esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Studies  
The impact of wind speed, wind direction, and metrological conditions on the variation in FCL in a wind-farm-integrated power system was observed by using the wind-speed prediction model, i.e., hybrid ANFIS-SARIMA. To increase the reliability of the power system, relay settings are updated continuously according to the predicted variation in FCL caused by wind speed. HHO-LP is incorporated to find the optimal values of TMS and \(\mathrm{I_{p}}\) for a minimization of the total operation time of primary and backup relays for the fastest fault isolation.  
The short-term interval (5 min) wind-speed data for the year 2019 collected from the southern parts of Pakistan, Jamshoro city, Sindh province, were used to train the ANFIS-SARIMA model. The latitude and longitude of this geographical location are 25.4007 and 68.2662, respectively. The predicted and actual wind speed, wind direction, and metrological conditions for one day in all four seasons are  
Figure 6: The hybrid ANFIS–seasonal autoregression integrated moving average (SARIMA) forecasting algorithm coupled with the hybrid Harris hawks optimization and linear programming (HHO–LP) optimization algorithm.
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 1 Introduction
To address these limitations, this study presents an integrated deep learning framework that combines the image classification model GoogleNet with the semantic segmentation model SegNet for crack detection in historic buildings. GoogleNet is used to perform global feature extraction and image-level classification [35; 36], while SegNet facilitates pixel-level segmentation of crack boundaries and patterns. To convert image data into real-world measurements, a dual-laser system is employed for pixel-to-length calibration. By integrating the classification capabilities of GoogleNet with the segmentation precision of SegNet, this study presents a practical and efficient end-to-end workflow for crack detection, delineation, and quantification. Although the architecture is based on established deep learning models, the novelty of this work lies in its integration within a unified framework specifically designed for heritage crack analysis. The proposed approach combines image classification for preliminary screening with semantic segmentation for detailed mapping, enhanced by a field-based dual-laser calibration technique. The key contributions of this study are as follows:  
* A two-stage workflow is introduced, wherein image classification is first employed to identify images containing potential cracks, followed by semantic segmentation to delineate crack regions. This approach offers a lower computational cost compared to more complex instance segmentation models. * By integrating segmentation outputs with a simple yet effective field-based tool (parallel lasers), the method facilitates accurate estimation of crack lengths. * The proposed framework demonstrates strong adaptability to varying lighting conditions and background textures typical of heritage surfaces. Its lightweight design also enables potential deployment on portable platforms. Overall, this study presents an effective interdisciplinary methodology that bridges deep learning techniques with heritage conservation practices, offering practical applications for digital documentation, preservation, and structural monitoring of historic buildings.
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 3 Results and Discussion  
### Materials Characterization by Thermogravimetry and XRD  
Pyrolysis of the cotton at 320 \({}^{\circ}\)C is associated with the degradation of cellulose and its structure, producing sample CB. At temperatures above 300 \({}^{\circ}\)C, the degradation of the carbohydrate contributes to an increased fixed carbon content, which reaches 6 wt.% in sample CB.  
Sample RHB was prepared by the pyrolysis of rice husks at 480\({}^{\circ}\) C, while sample ARHB was synthesized by chemically activating sample RHB at 700\({}^{\circ}\) C. In this higher temperature range, the lignocellulosicose matrix undergoes significant decomposition, releasing large amounts of volatile matter and increasing the carbon fraction in the biochar. The amount of fixed carbon in both samples is about 10 wt.%, as shown in Figure 1. The pyrolysis process is associated with an increase in the sp\({}^{2}\) content of biochar, which is proportional to the pyrolysis temperature [38].  
In general, the solid pyrolysis residue retains the structure of the lignocellulosic matrix if the latter has not been subjected to chemical pretreatment. The typical three well-defined crystalline peaks in cellulose around 16\({}^{\circ}\), 22\({}^{\circ}\), and 35\({}^{\circ}\) 20, as reported by [PERSON] et al., are not present in the spectrum of the CB sample (Figure 2) [39]. Despite the low pyrolysis temperature, the two prominent peaks at 24.5\({}^{\circ}\) and 43\({}^{\circ}\) 20 degrees in the CB sample indicate the presence of an ordered carbon structure. The preliminary removal of Si from the lignocellulosic matrix, associated with hydrolysis processes, leads to more disordered carbon structures in the RHB and ARHB samples, as shown in Figure 2.  
The adsorption of silver onto ARHB resulted in structural changes, as evidenced by the appearance of new diffraction peaks in the 10-35\({}^{\circ}\) 20 range (Figure 2b). These peaks suggest the formation of silver-related crystalline phases, potentially due to the presence of silver nanoparticles, silver oxide (Ag\({}_{2}\)O), or silver clusters interacting with the biochar surface. Such transformations could arise from the reduction in Ag ions or their complexation with functional groups on the biochar, leading to distinct XRD features.  
Figure 1: TG analysis of the biochars, heating rate 10 \({}^{\circ}\)C min\({}^{-1}\); atmosphere—air.
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 2 Materials and Methods
### TDA Features Extraction
#### 2.2.2 Topological Data Analysis  
TDA provides insight into the underlying structure in data points of various dimensions [31], making it effective for the analysis of high-dimensional phase trajectory obtained by PSR, which is also considered as a high-dimensional point cloud due to its discrete form. Persistent homology (PH) is a TDA method for describing the latent structure of a point cloud which is computed during the filtration of the point cloud. Point cloud filtration using the Rips complex has been used for ECG analysis and has been proved to be effective, for example for atrial fibrillation detection [32]. Therefore, this study employs Rips complex filtration for TDA feature extraction.  
PH can be characterized by the Betti number, which implies \(n\)-dimensional holes in the Rips complex. Since the period between the start of the hole and its disappearance is considered as PH, it can be presented as a barcode, which is a table of PH and its corresponding Betti numbers [32]. Figure 4 demonstrates the barcode diagrams of a 10 s VF split frame and a 10 s nVF split frame when \(m\) is chosen as 6 and \(\tau\) is chosen as 12.  
Figure 4: Barcode diagram of VF and nVF split frames. (**a**) VF split frame with Betti number 0 to 2; (**b**) nVF split frame with Betti number 0 to 2.  
In this study, the SimBa method is used to obtain approximate results of Rips filtration for improving computational efficiency [33], and the extraction procedure of TDA features is described as follows:  
1. Obtain the barcode from the point cloud;
2. Calculate the PH duration length (PHDL) of each complex during the filtration process, which is defined as the difference between the radius when the complex disappears and the radius when the complex appears: \[\mathrm{PHDL}_{b}(i)=\varepsilon_{\mathrm{disappear}}-\varepsilon_{\mathrm{ appear}}(i=1,\ 2,\ 3,\ 4,\ \ldots,\ \mathrm{N}),\] (3) where \(b\) is the Betti number, \(\varepsilon\) is the radius during the filtration process, and there are N complexes in the barcodes; and,
3. Finally, the three statistical features of PHDL in the barcodes with Betti number \(b\) are calculated as follows: \[\mathrm{sum}_{b}=\sum_{i=1}^{\mathrm{N}}\mathrm{PHDL}_{b}(i),\] (4) \[\mathrm{mean}_{b}=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}}\mathrm{ PHDL}_{b}(i),\] (5) \[\mathrm{var}_{b}=\frac{1}{\mathrm{N}-1}\sum_{i=1}^{\mathrm{N}}\lvert \mathrm{PHDL}_{b}(i)-\mathrm{mean}_{b}\rvert^{2}\] (6)  
Totally, nine TDA features were calculated from the point clouds obtained from PSR; they are listed in Table 2.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 5 Conclusions  
Winter wheat is one of the most widely grown grain crops in the world, and pre-harvest insights into yields can help optimize management practices. In this study, we developed an ensemble learning framework for stacking information from multiple growth stages of the crop. The results showed that the ensemble model outperformed other individual growth stage models. In addition, coupling vegetation indices with LAI can effectively overcome the spectral saturation phenomenon and improve the prediction accuracy of the model. Our study demonstrated the effectiveness of Stacking technique for improving GY prediction accuracy (Figure A4), which can provide new ideas for yield estimation of other crops.  
Conceptualization, C.L.; Data curation, C.M.; methodology, Y.W.; software, Y.W.; formal analysis, Y.L.; writing original draft preparation, Y.W.; writing review and editing, C.L.; validation, Y.W.; investigation, W.C. and Z.X.; supervision, F.D.; visualization, J.L.; funding acquisition, C.L. and C.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Natural Science Foundation of China (41871333), the Important Project of Science and Technology of the Henan Province (212102110238) and Key scientific research project of Henan college and university (20B420002).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Measurement Dates** & \multicolumn{3}{c}{**2016–2017**} & \multicolumn{3}{c}{**2017–2018**} & \multicolumn{3}{c}{**2016–2018**} \\
**Treatments** & **MEAN** &  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Measurement Dates**} & \multicolumn{3}{c}{**2016–2017**} & \multicolumn{3}{c}{**2017–2018**} & \multicolumn{3}{c}{**2016–2018**} \\ \multicolumn{1}{c}{**Treatments**} & \multicolumn{1}{c}{**MEAN**} & \multicolumn{1}{c}{**SD**} & \multicolumn{1}{c}{**CV**} & \multicolumn{1}{c}{**MEAN**} & \multicolumn{1}{c}{**SD**} & \multicolumn{1}{c}{**CV**} & \multicolumn{1}{c}{**MEAN**} & \multicolumn{1}{c}{**SD**} & \multicolumn{1}{c}{**CV**} \\ \hline N\({}_{\text{2--W}}\)\({}_{\text{1}}\)\({}_{\text{--P}}\)\({}_{\text{2}}\) & 2.27 & 0.53 & 23.33 & 2.43 & 0.55 & 22.63 & 2.35 & 0.5  
Figure 17: Regression plots, density curves and correlation coefficients for grain yield predictions generated by the four growth stages. In the four key growth stages. (**a**) Without LAI; (**b**) With LAI. Significant (***) at \(p<0.001\).
## 2 Materials and Methods
Each Operator was given one teeth pair embedded in an acrylic resin block consisting of one tooth from the DSC and one from the DFC group, de-bonding instruments, and instructions to remove brackets and composite using orthodontic de-bonding pliers under assistance (aspiration of the residuals and holding of the UV lamp). The scopes of the research were blinded so the operators performed the de-bonding without knowing the effects of the UV lamp used on the composite of both DFC and DSC. The use of UV light to evidence the presence of composite remnants, even without fluorescent particles, has previously been evidenced as a positive outcome factor in past research [7; 8; 9; 10; 11]; therefore, blinding was necessary to avoid bias. The UV lamp used in the study had the following characteristics: 360-370 nm min 20 mW/cm\({}^{2}\) (which is harmless for biological tissues but requires eyes protection). It was held a maximum of 50 cm from the tooth in order not to dissipate the light and not to be in contrast with the environmental light to decrease potential risk of bias of light change due to different times of the day and weather conditions. Operators wore protective eyeglasses to avoid damage related to the use of UV light. The simplified de-bonding procedure was performed and composite was removed with the aid of a 12-flute tungsten carbide bur under UV lamp. The procedure was performed under the same conditions for both teeth from the two sample groups. The duration of the removal was measured for a subgroup of 20 teeth with similar anatomic structures (canines and incisors) right after the removal of the orthodontic bracket at the start of the removal with burs performed by similarly experienced operators. The duration of the procedure was measured only for a subgroup (\(n=20\)) of frontal teeth with similar anatomy (10 incisors and 10 canines) to decrease the risk of bias related to the anatomical variance (Figure 4). * Grade 0: visible composite residuals OR significant damage to the enamel surface visible without 5\(\times\) magnification from any view OR any anatomical change to the tooth;
* Grade 1: minor enamel scratching or damage not exposing the dentine, which was partially visible without 5\(\times\) magnification from some views or/and composite residuals visible using 5\(\times\) magnification only;
* Grade 2: absence of scratches or presence of minor scratches in the enamel surface, visible only with the use of 5\(\times\) magnification. All the collected data were analyzed using statistical software (SPSS 20, IBM) by calculation of descriptive statistics and Pearson Two-Tailed test, and Mann-Whitney \(U\) tests (independent samples) for small sample sizes were used for group comparison of ARI and duration of procedure; \(p\)-value \(<0.05\) was considered statistically significant (Figure 5).
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 3 Results  
### Comparing Along-Track Coverage  
To assess the observation efficiency of HY-2 series satellites, we analyzed the distance to the nearest observation of the reference groups and evaluation groups at different latitudes. The shorter the distance to the nearest observation, the higher the density of orbital coverage. Generally, the orbital densities of three regions are, in descending order, high latitudes, middle latitudes and low latitudes. Regarding the spatial distribution of reference groups, it varies at different latitudes. At high latitudes (Figure 3a), the orbital  
Figure 2: January 2019 of (**a**) DUACS DT2018 sea level anomaly (SLA) products; (**b**) SLA obtained by our optimal interpolation (OI) analysis; and (**c**) differences between two datasets. Unit of SLA in cm.  
coverage shows a decrease from top to bottom. At middle latitudes (Figure 3d), it is an hourglass shape, with a lower orbital coverage collected at the left and right edges. At low latitudes (Figure 3g), the orbital coverage is similar in all locations, except for low values at the north-west and south-west corners. In evaluation groups, the average distances to the nearest observation are decreased to 54 km at high latitudes (Figure 3b), 102 km at middle latitudes (Figure 3e) and 116 km at low latitudes (Figure 3h). The distance decreased by 43% at high latitudes (Figure 3c), 47% at middle latitudes (Figure 3f) and 35% at low latitudes (Figure 3i). Compared to the reference groups, it is a significant enhancement in the observation efficiency of evaluation groups.  
It is worth noting that the increase in orbital density may reduce the tuned parameters in the OI algorithm. As can be seen from Table 2, for the same regions, the correlation scales of the evaluation groups decrease at both middle and high latitudes. When the along-ground tracks are sparse, observations cannot be effectively interpolated, which leads to a reduction in the accuracy of the gridded products. Thus, a suitable increase in correlation scales would prevent the results from containing fewer observations, improving the accuracy of the gridded products. However, correlation scales that are too large would smooth out the gridded products, leaving them lacking details. In summary, the selection of correlation scales is a compromise between the characteristics of the SSH field and the sampling capability of altimeter constellation.  
Figure 3: Orbital coverage distribution of reference groups and evaluation groups at (**a,b**) high latitudes, (**d,e**) middle latitudes, and (**g,h**) low latitudes. The value of each figure is the distance to the nearest along-track observation, which is used to represent orbital coverage. Time series of daily orbital coverage of reference groups and evaluation groups at (**c**) high latitudes, (**f**) middle latitudes, and (**i**) low latitudes. Unit of distance is km and unit of time is day.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## 2 Results
### FOXM1 Isoform Expression in GTEx Normal, TCGA Normal, and TCGA Pan-Cancer Tissues  
FOXM1 has three known splice variants: FOXM1a, b, and c, which encode proteins with varying activities [53; 16; 54]. Current knowledge of _FOXM1_ isoform expression in different cancer types is inconsistent and has not been defined in relation to normal tissues or paired normal and tumor samples. To address these questions, we determined _FOXM1_ isoform expression in normal and tumor tissues using TOIL GTEx and TCGA RNA-seq datasets from UCSC Xena. We found that _FOXM1c_ was the predominant isoform expressed in both normal and tumor tissues, followed by _FOXM1b_ and _FOXM1a_ (Figure 8A). Additionally, all three _FOXM1_ isoforms had increased expression in paired tumor vs. normal samples (Figure 8B-D). Comparing the ratios of isoform expression between tumor and normal, all three isoforms showed a similar increase in expression in tumors (Figure 8E). Thus, the observed increase in overall FOXM1 expression in cancer is not due to differential expression of any particular isoform. We next used the CCLE cancer cell line RNA-seq dataset to compare _FOXM1_ isoform expression, and again found that _FOXM1c_ showed the highest expression (Figure S4A). Finally, using RT-qPCR with isoform-specific primers, we observed that _FOXM1c_ was the  
Figure 7: FOXM1 and CCNE1 expression correlation in pan-cancer and high-grade serous ovarian cancer (HGSC) tissues. (**A**) Correlation of _FOXM1_ expression and _CCNE1_ mRNA expression (RNA-seq RSEM, log2(norm count +1)) in TCGA pan-cancer tissues. (**B**) Correlation of FOXM1 expression and CCNE1 protein expression ((RPPA, pan-can normalized) in TCGA pan-cancer tissues. (**C**) Correlation of _FOXM1_ expression and _CCNE1_ mRNA expression (RNA-seq RSEM, log2(norm count +1)) in TCGA HGSC tissues. (**D**) Correlation of FOXM1 expression and CCNE1 protein expression (RPPA, pan-can normalized) in TCGA HGSC tissues.  
predominantly expressed isoform in a panel of cancer cell lines, followed by _FOXM1b_ and _FOXM1a_ (Figure S4B) [25]. Together, these data provide strong evidence that _FOXM1c_ is the highest expressed _FOXM1_ isoform in both normal tissues and cancer.
## 3 Antenna Single Element  
Due to its conformability, integration and low cost, the antenna unit is implemented in planar technology by means of a microstrip patch as a radiating element [32]. The dual-polarization characteristic of the antenna leads to the use of a quadratic patch.  
Due to the resonant nature of the microstrip patch and its narrow-band nature, a bandwidth enhancement technique by using a stacked dual patch configuration is applied, in order to achieve the required antenna bandwidth of 150 MHz specified by the SAR sensor requirements. The aperture coupling feeding technique [33] allows an independent implementation of the radiation and feeding part of the antenna, becoming a versatile method to accomplish the dual polarization excitation.  
Figure 1: F-SAR Antenna carrier.  
The aforementioned design considerations lead to a multi-layer structure as it can be seen in Figure 2. The design steps of the antenna element are explained in detail next. The electromagnetic analysis and optimization process of the structure were performed using HFSS, a High Frequency Simulation software tool, part of the Ansys Electromagnetics Desktop environment.
# Engineering of Ti:Sapphire Lasers for Dermatology and Aesthetic Medicine
## 2 Experimental Results and Discussion
### The Influence of Pump Fluence Distribution on Ti:Sapphire Laser Operation
The scattering coefficients (\(\alpha\)) for the comparison of several materials are presented in Figure 9:2  
One can see that the induced scattering coefficient here is larger than the appropriate coefficients of the intrinsic scattering of indicated optical materials. Therefore, it is not surprising that the influence of pump light spatial modulation on Ti:Sapphire laser generation is considerable. We can conclude the following:  
1. The induced scattering in our application caused a positive effect, leading to the synchronization of laser generation over aperture. This allowed us to obtain stable subnanosecond pulses from the Ti:Sapphire laser under nanosecond pumping and increase pulse energy in comparison to subnanosecond pumping. Scattering intensity \(<\)1-2% is sufficient for synchronization if the scattering angles are large enough. Angles larger than \(\sim\)30 mrad (estimated from our pump patterns) are sufficient; angles \(<\) 5 mrad are definitely insufficient. 2. The concomitant increase in the angular divergence to 15-20 mrad in our application was not critical. More than 90% of the laser energy can be delivered to the target using a standard articulated arm. It is worth mentioning that small-angle scattering did not reduce the energy efficiency in the lasers with large N\({}_{\text{F}}\) because the scattered light was not lost, but still participated in the generation process. 3. The induced scattering, which is an additional mechanism for the synchronization, of course, cannot compensate for big differences in w(x, y) at low spatial frequencies. The parameters of the homogenizers must be properly selected to eliminate such differences. Hypothetically, it is possible to obtain synchronous laser generation in such laser without the assistance of scattering in the case of flat-top w(x, y). However, a similar distribution in practice can be realized only with pump sources of lower coherence, for example, with Nd:glass lasers. It is notable that small-angle scattering for synchronization in the Ti:Sapphire laser can also be produced, for example, by the deposition of special coatings on the surface of a laser \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **\(\alpha\), Intrinsic, cm\({}^{-1}\)** & **Ref.** \\ \hline Ruby and Ti:Sapphire & 0.001 & [29] \\ \hline Nd\({}^{3+}\):YAG crystal & 0.002 & [29] \\ \hline Nd\({}^{3+}\):YAG ceramic & 0.004 & [33] \\ \hline
**Material** & **\(\alpha\), induced, cm\({}^{-1}\)** & \\ \hline Ti:Sapphire with MLA & 0.053 & Calc. \\ \hline Ti:Sapphire with DD & 0.077 & Calc. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scattering coefficients of optical materials. Adopted with permission from ref. [18]. Copyright 2019 © The Optical Society. cavity. However, such technical solution seems unreasonable, because, as a rule, nonuniformities in the coatings considerably reduce their LIDT, which is not acceptable here.