esa-sceva/satcom-chunk-collection huggingface dataset:
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 1 Introduction
### The Applied Methodology  
Two issues are addressed here. The first one deals with the best choice of the method of effective detection and assessment of the locking phenomena. The second issue is related to the numerical methods of removing the phenomena.  
It results from the above literature survey that the available knowledge on the locking phenomena allows understanding of the nature and sources of appearance of the phenomena. The accumulated knowledge on the phenomenon allows also for a priori determination of the solution convergence of the problems where the phenomena appear. The main difficulty in the direct application of these results in the numerical analysis of any arbitrary thin-walled structure is that the available results concern the specific model problems which may differ to the arbitrary problem under consideration.  
The second conclusion from the literature survey is that the most effective way of removing the locking phenomena lies in application of the higher-order longitudinal \(p\)-approximation of the displacement field in the analyzed thin-walled structure or a thin-walled part of the complex structure.  
It also results from the literature that some detection methods of the phenomena exist. Among these methods, the approach proposed in [37; 38; 39] seems to be best suited for adaptive analysis. This approach is based on the same numerical techniques that are applied in the error-controlled adaptivity.  
The main feature of the proposed a posteriori detection, assessment and removal of the locking phenomena is that the adaptation process requires four steps, instead of the standard three steps of the error-controlled \(hp\)-adaptivity proposed in [40]. The additional step of adaptation, which lies in initial mesh modification, incorporates not only the automatic removal of the locking phenomena but also the automatic resolution of the boundary layers [38]. The main idea standing behind the additional adaptation step is to move the numerical solution, obtained with use of \(hp\)-approximations [41; 42], to the asymptotic convergence range. Within this range, the standard \(h\)- and \(p\)-adaptation steps can be made based on the \(hp\)-convergence theorem and upper-bounding values of the global error estimates from the equilibrated residual method [43; 44].  
Finally, it should be noted that our detection and assessment tools are based on sensitivity analysis, not on the estimated error values themselves. Thanks to this, requirements concerning the error estimation can be relaxed.
# Fibr Facilitates Mesenchymal Stem Cells to Ameliorate Rats with Polycystic Ovary Syndrome
## 2 Result
### Serum Hormone Levels  
Considerable variations in serum hormonal levels were observed after UC-MSCs and UC-MSCs mixed with fibrin were transplanted into the ovaries of PCOS rats. The PCOS rats showed significantly higher testosterone levels (0.387 \(\pm\) 0.176 ng/mL) compared to the normal group. UC-MSCs administration significantly reduced serum testosterone levels (0.209 \(\pm\) 0.088 ng/mL, 0.062 \(\pm\) 0.011 ng/mL, \(p\) < 0.01). However, UC-MSCs mixed with fibrin reduced testosterone levels to the levels observed in normal rats more efficiently, suggesting that UC-MSCs + fibrin administration may be potentially utilized in the treatment of hyperandrogenemia in PCOS patients. Furthermore, a significant difference in testosterone levels was observed between the UC-MSCs group and UC-MSCs + fibrin group (\(p\) < 0.01, Figure 5D), revealing that UC-MSCs can restore androgen levels in PCOS more efficiently.  
Significantly reduced concentrations of estradiol (E2) and progesterone were observed in the PCOS rat model compared to the control group as expected. UC-MSCs + fibrin treatment completely restored serum E2 levels in PCOS rats as observed in normal rats within two weeks (Figure 5A), whereas UC-MSCs alone also showed a significant increase in E2. However, treatment with UC-MSCs alone effectively restored progesterone levels to normal levels, whereas UC-MSCs + fibrin treatment increased mean progesterone levels (\(p\) < 0.000001) higher than the normal levels (Figure 5B). In PCOS rats, higher serum gonadotropin (LH/FSH) levels were observed compared to normal rats, whereas UC-MSCs with and without fibrin induced a reduction in serum gonadotropin levels to that in the control group (Figure 5C). These results collectively suggest that UC-MSCs with the fibrin scaffold had a greater impact on restoring hormonal dysfunction in the PCOS rat model.  
Figure 4: Comparison between ovary size and follicular morphology after implantation. **(A)** A representative ovary from various groups showing relative ovary size. **(B)** Ovarian and follicular morphology in various groups were examined by haematoxylin and eosin (H&E) staining. Ovaries with thicker granular layer and a higher number of cells in the corpus luteum were assessed in the normal and UC-MSCs + fibrin group. Black arrow: cystic follicle with fewer granules, red arrow: follicle with more granules, white triangle: corpus luteum (scale bar, 500 μm). **(C)** Mean body weight did not significantly differ among all groups. **(D)** Ovary weight significantly differed between the PCOS and normal groups (****\(p\) < 0.001) and between PCOS and UC-MSCs + fibrin groups (\(\text{\textasci{*}}\,p\) < 0.5).
## 5 Evaluations
### Factors of Evaluation  
In 1:1 authentication, the biometric information provided is classified as belonging to an authorized user. The false acceptance rate (FAR) is the rate at which biometric information belonging to an unauthorized user is incorrectly accepted as that of an authorized user. The false rejection rate (FRR) is the rate at which biometric information belonging to an authorized user is incorrectly rejected as that of an unauthorized user. The true acceptance rate (TAR) is the rate at which the biometric information of an authorized user is correctly accepted as such. These are defined by the following equations:  
\[\text{FAR} =\frac{T_{FA}}{N_{auth}} \tag{5}\] \[\text{FRR} =\frac{T_{FR}}{N_{auth}}\] (6) \[\text{TAR} =1-\text{FRR} \tag{7}\]  
where \(N_{auth}\) is the number of authentications performed, \(T_{FR}\) is the number of authorized users incorrectly rejected, and \(T_{FA}\) is the number of unauthorized users incorrectly accepted. When performing classifications, it is necessary to set a threshold that determines the minimum probability of true acceptance. If the threshold is set low, it is more difficult to incorrectly reject an authorized user and easier to incorrectly accept an unauthorized user. Contrarily, if the threshold is set high, it becomes easier to reject authorized users. Therefore, as illustrated in Figure 5(a), there is a trade-off between FAR and FRR. The graph shown in Figure 5(a), which indicates the relationship between FAR and FRR, is known as a detection error tradeoff (DET) curve. The intersection between the FAR and FRR is known as the equal error rate (EER), which is used as a performance indicator of biometric authentication. The lower the EER, the higher the authentication accuracy. The graph shown in Figure 5(b), which indicates the relationship between TAR and FAR, is known as a receiver operating characteristic (ROC) curve. The area below the ROC curve is referred to as the area under the curve (AUC) and is used as a performance indicator of biometric  
Figure 5: Overview of bilateral ear acoustic authentication system.  
authentication. The AUC varies from 0 to 1, that is, if there are no authentication mistakes, the AUC is one, and in the case of accidental authentication, 0.5. Therefore, the higher the AUC, the higher the authentication accuracy.
## 2 The Permanent Facility for Altimeter Calibration
### Infrastructure and Instrumentation
#### 2.1.2 The CRS1 Cal/Val Site  
The CRS1 Cal/Val site is located in a small, well-protected fishing harbor at the tip of southwestern Crete. Its establishment was initiated in 2007 primarily for the calibration of the American Navy's Geostat Follow-On (GFO) altimetric mission (Figure 8) as it is almost under a crossover of its ascending and descending orbits. The site had been fully operational in March 2008 but unfortunately it did not serve that purpose as an onboard problem of the GFO satellite led to its termination on 26 November 2008. A decision was then made to continue to maintain the CRS1 Cal/Val site as operational to monitor tectonic deformation and tsunamis.  
Then later in August 2011, the launch of the Chinese HY-2A altimeter mission provided the impetus for re-activating the CRS1 as a satellite altimetry Cal/Val site. The HY-2A descending Pass No. 280 happens to pass close by and only 9 km west of the CRS1 Cal/Val site (Figure 8).  
A set of a pressure and a radar tide gauges has been operating at the CRS1 Cal/Val site, supported by a GNSS receiver and a meteorological sensor (Figure 9).  
Till now, the CRS1 Cal/Val site has been primarily employed for the calibration of the HY-2A altimeter [31]. It is also planned to act as a calibration site for both Sentinel-3A (Pass No. 278) and  
Figure 8: The CRS1 Cal/Val site has been initially installed next to a GFO crossover (**left**) but later it has been used to calibrate the Chinese HY-2A altimeter (**right**).  
Figure 7: The Gavdos Cal/Val facility provides sea-surface calibration for the Jason series (Red: Pass No. 18-descending and No. 109-ascending), SARAL/AltiKa (Yellow: Pass No. 571) and Sentinel-3A (Green: Pass No. 14-descending and No. 335-ascending).  
Sentinel-3B (Pass No. 014) altimeters as a crossover of these satellites takes place 20 km west of CRS1 in open and deep ocean (3000 m deep, Figure 10).
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
###### Abstract  
Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOv4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.  
transmission line; insulator; improved YOLOv4; lightweight convolutional neural network defect detection +
Footnote †: journal: Applied Sciences
# Agricultural Waste-Based Biochar for Agronomic Applications
## 8 Conclusions  
This review deals with the beneficial role of biochar in different agricultural applications. Biochar'sphysico-chemical properties, which depend on many variables such as biomass source, pyrolysis conditions, biochar ageing, determine its various agronomic functions. Biochar can act as a soil amendment, as a co-substrate in composting and as a feed additive to improve livestock productivity. The effect of biochar amendments in mitigating greenhouse gas emissions has also been proven. However, and based on literature findings, benefits of biochar application in agriculture are often limited to specific conditions. For instance, biochar can significantly improve soil properties and crops yield for certain soils and certain plants, while showing no significant effect or sometimes negative effect on other soils. This can be due to the large variety in biochar properties but also to the variety in soil types, crops specificity, climate conditions and the difficulty to predict the real interaction between biochar and soil nutrients and contaminants. This makes it difficult to decide on the appropriate biochar for the appropriate agricultural application. Hence, the real challenge is not the feasibility and efficacy of biochar application, but rather the selection of the appropriate biomass and pyrolysis conditions to produce specific biochars for specific purposes. Moreover, most of the studies presented so far were performed under laboratory conditions, which make the underlined conclusions unsuitable to cover all biochar effects and to define all limitations that could be related to its application. More experiments are therefore needed to evaluate the longevity of biochar's impact in agriculture under realistic conditions.  
Conceptualization, G.E. and M.L.; writing--original draft preparation, G.E. and M.L.; writing--review and editing, G.E. and M.L. Both authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 5 Challenges and Future Research Directions
### Standardization  
The prominent organizations such as IEEE and ITU have not yet finalized the standardization and regulation of blockchain technology. As a result, adequate rules, guidance and regulations are required in the real-time deployment of blockchain with the UAV network. Thus, it is necessary to develop technological standards and recommendations that will make the deployment of UAVs over 6G communication channels simple and efficient. It is difficult to acquire blockchain in real-world 6G enabled UAV networks without the blockchain technology standardization [13].
## 3 Results and Discussion
### Ar Broadening Coefficient  
The HCl gas molecule is not only reactive but also very sticky, similar as NH\({}_{3}\). As we have shown in our previous study [20], the system equilibrium condition can only be reached after passivation. Actually, during this passivation process, HCl molecules stick to the inner surface of the system, while those molecules came into the gas flow again during the evacuation process. We made use of this property of the HCl molecule for foreign broadening coefficients' measurements.  
After flushing the cell with HCl/N\({}_{2}\) gas mixture for about an hour to passivate the system, the cell was evacuated using a chemistry vacuum pump to the lowest possible pressure to minimize the HCl self-and N\({}_{2}\)-broadening effects. Due to the degassing effect, HCl molecules were released from the inner surface. After the evacuation process, pure Ar was filled into the gas cell to a certain pressure. Therefore, in the cell, there were trace HCl (tens of ppm level, but sufficient to measure) and buffer gas Ar.  
The HCl-Ar broadening measurements were carried out after those processes. The spectrum treatment was the same as for CO\({}_{2}\) and N\({}_{2}\) broadening measurements in Sections 3.1 and 3.2. Figure 4a shows the measured HCl absorption spectrum in Ar (160.5 mbar, 22 \({}^{\circ}\)C), the fitting residuals were around (1\(\sigma\)) 4.5 \(\times\) 10\({}^{-4}\) (optical density), which corresponds to a SNR of 351 for the spectrum of 27.3 ppm HCl in Ar. The measured Lorentzian widths under different pressures are shown in Figure 4b, the linear fit has an intercept of (1.31 \(\pm\) 6.29) \(\times\) 10\({}^{-4}\) cm\({}^{-1}\). The measured HCl-Ar broadening coefficient from the linear fit slope is \(\gamma_{\rm Ar}\) = 26.23(78) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm at 295 K.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 2 Data and Methods
### Multi-Image Color Match  
The features in the Antarctic satellite images are relatively simple compared with the surface features at the lower latitudes of the earth, and the reflectance of ground object features varies greatly (such as oceans and ice/snow). If an improper method is used in the process of color stretching in an Antarctic image, it is easy to cause excessive stretching and color distortion of some ground object features. Based on histogram specification theory, this study adopts different mapping methods for low-reflectance and high-reflectance features in the process of histogram mapping between the original image and reference image. Meanwhile, to prevent gray-level discontinuity in the image mapping, gray-level linear stretch transformation is used in the mapping rule.  
Assume that T (T \(\in\) [0, 255]) is the threshold of low-reflection objects and high-reflection objects in an Antarctic satellite image, and R(x,y) and O(x,y) are the reference image and the original image, respectively. Then, histogram equalization transformation is performed for the original image and the reference image:  
\[\mathrm{F_{R}(x,y)}=\begin{cases}\mathrm{R(x,y)}&\mathrm{R(x,y)}<\mathrm{T}\\ \mathrm{T}+(255-\mathrm{T})\sum\limits_{\mathrm{R(x,y)=T}}^{255}\mathrm{P_{R}( R(x,y))}&\mathrm{R(x,y)}\geq\mathrm{T}\end{cases} \tag{5}\]  
\[\mathrm{F_{O}(x,y)}=\begin{cases}\mathrm{O(x,y)}&\mathrm{O(x,y)}<\mathrm{T}\\ \mathrm{T}+(255-\mathrm{T})\sum\limits_{\mathrm{O(x,y)=T}}^{255}\mathrm{P_{O}( O(x,y))}&\mathrm{O(x,y)}\geq\mathrm{T}\end{cases} \tag{6}\]  
where \(\sum\limits_{\mathrm{R(x,y)=T}}^{255}\mathrm{P_{R}(R(x,y))}\) and \(\sum\limits_{\mathrm{O(x,y)=T}}^{255}\mathrm{P_{O}(O(x,y))}\) represent the cumulative histogram transformation of the images R(x,y) and O(x,y), respectively, and \(\mathrm{F_{R}(x,y)}\) and \(\mathrm{F_{O}(x,y)}\) are the histogram equalized results of the reference image and the original image, respectively. According to the mapping rule of minimum \(\left|\mathrm{F_{O}-F_{R}}\right|\), after histogram equalization conversion, assume that the conversion value of the original image O(x,y) gray value range \(\left[\mathrm{M_{i},M_{j}}\right]\left(0\leq\mathrm{M_{i}}\leq\mathrm{M_{j}} \leq 255\right)\) is \(DN_{O}\), and the conversion value of the reference image R(x,y) gray value range \(\left[\mathrm{N_{i},N_{j}}\right]\left(0\leq\mathrm{N_{i}}\leq\mathrm{N_{j}} \leq 255\right)\) is \(DN_{R}\), and the \(\left|\,DN_{O}-DN_{R}\,\right|\) is the smallest. Then the gray value of the output image is mapped as  
\[\mathrm{I}=\begin{bmatrix}\mathrm{M-M_{i}}\\ \mathrm{M_{j}-M_{i}}\end{bmatrix}*\left(\mathrm{N_{j}-N_{i}}\right)+\mathrm{N_ {i}},\ \left(\mathrm{M_{i}}\leq\mathrm{M}\leq\mathrm{M_{j}}\right)\end{cases} \tag{7}\]  
The mapping value I for the gray level M of the original image O(x,y) can be calculated using the above mapping rule, and the gray level of the original image can be converted according to this formula to achieve color correction. The workflow of multi-image color matching processing is shown in Figure 2.
# Image-Captioning Model Compression
## 4 Experiments
### Decoder Pruning
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Pruning Coefficient** & **B@1** & **B@4** & **M** & **R** & **C** & **S** & **Size** & **NNZ** \\ \hline \multicolumn{8}{c}{Up-Down} \\ \hline \(\alpha=0\) & 79.4 & 35.2 & 27.1 & 56.6 & 119.2 & 20.9 & 68.8 MB & 18 M \\ \(\alpha=\textbf{0.1}\) & **79.2** & **35.1** & **26.9** & **56.3** & **118.4** & **20.8** & **62 MB** & **16.2 M** \\ \(\alpha=0.3\) & 77.5 & 32.6 & 25.7 & 54.7 & 109.4 & 19.5 & 48.2 MB & 12.6 M \\ \hline \multicolumn{8}{c}{AoANet} \\ \hline \(\alpha=0\) & 79.5 & 38.5 & 29.5 & 58.3 & 129.1 & 22.1 & 100.7 MB & 26.4 M \\ \(\alpha=\textbf{0.5}\) & **79.3** & **38.2** & **29.1** & **58.3** & **127.6** & **21.5** & **50.4 MB** & **13.2 M** \\ \(\alpha=0.7\) & 75.5 & 34.1 & 26.6 & 56.3 & 112 & 19 & 30.2 MB & 7.9 M \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of all of tested models’ decoders pruned using different pruning coefficients. Both “Size” and “NNZ” columns refer to the decoder.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 2 Basic Concepts of the Wind Power and Turbines
### Classic Methods in the Field of Wind Energy  
Predictive maintenance is heavily debated in the wind energy industry because defect prevention reduces production costs. Classical methods are used to predict defects, but ML approaches offer undeniable advantages over traditional methods.  
Classic predictive maintenance methods are based on techniques such as the following:  
* Vibration analysis measures the vibration frequencies of mechanical components, such as bearings or wind turbine shafts [39]. Different elements are analyzed through vibrations. [PERSON] et al. [40] studied the vibrations of traction cables that affect the wind turbine blades' static tests. The study analyzed the influence of cable length and pulley position to provide recommendations to avoid resonance and reduce vibrations. If the amplitude of the vibrations exceeds a preset threshold, the system triggers an alert.
* Statistical process control uses historical data to identify deviations from normal parameters. For example, if the temperature of a motor rises above a specific value for more than several minutes, this indicates a problem. The analyzed studies emphasize the need for the statistical monitoring of wind energy. Numerical models are evaluated using statistical comparisons between simulated data and meteorological observations, emphasizing regional wind variability and underestimation or overestimation errors [41]. The article by [PERSON] et al. [42] used statistical distributions, such as Weibull, generalized extreme value (GEV), etc., to evaluate the wind energy potential. In the paper [43], the simulation of blade icing is optimized through statistical analyses that correlate factors such as wind speed, temperature, and liquid water content. This approach highlights nonlinear relationships for prediction. Nonlinearity highlights the need to explore ML models that offer better results in these scenarios.  
These methods work well in simple scenarios where defects are addressed linearly, described by a small number of variables. However, they encounter difficulties when it comes to managing complex systems. Wind turbines involve many sensors in maintenance processes that monitor environmental parameters. Nonlinear interactions between components characterize this behavior.  
This drawback is managed by the ability of ML models to analyze large amounts of data and learn from the system's behavior. The algorithms presented in this paper belong to the ML category. They simultaneously process multiple variables and identify hidden correlations that are impossible for humans to detect, as well as for applications that do not include AI components. This human incapacity is explained by the inability to perform complex calculations beyond those of a computing system. Additionally, processing a volume of data as large as that handled by an AI-equipped computing system is impossible. For these reasons, ML models are superior to traditional methods.
## 3 The ASLM Architecture
### Model Description  
In this section, we discuss the ASLM model's entire recommendation process and the main system components with the adopted technologies we introduced.  
As illustrated in Figure 2, ASLM integrates the modeling of the user's Long-term and augmented Attention-based Short-term Intent Layers into one single architecture. The Long-term Preference Layer is evolved from the Inter-session RNN used in [7], and the model proposed in it is regarded as a baseline to compare with ASLM model.