esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Target Displacements in RC Buildings for 2023 Turkiye Earthquakes
## 4 Comparison of Target Displacements
Subsequently, target displacements (m) for the predicted PGAs in the two earthquake codes and hazard maps used in Turkiye were obtained separately for the sample RC model. Structural analyses were carried out for the ground motion level, which had a 10% probability of being exceeded in 50 years (repetition period 475 years), a standard design earthquake ground motion level in the last two earthquake codes. Target displacements used to determine the performance of structures were obtained separately for the measured and predicted PGAs in the two most recent earthquake codes. The comparison of the target displacements for measured and predicted PGAs is illustrated in Table 10.  
When Table 10 is examined, it is observed that the limit states are adequately represented for Kilis, Diyarbakir, and Elazig provinces. However, the target displacement values predicted for the remaining provinces cannot adequately represent the target displacement values measured during earthquakes. For Hatay, Kahramanmaras, Adiyaman, Sanlurfa, Malatya, Osmaniye, Adana, and Gaziantep, where the target displacements were exceeded, the analyses were also performed for the recurrence period is 2475 years (ground motion level with a 2% probability of exceedance in 50 years). A comparison of the obtained limit state values is shown in Table 11.  
The obtained results also coincide with the damage distribution findings made after the earthquake. It is especially evident that the larger-than-expected acceleration values largely ignored ground conditions. Its importance in terms of revealing its effect has been emphasized once again. While the largest earthquake ground motion level, DD-1, was not exceeded for Osmaniye and Adana, it was exceeded for other provinces. This clearly  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Province**} & \multicolumn{3}{c}{**Current Earthquake**} & \multicolumn{3}{c}{**TSDC-2007/DD-2**} & \multicolumn{3}{c}{**TBEC-2018/DD-2**} \\ \cline{2-10}  & **DL (m)** & **SD (m)** & **NC (m)** & **DL (m)** & **SD (m)** & **NC (m)** & **DL (m)** & **SD (m)** & **NC (m)** \\ \hline Hatay & 0.357 & 0.458 & 0.794 & 0.102 & 0.131 & 0.227 & 0.114 & 0.146 & 0.253 \\ Kahramanmaras & 0.530 & 0.681 & 1.180 & 0.102 & 0.131 & 0.211 & 0.097 & 0.124 & 0.216 \\ Adiyaman & 0.229 & 0.294 & 0.509 & 0.077 & 0.098 & 0.170 & 0.061 & 0.079 & 0.137 \\ Kilis & 0.014 & 0.018 & 0.031 & 0.026 & 0.033 & 0.057 & 0.053 & 0.068 & 0.117 \\ Diyarbakir & 0.030 & 0.039 &  
indicates that the earthquake hazards were larger than the predicted earthquake hazards for these provinces.
# Accurate Determination of Geographical Origin of Tea Based on Terahertz Spectroscopy
## 5 Conclusions  
This study demonstrated that THz spectrum can provide accurate and qualitative information regarding four types of green tea with varying origin. The THz spectra combined with the proposed GA-SVM model can be successfully used to identify the geographical origins of green teas, at up to a 96.25% identification rate. In effect, the proposed algorithm enhances the identification accuracy of the THz spectra of green tea. This simple approach involves non-destructive sample preparation and rapid processing and identification. THz spectroscopy can be effectively applied to identify types of green tea with various origins.  
This approach would also apply to the quantitative detection of other types of samples. As of now, it is not possible to detect subtle differences in the same type of green tea from different fields within the same region; we will attempt to secure a method for doing so in our next research project. As-is, the proposed approach may be a valuable and versatile on-line quality-control tool for foods, drugs, and health products.  
This work is supported by the Chinese Ministry of Science and Technology (Project No. 2012 BAK04B03), National 973 Program of China (Project No. 2015 CB755401) and the Chongqing Science and Technology Commission (Project No. cstc2013 cyjC00001 and cstc2014 jcyjA10036). [PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments. [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Non-Parametric Signal Analysis
#### 2.3.3 Time-Frequency Domain Analysis  
The analysis of the electrical signal of the _Aloe vera_ var. chinensis _(A. vera)_ in the time-frequency domain was carried out using the wavelet transform. This mathematical tool allows decomposing signals in terms of scale and time, providing a more detailed representation than traditional transforms such as the Fourier transform. Equation (2) defines it as:  
\[W_{\psi}(a,b)=\sum_{n=-\infty}^{\infty}x[n]\psi_{a,b}[n] \tag{2}\]  
where \(W_{\psi}(a,b)\) is the wavelet coefficient, \(x[n]\) is the input signal in the time domain, \(\psi_{a,b}[n]=\frac{1}{\sqrt{a}}\psi\left(\frac{n-b}{a}\right)\) is the scaled and translated wavelet function, \(a\) is the scale parameter, \(b\) is the translation parameter, and \(\psi\) is the mother wavelet function.  
In this work, different configurations of wavelet functions and decomposition levels were tested to identify the optimal combination to represent the signal properties. Four wavelet functions were selected: \"db3\", \"sym4\", \"coif1\" and \"haar\". These functions have specific frequency-time domain localization properties, which make them suitable for different signal characteristics. \"db3\" is ideal for capturing smooth transitions and complex patterns, while \"sym4\" offers a symmetry that makes it suitable for continuous signals. On the other hand, \"coif1\" specializes in detecting transient events, and \"haar\" is efficient for representing abrupt changes. These wavelet functions were combined with four levels of decomposition (4, 5, 6, and 8), exploring a wide range of scales, from high frequencies, associated with fast events, to low frequencies, reflecting global characteristics of the signal. The signal was normalized by dividing each value by the absolute maximum. Subsequently, TWD was carried out. Two metrics were calculated to evaluate the quality of each combination of wavelet function and decomposition level: total energy and entropy. Total energy measures the amount of information captured by the coefficients, while entropy reflects how this energy is distributed among the levels, indicating the degree of concentration or dispersion of information. The development was carried out in Python 3.12.4 and the PyWavelets library has been used [13].
## 7 Experimentation and Results
### Analysis of Pareto-Optimal Set of the Four Algorithms  
As we introduced previously, the multi-objective optimization problems are a set of conflict optimization objective functions that consist of minimization and maximization functions. Pareto optimality concept is emerged to manage the trade-offs between these objectives. This concept is proposed by [PERSON] in 1906 [73]. Pareto optimality operates mainly based on the Pareto front set, which is used to balance the conflict objective functions. Based on the Pareto front of each objective function, two concepts should be defined as follows: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Objective** \\ **Functions** \\ \end{tabular} } & \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Std. Dev.**} & \multirow{2}{*}{**Std. Error**} & \multicolumn{2}{c}{**95\% Confidence Interval for Mean**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} \\ \cline{5-8} \cline{7-9}  & & & & & & **Lower Bound** & & **Upper Bound** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Energy \\ Efficiency \\ \end{tabular} } & SPEA2 & 0.285 & 0.181 & 0.013 & 0.260 & 0.310 & 0.096 & 0.602 \\ \cline{2-8}  & MOSFP & **0.401** & 0.185 & 0.013 & 0.375 & 0.427 & 0.096 & 0.602 \\ \cline{2-8}  & OMOPSO & 0.392 & 0.188 & 0.013 & 0.366 & 0.418 & 0.096 & 0.602 \\ \cline{2-8}  & NSGA-II & 0.387 & 0.186 & 0.013 & 0.361 & 0.413 & 0.096 & 0.602 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Packet \\ Throughput \\ \end{tabular} } & SPEA2 & 0.398 & 0.828 & 0.059 & 0.282 & 0.513 & 0.010 & 3.161 \\ \cline{2-8}  & MOSFP & **0.791** & 1.074 & 0.076 & 0.642 & 0.941 & 0.010 & 3.154 \\ \cline{2-8}  & OMOPSO & 0.776 & 1.079 & 0.076 & 0.626 & 0.926 & 0.010 & 3.154 \\ \cline{2-8}  & NSGA-II & 0.725 & 1.054 & 0.075 & 0.578 & 0.872 & 0.010 & 3.158 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} End-to-End \\ Delay \\ \end{tabular} } & SPEA2 & 1.105 & 0.422 & 0.030 & 1.047 & 1.164 & 0.240 & 1.493 \\ \cline{2-8}  & MOSFP & **0.840** & 0.454 & 0.032 & 0.777 & 0.903 & 0.240 & 1.492 \\ \cline{2-8}  & OMOPSO & 0.858 & 0.459 & 0.032 & 0.794 & 0.922 & 0.240 & 1.492 \\ \cline{2-8}  & NSGA-II & 0.874 & 0.453 & 0.032 & 0.811 & 0.937 & 0.240 & 1.493 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} End-to-End \\ Latency \\ \end{tabular} } & SPEA2 & 6.597 & 4.927 & 0.348 & 5.910 & 7.284 & 0.279 & 15.579 \\ \cline{2-8}  & MOSFP & **3.880** & 4.539 & 0.321 & 3.247 & 4.513 & 0.279 & 15.533 \\ \cline{2-8}  & OMOPSO & 4.137 & 4.741 & 0.335 & 3.476 & 4.798 & 0.279 & 15.535 \\ \cline{2-8}  & NSGA-II & 4.185 & 4.673 & 0.330 & 3.534 & 4.837 & 0.279 & 15.601 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison between SPEA2, MOSFP, OMOPSO, and NSGA-II for four objective functions. The highlighted background with bold font represents the best average for the respective objective function.
## 6 Current Challenges and Future Directions  
There are several challenges with the current authentication protocols in terms of efficiency, overhead, cost, delay, privacy, _etc._ One of the major concerns is that unfortunately, there is no such protocol that exists that provides mutual authentication among HAN environments (HAs, SM, HAN-GW), energy provider, BAN-GW, NAN-GW, and AMI network (SM, AG/collector, RTU, and CC). Further, we suggest emphasizing the importance of user involvement when authenticating the HAs (during initial setup), since they generate and send data that belong to a particular user. Further, there is not yet an authentication protocol that fulfills all the proposed security and performance objectives in the SG network. The privacy protection in the home environment as well as in the V2G network is an important requirement for the SG. The protocol must not reveal the confidential and private information related to any entity involved in the authentication process. The access-based scheme is a good solution for providing the sufficient and required access to each entity after the authentication. Further, the V2G network also needs a secure and efficient batch verification-based authentication protocol with manageable overhead, and low end-to-end delay. Additionally, integration of device-to-device (SM-to-HAs, V-to-V) and device-to-network (SM-to-HAN-GW, V-to-RTU/NAN-GW) mutual authentication is needed in the design of future protocols for the home as well as V2G systems.  
Lastly, a secure and efficient SCADA architecture (including RTU, IED, programmable logic controller, phasor data consolidator (PDC), PMU) with the optimum routing backbone network is required in order to strengthen the capability to perform secure data transfers in the SG, as the generic industrial control system (ICS)/SCADA system does not consider the existing security issues.
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
###### Abstract  
An airport slot is the core resource in the air transportation system. In most busy airports in China, airline demand significantly exceeds the available slot capacity. Scientific and reasonable slot allocation techniques and methods can improve the operational efficiency and benefits of multi-airport systems. Existing research has predominantly addressed slot allocation optimization for individual airports; however, there are differences in the functional positioning and resource allocation during multi-airport slot optimization, which makes cooperative optimization in the context of multi-airport slot allocation difficult. The dynamic sharing of airspace capacity in multi-airport systems is crucial for optimizing airport slot allocation and improving resource utilization efficiency. This study develops a multi-objective optimization model incorporating coupled airspace capacity relationships within multi-airport systems and the fairness of airlines and airports in order to realize the optimal utilization of multi-airport system resources, considering specialized 24 h airport slot coordination parameter patterns and slot firebreaks in China. Finally, the validity and scalability of the model are verified using real flight data from three airports in the Beijing airport terminal area, and simulations are conducted to verify the model. The findings provide a solid reference for the optimization of airport slot timetables in multi-airport systems, having both important theoretical value and practical significance.  
multi-airport system; flight schedule; slot allocation; collaborative Article
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 4 Calculation Procedure of Main Circuit Parameters
### Flow Chart of the Main Circuit Parameter Design  
The corresponding flow chart is shown in Figure 6. To illustrate the calculation procedure more clearly and specifically, the detailed steps are labelled in every flow chat box.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Name of the Parameters** & **Symbol of the Parameters** \\ \hline  & DC power & \(P_{\text{dR}}\) and \(P_{\text{dl}}\) \\ \cline{2-3}  & DC voltage & \(U_{\text{dR}}\) and \(U_{\text{dl}}\) \\ \cline{2-3}  & DC current & \(I_{\text{d}}\) \\ \cline{2-3}  & Firing angle & \(\alpha\) \\ \cline{2-3}
**Target main circuit** & Extinction angle & \(\gamma_{\text{app}}\) and \(\gamma_{\text{real}}\) \\ \cline{2-3}  & Overlap angle & \(\mu_{\text{R}}\) and \(\mu_{\text{I}}\) \\ \cline{2-3}  & Reactive power & \(Q_{\text{R}}\) and \(Q_{\text{I}}\) \\ \cline{2-3}  & On-load tap changer of the converter & \(n_{\text{TR}}\) and \(n_{\text{TI}}\) \\  & transformer & \\ \hline  & Rated AC line voltage & \(U_{\text{acNR}}\) and \(U_{\text{acNI}}\) \\ \cline{2-3}  & AC line voltage & \(U_{\text{acR}}\) and \(U_{\text{acI}}\) \\ \cline{2-3}  & Rated ideal no-load DC voltage & \(U_{\text{dioNR}}\) and \(U_{\text{dioNI}}\) \\ \cline{2-3}
**Intermediate** & Ideal no-load DC voltage & \(U_{\text{dioR}}\) and \(U_{\text{diol}}\) \\ \cline{2-3}
**parameters** & Minimum firing angle & \(\alpha_{\text{Tmin}}\) \\ \cline{2-3}  & Minimum apparent extinction angle & \(\gamma_{\text{appmin}}\) \\ \cline{2-3}  & Minimum real extinction angle & \(\gamma_{\text{realmin}}\) \\ \cline{2-3}  & Minimum reactive power & \(Q_{\text{Rmin}}\) and \(Q_{\text{Imin}}\) \\ \cline{2-3}  & consumption & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The summary of the parameters during the calculation procedure.
## 2 Materials and Methods
### Measurements of Sap Flow and Environmental Factors  
Sap flux density (SFD) of the tree stems was measured with the thermal dissipation method using self-made Granier-type sensors [34] installed 1.5 m above the ground. Eachsensor consisted of two probes (2 cm long and 2 mm in diameter). The upper probe included a heater supplied with 0.2 W constant power. The heat was dissipated into the sapwood and provided an estimate of the vertical sap flux surrounding the probe. For each sample tree, one sensor was installed at 0-2 cm depth in the sapwood, with the two probes separated by 15 cm. All sensors were installed on the north side of the stem and covered by a shelter of reflective insulation to avoid thermal disturbance by radiation, wind, and rainfall. Each tree was continuously monitored from mid-April (before bud break) to the end of November (after all the leaves had fallen) at 30 sec intervals, and 30 min averaged data were recorded on a data logger (CR1000, Campbell Scientific, Logan, UT, USA) with a peripheral multiplexer (AM16/32, Campbell Scientific, Logan, UT, USA). However, occasionally, data loss occurred due to equipment failure caused by frequent lightning and animal attacks and also due to power supply problems during prolonged rainy days in July and September (Figure A3). The recorded temperature difference between the two probes was converted into the SFD, as described by [PERSON] [34]. The highest temperature difference (\(\Delta T_{\text{max}}\)) was defined as the zero-flux condition for each day. The \(\Delta T_{\text{max}}\) values determined for each day were generally similar throughout the experimental period (data not shown).  
Solar radiation above the canopy was measured using a solar radiometer (LI-200, Li-Cor, Lincoln, NE, USA), and air temperature and humidity in the canopy layer were measured using a thermohydrodynamic (CS215, Campbell Scientific Inc., Logan, UT, USA). Meteorological data were sampled and recorded at the same frequency as the sap flow measurements and on the same data logger. The vapor pressure deficit (VPD), calculated from the temperature and relative humidity using the Tetens formula [35], was also recorded on the data logger using a customized logging program written in CRBasic (Campbell Scientific Inc., Logan, UT, USA).
## 3 Results
### Feature Extraction  
We applied the feature extraction step on the GGO areas identified by our segmentation algorithm. In Figure 5 we show the Pearson's correlation matrix between each pair of observed features. The cluster plot highlights the existence of multiple groups of strongly correlated features. The first set of clusters are given by the features related to the roundness, elongation, and distance features. The most prominent cluster is composed of the Haralick features: energy, entropy, inertia, and cluster prominence.  
In Figure 6 we show the relation between feature distributions and labels. Using the median of the feature distributions as a threshold, we estimated the percentage of the patients associated with each label who are above this threshold. A result of 0.5 (white-like cells) indicates non-specificity of the variable for that individual label, i.e., a uniform distribution of the feature. A result greater than 0.5 (red-like cells) or smaller than 0.5 (blue-like cells) indicates a high/low percentage of patients for whom the feature values are greater/smaller than the median of the distribution, respectively. This representation allows a visual analysis for the identification and selection of the most informative features related to each label.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Technique** & **F\({}_{1}\) Score** & **Sensitivity** & **Specificity** & **Precision** \\ \hline Fan2020 [66] & InfNet & 0.579 & 0.870 & 0.974 & 0.500 \\ Fan2020 [66] & SemiInfNet & 0.597 & 0.865 & 0.977 & **0.915** \\ Muller2020 [49] & U-Net & 0.761 & 0.739 & **0.999** & - \\ Jun2020 [52] & 3D U-Net & 67.3 \(\pm\) 22.3 & - & - & - \\ Jun2020 [52] & 2D U-Net & 60.9 \(\pm\) 24.5 & - & - & - \\ Qingsen2020 [65] & U-Net & **0.726** & **0.751** & - & 0.726 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison between the results of various segmentation techniques applied on the same datasets. For each column the maximum value was indicated with bold font.  
Figure 5: The Correlation Matrix between the estimated features. We report for each pair of features the Pearson’s correlation coefficient. In red are highlighted the positive correlations, while in blue the negative ones. White-like colors identify the features with low correlation.
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 4 Simulation and Results
In Figure 3, we demonstrate the effect of the total power budget on the system throughput by assuming that the total available power at the relay node is half of the power at BS. It can be noted that we got a higher end-to-end rate by the increase in the total power budget. Further, the graph of JntS1S2 scheme is always at the top at each data point. On the other hand, it was nice to observe that NBARS had no significant benefits over any of our other proposed scheme at any point. It was very interesting to observe the consequences of relay position on the throughput of our designed algorithms, as shown in Figure 4. Similar to [6], it can be noted here as well that our proposed schemes gave their best performance at the point where the relay node was near the center of the source and destination nodes. Figure 2: Throughput (\(\text{bps}/\text{Hz}\)) versus total power budget. Further, the JntS1S2 solution outperforms at all points. The BARNS1 and BARNS3 schemes meet the JntS1S2 solution when the relay is placed near to either end. In Figure 5, we show the performance of our network by varying the state of the buffer (amount of data present in the queue before the start of transmission). It was nice to observe that the throughput of the system improved by increasing the amount of data in the queue. This was because it increased the upper bound on the second link to be selected for more number of time slots, which in turn improves the overall performance. Moreover, it can be clearly seen that there was no effect on the state of the buffer on NBARS. Figure 4: Throughput (\(\mathrm{bps/Hz}\)) versus relay position (the distance of the relay from base station (BS)). \[\frac{1}{2}\sum_{i=1}^{N}R_{i,t}^{1}\geq\frac{1}{2}\sum_{i=1}^{N} R_{i,t}^{2}\ \forall\ t\]0 Throughput (\(\mathrm{bps/Hz}\)) versus total power budget (power at the relay is half of the source node’s available power.