esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Methodology
### M Component: Stepwise Topic Cluster Aggregation and Model Training  
In the final phase, fastTNT cumulatively assesses the ranked topic clusters, beginning with the top-ranked topic cluster (\(TC_{R1}\)) and in a stepwise manner incorporating the remaining ones (i.e., \(TC_{R2}\), \(TC_{R3}\)) until all topic clusters up to the kth rank are merged. This process creates an aggregated set of words for each accumulation, from which two  
Figure 6: The working mechanism of the S component (feature importance-based topic scoring).  
class sub-datasets are extracted from the training and testing datasets (\(D_{train}\) and \(D_{test}\)). Subsequently, the fastTNT manages the training and testing of the machine learning model, employing Random Forest. Ultimately, it identifies the optimal topic cluster subset from all potential subsets, selecting the subset with the highest performance, which indicates the most discriminative power for the text classification task. This subset consists of \(r\) topic clusters {\(TC_{R1}\), \(TC_{R2}\),..., \(TC_{Rr}\)}, where \(r\) < \(k\). Figures 7 and 8 depict the working mechanism of the M component over the first two iterations.  
For instance, in the first iteration (refer to Figure 7), fasTNT utilizes only the top-ranked topic cluster. The words within this topic cluster are used to create training and testing sub-datasets from \(D_{train}\) and \(D_{test}\), respectively. Each sub-dataset has dimensions of \(d_{train}\)\(\times\) (\(b+1\)) and \(d_{test}\)\(\times\) (\(b+1\)), where \(d_{train}\) and \(d_{test}\) denote the number of documents in the training and testing datasets, respectively, and \(b\) denotes the number of words in the topic cluster. Subsequently, fasTNT trains a Random Forest model using the training sub-dataset and evaluates its performance using the testing sub-dataset.  
In the second iteration (refer to Figure 8), fasTNT combines the top-ranked and second-ranked topic clusters. The words from these two clusters are aggregated and used to generate new training and testing sub-datasets from \(D_{train}\) and \(D_{test}\), with each sub-dataset having dimensions of \(d_{train}\)\(\times\) (\(2b+1\)) and \(d_{test}\)\(\times\) (\(2b+1\)), respectively. The Random Forest model is again trained and evaluated with these extracted sub-datasets.  
This iterative process continues, progressively accumulating topic clusters until all \(k\) topic clusters have been incorporated. Each iteration refines the model by incrementally adding topic clusters, aiming to identify the optimal subset of topic clusters from all potential subsets that result in the highest performance (i.e., accuracy). This optimal subset is then used to train the final model, ensuring that the most relevant topic clusters are included for superior classification accuracy.  
Figure 7: The working mechanism of the M component (first iteration). The red border covers the topic clusters and their corresponding word lists utilized for the training and testing processes in the specified iteration.
## 2 Soiling-Related Pollen Topics
### Pollen Aggregation and Disaggregation  
As already mentioned, some types of vegetation disperse aggregated pollen grains (\"pollen clumps\") instead of solitary pollen grains. For example, holm oaks and oaks (_Quercus_ sp.), willows (_Salix_ sp.) and elms (_Ulmus_ sp.) are atypical anemophilous trees because they disperse pollen clumps [10]. Furthermore, pollen disaggregation is sometimes possible: during their aerial dispersion, larger pollen clumps may fragment into smaller ones due to the exposure to turbulent wind [42]. In any case, the formation of larger airborne particles will influence the soiling potential of pollen by affecting its aerial transport, its deposition processes, its adhesion forces, etc.  
Figure 1: Single-grain size distribution of pollen of common Mediterranean plants (data gathered from [11; 37; 38]). The colored bottom bar and the black horizontal lines represent the grain size classes and the grain size spans of the vegetal families, genres or species, respectively.  
Figure 2: Distribution of the vegetal species per pollen size class (columns) and per pollen dispersal unit (colored bars). This graph is for indicative purposes: the data sampling was limited to the 3280 vegetal species included in the pathological database PalDat (last accessed: 12 June 2020) [34].  
In most cases, pollen aggregation takes place thanks to some viscous fluid which holds the pollen grains together: pollenkitt (which is the most common one and resides over the exine and/or within the exine cavities), tryphine (only in Brassicaceae), elastoviscine (in Orchidaceae and Asclepiadaceae)... [43]. In general, pollenkitt is abundant in entomophilous pollen (e.g., 60% and 30% mass content in dandelion and sunflower, respectively), it is present to a lesser extent in ambophilous pollen (e.g., 8% mass content in olive) and it is essentially missing in anemophilous pollen (e.g., still 10% mass content in poplar; absent among almost all _Gymnosperms_) [43; 44; 45]. Finally, pollen aggregation is also influenced by other factors, such as the relative humidity or the time elapsed since the anther dehiscence [39].
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 1 Introduction
Inspired by this, this paper first analyzes the relationship between SVD-based watermarking and the pixel values in the spatial domain, and then proposes a new robust image watermarking scheme using the mathematical characteristics of SVD. Unlike the common SVD domain watermarking schemes, a binary watermark is embedded into the largest singular values of the selected image blocks in the spatial domain. To guarantee the security of this algorithm, the Arnold transform is utilized to encrypt the watermark. Compared with the existing SVD domain watermarking schemes, the proposed method maintains good robustness against signal processing attacks and geometric attacks. Since the watermark embedding process is performed in the spatial domain, the proposed method avoids the false positive problem existing in traditional SVD-based watermarking schemes and reduces the computational complexity. The remainder of this paper is organized as follows. In Section 2, the basic concepts of SVD and Arnold transform are described briefly. Section 3 analyzes SVD-based watermarking and its impact on the spatial domain. The proposed watermarking scheme is developed in Section 4. Experiments and performance analysis are documented in Section 5. The conclusions and future work are given at the end of this paper.
## 6 Conclusions  
A review of optical current sensors for high voltage current sensing applications, including the different configurations, physical principles, advantages and disadvantages and state-of-the-art including commercially available systems, was presented.  
Figure 18: Commercially available prototypes for high voltage electrical current measuring based in optical systems: **(a)** AIRAK’s Overhead Mountable Optical Current Sensor **(b)** NxtPhase’s NXCT Optical current sensor **(c)** ABB’s MOCT (Magneto-Optical Current Transformer) **(d)** SIEMENS’s Optical current sensor.  
In all-fiber sensors, immunity against external currents/magnetic fields can be easily obtained, although linear birefringence can present a serious limitation to the performance of the sensor. Commercially available sensors for DC current measurement have been shown to measure up to 500 kA with 0.1% accuracy and for AC current measurement up to 63 kA\({}_{\mathrm{RMS}}\) with 0.1% accuracy.  
Bulk magneto-optical sensors are more robust when compared to all-fiber sensors, being more protected against mechanical and thermal gradients, vibrations and other noises, although immunity against external currents/magnetic fields is harder to obtain. They have also higher Verdet constants and low linear birefringence which allows for high sensitivities over high measurement ranges. Commercially available sensors which allow for AC current measuring from 20 A to 20,000 A with maximum errors of 2% and sensors which allowed to measure up to 720 kA have been demonstrated.  
With regard to magnetic force optical current sensors, the use of magnetostrictive materials, such as Terfenol-D, attached to an FBG in an optical fiber can present an interesting solution. Using this idea, a prototype was able to measure current in a range of 100-1000 A for a frequency signal at 60 Hz. Sensors able to measure DC current have also been demonstrated.  
Lastly, Hybrid sensors can be explored in order to try to combine the best properties of conventional technology and new (optical) technology. The insulation provided by an optical fiber could reduce the cost of the insulation needed for a conventional current transducer. A very important issue regarding Hybrid sensors is that they can be used as a first step to start implementing optical current sensors in the industry, before exclusively optical current sensors can be further developed.
# Molecular Detection of _Acarapis woodi_ Using Hive Debris as Innovative and Non-Invasive Matrix
## 2 Materials and Methods
### Molecular Analyses  
Adult honey bees were first homogenized in 20 mL of Phosphate Buffer Solution (PBS) 1X, and then 2 mL were centrifuged at 16,000\(\times\)\(g\) for 5 min and the supernatant was discarded. The DNA extraction was performed with NucleoSpin(r) Tissue Kit following the manufacturer instructions ([PERSON]; Duren, Germany). One gram per each debris was incubated for two hours at 37 \({}^{\circ}\)C in 10 mL PBS 1X in agitation and two mL were later subject to DNA extraction with the NucleoSpin(r) Tissue Kit following the manufacturer instructions ([PERSON]; Duren, Germany). The quality and the integrity of the extracted DNA was determined by amplifying a fragment of 181 bp from the housekeeping gene \(\beta\)-actin with a real time PCR, as described by [PERSON] et al. [7] Two specific molecular techniques for the identification of _A. woodi_ were executed. The first PCR protocol amplifies a region of 180 bp of the subunit I of cytochrome oxidase (COI) and was performed as described by [PERSON] et al. (2015) using the following prime pairs: AWFor GGAATTAGATCTGTTAGTTGGTC and AWRev GAATCAATTTTCCA
## 4 Experimental Results
### Ablation Experiments
It can be seen from the results in Table 3 that after the introduction of CAM, the recall rates of Att-UNet, Laplacian, U-Net, and UNeXt have all improved. However, the recall rate of the proposed method is still the highest, indicating that it has a stronger detection ability in cloud segmentation. The F1 scores have all improved, but the F1 score of the proposed method is still the highest, indicating that it has achieved the best balance between the accuracy and recall rate of cloud segmentation. The error rates have all decreased, but the error rate of the proposed method is still the lowest, indicating that its segmentation  
Figure 11: SWINSG comparative experimental prediction plots. Figure 12: SWINSEG comparative experimental prediction plot. results are the most accurate. The MIoU values have all increased, but the MIoU value of the proposed method is still the highest, indicating that the overlap degree between its segmentation result and the real label is the highest, and the segmentation accuracy is the highest. With regard to the occasional performance degradation observed in Table 3 (e.g., AttUNet+CAM's lower F1 score for nighttime images), the following factors are more likely responsible. Multi-scale CAM's dynamic weighting \(\alpha\) might overfit to coarse cloud structures while neglecting fine details (e.g., thin cirrus edges) when fused with shallow features. This is exacerbated in models like Att-UNet, where skip connections already prioritize high-level semantics [41]. For compact models (e.g., UNeXt [44]), adding CAM introduces redundant parameters without proportional gains, leading to suboptimal feature fusion. CAM's upsampled activations often blur cloud boundaries (Figure 8), conflicting with precise segmentation needs. While the proposed multi-scale CAM (Figure 3) alleviates this via hierarchical features, residual errors persist in complex scenes. The dependence of CAM on advanced features will intensify noise in low-light environments (Section 4.2). Adaptive adjustments specific to the architecture (such as adding low-level skip connections) may alleviate this problem--this is the current research focus. We will conduct more in-depth research on the performance of CAM in future work. The ablation study presented in Table 4 systematically evaluates the contributions of each proposed module to the overall performance of our model. The baseline architecture,
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 3 CRCScatter Decoder Design
### CRC Reverse Decoder
#### 3.2.2 How to Reverse the Unknown Data from CRC32 Value?  
The algorithms of CRC utilize forward or reverse methods to compute the value of the CRC register from the input data bits \(a\). A highly relevant problem is knowing how to reversing the unknown data bits from the initial and final state values of CRC32. Assuming the unknown data length \(l\) is known, we can employ brute-force search to find the possible original data from all \(2^{l}\) data sequences. When the data length \(l\) is greater than 32 bits, the number of solutions is \(2^{l-32}\). If the data length does not exceed 32 bits, the unknown data sequence is unique. The relationship between the number of solutions \(N_{l}\) and the data length \(l\) can be expressed as follows:  
\[N_{l}=\left\{\begin{array}{ll}2^{l-32},&l>32\\ 1,&l\leq 3  
Through the equivalence relation of (6) and the equation (3), we can derive a new Equation (7) to calculate the data bits \(a\), as shown in Figure 5.  
\[a=crc\_reverse(r,r^{\prime}). \tag{7}\]
## 2 Mathematical Modeling
### Nanofluid Thermophysical Properties  
SiO\({}_{2}\)-water nanofluid was used in this study and the thermo-physical properties are given in Table 2[24]. The effective density, specific heat, thermal expansion coefficient of the nanofluid are given by the following formulas:  
\[\rho_{nf}=(1-\phi)\rho_{f}+\phi\rho_{p}, \tag{7}\]  
\[(\rho c_{p})_{nf}=(1-\phi)(\rho c_{p})_{f}+\phi(\rho c_{p})_{p}, \tag{8}\]  
\[(\rho\beta)_{nf}=(1-\phi)(\rho\beta)_{f}+\phi(\rho\beta)_{p}, \tag{9}\]  
where the subscripts \(f\), \(nf\) and \(p\) denote the base fluid, nanofluid and solid particle, respectively.  
The effective thermal conductivity of the nanofluid includes the effect of Brownian motion. In this model, the effects of particle size, particle volume fraction and temperature dependence are taken into account and it is given by the following formula [25]:  
\[k_{nf}=k_{st}+k_{Brownian}, \tag{10}\]  
where \(k_{st}\) is the static thermal conductivity as given by [26]  
\[k_{st}=k_{f}\left[\frac{(k_{p}+2k_{f})-2\phi(k_{f}-k_{p})}{(k_{p}+2k_{f})+\phi (k_{f}-k_{p})}\right]. \tag{11}\]  
The interaction between the nanoparticles and the effect of temperature are included in the models as  
\[k_{Brownian}=5\times 10^{4}\times 1.9526\times(100\phi)^{-1.4594}\phi\rho_{f}c_{p,f}\sqrt{\frac{\kappa_{b}T}{\rho_{p}d_{p}}}f^{\prime}(T,\phi), \tag{12}\]  
where the function \(f^{\prime}\) is given in [25].  
The effective viscosity model of the nanofluid was given in [27]  
\[\mu_{nf}=\mu_{f}\frac{1}{\left(1-34.87\left(\frac{d_{p}}{d_{f}}\right)^{-0.3} \phi^{1.03}\right)}, \tag{13}\]  
where the average particle size of the fluid is given as [27]:  
\[d_{f}=\left(\frac{6M}{N\pi\rho_{f}}\right)^{1/3}, \tag{14}\]  
with \(M\) and \(N\) denoting the molecular weight and Avogadro number.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Property** & **Water** & **SiO\({}_{2}\)** \\ \hline \(\rho\) (kg/m\({}^{3}\)) & 998.2 & 2200 \\ \(c_{p}\) (J/kg K) & 4812 & 703 \\ k (W/mK) & 0.61 & 1.2 \\ \(\mu\) (N s/m\({}^{2}\)) & 0.001003 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical properties of base fluid and SiO\({}_{2}\) nanoparticle [28].
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
## 3 Results  
### Free Surface Elevation Time Series  
The free surface elevation is measured via wave gages which are placed offshore, near shore and in the vicinity of the island. The gages placed in the vicinity of the island make it possible to record and analyze the effect of the island on the propagation of the single solitary wave and wave currents. The comparison of the results regarding the free surface elevation time series is given in Figure 3.  
Figure 3: Comparison of free surface elevation (FSE) results: (**a**) \(X\) = 7.5 m and \(Y\) = 0.0 m at Gage 1; (**b**) \(X\) = 13.0 m and \(Y\) = 0.0 m at Gage 2; (**c**) \(X\) = 21.0 m and \(Y\) = 0.0 m at Gage 3; (**d**) \(X\) = 7.5 m and \(Y\) = 5.0 m at Gage 4; (**e**) \(X\) = 13.0 m and \(Y\) = 5.0 m at Gage 5; (**f**) \(X\) = 21.0 m and \(Y\) = 5.0 m at Gage 6; (**g**) \(X\) = 25.0 m and \(Y\) = 0.0 m at Gage 7; (**h**) \(X\) = 25.0 m and \(Y\) = 5.0 m at Gage 8. Black line represents benchmark data, red line represents numerical results.  
Figure 2: Model parameters: (**a**) bathymetry of the numerical model, NAMI DANCE; (**b**) incoming wave.  
The results revealed that depth-averaged NSW equations are able to satisfactorily predict the free surface elevation at gages 1, 4, 7, and 8. However, these equations fail to accurately estimate the free surface elevation in the vicinity of the island, i.e., at gages 2, 3, 5, and 6. It is known from the experimental observations that turbulence and relatively large wave currents are formed in the vicinity of the island; these are considered to play a role in the underestimation of maximum wave amplitudes.
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 3 Impact of Temperature Drift on a Fiber Optic Current Sensor
### Comsol Simulation Models
#### 3.1.2 Material Selection  
The material, relative dielectric constant \(\varepsilon_{r}\), relative magnetic permeability \(\mu_{r}\), and refractive index n in each simulation region are shown in Table 1.  
When the current was set to 2 kA, the Verdet constant increases linearly with temperature at 0.7 \(\times\) 10\({}^{-4}\) rad/(A\({}^{\circ}\)C), \(\delta_{0}\) = 0, and \(Q\) = 5.89 \(\times\) 10\({}^{-3}\) rad/\({}^{\circ}\)C. Temperature fluctuations were set to \(\Delta T\) to 0 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C. The relationships between the rotation angle \(\theta\) and the transmission distance \(L\) for different temperature changes are shown in Figure 4, and the maximum rotation angle \(\theta_{max}\) and the corresponding angle error \(\Delta\theta\) are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Region** & **Material** & \(\varepsilon_{r}\) & \(\mu_{r}\) & \(n\) \\ \hline Wire & Copper & 1 & 0.9999 & – \\ Air region & Air & 1 & 1 & 1 \\ Sensing fiber & SiO\({}_{2}\) & 2.09 & 1 & 1.45 \\ \hline \end{tabular}
\end{table}
Table 1: Material Parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Temperature Fluctuation \(\Delta T\) (\({}^{\circ}\)C)** & **0** & **20** & **40** \\ \hline \(\theta_{max}\) (\({}^{\circ}\)) & 6.482 & 5.979 & 5.502 \\ \(\Delta\theta\) (\({}^{\circ}\)) & 0 & \(-\)0.503 & \(-\)0.980 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum rotation angle \(\theta_{max}\) and angle error \(\Delta\theta\) for different temperature changes.  
Figure 3: Geometric model of optical fiber units.  
One can infer from Figure 4 that the rotation angle is almost linear with transmission distance when \(\Delta T=0\)\({}^{\circ}\)C. When \(\Delta T\
eq 0\)\({}^{\circ}\)C, \(\theta\) is no longer linear with \(L\) as the transmission distance increasing, the curve becomes nonlinear and also the maximum rotation angle reduces. This is due to the cumulative effects of temperature drift with increased transmission distance because the fiber signal is multiplied by a coefficient that is less than 1. The greater the temperature fluctuation, the larger is the deviation of the rotation angle. According to Table 2, a greater temperature fluctuation results in greater deviation in the rotation angle, which is consistent with Equation (12).
# Calculation of Blasting Damage Zone Radius of Different Charge Structures in Burnt Rock
## 7 Conclusions  
The radius of the failure area after the blasting fracture process of burnt rock when affected by a joint fissure does not conform to the existing theoretical calculation formula, and the distribution law of the failure area also changes. The fracture area is large, and the fracture extension and expansion area are small. Therefore, in view of the blasting situation of burnt rock, based on the combined action of stress wave and explosive gas, combined with damage theory, the damage area radius of burnt rock was deduced by introducing a damage factor. The main conclusions of this study are as follows:  
Figure 11: Blasting results of test area.  
Figure 12: Size chart of blasting funnel radius.  
There were three stages of blasting failure mode: shock wave, compressive stress wave and seismic wave, and then the rock failure area was divided into a crushing zone, fracture zone and vibration zone. The propagation of radial cracks after the blasting of burnt rock was affected by joint fissures, which could not be better extended, which resulted in a smaller range of crack areas and no damage to the rock mass in the vibration area.
2. Combined with the theory of rock damage mechanics, a damage factor characterizing the deterioration of rock was introduced into the rock mechanics parameters, and some mechanical properties of fractured rock mass and the calculation formula of stress wave attenuation were obtained.
3. According to the [PERSON] criterion, through the stress wave propagation law of fractured rock mass and the expression of three-dimensional stress state of rock, the formulas for calculating the radii of blasting failure area of burnt rock with coupled charge and uncoupled charge structures were derived.
4. In addition, it is recommended that the mine adhere to the principle of safety first regarding blasting design to ensure the crushing effect while saving explosives and reducing the impact on the environment. The blasting parameters were designed reasonably and accurately according to the rock characteristics and working conditions, and the construction management and personnel training were strengthened. At the same time, we should actively introduce advanced technology and equipment, improve blasting efficiency and safety, formulate emergency plans to deal with emergencies and comprehensively ensure the smooth progress of blasting operations.  
Conceptualization, methodology, formal analysis, writing--original draft, and writing--review and editing, Z.J.; formal analysis, visualization, validation, methodology, and writing--review and editing, W.L.; funding acquisition, supervision, and writing--review and editing, H.L. and J.Y. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflicts of interest.