esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 3 Overview of Data and Preprocessing
### Feature Specification and Explanation  
This subsection provides a concise overview of the features used in this study, categorized by type and relevance. Table 6 summarizes features from the raw dataset, engineered attributes, and newly introduced variables.  
* **Performance Metrics**: Include key statistics such as goals, _xG_, assists, key passes, _xGChain_, and _xGBuildup_, reflecting direct and indirect offensive contributions.
* **Disciplinary Actions**: Captured through yellow/red cards, and normalized measures like per game or per minute rates, indicating player discipline and its impact.
* **Playing Time & Efficiency**: Combines games, minutes played, and rate-based metrics such as _gpm_ and _apm_ to evaluate contribution relative to time on the field.
* **Positional & League Influence**: Includes categorical variables for position, position encoding, and the _league_weight_ to model contextual competitiveness.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**League** & **Average Annual Salary (GBP)** & **UEFA Coefficient** & **League Weight** \\ \hline EPL (England) & GBP 3,433,450 & 106.624 & 1.0000 \\ La Liga (Spain) & GBP 1,950,083 & 87.739 & 0.6695 \\ Bundesliga (Germany) & GBP 1,568,559 & 82.581 & 0.5845 \\ Serie A (Italy) & GBP 1,477,455 & 92.668 & 0.6060 \\ Ligue 1 (France) & GBP 1,161,575 & 69.093 & 0.4620 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _league_weight_ calculation based on average annual salary and UEFA coefficients for top five leagues.  
* **Team & Nationality Encoding**: Encoded club and country data preserve privacy while allowing contextual analysis. This classification improves interpretability and ensures balanced integration of diverse factors in evaluating player performance and modeling outcomes.
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 2 Assessment of Hidden Ecological Factors
### Presentation of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement/Structural Model** & **Parameter** & **Estimate** & **Centroid (A)** & **Factorial (B)** & **Path Weighting (C)** \\ \hline Minimum temperature \(\longleftarrow\) FactorI & \(\lambda_{1,1}\) & 0.9479 & 0.9479 & 0.9495 & 0.9495 \\ Relative humidity \(\longleftarrow\) FactorI & \(\lambda_{1,2}\) & 0.9910 & 0.9910 & 0.9903 & 0.9903 \\ Maximum temperature \(\longleftarrow\) FactorII & \(\lambda_{2,1}\) & 0.8816 & 0.8816 & 0.8675 & 0.8675 \\ Solar radiation \(\longleftarrow\) FactorII & \(\lambda_{2,2}\) & 0.8735 & 0.8735 & 0.8873 & 0.8873 \\ Precipitation \(\longleftarrow\) FactorIII & \(\lambda_{3,1}\) & 0.9849 & 0.9849 & 0.9852 & 0.9852 \\ Wind speed \(\longleftarrow\) FactorIII & \(\lambda_{3,2}\) & 0.0017 & 0.0017 & 0.0031 & 0.0031 \\ FactorI \(\longrightarrow\) FactorII & \(\beta_{1,2}\) & \(-0.3248\) & \(-0.3248\) & \(-0.3302\) & \(-0.3302\) \\ FactorII \(\longrightarrow\) FactorIII & \(\beta_{2,3}\) & \(-0.2774\) & \(-0.2774\) & \(-0.2690\) & \(-0.2690\) \\ FactorI \(\longrightarrow\) Malaria incidence & \(\gamma_{1}\) & 0.9700 & - & - & - \\ FactorII \(\longrightarrow\) Malaria incidence & \(\gamma_{2}\) & 0.7700 & - & - & - \\ FactorIII \(\longrightarrow\) Malaria incidence & \(\gamma_{3}\) & 0.4900 & - & - & - \\ Maximum number of iterations & - & - & 12 & 15 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Factor scores for path coefficients in the PLS-PM using three weighting schemes.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Evaluation Indicators  
The average of the absolute error (MAE) is used to reflect the error between the predicted value and the ground truth:  
\[\text{MAE}=\frac{1}{D}\sum_{i=1}^{D}\abs{\left(\hat{\theta}_{i},\hat{\varphi}_ {i}\right)-\left(\hat{\theta}_{i},\varphi_{i}\right)}, \tag{6}\]  
where \(D\) is the number of sources within a sample, \(\left(\hat{\theta}_{i},\hat{\varphi}_{i}\right)\) is the predicted DOA, \(\left(\theta_{i},\varphi_{i}\right)\) is the ground truth, and \(\left(\hat{\theta}_{i},\hat{\Phi}_{i}\right)-\left(\hat{\varphi}_{i},\varphi _{i}\right)\) is the angular distance between the predicted and actual DOA.  
Root Mean Square Error (RMSE) measures the error between the predicted value and the true value and is sensitive to outliers in the data. The definition is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{D}\sum_{i=1}^{D}\bigl{(}\left(\hat{\theta}_{i}, \hat{\Phi}_{i}\right)-\left(\hat{\theta}_{i},\varphi_{i}\right)\bigr{)}^{2}}. \tag{7}\]The MAE and RMSE below are obtained for all samples. We also use Acc5\({}^{\circ}\), Acc10\({}^{\circ}\), and Acc15\({}^{\circ}\) to demonstrate the percentage of samples that are estimated correctly with their MAE less than 5\({}^{\circ}\), 10\({}^{\circ}\), and 15\({}^{\circ}\), which are defined as follows:  
\[\text{Acc}=\frac{K_{\text{c}}}{K}\times 100\%. \tag{8}\]  
where \(K\) and \(K_{\text{c}}\) are the total number of samples and the number of correctly estimated samples.
## 3 Results  
### Fatty Acid Composition in the Tested Samples  
Table 1 presents the GC analysis results of the major fatty acid contents of the analyzed commercial poppy seed oil samples and potential adulterants, including canola oil, maize (corn) oil, soybean oil, and sunflower oil. These specific adulterant oils were selected for their common use and affordability/cost-effectiveness when compared to poppy seed oil. After the GC analysis findings and literature review, 9 out of 39 commercial poppy seed oil samples were identified as adulterated. In addition, GC analysis showed the major fatty acids of poppy seed oil, which were palmitic (16:0), stearic (18:0), oleic (18:1n-9), linoleic (18:2n-6), and linolenic (18:3n-3) acids as presented in Table 1. Among these, oleic and linoleic acids were found to be the most predominant fatty acids in authentic poppy seed oil samples, constituting about 20.92 \(\pm\) 2.90% and 66.68 \(\pm\) 2.88%, respectively, while in Table 1, these are in agreement with values reported in the literature for authentic poppy seed oils [4,6]. As a possible adulterant, commercial canola oil samples showed notably higher oleic acid content (~64%) and linolenic acid content (~7%) compared to poppy seed oil, while commercial maize (corn) oils displayed higher levels of palmitic (12%) and oleic (32%) acids than poppy seed oil (Table 1). The analyzed commercial soybean oil samples revealed higher levels of palmitic, oleic, and linolenic acids, while commercial sunflower oil samples exhibited significantly higher oleic acid content (Table 1). Our GC results for canola, maize (corn), soybean, and sunflower oils were consistent with the literature [32, 33, 34]. Even though four oils (canola, maize (corn), soybean, and sunflower) were considered potential common adulterants for the poppy seed oil, none of them showed a similar fatty acid composition to the tested poppy seed oils (Table 1). Nevertheless, it is crucial to mention that their blends could potentially yield a fatty acid composition similar to the poppy seed oil. A 2006 study by [PERSON] and colleagues revealed that certain regional oil producers frequently blended the less expensive sunflower oil with the rarer and more costly poppy seed oil. This practice was motivated by economic factors and the similarity in the fatty acid profiles of the two oils [6]. However, the same researchers also pointed out that, although similar to poppy seed oil, \"natural\" sunflower oil falls under the category of \"linoleic acid-rich\" oils. It is worth mentioning that plant breeding has led to the development of sunflower cultivars with elevated concentrations of alternative fatty acids, notably palmitic and oleic acids [6]. Therefore, the observed variation in fatty acid composition between sunflower oil and poppy seed oil in our tested samples is understandable.
## 2 Overview of the Power Ramp-Rate Control Methods
### Ramp Saturation (RS) Method  
The RS is one of the most common and easy-to-implement PRRC methods. This method evaluates the PRR of the PV and absorbs or delivers energy from the ESS to saturate the PRR at the output. The RS method can be expressed as [12; 13]:  
\[\mathrm{P_{ess}[k]=}\left\{\begin{array}{rcl}\mathrm{P_{pv}[k-1]PRR_{max}-P_{pv }[k],}&\mathrm{when}&\mathrm{PRR_{pv}[k]~{}>~{}PRR_{max}}\\ -\mathrm{P_{pv}[k-1]PRR_{max}-P_{pv}[k],}&\mathrm{when}&\mathrm{PRR_{pv}[k]~{}< -PRR_{max}}\\ 0,&\mathrm{when}&\left|\mathrm{PRR_{pv}[k]}\right|~{}\leq~{}PRR_{max}.\end{array}\right. \tag{3}\]  
As \(\mathrm{PRR_{max}}\) decreases, the ESS VSC will require more energy to smooth power transitions at the output. Thus, the DoD of the ESS will increase because the SOC will reach lower values.
# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Other CADD Applications
The de novo sequence redesign model does not suffer from a restrictively cumulative effect for future directions. Currently, mainly qualitative or semi-quantitative methods are used in MD simulation to calculate the binding affinity with accurate free energy. Accurate free energy prediction methods, such as alchemical free energy method (AFEM) and absolute binding free energy (ABFE), greatly improve the efficiency of CADD, although it is extremely demanding and costly [139]. Free energy prediction models that are more accurate and efficient than molecular docking and less computationally demanding than AFEM, including Poisson-Boltzmann surface area (MM /PBSA) and Born surface area (MM /GBSA) generalized molecular mechanics, have already been used in current research, such as the discovery of RAS inhibitors [140]. The discovery of allosteric binders of RAS can also be empowered with free energy prediction methods. For example, the naphthyridinone scaffold was identified as a novel covalent allosteric binder for KRAS\({}^{\text{G12C}}\) in free energy perturbation models [141]. As a more promising CADD method, machine learning (ML) algorithms, such as neural networks and the transformer, are developing suitable models to predict target protein structure and discover potential compounds. Other CADD methods, including molecular docking, QSAR, and pharmacophore modeling, also benefit from machine learning algorithms [142; 143]. In molecular docking, ML is used for scoring functions that translate protein-ligand interactions into descriptors. In this way, effective scoring function models such as a NN Score and a RF Score can be built. In QSAR and pharmacophore modeling, ML improves the accuracy of molecular comparison and descriptor identification. It is foreseeable that machine learning algorithms with high efficiency will be increasingly used for RAS inhibitor discovery in the future.
## 3 Thermography Data Collection and Preparation
### Transformation Process for Aligning Real and Synthetic Thermographic Image Datasets
 \tag{3}\]  
The random variable matrix \(RM\) is a matrix composed of values randomly selected from a uniform distribution between 0.9 and 1.1. The random variable matrix \(RM\) is expressed as shown in Equation (4). \[RM= \begin{bmatrix}RM_{1,1}&RM_{1,2}&\cdots&RM_{1,W}\\ RM_{2,1}&RM_{2,1}&\cdots&RM_{2,W}\\ \vdots&\vdots&\ddots&\vdots\\ RM_{H,1}&RM_{H,2}&\cdots&RM_{H,W}\end{bmatrix}  \tag{4}\]  
Figure 7: The 3D surface graph and Histogram of the Restructured \(SI^{pz}\) after passing through the Product Layer and \(SI^{nz}\) after Passing through the Noise Layer. In the noise layer, the reconstructed \(SI^{yz}\) is combined with the style transfer matrix \(STM\) and the random variable matrix \(RM\), which uses the Hadamard product operation, as shown in Equation (5). \[nz_{i,j}=RM_{i,j}\left(0.5\times pz_{i,j}+0.5\times STM_{i,j}\right) \tag{5}\]  
\(SI^{yz}\) is reconstructed into the \(SI^{yz}\) matrix, consisting of the \(nz\) values obtained through the computational process in the noise layer. As shown in Figure 7b, the 3D surface graph and histogram of the reconstructed \(SI^{yz}\) distribution confirm that it has been transformed to exhibit a distribution similar to that of the real specimen thermographic image \(RI\). The entire synthetic specimen temperature data were fed into the transformation module, and the resulting \(SI^{yz}\) matrix was converted into an image and assigned to the synthetic thermographic image dataset, which was then used as training data for the deep learning model. For the RD29 specimen, images showing the synthetic thermographic images before and after preprocessing, which vary according to the defect size, are attached in Appendix A. Table 5 shows the distribution of data by class in the synthetic thermographic image dataset. The non-defect class is composed of 14% of the non-defect class data from the real thermographic image dataset.
# A Virtual Experiment for Learning the Principle of Daniell Cell Based on Augmented Reality
## 2 System Development
### Operating Procedure  
The learning objective of the virtual experiment is to design an electrochemical battery using the electrolyte solutions and observe the electrochemical reaction to understand its operating principle. The experimental equipment includes a dropper, an Erlenmeyer flask, a U-shaped tube, tweezers, two beakers, and a galvanometer with black/red connecting wires and alligator clips. The operating procedure of the Daniell cell experiment is described below:  
1. Add 200 mL of zinc sulfate solution and 200 mL of copper sulfate solution (0.1 M) to the beakers.
2. Put the zinc strip in the zinc sulfate solution and the copper strip in the copper sulfate solution.
3. Connect the zinc strip and the galvanometer with a black alligator clip and connect the copper strip and the galvanometer with a red alligator clip.
4. Pour 200 mL of potassium nitrate solution (1 M) into the U-shaped tube and plug its open ends with cotton. Keep air out of the U-shaped tube to avoid the blocking of current.
5. Put both ends of the U-shaped tube (salt bridge) into the two beakers.
6. When the zinc strip and copper strip are connected to the galvanometer, determine if there is current and its direction by observing the pointer on the galvanometer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Macroscopic and Tangible Subjects** & **Submicroscopic Explanation** & **Symbolic Representation** \\ \hline \(\bullet\) & Zinc ion Zn\({}^{2+}\) formed by the atom & \(\bullet\) & Oxidation \\ \(\bullet\) & Using AR cards to set up the & Zn losing electrons & equation: Zn \(\rightarrow\) Zn\({}^{2+}+2e^{-}\) \\ experimental environment & \(\bullet\) & Copper ion Cu\({}^{2+}\) reduced to copper & \(\bullet\) & Reduction equation: \\ \(\bullet\) & Observing phenomena: pointer on the galvanometer, color of copper & \(\bullet\) & KNO\({}_{3}\) dissociated into nitrate ions & Cu\({}^{2+}+2e^{-}\rightarrow\) Cu \\ sulfate solution & & NO\({}_{3}^{-}\) and potassium ions K\({}^{+}\) & \(\bullet\) & Dissociation in salt bridge: \\  & & & KNO\({}_{3}\rightarrow\) K\({}^{+}+\) NO\({}_{3}^{-}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three levels of chemistry knowledge for designing the virtual experiment.  
After connecting the two beakers with the salt bridge, observe if the pointer on the galvanometer moves to determine if there is current generated by the Daniell cell, and also measure whether the mass of the zinc strip and the copper strip has changed.
# Optimizing Service Placement for Microservice Architecture in Clouds
###### Abstract  
As microservice architecture is becoming more popular than ever, developers intend to transform traditional monolithic applications into service-based applications (composed by a number of services). To deploy a service-based application in clouds, besides the resource demands of each service, the traffic demands between collaborative services are crucial for the overall performance. Poor handling of the traffic demands can result in severe performance degradation, such as high response time and jitter. However, current cluster schedulers fail to place services at the best possible machine, since they only consider the resource constraints but ignore the traffic demands between services. To address this problem, we propose a new approach to optimize the placement of service-based applications in clouds. The approach first partitions the application into several parts while keeping overall traffic between different parts to a minimum and then carefully packs the different parts into machines with respect to their resource demands and traffic demands. We implement a prototype scheduler and evaluate it with extensive experiments on testbed clusters. The results show that our approach outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic.  
service placement; cluster scheduling; network optimization; resource management; microservice architecture; cloud computing +
Footnote †: journal: Applied Sciences
## 3 Results
However, as seen in Figure 9, VNA measurements of the power that returns to the source (S11) in the transmitter circuit reveal a drop in impedance (peak in transmission) at 55.5 kHz, as we can see in Figure 10. Measurements of the power absorbed by the VC circuits (S21) show a distribution of resonance frequencies, with VC 1, 2, 4 and 5 resonating at 55.5 kHz, while coil 3 resonates at 60 kHz. These measurements do not fully represent the actual operation of the charging process. Since the power of the VNA is insufficient for providing the voltage required for activating the rectifying circuits, we were forced to  
Figure 8: Energy transfer efficiency in the small VC mode. Blue curve: simulation results. Yellow dots: measurement results. short-circuit the other VC circuits every time a specific VC circuit was measured. In such a case, each coil \"sees\" all others and its induced current flows so as to cancel the change in flux in the others. The problem becomes a coupled set of resonators which requires special optimization [21] to be performed in future studies. As a result, each coil inductance is practically enhanced by this mutual-inductance. For a given capacitor, this increase in inductance reduces the resonance frequency, in accordance with our findings. Although this effect of the mutual VC coupling is less pronounced in the actual operation, we intend to extend this work to include the effect of the mutual inductance in the design so that the resonance frequency is brought back to the desired range. We also expect to achieve a better quantitative match between the simulation and the experimental results. The low Q-factor, defined as \(\mathrm{f}/\Delta\mathrm{f}\), can be extracted from Figures 9 and 10. These values are low (on the order of 10) and result from the self-inductance of the small VC model's coil being approximately two orders of magnitude smaller than those presented in Table 2 for the full-scale model. The load resistor was kept at 0.1 \(\Omega\) for both VC models. Significantly higher Q-factor values, around 850, are expected for the full-scale VC model.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 2 Time-Cooperative Reentry Problem for Multi-Hypersonic Vehicles
### Constraint Conditions  
To ensure the safety of the reentry maneuvering flight of a hypersonic vehicle, each vehicle needs to meet the hard constraints of heat flux rate, dynamic pressure, and overload during the reentry flight. The three constraints can be expressed as follows:  
\[\left\{\begin{array}{l}N_{l}=\sqrt{L^{2}+D^{2}}/m\\ q=0.5\rho V^{2}\\ \dot{Q}=K_{Q}\rho^{0.5}V^{3.15}\\ \end{array}\right.. \tag{3}\]  
where \(N_{l}\), \(q\), and \(\dot{Q}\) denote the overload, dynamic pressure, and heat flux rate, respectively, and \(K_{Q}=7.9686\times 10^{-5}\,\mathrm{Js}^{2}/(\mathrm{m}^{3.5}\mathrm{k}\mathrm{g}^{ 0.5})\) is the heat flux constant.  
In addition, when the HGV enters the glide phase, it also needs to satisfy the QEGC. The specific form is as follows:  
\[\frac{L}{m}\cos\sigma+(\frac{V^{2}}{r}-g)=0. \tag{4}\]  
The reentry flight control constraint can be written as below:  
\[\left\{\begin{array}{l}|\alpha|-\alpha_{\mathrm{max}}\leq 0\\ |\sigma|-\sigma_{\mathrm{max}}\leq 0\\ \end{array}\right.. \tag{5}\]  
where the subscript \"max\" denotes the maximum value of the control. The control quantity can be written in vector form as \(\mathbf{u}=[\alpha,\sigma]^{\mathrm{T}}\).  
The purpose of hypersonic reentry trajectory optimization is to control the aircraft to reach the specified area or the specified target point. In this paper, the mission target is specified as the terminal area, and the terminal constraint is recorded as follows:  
\[\left\{\begin{array}{l}\left|r(t_{f})-r_{f}\right|\leq\Delta r\\ S(t_{f})-S_{f}\right|\leq\Delta S\\ V(t_{f})-V_{f}\right|\leq\Delta V\\ \end{array}\right.. \tag{6}\]  
where \(t_{f}\) is the terminal time, the subscript \"\(f\)\" denotes the expected state value at the terminal time, and \(\Delta r\), \(\Delta S\), and \(\Delta V\) denote the acceptable error of the altitude and range at the terminal time, respectively.  
In addition, the cooperative reentry problem studied in this paper considers the attack time consistency constraint:  
\[t_{f,i}=t_{f},i=1,2,\cdots,n. \tag{7}\]
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Challenges
#### 4.1.2 Robust and Scalable Solutions  
Realising robust, dynamic, and adaptive O-RAN-based IoT solutions that shrink or scale based on the operating environment requirements is hectic. This is because of the diverging QoS and reliability criteria to be satisfied by the emerging systems. For example, the systems are expected to scale either vertically or horizontally to accommodate the increasing number of connected devices and be equipped with efficient network management mechanisms for handling their dynamic resource allocation based on the demands of IoT applications.  
Similarly, as the system complexity grows, the attack surface for potential security threats also increases. Conflicts may also emerge when the IoT devices employ incompatible communication protocols with the O-RAN architecture.  
Scalability challenges may also arise when IoT systems need to ensure seamless cross-domain integration with legacy networks, cloud, or data centers. In addition, the vast amount of data generated will require scalable analytics to generate relevant insights that benefit the operating environment. The entire system lifecycle management strategies need to be scalably dynamic and efficient, starting from onboarding, provisioning, and firmware updates. Optimized communication protocols and mechanisms that extend the battery life of these devices will also be required. The system will need to handle a massive variety of new IoT device applications and services to be developed in the future. In other words,resilient solutions that scale to the operating environment dynamics remain open for the research community to solve.
# Towards a Collection of Security and Privacy Patterns
## 4 Privacy Patterns
### Undetectability
#### 4.3.2 Steganography  
A key enabler of undetectability is the use of steganography, selecting specific steganographic techniques depending on the medium used for communication. Steganography (from the Greek word \"\(\Sigma\tau\epsilon\gamma\alpha\
u\gamma\rho\alpha\sigma\iota\alpha\)\", translated to \"covert writing\") focuses on techniques allowing undetectable transmission of messages by embedding them into innocuous carriers. These carriers are referred to as \"stego-mediums\" and they may include text messages, images and video, audio, and others (e.g., using unused space in storage devices or the noise in communication channels). Depending on the stego-medium, different steganographic techniques can be employed.  
Therefore, in contrast to cryptography, which is used to hide the contents of a message, steganography focuses on hiding the message itself.