esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Governing Equation  
The common model used to describe heat transfer in biological tissue is the Pennes bioheat transfer equation, which is given as [21]:  
\[\rho\ c\ \frac{\partial T}{\partial t}=\cdot(k\ T)-Q_{p}+Q_{m}+Q_{h}, \tag{1}\]  
where \(\rho\) is the tissue density (kg m\({}^{-3}\)), \(c\) is the specific heat of tissue at constant pressure (J kg\({}^{-1}\) K\({}^{-1}\)), \(T\) is the temperature of tissue (K), and \(k\) is the thermal conductivity of the biological tissue (W m\({}^{-1}\) K\({}^{-1}\)). \(Q_{P}\) is the heat loss due to blood perfusion (W m\({}^{-3}\)), \(Q_{m}\) is the volumetric heat produced by metabolism (W m\({}^{-3}\)), and \(Q_{h}\) is the heat source generated by the RF current (W m\({}^{-3}\)) from the active electrodes. Generally, the terms \(Q_{p}\) and \(Q_{m}\) are ignored because of their insignificant contributions to cardiac ablation, compared with the other terms in the Pennes equation [21]. In the RF range used in cardiac ablation (approximately 500 kHz), the biological tissue can be considered as a total resistive medium. The heat source \(Q_{h}\) refers to Joule heating and can be expressed by solving the quasistatic electrical problem  
\[Q_{h}=\sigma|E|^{2}, \tag{2}\]  
where \(\sigma\) is the electrical conductivity (S m\({}^{-1}\)) of the biological tissue; and \(E=-\
abla V\) is the electric field intensity (V m\({}^{-1}\)), which is calculated from the gradient of the associated electric potential \(V\) (V). If no internal electric sources exist in the heat transfer system, the electric potential \(V\) will satisfy:  
\[\cdot\sigma\ V=0, \tag{3}\]The application of RF power inflics irreversible lesions on the biological tissue. Previous studies concluded that the temperature field enclosed by the 50 \({}^{\circ}\)C isothermal surface after ablation activity reflects the effective lesion volume [19; 20].
## 3 Results  
In order to make the discussion easier, we will briefly illustrate first the flow evolution for a fixed aspect ratio (\(\Gamma=1\)) as a function of the Reynolds number. For \(Re\leq\mathcal{O}(1)\), the dynamics is dominated by viscosity with the flow smooth and steady, without separations; this is the Stokes regime (Figure 4a), where between the upstream and downstream flows there is an approximate symmetry that becomes more exact the more \(Re\to 0\). As Re is increased, the flow remains steady and a first recirculation can appear: however, this recirculation is not attached to the torus, but located downstream in the wake. This regime is generated by the competing effects of blockage of the torus and downstream convection of the mean flow. Owing to the vorticity distribution within the bubble, it will be referred to as Hill regime, and it can exist only in a narrow range of aspect ratios (around \(\Gamma=1\)) and Reynolds numbers (Figure 4b). A further increase of \(Re\) produces separations attached to the torus that in some cases can coexist with the Hill recirculation (Figure 4c), but eventually only recirculations attached to the torus surface can survive (Figure 4d). When the Reynolds number is still augmented, the flow becomes unsteady (Figure 4e) with a wake that oscillates in time and sheds compact vortices. The last regime is observed for even higher \(Re\) when the flow becomes three-dimensional and also the azimuthal symmetry is lost (Figure 4f).  
The above scenario can change considerably depending on the aspect ratio of the torus: in the following sections, we will illustrate the flow dynamics in the various regimes for different values of \(\Gamma\).
# Comprehensive Insights into the Molecular Basis of HIV Glycoprotein
## 2 Proteins Engaged in HIV Entry
### Env/gp160  
HIV-1 enters the host cell by fusing its lipid envelope with the host cell's plasma membrane. This fusion process is facilitated by the HIV-1 envelope (Env) glycoprotein, specifically the gp160 complex, which is embedded in the viral lipid envelope. The Env glycoprotein serves as a key antigen, eliciting robust antibody responses, and is a primary focus in developing HIV-1 vaccines and therapeutic agents [28]. The viral _env_ gene encodes the Env protein. It is initially translated as the precursor transmembrane protein gp160, which subsequently undergoes cleavage into two subunits: gp120 and gp41. Structurally, Env forms a dimeric spike-like protein on the virion surface, characterized by its low density per virion, constituting 50% carbohydrate mass. Each trimer consists of three identical molecules, each comprising a cap-like gp120 region and a stem-like gp41 region [29; 30; 31; 32; 33].
## 1 Introduction
Additionally, while reinforcement learning and AI models for robotic control have been increasingly explored [11], their practical deployment on dynamic construction sites continues to be a challenge. However, the existing literature exhibits critical limitations. First, prior reviews have predominantly focused on regional applications, lacking a global synthesis of trends. Second, studies often isolate technical advancements from their socio-economic implications, such as workforce retraining or ethical governance. Third, despite the proliferation of bibliometric analyses in adjacent fields, construction robotics remains understudied through a systematic, data-driven lens. In summary, the current research illustrates the transformative potential of robotics in construction for modular prefabrication, AI-driven decision making, and human-robotcollaboration [18]. However, widespread integration depends on overcoming technical, economic, and regulatory challenges. Future research must address these obstacles while refining experimental implementations of emerging robotic technologies, bridging the gap between conceptual innovation and industry adoption [19]. The integration of robotics in the construction industry is advancing through practical applications such as modular prefabrication, 3D printing, and autonomous site management, supported by AI, BIM, and sensor-enabled systems, while future opportunities include collaborative human-robot systems and swarm robotics [20]. Despite significant advances in robotics for the construction industry, several research gaps and unresolved challenges remain. Starting from the above problems, this study tries to interpret the current trends in and the future development of construction robots using the bibliometric analysis method [21]. With this goal, this review aims to achieve the following: (1) to map global research trends in construction robotics (2002-2024) through bibliometric analysis, identifying dominant clusters and collaboration patterns; and (2) to quantify interdisciplinary linkages between robotics, AI, and socio-technical domains. Through integrating fragmented insights into a cohesive framework, this work contributes to the transition toward Construction 4.0, where intelligent automation aligns with sustainable and inclusive industry practices.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 5 Discussion
### Potential of the Combined Use of SAR and Optical Remote Sensing for Snow Cover Mapping  
The potential of active SAR as a complement to optical sensors for SCA mapping lies in its capability for wet snow dynamics monitoring [87]. Radar backscatter decreases and the one-way penetration depth of the radar signal drops drastically as liquid water content increases due to the high dielectric losses of water [88; 89]. Radar signals at C- and X-band frequencies are reflected and scattered at the surface and within the uppermost centimeters of the snowpack due to liquid water content increase, whereas in the case of dry snow, the backscatter is strongly dominated by the contribution of the snow/ground interface [90]. Thus, it can be inferred that the use of active SAR for SCA mapping is primarily effective in snowmelt areas. Several studies have indicated the potential of combining SAR with optical sensors for SCA monitoring [91; 92]. For instance, the Copernicus Land Cover Monitoring Service developed a gap-filled fractional SCA product by combining SAR (Sentinel-1) and optical (Sentinel-2) satellite constellations through spatial and temporal gap-filling over major European high mountain areas. An overall quality assessment of this product revealed a relatively consistent and good performance of HR$&I$ SWS for the spatial gap-filling of optical SCA throughout the snow season. This contrasts with our study's findings, which show a significant increase in the spatial gap-filling of MODIS SCA in spring compared to winter. These discrepancies can be attributed to the focus of our study on mid-latitude mountains, whereas the Copernicus assessment included both mid-latitude and high-latitude mountainous systems. Additionally, differences in the revisit times of the MODIS and Sentinel-2 sensors could explain the variations in the percentage of gap-filled optical SCA. Nonetheless, both studies suggest a high potential for the combined use of SAR and optical sensors.  
In this research, we found that the Copernicus HR$&I$ SWS product provides a substantial amount of additional SCA data in Pyrenees in the spring season. Taking into account the sparse distribution of forest density in the main snow-dominated Iberian mountains, together with the different patterns observed in cloud coverage and the duration of the melting period, we can infer that wet snow (HR$&I$ SWS product) could contribute even more significantly to additional SCA information in the Sierra Nevada. In contrast, the contribution is expected to be less in the Cantabrian system, Central System, and Iberian Range. However, a more comprehensive analysis is needed to evaluate the contribution of wet snow to the SCA dynamics in the rest of the mountain ranges of the Iberian Peninsula. This analysis should consider other snow variables such as snow depth or snow water equivalent to provide more detailed understanding.
## 3 Results and Discussion
### Interfacial Tension Measurements (IFT)
#### 3.3.3 Effect of NaCl on IFT  
The study was conducted to determine the effect of NaCl concentration on the DIFT of betaine surfactant solution at the optimal concentration and Xinjiang crude oil. It was observed that the lowest interfacial tension (IFT\({}_{\text{min}}\)) gradually decreased with the increase in Na\({}^{+}\) concentration and reached the minimum values of 3.0 \(\times\) 10\({}^{-4}\) mN/m and 1.5 \(\times\) 10\({}^{-4}\) mN/m when the Na\({}^{+}\) concentration was 5%. This indicates that NaCl and betaine surfactants have synergistic effects in aqueous solution. Meanwhile, when the concentration of Na\({}^{+}\) reached 10%, DCB and DSB can still reach the state of ultra-low interfacial tension, and both of them had good tolerance to monovalent cations.  
It can also be discovered from the Figure 5 that, with an increase in Na\({}^{+}\) concentration, the equilibrium interfacial tension value (IFT\({}_{\text{equ}}\)) of both betaine surfactants showed a trend of first decreasing and then increasing. DCB was lowest (1.9 \(\times\) 10\({}^{-2}\) mN/m) when the Na+ concentration was 2%, and DSB was the lowest (1.5 \(\times\) 10\({}^{-3}\) mN/m) when the Na+ concentration was 5%. The reason is that the surfactant molecules in solution can form a diffusion double electric layer and Na\({}^{+}\) will compress the double electric layer, giving the surfactant molecules in the interface layer a more compact arrangement, leading to a gradual reduction in IFT\({}_{\text{equ}}\)[30]. However, as the concentration of Na\({}^{+}\) continues to increase, the addition of counter ions can shield the charge of the ionic surfactant, destroy the hydration structure around ions, and enhance the hydrophobicity of the surfactant, which leads to the transfer of more surfactant molecules to the oil phase, a decrease in the surface active molecular weight on the oil-water interface, and an increase in IFT\({}_{\text{equ}}\)[31]. In addition, enhanced hydrophobicity makes surfactant molecules more likely to associate to form micelles, and the number of single surfactant molecules at the oil-water interface decreases, while IFT\({}_{\text{equ}}\) increases. This rule also applies to Ca\({}^{2+}\) and Mg\({}^{2+}\).  
Figure 4: Effects of temperature on IFT between the optimum concentrations of surfactants and Xinjiang crude oil for DCB (**a**) and DSB (**b**).
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Double-Balanced Down-Conversion Mixer Noise Calculation and Simulation  
The noise sources in linear circuits are typically considered to be stationary, but in mixers, the generated noise changes periodically over time, resulting in cyclo-stationary noise processes. This occurs when the dominant noise sources are switched back and forth by the local oscillator [1; 16; 17; 18]. Because of its nonlinear nature, calculating noise requires a nonlinear summation of noise from the RF and LO ports to determine the noise power at the IF. A simulation-assisted approach for these computations that does not rely on harmonic balance techniques has been described in the literature [19; 20; 21].  
Finding the noise-generating mechanism in a single-balanced mixer and doubling the contribution to the IF port are prerequisites for comprehending noise in a double-balanced differential mixer. The analysis will be much easier compared to a Gilbert cell, but it will still offer valuable information on the noise performance of the active mixer. Throughout these calculations, it will be assumed that the output is always taken differentially. The summary shows the main causes of noise and a simple approach for computing the spectral densities of this noise in a single-balanced mixer. The major mechanisms contributing to noise are as follows:  
* Once the switching transistors (Q2 and Q3) are fully switched, any noise in the signal path, such as noise from the RF input stage, is manifested at the output as differential noise. This occurs because only one of the outputs is active at a time, and the other switching transistor has a lower impact on the noise.  
Figure 10: IF spectrum at 145 MHz.  
Figure 9: Voltage and current waveform of Q2.  
When the states of Q2 and Q3 switches change, the noise from the RF input stage Q1 is observed as a common mode and does not add any contribution to the differential output current. However, the switch devices share the DC current and introduce differential noise to the output. In the current commuting mixer, the duration of this period is determined by the magnitude of the LO and decreases with a stronger LO drive.
* Furthermore, significant noise may be present in the LO signal during the switch phase, which could have a similar impact on the output as the noise produced by Q2 and Q3.
* The current-to-voltage stage output consistently introduces differential noise. When a resistive load is used to implement this stage, it typically adds thermal noise.  
The noise figure is obtained by combining linear sources and linear sources that have been influenced by the periodic and time-varying characteristics of the circuit [22; 23; 24]. The detailed analysis is given in [3].
# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 3 Preliminaries
### Arabic Ontology Learning
#### 3.3.3 The Semantic Relations Theory  
Underlying semantic relations for Arabic text show a great deal of variety [67]. The three semantic relationships considered in the current work can be explained with the following examples of biomedical concepts from our corpus:  
* Synonymy. This relationship type aims concepts that hold nearly similar meanings. For instance, the concepts \(\mathfrak{s}_{\text{\tiny{2}}\!\!
## 2 Materials and Methods  
All commercially obtained chemicals were of analytical grade, used without further purification, and supplied by Oxford University in Oxford, UK. \({}^{1}\)H and \({}^{13}\)C NMR spectra were recorded at 400 MHz using a Bruker Spectrospin DPX-400. All chemical shift (\(\delta\)) values are given in parts per million. Low-resolution mass spectra were recorded on a Waters Micromass LCT Premier XE spectrometer. Accurate masses were determined to four decimal places using Bruker uTOF and Micromass GCT spectrometers at the Chemistry Research Laboratory of the University of Oxford. Dialysis tubing was performed on a Dispo-Biodialyzer MWCO 1 kDa and Cuprisorb(r) as the copper-adsorbing resin. HPLC purification was acquired with the Waters HPLC or a UV-Vis spectrometer (Cary 60 UV-Vis, Agilent Technologies, Santa Clara, CA, USA). Elemental analysis for H, C, and N was carried out on a Perkin Elmer 2400 Series II CHNS Analyzer. Metal ions were analyzed on an Inductively Coupled Plasma Mass Spectrometry on an Agilent 7700 ICP-MS instrument.  
The family of complexes in this study were the heterolanthanide compounds based on azide-alkyne Huisgen cycloadditions built like a potential CA: a tetramer with a certain rotational impediment, an open trimer, and another closed. The first one was designed using DOTA units, a 1,2,3-triazole ring, and 1,4-phenylenediamine (Figure 1). The second was a trimer Gd\({}^{3+}\)-Tb\({}^{3+}\) complex with an open configuration to measure the relaxivity in CA-hindered and a tetramer in a closed configuration with high rigidity (Figures 2 and 3).  
The different synthetic routes used for the synthesis of compound 18 are shown in Table 1. Every single reaction was performed in duplicate, purified by flash column chromatography (FCC) and preparative TLC using as eluents DCM (90%):MeOH (9.9%):TEA (0.1%) for starting materials and toluene (30%):hexane (69.9%):TEA (0.1%) for the expected product, resulting in minimal amounts of product, enough for the \({}^{1}\)H-NMR and MS studies.
## 4 Online Control Strategy
### Online Controller Verification
Therefore, the DP-A-ECMS-based neural network control strategy has a higher robustness, and the fuel consumption in the simulation will be closer to the offline optimization results. Figure 12 shows the change curve of the SOC during the entire driving cycle. The best SOC curves obtained by the DP-A-ECMS algorithm and DP algorithm offline calculation are added as a reference. The solid red line represents the online control strategy based on the DP-A-ECMS algorithm proposed in this article, and the solid green line represents the neural network control strategy based on the traditional DP algorithm. It can be seen that the offline calculation results of the DP-A-ECMS and DP are extremely close. The neural network online control strategy based on the DP-A-ECMS algorithm is closer to the global optimal solution. Table 3 shows the vehicle fuel consumption of the two methods. It can be seen that the fuel economy of the method proposed in this paper is increased by 2. From the verification results of the online controller, it can be seen that the BP neural network can output online equivalent factors close to the theoretical optimal value. The online speed recognition of the neural network is very fast, and the average calculation speed in the computer simulation process is 4.8 ms. The works [24; 26] pointed out that A-ECMS exhibited a good real-time performance, stability, and accuracy in both hardware-in-the-loop experiments and real-vehicle road tests. Therefore, the online control strategy proposed in this paper can effectively improve the fuel economy of PHEV in the real world. Table 4 compares the existing online control strategy with the method proposed in this paper in terms of the fuel economy, real-time performance, and robustness. The indicator of fuel economy is the fuel consumption per 100 km under 10 NEDC driving conditions. With the support of optimization theory, the optimization-based strategy and neural network-based strategy have great advantages compared with the RB strategy, which is empirical. The evaluation index of the real-time performance is the average calculation time per step. The rule-based strategy is essentially based on table lookup, the instantaneous optimization strategy is a comparison of a limited number of values, and the neural network strategy adds a parameter recognition process to the comparison. Therefore, the computation speed of these three strategies is very fast. It is difficult to unify the evaluation criteria of robustness. However, as we all know, the rule-based strategy can adapt to most working conditions, while optimization-based strategies can only be applied to a single optimized driving condition. Neural network-based strategies are related to the generalization of neural networks. As mentioned above, the neural network based on DP-A-ECMS has better generalization. Therefore, the table gives a subjective evaluation of the robustness of different strategies.
## 3 Results and Discussion
### HA-MVD Process Facing Walnut Shell Breaking
#### 3.2.5 Kernel Color Parameter  
Color is one of the most important quality indicators of dried products. As summarized in Table 4, \(\Delta\)E\({}^{\star}\) ranged from 5.92 to 10.94. The response surfaces of each variable to \(\Delta\)E\({}^{\star}\) are presented in Figures 12D and 13D. The results show that the interaction effects of HA temperature-microwave power and MC of the transition point-MV thermostatic temperature are significant for \(\Delta\)E\({}^{\star}\) (Table 5). We found that \(\Delta\)E\({}^{\star}\) decreases and then increases with increasing HA temperature and MV thermostatic temperature (Figures 12D and 13D). The color darkening of food materials during drying might be caused by both enzymatic and non-enzymatic browning reactions [45, 46]. Specifically, the low-temperature conditions require a long duration of drying, thus resulting in increasing browning [40]. Also, the heating at the high temperature induced the Maillard reaction of amino acids and reduced sugar inside kernels and caused more non-enzymatic browning [47, 48]. Meanwhile, we also found that the \(\Delta\)E\({}^{\star}\) of walnuts dried at 600 W is lower compared to those dried at higher power (Figure 12D). This is due to the fact that the higher temperature of kernels at higher microwave power levels leads to a higher degree of browning [44].  
As can be seen from Figure 12D, the combination of high HA temperatures (65-70 \({}^{\circ}\)C) and microwave power (650-700 W) results in a significant increase in the \(\Delta\)E\({}^{\star}\). This suggeststhat the combination of higher HA temperatures and microwave power can exacerbate the browning of kernels during drying. As shown in Figure 13D, the \(\Delta\)E* is smaller at the higher MC of the transition point and lower MV thermostatic temperature. This implies that earlier access to low-temperature MVD can significantly reduce the color change of kernels. Moreover, a significant negative linear effect between the MC of the transition point and \(\Delta\)E* confirms that MVD has a smaller effect on kernel color than HAD (Table 5). This is likely due to the lack of oxygen preventing enzymatic oxidation when the MV is applied [49].