esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MLOps Pipeline Design
### Automatic Model Deployment and Update  
Automated model deployment and update is a process that automates the integration of machine learning models into production environments and the management of updates to these models. Tools that can be used in the automatic model deployment and update phase include continuous integration and continuous deployment (CI/CD) tools such as Jenkins, Spinnaker, ad Argo CD. There are container management systems such as Kubernetes and Docker, and platforms that offer machine learning-specific features such as MLflow and TFX (TensorFlow Extended). These tools enable the model to be seamlessly integrated into the live environment, monitor its performance, and make automatic updates when necessary.  
During the automatic model deployment and update phase, Docker ensures that models are packaged in containers and run consistently and isolated in any environment. This process starts by using a structure in the Dockerfile where the application and its dependencies are defined. A Docker image is created and can be stored in registries such as Docker Hub. Then, using the created image, containers are quickly deployed and run in the production environment or development and test environments. This method ensures the consistency and portability of the application across different environments. Figure 7 shows the Docker architecture used in the model distribution structure used in this study.  
Figure 7: MLOps Docker architecture.  
In the architecture shown in Figure 7, hardware with an i7 processor and 16 GB RAM capacity was used as the infrastructure. Windows 10 was preferred as the operating system. In the Docker container engine section, a structured container containing dependencies for artificial intelligence models was created. Libraries such as NumPy and scikit-learn were defined as requirements. Efficient use and sharing of resources are ensured without requiring separate operating systems. Thanks to the container engine, system resources are effectively shared through the Docker engine while applications are run in isolated environments. This approach contributed to speeding up deployment and increasing application consistency. In the APP section, an API was developed using the FastAPI library. API requests trigger the base model in the container engine section. When the base model Docker is first started up, it is loaded into memory and stands ready for API requests.
## 4 Conclusions  
In this study, a no-reference air traffic control speech quality assessment method was developed by optimizing the details of shallow feature extraction and improving the ResNet-BiLSTM network structure. The experimental results demonstrated that the proposed model accurately estimates the quality of the air traffic control speech and shows a high consistency with subjective evaluations. This provides a feasible solution for air traffic control speech quality assessment.  
In the future, we will continue our commitment to enhancing and expanding the practicality and depth of this research. Specifically, we have planned the following initiatives: Firstly, our focus will be on expanding the real air traffic control speech database to encompass a wider range of scenarios and diverse categories of speech samples. This expansion aims to improve the robustness of our model, ensuring its exceptional performance across various contexts. Secondly, we intend to develop a rating standard tailored to air traffic control speech, following the subjective evaluation method proposed by ITU, and apply this standard to rate our speech database. Through subjective evaluations, we will achieve a more accurate quantification of speech quality and validate our model's performance in real-world scenarios. Additionally, we will continuously refine and optimize the network structure to enhance its capability to accurately predict air traffic control speech quality scores in complex speech environments. This will involve improvements in feature extraction and deep learning architectures to better capture the critical features of speech quality. Lastly, we will conduct an in-depth analysis of the specific factors that influence air traffic control speech quality and convert them into actionable metrics. This will facilitate a deeper understanding of speech quality and further improvements in this domain.  
Conceptualization, Y.W. and G.L.; methodology, G.L.; software, Q.F.; validation, Y.W., G.L., and Q.F.; formal analysis, Y.W.; investigation, G.L.; writing--original draft preparation, Y.W. and G.L.; writing--review and editing, Y.W., G.L., and Q.F.; project administration, Y.W.; funding acquisition, Y.W. and Q.F. All authors have read and agreed to the published version of the manuscript.
## 4 Experimental Results and Analysis
### Performance Analysis and Comparison
#### 4.1.2. Pest Classification Results  
The high capacity to correctly classify different species of pests is of key importance for the reliability of the method. To this aim, model B is considered. Figure 10 shows the confusion matrix plot of the designed method that highlights the performance of pest classification in terms of predicted and actual classes. The TP values for each class are reported along the diagonal of the confusion matrix. The higher the accuracy value of the classification task, the darker the color of the diagonal. The process of distributing overnumbered samples into several subcategories leads to the generation of highly correlated classes. The model benefits from an equal data distribution but struggles to make the correct subclass prediction in case of samples generated from the same original class (having, therefore, common visual features). This trend is shown in the diagonal-block distribution of the confusion matrix as follows (Figure 10): each sub-block along the diagonal contains all the classification errors generated when the model confuses the original class with one of the generated subclasses. The bigger the block, the higher the number of the subclasses generated by the proposed method. However, these errors must not be considered as actual misclassifications in the performance evaluation phase because those instances belong to the same class in the original dataset, regardless of the subclass they belong to after balancing. For the sake of visualization, only a portion of the confusion matrix is reported in the figure. In the enlarged view, all the categories falling in each sub-block along the diagonal actually belong to the pest class highlighted in red. The color gradation shows the magnitude of the classification errors as follows: the darker the color, the higher the number of misclassified instances. In Table 3, a comparative analysis of the performance achieved by model B in the classification task is summarized with the findings of other techniques indicated in the literature that adopt the same dataset as the test bench. The methods taken into account for the benchmarking have been detailed in Section 2. The results of the method validate the performance of model B. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Acc \%** & **F1** & **P** & **R** & **Y** & **T** \\ \hline
[9] & 73.7 & - & - & - & 2023 & Lightweight CNNs + embedded attention \\ \hline
[10] & 71.32 & - & - & - & 2021 & Lightweight CNNs + embedded attention \\ \hline
[11] & 82.43 & 82 & 83 & 81 & 2023 & Faster-RCNN + MobileNet \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection and classification performance comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Acc** & **F1** & **IoU** & **P** & **R** \\ \hline
[18] & 68.74 & 59.39 & 0.621 & 61.72 & 57.46 \\
[27] & - & 57.50 & - & 57.40 & - \\
**Our method** & 74.77 & 78.63 & 0.79 & 82.25 & 74.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection and classification performance comparison.
# Fast Stepwise Inertial Control Scheme of a DFIG for Reducing Second Frequency Drop
## 2 Doubly-Fed Induction Generator Model  
Based on the theory of aerodynamics, the mechanical power of the wind turbine is a function of air density (\(\rho\)), power coefficient (\(c_{p}\)), rotor radius (\(R\)), and wind speed (\(v_{w}\)):  
\[P_{m}=\frac{1}{2}\rho\pi R^{2}v_{w}^{3}c_{p}(\lambda,\beta) \tag{1}\]  
\[c_{p}(\lambda,\beta)=0.645\{0.00912\lambda+\frac{-5-0.4(2.5+\beta)+116\lambda _{i}}{e^{21\lambda_{i}}}\} \tag{2}\]  
\[\lambda_{i}=\frac{1}{\lambda+0.08(2.5+\beta)}-\frac{0.035}{1+\left(2.5+\beta \right)^{3}} \tag{3}\]  
\[\lambda=\frac{\omega_{r}R}{v_{w}} \tag{4}\]  
where \(\beta\) is the pitch angle and \(\lambda\) is the tip-speed ratio.  
The mechanical dynamics are displayed in a two-mass shaft model, as indicated by the following equations [36]:  
\[2H_{t}\frac{d\omega_{t}}{dt}=T_{m}-T_{ls} \tag{5}\]  
\[2H_{g}\frac{d\omega_{r}}{dt}=T_{hs}-T_{em} \tag{6}\]  
\[T_{ls}=K(\theta_{t}-\theta_{ls})+B(\omega_{t}-\omega_{ls}) \tag{7}\]  
where \(T_{m}\) is the mechanical torque from the turbine; \(T_{em}\) is the generator electrical torque, \(H_{t}\) is inertia from the turbine; \(H_{g}\) is the generator inertia constant; \(T_{ls}\) and \(T_{ls}\) are the torques of the low-speed and high-speed shafts, respectively; \(\omega_{t}\) is the turbine rotor speed; \(\omega_{r}\) is the generator rotor speed; \(K\) is the spring constant; \(\theta_{t}\) and \(\theta_{ls}\) are the torsional twists of the rotor and low-speed shaft, respectively; \(B\) and \(\omega_{ls}\) are the damping constant and rotor speed of the low-speed shaft, respectively.  
Figure 1 illustrates the vector control of the rotor side converter, which adjusts the voltage and active power injected into the grid through the inner current controller.  
In (1), the DFIG captures the maximum power from wind. Substituting (4) into (1), the expression of the power reference for MPPT operation, \(P_{MPPT}\), is described as:  
\[P_{MPPT}=\frac{1}{2}c_{P,\text{ max}}\rho\pi R^{2}(\frac{\omega_{r}R}{\lambda_{opt }})^{3}=k_{g}\omega_{r}^{3} \tag{8}\]  
where \(k_{g}\) is set to 0.512. In addition, \(c_{P,\text{ max}}\) is the maximum value of \(c_{P}\) and is set to 0.5. \(\lambda_{opt}\) is the optimal tip-speed ratio and is set to 9.95.
# Identification of Complicated Lithology with Machine Learning
## 3 Data Selection and Practical Application
### Model Construction
#### 3.4.3 Random Forest Prediction of Felsic Content  
First, we selected 20% of the predictive factors for training. After achieving the best training results, we input the predictive factors of the test set into the trained model to obtain the predicted results. The evaluation metrics of the predicted results are shown in Table 6.  
The random forest model performs similarly to SVR under the condition of 20% training samples (Table 6), with an MAE of 0.12, an MSE of 0.022, an RMSE of 0.15, and an R\({}^{2}\) of 0.39. Although its MSE and RMSE are slightly higher than those of SVR, the MAE and R\({}^{2}\) of the two are basically the same, indicating that random forest and SVR have similar predictive abilities under the condition of small samples. The prediction results are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline
**Random Forest** & 0.12 & 0.022 & 0.15 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Random forest model of 20% of the training samples in Well Lu 1 predicting the evaluation index of the felsic quality.  
Figure 14: Effect diagram of 20% of the random forest training samples predicting felsic quality in Well Lu 1.  
Figure 13: Effect diagram of 80%-training-sample SVR prediction of felsic content in Well Lu 1.  
From Table 7, it can be seen that the MAE of the random forest model is 0.11, the MSE is 0.020, the RMSE is 0.14, and the R\({}^{2}\) is 0.43. Compared to the results from training with 20% of the predictive factors, the MAE, MSE, and RMSE from training with 80% of the predictive factors have all slightly decreased, and the R\({}^{2}\) has slightly improved. The prediction results are shown in Figure 15.  
As shown in Figure 15, the fluctuations in the predicted values are generally consistent with those of the actual values. Compared to Figure 14, the alignment of the peaks and troughs has improved.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.1 Flyback and Forward Converters  
Flyback converters and forward converters are single-switch isolated DC-DC converters that are popular for low-power applications due to their simple design and low component count. A multiple-output converter based on a flyback converter was proposed in [92]. The topology provides advantages like good load regulation, line regulation, and high efficiency. Ref. [93] proposed an isolated multiple-output forward converter with magnetic feedback. The converter is reliable, as the magnetic feedback and controllers are not affected by the environmental conditions in space. However, topologies based on flyback or forward converters exhibit high semiconductor switching stresses and have low efficiency. Moreover, they exhibit low transformer utilization [94].
## 2 Atmospheric Spectroscopy  
Diode Laser spectroscopy provides interesting advantages related to its high selectivity and sensitivity in the detection of gases. Semiconductor diode lasers emitting in the near infrared (NIR) have played an important role because of their continuous mode and tunablity as well as their relative low amplitude noise. The main reason is that most atmospheric pollutant molecules feature suitable absorption lines in the near infrared spectral region.
## 1 Introduction
Also, the impact of the aggregator's bids on prices is assessed using a bi-level problem, however the impact of the prices offered by other aggregators is not discussed. The problem of decision-making of the interaction between the parking lot and distribution system operator in a bi-level framework is provided in [23]. It gives a model for participation of parking lots in both energy and reserve markets in order to compensate renewable generation and load uncertainties. In [24], authors propose an integrated model of plug-in EVs and renewable distributed generators in a market floor where users can sell back the energy generated from their renewables or the energy stored in their plug-in EVs. In this paper, a bi-level stochastic model for the decision-making of an EV aggregator in a competitive electricity market is proposed. Then an equivalent single-level linear formulation based on the Karush-Kuhn-Tucker (KKT) optimality conditions and duality theory is used to combine the upper and lower levels. This concept has been applied to a hub manager problem in [25] and to a retailer problem in [26], previously. Here, this concept is extended to an EV aggregator decision-making problem while considering the reaction of EV owners to the selling prices offered by the aggregator and its rivals. The main contributions of this paper are as follows:  
* Proposing a stochastic bi-level model for the decision making of an EV aggregator in which the profit of the aggregator is maximized in the upper level and the cost paid by the EV owners is minimized in the lower level;
* Considering the reaction of consumers to the offered selling prices by the aggregators in a competitive environment;Considering risk aversion measurement to decrease the unfavorable influence of risk in the decision-making program. The remaining sections of this paper are organized as follows: Section 2 describes the features of the problem; Sections 3 and 4 presents the problem formulation of the decision-making of an EV aggregator as a two-stage stochastic programming model; and Section 5 provides numerical results and discussion. Finally, a relevant conclusion is drawn in Section 6.
## 2 Passive Radar Performance Principle
### System Coverage Limited by Sensitivity  
Expression Equation (5) can be re-written as in Equation (6). The loci corresponding to \(R_{T}\cdot R_{R}=constant\) is known as the oval of Cassini. For a required system sensitivity, \(P_{Rmin}\), a value of \(R_{R}\cdot R_{T}\) is obtained.  
\[(R_{R}R_{T})^{2}=\frac{P_{T}G_{T}G_{R}\lambda^{2}\sigma_{bis}}{\left(4\pi \right)^{3}P_{R}}\cdot A \tag{6}\]  
In the considered bistatic geometry, the passive radar is located on the Earth's surface (Figure 6). Assuming a baseline length \(L=500\) km and \(R_{R}<20\) km, \(L/(R_{T}\cdot R_{R})>2\), and the Cassini oval breaks into two parts, one centered on the OI and the other on the PR.  
As an example of the impact of the \(L/(R_{T}\cdot R_{R})\) relation on the coverage area geometry, a simple study is presented in Figure 7. Assuming a normalized value \(R_{T}\cdot R_{R}=1\) km\({}^{2}\), different coverage areas are represented for \(L/(R_{T}\cdot R_{R})\in\{1.5,2,3\}\).  
Figure 6: Geometry of the PAZ-based passive radar: \(\beta\) is the bistatic angle and \(\alpha\) is the incidence one.
## 5 Conclusions  
Using this extended material model, good comparability with experimental results can be achieved. This can be seen particularly well in Figure 12. Especially in the area of elastic deformation (characterized by Young's modulus) and plastic deformation up to the uniform elongation (characterized by the maximum force value), good agreement between experiment and simulation is achieved and even with regard to the elongation at fracture, the 10% deviation of the numerical and experimental results is still acceptable. Moreover, the temperature development was predicted by the numerical simulation with good accuracy for all strain rates considered in this study (see Figures 13 and 14).  
Thus, our study provides the following important results and conclusions:  
* Taking into account the actual strain rate for each of the tests performed is necessary since this value is often not the same as the set value for the machine.
* The plastic material model must take into account a range of strain rates exceeding the nominal strain rate of the test by several times in order to reproduce localization effects with sufficient accuracy.
* The sophisticated choice of the measuring point allows recording the maximum temperature in the fracture region with a high-speed pyrometer for strain rates of up to several hundred per second.
* The results of the experiment and simulation are well comparable using the provided material model.  
Conceptualization, E.G., S.W., F.R. and V.P.; methodology, E.G. and V.P.; software, E.G., F.R. and M.T.; validation, E.G., F.R. and V.P.; investigation, E.G.; resources, V.P. and V.K.; writing--original draft preparation, E.G.; writing--review and editing, E.G., S.W., F.R. and V.P.; visualization, E.G.; supervision, V.P., V.K. and A.B.; project administration, E.G. and V.P.; funding acquisition, V.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant numbers PS 68/2-3 and BR 3500/19-3.
# On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
###### Abstract  
Dual-frequency GNSS data processing is currently one of the most useful techniques for sounding the ionosphere. Hence, this work was aimed at the evaluation of ground-based GNSS data for the continuous monitoring of polar patches in both hemispheres. In this contribution, we proposed to use epoch-wise relative STEC values in order to detect these structures. The applied indicator is defined as a difference between an undifferenced geometry-free linear combination of GNSS signals and the background ionospheric variations, which were assessed with an iterative algorithm of four-degree polynomial fitting. The occurrence of patches during the St. Patrick geomagnetic storm was performed for validation purposes. The first part of the work confirmed the applicability of the relative STEC values for such investigations. On the other hand, it also revealed the limitations related to the inhomogeneous distribution of stations, which may affect the results in both hemispheres. This was confirmed with a preliminary cross-evaluation of GNSS and in situ SWARM datasets. Apart from the periods with a well-established coincidence, the opposite situation, when both methods indicated different parts of the polar ionosphere, was also observed. The second part of this contribution depicted the feasibility of continuous patch detection for both regions, and thus the interhemispheric comparison of the analyzed structures. It has demonstrated the strong disproportion between patches in the northern and southern hemispheres. This discrepancy seems to be related to the different amount of plasma propagating from the dusk sector, which is justified by the values of relative STEC at mid-latitudes. The observed structures are also strongly dependent on the orientation of the interplanetary magnetic field.  
GNSS; ionosphere; polar patches +
Footnote †: journal: Remote Sens. **2018**, _10_, 2044; doi:10.3390/rs10122044
# Traffic-Aware Optimization of Task Offloading and Content
## 3 System Model and Problem Formulation
### Execution Time and Energy Consumption Model
#### 3.2.2 Execution Time and Energy Consumption of Edge Task Computing  
When task \(q^{i,n}\) is offloaded to the MEC server, the process involves the following steps: the \(n^{th}\) vehicle uploads the task's input data to the MEC server via the BS/RSU. The MEC server allocates computational resources for task processing and returns the result to the vehicle. The time of tasks offloaded to MEC server m can be as described below:  
\[TM_{q}^{i,n}(t)=\alpha_{i,n}^{m}\times(\frac{d_{q}^{i,n}}{v_{n}^{i}(t)}+\frac{ c_{q}^{i,n}}{fm_{i}}+\frac{\phi_{q}^{i,n}}{v_{m}}) \tag{3}\]  
where the first part enclosed in parentheses in Equation (3) represents the offload time to the edge server, the second part denotes the execution time delay for processing \(q^{i,n}\), and the final part accounts for the feedback time delay of processing results. Here, \(fm_{i}\) is the computing capability of edge servers in region \(i\), \(v_{n}^{i}(t)\) signifies the data offload rate from the \(n^{th}\) vehicle to the mobile edge server within region \(i\) at time slot \(t\), \(o_{q}^{i,n}\) is the size of the task processing outcome, and \(v_{m}\) is the backhaul transmission rate of edge server \(m\).  
This means the edge energy consumed is  
\[EM_{q}^{i,n}(t)=\alpha_{i,n}^{m}\times(c_{q}^{i,n}\times(fm_{i})^{2}\times \varsigma+\frac{o_{q}^{i,n}}{v_{m}}\times\delta^{m}) \tag{4}\]  
where \(\delta^{m}\) is the offload capability for edge server \(m\).