esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Mechanism of Series-Flow of Pollutants between Consecutive Tunnels by Numerical Simulation
## 3 Results and Discussions
### Construction of Series-Flow Ratio Model
Substituting \(m\) into Equation (12) obtains:  
\[Q_{s}=0.5m\pi R^{2}u_{\max}c_{\max}=mS\phi_{x}=S\phi_{x}-8.94(4.25+\Delta L/D )^{-1.5}S\phi_{x} \tag{13}\]  
where \(\phi_{\mathrm{x}}=u_{\max}c_{\max}\) refers to the maximum flux of pollutant quantity on the jet cross-section, and the second term in the formula of \(m\), \(8.944(4.254+\Delta L/D)^{-1.5}\), which represents the heterogeneity of the flux distribution of pollutant, gradually fades out as \(\Delta L\) increases while the pollutant diffusing makes the flux on the jet cross-section more and more uniform. Combing Equations (6), (10), (11), and (13), \(\varphi_{s}\) can be calculated as:  
\[\varphi_{s}=1-0.0328\Delta x/D \tag{6}\]0  
Using the simulation results of different values of \(\Delta L/D\) and \(u_{\mathrm{up}}/u_{\mathrm{down}}=1\), the least square fitted values of k and n are \(2.228\) and \(0.335\), respectively, with the goodness of fit \(\mathrm{R}^{2}=0.992\). Combining Equations (6) and (14),\(f_{1}(\Delta L/D)\) can be obtained as follows:  
\[f_{1}(\Delta x/D)=\left\{ \begin{array}{cc}1-0.0328\Delta L/D&(\Delta L/D \leq 7.0)\\ 3.62\big{[}1-8.944(4.254+\Delta L/D)^{-1.5}\big{]}(\Delta L/D)^{-0.67}&(\Delta L /D>7.0)\end{array} \right. \tag{15}\]  
With Equation (15), the least square fitting by use of the simulation results of different \(u_{\mathrm{up}}/u_{\mathrm{down}}\) gives \(f_{2}(u_{\mathrm{up}}/u_{\mathrm{down}})=(u_{\mathrm{up}}/u_{\mathrm{down}}) ^{-0.88}\), \(\mathrm{R}^{2}=0.988\). Consequently, the series-flow model can be expressed as:  
\[\varphi_{s}=\left\{ \begin{array}{cc}min\big{\{}(1-0.0328\Delta L/D)(u_{ \mathrm{up}}/u_{\mathrm{down}})^{-0.88},1\big{\}}&(\Delta L/D\leq 7.0)\\ min\big{\{}3.62\big{[}1-8.944(4.254+\Delta L/D)^{-1.5}\big{]}(\Delta L/D)^{-0.67 }(u_{\mathrm{up}}/u_{\mathrm{down}})^{-0.88},1\big{\}}&(\Delta L/D>7.0)\end{array} \right. \tag{16}\]  
Figure 10 shows the comparison between prediction results of series-flow model and CFD simulation data. As can be seen, Equation (16) accurately describes the influence of \(\Delta L/D\) and \(u_{\mathrm{up}}/u_{\mathrm{down}}\) on \(\varphi_{s}\).
# Effects of Three Different Withering Treatments on the Aroma of White Tea
## 2 Materials and Methods  
### Chemicals and Samples of Different Withering White Teas  
Distilled water was obtained from Watsons Water Company (Guangzhou, China). Dichloromethane was purchased from TEDIA (Fairfield, OH, USA) and was chromatographically pure; it was used for experiments after redistillation. Ethyl decanoate was 99% pure and was purchased from Aladdin (Shanghai, China) and was diluted to 1 mg/mL in absolute ethanol as an internal standard. Finally, n-alkanes (C6-C40; Sigma-Aldrich, St. Louis, MO, USA) were used to calculate retention indices to identify volatile compounds.  
The samples were produced at the Zhongcha factory (Songxi County, Nanjing City, Fujian Province, China). Fresh leaves (_Camellia sinensis_ (L.) [PERSON]) with one bud and two or three leaves were picked in April 2021. Two local tea masters with more than 20 years of experience in white tea production monitored the tea leaves in turn. The fresh tea leaves were evenly distributed for withering in accordance with traditional processing methods in the three aforementioned approaches. (Figure 1). The number of fresh leaves used in each method is adjusted according to the capacity of the withering tank and the bamboo plaque, with approximately 50 kg of fresh leaves used in the withering tank and 10 kg of fresh leaves used in each sunlight withering and indoor withering. For SWT, the fresh leaves were spread on a bamboo plaque for withering under the sunlight during the day; the plaques and tea were retrieved and placed indoors at night (Figure 1a). For IWT, fresh leaves were laid on bamboo plaques and placed in a room to wither (Figure 1b). For WWT, fresh leaves were placed in a withering-tank comprising a tank and an engine (YY802-4, Shanghai Guantao Electromechanical, Shanghai, China) and withered by blasting at 30 \({}^{\circ}\)C (Figure 1c). To ensure a consistent degree of withering among the samples, the moisture of the leaves at the end of withering was controlled at about 10%, as shown in Table S1. Moreover, to ensure the quality of white teas, the finished samples were placed in a tea dryer (6 CHZ-9B, Fujian Tea Machinery Intelligent Technology, Fujian, China) at 85 \({}^{\circ}\)until they were completely dry after the completion of withering was confirmed by the tea masters again.  
Sunlight withering, withering tank withering, and indoor withering were performed once, and three different batches of samples were made for each treatment. After returning to the laboratory, 200 g of samples from different batches of the same treatment were mixed separately, packed in tinfoil bags, and stored in a \(-\)20 \({}^{\circ}\)C refrigerator. Finally, three finished white teas produced using SWT, IWT, and WWT were collected.
# Construction of a Chemical Kinetic Mechanism of Five-Component Surrogate Fuel for RP-3 Kerosene
## 4 Result and Discussion
### Verification of Single-Component Combustion Parameters
#### 4.2.2 n-Tetradecane  
Figure 16 presents the verification of the ignition delay time for n-tetradecane fuel. The experimental data are from Shen [52], with \(\varphi\) = 0.5 and 1.0, P = 1.2 MPa and 4.0 MPa, and T from 900 to 1300 K. The results suggest that under the relatively low-pressure condition of 1.2 MPa, regardless of \(\varphi\), the mechanism constructed in this research can effectively predict the ignition behavior of n-tetradecane. When the pressure is elevated to 4.0 MPa, the predicted curves of the reaction mechanism exhibit considerable discrepancies from the experimental data within the temperature range below 1000 K. It is speculated that this is because of the significant pressure sensitivity of the low-temperature reaction part of n-tetradecane in the mechanism, resulting in an increased reaction rate under high-pressure conditions and a weakened prediction performance. Nevertheless, from the overall prediction situation, the coupled mechanism can essentially satisfactorily meet the prediction requirements for the ignition delay time of the n-tetradecane component.  
Figure 14: Verification of laminar flame speed for n-dodecane [23; 41; 51].  
Figure 15: Verification of oxidation products of n-dodecane.  
Figure 17 validates the laminar flame speed of n-tetradecane. The experimental data were derived from the measurements of [PERSON] [23] and Li [53], respectively. It can be observed from the figure that regardless of the pressure value, the simulation results effectively reflect the variation trend of the laminar flame speed of n-tetradecane with the equivalence ratio and are in good accordance with the experimental values.
# Novel Chitosan Derivatives and Their Multifaceted Biological Applications
## 3 Biological Applications of ChDs
### Antimicrobial Applications
Their antifungal activity against _Monilinia frticicola_, _Clatosporium cucumerinum_, _Colletotrichum lagenarium_, and _Fusarium oxysporum_ was found to be superior compared to native chitosan [90]. Novel water-soluble ChDs containing 1,3,4-thiadiazole group were synthesized including 1,3,4-thiadiazole (TPCTS), 2-methyl-1,3,4-thiadiazole (MTPCTS), and 2-phenyl-1,3,4-thiadiazole (PTPCTS). MTPCTS inhibited _Colletotrichum lagenarium_, _Phomopsis asparagi_ and _Monilinia frticicola_ at 1.0 mg/mL [92]. [PERSON] et al. [97] reported the synthesis of two novel cationic ChDs modified with quaternary phosphonium salts via trimethylation, chloride acetylation and quaternization with tricyclohexylphosphine and triphenylphosphine. The antifungal activities of ChDs against _Phomopsis asparagi_, _Watermelon fusarium_, _Colletotrichum lagenarium_, and _Fusarium oxysporum_ showed significant antifungal efficiency compared to native chitosan. A chitosan derivative of 2-thiophene carboxaldehyde was found to be active against _Escherichia coli_. Novel chitosan oligosaccharide derivatives (COS-All-Tio) have been employed for shrimp preservation. Six dominant spoilage bacteria _Shewanella putrefaciens_ (RMS1)_, _S. putrefaciens_ (S2)_, _Pseudomonas weihensteplanensis_ (P1)_, _P. gessardi_ (P2)_, _Aeromonas besiarum_ (A1) and _Aeromonas molluscorum_ (A2) that are actively involved in spoilage of prawns were used. COS-All-Tio ChDs could inhibit the growth of all these bacteria. It was observed that COS-All-Tio could inhibit bacterial growth by influencing their membrane integrity, resulting in the leakage of the bacterial intracellular substances [87]. [PERSON] et al. [86] report a new biodegradable and biocompatible chitosan-derived cationic antibacterial polymer, 2,6-diamino chitosan (2,6-DAC). 2,6-DAC demonstrated excellent broad-spectrum antimicrobial activity against clinically relevant and multidrug-resistant (MDR) bacteria, including _Listeria monocytogenes_, _Staphylococcus aureus_, _Escherichia coli_, _Klebsiella pneumoniae_, _Pseudomonas aeruginosa_ and _Acinetobacter baumannii_. In vivo biocompatibility of 2,6-DAC was also tested; 2,6-DAC did not cause any weight loss nor significant changes in liver and kidney biomarkers or blood electrolytes. 2,6-DAC was also used together with novobiocin and rifampicin to inhibit _A. baumannii_ in murine intraperitoneal and lung infection models.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 2 The HERMES Pathfinder Mission
### Scientific Payload  
The HERMES Scientific Payload was designed by the Istituto Nazionale di Astrofisica (INAF) and has the task of performing high resolution spectral-timing analysis of long and short GRBs [20; 21; 22; 23; 24; 25; 26; 27]. Whenever a GRB is detected, the estimated location of the CubeSat and the timestamp of the detection are saved in order to triangulate the GRB direction with an accuracy of several degrees (depending on the nature of the GRB and the configuration of the triplet observing the event). The time accuracy is provided by a chip-scale atomic clock integrated in the PL which is maintained synchronized with the GPS time via PPS synchronization. To ensure the atomic clock timing accuracy, the component temperature must be kept above \(-10\,\mathrm{\SIUnitSymbolCelsels}\)[28]. For what concerns GRB detection and energy time history recording, the PL relies on 120 Silicon Drift Detectors (SDDs) and 60 GAGG crystals, with each crystal coupled with two SDDs. More details on the high energy transient detection can be found in [21]. SDDs constitute the core of the instrument, and they are required to operate below \(-5\,\mathrm{\SIUnitSymbolCelsels}\) to limit the amount of leakage current due to on-orbit radiation damage, guaranteeing the instrument spectroscopic performance and the required lifetime. In order to keep such low temperatures, the PL structural panels act as a selective surface thanks to an Aeroglaze coating [29] that reflects the majority of the sunlight and maximizes the radiative heat flux emission in the infrared spectrum. Figures 2 and 3 show the  
Figure 1: HERMES CubeSat.  
breakdown of the PL internal components and the associated nomenclature, adopted to identify the PL subsystems in the remainder of the paper.
# Secure Localization in the Presence of Colluders in WSNs
## 3 Background, Models and Assumptions
Which category an attacker belongs to depends on how it reports its location. A category one attacker lies by supplying fake locations generated at random. A category two attacker provides fake location in cooperation with two other malicious nodes, without aiming at escaping detection. For instance, we include in this category nodes that may provide false locations in order to affect the positioning process of those location-unknown sensor nodes requesting collaboration  
Figure 1: Communication range scenarios of two sensors nodes \(A\) and \(B\). (**a**) Both \(A\) and \(B\) are inside the communication range of each other. (**b**) Both \(A\) and \(B\) are out of the communication range of each other. (**c**) \(A\) is in the communication range of \(B\), but \(B\) is out of the communication range of \(A\). from their neighbours Such colluders do not implement any attack techniques to evade detection from neighbouring nodes. Finally, category three attackers consistently lie in cooperation with other colluders, with the goal of misleading the positioning process of location-unknown nodes, as well as enforcing techniques to evade detection. Category three attackers are assumed to conduct _evil ring_ attack techniques (reported in [16]), for example by reporting false positions on a circle centred at the victim's location and of a radius corresponding to the distance between the attacker and victim. Finally, we emphasize that colluders of all three categories can compromise data integrity, for example by forging colluded _CCLs_. Let \(r_{A}\) denote the communication range of a location-unknown sensor \(A\). Let \(d_{A}\) be a disc of radius \(r_{A}\) centered at \(A\). Let \(K\in\mathcal{K}\) be a location-known sensor with communication range \(r_{K}\). Let \(d_{K}\) be a disc of radius \(r_{K}\) centred at \(K\). Let \(l\) denote the number of truth-telling sensors that: (1) are in the intersection of \(d_{A}\) and \(d_{K}\); and (2) have _bi-directional_ communication with both \(A\) and \(K\). Let \(m\) denote the exact amount of colluders that intersect \(d_{A}\) and \(d_{K}\). We postulate that for \(A\) to systematically detect all colluders, the number of truth-telling sensors \(l\) should be at least two more than \(m\). In what follows, we present algorithmic solutions to address the third category of colluders, which are not detectable by previous algorithms in the literature. We emphasize that algorithms that can handle colluders of category three clearly can also handle the two other categories of colluders.
## 3 Results
### Identification of Volatile Organic Compounds by GC-IMS
#### 3.3.2 Analysis of GC-IMS Fingerprints  
HS-GC-IMS was employed to identify and analyze VOCs in the four fermentation groups, resulting in the detection of 75 signal peaks corresponding to 73 VOCs. These included nineteen esters, eleven alcohols, ten ketones, eight aldehydes, two terpen, one acid, and fourteen other compounds (Table S1). Most of these VOCs were detected predominantly in their monomeric forms. Notably, 1-Propanol, 2-methyl and 2-pentanone were also detected in their dimeric forms, likely due to their concentration or high proton affinity [13]. To facilitate a clearer comparison of VOC differences among the four fermentation groups, a volatile fingerprint spectrum was generated using the Gallery Plot. In this plot, each row represented a sample, while each column corresponded to a VOC. The color intensity reflected the VOCs' concentration, with brighter colors indicating higher levels. Numbers in the fingerprint indicated compounds with responses unmatched in the spectral library [28]. As shown in Figure 3, the differences among the fermentation groups ET008-c54, SY, EC-1118, and NM-8 were mainly reflected in regions A, B, and C.  
In Region A, esters such as ethyl caprylate (apple, pineapple aroma), isopentyl formate (fruity aroma), 2-methylpropyl butanoate (sweet, fruity aroma), and amyl isovalerate (fresh, fruity aroma) were identified. Aldehydes, including \(\beta\)-cyclocitral (rose, herbal aroma), 3-methylbutanal (peach aroma), propanal (nutty aroma), and 2-pentanone (sweet, fruity aroma) were also detected. These aroma compounds were present at significantly higher levels in the SY and ET008-c54 fermentation groups, compared to NM-8 and EC-1118. In Region C, the VOCs in the NM-8 fermentation group were significantly lower than in the other groups, particularly for compounds like amyl isovalerate (fresh apple aroma) and styrene (balsamic, floral aroma). Additionally, Region B showed that \(\alpha\)-terpineol (floral aroma) and heptyl acetate (green, fruity aroma) were significantly more abundant in the SY fermentation group compared to others.  
The GC-IMS results indicated distinct VOC concentration differences in the GAB fermented by the four _S. cerevisiae_ strains, primarily observed in Regions A, B, and C. To further clarify the specific VOC differences among fermentation groups, GC-MS was  
Figure 3: Volatile characteristic fingerprint spectra of the different fermentation groups. The differences among the fermentation groups ET008-c54, SY, EC-1118, and NM-8 were mainly reflected in regions A, B, and C.  
employed for quantitative analysis of the VOCs, allowing for the identification of key differential metabolites across the groups.
# A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks
## 3 Hybrid Handover Scheme
### The MDP Setup
, \tag{9}\]  
Considering the early access option to the next VLC AP for all states of \((s,s^{\prime},2)\), the transition rate after action \(p(i)\) is written as  
\[\Pr_{i\to i^{\prime}}\{(s,s^{\prime},2)\}=[1-\frac{1}{2}|p(i)|(1-p(i))]\Pr_{i \to i^{\prime}}\{(s,s^{\prime},2),p(i)\}+\frac{1}{2}|p(i)|(1-p(i))\Pr_{i\to i^{ \prime}}\{(s^{\prime},\text{Off},1),p(i)\} \tag{10}\]If \(p(i)=0\) or 1, the transition rate \(\Pr_{i\to i^{\prime}}\{(s,s^{\prime},2)\}\) equals \(\Pr_{i\to i^{\prime}}\{(s,s^{\prime},2),p(i)\}\). If \(p(i)=-1\), which means that early HHO is performed, the state transits to \((s^{\prime},\text{Off},1)\). Therefore, \(\Pr_{i\to i^{\prime}}\{(s^{\prime},\text{Off},1),p(i)\}\) is the transition rate after early HHO. The objective of the proposed handover decision problem is to minimize the weighted sum of the consumption for packet transmission and delay, as well as the switching cost. Therefore, the reward of this MDP is made up of two parts:  
\[g(i,p(i))=\xi_{1}g_{1}(i,p(i))+\xi_{2}g_{2}(i,p(i)), \tag{11}\]  
where \(g_{1}(i,p(i))\) represents the sum of the energy cost for the packet transmission and latency, which is written as  
\[g_{1}(i,p(i))=|p(i)|E_{\text{V}}+(1-p(i))(1+p(i))E_{\text{R}}+\zeta b, \tag{12}\]  
where \(E_{\text{V}}\) and \(E_{\text{R}}\) are the energy consumed by the VLC and RF transmission, respectively. \(\zeta b\) is the delay cost incurred by the packets in the queue, with \(\zeta\) the trade-off factor for a packet waiting to be processed. For analytical simplicity, the illumination energy of the LED lamps and the running cost of the base-stations are not included in \(E_{\text{V}}\) and \(E_{\text{R}}\). \(g_{2}(i,p(i))\) is the handover cost that takes the signaling cost of the link switching and the handover latency into consideration,  
\[g_{2}(i,p(i))=|p(i)-w|(2-|p(i)-w|)(E_{V\text{HO}}+\theta\lambda\tau_{V\text{HO }})+\frac{1}{2}|p(i)-w|(|p(i)-w|-1)(E_{H\text{HO}}+\theta\lambda\tau_{H\text{ HO}}), \tag{13}\]  
where the switching signaling cost for VHO and HHO are denoted by \(E_{\text{VHO}}\) and \(E_{\text{HHO}}\), respectively. The latency for VHO and HHO is represented by \(\tau_{V\text{HO}}\) and \(\tau_{H\text{HO}}\) so that the packets accumulated in the buffer during the VHO and HHO process are denoted by \(\lambda\tau_{V\text{HO}}\) and \(\lambda\tau_{H\text{HO}}\). \(\theta\) is the latency coefficient decided by the delay requirement. \(\xi_{1}\) and \(\xi_{2}\) are the balance coefficients for \(g_{1}(i,p(i))\) and \(g_{2}(i,p(i))\). In simulations, \(\xi_{1}>\xi_{2}\). This is because the QoS of the vehicular communication has to be guaranteed, although we aim to minimize the switching cost as well. Hence, the weight factor for the switching cost \(\xi_{2}\) is smaller.
## 2 Materials and Methods
### Measurement of Serum Tropomyosin-Specific IgE Levels  
Serum levels of shrimp tropomyosin-specific IgE were measured by ELISA, as described in a previous study [16]. In brief, 96-well ELISA plates were coated overnight at 4 \({}^{\circ}\)C with rMet e 1 (10 \(\mu\)g/mL) in carbonate buffer (0.05 M Na\({}_{2}\)CO\({}_{3}\), 0.13 M NaHCO\({}_{3}\), 0.003 M NaN\({}_{3}\) (pH 9.6)), blocked with 5% FBS/PBS for 2 h, and then incubated with serum samples from all mice (1:10) overnight at 4 \({}^{\circ}\)C. After washing with PBS/0.05% Tween-20, the plates were incubated with biotin-conjugated anti-mouse IgE (1:1000, Pharmingen, San Diego, CA, USA) and Avidin D peroxidase (1:1000, Vector Laboratories, Burlingame, CA, USA) at room temperature for 1 h and 30 min, respectively. The plates were then washed and developed for 15 min using the TMB substrate reagent set (BD Biosciences, Franklin Lakes, NJ, USA). The reaction was terminated by addition of 2 N H\({}_{2}\)SO\({}_{4}\), and the optical density at 450 nm was measured using a microplate reader. Serum was prepared from blood samples collected from the mice 24 h after sensitization (days 1, 8, and 16) and the challenge (days 29 and 36).
# A Data-Driven Model of Cable Insulation Defect Based on Convolutional Neural Networks
## 4 Results and Discussion  
### Accuracy Analysis of Data-Driven Models  
The original data set is divided into a training set and a test set. After generating a model by learning the data in the training set, the data in the test set is predicted to verify the accuracy of the model. The model's accuracy is expressed by the accuracy, and the accuracy r is calculated as shown in Equation (10).  
\[r=\sqrt{1-m/n} \tag{10}\]  
In the above equation: \(m\) is the sum of the squares of the actual and predicted values, \(m=\sum\left(l_{true}-l_{predict}\right)^{2}\); where \(l_{true}\) is the true value; \(l_{predict}\) is the predicted value; and \(n\) is the sum of the squares of the differences between the true and predicted values.  
By varying the cable operating voltage of the cable model, the accuracy \(r\) of the convolutional neural network learning model training set and test set for the electric field strength of the cable was 0.971 and 0.967 respectively. The electric field intensity values calculated by the learning model based on the convolutional neural network algorithm for the single and multiple impurity cases were compared with those calculated by finite elements and the results are shown in Figure 3. Where the \(x\)-axis is the electric field strength value calculated by the finite element and the \(y\)-axis is the value calculated by the convolutional neural network learning model. As can be seen by the graph below, the learning model fits the data in this case better than the finite element calculated data. However, it can also be seen that there are some points with large deviations in the prediction results. This situation may be caused by the regression principle of the convolutional neural network because the nonlinearity of the electric field intensity near the impurity points is stronger, and the stronger the nonlinearity of the data, the worse the learning effect of the neural network method in the case of insufficient data information. Generally speaking, expanding the dimensionality of the input feature quantity to include more influencing factors can make the judgment more accurate and further improve the prediction accuracy of the prediction model.  
Figure 2: Flowchart of data-driven calculation based on CNN.