esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The article summarizes the results of a comprehensive study of the influence of milking system operating conditions on the accuracy of the light-scattering sensor. Based on the results, the authors put forward several recommendations for ensuring optimal operating conditions for milking systems, including the integration of a light-scattering sensor into them. The presence of _S. aureus_ and _S. epidermidis_ was detected, but their number proved to be much less than the one that could interfere with the light-scattering sensor (1.5 \(\times\) 10\({}^{3}\) was detected, while a concentration of 1 \(\times\) 10\({}^{5}\) would have an impact). When even milk from healthy animals is a suitable environment for the growth of pathogenic microflora, it is necessary to wash the milk hoses at a temperature of at least 80 \({}^{\circ}\)C. Modeling the milk flow in the measuring chamber of the light-scattering sensor showed a stable flow without stagnation. This will ensure that the milk in the measuring chamber is refreshed in time to provide up-to-date measurements of milk quality parameters, and that the milk hoses are rinsed properly. The accuracy of the light-scattering sensor will be quite high if we exclude the values of the first \(\sim\)40 s after the start of the milking system operation, since milk residues and the filling of the milk hoses significantly reduce the accuracy of the  
Figure 7: Light-scattering sensor accuracy tests with 4.31% milk.  
device performance. The study results confirm that the light-scattering sensors are resistant to such factors of the milking system operation as contamination by pathogenic microflora (_S. aureus_ and _S. epidermidis_) and the low-quality rinsing of milk hoses.  
Conceptualization, A.R.K. and S.S.Y.; methodology, A.R.K., A.S.D., D.Y.P. and A.V.S.; software, S.S.Y. and M.E.A.; validation, A.Y.I., A.S.D. and D.Y.P.; formal analysis, D.Y.P. and S.V.G.; investigation, A.R.K., S.S.Y., A.V.S., M.E.A., D.N.I. and S.V.G.; resources, S.S.Y. and D.N.I.; data curation, A.R.K.; writing--original draft preparation, A.R.K. and S.S.Y.; writing--review and editing, A.V.S.; visualization, A.R.K. and S.S.Y.; supervision, A.Y.I., A.S.D. and S.V.G.; project administration, A.R.K. and D.Y.P.; funding acquisition, A.S.D. and D.Y.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Ministry of Science and Higher Education of the Russian Federation for large scientific projects in priority areas of scientific and technological development (grant number 075-15-2020-774).  
Not applicable.  
All data from this study are included within this manuscript.  
The authors declare no conflict of interest.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 2 Implementation of a Dual-Band RF Wireless Power Transfer (WPT) System
### Design of the Rx Antennas  
In order to conduct various WPT experiments according to the operating frequency, as shown in Figure 1, three types of antennas are implemented as Rx antennas: a 2.4 GHz patch antenna, a 5.8 GHz patch antenna, and a dual-band (operating at 2.4 and 5.8 GHz) patch antenna. For polarization matching with the Tx array antenna, all of these Rx antennas are designed to operate RHCP at their respective operating frequencies.
###### Abstract
Along this line, several self-labeled algorithms have been proposed in the literature, while some of them exploit ensemble methodologies and techniques. Democratic-Co learning [12] is based on an ensemble philosophy since it uses three independent classifiers following a majority voting and a confidence measurement strategy for predicting the values of unlabeled examples. Tri-training algorithm [13] utilizes a bagging ensemble of three classifiers which are trained on data subsets generated through bootstrap sampling from the original labeled set and teach each other using on majority voting strategy. Co-Forest [14] utilizes bootstrap sample data from the labeled set in order to train Random trees. At each iteration, each random tree is reconstructed by newly selected unlabeled instances for its concomitant ensemble, utilizing a majority voting technique. Co-Bagging [15] trains multiple base classifiers on bootstrap data created by random resampling with replacement from the training set. Each bootstrap sample contains about 2/3 of the original training set, where each example can appear multiple times. Recently, a new approach has been given by [PERSON] et al. [2; 8; 16; 17] and [PERSON] [18] in which some ensemble self-labeled algorithms are proposed based on voting schemes. The proposed algorithms exploit the individual predictions of the most efficient and frequently used self-labeled algorithms using simple voting methodologies. Motivated by these works, we propose a new semi-supervised self-labeled algorithm which is based on a sophisticated ensemble philosophy. The proposed algorithm exploits the individual predictions of self-labeled algorithms, using a new weighted voting methodology. The proposed weighted strategy assigns weights on each component classifier of the ensemble based on its accuracy on each class. Our main aim is to measure the effectiveness of our weighted voting ensemble scheme over the majority voting ensembles, using identical component classifiers in all cases. On top of that, we want to verify that powerful classification models could be developed by the adaptation of advanced ensemble methodologies in the SSL framework. Our preliminary numerical experiments prove the efficiency and the classification accuracy of the proposed algorithm, demonstrating that reliable prediction models could be developed by incorporating ensemble methodologies in the semi-supervised framework. The remainder of this paper is organized as follows: Section 2 presents a brief survey of recent studies concerning the application of machine learning for the detection of lung abnormalities from X-rays. Section 3 presents a detailed description of the proposed weighted voting scheme and ensemble algorithm. Section 4 presents a series of experiments carried out in order to examine and evaluate the accuracy of the proposed algorithm against the most popular self-labeled classification algorithms. Finally, Section 5 discusses the conclusions and some research topics for future work.
## 3 Results and Discussion
### Sensory Evaluation  
The results of sensory evaluation are shown in Table 5. In general, off-flavors increased more rapidly in milk stored at 10 \({}^{\circ}\)C than at 4 \({}^{\circ}\)C and 7 \({}^{\circ}\)C. After 14 days-storage, the overall acceptability score of milk stored at 4 \({}^{\circ}\)C was 8.00 \(\pm\) 0.29, followed by at 7 \({}^{\circ}\)C (7.50 \(\pm\) 0.00) and 10 \({}^{\circ}\)C (7.00 \(\pm\) 0.00). Similarly, the score of taste and aroma at day 14 was (7.50 \(\pm\) 0.00, 7.17 \(\pm\) 0.60), (7.00 \(\pm\) 0.00, 6.67 \(\pm\) 0.33) and (6.67 \(\pm\) 0.33, 6.33 \(\pm\) 0.17) at 4 \({}^{\circ}\)C, 7 \({}^{\circ}\)C and 10 \({}^{\circ}\)C, respectively. The overall acceptability of milk samples, after 19 days of storage, at 4 \({}^{\circ}\)C was (score 6.00 \(\pm\) 0.00) going to spoil, and at 7 \({}^{\circ}\)C and 10 \({}^{\circ}\)C were badly spoiled, with scores of respectively, 5.17 \(\pm\) 0.17 and 4.00 \(\pm\) 0.00. Overall, the obtained sensory results agree with those of [PERSON] et al. [27], who reported that storage-induced changes in sensory characteristics, volatiles and primary amines depend on both differences in lactose content and the applied heat processing, as well as with the results obtained by [PERSON] et al. [28], who reported that aldehyde and ketone compounds may play a major role in controlling the oxidized flavor, which was influenced by pre-heating as well as concentration and drying during milk powder production. The standard plate count (SPC, Log\({}_{10}\)) was (2.91 \(\pm\) 0.03 CFU/mL, 5.77 \(\pm\) 0 CFU/mL and 14.88 \(\pm\) 0.03 CFU/mL), at day 14, and (5.78 \(\pm\) 0.01 CFU/mL, 17.25 \(\pm\) 0.03 CFU/mL and 21.89 \(\pm\) 0.01 CFU/mL), at day 19, respectively, at 4 \({}^{\circ}\)C, 7 \({}^{\circ}\)C and 10 \({}^{\circ}\)C. This has previously been noted by [PERSON] [29] and [PERSON] [30], who reported that the most reliable method to judge fresh milk quality is sensory evaluation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{1}{c}{**Storage Days**} & \multicolumn{1}{c}{**Mean**} \\ \cline{3-14}  & **Temp.
## 2 Approach and Implementation
### System Framework  
The developed system was modelled closely following a service-orientated framework--by which, each module is developed as a micro-service application that plays a specific and detected role within the cloud system stack--abstracted to four primary modules, formally named, Data Process Queue, Data Management Frontend, Data Management Backend and the Web Proxy (Figure 3).  
The Data Management Front micro-service handles all HTTP proxy traffic directed by the Web Proxy through a public SSL (Secure Socket Layer) pipeline, rendering the static frontend HTML application for system interaction by the user. The Data Management Backend was implemented to handle the process of storing all inhomogeneous data sources conformed to the revised data structure. The Data Management Backend also provides an Application Program Interface (API) to the stored and index data sources. Finally, the Data Process Queue was implemented to handle aggregation of varying inhomogeneous data sources, conforming the data sources to a single uniform Athdata structure to be stored within the backend. The Process Queue also provides visualisation functionality with real-time rendering of time series datasets, allowing the collaboration of operators for processing of distributed data sources.
## 1 Introduction
### Motivation  
The RANS models provide reasonable estimates of the average flow field but their accuracy is limited in the presence of transition or separation since they rely on some simplifications which are approximately satisfied only in fully turbulent and attached flows. In recent years, several research efforts have been devoted to the development of new turbulence models that can avoid the limitations of classical RANS models. On one side, the growing computational power allows us to perform scale-resolving simulations which can provide high-fidelity databases and insight into turbulence physics. On the other side, the development of new machine learning techniques also paves the way for the application of these techniques in the prediction of turbulent separated flows. The use of machine learning in fluid dynamics has been investigated with several strategies [12]: field inversion and machine learning [13; 14], correction of the Boussinesq assumption in the computation of the Reynolds stresses [15; 16], evaluation of the subgrid stresses in LES [17; 18], RANS modeling of vortex breakdown in complex flows [19], transition modeling [8; 20]. The original field inversion and machine learning (FIML) paradigm was originally introduced by [13], who proposed to correct the RANS error by introducing a multiplier in the production term of the turbulent eddy viscosity transport equation. This approach was applied and investigated for several applications and provided encouraging results. However, in some problems, it is difficult to perform the regression step through machine learning because it is not clear what input flow features should be considered and it could be impossible to find a set of local flow features that allows the evaluation of the local correction factor through a function. For this reason, the FIML paradigm is considered in this work but then an alternative approach is investigated.
## 2 Materials and Methods
### Data Analysis and Machine Learning Models
#### 2.2.3 Training and Validation of Machine Learning Models  
Top of Form  
To enable proper comparison of the coefficients and account for different material behavior, three identical but independent regression models were trained--one for Aluminium, one for concrete, and one for ABS. The data were randomly and blindly split into training and test sets with a 70/30 split. The choice of a 70% training data split ensures a sufficiently large dataset for training the machine learning models. Having a substantial amount of data for training helps the models capture the underlying patterns and variations in crack propagation accurately. It provides a robust foundation for the models to learn from and develop predictive capabilities. Allocating 30% of the dataset to the validation set allows for a substantial portion of data to evaluate the trained models' performance. Sizable test data helps assess the generalization ability of the models, determining how well they perform on unseen data. This split ensures a meaningful evaluation of the model's predictive accuracy and provides statistical confidence in their performance metrics. The 70/30 split strikes a balance between mitigating overfitting and underfitting issues. If the training set were too small (e.g., 50/50 split), the models might not have enough data to learn effectively, potentially resulting in poor generalization and overfitting. Conversely, if the training set were too large, the risk of underfitting increases as the models may not adequately capture the complexity of crack propagation patterns. The 70/30 split attempts to optimize this balance [7; 25; 26; 27; 28].  
Model hyperparameters were adjusted to minimize mean squared error and achieve satisfactory performance. This approach ensured that the model was always evaluated on previously unseen data, making the evaluated performance more representative of a real-world predictive problem. To prevent any compromise to the model's performance due to breaking the \"test on unseen data\" principle during training, a K-fold cross-validation methodology was utilized. This methodology is illustrated in Figure 3 and ensured that the model was evaluated using unseen data during training.  
Figure 3: K-Fold cross-validation schematic.
## Appendix C Development of the Time-Limited Fractional Frequency Shift
Accordingly, in time-domain the input-output relation in continuous time becomes  
\[y(t)=\frac{1}{2F}\sum\limits_{m=0}^{M}x(t)e^{j2\pi(m-M)2 Ft}Sinc(m-q) \tag{14}\]  
In this application, the time gating intervals are not symmetric about 0 and they alternate in sign at every half period of the modulating frequency, \(F\), so the spectrum envelopes will be different, but the same fractional pseudo-Doppler shifts, \(Fkd/(2R)\), will be observed in their envelopes. These shifts are not directly visible in the simulation results because the spectrum envelopes are not plotted, only the spectral replicas at the repetition frequencies according to (11), with the correspondence of \(q=kd/(4R)\) in \(\mu=q2F=Fkd/(2R)\). As in the case of the fractional sample delay filter, a similar structure based on (A13) can be used in the \"equalization\" or recovery of OAM modes in the frequency domain (but still has to be implemented in time domain as discussed above). In this application, the recovery algorithm can be more deterministic because the necessary parameters (those defining the fractional shift \(q\) above) can be made known to the receiver. For reference and to complete the duality statement, the following structure is formulated as a basis for the OAM recovery algorithm. The structure in Figure A6 is a functional representation of (A14) truncated to \(M+1\) terms and can be used to recover a desired OAM mode from a superposition of all OAM modes that appears at the output of the gating block of the pseudo-Doppler modulation subsystem of the receiver front end. Note that one set of coefficients \(\{G(m)\}\) is required to recover each order of OAM mode. It is important to remember that the entire broad-band spectrum containing \(M\) harmonics of the pseudo-Doppler modulation, \(2F\), and the associated spectral replicas of the transmitted signal. Note also that the input and output signals in Figure A6 are still in continuous-time domain, and may also be in analog form.
# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 4 Results
### Classification Results  
With the feature vectors created according to the description mentioned above, we performed a test to check the ability of the handcrafted features to distinguish correctly between cervical cancer grades. The tests were performed on images from the database described in Section 3. At first, we checked the classification accuracy for the 27 features as a baseline. Then the reduced feature vector classification was performed. Furthermore, we compared these results with the classification results for features extracted with VGG16 convolutional neural networks. The obtained results are summarized in Table 3. Each classifier was presented with a full feature vector (baseline), five reduced feature vectors, and a CNN feature vector. Looking at the results, we can easily notice that the SVM classifier outperformed other classifiers for all feature vectors (see Figure 5). We cannot see this dependency for all remaining classifiers. In Table 4, the recorded _precision_ and _recall_ values for all classifier types are presented. From the table we can see that the highest _precision_ was noted for Friedman, stepwise and significant feature selection methods. In case of _recall_ (see Figure 6) the highest values were observed for the significant feature selection method reaching 91.40% when the SVM classifier was used. The same observation was made for _precision_, as can be seen in Figure 7. Another important discovery is that feature reduction not only highly increased the accuracy of the proposed framework but also enhanced the _precision_ and _recall_ of the system. Only in the case of Kormogolov-Smirnov test and stepwise feature selection method did we not observe such an increase. Figure 4: Reduction rate visualization. Figure 5: Classifier performance for reduced data sets. Taking the above into account, we can see that SVM is the best classifier for which we have recorded the highest accuracy values in all cases. When we combine this classifier with the significant feature selection method (highest accuracy for SVM), we can see that the _precision_ and _recall_ values are also very high. What is also important to note is that this combination allows one to reach the accuracy similar to CNN feature vector and SVM classifier. Furthermore, an efficiency comparison between CNN-based feature extraction and conventional methods showed that the latter is more efficient. For a single cell image, the processing time for conventional methods was approximately 3.25 s, compared to 4.47 s for VGG16, making conventional methods 1.38 times faster. At the same time, memory usage was also slightly lower for conventional methods (0.0208 GB vs. 0.0246 GB). Considering this, we can propose a classification system based on SVM and a feature vector consisting of the features selected with the significant feature selection method. Taking that into account, it can be noted that the presented findings demonstrate the potential of combining handcrafted features with feature selection for accurate and efficient diagnosis.
## 3 Analysis of the Coal Pillar Width under the Influence of the Water-Logged Goal
### Calculation of the Width of the Plastic Zone of Waterproof Coal Pillars
\tag{9}\]  
where \(a\), \(b\), \(c\), \(c_{1}\), \(c_{2}\) are constants to be determined  
By substituting the stress boundary condition at the interface between the plastic zone and the elastic zone into Equation (16), the width of the plastic zone of the zone waterproof coal column can be calculated as:  
\[l_{1}=\frac{h\lambda}{2\tan\varphi_{0}}\ln\frac{K_{1}\gamma H+\frac{C_{0}}{\tan \varphi_{0}}}{\frac{C_{0}}{\tan\varphi_{0}}+\frac{P_{x}}{\lambda}}+(\frac{h}{2 }-y)\tan\varphi_{0} \tag{18}\]  
During actual mining operations, different mining methods, the speed of working face mining, and other factors can affect the plastic zone of the coal pillar, resulting in an expansion of the range of the plastic zone. Therefore, the mining disturbance factor was also considered. Furthermore, the lateral constraints of the coal pillar gangs are uncertain due to the mining of the working faces. In order to ensure that the width of the coal pillar calculated in Equation (18) meets the safety requirements for the working faces, Px must be 0. By substituting this value into the above formula, the following expression can be used to calculate the width of the plastic zone:  
\[l_{1}=\frac{hd}{2\tan\varphi_{0}}\left[\ln\left(1+\frac{K_{1}\gamma H}{C_{0}} \tan\varphi_{0}\right)^{\lambda}+\tan^{2}\varphi_{0}\right] \tag{19}\]  
where \(h\) is the height of coal pillar (in m), \(d\) is the mining influence factor, \(d\) = 1.5-3.0, \(\varphi_{0}\) is the friction angle at the interface between the coal seam and the roof and the floor plate (in \({}^{\circ}\)), \(K_{1}\) is the stress concentration factor of coal body in the plastic zone, \(\gamma\) is the rock volume force (in kN/m\({}^{3}\)), \(H\) is the depth of the coal seam (in m), and \(C_{0}\) is the cohesive force at the interface between coal seam and top and bottom plate (in MPa). Through the collection of information and on-site research, the relevant parameters of the Cuijiagou 4-2# working face were obtained as follows: \(h\) = 12 m, \(H\) = 600 m, \(K_{1}\) = 2.5, rock volume force \(\gamma\) = 25 kN/m\({}^{3}\), mining influence factor \(d\) = 1.5, \(C_{0}\) = 3.5 MPa, and \(\varphi_{0}\) = 30\({}^{\circ}\). The lateral pressure coefficient was calculated as:  
\[\lambda=\frac{\sigma_{x}}{\sigma_{y}}=\frac{1-\sin\varphi}{1+\sin\varphi} \tag{20}\]  
where \(\varphi\) is the angle of friction within the coal (in \({}^{\circ}\)). Solving Equation (20) yields \(\lambda\) = 0.3. By substituting the above parameters into Equation (19), the following was obtained:  
\[l_{1}=\frac{12\times 1.5}{2\times\tan 30^{\circ}}\left[\ln\left(1+\frac{2.5 \times 2.5\times 6}{3.5}\times\tan 30^{\circ}\right)^{0.3}+\tan^{2}30^{ \circ}\right]=14.26\ \text{m}\]