esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Rainfall Duration on the Degree of Saturation
On the other hand, the slope with SW-SM soil (see Figure 8) exhibits slower saturation due to the higher fines content, which reduces soil permeability. At a height of 12 m, the upper level reaches 100% saturation at 9.6 h, showing a more gradual increase compared to SP-SM. At 9 m, saturation begins at 2.4 h and stabilizes at 100% at 16.8 h. At 6 m, the process starts at 7.2 h, reaching 100% at 24 h, while at 3 m, saturation begins at 12 h and reaches 94% within the same period. At the base of the slope (0 m), the increase in saturation is minimal, starting at 19.2 h and reaching only 75%. This demonstrates that infiltration is more gradual and limited in depth due to the low permeability of SW-SM soil. In Figures 7 and 8, it is observed that, in terms of saturation speed, SP-SM soil responds quickly to infiltration, achieving a stable saturation of approximately 93.5% at the slope's top (H = 12 m) in just 1.2 h. This is attributed to its low fine content, facilitating rapid drainage and less water storage. In contrast, SW-SM soil shows a slower saturation process due to its higher fines content, reaching 100% at the same level of H = 12 m after 9.6 h. This behavior reflects the influence of SW-SM's lower permeability, which retains water in the soil profile for a longer time. Regarding the maximum saturation reached, it can be observed that, in SP-SM soil, saturation stabilizes between 93.5% and 94% throughout most of the profile without achieving full saturation. This is explained by the granular structure of poorly graded sand, which allows the formation of preferential pathways that facilitate water flow and reduce pore retention capacity [51]. On the other hand, in SW-SM soil, water infiltrates more slowly. Still, it achieves full saturation (100%) in the upper layers, demonstrating a higher water retention capacity due to its fine content and compact structure. However, in the lower layers (H = 0 m), this maximum saturation is not reached, stabilizing around 75%, indicating a limitation in the depth propagation of infiltration due to the soil's low permeability. In summary, the analysis of the increase in the degree of saturation highlights significant differences between the two soil types studied. Due to its granular structure and lower fine content, SP-SM soil exhibits a rapid initial response to infiltration, stabilizing between 93.5% and 94% across most of the profile without achieving full saturation, reflecting a limited water retention capacity. In contrast, SW-SM soil shows a slower and more progressive process, reaching 100% saturation in the upper layers. It demonstrates its higher water storage capacity due to its better gradation and fines content. However, in the deeper layers, both soils exhibit limitations in saturation propagation, with SW-SM reaching only 75% at the base of the slope. These results underscore how the hydro-mechanical properties of soils influence infiltration dynamics and their distribution within the slope.
# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 3 Proposed Methodology  
This research has developed a learning model that integrates LSTM and CNNs to forecast the demand for electric vehicles. Forecasting the demand for electric vehicles is crucial for various purposes, including vehicle adoption, marketing strategies, and infrastructure planning. The proposed methodology in this context attempts to enhance both the precision and efficacy of forecasting electric car demand by utilizing deep learning techniques. Figure 1 shows the broad schematic illustration of the suggested concept.  
The proposed EVs-PredNet approach includes normalization, convolution, ReLU activation, maximum pooling, LSTM, and dense layers, as illustrated in Figure 1. The input parameters were normalized using min-max normalization initially. A convolution procedure with a 3 \(\times\) 3 kernel size was applied to the normalized features, followed by a ReLU activation phase. This process was repeated twice. The first convolution process used 8 filters, while the second convolution process used 16 filters. Maximum pooling was then applied using a 2 \(\times\) 2 kernel size. The feature map was sent to a 50-unit LSTM network, followed by a flattening layer. Two dense layers were used, each containing 500 and 200 neurons. Table 2 shows the general structure and characteristics of the proposed model.  
CNNs and LSTM are two basic structures widely used in the field of deep learning and provide effective results, especially in the analysis of time series data and sequential data. CNNs (Convolutional Neural Networks) identify important features in the data using convolutional operations for feature extraction. This is a method widely used, especially in image processing and signal data. CNN's deep layers are powerful for discovering complex relationships in the data and transferring them to the model. LSTM (Long Short-Term Memory) is an effective model for understanding long-term dependencies in time series data and how information from the past affects current predictions. LSTM was developed to solve the long-term dependency problems experienced by ordinary feedback neural networks. Gates (forget, input, and output) in the LSTM cell structure dynamically determine which information is remembered or forgotten at each step. The following sections provide more information on CNNs and LSTM, which form the theoretical basis of the proposed model.
## 3 Simulation Results
### Results
These results emphasize the importance of phase-noise-resilient algorithms like CA-CFAR for practical radar deployments. Figure 7: Probability of detection versus SNR under phase noise of \(-\)80 dBc/Hz. With more pronounced phase noise, all algorithms experienced further degradation in performance. CA-CFAR remained the most resilient, reaching full detection at low SNR levels. LTSP maintained competitive performance and outperformed FFT and MPM in mid-SNR regimes. The SVD-based detector remained ineffective below 15 dB, indicating its limited practicality in highly phase-noised conditions without additional stabilization or preprocessing. a favorable trade-off between robustness and implementation complexity in embedded FMCW radar systems.
# Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results
## 2 Materials and Methods
### Phantom Experiments and Quality Metrics  
A complete test setup is presented in Figure 6. An evaluation of the system performance was carried out using a tissue-mimicking Model 049A elasticity Q/A phantom (CIRS, Norfolk, VA, USA). The phantom had four stepped cylinder mass targets of known stiffness and six diameters. Before performing the measurements, standard real-time B-mode imaging was used for probe placement over an inclusion. For all subsequent methods, the probe position was fixed using a handle, which was aimed at a given target. The phantom region with no inclusions was used for the homogeneous phantom experiments. To evaluate the performance of each push generation method (FCUSE, SSI and FCUSE-SSI) and for comparison purposes, a number of quality metrics were calculated. The shear wave motion energy in each pixel \(SW_{E}(x,z)\) was calculated as:  
\[SW_{E}(x,z)=\sum_{i=1}^{N}v_{zf}(x,z,i)^{2} \tag{8}\]  
where \(N\) is the number of samples in the slow-time domain and \(v_{zf}\) is the shear wave axial velocity after the frequency and directional filtering. The \(SW_{E}(x,z)\) maps were calculated separately for both the \(RL\) and \(LR\) datasets and were then added together.  
In the homogeneous phantom experiment, the bias and SNR of the stiffness map was obtained as follows:  
\[Bias=\frac{SWS_{E}-SWS_{N}}{SWS_{N}}\cdot 100\% \tag{9}\]  
Figure 6: The setup that was used in this work. Left to right: the L7-4 probe over the CIRS 049A elastography phantom, the us4R-lite portable research platform with the TX extension board mounted and connected to the PC with Mathworks MATLAB software running.  
\[SNR_{dB}=20 log_{10}(\mu_{SWS}/\sigma_{SWS}) \tag{10}\]  
where \(SWS_{E}\) and \(SWS_{N}\) are the estimated and nominal shear wave speed values, respectively, \(\mu_{SWS}\) is the mean value and \(\sigma_{SWS}\) is the standard deviation of the selected area of the stiffness map.  
In the heterogeneous phantom experiments, the contrast to noise ratio (CNR) was derived using the mean SWS values of the inclusion and the background, \(\mu_{I}\) and \(\mu_{B}\), and the standard deviations of those values, \(\sigma_{I}\) and \(\sigma_{B}\), within manually extracted regions of the stiffness map:  
\[CNR_{dB}=20 log_{10}\left(\frac{|\mu_{I}-\mu_{B}|}{\sqrt{\sigma_{I}^{2}+\sigma_ {B}^{2}}}\right) \tag{11}\]
## 3 Results
### Sensitivities to Different UV Wavelengths
Evaluation of Extracellular Protein Content and Sensitivity to UVA among Mutants of General Secretion Pathway Genes  
To investigate the relationship between sensitivity to UVA and the mutation of the general secretion pathway gene, we measured the extracellular protein of the _VP0136_ and _VP0140_ mutants. The content of extracellular protein in the _VP0136_ and _VP0140_ mutants was significantly higher than that in WT (Figure 5a), but the intracellular proteins were not different among WT and the mutants (Figure 5b). The gene complementation of VP0136 (p_VP0136_/_VP0136_) or VP0140 (p_VP0140_/_VP0140_) significantly decreased the content of extracellular protein and increased UVA sensitivity (Figure 5a,c), whose levels were the same levels in WT (Figure 5a,c). From these results, membrane permeability controlled by the proteins of general secretion pathway might be associated with UVA sensitivity. Intracellular Concentrations of Uliquinone-8 and Sensitivity to UVA Irradiation among Strains with Mutations in Uliquinone Biosynthesis Genes  
In _E. coli_ and _Vibrio_ spp., the UQ tail includes eight isoprene groups and as such is designated UQ-8 [24]. Biosynthesis of UQ-8 in _E. coli_ relies on _ubi_ genes [25]. In order to explore the relationship between sensitivity to UVA and mutations in genes responsible for ubiquinone biosynthesis, we measured intracellular concentrations of UQ-8 (C\({}_{51}\)H\({}_{72}\)O\({}_{2}\)) in _VP0097_ and _VP0315_ by LC-TOFMS. The positive ions associated with UQ-8 (mass-to-charge ratio [m/z] = 727.5673 [M + H] and 749.5490 [M + Na]) were detected at a retention time = 44.8 min (Figure 6a). We detected a lower concentration of UQ-8 in the _VP0315_ mutant than in the WT; interestingly, the _VP0097_ mutant exhibited a higher UQ-8 concentration than the WT (Figure 6a,b). The gene-complemented strains, _pVP0097_/_VP0097_ and _pVP0315_, maintained levels of both the intracellular UQ-8 concentration and UVA sensitivity that were similar to the WT (Figure 6b,c). From these results, we conclude that both VP0097 and VP0315 serve to regulate UQ-8 concentrations and also the sensitivity to UVA irradiation. Figure 4: Intracellular ATP concentration and sensitivity to UVA irradiation in the mutants of F\({}_{0}\)F\({}_{1}\)-type ATP synthase genes. (**a,b**) ATP concentrations in response to gene complementation of F\({}_{0}\)F\({}_{1}\)-type ATP synthase gene mutations, p_VP3069_/_VP3069_ and p_VP3075_/_VP3075_. (**c,d**) WT and mutant strains in the absence (**) or presence of 20 mM L-serine (**). Intracellular ATP concentration was measured by a luciferase/luciferin reaction as described in the materials and methods section. (**c,d**) Sensitivity to UVA irradiation was measured as described in the materials and methods section. Values shown are means \(\pm\) SD (n = 3–7, n = number of independent experimental replicates). * \(p\) \(<\) 0.05 or ** \(p\) \(<\) 0.01 versus WT.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 3 Results and Discussions
### Unconfined Compression Strength  
Unconfined compression strength (UCS) tests were performed as per ASTM standard [37] with an axial strain rate of 1 %/min. The size of sample was 10 cm--height and 5 cm-diameter. A-state-of-the-art automated triaxial compression testing machine was employed to assess the strength. In addition, effect of moisture content is also studied on the UCS of the DS-RS mixtures. To maintain a uniform moisture content inside the specimen, the specimens were confined by the rubber membrane. A large amount of the mixture was prepared for UCS tests at each sediment content to ensure the reproducibility of the test results. Hence, identical samples were tested, and the average value was obtained. The detailed effects of moisture content on unconfined compression strength of each dune sand-reservoir sediment mixture and their corresponding regression models have been presented in Figure 9. Each equation with different parameters outlines the effects of reservoir sediments content on moisture--UCS relationship. A regression model between sediment content and unconfined compressive strength is shown in Figure 10.  
Figure 9: Effect of moisture content on unconfined compressive strength of sand-sediment mixes.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 3 Results and Discussion
### Removal of As(III) from Aqueous Solutions
#### 3.2.3 Evaluation of Anti-Ion Interference Capability and Reusability  
In the real environment, other anions and cations were certain to be found in the arsenic-containing effluent. To explore the practical application capability of the nZVI/MXene@CNTs, several common interfering ions in water bodies (Ca\({}^{2+}\), Mg\({}^{2+}\), Cl\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\)) were used in the As(III) removal experiment. As shown in Figure 4d, at the 0.01 mol/L concentration of interfering ions, the effect of the As(III) removal rate was in the order of Mg\({}^{2+}\) (98.60%) > Ca\({}^{2+}\) (98.09%) > Cl\({}^{-}\) (89.21%) > SO\({}_{4}\)\({}^{2-}\) (87.81%). The reason was that the cation would not compete with the negatively charged H\({}_{2}\)AsO\({}_{3}\)\({}^{-}\) for adsorption sites and had basically no effect on the removal of As(III), while the anion would, and then the removal efficiency was restricted [57]. Generally, the nZVI/MXene@CNTs showed a good practical applicability against interference.  
Reusability is usually one of the most significant factors to evaluate the performance of adsorbents. Therefore, in addition to the adsorption performance research, the reusability of the materials after desorption should be assessed. In this experiment, 1 mol/L NaOH was used to regenerate the adsorbed composite material, and then employed for further adsorption tests, as shown in Figure 4e. After five cycles, the removal of As(III) by the elution-regenerated nZVI/MXene@CNTs decreased by about 14.25%, which might be owing to a decline in the activity of the number of functional groups and surface active sites. The results showed that the nZVI/MXene@CNTs had outstanding reusability properties and could be employed in true water-treatment procedures.
## 3 Results
### Potential Causes of FAB Occurrence  
The lake monitoring data show that Bear Lake is warming, as evidenced by both a long-term decrease in ice period from 1923 to 2021 (Figure 2A, linear curve fit: y = \(-\)0.368x + 774, R\({}^{2}\) = 0.06, \(p\) = 0.011), and a recent (2009 to 2020) significant increase in summer water temperatures (Figure 2C, linear curve fit: y = 0.198x \(-\) 378, R\({}^{2}\) = 0.18, \(p\) = 0.0096), with no significant change in springtime water temperatures (Figure 2B, \(p\) = 0.51). Specifically, long-term ice cover data indicated that Bear Lake had last frozen over in the winter of 2016-2017, meaning that the lake had (as of winter 2020-2021) experienced four consecutive ice-free winters. The longest prior run of consecutive ice-free winters since 1923 had been three years, which occurred fairly recently (1998-2000). Prior to 1998, there were only two instances in which the lake did not freeze over for two consecutive years (1953-1956, and 1977-1978).  
Analyses of stable isotopes from FAB and non-FAB materials that were collected in June 2021 indicated that the \(\delta^{15}\)N values were not significantly different between the sites (Figure 3, one-way ANOVA, [PERSON]'s post hoc test at \(p<0.05\)). In October 2019, the \(\delta^{15}\)N values of the FABs that were collected at the west site were significantly higher than in 2021 (Figure 3, one-way ANOVA, \(p=0.006\)). However, the N content in periphyton tissues was significantly higher in the FABs than in the non-FABs (Figure 3, one-way ANOVA, \(p=0.015\)). The \(\delta^{15}\)C values of the FABs were significantly lower than those of the non-FABs (Figure 3, one-way ANOVA, \(p<0.0001\)). Similarly, the C content of the FABs was significantly higher than that of the non-FAB tissues (Figure 3, one-way ANOVA, \(p<0.0001\)).  
Figure 2: (**A**) Number of ice days, (**B**) surface water temperature in spring (April-June) and (**C**) surface water temperature in summer (July–August) in Bear Lake. Trends were significant for numbers of ice days (linear curve fit: \(y=-0.368\)x + 774, R\({}^{2}=0.06\), \(p=0.011\)) and water temperature in summer (linear curve fit: \(y=0.198\)x \(-\) 378, R\({}^{2}=0.18\), \(p=0.0096\)).
## 2 Geological and Stratigraphic Setting  
Labuan Island is part of the Brunei-Sabah Basin [8]. It is a plunging anticline that mainly controls the topography of the Island, where the highest elevation is 85 m [9]. Labuan is situated 8 km west of the Sabah shores and north of Brunei, in the southeast of the South China Sea (Figure 1). Sabah is located in the north of Borneo, and covers an area of about 73,000 km\({}^{2}\)[10]. Figure 2 presents outcrops of the Belait Formation sandstone with coal intercalations (A), the interbedded fine-grained sandstone of the Temburong Formation (B), and the lower Belait Formation massive sandstone (C).  
Figure 1: A locational map of Labuan Island in relation to the South China Sea and Borneo.  
The Sabah Basin is divided into four distinct areas--the western lowlands, west cordillera, central uplands, and eastern lowlands. This study focused on Labuan Island as part of the Sabah Basin, northwest of Borneo Island. The island's stratigraphy shows four formations. The Crocker and Tembuorng formations represent the deep marine, while the shallow marine sequence is represented by the Setap and Belait Formations. The two groups are separated by the Middle Miocene Age's unconformity, known as the deep regional unconformity (DRU) [11]. This paper will focus on two formations: Tembuorng, and Belait (Figure 3). These formations reflect their offshore equivalent stages: II, III; and IV, respectively [12]. Tembuorng and Crocker structurally form the central core of the anticline. Setap Shale is characterized as a shallow marine argillaceous shale intercalated with siltstone and limestone [13], deposited during the Early to Middle Miocene [9]. Setap shale covers the central area of the island [14]. Belait was deposited during the Middle to Late Mioceneand considered to be a shallower firing upward formation with a texture that starts with the conglomerate, to pebbly sandstone at the bottom and shale and coal on top [15]. Belait's base was considered to be an erosional part, as it descriptively matches the DRU in the offshore sequences [16]. Several researchers have attempted to investigate the geological history of the Labuan Island from several perspectives.There were numerous studies, especially over the previous couple of years. [PERSON] and [PERSON] [12] have conducted a sedimentological study on the base of the Belait Formation to provide some information about the offshore equivalents. They did not cover much about petrophysics in their study. [PERSON] et al. [9] studied the geologic properties through eight outcrops, but with no details about their petrographical or petrophysical aspects. [PERSON] and [PERSON] [17] studied Belait outcrops and used the presence of facies to detect the continuity of the formation. [PERSON] [13] presented a field facies analysis on some outcrops based on their field characteristics, although there was no report on laboratory analyses.  
Figure 2: (**A**) The upper Belait Formation sandstone with coal intercalations, (**B**) the interbedded fine-grained sandstone of the Tembuorng Formation, and (**C**) the lower Belait Formation massive sandstone.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### Causes of Failure of RF MEMS Switches  
For RF MEMS switches with capacitive switching principle at low-power handling (1 mW and below) the main failure mechanism is the electric charge of the dielectric film. The solution to this problem is the use of control voltages with variable polarity [39; 40]. For medium-power handling switches (10-100 mW) the main failure mechanisms are related to the electric charge of the dielectric film and failure due to high current density. Problems of failure due to high current density are usually solved by increasing the thickness of the membrane [41; 42]. For high-power handling switches the main mechanisms are random tripping and high current density. Random triggering occurs at a voltage in the RF transmission line comparable to the pull-down or return voltage. The solution to this problem is to increase the stiffness of the switch design and control the membrane using separate electrodes [43; 44].  
Figure 3: Schematic view of the capacitive RF MEMS switch.  
Figure 2: Schematic view of the resistive RF MEMS switch.  
For RF MEMS switches with a resistive switching principle at low-power handling (less than 1 mW), the main failure mechanisms are erosion, contact hardening and the formation of dielectric films on the contact. The solution to this problem lies in the selection of materials with the best contact characteristics [45; 46]. Medium-power handling switches (10-100 mW) fail due to high contact current density and contact microwelding. This problem is solved by the selection of materials with better contact characteristics [47; 48; 49; 50]. For high-power handling switches (100 mW or more), the main failure mechanism is temperature rise, high current density and microwelding of contacts. This problem is solved by constructive work on cooling the contact area, the selection of materials contact [35; 51; 52].