esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Optoelectrokinetics-Based Manipulation and Fabrication of Micro/Nanomaterials
## 2 Optoelectrokinetics (OEK) Chips and Their Working Principles
A P3 HT/PCBM bulk-heterojunction polymer was demonstrated as an alternative to a-Si:H for micro/nanomanipulation [47]. Figure 2 shows the structure of an OEK chip made up of this material. P3 HT/PCBM serves as a photosensitive material to respond to optically-projected images and produce a non-uniform electric field, thereby manipulating and isolating differently sized polystyrene beads [48]. The P3 HT/PCBM polymer for making this type of chip is created by spinning-coating at a relatively low temperature. Compared to a-Si:H based OEK chips, OEK chips based on this material are much easier to fabricate, except that the fabrication process of the latter requires preventing the polymer film from being collapsed by either water or oxygen. OEK chips based on this material can bend up and down freely to produce concave or convex curvatures, which leads to significantly higher efficiency in separating and concentrating differently sized polystyrene beads [49]. Another kind of OEK chip was devised based on T\({}_{i}\)OP\({}_{c}\), an organic photosensitive material. As shown in Figure 3[50], the chip has the same structure as the one indicated in Figure 1. This chip can be easily fabricated only by spinning-coating and baking techniques. This chip is fabricated in a much easier way than an a-Si:H based chip. It has been demonstrated that this chip can perform real-time manipulation of picobubbles [51] and droplets [52] as well as cell patterning [53]. However, problems remain with the long-term stability of this OEK chip. Figure 1: Schematic illustration of optoelectrokinetics (OEK) chip (reproduced from Ref. [42]). Figure 2: Structure of P3 HT/PCBM polymer-based OEK chip (reproduced from Ref. [47]).
# FE-P Net: An Image-Enhanced Parallel Density Estimation Network for Meat Duck Counting
## 2 Materials and Methods  
### Image Acquisition for Meat Duck Counting in Large-Scale Farm  
The dataset used in this experiment was collected from a poultry house located in Lishui District, Nanjing City. Spanning approximately 500 square meters, the poultry house uses net-bed breeding methods. Data were collected over June and July 2023 using Hikvision Ezviz CS-CB2 (2 WF-BK) cameras, which have a resolution of 1920 \(\times\) 1080 and a viewing angle of about 45\({}^{\circ}\). Over 136 min of video footage were recorded, with frames sampled every 30 s. After filtering out insect-obscured images, 242 usable images were obtained, containing 20,550 meat duck instances, averaging 84.92 instances per image.  
As shown in Figure 1, the collected images exhibit low resolution, significant occlusion between targets, considerable variations in individual sizes, and varying densities across different regions. These factors present substantial challenges for detection tasks.
## 4 State of Art
### All-Fiber Sensors
Since the linear birefringence is a reciprocal effect, the phase difference introduced by the linear birefringence will be compensated [19]. Figure 7: Typical configuration of an optical current sensor with flint fiber. A sensor using reflected light propagation for compensation of linear birefringence, made of low-birefringent flint fiber with a very low photo-elastic constant, achieved the accuracy required for the 0.1% class of current metering transformers in the range of 1 kA [21]. Commercial fiber-optic DC current sensors have also been demonstrated [22] being able to measure up to 500 kA with an error of 0.1%. More recently, a signal processing methodology based on artificial neural networks (ANN) was developed, which processes signals from a typical optic current sensors and a thermometer, achieving higher accuracy with temperature and non-linearity compensation [23]. The great benefit of ANN is to get a transfer function for the measurement system taking in account all variables, even those from unwanted and unknown effects, providing a compensated output, after the ANN training session. Some optical electric current sensors are already commercially available. Considering all-fiber current sensors, for instance, ABB and NXTphase are two companies that have available sensors with working principle identical to the one shown in Figure 6. The first company assures a range of operation up to 500 kA and 0.1% accuracy [24]. The second company permits DC and AC measurements with a range of operation from 1 to 63 kA\({}_{\mathrm{RMS}}\) being also in the 0.1% class category [25,26].
## 4 Results and Discussion
### Results of Quasi-Static Tests
#### 4.1.2 Single-Yarn Pullout Tests  
The force-slip curves of quasi-static single-yarn pullout from plain and SHCC matrices are plotted in Figure 9. The slip of the carbon yarns was recorded by LVDTs attached directly to the specimens. The force-slip curve can be divided into three stages, as shown in Figure 9d. According to the pullout load-slip model presented in [30; 31], stage I corresponds to the linear elastic stage, and is followed by the gradual debonding stage II, which terminates at the end of the debonding process. Stage III represents the pullout process influenced mainly by the yarn-matrix interfacial friction.  
The relatively small embedment length of the yarns in combination with the weak affinity of both the carbon filaments and the styrene-butadiene coating to the cementitious matrix led to a complete pullout of the yarns. The peak forces for carbon yarn pulled out from plain matrix and PE-SHCC are nearly identical, with average forces of 441 N and 440 N, respectively. In contrast, the addition of PBO fibers led to a considerably higher bond strength, with an average peak pullout force of 530 N; see Table 6. This could be traced back to the mitigation of shrinkage-induced micro-cracking as ensured by PBO fibers [32]. However, a more detailed analytical investigation of the yarn-matrix interface should bring more clarity to this phenomenon. Note that in previous studies, such as [12], the addition of short alkali-resistant glass fibers and carbon fibers had a positive effect on the yarn-matrix bond strength. It could be that the poor wettability of the PE fibers, as well as their lower stiffness could be the reason for a lower bond strength in comparison to PBO-SHCC.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **TRC-M** & **TRC-HDPE** & **TRC-PBO** \\ \hline Average crack width at the ultimate strain level [\(\upmu\)m] & – & 88 & 29 \\ Average crack spacing at the ultimate strain level [mm] & – & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average crack width and spacing at the ultimate strain level for the representative specimens subjected to uniaxial quasi-static tension tests (localization crack excluded).
## 2 The Formulation and Resolution of Equations for Tunnel Load and Deformation  
### The Formulation of Equations Neglecting the Influence of Lateral Soil Action  
Figure 1 presents a simplified schematic representation of the computational model used in this paper, wherein the tunnel is modelled as a Euler-Bernoulli beam supported by a Vlasov foundation, replicating the interaction between the tunnel structure and the surrounding soil.  
In the Vlasov foundation model, the relationship between the foundation reaction \(p(x)\) and the tunnel settlement \(u(x)\) is given follows:  
\[p(x)=ku(x)-2t\frac{\text{d}^{2}u(x)}{\text{d}^{2}x} \tag{1}\]  
where \(k\) is the foundation reaction coefficient, and \(t\) is the soil layer shear coefficient, representing the degree of soil deformation continuity. These two can be calculated using the following formulas:  
\[k=\frac{(1-v)E_{\text{s}}}{(1+v)(1-2v)}\int_{0}^{T}\biggl{(}\frac{\text{d}h(z) }{\text{d}z}\biggr{)}^{2}\text{d}y \tag{2a}\] \[2t=\frac{E_{\text{s}}}{2(1+v)}\int_{0}^{T}h^{2}(z)\text{d}y \tag{2b}\]  
where \(E_{\text{s}}\) represents the elastic modulus of the soil, \(T\) stands for the thickness of the elastic foundation layer, and is set to \(T=2.5D\) in this work [19], \(D\) denotes the tunnel diameter, and \(h(z)\) corresponds to the attenuation function within the Vlasov foundation model, which accounts for the diminishing effect of the foundation soil along the deformation direction to determine the values of \(k\) and \(t\). Typically, this function can manifest as either a linear or exponential relationship. For the purposes of this paper, \(h(z)\) is defined as \(h(z)=(T-z)/z\).  
On such basis, the force balance relationship of the tunnel under the influence of dewatering determines the settlement equation of the tunnel as follows:  
\[EI\frac{\text{d}^{4}u(x)}{\text{d}x^{4}}+p(x)D=q(x)D \tag{3}\]  
Figure 1: Vlasov foundation beam model diagram: (**a**) front view; (**b**) side view.  
where \(EI\) represents the bending stiffness of the tunnel, with \(E\) being the elastic modulus of the whole tunnel, and \(I\) denoting the moment of inertia of the tunnel cross-section. Furthermore, \(q(x)\) represents the additional load induced by dewatering. Substituting Equation (1) into Equation (3) gives:  
\[EI\frac{\mathrm{d}^{4}u(x)}{\mathrm{d}x^{4}}-2 tD\frac{\mathrm{d}^{2}u(x)}{ \mathrm{d}x^{2}}+kDu(x)=q(x)D \tag{4}\]
# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 6 Discussion
### Computing Resource Requirement  
The model was trained and tested on a computer containing 16 GB of Memory, 2.90 GHz CPU and a GPU (NVIDIA GeForce 930 MX) utilised by LSTM and CNN. It took 0.00067 s/sample to extract features from the fusion data of 53 users containing 10 min of the physiological signal. To develop an SVM model for group authentication with three users, the training time was 0.0004 s/sample and the testing time was 0.000066 s/sample. The GAM model training took 0.000517 s/sample and the testing time was 0.000099 s/sample. In single-user authentication mode, the SVM model training time was 0.00035 s/sample and testing time 0.000066 s/sample. The GAM model training took 0.00041 s/sample and the testing time was 0.000077 s/sample. The LSTM model training time was 0.065 s/sample and the testing time was 0.00081 s/sample. Sample feature extraction time and testing time are promising.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 4 Simulation Results and Discussion
### Statistical Analysis for R.T.C. France Solar Cell
#### 4.2.1 Single Diode Model
It gives the lowest values of the minimum, maximum, mean, and standard deviation of the \(RMSE\), at 0.000986022, 0.000986205, 0.00098603, and 3.35307 \(\times\) 10\({}^{-8}\), respectively. Meanwhile, the second-best \(RMSE\) (0.000986023) is achieved by the BSDE, followed by MRFO, EO, CSO, MPA, BSA, and GWO. Figure 8 displays the RMS values of the 30 runs for the R.T.C. France SDM. This figure shows the significant robustness feature of the proposed TFWO since all the acquired values of the \(RMSE\) based on TFWO are the lowest values compared with the other methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{_RMSE_} \\ \cline{2-5}  & **Min.** & **Max.** & **Mean** & **SD** \\ \hline TFWO & 0.00098602 & 0.00098620 & 0.00098603 & 3.353 \(\times\) 10\({}^{-8}\) \\ MRFO & 0.00098603 & 0.00105788 & 0.00100505 & 2.143 \(\times\) 10\({}^{-5}\) \\ BSDE & 0.00098602 & 0.00103025 & 0.00099520 & 1.056 \(\times\) 10\({}^{-5}\) \\ MPA & 0.00098736 & 0.00481175 & 0.00217485 & 0.00065237 \\ EO & 0.00098603 & 0.00105604 & 0.00100209 & 1.783 \(\times\) 10\({}^{-5}\) \\ CSO & 0.00098618 & 0.00130296 & 0.00105888 & 8.095 \(\times\) 10\({}^{-5}\) \\ GWO & 0.00100823 & 0.03816637 & 0.00637283 & 0.0112567 \\ BSA & 0.000989471 & 0.001161862 & 0.001037488 & 4.42885 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of \(RMSE\) for R.T.C. France SDM. Figure 5: Convergence curves for R.T.C. France SDM. Figure 6: Characteristic curves for R.T.C. France SDM based on parameters extracted from TFWO: (**a**) Current-voltage (I–V) ch/s and (**b**) power–voltage (P–V) ch/s. Figure 7: Error values for R.T.C. France SDM based on parameters extracted from TFWO: (**a**) Current error values and (**b**) power error values.
# A Novel MEMS Capacitor with a Side Wall for High Tuning Ranges
## 5 Theoretical Analysis  
Table 2 shows the optimal design variable values and the initial value. Figure 5 shows the electric force and mechanical force graph in accordance with the optimization value.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Design Variables**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Lower Bound**} & \multirow{2}{*}{**Initial**} & \multirow{2}{*}{**Optimal**} & \multirow{2}{*}{**Upper Bound**} \\ \hline \(x_{1}\) & \(\mu\)m & 0 & 25 & 0 & 50 \\ \hline \(x_{2}\) & \(\mu\)m & 50 & 75 & 100 & 100 \\ \hline \(x_{3}\) & \(\mu\)m & 100 & 125 & 135.467 & 150 \\ \hline \(x_{4}\) & \(\mu\)m & 150 & 170 & 200 & 200 \\ \hline \(y_{1}\) & \(\mu\)m & 0 & 0.000 & 0.305 & 0.305 \\ \hline \(y_{2}\) & \(\mu\)m & 0.305 & 0.505 & 0.547 & 0.610 \\ \hline \(y_{3}\) & \(\mu\)m & 0.610 & 0.800 & 0.61 & 0.915 \\ \hline \(y_{4}\) & \(\mu\)m & 0.915 & 1.100 & 0.924 & 1.22 \\ \hline \(y_{5}\) & \(\mu\)m & 1.22 & 1.230 & 1.223 & 1.24 \\ \hline \hline \end{tabular}  
\(\times 10^{-4}\)  
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{\(\times 10^{-4}\)} & \multirow{2}{*}{_Optimization of the proposed capacitor_} \\ \cline{3-3}  & & DC bias= 9.1 Volt \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \end{tabular}  
\end{table}
Table 2: Initial and optimal design variables values.  
Figure 5: The electric force and mechanical force graph in accordance with the optimization value.  
We confirmed that the displacement value according to the voltage (7.4 V\(\sim\)pull-in point). Table 3 shows the equilibrium points in accordance with electrical force and mechanical force nodes.  
The mathematical result is that the conventional research used a pull-in point at 7.4 volts, but the pull-in point of this paper is approximately 9.1 volts. That is, the voltage's availability increased by about 23%. Moreover, displacement increased by approximately 7% at the pull-in point. Therefore, this novel MEMS capacitor improved the performance by about 30% according to the following expression: \(Q=CV\).
## 5 Conclusions  
The Monte Carlo simulation was utilized to assess the DNA damage induced by irradiating an iron-gold nanoparticle heterojunction with kV photon beams. This heterojunction serves as both a radiation dose enhancer and a contrast agent for MRI in image-guided nanoparticle-enhanced radiotherapy. The findings indicate that DNA damage increases as the percentage of gold volume in the heterojunction rises, primarily due to photoelectric enhancement. Moreover, a lower photon beam energy of 50 keV leads to greater DNA damage compared to energies of 100 keV and 150 keV. The results suggest that to achieve effective cancer cell kill through DNA damage, the gold volume should be equal to or greater than 50% in the iron-gold nanoparticle heterojunction. These Monte Carlo results hold significance for clinicians and radiation personnel involved in designing functionalized nano-theranostic agents used in image-guided nanoparticle-enhanced radiotherapy. The forthcoming tasks involve fine-tuning the nanoparticle size and concentration to enhance the biocompatibility with healthy tissue. In addition to the current efforts, future work will involve developing a comprehensive multi-scale model that integrates simulations of the tumor, cell, and DNA. This integrated model will enable the estimation of the dose multiplication factor, which is crucial for assessing the cumulative effect of radiation on DNA damage in relation to the total dose received in radiotherapy.  
Conceptualization, J.C.L.C.; methodology, J.C.L.C.; software, C.A.S. and J.C.L.C.; validation, C.A.S. and J.C.L.C.; formal analysis, C.A.S.; investigation, C.A.S.; resources, C.A.S. and J.C.L.C.; data curation, C.A.S.; writing--original draft preparation, J.C.L.C.; writing--review and editing, C.A.S. and J.C.L.C.; visualization, C.A.S. and J.C.L.C.; supervision, J.C.L.C.; project administration, J.C.L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] from the University of Toronto, Canada, and [PERSON] from the Ryerson University, Canada, for their assistance in the MC simulation using the Geant4-DNA code.  
The authors declare no conflict of interest.
## 5 Experiments and Discussions
### Hardware Experiments and Analyses
#### 5.3.1 Periodic Sign Waveforms  
This experiment mainly focuses on checking the performance of periodic sequences generated by GTX. Figure 14 shows the time-domain appearance and the spectrum of \(p_{i}(t)\). From the time-domain, we can see the waveform is not actually square. However, its spectrum consists of concentrated energy peaks which we call Dirac functions. Corresponding to the Equation (6), we can see that the role of the periodic sequences is to move all the signal residing in different frequency bands to baseband. And this make it possible for our system to sample at a low rate and then sense it.  
Figure 14: Time and frequency domain of periodic sequences.  
Figure 13: Two simulation results of the hardware prototype. (**a**) Detection performance with different \(m\) and SNR; (**b**) Detection performance comparison between SBL and OMP.
## 2 Mathematical Model
### Economic Assessment and \(Co_{2}\) Emission Calculations
\[M_{\text{CO}_{2},ins}=\frac{\rho_{ins}x_{ins}f_{ins}}{N} \tag{15}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Heating Source** & **Emission Factor, f\({}_{\text{h}}\)** & **Lower Heating Value, H\({}_{\text{u}}\)** & **Efficiency, \(\eta\)** & **Price, C\({}_{\text{f}}\)** \\ \hline \multicolumn{5}{c}{**(kgCO\({}_{2}\)/kWh)**} & **(\%)** \\ \hline Natural Gas & 0.194 & \(34.526\times 10^{6}\) J/m\({}^{3}\) & 93 & 0.327 USD/m\({}^{3}\) \\ Electricity & 0.588 & \(3.599\times 10^{6}\) J/kWh & 99 & 0.1059 USD/kWh \\ Fuel Oil & 0.268 & \(40.594\times 10^{6}\) J/kg & 80 & 0.734 USD/kg \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Emission factors, lower heating values, efficiencies, and prices of heating sources [33; 44]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wall Components** & **_Emission Factor, f\({}_{\text{f}}\)_ & _Density, \(\rho\)_ & _Conductivity, k_ & _Price, C_ \\ \hline \multicolumn{5}{c}{**(kgCO\({}_{2}\)/kg)**} & **(kg/m\({}^{3}\))** & **(W/mK)** & **(USD/m\({}^{3}\))** \\ \hline Expanded Polystyrene (EPS) & 3.51 & 20 & 0.036 & 100 \\ Extruded Polystyrene (XPS) & 3.83 & 30 & 0.037 & 150 \\ Mineral Wool (MW) & 1.16 & 55 & 0.04 & 130 \\ Polyurethane Foam (PUR) & 4.47 & 40 & 0.036 & 200 \\ Plaster & 0.36 & 1800 & 0.87 & 90 \\ Light Concrete & 0.09 & 1700 & 0.71 & 85 \\ Reinforced Concrete & 0.12 & 2400 & 2.5 & 120 \\ Brick & 0.31 & 1200 & 0.45 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Emission factors, physical properties, and prices of wall components [45; 46; 47; 48; 49; 50]. \[M_{CO_{2},plast}=\frac{\rho_{plast}x_{plast}f_{plast}}{N} \tag{16}\]  
\[M_{CO_{2},wall}=\frac{\rho_{wall}x_{wall}f_{wall}}{N} \tag{17}\]  
\[M_{CO_{2},heat}=\frac{0.024\;HDDf_{h}}{\left(\frac{1}{h_{i}}+\frac{1}{h_{o}}+ \frac{x_{plast}}{k_{plast}}+\frac{x_{wall}}{k_{wall}}+\frac{x_{ins}}{k_{ins}} \right)\eta} \tag{18}\]  
\[M_{CO_{2},cool}=\frac{0.024\;CDDf_{c}}{\left(\frac{1}{h_{i}}+\frac{1}{h_{o}}+ \frac{x_{plast}}{k_{plast}}+\frac{x_{wall}}{k_{wall}}+\frac{x_{ins}}{k_{ins}} \right)COP} \tag{19}\]  
\[M_{CO_{2}}=M_{CO_{2},ins}+M_{CO_{2},plast}+M_{CO_{2},wall}+M_{CO_{2},heat}+M_{ CO_{2},cool} \tag{20}\]  
Within these equations, \(\rho\) indicates the density of the relevant materials, while \(f\) denotes the emission factor of heating sources and wall components. Tables 1 and 2 present the relevant values.
# Linomycin-Induced Transcriptional Alterations in the Green Alga _Raphidocelis subcapitata_
## 4 Discussion
### Genes involved in Photosynthesis  
The exposure of _R. subcapitata_ to 0.5 and 5 \(\upmu\)g L\({}^{-1}\) LIN for 7 days led to the promotion of pathways of photosynthesis: antenna proteins and porphyrin and chlorophyll metabolism pathways, suggesting that the photosynthesis activity was increased. Genes _llca2_, _llca4_, and _llcb1_ in low LIN treatment groups and _llca4_, _llcb1_, and _llcb2_ in the medium were upregulated. _Lhcb1_ and _llcb2_ encoding light-harvesting chlorophyll a/b binding proteins, which serve as supplementary roles in the process of state transitions in _Arabidopsis_[30]. Gene _llca4_ is associated with the red-most fluorescence emission of photosystem I of the antenna complex, which contains two stromal binding sites with chlorophylls. The upregulated_Lhca4_ may thus promote the binding of chlorophyll and further lead to an increase of photosynthesis activity in the medium group [31]. The conjecture was proved as well by the increased expression levels of genes _chlD_, _chlH_, _hemA_, _hemD_, _hemE_, _hemL_, and _por_ in porphyrin and chlorophyll metabolism pathway in medium LIN treatment samples. These elevated genes suggested the promotion of the synthesis of chlorophylls a and b, which may further promote the process of light capture and energy transition [32]. A similar effect was observed on the _Euglena gracilis_ after chloramphenicol exposure [33]. While the growth inhibition test demonstrated that there is no significant increase in algal growth at low (0.5 \(\upmu\)g L\({}^{-1}\)) LIN exposure, and significant growth inhibition at 5 \(\upmu\)g L\({}^{-1}\). This suggested that for this antibiotic LIN, photosynthesis is more sensitive than growth, presumably due to photosynthesis is the first procedure in converting light to energy [34, 35]. Similar effects were detected in the toxicity tests of tylosin to _R. subcapitata_[35]. A decreased content of chlorophyll was detected when the concentration of antibiotics rises. For instance, after 0.5 mg L\({}^{-1}\) ROX exposure of _Chlorella pyrenoidosis_, the content of chlorophyll was reduced remarkably [36].  
In the high-LIN dosing groups, the majority of upregulated genes were detected in the pathway of carbon fixation in photosynthetic organism. C3 photosynthetic pathway also known as Calvin Benson cycle, which with C4-dicarboxylic acid cycle (C4 photosynthetic pathway) play important roles in carbon fixation involved in the photosynthesis [37]. In this study, the expression level of genes _rpa_, _gapa_, and _aldo_ in the Calvin Benson cycle at high-LIN exposure were mostly up-regulated (Table 1). It suggested the enhancement of C3 cycles at high-LIN exposure is conducive to accelerate CO\({}_{2}\) concentrating process [9, 38]. At the same unfavorable conditions, the promotion of the carbon fixation pathway in _R. subcapitata_ was detected after 0.09 mg L\({}^{-1}\) roxithromycin and 0.12 mg L\({}^{-1}\) erythromycincin, respectively [9, 10].