esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellulose and Vanadium Plasmonic Sensor to Measure Ni\({}^{2+}\) Ions
## 3 Results and Discussion
### SPR parameters  
In addition to sensitivity and binding affinity parameters, full width at half maximum (FWHM) is also an important parameter, which the value should be as small as possible for a good sensor. A smaller value of FWHM implies a narrow and sharp reflectance curve, signifying a better detection accuracy of measurements [67, 68, 69]. The variation of FWHM with respect to Ni(II) ion concentrations is shown in Figure 6. It was observed that after the detection of 2 ppb of Ni(II) ion, the FWHM increased until the detection of 20 ppb of Ni(II) ion. The reason for this was due to the increase in the thickness of the sensing layer, which leads to damping of incident light in the sensing layer, which in turn resulted in broadening of the reflectance curve [70, 71, 72, 73, 74]. The smallest FWHM was obtained at the detection of 2 ppb of Ni(II) ion, for which the detection accuracy (DA) was maximum. DA was inversely proportional to the FWHM. As is clearly shown in Figure 5, the proposed sensor showed the highest DA when exposed to 2 ppb of Ni(II) ion.  
Figure 7 demonstrates the variation of SNR with the concentration of Ni(II) ion. The SNR is an important measurement to reduce the sensor's noise or improve the sensor intensity, indicated by higher SNR. The SNR can be calculated using the following equation:  
\[SNR=\frac{\Delta\theta_{SPR}}{FWHM}=\Delta\theta_{SPR}\times DA, \tag{2}\]  
As a result, the SNR increases through the detection of Ni(II) ions. In addition, when comparing to other SNR of the SPR sensor in Table 3, the SNR of the Au/MPA/V-CNCs-CTA thin-film-based SPR sensor is increased. The increased SNR can result in faster detection of analyte concentrations that were previously lost in the noise. At the end of this study, the overall performance parameter analysis of the proposed sensor with respect to Ni(II) ion concentrations is given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials Used** & **LOD 1** & **Sensitivity** & **Binding Affinity** & **SNR 2** & **Ref.** \\ \hline CTA-NCC/GO 3  & 10 ppb & 0.001\({}^{\circ}\) ppb\({}^{-1}\) & 1.620 \(\times\) 10\({}^{3}\) M\({}^{-1}\) & 0.0191 & [36] \\ Ppy-CHI 4  & 10 ppb & - & 1.672 \(\times\) 10\({}^{4}\) M\({}^{-1}\) & - & [65] \\ AgNPs-3 MPS 5  & 500 ppb & - & - & - & [66] \\ V-CNCs-CTA 6  & 2 ppb & 0.068\({}^{\circ}\) ppb\({}^{-1}\) & 1.819 \(\times\) 10\({}^{6}\) M\({}^{-1}\) & 0.0276 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface plasmon resonance for Ni(II) ion detection presented in the previous report.  
Figure 6: Full width at half maximum and detection accuracy of Au/MPA/V-CNCs-CTA thin film when detecting Ni(II) ion solution ranged from 2 ppb to 50 ppb.
## 2 Research Objects and Numerical Methods
### Governing Equation and Turbulence Model  
The governing equations mainly include mass conservation equations, momentum conservation equations and energy conservation equations. The governing equations for its main variables can be expressed in the following general forms:  
\[\frac{\partial(\phi\phi)}{\partial t}+\mathrm{div}(\rho UL\phi)=\mathrm{div} \big{(}\Gamma_{\phi}\mathrm{grad}\phi\big{)}+S_{\phi} \tag{5}\]  
where \(\rho\) is density, \(t\) is time, \(U\) is speed, \(\Gamma_{\phi}\) is generalized diffusion coefficient, \(S_{\phi}\) is the generalized source term. \(\Phi\) is a general variable, and \(\Phi\) = 1 in mass equation. In the momentum equation, let \(\Phi\) take \(u\), \(v\), \(w\) respectively. In the energy equation, \(\Phi\) = T. And in the mass equation, let \(S_{\phi}\) take 0.  
Figure 3: Geometric model of fluid domain.  
Figure 2: Profiles of blades: (**a**) 1 st stator; (**b**) 1 st rotor; (**c**) 2 nd stator; (**d**) 2 nd rotor; (**e**) 3 rd stator; (**f**) 3 rd rotor.  
A two-equation \(k\)-\(\omega\) SST turbulence model is taken, which has a faster calculation speed and higher calculation accuracy for complex flows such as boundary layer flow. This model combines the advantages of the \(k\)-\(\epsilon\) turbulence model and \(k\)-\(\omega\) turbulence model. Other scholars [20; 21] have compared the numerical results of this turbulence model with the experimental results in the process of turbomachinery research, and the results show that they are in good agreement. \(k\) equation and \(\omega\) equation are as follows [22; 23].  
\(k\) equation:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho u_{i}k)}{\partial x_{ j}}=P_{k}+\frac{\partial}{\partial x_{j}}\Bigg{[}(\mu+\sigma_{k}\mu_{t})\frac{ \partial k}{\partial x_{j}}\Bigg{]}-\beta^{*}\rho\omega k \tag{6}\]  
where \(k\) is the unit mass fluid turbulent pulsating kinetic energy, \(P_{k}\) is the turbulent energy generation, \(\beta^{*}\) is the turbulence model coefficient, \(\omega\) is the turbulent kinetic energy dissipation rate, \(\mu\) is the kinematic viscosity coefficient, \(\mu_{t}\) is the turbulent viscosity coefficient, \(\sigma_{k}\) is the \(k\) equation coefficient.  
\(\omega\) equation:  
\[\frac{\partial\rho\omega}{\partial t}+\frac{\partial\rho u_{j}\omega}{ \partial x_{j}}=\frac{\gamma}{v_{t}}P-\beta\rho\omega^{2}+\frac{\partial}{ \partial x_{j}}\Bigg{[}(\mu+\sigma_{\omega}\mu_{t})\frac{\partial\omega}{ \partial x_{j}}\Bigg{]}+2(1-F_{1})\frac{\rho\sigma_{\omega}{}^{2}}{\omega} \frac{\partial k}{\partial x_{j}}\cdot\frac{\partial\omega}{\partial x_{j}} \tag{7}\]  
where \(\gamma\) and \(\beta\) are turbulence model coefficients, \(\
u_{t}\) is vortex viscosity, \(\sigma_{\omega}\) is the \(\omega\) equation coefficient. \(F_{1}\) is the functional function with 1 at the near wall and 0 at the far surface.
## 3 Solution Methodology
### Trapping Characteristics  
Trapping for different values of Deborah number \(\Gamma\), electroosmotic parameter \(m_{e}\) and maximum electroosmotic velocity \(U_{\text{Ibs}}\) are shown in Figures 6-8. Figure 6a-d reveal a streamline structure for various values of \(\Gamma\). The size of the enclosed bolus decreases as \(\Gamma\) increases. It means that size of trapped bolus is strongly affected by changing the fluid. Similarly, Figure 7a-d visualize that the number of enclosed bolus increases with rise in electroosmotic parameter \(m_{e}\). It scrutinizes that as the \(m_{e}\) increases, the enclosed bolus strongly appears in EDL. It means that more fluid can be trapped in the presence of more electric field. Figure 8a-d illustrate streamline makeup for \(U_{\text{Ibs}}\). It demonstrates that the accumulation of streamline reduces with rise in \(U_{\text{Ibs}}\). Thus, stronger \(U_{\text{Ibs}}\) means stronger the external electric field, the number of enclosed significantly decreases for large values of bolus \(U_{\text{Ibs}}\).
## 4 Discussion
### Temporal Trend Pattern of \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) Across Ecosystems  
For \(S_{\text{GPP}}\), only shrubland showed a significant increase, while wetland exhibited a non-significant decrease (Figure 8), and grassland showed a non-significant downward trend. In contrast, for \(S_{\text{ER}}\), grassland exhibited a highly significant downward trend, while shrubland showed a significant increase over time (Figure 9). The temporal variations in \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) between grassland and shrubland were notably different, which is a noteworthy observation. As global warming advances, total precipitation has increased [78]. Coupled with the CO\({}_{2}\) fertilization effect, this has contributed to the rise in global vegetation productivity [79; 80; 81]. Concurrently, the expansion of leaf area in vegetation has led to higher water consumption [82], establishing a balance between the increased availability of water and the elevated demand for it.  
Studies have shown that grassland exhibits a broadly positive, symmetric response to precipitation changes [39], indicating a reduced water limitation on carbon dioxide flux. In contrast, the temporal trend of \(S_{\text{GPP}}\) in forest remains more stable, likely due to multiple factors. First, rising atmospheric CO\({}_{2}\) concentrations have enhanced forest water-use efficiency [83]. Additionally, the complex vertical structure of forest provides resilience to environmental changes [84]. These factors collectively contribute to the stability of forest \(S_{\text{GPP}}\) trends. For \(S_{\text{ER}}\), the increased precipitation enhances soil respiration due to the strong water retention capacity of the soil [85; 28]. Studies show that moderate increases in precipitation significantly boost microbial respiration, while root and microbial respiration only decline significantly under extreme precipitation increases [86]. Therefore, in ecosystems with relatively mild water resource limitations, an increase in precipitation results in a more pronounced declining trend in \(S_{\text{ER}}\). It is noteworthy that while both \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) in shrubland exhibit a consistent upward trend over time, the increase in \(S_{\text{GPP}}\) is markedly more pronounced than that of \(S_{\text{ER}}\). This discrepancy may be attributed to the higher water use efficiency of vegetation in water-limited areas [87], as GPP typically responds more rapidly to changes in precipitation compared to ER [58].
## 3 Results  
### Input Selection Using FS Technique  
An FS technique was used to reduce the dimensionality of the data and select the most important input variables. Table 4 shows the results obtained from the FS technique. The FS technique reduced the number of input variables from six to five. Thus, FS identified the maximum charge per delay (MC), hole depth (HD), stemming (ST), powder factor (PF), and distance (D) as the most important input variables. These variables were then used to develop five ML models to predict the PPV.
## 2 Materials and Methods
### Image Processing  
All images were processed using the software CASSIA version 2.0 developed by our group [35]. The comprehensive [18]FDG PET/CT image processing methods utilized for the quantification of the myocardial metabolic activity was detailed in previous work [33]. Briefly, imaging data must be preprocessed prior to feature extraction following Image Biomarker Standardisation Initiative (IBSI) recommendations [36]. Volume of interest (VOI)segmentation of the left ventricle [18]FFDG metabolic activity, gray level discretization, and radiomic feature extraction were computed prior to radiomic data analysis. A standardized uptake value (SUV) was considered to quantize the PET images and compute the radiomic parameters. The segmentation of the metabolically active left ventricle volume in the PET image is conducted by thresholding (determining an optimal reference threshold within the left ventricular cavity for the segmentation of the left ventricle active metabolic volume [37]).  
Radiomic features were extracted from the metabolically active left ventricle volume in the original non-filtered [18]FFDG PET images by means of the open-source Python version 3.6.2 package Pyradiomics [38] with the default parameters, except resampledPixelSpacing set to 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\); binWidth set to 0.125 SUV; resegmentMode set to absolute; and resegmentRange set to [0, 40.94] SUV (according to the SUV range of the study population). The radiomic feature extraction with the specified parameter configuration yielded a total of 94 image-based features: 19 First Order Statistics, 24 Gray Level Co-occurrence Matrix (GLCM), 16 Gray Level Run Length Matrix (GLRLM), 16 Gray Level Size Zone Matrix (GLSZM), 5 Neighboring Gray Tone Difference Matrix (NGTDM), and 14 Gray Level Dependence Matrix (GLDM). The comprehensive description of each radiomic feature, in compliance with the IBSI [36], can be accessed in the Pyradiomics documentation available at the Pyradiomics website ([[https://pyradiomics.readthedocs.io/en/latest/](https://pyradiomics.readthedocs.io/en/latest/)]([https://pyradiomics.readthedocs.io/en/latest/](https://pyradiomics.readthedocs.io/en/latest/)), accessed on 11 November 2024).  
Lastly, the \(H_{Rad}\) index for the quantification of the heterogeneity of the metabolic uptake patterns is proposed. According to the definitions provided by the IBSI [36], the radiomic variables were assigned a contribution coefficient (\(C_{i}\)) whether they relate to the heterogeneity (\(C_{i}\) = 1) or homogeneity (\(C_{i}\) = \(-\)1) of the distribution of pixel intensities, or if they do not describe heterogeneity or homogeneity at all (\(C_{i}\) = 0). The specific contribution coefficients assigned to each feature can be found in Supplementary Table S2. Therefore, the \(H_{Rad}\) index is computed as follows:  
\[H_{Rad}=\sum_{i=0}^{N}C_{i}\times F_{i}, \tag{1}\]  
with \(N\) being the number of extracted radiomic features, \(F_{i}\) the value of the radiomic feature, and \(C_{i}\) the specific contribution coefficient defined for that feature according to the definitions provided by the IBSI [36].
## 4 Experimental Results
### Uncertainty Analysis
\[Q_{PCM}=\frac{h_{As}}{\delta}\times V_{s}-C_{box}\times V_{box} \tag{32}\]  
Uncertainty analysis was performed using the heat of absorption test data of erythritol in a container with temperature step from 120 to 121 \({}^{\circ}\)C. Equation (33) gives the uncertainty composition formula for each temperature step. \[u_{Q_{PCM}}=\sqrt{\left(\frac{\partial Q_{PCM}}{\partial h_{As}}\times u_{h_{As }}\right)^{2}+\left(\frac{\partial Q_{PCM}}{\partial\delta}\times u_{b} \right)^{2}+\left(\frac{\partial Q_{PCM}}{\partial V_{s}}\times u_{V_{s}} \right)^{2}+\left(\frac{\partial Q_{PCM}}{\partial V_{box}}\times u_{Q_{ box}}\right)^{2}+\left(\frac{\partial Q_{PCM}}{\partial V_{box}}\times u_{Vbox} \right)^{2}} \tag{33}\]  
The uncertainty of areal enthalpy depends on the uncertainty of the heat flow measurement, which was 1.99%. In this temperature step, \(h_{As}=3.90\times 10^{3}\) kJ/m\({}^{2}\). As such, the standard uncertainty of \(h_{As}\) (\(u_{hAs}\)) was 77.61 kJ/m\({}^{2}\). The standard uncertainty sources of thickness measurements include vernier caliper measurement uncertainty, specimen thermal expansion uncertainty, and thermal expansion measurement uncertainty. Thus, the standard uncertainty of \(\delta\) (\(u_{S}\)) was 9.69 \(\times\) 10\({}^{-6}\) m. The volume uncertainty depends on the uncertainty of the thickness measurement. As such, it can be calculated that the standard uncertainties of the specimen (\(u_{V_{S}}\)) and the container (\(u_{V_{box}}\)) were 1.16 \(\times\) 10\({}^{-6}\) m\({}^{3}\) and 1.56 \(\times\) 10\({}^{-6}\) m\({}^{3}\), respectively. Furthermore, the volume specific heat of the container was measured by a HotDisk analyzer. The measurement accuracy of C\({}_{\text{p-304}}\) was 7%, which was subject to uniform distribution. Meanwhile, C\({}_{\text{p-304}}\) = 3.99 kJ/m\({}^{3}\) in this temperature step, so the standard uncertainty of C\({}_{\text{p-304}}\) is 3.99 \(\times\) (7%/\(\sqrt{3}\)) = 0.16 kJ/m\({}^{3}\). In addition, \(\delta\) = 0.05 m, \(V_{s}\) = 4.50 \(\times\) 10\({}^{-3}\) m\({}^{3}\), and \(V_{box}\) = 1.14 \(\times\) 10\({}^{-3}\) m\({}^{3}\). By substituting all the parameters into Equation (33), it can be calculated that the standard uncertainty of \(Q_{PCM}\) (\(u_{QPCM}\)) was 6.99 kJ. As shown in Figure 14, the heat of absorption from 120 to 121 \({}^{\circ}\)C was \(Q_{PCM}\) = 3.51 \(\times\) 10\({}^{2}\) kJ. Therefore, the relative standard uncertainty can be calculated in Equation (34). It can be concluded that the relative standard uncertainty mainly depends on the uncertainty of the heat flow measurement. \[u^{\prime}{}_{Q_{PCM}}=\frac{u_{Q_{PCM}}}{Q_{PCM}}\times 100\%=1.99\% \tag{34}\]
## 4 Security Threats
### Implementation
#### 4.4.3 Channel Usage Violation  
There are numerous approaches proposed to date for sharing the available spectrum in a DSA network in the frequency domain [59]. They usually model spectrum sharing as a single (or joint, in case of complex cross-layer approach) problem of optimal assignment of channels to nodes. This channel allocation should be done in a way that no nodes with overlapping coverage are assigned to the same channel, or in case such a requirement cannot be totally satisfied, this allocation shouldminimize as much as possible the interference caused by simultaneous packet transmissions. Among the main aims of these approaches are to preserve the optimal--in terms of efficiency and spectrum utilization--operation of the managed DSA network, as well as to satisfy each node's needs. Obviously, all nodes in a DSA network must obey the rules related to such allocations for enabling them to achieve their goals. But, as stated before, this may not be always the case and a malicious node may choose to violate these rules.  
An adversary may attempt to transmit packets over a channel, even if he has no right to do so [57]. Such a channel may never have been assigned to him or could have been assigned to him at a previous time and since retracted. Additionally, this may already be occupied by one of his competitors, or even by a Primary User. Such unanticipated packet transmissions may result in significant interference problems, degrading each \"attacked\" channel's quality. This attack can also be instigated with the aim of lowering the reputation, as well as the price, of special, high-quality \"expensive\" channels [49].
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 2 Methodology
### Extraction of Repeways  
Further segmentation and extraction are performed on the cableway point cloud that has been initially obtained. By minimizing the sum of squared errors, the least squares method determines the best function-matching relationship between variables [30; 31; 32; 33]. The results are affected by some \"unfavorable\" data since the method uses all data for calculations during data fitting. The principal component analysis (PCA) method was proposed by [PERSON] in order to evaluate each sampling point's \"neighborhood\" as well as to calculate normal vectors and plane fittings [34; 35; 36; 37; 38; 39]. A PCA can be understood as geometric optimization of least squares, or as an equivalent of maximum likelihood estimation, and it is more accurate and efficient than least squares methods. This method is, therefore, widely used in the fitting of planes. A combination of the two algorithms is presented in this paper in order to mitigate the influence of \"unfavorable\" factors on point cloud data and increase the accuracy of feature extraction.
## 2 Materials and Methods  
### Geological and Hydrogeological Setting  
The \"Coastal Aquifer between Cecina River and San Vincenzo\" SSWB is located in Livorno province of Tuscany, central Italy (Figure 1). The annual cumulative precipitation is nearly 700 mm and the main rivers draining the area are rivers Fine and Cecina, which drain the north and south areas respectively. This area is also densely populated and subjected to a high demand of drinkable water, especially during the summer [14] when the area inhabitants greatly increase due to tourism.  
The SSWB studied is constituted by quaternary deposits (Figure 1) formed as a sequence of permeable gravel and sand layer (k \(\cong\) 10\({}^{-2}\) m/s) separated by impermeable silty-clayey deposits. The bedrock of this sequence is represented by: (i) the sands and clays with Arctic islandica ([PERSON], 1767) in the northern part of the study area; (ii) the low-permeability Ligurian units in the southern part; and (iii) clayey deposits of uncertain stratigraphic position in the area between the Cecina River and Bolgheri village. The hydraulic head of this coastal water body appears to be consistent to the ones from the nearby \"Cecina Valley\" and \"Between River Fine and Cecina River\" SSWBs, probably due to the discontinuous nature of the impermeable deposits and the presence of several boreholes that connect the permeable layers situated at different depths. Thus, this multi-layer system can be considered to behave as a single-layer aquifer [14]. Isotopic studies carried out in the late 1990s [14], provided evidence that this coastal SSWB is recharged both by local precipitations and infiltrating meteoric waters from the nearby hills where low permeability formations crop out. Previous results by [5; 14] have shown that the characteristic ions for the majority of the groundwaters in this SSWB are HCO\({}_{3}\)\({}^{-}\) and Ca\({}^{2+}\), although Cl\({}^{-}\) is also  
Figure 1: Geological scheme of the study area and the surrounding hills. Legend: 01—Quaternary (post-“Villafranchian”) continental and coastal deposits; 02—Magmatic rocks; 06—Ruscinian and “Villafranchian” continental succession; 07—Pliocene-Pleistocene marine succession; 08—Messinian syn- and post-evaporite succession; 09—Messinian pre-evaporite marine succession; 10—Lower Turolian (Upper Tortonian) fluvio-lacustrine succession; 11—Middle Burdigalian-Lower Tortonian marine deposits (Tyrrhenian Epiligurides Auctt.); 13—Internal Ligurian Domain; 14—Extenral Ligurian Domain; 15—Subligurian Domain; 18—Tuscan Nappe; 21—Tuscan Metamorphic Units; blue line—Tectonic contact; red line—fault; blue dashed line—studied aquifer.  
important. On the other hand, SO\({}_{4}\)2\({}^{-}\) may become important in the water wells draining gypsum or anhydritic lithologies that crop out in the nearby hills [14].
## 4 Conclusions  
This paper presents an all-weather integrated defogging and drivable area detection algorithm based on deep learning, designed to address the challenges of recognizing unstructured roads and achieving clear environmental perception under adverse weather conditions in current autonomous driving systems. The algorithm improves the Lite-Mobilenetv2 feature extraction module and incorporates a pyramid pooling module integrated with an attention mechanism, enhancing the speed and accuracy of feature extraction. Additionally, a defogging preprocessing module suitable for real-time detection is introduced, transforming foggy images into clear ones to improve the precision of drivable area detection.  
In the drivable area detection component, the algorithm replaces the DeepLabv3+ module with grouped convolutions and depthwise separable convolutions, significantly reducing the network depth, computational load, and parameter count, which further accelerates detection and prevents overfitting. In the atrous spatial pyramid pooling module, the introduction of an attention mechanism optimizes the encoder's structure, leading to higher processing efficiency and accuracy. The defogging preprocessing module uses a physics-based defogging algorithm that enhances the traditional Dark Channel Prior method by adding weighted aggregation-guided filtering and quadtree search for atmospheric light values. This improvement effectively mitigates halo effects and exposure issues at the image edges while enhancing image detail.  
The experimental phase employs a transfer learning-based training method, constructing an all-road-condition semantic segmentation model by training on four datasets containing structured and unstructured roads, with and without fog. This strategy not only reduces computational burden but also significantly improves detection accuracy. Experimental results show that the proposed algorithm increases detection efficiency by 3.84% compared to existing algorithms.  
Future research will integrate sensor technology to further improve drivable area recognition, optimize the algorithm for processing video data, and enable automatic switching between foggy and non-foggy modes, thereby better adapting to real-world application scenarios.  
Methodology, C.L.; Validation, Q.W.; Formal analysis, Q.W.; Investigation, Q.W.; Resources, C.L.; Writing--original draft, Q.W.; Writing--review & editing, C.L.; Visualization, C.L.; Supervision, Y.L.; Project administration, Y.L.; Funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
Supported by Science and Technology Department of Sichuan Province No. 2023 YFG0317.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.