esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Importance of the Molecular Backbone: Conjugated Molecular Wires  
In the previous section, we have focused the discussion on alkane molecular chains and we have considered the different parameters which can influence the electronic transport in the junction and consequently the attenuation factor. In particular, we have considered the anchoring groups, in symmetric or asymmetric junctions, and the electrodes, with different metals and with a graphitic tip. Without being exhaustive, it is important also to consider what happens when using another molecular backbone, like conjugated molecular wires. To this end, we chose to have a short look at polyphenylene chains, which are also very common and popular for molecular electronics. These polymers are very interesting, since, as presented in Figure 1 and [15], their attenuation factors are around three times smaller than for the alkane chains, making these molecular wires much more conductive. Hence, with what we have seen by using graphene electrodes, we can expect an even higher conductance for these polymers used in hybrid molecular junctions.  
Therefore, hybrid Au-graphene and standard Au-Au molecular junctions have been studied through I(s) measurements and DFT calculations, with polyphenylenes, using thiol and amine anchoring groups, as a direct comparison with alkane-based molecular junctions [50].  
Surprisingly, the electronic behavior is very different from what was expected. Indeed, either experimentally or theoretically, almost no difference has been found for the attenuation factors determined for Au-Au and Au-graphene junctions. Moreover, the different anchoring groups, thiol and amine, did not bring any significant difference. Again, this can be better understood from the DOS calculations represented in Figure 8. The projected DOS on the molecular part (including the anchoring group) reveals that there is almost no difference in the electronic structure of each molecular junction. This means that, in this case, the use of a graphene electrode does not break the symmetry, as was the case for the alkane chains. This is further illustrated in Figure 8 with the representation of the spatial extension of the HOMO level for both Au-Au and Au-graphene junctions. In both cases, we can observe a molecular orbital of \(\pi\) symmetry which propagates well along the molecular chain. However, we can also observe that the coupling to the electrodes remains the same in both junctions, despite the introduction of the graphene electrode. In other words, for this system, there is no symmetry breaking induced by the introduction of the graphene electrode. Consequently, the coupling to the electrode or the anchoring group used will have no effect on the electronic structure of the system and therefore on the electronic transport. Hence, the polyphenylene molecular chain solely drives the attenuation factor. From a theoretical point of view, in this case, the Simmons model would not help in discriminating the effect of the anchoring group, whereas a complex bandstructure calculation would probably give the correct attenuation factor, independently of the electrodes or the anchoring groups.
# Study on the Disturbance of Existing Subway Tunnels by Foundation Sloping Excavation
## 4 Validation of the Numerical Simulation  
### Calculation and Element Parameters  
In order to verify the correctness of the theoretical formulation by comparison, a model of the pit excavation was constructed using the finite difference software FLAC3D. The excavation area of the foundation pit is 41.3 \(\times\) 22.0 \(\times\) 7.6 m. The distance between the existing metro line and the foundation is 3.0 m. The support structure is in the form of an underground diaphragm wall with internal bracing, with a wall thickness of 1.0 m and an embedded depth of 10.0 m. The support structure has a high stiffness. The tube sheet of the existing tunnel has an external diameter D of 6.0 m and a thickness of 0.3 m. A reinforced concrete tube sheet of C40 strength was used. According to the available research, the unloading index of the pit sidewall is taken as \(a\) = 0.2, assuming that the soil is homogeneous in the calculation model, and the soil parameters are the weighted average of the parameters of different soil layers within the excavation area of the pit. The Mohr-Coulomb elastoplastic model was selected as the constitutive relation of soil, and soil parameters of each layer were determined according to the engineering investigation report. The linear elastic model was adopted for the diaphragm wall and existing tunnel segment [29; 30]. Table 2 shows the values of each theoretical calculation parameter and numerical simulation material.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 7 Conclusions  
In this research, the ED-MORFO algorithm is proposed, analyzed, and well-organized to recognize the energy-efficient clustering and routing in a WSN. Here, the ED-MORFO is recommended to reach the base station with minimum energy consumption. Initially, the CH selection is based on a variety of criteria; the primary goal of improving the network lifetime is achieved while lowering energy consumption. After choosing the CH, the proposed EDMORFO algorithm is used to choose the best route for transmitting data to the BS; thus, the energy-efficiency of the WSN is improved. The proposed ED-MORFO combination is tested for a system size, with node counts ranging from 100 to 500, and the energy efficiency is investigated through network analysis using PDR, energy consumption, throughput, and network lifetime. In contrast to traditional and cluster-dependent routing systems, such as MCH-EOR and RDSAOA-EECP, the simulated outcomes of ED-MORFO are superior. According to the simulation results, the proposed ED-MORFO outperforms existing protocol systems in all characteristics, by reducing energy consumption by up to 0.46 J. It also achieves a PDR of 99.4%, a PLR of 0.6%, an end-to-end delay of 11 s, a routing overhead of 0.11, a maximum throughput of 0.99 Mbps, and a network lifetime of up to 3719 s. In the future, the proposed approach can be tested with various specification parameters and a variable number of node counts; in addition, a hybrid optimization-based routing procedure can be implemented to achieve better energy consumption results.  
The paper investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization were conducted by R.N. and G.M. The paper conceptualization, and software were conducted by M.R.H. The validation, formal analysis, methodology, supervision, project administration, and funding acquisition of the version to be published were conducted by A.M. and P.B.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Low-Frequency Square Kilometer Array Pattern Optimization via Convex Programming
## 4 Results and Discussion

Table 1: Comparison of the performance parameters achieved by respectively adopting uniform and optimized weighting of the SKA-Low station’s elements. larger configurations. Therefore, by virtue of its adaptability to increasingly complex radio telescope systems, the proposed method also has a relevant practical scope.
## 3 Materials and Methods  
### General Experimental Procedures  
UV spectra were recorded using a UV8000 UV/Vis spectrophotometer (Shanghai Metash Instruments Inc., Shanghai, China). Optical rotation data were measured on the basis of the Rudolph Autopol IV automatic polarimeter (Rudolph Reaearch Analytical, Newburgh, NY, USA). ECD spectra were measured using a Chirascan spectrometer (Applied Photophysics inc., Leatherhead, Surrey, UK). HRESIMS data were measured using a Xevo G2 Q-TOF mass spectrometer (Waters, Milford, MA, USA). The \({}^{1}\)H, \({}^{13}\)C, HSQC, COSY, HMBC, and NOESY spectra were measured on the basis of the Bruker Avance 400 FT NMR spectrometer (Bruker Company, Fallanden, Switzerland) with tetramethylsilicane (TMS) as an internal standard. Semi-preparative HPLC was performed using an Alltech LS class pump with a model 201 variable wavelength UV/Vis detector, and a YMC packed ODS-A (250 \(\times\) 10 mm, 5 \(\upmu\)m) column (YMC Co., Ltd. Kyoto, Japan) was used for the purification. Column chromatography (CC)  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**Anti-NO (\%)**} & \multicolumn{2}{c}{**Cell Viability Inhibition (\%)**} \\ \cline{2-5}  & **20 \(\upmu\)M** & **10 \(\upmu\)M** & **20 \(\upmu\)M** & **10 \(\upmu\)M** \\ \hline
**1** & 33.5 \(\pm\) 1.5 & 10.2 \(\pm\) 2.0 & 0.7 \(\pm\) 0.1 & 0.3 \(\pm\) 1.2 \\
**2** & 34.0 \(\pm\) 1.4 & 22.7 \(\pm\) 1.4 & 4.6 \(\pm\) 2.6 & -1.1 \(\pm\) 0.4 \\
**3** & 22.7 \(\pm\) 1.5 & 13.2 \(\pm\) 1.3 & 4.1 \(\pm\) 7.6 & 3.3 \(\pm\) 3.1 \\
**4** & 28.3 \(\pm\) 0.7 & 18.0 \(\pm\) 2.0 & 3.5 \(\pm\) 3.7 & 2.01 \(\pm\) 1.4 \\
**5** & 39.1 \(\pm\) 1.6 & 25.1 \(\pm\) 0.8 & 10.0 \(\pm\) 0.2 & 3.2 \(\pm\) 1.7 \\
6 & 101.4 \(\pm\) 2.4 & 94.4 \(\pm\) 0.0 & -1.6 \(\pm\) 5.1 & -0.8 \(\pm\) 3.6 \\
**7** & 55.0 \(\pm\) 1.4 & 35.4 \(\pm\) 2.4 & 4.1 \(\pm\) 3.8 & -1.5 \(\pm\) 3.5 \\
**8** & 30.7 \(\pm\) 0.8 & 18.0 \(\pm\) 0.8 & 1.8 \(\pm\) 4.7 & -0.1 \(\pm\) 8.2 \\
**9** & 39.3 \(\pm\) 0.7 & 30.4 \(\pm\) 1.9 & 2.3 \(\pm\) 0.1 & -1.1 \(\pm\) 3.7 \\
**10** & 44.8 \(\pm\) 0.7 & 33.0 \(\pm\) 0.7 & 0.4 \(\pm\) 1.3 & 0.2 \(\pm\) 1.8 \\
**11** & 42.7 \(\pm\) 1.3 & 30.8 \(\pm\) 2.6 & 1.9 \(\pm\) 2.4 & -0.5 \(\pm\) 3.1 \\ \hline \end{tabular}
\end{table}
Table 3: Inhibitory effects of **1–11** against NO production in LPS-activated BV-2 microglia cells and their cytotoxicities against BV-2 microglia cells.  
was carried out using a Sephadex LH-20 (Amersham Biosciences, San Francisco, CA, USA), ODS-A-HG (YMC Co., Ltd. Kyoto, Japan), and silica gel (Qingdao Marine Chemistry Co., Ltd., Qingdao, China). The TLC analyses were carried out with the precoated silica gel plates by heating after spraying with vanillin sulfuric acid chromogenic reagent (Xilong Scientific Co., Ltd., Shantou, China).
## 2 Materials and Methods
### Measuring Impedance in Non-Newtonian Materials
#### 2.2.3 Spectroscopy Analysis Device  
Impedance measurements have been performed with a state-of-the-art infrastructure, i.e., a Solatron modulabXm impedance analyzer (Solatron Analytical, UK), see Figure 4. Through the Solatron analyzer a 100 mV r.m.s. sinusoidal voltage has been applied to the outer couple of electrodes of the measurement probe which was immersed into the glass tube containing the analyzed sample. The impedance of the samples has been analyzed in the 1 Hz-1 MHz frequency range. The electrochemical cell consisted of a plastic cylindrical tank with an inner diameter of 20 mm and a length of 30 mm. The electrodes used to perform the experiments were screen-printed electrodes from Dropsens. These electrodes are chosen since they exhibit high electrochemical activity and good repeatability [29]. The counter and working electrodes are platinum (4 mm diameter) and the reference electrode is silver.  
The _Modulab XM_ is a highly versatile electrochemical test system that measures the characteristics of a wide range of electrochemical systems including organic/inorganic, specialized corrosion, electroplating and energy cells. Reference grade system components (potentiostat/ galvanostat, frequency response analyser and optional high voltage amplifier) are combined in a single unit, avoiding the need for stacking and wiring separate units. The device communicates via an Ethernet link to an external PC, running XM-studio ECS software for control and monitoring purposes. A very high sampling rate is available for recording the data samples and stored in real-time in situ buffer memory slots. The system can export data in real-time (in stack data format) for raw data processing or can deliver at end-experiment the estimated impedance. For a broader scope of utility of raw data samples, a time- and frequency domain analysis can be performed, hence providing both  
Figure 3: Illustration of a polar plot for the Randle’s circuit impedance.  
time-based signals (impedance) as well as image-based data formats (spectrograms). This mixed time-and-image information is of particular interest for applying deep learning tools, as we recently did in a medical application [30].
## 4 Results
### Comparative Analysis with Traditional Methods
#### 4.8.2 Scenario-Based Analysis  
Testing under various operational conditions revealed distinct performance differences. During normal operations, LSTM achieved 95.24% accuracy in fixture demand prediction, compared to 80.95% for queueing theory and 82.93% for traditional codes. Peak usage periods (8:00-10:00 a.m., 12:00-2:00 p.m.) showed even greater disparities, with LSTM maintaining an average error of only 4.76% versus 19.05% for queueing theory and 17.93% for traditional codes.  
Special events such as conferences and training sessions demonstrated the LSTM model's adaptability, with error rates of 6.82% and adaptation times under 15 min. In contrast, queueing theory showed persistent errors of 23.45%, while building codes provided no adaptive capability. Under variable occupancy patterns, LSTM maintained accuracy within 5% for occupancy variations up to \(\pm\)30%, while queueing theory accuracy degraded by 2.5% for every 10% change in occupancy.
## 4 Conclusions
### Nomenclature  
\begin{tabular}{l l} AP & Ship after perpendicular [-] \\ \(\alpha\) & Angle of attack (AOA) [\({}^{\circ}\)] \\ \(\beta\) & Closure coefficient in turbulence model [-] \\ \(C\) & Chord length [m] \\ \(C_{L}\) & Lift coefficient [-] \\ \(C_{D}\) & Drag coefficient [-] \\ \(D\) & Experimental data [-] \\ \(D_{P}\) & Propeller diameter [m] \\ \(dT\) & Local thrust [N] \\ \(dQ\) & Local torque [N\(\cdot\)m] \\ \(\Delta x\) & Thickness of propeller actuator disk [m] \\ \(E\%D\) & Error [-] \\ \(f\!b_{x}\) & Axial body force [N] \\ \(f\!b_{\theta}\) & Tangential body forces [N] \\ FP & Ship front perpendicular [-] \\ \(i\) & Grid point index in one RANS cell [-] \\ \(J\) & Propeller advance coefficient [-] \\ \(k\) & Turbulence kinetic energy [m\({}^{2}\)\(\cdot\)s\({}^{-2}\)] \\ \(k_{1}\) & Empirical correction for finite blade width [-] \\ \(k^{\prime}\) & Coefficient to produce mid- or 25\% chord line [-] \\ \(KT\) & Propeller thrust and torque coefficient [-] \\ \(KQ\) & Propeller torque coefficient [-] \\ \(L\) & Model ship length (=\(Lpp\)) [m] \\ \(Lpp\) & Length between perpendiculars (=\(L\)) [m] \\ \(n\) & Propeller rotation rate [rps, s\({}^{-1}\)] \\ \(n_{b}\) & Which propeller blade in number order [-] \\ \(\upsilon_{t}\) & Turbulence viscosity [m\({}^{2}\)\(\cdot\)s\({}^{-1}\)] \\ \(N\) & Number of propeller blades [-] \\ \(N_{i}\) & Grid number in axial direction [-] \\ \(N_{j}\) & Grid number in lateral direction [-] \\ \(N_{k}\) & Grid number in vertical direction [-] \\ M & Million [-] \\ \(\omega\) & Specific turbulence dissipation rate [s\({}^{-1}\)] \\ \(p\) & Power parameter [-] \\ \(p\) & Pitch [m] \\ \(P_{S}\) & Pressure [N\(\cdot\)m\({}^{2}\)] \\ \(\phi\) & Level set function [-] \\ \(r\) & Radial coordinate (position) in cylinder/propeller coordinate [m] \\ \(r_{i}\) & Distance between foil 25\% chord position and grid point [m] \\ \(R\) & Propeller radius [m] \\ Re & Reynolds number [-] \\ Re\({}_{eff}\) & Effective Reynolds number [-] \\ \(R_{G}\) & Grid independence indicator [-] \\ \(S\) & Simulation value [-] \\ \(S_{1}\) & Simulation value of fine grid [-] \\ \(S_{2}\) & Simulation value of medium grid [-] \\ \(S_{3}\) & Simulation value of coarse grid [-] \\ \(\mathbf{S_{b}}\) & Source term (vector) [N\(\cdot\)m\({}^{-3}\)] \\ \(t\) & Time [s] \\ \(\theta_{s}\) & Propeller skew angle [\({}^{\circ}\)] \\ \(u\) & Axial flow velocity [m/s] \\ \(\mathbf{u}\) & Vector form of velocity field [m/s] \\ \(\mathbf{u}_{G}\) & Grid velocity [m/s] \\ \(u_{i}\) & Axial velocity at grid point [m/s] \\ \(u_{q}\) & Axial velocity at foil 25\% chord position [m/s] \\ \end{tabular}
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.2 Recall Metric  
This evaluation metric calculates the ratio between the correct case predicted as positive over actual positive cases.  
\[\text{Recall}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Negative}}\]  
The recall metric provides us the intuition of the ability to find all cases that are only relevant in the dataset. This metric relates to opportunity costs; if there are many false negatives, then there are opportunities lost every time.
# The Effects of Plasma-Activated Water on Heavy Metals Accumulation in Water Spinach
## 3 Results and Discussion  
### Physicochemical Properties of Irrigation Water  
The generation of PAW can be confirmed by analyzing the acidity, conductivity, ORP, and concentration of ROS and RNS in the water [14]. [PERSON] et al. mentioned that the increase in the NO3- and NO2- contents in PAW could be the main reason behind enhanced plant growth and seed germination [14]. In this study, RO water was used as the water source and treated for 5, 10, 15, and 20 min to generate PAW for the selection of the most favorable PAW as irrigation water. The physiochemical characteristics of PAW under different treatment times are summarized in Table S1. In a nutshell, the NO3- and NO2- concentrations, as well as the EC, ORP and H2O2 values, increased with increasing treatment time, except that the pH values of PAW decreased with increasing treatment time (Figures S1-S4).  
Figure 2 illustrates the concentration of NO3- generated by plasma in this study. The concentration of NO3- increased from untreated (control) to 20-min treatment. The concentration of NO3- decreased in the order 20 min \(>\) 15 min \(>\) 10 min \(>\) 5 min \(>\) control. The increasing trend nitrate concentration with increasing treatment time was similar to results reported in some other studies [21; 22]. The findings of [PERSON] et al. [21] indicated that NO2- and NO3- are formed in water, with the constant rate following zero-order kinetics, indicating a direct effect of the plasma. Meanwhile, they also reported that air microplasma treatment resulted in the highest concentration of nitrites and nitrates, followed by nitrogen plasma and oxygen plasma. [PERSON] et al. [22] also mentioned that the concentrations of NO2-, NO3-, and H2O2 increased with increasing discharge current or activation time [22]. Figure 3 depicts the concentrations of NO2- in PAW treated for different lengths  
of time in this study. However, the NO2- concentration of PAW in this study do not follow the time-dependent trend with treatment suggested by other researchers. NO2- was not detected in RO water, but it was present when water dissolved the ions resulting from the breakdown of nitrogen and oxygen in the plasma treatment. The concentration of NO2- was highest after 10-min PAW treatment (PAW10), followed by PAW20 > PAW15 > PAW5. Furthermore, some studies in the literature have reported that no NO2- was detected after 1 and 5 min of plasma activation [23]. NO3- plays important roles as the nutrient that leads to the production of amino acids and nitrogen compounds and as signal molecules in plant development and metabolism. Hence, PAW treatment for 20 min was selected for the irrigation water throughout the whole plantation period of water spinach.
## 4 Experiments and Results
### Ablation Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Group** & **Accuracy** & **Precision** & **AUC** \\
**Architecture Blocks** & & & \\ \hline \(i=1\) & 68.6\% & 69.4\% & 0.90 \\ \(i=2\) & 65.1\% & 67.8\% & 0.88 \\ \(i=3\) (ours) & 69.8\% & 70.3\% & 0.91 \\ \(i=4\) & 54.7\% & 57.4\% & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study: effect of varying the number of group architecture blocks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **AUC** \\ \hline Model 1 (remove CA) & 68.6\% & 70.2\% & 0.89 \\ Model 2 (remove SA) & 67.4\% & 70.1\% & 0.88 \\ Ours (FFA + BiGRU) & 69.8\% & 70.3\% & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study: effects of channel and spatial attention for spatial feature fusion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Basic** & **Accuracy** & **Precision** & **AUC** \\
**Block Structures** & & & \\ \hline \(i=17\) & 60.5\% & 60.9\% & 0.86 \\ \(i=18\) & 62.8\% & 64.1\% & 0.87 \\ \(i=19\) (ours) & 69.8\% & 70.3\% & 0.91 \\ \(i=20\) & 62.8\% & 64.5\% & 0.87 \\ \(i=21\) & 65.1\% & 68.5\% & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study: effect of varying the number of basic block structures. 66.3%, respectively, while that of the proposed model, which combines these two modules, is 69.8%. This indicates that the combination of spatial and temporal feature extractors can capture the underlying information in music audio more comprehensively. The results suggest that the extraction of both spatial and temporal features is crucial for music emotion classification.
## Computational Methods
### Calphad Method  
The CALPHAD method is utilized here to calculate the heat of formation, heat capacity, and Gibbs energy of UI\({}_{3}\) as functions of temperature for comparison with our first-principle-theory data to validate our model. Generally, the major function of the CALPHAD scheme is to model the Gibbs energy of individual phases pertaining to binary and ternary systems to best reproduce critically reviewed phase diagrams and thermodynamic properties, and then leverage the assessments of these \"building blocks\" to predict phase stability and thermodynamic properties of multicomponent systems [26; 27; 28]. CALPHAD works iteratively to generate a set of functions and parameters describing the Gibbs energy of appropriate phases and when such functions and parameters have been determined, they are compiled in a database that can be applied to calculated thermodynamical properties of multi-component systems.  
In addition to comparing CALPHAD data and first-principles modeling, we realize that the coupling of CALPHAD and the theoretical results enhances the capability of thermodynamic modeling to consider material systems with many thermodynamic unknowns. First, ab initio data (e.g., the heats of formation and transformation) can directly provide important parameters to the CALPHAD modeling framework when lacking experimental data. Second, it is worth mentioning that the procedure of parameter optimization and error minimization within in the CALPHAD approach is an inverse problem with infinite degrees of freedom [29]. Therefore, a large number of combinations of values of parameters determined by the user can result in phase diagrams that coincide. Therefore, the use of DFT-derived properties associated with the CALPHAD assessment constrains the optimization and validates the final thermodynamical database, not only from a phase-diagram standpoint, but also from an energetics standpoint. In the literature, one readily finds first-principles-informed CALPHAD assessments for actinides [30; 31; 32].  
There is no available experimental phase diagram for the U-I system, thus no CALPHAD assessment can be performed across the whole composition range. However, thermochemical data of the UI\({}_{3}\) compound have been compiled by the Scientific Group Thermodata Europe (SGTE) and the corresponding CALPHAD functions are used in the present study through the SSUB6 Substances Database of the Thermo-Calc software (2019b version) [33; 34]. Following the present work, ab initio predictions of specific heat and free energy versus temperature for actinide systems are extremely useful to the CALPHAD community.