esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### LC-MS/MS Analysis of Neurotransmitter Monoamines and Metabolites  
After the FST, TST, and OFT, the levels of NE, EP, 5-HT, DA, DOPAC, 5-HIAA, GABA, and choline in mouse hippocampus tissues were quantitatively determined based on calibration curves and then normalized. In the hippocampus, five compounds were detected; NE, EP, 5-HT, GABA, and choline levels of E100 and/or E300 were significantly higher than those of N with dose-dependencies (NE, \(p<0.01\); EP, \(p<0.05\); 5-HT, \(p<0.05\); GABA, \(p<0.01\); choline, \(p<0.01\); Figure 4). The concentrations of NE, EP, 5-HT, GABA, and choline were \(0.16\pm 0.0021\), \(1.84\pm 0.25\), \(0.12\pm 0.0035\), \(542.5\pm 94.7\), and \(178.6\pm 27.9\) ng/mg protein in control; those in CUMS-treated negative controls were decreased, those in positive controls were increased, and those in E100 and E300 were increased to the positive controls (Table S1). Levels of DA, 5-HIAA, and DOPAC were too low to be detected.  
Figure 3: Behavioral analysis of the open field test (OFT) after intraperitoneal injection of fluoxetine and oral administration of the extract into mice (\(n=7\)). (**A**) Total distance; (**B**) Zone distance; (**C**) Time in zone. C: normal control; N: negative control (CUMS); P: positive control (CUMS + 15 mg/kg fluoxetine); E100 and E300: doses of the extract (CUMS + 100 and 300 mg/kg, respectively). #: Kruskal-Wallis test; *: Mann-Whitney test. #: \(p<0.05\); ***: \(p<0.001\); *: \(p<0.05\); *: \(p<0.01\).  
Figure 2: Behavioral analyses of the forced swimming test (FST) (**A**) and tail-suspension test (TST) (**B**) after intraperitoneal injection of fluoxetine and oral administration of the extract into mice (\(n=7\)). C: normal control; N: negative control (chronic unpredictable mild stress (CUMS)); P: positive control (CUMS + 15 mg/kg fluoxetine); E100 and E300: doses of the extract (CUMS + 100 and 300 mg/kg, respectively). #: Kruskal-Wallis test; *: Mann-Whitney test. ###: \(p<0.001\); *: \(p<0.05\); *: \(p<0.01\).
## 2 Amplifier Configuration
Considering a single-pump OPA, the four wave mixing (FWM) equations for the parametric pump (\(P_{P}\)), signal (\(P_{s}\)) and idler (\(P_{i}\)) are expressed by Equations (5)-(9) [16]. \[\frac{dP_{P}}{dz}=-4\gamma(P_{P}^{2}P_{s}P_{i})^{\
u 2}\sin\phi-\alpha P_{P} \tag{5}\]  
Figure 1:0  
Figure 1:1  
Figure 1:2  
Figure 1:3  
where \(\gamma\) is the fiber nonlinearity coefficient and \(\Delta\beta\) is the linear phase mismatch. \(\omega_{P}\), \(\omega_{s}\) are the pump and signal frequencies, and \(\omega_{o}\) is the fiber zero dispersion frequency. Here, \(\varphi(z)\) describes the relative phase difference between the four involved light waves [16]. The linear phase mismatch \(\Delta\beta\) is related to the fiber third- and fourth-order dispersion parameters \(\beta_{3}\) and \(\beta_{4}\), as in Equation (7) [7,17]. Note that the inclusion of the fourth-order dispersion parameters \(\beta_{4}\) is very important, as its omission may lead to either overestimation or underestimation of the gain bandwidth [7]. In our model, we have assumed that the state of polarization (SOP) on the incoming signals and pumps are aligned. In a real system, this may not be always true. Hence, this may cause polarization-dependent gain (PDG) for the amplifier. However, polarization diversity techniques (PDTs) can minimize the PDG. For example, in [18] a single-pump OPA achieved gain independent of the signal SOP using PDT. In this approach, the pump and the signals are launched into a polarization beam splitter (PBS), before entering the HNLF. The incoming signals are split into their two orthogonal polarizations. The transmission and reflection port of the PBS are connected in a closed  
Figure 1:4 Derived Raman gain efficiency at 1260 nm and 1280 nm from the measured Raman gain efficiency of 1453 nm. loop to the optical fiber. A polarization controller (PC) within the loop is used to adjust the SOP of the pump. The two orthogonal polarization components are recombined through the PBS and exit the amplifier. This PDT was also used in a field-trial evaluation of an OPA configured as an in-line amplifier [19]. The field trial demonstrated that the PDT can reduce PDG to as low as ~0.04 dB. A polarization-independent Raman-assisted OPA (for OPA gain enhancement) using both depolarized Raman and parametric pumps has also been demonstrated with a PDG of <1 dB [20]. Hence, similar approaches can be used in our proposed HROPA design.
# Knitted Microwave Transmission Line for Wearable Electronics
## 1 Introduction
In one technological process, a 3D structure was fabricated that consists of a conducting strip on top of a dielectric structure that separates it from the conducting ground plane. The following sections present the transmission line design and preliminary computer simulations. This is followed by a detailed description of the knitting technology that was used for the prototype fabrication. Later, the results of measurements obtained with the prototype transmission line are presented and compared to the results of simulations. It is shown that the proposed structure can be used for microwave wearable systems that operate below 2.4 GHz ISM band.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol  
The PQC standalone PKE protocol relies entirely on the security of the chosen PQC algorithms for both key exchange (KEM) and data encryption. We analyze its strengths and weaknesses, detailing the assumptions underlying its security.
###### Abstract  
Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization--energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars' policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties.  
Keywords:dual robust optimization; risk aversion; energy and environmental systems; vehicular emissions; multiple uncertainties +
Footnote †: journal: Applied
## 3 Results and Discussion  
### The Configuration Parameters for the Optimal Constellations  
By applying the PSO algorithm to search for the optimal configuration parameters for 8-, 24-, 60-, and 120-satellite 2D-LFCs for global GNSS-R observations, the four optimal constellations were obtained and are presented in Table 1.  
Figure 1: Geometry of the specular reflection points.  
From Table 1, it can be seen that the inclinations were generally high for the optimal 2D-LFCs as the number of the LEO satellites varied among 8, 24, 60, and 120. Specifically, the inclinations of the optimal 8- and 120-satellite LEO constellations were close to that of the sun synchronous orbiting satellite, and the optimal 24- and 60- satellite ones were composed of near polar orbiting satellites. In addition, the number of orbit planes for each of the four constellation configurations was optimized to the maximum value. To be specific, the optimized value for \(N_{o}\) was 8 for the 8- and 24- satellite LEO constellations, while it was 10 for the 60- and 120-satellite LEO constellations. As stated in Section 2.1, the number of orbit planes was set to be less than or equal to 10 in the simulation for cost consideration. For a 2D-LFC composed of a certain number of satellites, the greater the orbit planes, the faster the coverage goal for GNSS-R observations will be reached. When comparing the fitness values of the four optimized constellations (i.e., the minimum lengths of time to reach the percentage coverage of 90%), the optimized 120-satellite LEO constellation took the shortest time of about 1 h, while the optimized 8-satellite LEO constellation took the longest time of about nine hours. What needs to be mentioned is that the fitness values of the optimized 60- and 120-satellite 2D-LFCs were very close, while the optimized 24-satellite 2D-LFC had a great improvement in fitness value when compared with the optimized 8-satellite 2D-LFC.
# Numerical Investigation of Thermal-Flow Characteristics in Heat Exchanger with Various Tube Shapes
###### Abstract  
In this study, eight configurations of oval and flat tubes in annular finned-tube thermal devices are examined and compared with the conventional circular tube. The objective is to assess the effect of tube flatness and axis ratio of the oval tube on thermal-flow characteristics of a three-row staggered bank for Re (2600 \(\leq\) Re \(\leq\) 10,200). It has been observed that the thermal exchange rate and Colburn factor increase according to the axis ratio and the flatness, where O1 and F1 provide the highest values. O1 produces the lowest friction factor values of all the oval tubes at all Re, and F4 gives 13.2-18.5% less friction than the other tube forms. In terms of performance evaluation criterion, all of the tested tubes outperformed the conventional circular tube (O5), with O1 and F1 obtaining the highest values. The global performance criterion of O1 has been found to be 9.6-45.9% higher as compared to the other oval tube geometries at lower values of Re, and the global performance criterion increases with the increase in flatness. The F1 tube shape outperforms all the examined tube designs; thus, this tube geometry suggests that it be used in energy systems.  
heat exchanger; tube shapes; flat tube; oval tube; annular fin; thermal-hydraulics; CFD simulation +
Footnote †: journal: Applied Sciences
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 3 Two-Stage Quantization Method
### Mapping  
With PSO-K-means, each layer is divided into a certain number of groups determined by the quantization bit-width, and each group shares one centroid. To map cluster centroid into fixed-point numbers, a common practice is to use uniform partition (the process is shown in Figure 7). Through mapping, integer operations can be used to speed up the matrix operations of neural networks. The detail is shown in Algorithm 3.  
For this paper, we define the following symbols:  
* \(r\) denotes a real number;
* \(q\) denotes an n-bit integer.  
\[r=S(q-Z) \tag{8}\]  
Equation (8) shows the mapping of fixed-point q and floating-point r, where \(S\) and \(Z\) are the quantization parameters. Each layer of weights or activations shares a set of quantization parameters.  
Note that the object to be quantified in this paper is the cluster centroids rather than the entire weight matrix. For the weight quantization, the core idea is to map the cluster centroids to a set of discrete, evenly spaced grid points which span the entire range. For example, with 8-bit quantization, the quantization range needs to be set to a symmetrical interval of [\(-\)127,127] instead of (\(-\)128,127].  
Figure 7: The proposed DNN quantization framework with PSO-K-means and mapping.  
```
0: weight matrix after clustering, Quantization bit-width n
0: Result set \(Q_{c}=\{Q_{1},Q_{2}\ldots Q_{k}\}\)
1: a = min \(D_{c}\), b = max \(D_{c}\)
2:\(S(a,b,n)=\frac{b-a}{Z-1}\)
3: Calculate the quantization range
4: q(r,a,b,n) = \(\frac{r}{S(a,b,n)}+Z\)
5: Convert weights to fixed-point numbers
6:return Result set
```  
**Algorithm 3** Mapping.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 1 Introduction
### Background on the Barometric Formula  
The atmospheric pressure is the weight exerted by the overhead atmosphere on a unit area of a surface. The barometric formula describes how this atmospheric pressure is reduced when the altitude is increased and vice versa. The unit of pressure is 1 hPa = 1 mbar = 100 Pa.  
The barometric formula reads:  
\[P=P_{b}*\left[\frac{T_{b}}{T_{b}+L_{b}*(h-h_{b})}\right]^{\frac{g_{0}+M}{R+L_{ b}}} \tag{1}\]  
where \(h_{b}\) is the reference altitude, \(T_{b}\) and \(P_{b}\) are the temperature and pressure at the reference point, \(L_{b}\) is the standard temperature lapse rate of 6.49 K/km, \(P\) is the pressure at the current point at height \(h\), \(R\) is the universal gas constant 8.3144621 J/K/mol, \(g_{0}\) is the earth's gravity acceleration 9.80665 m/s\({}^{2}\) and \(M\) is the molar mass of the earth's air 0.0289644 kg/mol.  
Equation (1) can be altered for estimating altitude to give the following:  
\[h=h_{b}+\frac{T_{b}}{L_{b}}*\left[\left(\frac{P}{P_{b}}\right)^{-\frac{R+L_{ b}}{g_{0}+M}}-1\right] \tag{2}\]  
The barometer equation is valid within a few kilometers of the earth's surface, within which the lapse rate, gravity acceleration and air composition can be considered constant, given that \(P_{b}\) and \(T_{b}\) consistently refer to the reference height \(h_{b}\). According to the barometric formula, a 1 mbar difference in pressure, with a 15 \({}^{\circ}\)C ambient temperature, leads to a 8.33 m altitude change, while a 1 m change of altitude leads to a 0.1201 mbar change in pressure.
# A Fast Method for Calculation of Marine Gravity Anomaly
###### Abstract  
Gravity data have been playing an important role in marine exploration and research. However, obtaining gravity data over an extensive marine area is expensive and inefficient. In reality, marine gravity anomalies are usually calculated from satellite altimetry data. Over the years, numerous methods have been presented for achieving this purpose, most of which are time-consuming due to the integral calculation over a global region and the singularity problem. This paper proposes a fast method for the calculation of marine gravity anomalies. The proposed method introduces a novel scheme to solve the singularity problem and implements the parallel technique based on a graphics processing unit (GPU) for fast calculation. The details for the implementation of the proposed method are described, and it is tested using the geoid height undulation from the Earth Gravitational Model 2008 (EGM2008). The accuracy of the presented method is evaluated by comparing it with marine shipboard gravity data. Its efficiency is demonstrated through comparison with the conventional sequential method. The tests demonstrate that the proposed method can be employed for accurately calculating marine gravity anomalies and provides an advantage on computational efficiency.  
marine gravity anomaly; the inverse Stokes formula method; GPU parallel computing Article  
A Fast Method for Calculation of Marine Gravity Anomaly  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], H. A Fast Method for Calculation of Marine Gravity Anomaly. _Appl. Sci._**2021**, _11_, 1265. [[https://doi.org/10.3300/appl11031265](https://doi.org/10.3300/appl11031265)]([https://doi.org/10.3300/appl11031265](https://doi.org/10.3300/appl11031265))  
Academic Editor: [PERSON]  
Received: 24 December 2020  
Accepted: 26 January 2021  
SubSubject: 30 January 2021  
**Publisher's Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright**: **© 2021 by the authors. Licence MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Experimental and Numerical Investigation of the Mesoscale Size Effect in Notched Woven Composites
## 3 Results
### Experimental Results  
Experimental mean curves are shown in Figure 5. In the load-displacement plot (Figure 5a), which uses data from the test machine's load cell and displacement transducer, the specimens display different stiffnesses and maximum loads. As expected, both stiffness and maximum load are highest for the plain specimen, and decrease following hole size. In the stress-strain plots (Figure 5b), all specimens show a similar tensile modulus, whereas hole size affects the nominal ultimate tensile strength of the specimens. The strain values reported were computed by averaging the DIC surface strain over the entire specimen AOI, while the engineering stress value was computed as the ratio between the applied load and the unnotched section area. All specimens showed mostly linear behavior throughout the test, with some non-linearity arising before the final failure. A brittle failure along a line passing through the hole was observed for all the tested specimens.  
Test results for ultimate tensile strength are collected in Table 2. Three specimens were tested per hole size. UTSnom is the nominal tensile strength calculated over the gross (unnotched) section, while UTSnet was obtained considering the reduction in section due to the hole. UTSnom exhibits a monotonic decreasing trend with increasing hole sizes. The specimens with the smallest hole size show the highest UTSnet and low variability; larger hole specimens have lower values but much more significant dispersion in the results. Overall, however, dispersion in the results appears low, with a maximum coefficient of variation of 2.81%.  
Figure 6 shows an example of DIC-determined strain fields for a notched specimen. Longitudinal strain is the highest in entity overall. Diagonal high-strain bands corresponding to longitudinal rows are readily observable. Faint high-strain bands in the opposite diagonal direction are the result of the intermediate 90\({}^{\circ}\) ply showing through. An interaction between the material distribution and the open hole concentration is visible in the area around the notch. Strain concentration is not symmetrical with respect to the hole. The strain field cannot be adequately described by a homogeneous material model. Transverse strain (Figure 6b) shows the lowest values, as well as few banding and concentration effects. Shear strains (Figure 6c) are also close to null far from the hole, but rise in 45\({}^{\circ}\) directions around the hole. The maximum value remains lower than that of longitudinal strain, and the strain field seems less affected by the material distribution.
# A Study on Operator Allocation in Consideration of Fatigue in Cell Manufacturing System +
## 6 Conclusions  
In this study, we proposed an operator allocation model that considers the decrease in proficiency due to fatigue. First, a comparison between the conventional method and the proposed method was conducted through computer simulations. The results show that the proposed method can consider the negative effects of working time due to fatigue and reduced working time due to proficiency. On the other hand, it was found that fatigue cannot be considered in some cases, depending on the combination of the fatigue index and the learning rate. More accurate simulation results can be expected by investigating the differences in results based on the combination of the learning rate and fatigue index.  
Next, to verify the effectiveness of the proposed method, an actual assembly experiment was conducted based on the operator allocation obtained from the computer simulations, and the process of proficiency development was compared. As a result, although there was a difference between the calculated and measured assembly times, no significant difference was observed.  
On the other hand, significant differences between operators were noted. This is considered to be due to the fact that, in this experiment, the operators' skills and learning were treated uniformly. If the differences in the operators' skills, learning rates, and fatigue indices can be considered, it is expected that the gap between the calculated values and the measured values will be reduced.  
However, due to time constraints, this experiment was unable to individually consider each operator's skill index, learning rate, and fatigue index. If these factors were to be considered, it would be possible to individually evaluate these parameters through preliminary experiments and incorporate them into the model. When applying this approach to a real-world setting, it is possible to calculate individual parameters by analyzing performance over several cycles of tasks; however, this may involve certain costs. Therefore, estimating general parameters based on specific workplace and task conditions and applying them to the model is considered a practical and cost-effective approach.  
Future works will include creating a model that reflects differences in operators' skill and proficiency, as well as developing a model that considers other human factors.  
Conceptualization, M.E. and H.H.; methodology, M.E.; software, M.E.; validation, M.E. and H.H.; data curation, M.E. and H.H.; writing--original draft preparation, M.E.; writing--review and editing, H.H. All authors have read and agreed to the published version of the manuscript.  
The authors did not receive support from any organization for the submitted work.  
Raw data were generated at Author. Derived data supporting the findings of this study are available from the corresponding author [PERSON] on request.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Parameter Optimization
#### 4.2.2 Learning Rate Selection  
The learning rate is an essential factor that affects the speed of model construction. If it is set too large, the loss can explode; if it is set too small, it can lead to a slow loss reduction. Three sets of learning rates (0.01, 0.001, and 0.0001) were used to decrease the gradient and determine the most appropriate learning rate. To prevent the learning rate from decreasing too rapidly along the gradient, an additional group of learning rates (0.0004) was set for the control test. The experimental results are presented in Figure 12. Figure 12 shows that the model training time generally tended to increase and then decrease, and the overall classification accuracy reached its maximum when the learning rate was set to 0.001. Therefore, the learning rate for the model input was set as 0.001.