrics  
The evaluation metrics that we adopted in this work were accuracy (ACC) and the quadratic weighted kappa (QWK). Briefly, ACC measures the percentage of correct predictions on the test data. As shown in Equation (3), it can be calculated by dividing the number of correct predictions, including true positives (TPs) and true negatives (TNs), by the number of total predictions, namely, the test set size. On the other hand, QWK is used to measure the amount of similarity between predictions and ground truth. The definition of QWK involves the following elements for an N-class classification problem: \(O\) is an \(N\times N\) histogram matrix (i.e., the confusion matrix) where \(O_{i,j}\) corresponds to the number of ISUP grade \(i\) that receives a predicted value \(j\); \(W\) is an \(N\times N\) matrix of weights that represent the differences between actual and predicted values, and \(W_{i,j}\) is given in Equation (4); \(E\) is another \(N\times N\) histogram matrix of expected outcomes calculated as the outer product between the actual rating's histogram vector and the predicted rating's histogram vector. Both \(E\) and \(O\) are normalized such that each has a sum of 1. Equation (5) shows that how QWK can be calculated. QWK is more informative than ACC when working with imbalanced data. For our task, Table 1 shows that samples with ISUP grades 0 and 1 are more than the rest samples combined, indicating a moderate imbalanced sample distribution. To this end, QWK was chosen as the secondary performance metric.  
\[ACC=\frac{TP+TN}{\text{test set size}} \tag{3}\]  
\[w_{i,j}=\frac{(i-j)^{2}}{(N-1)^{2}} \tag{4}\]  
\[QWK=1-\frac{\sum_{i,j}w_{i,j}O_{i,j}}{\sum_{i,j}w_{i,j}E_{i,j}} \tag{5}\]
## 2 Space Debris--A Primer
### The Not Useful Objects  
As can be imagined from the discussion in the preceding paragraphs, satellite collisions are one source of the debris circling the Earth. This debris is then a problem of our own creation, whether intended or not. So far, we have been lucky--we have not had an explosive growth in the amount of debris up in orbit; the growth has been steady, but not explosive. Nations that launch missions are aware of the debris problem and the need to limit the creation of additional debris. The steps taken to avoid the unnecessary creation of space debris, as well as the effectiveness of these mitigation strategies, vary from nation to nation.  
Figure 1: The crowded night sky (Image © 2024 [PERSON]/AmazingSky.com; used by permission).  
For most spacecraft launches, a significant component of mission planning has always been a review of the entire mission and its orbits from the perspective of possible encounters with larger pieces of orbital debris. We did have a wake-up call, though, in 2009, when a spent Cosmos booster hit a (very much alive and functioning) Iridium satellite, destroying the satellite and creating approx. 2000 large (i.e., 10 cm or larger) pieces of space debris. A replacement was soon moved into position, but this event effectively served as a notice to the space community that such an event could happen again.  
Figure 2 below shows that the debris population has been, overall, growing steadily, and that any time there is some artificial perturbation of the environment (as a result of an anti-satellite test, or ASAT, for example), the number of larger objects in Earth orbit leaps up by several thousand. For example, in Figure 2, the labeled increases in the number of LEO objects are sudden increases due to (1) the 2007 Fengyun ASAT test by China, (2) the accidental collision between Iridium 33 and Cosmos 2251 in 2009, and (3) an ASAT test conducted by Russia in late 2021. In early 2019, India performed an ASAT test as well, but the debris created by this test quickly decayed and fell back to Earth. By late summer, fewer than 50 trackable debris particles remained in orbit out of the several hundred originally created [11]. Also in Figure 2, the frequent minor perturbations in the plotted curves are likely due to debris fragments resulting primarily from debris-on-debris collisions in Earth orbit. Figure 2 also shows a recent steady increase in the number of LEO objects due to the steady launching of mega-constellation satellites since 2019.
###### Abstract  
A square patch metasurface is designed, simulated, fabricated, and experimentally tested to investigate polarization characteristics quantitatively. The metasurface consists of one layer unit cell in the form of a square patch with one via and a lumped resistor, which is used for harvesting RF (radio frequency) energy. FR4 dielectric is used as a substrate supported by a metal ground plane. Polarization-dependent properties with specific surface current patterns and voltage dip are obtained when simulating under normal incidence of a plane wave. This characteristic results from changes in surface current conditions when the polarization angle is varied. A voltage dip appears at a specific polarization angle when the surface current pattern is symmetrical. This condition occurs when the position of the lumped resistor from the center of the patch is perpendicular to the linearly polarized incident electric field. A couple of 10 \(\times\) 10 arrays with different resistor positions are fabricated and tested. The experimental results are in good agreement with the simulated results. The proposed design demonstrates a symmetric unit cell structure with one via and a resistor that exhibits polarization-dependent behavior for linear polarization. An asymmetric patch design is explored through both simulation and measurement to mitigate polarization dependence by suppressing the dip behavior, albeit at the expense of reduced absorption efficiency. This study provides a complete polarization analysis for both symmetric and asymmetric patch metasurfaces with a single via and a single lumped resistor, and introduces a predictive relation between the position of the resistor relative to the center of the patch and the resulting voltage dip behavior.  
metasurface; polarization-dependent; RF energy harvesting Article
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 7 Discussion  
### Rank  
In this experiment, the highest prediction accuracy was often observed when the rank was either 6 or 7, suggesting that an appropriate rank was selected. Many matrix  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Rank**} & \multicolumn{5}{c}{**RMSE**} \\ \cline{2-6}  & **Matrix100** & **Matrix80** & **Matrix60** & **Matrix40** & **Matrix20** \\ \hline
1 & 0.463 & 0.490 & 0.578 & 0.524 & 0.715 \\
2 & 0.469 & 0.554 & 0.605 & 0.432 & 0.507 \\
3 & 0.453 & 0.352 & 0.602 & 0.330 & 0.519 \\
4 & 0.279 & 0.261 & 0.370 & 0.394 & 0.363 \\
5 & 0.278 & 0.204 & 0.289 & 0.468 & 55,392,383 \\
6 & 0.278 & 0.166 & 0.138 & 0.494 & 0.369 \\
7 & 0.176 & 0.166 & 0.286 & 0.411 & 0.393 \\
8 & 0.255 & 0.307 & 0.290 & 0.493 & 0.668 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction results of PPV using WNMF (Comparison between Matrix100 and Matrix20–80).  
factorization methods, including WNMF, have the characteristic of changing prediction accuracy depending on this rank. Generally, it is said that as the rank number increases, the prediction accuracy improves, and as the rank number decreases, the prediction accuracy drops [23]. However, an excessively large rank number might increase the computational cost, possibly reducing the prediction accuracy. Thus, selecting the appropriate rank number is crucial. In this experiment, the prediction accuracy was compared by changing the rank number from 1 to 8. Given that the highest prediction accuracy was observed at ranks 6 and 7, it can be inferred that the appropriate rank was chosen. Nonetheless, such a trial-and-error method requires significant time and computational cost. Assuming that the proposed method is used in actual settings, this approach could pose a significant challenge. Therefore, future research needs to develop a method to estimate the rank before executing WNMF. Already, cases where the rank is estimated before matrix factorization have been reported [23]. Building on these, the development of an optimal rank estimation method and the improvement of the proposed method are future challenges.
## 6 Discussion and Future Work
### Future Work  
Future development of the VARIoT database will be proceeded in three ways as a response to the existing challenges and limitations of the database. Still, we can improve the quality of information as well as its comprehensiveness. First, as there are still changes in the sources or in standards (for example, the recent introduction of CVSS version 4.0), maintenance works and small developments are needed. Thanks to that work, the VARIoT database will stay up-to-date both in the context of information standards regarding vulnerabilities and exploits as well as in the context of the ability to harvest information from sources which are subject to change.  
Secondly, an important part of the work will be performed by adding new, promising sources. Even though they can be dedicated to specific types of vulnerabilities or exploits or specific types of assets, if they would provide high-quality information, it will be worth adding them. However, it cannot provide information en masse, and the amount of work needed to obtain information from such sources is proportionally greater than that of bigger sources.  
Lastly, we plan to add new functionalities to the database. An example of such a new functionality is the development of enhanced search mechanisms (using search engines such as Google, Bing, or DuckDuckGo) for descriptions of new vulnerabilities or exploits in unstructured sources (such as blog posts). By automatically extracting such information and adding it to the database, we could significantly enhance both the number of vulnerabilities and exploits in the database as well as the comprehensiveness of information related to a specific vulnerability or exploit. We also plan to develop similar mechanisms aimed at harvesting information posted on sites on the dark web. This could be very beneficial, as it could potentially provide information about zero-days (i.e., vulnerabilities which are not patched). The prospective functionality also involves opening the database for user contributions. It could be beneficial as, due to various reasons, not all information in the VARIoT database is perfect. Accepting feedback from the community (about all information) will contribute to further improving the quality of the information in the database. Moreover, we will develop trust mechanisms (automatic evaluation of the reliability of information). This will enable us to assess how much specific information can be trusted and whether it should be used to make important decisions (for example, regarding risk management).
## 2 Initiation of the Project
### Quality Assurance and Documentation  
Quality Assurance (QA) is the set of measures oriented to make sure that the work done during the project is conducted consistently and according to the stakeholders' expectations. The standard defines the workmanship, processes and materials used during the project as well as the procedures and means of check the activities throughout the project. Additionally, QA allocates a contingency plan to manage any deviation from the original plan.  
ESA's standard on Quality Assurance [34] for satellites focuses on certain activities that will ensure the quality of the design and development process, but does not specify how to perform such activities. The activities can be categorized in eight general principles, i.e., controlling critical items, controlling nonconformance, managing alerts, establishing the means of proving acceptance authority, tracing parts and responsibilities, controlling metrology and calibration procedures, establishing measures on handling, storage and preservation of components, and defining statistical quality control procedures for batch production.  
Documentation plays a crucial role in performing QA as well as maintaining (and disseminating) the knowledge constructed throughout a project. It is also an effective way to keep track of poor practices or mistakes done during the development process. ESA's standard on Quality Assurance requires preparing an extensive set of documentation for every space project. However, some interpretations of such standards may lead a team to produce redundant documentation. Redundancy in documentation can not only reduce the efficiency by causing extra work of creating repetitive documents, but also cause confusion due to version mismatch. Therefore, care must be taken to avoid unnecessary redundancy and to update all sources of information when introducing changes into the documentation.  
The ESA's standard is mainly tailored to conventional space missions, due to their advanced complexity and high costs and associated risks. Nevertheless, even partial implementation of the standard can have a significant impact on improving the success rate of CubeSat missions, thus enhancing the chance of continuing CubeSat programs within small institutions. However, the application of such a standard could entail notable amounts of documentation that may end up challenging for some (small) CubeSat teams. To address this problem, ESA is currently working on the simplification of the QA requirements and documentation for CubeSats missions. Examples of some recent works are mentioned in References [35; 36].
# Superelastic Shape Memory Alloy Honeycomb Damper
## 2 Superelastic SMA Honeycomb Damper
### Theoretical Analysis of a Singular Hexagonal Cell  
Figure 4 illustrates a regular hexagonal cell that originates from a honeycomb structure. The figure illustrates that the cell wall has a length of \(l\) and a thickness of \(L_{h}\). The cell undergoes a reshaping process through bending when subjected to an in-plane load (specifically, along the y-axis in this study). As a result, each cell wall can be regarded as a nonlinear beam capable of withstanding significant displacements and rotations [10].  
According to the local coordinate system depicted in Figure 4, the honeycomb beam is simplified as a straight beam with a thickness of \(L_{h}\) and a length of \(l\). The beam will bend and move when the honeycomb is loaded (point \(a\) to point \(b\)). \(v(x)\) and \(h(x)\) are used to express the displacement vector of the points along the longitudinal and transverse directions. The Euler-Bernoulli beam theory postulates that the cross-section of the small strain beam stays perpendicular to the central axis both before and after deformation. In other words, when the beam experiences warping and transverse shear deformation, the transverse strain can be disregarded, and the plane stays flat and perpendicular to the central axis after deformation. According to the above assumption, the strain of any point with an initial local coordinate \((x,y)\) is given by:  
\[\varepsilon(x,y)=\gamma(x)+yk(x) \tag{1}\]  
where \(\gamma(x)\) is the axial strain function of each point on the central axis and \(k(x)\) is the bending curvature. The axial strain, \(\gamma(x)\), and bending curvature, \(k(x)\), can be computed by the vertical and horizontal displacement components as  
\[\gamma(x)=\sqrt{\left(1+\frac{dh}{dx}\right)^{2}+\left(\frac{dv}{dx}\right)^{ 2}}-1 \tag{2}\]  
\[k(x)=\frac{\left[\frac{dvd^{2}h}{dxdx^{2}}-\left(1+\frac{dh}{dx}\right)\frac{d^ {2}v}{dx^{2}}\right]}{\left[\left(1+\frac{dh}{dx}\right)^{2}+\left(\frac{dv}{ dx}\right)^{2}\right]} \tag{3}\]  
where \(v(x)\) and \(h(x)\) are the vertical and horizontal displacement components, respectively.  
Figure 4: Honeycomb beam in local coordinate system.  
The internal virtual work contribution of each node in the weak form of the equilibrium equation can be written as:  
\[\delta W_{l}^{I}=\int_{0}^{1}(N\delta e+M\delta k)dx \tag{4}\]  
\[N\equiv\int_{-L_{k}/2}^{L_{k}/2}\sigma dy \tag{5}\]  
\[M\equiv\int_{L_{k}/2}^{L_{k}/2}\sigma dydy \tag{6}\]  
where \(N(x)\) and \(M(x)\) are the combined axial forces and bending moments on the honeycomb wall, respectively, and \(\sigma\) is the axial stress on the honeycomb wall. Corresponding to the Euler-Lagrange equations of Equations (1)-(3), the consistency between them and the equilibrium equation has been proved in detail in the literature [10].  
It should be stated that such a three-dimensional deformation process can be completely solved by the method proposed by [PERSON] et al. [37].
## 4 Practical Implementation: Identification and Separation of Leukemic Cells from Healthy Ones
### Coupling with ANN  
The DMP model accounts for the physics and mechanics of the system, but, by itself, cannot discriminate between flexible and rigid cells. To achieve this goal, several additional particles (type-6) are added to the model. By taking advantage of the particle-neuron duality, we can make these particles behave like artificial neurons and, by connecting them in layers, we can make these neurons behave like an ANN. Figure 8 illustrates the logic behind the resulting deep multiphysics (DMP + ANN) model.  
Types 1-3 particles (DMP particles) only exchange forces, i.e., not information. They possess the property of position (which changes by the effect of forces), but do not have a neuron-like output. Type 1-3 particles, therefore, are pure DMP particles.  
Figure 7: Schematic representation of the particle types used in the simulations.  
Type-4 particles represent, at the same time, the root of the cilia and the input layer of the ANN. They exchange forces with the other particles in the computational domain (types 1-5) and information with the neuron particles (type-6). Type 4 particles, therefore, are hybrid particles with both DMP and neural properties.  
Type-5 particles, and in particular the particles next to the wall separating the two chambers (see Figure 6), are also hybrid. On the one hand, they constitute the output layer of the ANN and, as such, exchange information with the hidden layers. On the other hand, they belong to the DMP computational domain and exchange forces with other DMP particles. The particle-neuron duality integrates these two properties and, according to the information coming from the hidden layer, the forces acting on type-5 particles are switched on or off to open or close the separating gates.  
Finally, type-6 particles are pure neurons and are used to represent the hidden layers of the network. They exchange information with (i) other type-6 particles from another hidden layer, (ii) type-4 particles from the input layer and (iii) type 5 particles from the output layer. Since they do not exchange forces, they do not possess a position or a velocity property that can be modified by the forces.  
In the next section, I call ANN the combination of type-6 particles plus type-4 and type-5 when they act as respective input and output layers. I call 'DMP model' the combination of type 1-3 particles plus type-4 and type-5 when they act as DMP-particles.
# Game Approach to HDR-TS-PV Hybrid Power System Dispatching
## 3 **Hdr-Ts-PV** Game Model
### Characteristic Functions of HDR-TS-PV Cooperative Game  
In the non-cooperative game, each player can only achieve their best profits through their independent dispatching strategies. By forming coalitions, players have more options for coordinated dispatching, thus expanding the strategy set. Based on the original profit model, \(HDR\) and \(TS\) power stations can also profit by providing reserves for the \(PV\) plant, but their primary strategies are to dispatch output power. Therefore, the strategy sets of the \(HDR\) and \(TS\) power stations are still \(\Omega_{H}\) and \(\Omega_{T}\), respectively. The \(PV\) plant can reduce the curtailed power and penalty for abandoning the load by purchasing reserves; therefore, its strategy set also includes  
\[\Omega^{\prime}_{P}=\left\{R^{t}\big{|}\big{|}R^{t}+\xi^{t}_{PV}-P^{t}_{PV} \big{|}\leq\sigma P^{t}_{PV}\ \right\}, \tag{22}\]  
where \(R^{t}\) represents the reserves purchased by the \(PV\) plant, and \(\sigma\) represents the power fluctuation rate allowed by the grid. In the case of purchasing the reserves, the constraint in Equation (22) always limits the \(PV\) plant's output power within a range of \(\pm\sigma\) around the predicted value. Therefore, \(P^{t}_{PV}\) can be considered as the credible output power of the \(PV\) plant, and its corresponding installed capacity is the credible capacity of the \(PV\) plant, denoted by \(P_{PV}\). \(\xi^{t}_{PV}\) represents the actual output power corresponding to the credible capacity part of the \(PV\) plant.  
Because the actual installed capacity of the \(PV\) plant may be greater than the reserve capacity provided by the \(HDR\) power station, the installed capacity can be divided into non-cooperative and cooperative parts in the cooperative game.  
\[P^{install}_{PV}=P_{PV}+\overline{P}_{PV}, \tag{23}\]  
In a non-cooperative game, the players cannot use their respective resource characteristics and operating modes to achieve complementary advantages. Through cooperation, players can fully coordinate the dispatching of resources to maximize profits. The operating models and the profit models can be innovated to expand payoff sources, thereby increasing the overall payoff of the hybrid power system. The characteristic function of each coalition is described below.
# Application of Machine Learning to Assess the Quality of Food Products--Case Study: Coffee Bean
###### Abstract  
Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.  
Keywords:artificial intelligence; deep learning; convolutional neural networks (CNNs); coffee bean +
Footnote †: journal: Applied Sciences