esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification
## 3 Experiment Setup and Material  
### Laser Sensor and Microfluidic Chip  
The entire optofluidic cytometry system is shown in Figure 3a. A commercial 1310 nm distributed feedback (DFB) laser diode (Allwave Lasers Device Inc., Xian, China) packaged with a monitoring photodiode was employed as the laser source and the sensor. The laser diode was supported by a DC power driver (Rigo Inc., Suzhou, China), and the initial output power was measured to be around 500 \(\upmu\)W. The laser propagation axis realizes an angle of 82\({}^{\circ}\) with the flow direction (thus the incident angle with respect to the perpendicular to the chip plane is 8\({}^{\circ}\)). In order to obtain sufficient power density and thus a higher SMI signal level, the laser beam was tightly focused using a 1:1 doublet imaging system (C240 TME-C, Thorlabs Inc., Newton, MA, USA) of 8 mm focal length. The laser sensor and the optical arrangement were mounted on a 3D stage (Zolix Inc., Beijing, China) for precise alignment. By moving the laser diode, the laser focal spot was positioned exactly at the channel center, and the focused laser spot size was calculated as 5 \(\upmu\)m \(\times\) 1 \(\upmu\)m.  
The SMI signal was acquired through the monitor photodiode current variation. Then, the photodiode current was amplified using a home-made trans-impedance amplification circuit for signal enhancement, meanwhile, the current signal was transformed into voltage. Afterward, the voltage signal was captured using a high-speed data acquisition card (NI-6361 USB, NI Inc., Austin, TX, USA) at 2 MHz sampling frequency. All the data processing operation was controlled by a custom-made LabVIEW routine.  
As shown in Figure 3b, a homemade hydrodynamic channel was fabricated on polydimethylsiloxane (PDMS) by means of photolithography, and then thermally bonded on a glass substrate. To protect the living cell, the phosphate buffer saline (PBS) was employed as the solvent and the sheath liquid.  
Figure 3: The schematic graphs of the system. (**a**) The optofluidic flow cytometry system. (**b**) The hydrodynamic channel chip.  
Figure 2: The individual cell passage through the laser beam at time point of (**a**) t1, (**b**) t2, (**c**) t3.  
The cell samples and the sheath liquids were injected into the channel by two high-precision string pumps. The sample fluid was focused into a narrow stream using two sheath fluids on both sides, thus ensuring the cells or particles enter the optical detection region one-by-one [35].  
To achieve stable laminar flows for hydrodynamic focusing in the channel, 20-5-20 \(\upmu\)L/min sheath/sample/sheath flow rates was set to maintain a small Reynolds number (Re < 2000) [36]. The core liquid width was measured to be around 30 \(\upmu\)m, which was comparable with the maximum particle and cell diameters. Furthermore, such hydrodynamic focusing chip increases the detection throughput efficiently.
## 3 Crack Detection in Plates Using Dynamic Response (DR)
### Forward Problem for the DR of the Cracked Plate
#### 3.1.1 Plate of the Uniform Thickness (UT)
They noted that moving masses affected the plates' dynamics more than moving loads. [PERSON] et al. (2022) [139] established a methodology for analyzing the free vibrations of hexagonal plates, both intact and cracked, focusing on the impact of crack length and inclination on their natural frequencies and mode configurations under various boundary conditions. [PERSON] et al. (2022) [140] examined the vibrational effects of central apertures and cracks on the RP, observing a correlation between fundamental frequency and aperture diameter, with cracks reducing the frequency. [PERSON] et al. (2023) [141] focused on fatigue crack detection using local vibration techniques, validated by scanning laser Doppler vibrometer (SLDV) and electromechanical impedance spectroscopy (EMIS). [PERSON] et al. (2023) [142] investigated the vibration of cracked ferromagnetic plates subjected to in-plane forces and a magnetic field, focusing on the first mode of natural frequency. Finally, the damage and DR of structures subjected to impact or blast loads have been extensively discussed in numerous studies [143, 144, 146]. For example, [PERSON] et al. (2024) [147] investigated the damage and the DR of rectangular steel plates under near-surface blast conditions. Their study combined full-scale tests and numerical simulations to analyze how factors like explosion yield, height, angle, and steel plate thickness affected structural integrity. Finally, Table 2 summarizes significant forward research investigations related to damage identification in UT plates. Figure 3: Frequency response curve at different crack angles, where \(a_{4}\) is the peak amplitude at mode 4, and \(\sigma\)2 is the second perturbation parameter [132]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Lengths of the Sides of the Plate**} & \multicolumn{3}{c}{**CCFF**} & \multicolumn{3}{c}{**CCSS**} & \multicolumn{3}{c}{**SSSS**} \\ \cline{2-9}  & \multicolumn{3}{c}{**1-Mode Natural Frequency, \(\omega_{\text{mn}}\) (rad/s) for a \%-Crack Length, a = 0.05 (m)**} \\ \hline
**I\({}_{1}\) (m)** & **I\({}_{2}\) (m)** & **Intact** & **Damaged** & **Intact** & **Damaged** \\ \hline
1 & 1 & 80.46 & 70.56 & 445.67 & 403.78 & 77.58 & 71.12 \\
0.5 & 1 & 231.06 & 227.61 & 1161.77 & 1138.53 & 193.95 & 189.58 \\
0.5 & 0.5 & 321.85 & 282.24 & 1782.66 & 1615.12 & 310.32 & 284.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Natural frequencies of a damaged plate under various boundary conditions [7]. Figure 2: Variation of the first 15 natural frequencies with respect to crack (**a**) location ratio and (**b**) length ratio [99].
## 4 Fatigue Life Estimation
The material constants \(C\) and \(n\) in Equation (11) are listed in Table 7.  
Then, the fatigue life \(N_{f}\) of medium-carbon steel is computed as:  
\[N_{f}=\frac{9\Delta K_{th}^{2}\mathrm{G}}{\mathrm{E}(\mathrm{S}-\mathrm{S}_{ \mathrm{e}})^{2}\pi(1-\upsilon)a_{0}}+\frac{a_{0}^{(1-\frac{n}{2})}}{CS^{n} \beta_{1}^{n}\pi^{\frac{n}{2}}\left(\frac{n}{2}-1\right)} \tag{15}\]  
where the shear modulus \(G\) is 79.40 GPa, the elastic modulus \(E\) is 206.00 GPa, and the threshold of stress intensity factor \(\Delta K_{th}\) is 6.26. Substituting the material constants \(C\) and \(n\) in Table 7 into Equation (12), the mean fatigue life \(N_{f}\) of the medium-carbon steel with different average surface roughness is: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Average Surface Roughness (\(\upmu\))** & \(n\) & \(C\) \\ \hline \(R_{\rm a}=0.4\) & 9.84 & \(4.11\times 10^{-34}\) \\ \(R_{\rm a}=0.8\) & 9.44 & \(4.81\times 10^{-34}\) \\ \(R_{\rm a}=1.6\) & 9.80 & \(3.02\times 10^{-36}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Material constants \(C\) and \(n\) in Equation (11) with different average surface roughness. \[N_{f}=\left\{ \begin{array}{ll}\frac{9\times 6.26^{2}\times 79.4}{206\times( \text{S}-275)^{2}\times\text{m}\times(1-0.27)\times 1.188\times 10^{-3}}\\ +\frac{1.188^{(1-\frac{9.825}{10})}}{4.11\times 10^{-34}\times S^{9.835}\times(0.5\sqrt{ \pi})^{9.835}\pi^{\frac{9.835}{2}}(\frac{9.835}{2}-1)}\\ \frac{9\times 6.26^{2}\times 79.4}{206\times(\text{S}-260)^{2}\times\text{m}\times(1 -0.27)\times 2.376\times 10^{-3}}\\ +\frac{2.376^{(1-\frac{9.4}{2})}}{481\times 10^{-34}\times S^{9.46}\times(0.5\sqrt{ \pi})^{9.436}\pi^{\frac{9.436}{2}}(\frac{9.436}{2}-1)}\\ \frac{9\times 6.26^{2}\times 79.4}{206\times(\text{S}-255)^{2}\times\text{m} \times(1-0.27)\times 4.752\times 10^{-3}}\\ +\frac{4.752(1^{-\frac{9.49}{2})}}{3.02\times 10^{-36}\times S^{9.802}\times(0.5 \sqrt{\pi})^{9.802}\pi^{\frac{9.802}{2}}(\frac{9.802}{2}-1)}\\ \end{array} \right.,\Longleftrightarrow R_{\text{a}}=0.4\ \text{\mu m} \tag{16}\]  
When the average surface roughness is 0.4, 0.8, and 1.6, respectively, the mean fatigue life curves of medium-carbon steel calculated by Equation (16) are shown in Figure 9. It can be seen that the fatigue life estimation model agrees with the experiment value well. To validate the presented formulation for estimating the mean fatigue life of medium-carbon steel, six more fatigue tests were conducted and the experimental results are shown in Table 8. The stress amplitude was 420 MPa and was not equal to any stress amplitude in the previous experiments. The maximum and minimum estimation errors are 15.83% and 1.99%, respectively. Therefore, it is obvious that the presented model for estimating the mean fatigue life of medium-carbon steel gives good results for roughness \(R_{\text{a}}=0.4\ \text{\mu m}\), \(R_{\text{a}}=0.8\ \text{\mu m}\), and \(R_{\text{a}}=1.6\ \text{\mu m
# Two-Terminal Electronic Circuits with Controllable Linear NDR Region and Their Applications
## 6 Applications
### Simulation of VCO Characteristics
The designed VCO with MIWCM (\(m=2\), \(R_{L}=50\)\(\Omega\)) also has very low phase noise (\(-139.0\), \(-137.0\) dB\(\mathrm{c}/\mathrm{Hz}\) at \(0.1\) MHz offset) and is one of the best FOM (\(-205.1\), \(-203.9\) dB\(\mathrm{c}/\mathrm{Hz}\) at \(0.1\) MHz offset) in the tuning range. Thus, we can successfully use the proposed two-terminal circuits with NDR for constructing highly efficient microwave oscillators and VCOs.
# An Initial Load-Based Green Software Defined Network +
## 6 Performance Analysis
### The effect on the delay caused by ICWGA  
To acquire a more accurate statistical result for the increased delay of the paths, tests are done with 100 (times) * 100 (periods). The increased propagation delay and the increased number of the hops of all the paths are recorded and the distribution results are presented. Figures 5 and 6 show the tradeoff between energy saving and delay caused by ICWGA of the three topologies. For a more clear presentation, for each topology, only the results for half of the \(\lambda\) cases are shown. It can be seen that in each \(\lambda\) case, either for the propagation delay or for the improved number of hops, the percentage of 0 accounts for the largest part. For all the topologies, as \(\lambda\) increases the percentage of 0 gradually  
Figure 4: The results for average bandwidth utilization of different SND-Lib topologies. (**a**) Abilene.  
Figure 3: The results for energy saving ratio of SND-Lib topologies. (**a**) Abilene. (**b**) India. (**c**) Germany.  
increases except for the smallest of \(\lambda\) cases. As shown in Figure 5, for the increased propagation delay, 0 accounts for 85% when \(\lambda=50\) in the Abilene topology, 27% when \(\lambda=75\) in the India topology, and 30% when \(\lambda=75\) in the Germany topology. The other part accounts for a small percentage. For the increased number of hops, as presented in Figure 6, there even exist some regions corresponding to the negative increased hop numbers, which means the paths calculated by ICWGA pass fewer hops than the shortest paths. This is because the shortest paths are calculated based on the propagation delay in the simulation. Compared to the results for the propagation delay, the 0 parts account for bigger percentages, 92% when \(\lambda=50\) for the abilene topology, 52% when \(\lambda=75\) for the India topology, and 55% when \(\lambda=75\) for the Germany topology; the percentage of 1 accounts for the largest part in the other \(\delta_{h}\) cases.  
In real networks, in order to avoid affecting user experience, we can set a higher priority for the delay sensitive services and route them on the shortest paths, or employ other methods.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.8 Oxalate Salts  
Oxalates have been identified as agents capable of occluding dentin tubules, potentially leading to a reduction in dental permeability of up to 98% [130; 131]. The application of 28% potassium oxalate can facilitate the formation of calcium oxalate within the dentin tubules. For enhanced efficacy, dentin etching can be employed in conjunction with oxalates [131]. Specifically, oxalate salts, such as potassium oxalate and ferric oxalate [132], are believed to work through a mechanism of precipitation and formation of crystals within the dentin tubules [133]. Oxalate salts have the ability to react with Ca+2 present in the dentin structure. Indeed, when applied to the exposed dentin surface, these salts can react with the Ca+2 in the tubules and surrounding dentin, leading to the formation of insoluble calcium oxalate crystals. The formation of calcium oxalate crystals within the dentin tubules creates physical barriers, which can partially or completely block the dentin tubules [132; 133]. It is important for patients to be aware that oxalate treatments might cause tooth discoloration or lead to the formation of insoluble deposits on the teeth. As such, the potential aesthetic concerns associated with these treatments should be discussed with patients before their use.
# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 1 Introduction
The PlanetScope satellite constellation (more than 130 CubeSats with the 3-unit form factor of \(10\times 10\times 30\) cm, and a mass of approximately 4 kg), launched by Planet Labs, can provide the near nadir-view coverage of the earth on a daily basis at fine spatial resolutions (i.e., 3-5 m spatial resolution) [52]. With the improved spatial and temporal characteristics, the PlanetScope imagery can potentially address the mixed phenology issue due to landscape heterogeneity under relatively coarse satellite spatial resolutions, as well as resolve the spatial coverage and potential sensor limitations of near-surface remote sensing [53; 54; 55; 56]. It may shed light on how satellite-derived phenological measures can be aligned with field-observed crop phenological growth stages, particularly in spatially fragmented and heterogeneous landscapes. The PlanetScope time series has been found to improve the phenological characterizations in forest and rangeland systems [57; 58; 59; 60]. In agricultural systems, this high-resolution imagery has been explored to detect the crop souring and growth stages at the field scale [61; 62]. However, there is still a dearth of research rigorously validating the crop phenological growth characterization results from this high-resolution imagery with regard to on-the-ground crop stage progress. The goal of this study is to systematically assess near-surface phenological cameras and high-resolution satellite time series in reconciling sensor- and ground-based crop phenological characterizations. As the two most critical means to detect crop phenology at fine scales, near-surface and high-resolution remote sensing provide distinct yet complementary measures to facilitate direct comparisons to visually observed crop stages for phenological validations, a long-standing challenge in phenological studies. Specifically, we aim to (1) interpret the fine-scale sensor-derived phenological characteristics in the context of visually-observed crop phenological events, (2) evaluate the role of the near-surface phenological cameras in characterizing the crop phenological dynamics with a diverse range of corn and soybean PhenoCam sites, and (3) systematically compare a set of phenological measures from high-resolution PlanetScope imagery, PhenoCams, and visual observations for coupled ground and sensor-based crop growth characterizations. Despite the increasing use of PhenoCam-derived phenological metrics to validate satellite-based ones, the fine-scale crop phenological comparison between PhenoCam and PlanetScope imagery is lacking. The enhanced understanding of fine-scale phenological characterizations in association with ground crop growth observations will benefit more precise and sustainable stage-specific agricultural management, especially in spatially heterogeneous or smallholder agricultural systems.
## 10 Results and Discussion
### Message Overhead  
Message overhead refers to the additional data or information that is transmitted along with the actual payload or user data during communication between nodes or systems in a network. Figure 13 shows the evolution of the message overhead for the BCPS-RPL and GBG-RPL processes.  
Figure 13 illustrates the rising trend in the message overhead over time for both the BCPS-RPL mechanism and the GBG-RPL mechanism. The GBG-RPL mechanism consistently has a lower messaging overhead than the BCPS-RPL mechanism at each time interval, indicating a lighter communication load and less network congestion overall. Initially, both techniques show a similar message overhead as the network is being initialized and the trust calculations are being undertaken. However, after 20 min, the GBG-RPL technique considerably reduces the message overhead due to the fact that only trust parameters are being forwarded by the device layer and trust calculations are being carried out at the fog layer. Furthermore, the improved attack-detection time/rate contributed to the reduction in message overhead due to less frequent retransmissions of data/control packets.
###### Abstract  
In recent years, the construction of China's power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China's power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.  
power grid investment forecasting; influencing factors screening; support vector machine; differential evolution; grey wolf optimization
## 5 In Vitro Antimicrobial Susceptibility Pattern of the _B. burgdorferii_ Complex  
### Spectrum of In Vitro Susceptibility against Antimicrobial Agents  
Even though several antimicrobial agents have been tested for their in vitro activity against borreliae, the full spectrum of antibiotic susceptibility has yet to be defined. Furthermore, the resistance pattern of _B. burgdorferii_ s.l. is difficult to predict as it clearly differs from that of common Gram-negative bacteria (Table 2) [17; 52]. To further expand our knowledge of the in vitro susceptibility profile of the pathogen, attempts in recent years have been made to explore currently available antimicrobial agents against a larger number of borreliae isolates in order to better characterize the in vitro susceptibility of _B. burgdorferii_ s.l. [17; 52; 57].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Antimicrobials**} & \multirow{2}{*}{**Calorimetric MIC\({}_{90}\) a**} & **MIC Range (ug/mL)** & **MSC\({}_{90}\) b** & **MSC Range (ug/mL)** \\  & & **(Adopted from the Literature)** & **(ug/mL)** & **(Adopted from the Literature)** \\ \hline
**\#-lactamase** & & & & \\ Penicillin G & 1 & 0.03–8 & 16 & 0.05–50 \\ Amoxycillin & 1 & 0.03–2 & 32 & \textless{}0.03–3.2 \\ Piperacillin & 0.06 & \textless{}0.06–0.125 & 2 & 1.3–2.6 \\ Mezlocillin & \textless{}0.06 & \textless{}0.06–1 & 2 & 0.125–2 \\ Azlocillin & 0.125 & 0.125 & 4 & N.A. \\ Aztreonam & \textgreater{}64 & 2–\textgreater{}64 & \textgreater{}64 & \textless{}256 \\ Cefaclor & 16 & 16–128 & \textgreater{}64 & \textless{}128 \\ Loracarberf & 32 & 4–32 & \textgreater{}128 & 128–\textgreater{}128 \\ Cefithuten & 32 & 4–32 & \textgreater{}128 & 128–\textgreater{}128 \\ Cefixime & 1 & 0.25–4 & 32 & 0.8–32 \\ Cefuroxime & 0.25 & 0.06–0.5 & 16 & 0.25–\textgreater{}16 \\ Cefpodoxim & 4 & 1–8 & 32 & 32–\textgreater{}32 \\ Cefotaxime & 0.125 & 0.01–1 & 8 & 0.02–0.25 \\ Ceftriaxone & 0.03 & \textless{}0.01–0.125 & 2 & 0.02–3.81 \\ Sulbactam & 64 & 0.125–256 & \textgreater{}64 & \textgreater{}64 \\
**Carbapenems** & & & & \\ Meropenem & 0.25 & 0.012–0.5 & 16 & 0.5–32 \\ Faropenem & 4 & 0.03–8 & 64 & 8–\textgreater{}64 \\ Imipenem & 0.5 & 0.06–0.5 & 32 & 16–32 \\ Ertenenem & 0.125 & 0.015–0.125 & 4 & 0.5–4 \\
**Glycopeptides** & & & & \\ Vancomycin & 1 & 0.25–2 & 16 & 2–32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: In vitro activity of 50 antibiotics (grouped according to the corresponding class of antimicrobials) against _B. burgdorferii_ s.l. (updated and modified from [17; 52]).
###### Abstract  
In a complex stress field of underground mining or geotechnical practice, tension damage/failure in rock masses is easily triggered and dominant. Unlike metals, rocks are generally bi-modularity materials with different mechanical properties (Young's modulus, etc.) in compression and tension. It is well established that the Young's modulus of a rock mass is directly related to the presence of the fracture or joint, and the Young's modulus estimation for jointed rocks and rock masses is essential for stability analysis. In this paper, the tensile properties in joint rocks were investigated by using numerical simulations based on the discrete element method. Four influencing parameters relating to the tensile properties (joint dip angle, joint spacing, joint intersection angle, and joint density) were studied. The numerical results show that there is an approximately linear relationship between the joint dip angle (\(\alpha\)) and the joint intersection angle (\(\beta\)) with the tensile strength (\(\sigma_{t}\)), however, the changes in \(\alpha\) and \(\beta\) have less influence on the Young's modulus in tension (\(E_{t}\)). With respect to joint spacing, the simulations show that the effects of joint spacing on \(\sigma_{t}\) and \(E_{t}\) are negligible. In relation to the joint density, the numerical results reveal that the joint intensity of rock mass has great effect on \(E_{t}\) but insignificant effect on \(\sigma_{t}\).  
Keywords:jointed rock; uniaxial tension loading; numerical analysis; discrete element method +
Footnote †: journal: Applied