esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presented an extensive review of the available path-loss-prediction and channel models regarding the A2A and A2S propagation channels. State-of-the-art models were addressed and specific case studies were assessed in order to determine the inherent nature and behavior of those channels. According to the published results, both are highly influenced by many parameters, such as flight dynamics, distance and altitude, underlying terrain, velocity and trajectories, and, finally, atmospheric effects. A time-series rain-attenuation synthesizer that can be applied in both A2S and A2A links was presented, and the excess loss in the event of precipitation was evaluated. Finally, many intriguing research gaps were emphasized. These necessitate further measurements at new frequencies and flight altitudes, in propagation environments, with multielement antennas.  
Conceptualization, N.M. and A.D.P.; methodology, N.M.; validation, N.M., K.P. and A.D.P.; formal analysis, N.M. and K.P.; investigation, N.M. and K.P.; resources, A.D.P.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, N.M., K.P. and A.D.P.; visualization, N.M.; supervision, A.D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Predictive Analytics for Sucker Rod Pump Failures in Kazakhstan Oil Wells Using Machine Learning
## 3 Data
### Machine Learning Algorithms  
LightGBM is a comparatively recent framework with extensive machine learning and data science applications. A primary challenge with gradient boosting algorithms is that they evaluate all data to determine potential separation points, affecting performance. LightGBM has been adapted to enhance the efficiency of the optimal search process [11].  
Another reason is that boosting ensemble algorithms can deliver strong performance with multi-class imbalanced data. Various issues with multi-class imbalanced data have been addressed using ensemble learning techniques. This is why LightGBM was selected for this task [12].  
The aim of LightGBM algorithm is to obtain an estimate \(\widehat{F(X)}\) of the function \(F(X)\) mapping \(X\) (input features) to \(Y\) (target variable) with a minimization of the loss function \(L(Y,F(X))\).  
In gradient boosting, each new \(b_{i}\) algorithm (tree) is added to the already built composition:  
\[a_{i}(x)=a_{i-1}(x)+b_{i}(x) \tag{1}\]  
where \(x\) represents the input features for predictions. Such an algorithm corrects the answers of the algorithm \(a_{i}(x)\) to correct answers on the training set. If we consider several algorithms, the algorithm is as follows:  
\[a_{n}(x)=\sum_{i=1}^{N}b_{t}(x) \tag{2}\]  
where \(t\) is iteration, the iterative process of adding models (trees) to the ensemble. For the classification task, the loss function has several options, one of which is as follows:  
\[L(Y,F(x))=log(1+exp(-YF(x))) \tag{3}\]  
where \(F(x)=a_{n}(x)+s_{i}\), \(s=(s_{1},\dots,s_{l})\) is the vector of the shift (correction). Our loss function is as follows:  
\[\sum_{i=1}^{l}log(1+exp(-y_{i}*(a_{n}(x_{i})+s_{i})))\rightarrow\min_{s} \tag{4}\]  
The forecasting performance of machine learning models is assessed using a single statistical indicator: the F1 score [13]. The F1 score balances precision and recall, making it particularly useful when the class distribution is irregular. This metric effectively evaluates models dealing with imbalanced data, especially when classifying positive instances is crucial [14].  
The metrics used (F1 score, precision, and recall) were chosen over accuracy because precision alone can be misleading in imbalanced datasets, where the model may favor Class 0 (normal work) and fail to detect instances of Class 1 (failures). The F1 score, the harmonic mean between precision and recall, is a balanced metric effective for imbalanced datasets. Combining precision and recall, the F1 score reflects the model's effectiveness in detecting faults while minimizing false positives and false negatives. This balance is essential for our predictive model, as it must reliably identify failures without being overly influenced by the majority Class 0, thus ensuring effective detection of critical failures.
# Intra-Day Variability Observations of Two Dozens of Blazars at 4.8 GHz
###### Abstract  
Two dozens of radio loud active galactic nuclei (AGNs) have been observed with Urumqi 25 m radio telescope in order to search for intra-day variability (IDV). The target sources are blazars (namely flat spectrum radio quasars and BL Lac objects) which are mostly selected from the observing list of RadioAstron AGN monitoring campaigns. The observations were carried out at 4.8 GHz in two sessions of 8-12 February 2014 and 7-9 March respectively. We report the data reduction and the first results of observations. The results show that the majority of the blazars exhibit IDV in 99.9% confidence level, some of them show quite strong IDV. We find the strong IDV of blazar 1357 + 769 for the first time. The IDV at centimeter-wavelength is believed to be predominately caused by the scintillation of blazar emission through the local interstellar medium in a few hundreds parsecs away from Sun. No significant correlation between the IDV strength and either redshift or Galactic latitude is found in our sample. The IDV timescale along with source structure and brightness temperature analysis will be presented in a forthcoming paper.  
quasars: general; galaxies: jets; radio continuum:galaxies; ISM: structure; scattering +
Footnote †: journal: _Journal of the American Astronomical Society_
## 2 Data and Method
### Albedo Direct Estimation
regression was used here for band conversion is because of the linear nature of the BRDF kernel model used by MODIS. One thousand high-quality samples of surface BRDF from the MODIS albedo product (MCD43A) were randomly collected over North America for different seasons and land covers including vegetation, soil, and snow samples. Quality checks on the BRDF data were carried out to ensure that only the data flagged with the highest quality were selected and the directional reflectance calculated from the BRDF parameters for all the viewing geometries fell into valid data range [35,36]. Each set of samples contains the spectral and broadband kernel parameters. Having converted the surface kernel parameter data to HJ bands following Equations (2)-(5), radiative transfer simulations were carried out to simulate the HJ TOA reflectances. Surface shortwave broadband albedo was calculated from the broadband kernel parameters with given viewing geometries. Regression coefficients in Equation (1) were estimated based on the simulated TOA reflectance and surface albedo on an angular-bin basis (Table 2), which were then applied directly to the actual HJ TOA reflectances. An example of the linear regression performance based on the simulated data is shown in Figure 4.  
Figure 3: Comparison of HJ spectral albedos and the values estimated from the simulated MODIS spectral albedos using Equations (2)–(5): (**a**) HJ band 1; (**b**) HJ band 2; (**c**) HJ band 3; and (**d**) HJ band 4. All 245 spectrum samples are included in the scatter plots. The theoretical uncertainties of using HJ TOA reflectance to estimate surface broadband albedo can be constrained within \(\pm 0.025\), which indicates the feasibility of using HJ TOA reflectance data to estimate surface broadband albedo. The accuracy of black-sky albedo estimation is similar to that of white-sky albedo estimation under the same viewing geometries, which improves with the decrease in solar (view) zenith angle. One interesting finding is that under the same set of solar zenith angle and view zenith angle, angular observations sampled close to the perpendicular plane (relative azimuth angle = 90\({}^{\circ}\)) can lead to an improvement in albedo estimation over those sampled close to the principal plane (relative azimuth angle = 0\({}^{\circ}\) or 180\({}^{\circ}\)), particularly for larger solar zenith angles; however, the improvement is not significant.
## 3 Results and Discussion
It can be seen that it has higher thresholds, but the distance distinguishing the thresholds of ON/OFF and OFF/ON is wider. Figure 5b also gives the linear reflection spectra of the proposed metasurfaces, with the inset of field distribution. With the characteristic of field distribution almost unchanging, the FWHM is narrower after the geometry parameter is optimized. It reveals that the Q factor is higher and the resonance is stronger. Optical bistability from the metasurface relies on the resonance within the Kerr material. Stronger resonance leads to the enhancement and localization of the electric field, and thus lower bistability thresholds. However, it also means a narrower FWHM on the spectrum, and thus narrower distance-distinguishing thresholds of ON/OFF and OFF/ON. The threshold is the key parameter to characterize the performance of a bistable switch. Here, Table 1 compares the proposed metasurface with previous works. It can be seen that, although the \(\chi^{(3)}\) used in this work is weak, the optical bistability thresholds supported by the proposed metasurface after asymmetrical optimization have the lowest thresholds for both \(I_{\text{ON-OFF}}\) and \(I_{\text{OFF-ON}}\), and they have at least one order of lowering when comparing to the configuration of photonic crystal using the bound states in the continuum (BIC)  
Figure 6: (**a**) Nonlinear reflection spectra with increased incident light input intensity in the asymmetric device structure. (**b**) Nonlinear reflection spectra with reduced incident light input intensity in the same structure. The two dashed lines represent the resonant wavelength \(\lambda_{\text{res}}\) and the input wavelength \(\lambda_{\text{in}}\), respectively. resonance [28]. Furthermore, the angular dependence of the bistability performance is investigated in Figure 7. In order to excite the nonlinear bistable hysteresis loop, high-incident-intensity light is needed, which is usually obtained by focusing a laser beam. The inclined portion of the focused laser beam would degenerate the threshold. From Figure 5, it is evident that both the thresholds of \(I_{\text{ON-OFF}}\) and \(I_{\text{OFF-ON}}\) barely change under incident angle \(\theta\) < 10\({}^{\circ}\). The reason might lie in the same magnetic field direction between the incident x-polarization light and the excited mirror-induced MAS in the proposed metasurface. As the incident angle increases further, the height-length ratio becomes not suitable for the original magnetic hotspots, and both thresholds experience an exponential rise. The insensitive dependence on incident angle within the range of 0\(\sim\)10\({}^{\circ}\) facilitates the practical application of the miniaturized bistable switch.
## 3 Finite Element Modeling
### FE Model, Results, and Discussion
#### 3.2.2 Model Outputs  
Figure 15 shows the final von Mises and maximum principal stress and strains for compression prism. As can be seen, the maximum stress occurred at the bottom edges of prism, while the maximum strain is located at the middle of the prism. Obviously, this leads to the initiation of vertical tensile splitting cracks at the middle of the prism and spreading to the top and bottom units, which is similar to what was found experimentally. The maximum stress obtained from the numerical model was 8.67 N/mm\({}^{2}\) with a difference of 6% compared with that recorded from experimental results.  
Figure 16 shows the numerical results for the two shear models. Figure 16a presents the von Mises and principal stresses and strains for shear prism with 0.1 N/mm\({}^{2}\) axial constant stress. In addition, the load displacement curve was obtained and compared with the experimental one. Furthermore, Figure 16b describes the same output with different axial constant stress of 0.3 N/mm\({}^{2}\). Displacement for model (b) (with an axial constant stress of 0.3 N/mm\({}^{2}\)) is much higher than model (a) (with an axial constant stress of 0.1 N/mm\({}^{2}\)), which is mainly due to increasing the constant axial stress.  
Figure 16: _Cont._  
Figure 15: Compression prism F.E. results: (**a**) Principal stresses; (**b**) Strains; (**c**) Von Mises stresses.  
The maximum horizontal loads obtained numerically for the two models were 94.4 kN and 119.9 kN for model (a) (with an axial constant stress of 0.1 N/mm\({}^{2}\)) and model (b) (with an axial constant stress of 0.3 N/mm\({}^{2}\)), respectively. Moreover, the horizontal displacement corresponding to the maximum horizontal loads was 0.074 mm and 0.13 mm, respectively. These results show a difference not more than 10% compared with the experimental one. The stepped shape for the experimental load-displacement curves took place due to the fact that the load was manually applied with force control. Furthermore, Figure 16 shows the maximum von Mises stress that occurred at the location of applying the shear loading. The maximum strain is located in the horizontal joints for both models which agreed with the experimental results as shown in Figure 8. The amount of the horizontal displacement (slip) at the location of bed joint is relatively small, indicating a high degree of brittleness for the materials. Failure modes and load-displacement curves for the numerical models showed good agreement with the experimental results.
## 3 Proposal of Ground Station Direction Detection Method Using Color Markers  
### Color Marker Detection Algorithm  
Blob detection, an image processing method for detecting color markers, employs only color information, tends to be subject to noise, and has a high probability of false detection. Consequently, the blob detection method is inadequate for accurately detecting only the color markers installed at ground stations. To solve this issue, we employ the pattern information of multiple color markers in addition to the color information possessed by these color markers. Assume that multiple color markers are installed and their relative positions are known. We can detect only the color markers by matching the known position coordinates of the markers with some of the coordinate points obtained by the blob detection process.  
Figure 2 provides an overview of the proposed method. The underlying assumption is that a reference color marker (RCM) is placed as a center maker at a ground station, with color markers placed around the RCM to assist in RCM detection. The color information from the blob detection process is compared with the color marker placement information predicted on the image plane, and the RCM that best matches the prediction is detected. The detection of RCMs located at ground stations requires three processes:  
1. Blob coordinate detection;
2. Prediction of the marker drawing position in an image;
3. RCM detection.  
The subsequent sections provide a detailed description of each process.
## 4 Methodology  
### Segmentation and Reference Objects  
As is well known, the first step in the OBIA approach involves image segmentation to produce homogeneous and discrete objects. Later, these objects, rather than pixels, are used as the classification unit [36]. It is important to note that automatic segmentation is a crucial process in OBIA approaches. This process has already been successfully achieved on plastic greenhouses by [PERSON] and [PERSON] [9] through the multi-resolution segmentation algorithm included in the eCognition Developer software (Trimble, Sunnyvale, California, United States). Working on a time series of ASTER images for detecting outdoor crops, [PERSON] _et al_. [24] used the multi-resolution segmentation algorithm implemented in eCognition by equally weighting the green, red and NIR bands of all images with 15-m spatial resolution. In the same way, [PERSON] _et al_. [25] carried out their segmentation process by using eCognition's multi-resolution algorithm on a time series of 30-m GSD Landsat images for the detection and mapping of sugarcane in Brazil. In both cases, the agricultural plots had a much larger size than our greenhouses. Therefore, in the present work, the segmentation process will be based on the VHR orthoimage from WV2.  
On the other hand, we have to take into account that the final classification accuracy for horticultural crops under greenhouses is going to be highly dependent on the automatic segmentation process carried out on the WV2 orthoimage. Hence, we decided to conduct a manual digitizing in order to obtain the best possible segmentation on our objects of interest. This was already done in a similar way by [PERSON] _et al_. [10]. Therefore, 694 reference objects provided by Vicasol were manually digitized over the WV2 pan-sharpened orthoimage with 0.4-m GSD, covering a total of 571 ha (Figure 2). The individual average area of the monitored greenhouses was 8200 m\({}^{2}\). According to the ground truth data, the existing crops were tomato _Solanum bycopersicum_ L. (54% of the area), pepper _Capsicum annuum_ L. (27%), aubergine _Solanum melongena_ L. (10%) and cucumber _Cucumis sativus_ L. (9%). It is worth noting that tomato and pepper crops were composed of different varieties (e.g., Californian red pepper, Italian green pepper, yellow pepper, orange pepper, plum tomato, cocktail tomato, cherry tomato, truss tomato).
# Effect of Process Parameters on Mode Conversion in Submicron Tapered Silicon Ridge Waveguides
## 2 Structure and Analysis
### Taper Design for Efficient Mode Conversion
#### 2.2.2 (0.5. H) Ridge Waveguide (Ridge Height = Slab Height)  
The etch-depth of the ridge waveguide is now reduced and made equal to slab height. In this case, both are equal to 250 nm. We observe a substantial increase in mode conversion efficiency as the ridge height is reduced. From Figure 6, even for an ideal case with no sidewalls and symmetric SiO\({}_{2}\) cladding, the taper length for an efficient mode conversion is 985 \(\upmu\)m. With angled sidewalls, the required L\({}_{\text{tp}}\) is 750 \(\upmu\)m, representing a 25% reduction in the overall length. Finally, when angled sidewalls and air-cladding are considered, the corresponding L\({}_{\text{tp}}\) for mode conversion is 655 \(\upmu\)m, which corresponds to a 33.5% reduction compared to an ideal case. When compared with deeply etched ridge waveguide, the effect of angled sidewalls is more pronounced. Since it is not possible to fabricate waveguides without sidewalls, the maximum efficiency is 94.5% for low-loss TM tapers for the sidewall angle of 8\({}^{0}\). For TM polarization preserving taper, Figure 6b and 6c show FDTD propagation in an ultra-short L\({}_{\text{tp}}\) = 10 \(\upmu\)m for sidewall angles of \(\theta\) = 0\({}^{0}\) and \(\theta\) = 8\({}^{0}\), respectively.  
Figure 7 shows the light propagation along an adiabatic L\({}_{\text{tp}}\) = 200 \(\upmu\)m taper, similar to Figure 5 but for 0.5 H etch-depth. Compared to Figure 5, an increase in mode conversion efficiency for the same taper length is observed. It is evident that mode conversion in a tapered ridge waveguide is strongly dependent on etching depth. In a deeply etched waveguide, even when maximum asymmetry due to angled sidewalls and air top cladding is accounted, the conversion is only 56%, whereas, for a 0.5 H waveguide, it is 78%.  
Figure 5: The light propagation in the designed taper L\({}_{\text{tp}}\) = 200 \(\upmu\)m (**a**) \(\theta\) = 0\({}^{0}\) and n\({}_{\text{cl}}\) = 1.445 (**b**) \(\theta\) = 8\({}^{0}\) and n\({}_{\text{cl}}\) = 1.445 (**c**) \(\theta\) = 8\({}^{0}\) and n\({}_{\text{cl}}\) = 1. The total height of silicon is H = 500 nm, ridge height (h) = 0.6 H and simulations are done at 1550 nm wavelength.  
Figure 6: (**a**) The mode conversion efficiency (\(\eta\)) as the function of taper length (L\({}_{\text{tp}}\)) when TM\({}_{0}\) mode is launched. (**b**) Light propagation with \(\theta\) = 0\({}^{0}\) and n\({}_{\text{cl}}\) = 1.445. (**c**) Light propagation with \(\theta\) = 8\({}^{0}\) and n\({}_{\text{cl}}\) = 1.445. The total height of silicon is H = 500 nm, ridge height (h) = 0.5 H, and simulations are done at 1550 nm wavelength.  
Therefore, process parameters play an important role in mode conversion, and they must be accounted for in the PIC circuit design. Furthermore, a small change in the etch depth can cause a significant difference in the mode conversion.
## 2 Related Work  
The past few years have shown the increasing amount of data usage in healthcare [6], which has contributed to the development of new approaches based on ML and AI. However, since most healthcare data contain patient information, the privacy and security of data during data processing have become crucial factors. Several measures have been suggested to solve the privacy concerns, especially in distributed machine learning; one of the most prominent solutions is known as federated learning (FL) [7]. This section presents a literature analysis of the federated learning approach in the healthcare context, privacy-preserving methods, and improvements in the hierarchical learning architecture.  
FL has been employed across several healthcare areas as a method of applying model updates without data aggregations. Reference [8] can be regarded as the first work that proposed the idea of federated learning and showed its applicability in mobile and edge devices. Subsequently, the potential use of the framework in the field of healthcare was investigated, where institutions could train models for disease prediction, patient risk assessment, and treatment outcome prediction without actually sharing any patient information. The research failed to incorporate formal privacy interventions like Differential Privacy (DP) or Secure Multi-Party Computation (SMPC), leaving the system vulnerable to gradient leakage or reconstruction attacks. Similarly to [9], the authors used FL with EHRs to create predictive models for cardiovascular disease and cancer diagnosis. Nevertheless, these initial FL applications encountered several issues regarding scalability, computational cost, and the management of data heterogeneity across institutions. The homogeneous or IID data distribution across clients was also assumed by these methods, which is not often true in real-world healthcare practice where data are, more often than not, scarce, highly imbalanced, and non-IID, causing a sharp decline in model performance.  
In light of these challenges, various improvement to the basic FL framework were suggested by researchers. For instance, the FLBM-IoT and PPFLB models proposed optimizations for federated learning in IoT scenarios and with healthcare big data, aiming at minimizing the communication burden and enhancing model performance [10]. However, these models were still confined to single-tier aggregation and hence were not very useful in large healthcare networks [11]. Also, they did not have strong measures put in place that would guarantee privacy in the transmission of model updates, especially necessary in the healthcare sector.