esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Analysis
### Parameter Estimation in the Asymptotic Case
The counting rate from the non-random set in \(Z\) windows that cannot generate the secret key is:  
\[s_{1}^{Z}=2p_{z0}(1-p_{z0})\mu_{z}e^{-\mu_{z}}(p_{1}+p_{2}-p_{1}p_{2}), \tag{8}\]The counting rate from the random set in \(Z\) windows that can generate the secret key satisfies:  
\[s_{1}^{\prime}=s_{1}^{Z}-s_{1}^{Z}. \tag{9}\]  
Under the weak randomness condition, in order to obtain more information, [PERSON] attenuates the random part of the quantum states to ensure that the final error code only comes from the quantum states of the random part. The bit error rate in \(\widehat{X}\) windows after the attenuation operation is calculated:  
\[e_{1}^{\prime}=\frac{e_{1}^{X}}{p_{rand}1}=\frac{e_{1}^{X}s_{1}^{Z}}{s_{1}^{Z}- (p_{1}+p_{2}-p_{1}p_{2})}, \tag{10}\]  
[PERSON] and [PERSON] use the announced data from \(X_{1}\) windows to calculate the counting rate \(s_{1}^{Z}\), which is also the value for \(Z\) windows. The number of bits generated in \(Z\) windows could be calculated from this value. Moreover, the error rate of bits in \(X_{1}\) windows of intensity \(\mu\) and \(E_{\mu}^{X}\), the counting rate of intensity \(\mu\) and \(S_{\mu}\), and the counting rate of vacuum \(s_{0}\) can be observed, so we can calculate the upper bound value of the flipping rate [12]:  
\[e_{1}^{X}\leq e_{1}^{X,UI}=\frac{S_{\mu}E_{\mu}^{X}-e^{-2\mu}s_{0}/2}{2\mu e^{ -2\mu}s_{1}^{Z}}. \tag{11}\]  
In the asymptotical condition, the phase-flip rate satisfies \(e_{1}^{ph}=e_{1}^{X}\). Similarly, under the weak randomness model, the phase-flip rate satisfies \(e_{1}^{ph}=e_{1}^{\prime}\). Finally, we can distill the final key with an asymptotic key rate formula with weak randomness:  
\[R=2p_{z0}(1-p_{z0})\mu_{z}e^{-\mu_{z}}s_{1}^{\prime}\Big{[}1-H(e_{1}^{ph}) \Big{]}-fS_{Z}H(E_{Z}). \tag{12}\]  
where \(H(x)=-\text{log}_{2}(x)-(1-x)\text{log}_{2}(1-x)\) is the binary entropy function, \(S_{Z}\) is the observed counting rate of \(Z\) windows, \(E_{Z}\) is the corresponding bit-flip error rate and \(f\) is the efficiency of error correction.
## 4 Case Study and Economic Analysis
### Multi-Angle Economic Analysis
#### 4.3.2 Benefit of Wind Power Plants in Peak Shaving  
The cost of wind power in China has been continuously reduced in the last few years. Onshore wind power feed-in tariffs are trimmed to 0.067 $/(kWh) in 2018 and the generation cost is 0.061 $/(kWh), so the net revenue of wind power plants is 0.006 $/(kWh). The purchase price of wind power is consistent with the thermal power feed-in tariff in China, and the difference is subsidized by the state government. Based on the cases and operating strategy in Section 4.1 for all UPI and UOI participating in deep peak shaving, Figure 11 indicates the relationship of wind power utilization and wind power revenue with regard to the peak-shaving level under condition of 36% wind power penetration. It is clear that the revenue is positively related to peak-shaving levels, so wind power plants are the direct gainers in deep peak shaving programs. If the peak-shaving service market is established, wind power plants would be required to pay peak-shaving service fees, which will undoubtedly reduce their revenue. To ensure the benefits, it is necessary for wind power plants to further cut down the power generation cost, or on the other hand, they may take initiative to curtail wind power.  
Then, we consider the wind power utilization for coal-fired units with three different operating strategies as follows: (a) All UPI have to participate in deep peak shaving; (b) units with capacity larger than 100 MW participate in deep peak shaving, while capacity less than 100 MW can only participate in peak shaving of RPS stage; (c) units with capacity larger than 400 MW take part in deep peak shaving, while less than 400 MW can only participate in RPS stage. The results of these three operating strategies are shown in Figure 12.  
Figure 12 indicates that the units with smaller capacity have a greater impact on wind power utilization at the peak-shaving level of 55% and 60%, especially at the level of 55%. The wind power utilization at the peak-shaving level of 65% does not change much among three operating strategies; this means that the units with capacity more than 400 MW mainly undertake the peak-shaving task under that level. Hence, the peak-shaving effect of units with relatively larger capacity is more obvious at the high peak-shaving level. Figure 12 also shows a tendency where the more units engage in deep peak shaving, the higher the wind power utilization and the more revenue wind power plants will gain.  
Figure 11: Wind power utilization and revenue of wind power plants.
# Effect of Tree Quantity and Distribution on the Electric Field under Transmission Lines
## 1 Introduction
[18] monitored and analyzed the mid-frequency electric and magnetic fields in four areas, namely, an unveated area, lawn, shrubs, and tall trees, under the same operating conditions of 110 kV overhead transmission lines, and concluded that the effect of plants on the mid-frequency electric field is obvious; specifically, the weakening rate of the mid-frequency electric field of the tall trees is more than 90%. The above studies almost all concluded that trees can reduce the field strength of transmission lines, but they have not been applied to actual transmission and transformation projects. Most studies have shown that trees have a weakening effect on the electromagnetic field of transmission lines, but few are applied to specific scenarios, this paper focuses on the design of transmission line corridors, by changing the number and distribution of trees, to design appropriate transmission corridors, save the cost of actual engineering, and reduce the impact of transmission lines on residents' living and survival. Therefore, the paper proposed using the simulated charge method to calculate the power frequency electric field of transmission lines and obtain the distribution of the transverse electric field intensity at 1.5 m on the ground under different voltage levels. The conclusion is that the maximum electric field intensity of AC transmission lines occurs below the side conductors. Through the method of three-dimensional simulation calculation, the influence of tree size on the power frequency electric field of 500 kV transmission lines was obtained, which can provide a reference for reducing the power frequency electric field of ultra-high voltage transmission lines and reducing the width of transmission corridors. The detailed contributions and novelty of this paper are as follows:  
1. By conducting three-dimensional simulation calculations of the electric field on transmission lines with and without overhead ground wires, the model of transmission lines without overhead ground wires is more efficient and quick. 2. The maximum power frequency electric field of the transmission line occurs below the edge conductor, and when the phase angle of voltage A is 30 degrees, the electric field strength is symmetrical about the center of the transverse measurement point. This result indicates that the field strength below the edge conductor needs to be given special attention and protection. 3. The proposed method is feasible and effective, which has been verified by simulating the three-dimensional electric field of tree green belts at different locations. It further provides suggestions based on the research results for reducing the width of transmission corridors and serves as a reference for future research in this field.
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 3 Electrocatalytic Nanomaterials for Promoting EET
### Metal Nitrides  
Similar to TMCs, transition metal nitrides (TMNs) have also shown great potential for electrochemical energy conversion and storage (e.g., supercapacitors, batteries, and electrocatalytic reactions) due to their unique electronic structure, high electrical conductivity, good chemical stability, large volumetric energy density, and excellent electrocatalytic activity [139, 140]. However, the application of TMNs in promoting microbial EET in microbial electrochemical systems is relatively rare, with only two recent cases to our knowledge (Table 1). [PERSON] et al. prepared a W\({}_{2}\)N-MXene composite catalyst through the introduction of W\({}_{2}\)N nanoparticles into the Ti\({}_{3}\)C\({}_{2}\)Tx MXene for the purpose of improving the wettability, electrical conductivity, electron transfer efficiency, and microorganism attachment capability [130]. As a consequence, the MFC power density obtained by the W\({}_{2}\)N-Ti\({}_{3}\)C\({}_{2}\)Tx-decorated carbon cloth anode was, respectively, 52% and 84% higher than that obtained by the Ti\({}_{3}\)C\({}_{2}\)Tx-decorated carbon cloth anode and plain carbon cloth anode, with an obviously increased chemical oxygen demand removal and coulombic efficiency. [PERSON] et al. investigated the effect of the carbon felt anode modified with Fe\({}_{3}\)N and Fe\({}_{3}\)C nanoparticles on the MFC's electrical performance and found a significantly increased output voltage and maximum power density [131]. Although the cases are limited, it is also proved that TMNs can promote microbial EET. Of course, the specific effect mechanism needs to be further clarified.
## 2 Related Work  
### Traffic Situation Awareness Models in ITSs  
Previous research has presented several models of traffic situation awareness and alerting by constructing various kinds of ITSs. Many state-of-the-art models, including, but not limited to, deep learning [15] and wavelet-supported vector machines [16], have been utilized to estimate and report car crashes or accidents [17], roadside sulfur dioxide and nitrogen oxide data projections [18], identification of suspicious unlicensed taxis [19], the choice of transport mode [20], etc.  
Significant work on ITSs combined with weather-related features has been performed. [PERSON] et al. [21] reviewed the influence of adverse weather on ITSs before 2015, proving that the research in this area had great potential for investigation. [PERSON] et al. [22] summarized the features of accidents including adverse weather on the expressway to Busan, Korea, in addition to building a detection model based on videos and other sensors deployed on the expressway. [PERSON] et al. [23] made full use of the weather data along the freeway from Seoul to Gyeongpodae to assess levels of traffic congestion using multiple linear regressions. [PERSON] et al. [24] explored a scheme and system architecture to deploy forecasting models assisted by adverse weather in a real road network and discussed an efficient way to provide early warnings. [PERSON] et al. [25] explored the impact of adverse weather on traffic from a data-driven perspective. [PERSON] et al. [26] linked the reason for freeway accidents to weather from a micro perspective. As to machine learning methods, [PERSON] et al. [27] selected the vector and Bayesian methods to estimate changes in traffic flow over a short period of time caused by adverse weather. With the development of deep learning methods, [PERSON] et al. [28] chose a DBN (deep belief network) model with the input parameter of weather data and made the model reduce forecasting error.  
In terms of climate, [PERSON] et al. [5] provided an analysis of traffic safety issues in terms of climate change in Canada. [PERSON] et al. [29] investigated similar issues of the impacts on transport from the perspective of climate change rather than meteorological analysis. All the ITS models above provide a solid basis to determine the method and application for weather-affected traffic safety alerting, traffic flow control, and travel choice optimization. However, there are no works that map the relationships between weather and traffic situations in social cyberspace.
## 2 The Formulation of the Multi-Material Topology Optimization Problem
### The Mapping-Based Interpolation Function for Multi-Materials  
The mapping-based interpolation function is formulated based on the ratio of the p-norm to the 1-norm of the design variables, which was first proposed by [PERSON] [16]. It is simple and clear that the design variable assigned to each element can be directly used as an indicator of whether the corresponding material appears. The Young's moduli of each material interpolated by the mapping-based interpolation function can be expressed as follows:  
\[E\Big{(}\overline{x}_{1}^{e},\,\overline{x}_{2}^{e},\cdots,\overline{x}_{NM}^{ e}\Big{)}=\sum_{i=1}^{NM}(\psi_{i})^{n}(E_{i}-E_{void})+E_{void}\]  
\[\psi_{i}=\frac{\|\overline{x}^{e}\|_{p-norm}}{\|\overline{x}^{e}\|_{1-norm}+ \delta}\overline{x}_{i}^{e} \tag{4}\]  
\[\|\overline{x}^{e}\|_{p-norm}=((\overline{x}_{1}^{e})^{p}+(\overline{x}_{2}^{ e})^{p}+\cdots+(\overline{x}_{NM}^{e})^{p})^{1/p}\]  
\[\|\overline{x}^{e}\|_{1-norm}=\overline{x}_{1}^{e}+\overline{x}_{2}^{e}+ \cdots+\overline{x}_{NM}^{e}\]  
where \(\psi_{i}\) is the interpolation function and \(\delta\) is a very small value used to avoid the undefined mathematical operation zero divided by zero. The larger the value of \(\delta\), the more it affects the accuracy of the approximation. In application, it is set to be a small value \(\delta=1\times 10^{-9}\) to balance the accuracy and robustness. In addition, \(n\) is a penalization factor, which is used to drive the design variable convergence to 0 or 1. \(E_{i}\) is [PERSON]'s modulus of the \(i\)th material and \(E_{void}\) is a very small value representing [PERSON]'s moduli of void regions. \(\left\|\overline{x}^{e}\right\|_{p-norm}\) and \(\left\|\overline{x}^{e}\right\|_{1-norm}\) are the p-norm and the 1-norm of the projected design variables \(\overline{x}_{1}^{e}(i=1,\cdots NM)\), respectively. The influence of the p-norm parameter \(p\) will be studied in Section 5.2.  
The volume or mass of the structure is often constrained in topology optimization. Therefore, according to the mapping-based interpolation function, the volume or mass constraint for each material can be obtained by the following formula:  
\[Mass_{i}=\sum_{e=1}^{NE}\overline{x}_{i}^{e}(i=1,\cdots NM) \tag{5}\]
# The Evolution of Service Ecosystems Based on the Lotka-Volterra Model
## 5 Service Co-Opetition Evolution Experimental Analysis
### Numerical Analysis
#### 5.1.2 Relationship Between New Service Equilibrium State and Interaction Coefficients  
In studying the evolution process of new service types in the service ecosystem, it can be recognized that this process stems from the collaboration between different atomic service types. They interact and integrate, ultimately nurturing entirely new composite service types. Therefore, deeply exploring the co-opetition relationships among these original services is crucial for understanding and predicting the emergence of new service types. These co-opetition relationships determine the direction of service innovation and the potential development scale of new services. By accurately analyzing these interactions, we can better reveal how new service types emerge from existing services and how to develop strategies to promote positive service evolution.  
By modifying the interaction coefficients among the original atomic services in the model, setting \(\alpha_{12}\), \(\alpha_{21}\) to 0.4, 0.6, and 0.8, respectively, the equilibrium states are as follows.  
The Figure 8a shows that low competition \(\alpha_{12}=\alpha_{21}=0.4\) leads to slow composite service growth. The Figure 8b shows that the moderate competition \(\alpha_{12}=\alpha_{21}=0.6\) accelerates scale rise and maximizes its market share. The Figure 8c shows that high competition \(\alpha_{12}=\alpha_{21}=0.8\) stifles scale due to resource scarcity. In conclusion, a notable trend can be observed: as the intensity of competition among the original atomic services increases, this intense competitive environment appears to provide fertile ground for the development of emerging composite service types. With the rise in competition coefficients, new composite services are more capable of breaking through the existing market barriers, effectively eroding the market share of the original services. This phenomenon indicates that competition is not only a mechanism for market elimination but also a catalyst for innovation and progress. New composite services can find unique market positions in a highly competitive environment, thereby driving the service ecosystem towards greater efficiency and diversity.
# Numerical Simulation on Dynamic Behavior of Slab-Column Connections Subjected to Blast Loads
## 1 Introduction  
As explosive terrorism and explosion accidents continue to occur around the world, research on the behavior of structures under such extreme situations is increasing [1, 2, 3]. Most of these studies focus on how single members such as beams, columns, and slabs behave under explosive loads. Since the failure or large deformation of the joint can directly lead to the collapse or malfunction of the entire structural system, the study on the joint behavior is no less important than the study on single members. Although many studies have been conducted on the structural behavior of joints under static and dynamic loads, further studies are still needed to improve the understanding of joint behavior under explosive loads [4, 5, 6].  
In this study, the joints of the columns and slabs were investigated. In particular, the behavior of the joints under blast load was investigated according to the position of the column. In other words, the effect of different restraints of the joints on the blast-resistance performance was observed as the columns were located on the inside, edge, and corner of the slab. In addition, the behavior of slab-column joint with drop panel, which is a square portion provided above the lower column and below the slab, was investigated. Drop panels that provide increased shear strength and moment resistance are expected to be effective in improving the blast-resistance performance of slab-column joints. The experimental approach with explosives is really challenging, expensive, and difficult. Therefore, in this study, numerical analysis, one of the best options to discuss thisphenomenon, was performed using LS-DYNA, a general-purpose finite element analysis program whose reliability has been verified through many previous studies [7; 8].
## 2 Materials and Methods
### Volatile Compound Analysis  
Analysis on VOCs profile were carried out by repeated sampling on each ham of group S18 (\(n=10\)). Sampling took place at time 0 (R0, green ham) and after 1 (R1), 3 (R3), 6 (R6) months of ripening and at 12 (S12), 14 (S14), 16 (S16), and 18 (S18) months of seasoning. Fat was sampled using a 5-mm punch core positioned approximately in the same location every sampling time. After each sampling, the hole was filled with a mixture of land, salt, and pepper to prevent oxidation reactions and microbial contaminations. Subsequently, 1g of homogenized fat was grounded by liquid nitrogen and then transferred to 10 mL screw cap headspace vials adding for each sample 1 mL of distilled water and approximately 1 g of NaCl. The vials were supplemented with 40 \(\upmu\)L of internal standard mix (ethylacetate-d8; toluene-d8; ethyl hexanoate d11; hexanoic acid d11; 3,4-dimethylphenol), either isotopologues, i.e., deuterated analogues of compounds present in the samples, added to the samples immediately before the analyses [14]. The volatile compound profile was obtained by Solid Phase Microextraction Gas Chromatography-Mass Spectrometry (SPME-GC-MS) technique. An Agilent 7890 Chromatograph (Agilent, Santa Clara, CA, USA) equipped with a 5975A MSD with EI ionisation was used for analysis. A three-phase DVB/Carboxen/PDMS 75-\(\upmu\)m SPME fibre (Supelco, Bellafonte, PA, USA) was exposed in the head space of the vials at 60 \({}^{\circ}\)C for 30 min for volatile compound sampling after a 5-min equilibration time. A Gerstel MPS2 XL autosampler (GERSTEL GmbH & Co.KG, Mulheim an der Ruhr, Germany) equipped with a magnetic transportation adapter and a temperature-controlled agitator (250 rpm with on/cycles of 10 s) was used for ensuring consistent SPME extraction conditions. Chromatographic conditions were column J&W Innovax (Agilent, Santa Clara, CA, USA) 30 m, 0.25 mm, ID 0.5 \(\upmu\)m DF; injection temperature 250 \({}^{\circ}\)C, splitless mode, oven program 40\({}^{\circ}\) for 1 min then 2 \({}^{\circ}\)C/min to 60 \({}^{\circ}\)C, then 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, then 10 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, and then 25 \({}^{\circ}\)C/min to 260 \({}^{\circ}\)C for 6.6 min. Mass spectra were acquired within the 29-350 M/Z interval with an Agilent 5975C MSD spectrometer (Agilent, Santa Clara, CA, USA) at a scan speed in order to obtain three scans/s. The identification of volatile compounds was obtained by matching the peak spectra with library spectral database and by matching of the calculated Kovats index (KI) with the KI retrieved from literature. Data are expressed as normalized area ratios with the appropriate internal standard (IS) [15].
## 5 Spatially-Resolved Spectroscopic Technique for Optical Property Measurement
### Quality Evaluation of Horticultural Products
Overall, both \(\mu_{\rm a}\) and \(\mu^{\prime}_{\rm s}\) are useful for evaluation of apple quality, while \(\mu_{\rm a}\) seems better correlated with quality attributes of horticultural products than \(\mu^{\prime}_{\rm s}\). Combining \(\mu_{\rm a}\) and \(\mu^{\prime}_{\rm s}\) is generally more effective for firmness and SSC prediction than using them alone. This is presumably because both \(\mu_{\rm a}\) and \(\mu^{\prime}_{\rm s}\) are related to the maturity and physiological processes of fruit, and each parameter provides different aspects of information about the compositional and structural properties of the fruit. A study on the relationship between the optical properties and the microstructures and mechanical properties  
Figure 24: Mean spectra of (**a**) \(\mu_{\rm a}\) and (**b**) \(\mu^{\prime}_{\rm s}\) of 50 normal and defective ’Journey’ pickling cucumbers measured at different times after mechanical damage (1 h and 1 day). Reproduced with permission from [91], Copyright Springer, 2011. Figure 25: (**a,c**) Prediction of firmness and (**b,d**) soluble solids content (SSC) using the best combinations of \(\mu_{\rm a}\) and \(\mu^{\prime}_{\rm s}\) for the pooled set of ‘Golden Delicious’ (GD) and ‘Delicious’ (DL) apples. of apples during storage [120], revealed the decrease in \(\mu_{\text{s}}^{\prime}\) with fruit softening and the positive correlation of \(\mu_{\text{a}}\) and \(\mu_{\text{s}}^{\prime}\) with tissue firmness, elasticity and the cell's area and equivalent diameter. Hyperspectral imaging-based spatially-resolved technique provides a new, potentially useful means for assessing quality of apple and other agro-food products. However, the technique requires complicated mathematical modeling and is prone to error during the image acquisition and curve fitting. Moreover, the technique currently is only suitable for laboratory use due to slow imaging and data processing speed. Further work is needed to improve the accuracy and speed for optical property measurement.