esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Validation
### Flying Wing with Coupled Flight Dynamics and Aeroelasticity
#### 3.2.3 Stability Analysis  
Time-domain simulations of the nonlinear dynamic responses of the highly flexible aircraft with three different payload masses (122, 152 and 227 kg) were performed. The aircraft was initially at its trimmed state, and perturbation was introduced by adding a pre-defined flap deflection change: the flap deflection angle was ramped linearly up to 5\({}^{\circ}\) between 1 and 2 s, then ramped linearly back to its trimmed angle between 2 and 3 s, and finally kept constant.  
The simulation was conducted by the direct solution of the complete nonlinear dynamic equations by implicit predictor-corrector schemes with the mixed time-marching algorithms and a time step of up to 0.1 s. Figures 10-12 show the variations of altitude, airspeed and AOA of the mid-span (the center) of the aircraft, respectively.  
It can be seen in Figures 10 and 11 that, with a payload of 122 kg at the central pod, the phugoid mode of the highly flexible aircraft is neutrally stable, as the altitude and airspeed of the vehicle after perturbation oscillate with equal amplitudes. When the payload increases to 152 or 227 kg, the phugoid mode becomes unstable with increasing amplitudes of oscillations: the heavier is the payload, the higher is the frequency and the larger is the amplitude of the oscillation. For the differences between the two results given by [PERSON] [17] and [PERSON] [13], [PERSON] attributed them to the differences of damping of the models used, which the authors think was not explained precisely. In the opinion of the authors of the present paper, it is more likely to be the difference of payload masses that leads to the different results.  
In Figures 10 and 11, the exchange between potential energy and kinetic energy of the highly flexible aircraft can be seen clearly from the out-of-phase variations between the altitudes and airspeeds. Besides, the aircraft altitude loss indicates a loss of energy due to the unstable phugoid mode and constant thrust.  
Figure 11: Airspeed of flight with different payload.  
Figure 10: Altitude of flight with different payload.  
As shown in Figure 12, within several cycles of oscillations, the mid-span (also the whole aircraft) starts to experience very high AOAs in every cycle, which is the inevitable result of the excited unstable phugoid mode.  
It is to be noted that, since stall is not yet included in the present framework, the results at high AOAs cannot be considered to be the real motion of the aircraft. Once the dynamic stall is included, one would see the different responses at the highest altitudes, where the lift will be insufficient to balance the weight and the aircraft drops with the increase in velocity, resulting in instantaneous higher AOAs.  
In addition, it can be clearly seen that excellent agreements are made with the results supplied by [PERSON] [17] for 227 kg payload mass and [PERSON] [13] for 152 kg payload mass, which demonstrates the validity and accuracy of the presented framework and its corresponding computing codes.
# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 3 Methodology
### Transport Network Index Evaluation Model Based on CRITIC-Entropy Weight
(5) Calculate the redundancy degree of information entropy \(d_{j}\):  
\[d_{j}=1-e_{j} \tag{5}\]  
(6) Calculate the entropy weight of each indicator \(w_{j}^{\ (1)}\):  
\[w_{j}^{\ (1)}=\frac{d_{j}}{\sum_{j=1}^{n}d_{j}} \tag{6}\]  
(7) Calculate the variability of each indicator, represented by the standard deviation \(S_{j}\):  
\[\left\{ \begin{array}{c}\overline{x}_{j}=\frac{1}{n}\sum_{i=1}^{n}x_{ij}\\ S_{j}=\sqrt{\frac{\sum_{i=1}^{n}\big{(}x_{ij}-\overline{x}_{j}\big{)}^{2}}{n-1 }}\end{array} \right. \tag{7}\]  
In the CRITIC method, the standard deviation is usually used to calculate the indicator weights. The idea is to reflect the information contained in the indicators based on the fluctuation of the numerical differences within the indicators. The greater the fluctuation of the numerical differences, the higher the evaluation intensity of the indicator. The fluctuation of the numerical differences can be quantified by the standard deviation. Therefore, indicators with larger standard deviations will have higher weights in the CRITIC method. (8) Calculate the indicator conflict \(R_{j}\). The indicator conflict reflects the correlation between indicators. The smaller the correlation, the greater the conflict of the indicators. Generally, indicators with high conflict can reflect more information. Indicators with higher correlation have higher redundancy and less reference value. Therefore, indicators with higher conflict often have higher evaluation weights. The calculation of indicator conflict \(R_{j}\) utilizes the Pearson correlation coefficient. Here, \(r_{ij}\) represents the average Pearson coefficient of indicator \(j\) with other indicators in the \(i\) sample, reflecting the correlation of indicator \(j\) with other indicators. The calculation of indicator conflict is shown in the formula. \[R_{j}=\sum_{i=1}^{n}\big{(}1-r_{ij}\big{)} \tag{8}\]  
(9) Calculate the CRITIC weight of each indicator \(w_{j}^{\ (2)}\):  
\[w_{j}^{\ (2)}=\frac{S_{j}\times R_{j}}{\sum_{j=1}^{n}S_{j}\times R_{j}} \tag{9}\]  
(10) The CRITIC method has the advantage of considering the strength of the indicators' contrast and the correlation between data, while the entropy weight method indirectly reflects the importance of indicators based on the degree of dispersion. In order to complement the advantages of these two weighting methods, this study combines and improves the two methods, assuming that they have equal status, and constructs a key indicator evaluation model based on the CRITIC and entropy weight methods. Thus, it achieves the objective of taking the strengths of both weighting methods and calculating the total weight of each indicator \(w_{j}\), as shown in the formula:  
\[w_{j}=\frac{w_{j}{}^{(1)}+w_{j}{}^{(2)}}{2} \tag{10}\]  
The total weights \(w_{j}\) calculated based on the established CRITIC-entropy weight evaluation model are sorted to obtain the key indicators used to calculate the resilience of the bus network.
# Shape Effect and Accuracy Analysis of Rock Tensile Strength Test
## 5 Conclusions  
By conducting indoor splitting tests on disc specimens with different diameters but the same thickness-to-diameter ratio cast simultaneously, and performing numerical simulations of tensile strength on three different specimen shapes made of the same material, the following conclusions are drawn:  
(1) In the laboratory splitting tests, the measured tensile strength decreases with an increase in the diameter of the disc specimens and then stabilizes. This variation can be well fitted using an exponential function. The results of this splitting test can be accurately fitted using the power function \(\sigma\)t = 5.806 3\(D^{-0.526}\). Additionally, numerical simulations indicate that the tensile strength obtained from disc-splitting tests is less than the actual tensile strength. For field tests, it is recommended to multiply the results by a coefficient, with the correlation coefficient suggested as 1.089 based on the numerical simulations.  
(2) For dumbbell-shaped specimens, the tensile strength measured from direct tensile tests shows a slight decrease as the diameter of the central effective test area decreases. The measured tensile strength is also lower than the actual tensile strength. For field tests, the use of straight\(-\)transition dumbbell-shaped specimens is not recommended due to significant deviation. If arc-transition dumbbell-shaped specimens are used for direct tensile tests, it is recommended to multiply the results by a coefficient, with the correlation coefficient suggested as 1.234 based on the numerical simulations.  
(3) Compared to dumbbell-shaped specimens, the results from disc specimens are closer to the actual tensile strength of rocks. Additionally, disc specimens are easier to fabricate and more suitable for construction sites, whereas dumbbell-shaped specimens, which require machining on a lathe, are more appropriate for laboratory comparative studies. However, in field tests, both specimen types may develop microcracks during fabrication, which could lead to deviations in the final test results.  
Data analysis and writing, J.P.; writing\(-\)review and editing, J.Z.; funding acquisition, S.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Project of Natural Science Research of Shanxi Province: Technology for Improving Tunneling Efficiency by Local Thermal Damage Modification in Hard Rock Strata of Tunnels, grant number 202303021221042.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Related Works
### Global Standardization in ITU-R and 3 GPP  
ITU-R and 3 GPP have a cooperative relationship in the development of global mobile communication standards. ITU-R establishes comprehensive guidelines and recommendations to steer the evolution of IMT families, and ITU-R Working Party 5D (WP 5D) bears the responsibility for the comprehensive radio-system aspects of the terrestrial component within IMT systems, which encompass IMT-2000, IMT-Advanced (referred to as 4G or LTE), IMT-2020 (commonly known as 5G), and IMT-2030 (indicative of the forthcoming 6G) [31]. 3 GPP is responsible for crafting standards for radio interfaces of IMT families, aligning them with requirements stipulated by ITU-R. Figure 1a summarizes the ITU-R Recommendation and corresponding radio interfaces based on 3 GPP standards for IMT families. Furthermore, 3 GPP undertakes the formulation of technical standards for core networks and terminals, expanding upon ITU-R's recommendations as shown in Figure 1b. This collaborative effort ensures the worldwide compatibility and efficiency of communication systems, optimizing the utilization of allocated radio frequencies, which contribute to establish the unified communication ecosystems based on 3 GPP standards prevalent in the market.
# Unidirectional Slow Light Transmission in Heterostructure Photonic Crystal Waveguide
## 3 Model Design and Simulation Results
### Slow Light Properties
Figure 2: The geometry configuration of the one-way slow light photonic crystal waveguide (PCW). The upper half plane is still a titled \(45^{\circ}\) square lattice of Alumina PC as an ancillary cladding, and the lower half plane is a square lattice of MOPC with the rod radii of first two rows are precisely tuned. The radii of the Yttrium–Iron–Garnet (YIG) rods in the first and the second rows adjacent to the waveguide are given by \(r_{1}\) and \(r_{2}\), respectively. dispersion-compensation. Zero \(\beta_{2}\) values is observed in the range of 4.2786 GHz to 4.2925 GHz (the corresponding bandwidth is 0.324%) in the case of \(r_{1}=0.078a\), \(r_{2}=0.115a\). The dispersion of such slow light PCW can be pursued to achieve a much slower \(v_{g}\) and lower \(\beta_{2}\) values simultaneously by further adjusting the YIG rod radii of the third row adjacent to the waveguide. Notably, when the externally applied magnetic field is reversed, the slope of the dispersion curves have only negative values as shown in Figure 3b. It indicates that the direction of the group velocity can be controlled by the external magnetic field. The dispersion curve can be tailored further to purse larger NDBP values by adjusting the magnitude of the externally applied magnetic field \(H_{0}\). Based on the results of Figure 5, we keep the parameters \(r_{1},\ r_{2}\) and carry out the optimizations for the \(H_{0}\). As shown in Figure 6a, corresponding to the same PCW structures in Figure 5, higher NDBP values of 0.394, 0.347, 0.329, and 0.326 are obtained for \(H_{0}=1.277\times 10^{5}\) A/m, \(H_{0}=1.279\times 10^{5}\) A/m, \(H_{0}=1.274\times 10^{5}\) A/m, and \(H_{0}=1.277\times 10^{5}\) A/m, respectively. The corresponding GVD characteristics are illustrated in Figure 6b. It is shown that the GVD parameters \(\beta_{2}\) in the slow light region keep around \(10^{4}\left(\frac{a}{2\pi c^{2}}\right)\) for all four cases, and these values are smaller than the values in Figure 5b. Figure 4: Dispersion curves of the edge modes for parameters \(r_{1}=0.077a\), \(r_{2}=0.113a\); \(r_{1}=0.078a\), \(r_{2}=0.115a\); \(r_{1}=0.079a\), \(r_{2}=0.117a\); and \(r_{1}=0.08a\), \(r_{2}=0.118a\). under the (**a**) \(+z\) and (**b**) \(-z\) direction applied magnetic field, The thick solid lines represent the flat band region for slow light. \(\odot\) and \(\oplus\) indicate \(+z\) and \(-z\) direction externally applied magnetic field, respectively. Figure 5: (**a**) Calculated group indices \(n_{g}\) and (**b**) the corresponding \(\beta_{2}\) as a function of the frequencies for the four optimized PCWs.
## 5 Experimentation
### Results
#### Aggregation Interval (\(\hat{t}\)); Number of Inputs (\(\hat{z}\))  
Figure 15 shows that gathering congestion readings during a short time interval (\(\hat{t}\)) results in obtaining the best performance for all the scenarios. Hence, we can say that the best results are obtained when the controller generates directives at a higher rate. There are minor differences between the suitable intervals for each scenario.  
The best performance for the Taxis and the Campus scenarios is at a 60 s interval, and for the Info5 scenario (the scenario with the fewest nodes) is 30 s. Thus, it can be said that the fewer nodes there are, the higher rate of directives emitted by the controller is required. For the Info5 scenario, even though in the plot it seems that from the interval of 600 s the function is increasing, simulations up to an interval of 10,800 s with samples every 1800 s have been run, and the delivery ratio remains constant to the value obtained at the 600 s interval.  
Finally, the best interval time for the Emergency scenario is 120 s. This scenario drastically changes the message generation in the middle of the simulation. In that variable situation, it is understandable to have a wider time interval range for gathering congestion readings to mitigate the effects of the changes.  
On the other hand, regarding the number of entries in \(\tilde{M}\) (\(\hat{z}\)) used to calculate the congestion prediction, Figure 15 clearly shows that, for all the scenarios it is better to have a small \(\hat{z}\). This is fully understandable, as the fewer inputs we use, the newer the information is.
## 2 The BuMPeT Method
### Procedures for Evaluation of Underperformance
#### 2.3.3 Procedure to Discover Location of Additional Sources  
This procedure aims to discover the location of additional sources that might be measured by an observed meter. This procedure should be applied if suspicion exists that there might be an additional source (for example, after applying the procedure in Section 2.3.2). Once a query is executed based on the metadata model, it is possible to examine the spatial information of the resulting components. Spatial information may be stored in the form of a string containing a coordinate system tailored to the building, such as office numbers.  
In the following, we give a configuration to the example given in Figure 2, using the tree structure to the right. In this example we assign a PT, P, which evaluates whether the consumption from the lighting subsystem is compliant and uses meter X to acquire the necessary data. From Figure 2 (to the left) it is obvious that areas A, B, C, D, and E are submetered by the meter X. However, if there is suspicion that an additional component or area exists (meter U), that is being measured by the same meter (X), then a query to the metadata model may be performed. This query would examine the relationships between the meter X used by P and discover all the components that are being measured by it.  
By examining the information given regarding the physical location of the components, given as a result from the execution of the metadata query, we may be able to determine which of the components are not present in the same area that P is taking into account. This can be done through examining the location of each component and identifying those that are outside the scope of the PT area or missing location information.  
The discovery of additional components that are measured by the same meter that the PT is being run on, which the PT is unaware of, indicates a problem. This problem can resemble an inconsistency between the real world and the metadata model or an underspecified metadata model. Considering the fact that the threshold for the PT is an expected amount of energy per m\({}^{2}\), increasing or decreasing  
Figure 4: Query for collecting the wattages of lights being fed by each meter in SPARQL on the left. The sets are constructed from individual pairs in post processing. An illustration of the query question and example results on the right.  
the area will yield a different threshold. If the threshold is a different value, whether the test passes or fails might change as well.  
The query for this procedure requires a mapping from lights to room names. The query in Figure 5 extracts this information from the metadata model. Any light without spatial information will be missing from the resulting map.
## 2 Related Works  
There are many definitions of an anomaly. [PERSON] [5], in 1969, defined an outlier or an anomaly as \"An outlying observation, or outlier, appears to deviate markedly from other members of the sample in which it occurs\". [PERSON] [6], in 1980, defined it as \"an observation which deviates so much from other observations as to arouse suspicions that a different mechanism generated it\". [PERSON] and [PERSON] [7], in 1994, defined it as \"an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data\".  
In several situations, the same action can be interpreted as an abnormal or anomalous event because most anomaly detection techniques are based on the hypothesis that a pattern that deviates from previously acquired patterns is considered abnormal [8]. According to some studies [9; 10; 11], anomalies can be divided into three types:  
* **Point anomalies:** These occur when only the entity's data behave somewhat irregularly compared to the rest of the data. Most research on anomaly identification focuses on this form of anomaly because it is the most basic. A car in the middle of the road can be termed a point anomaly.
* **Contextual anomalies:** These occur when a data value behaves irregularly compared to the rest of the data in a particular context. The context includes the observer's subjectivity and overall perception of the situation. Parking a passenger car in a bus-only car park can be considered a contextual anomaly.
* **Collective anomalies:** These occur when a collection of data samples is considered abnormal compared to the real data. A group of people gathered at the exit of a door can be called a collective anomaly.  
Many approaches have been suggested within the scope of anomaly detection for both crowded and uncrowded environments. One study [12] highlighted the relationship between the number of moving objects in the clip and the complexity of the medium utilized to detect and identify abnormalities in the video: slightly crowded environment (10 square feet/per person), moderately crowded environment (4.5 square feet/per person), and crowded environment (2.5 square feet/per person).
# Effects of Different Extraction Methods on Vanilla Aroma
## 2 Results
### Investigation of the Effect of Different Extraction Methods on the Aroma Components of Vanilla Pods
#### 2.1.2 HS-SPME  
A total of 28 volatile compounds were identified by HS-SPME extraction of vanilla pod samples (Table 2). The samples contained 6 aldehydes, 6 phenols, 5 alcohols, 3 esters, 2 ketones, 2 hydrocarbons, 2 sesquiterpenes, 1 furan and 1 monoterene. The total peak area with HS-SPME was the largest and the total peak area of aldehydes was more than 5 times greater than that obtained with the other extraction methods (Table 1). In addition, the total peak areas of furans, alcohols and phenols were also higher than those obtained with the other extraction methods. The main components of vanilla pods analyzed by HS-SPME were phenol, 1-octen-3-ol, 2-pentylfuran, 1-octenol, guaiacol and vanillin. [PERSON] et al. [22] used HS-SPME to analyze _V. planifolia_ produced in Taiwan and detected a variety of monoterenes and sesquiterpenes. Among them, limonene, \(\alpha\)-copaene and \(\alpha\)-muurolene were also identified in the experiment, which can offer vanilla citrus, lemon and wood aromas. [PERSON] et al. [12] analyzed _V. planifolia_ using HS-SPME and showed that shikimate derivatives accounted for the majority of _V. planifolia_, and vanillin was the most abundant component. In addition, volatile compounds, such as benzaldehyde, \(p\)-anisaldehyde, \(p\)-hydroxybenzaldehyde, benzyl alcohol, \(p\)-cresol, guaiacol, creosol and \(p\)-anisyl alcohol, were all shikimic acid derivatives. In this experiment, such compounds accounted for approximately 92% of the components, among which vanillin was the most abundant, followed by guaiacol. Although guaiacol was abundant, it is generally considered to have a negative effect on vanilla pod aroma [23], and with increasing guaiacol content, the vanillin content tends to decrease [24]. \begin{table}
\begin{tabular}{l r r r r r r} \hline \hline \multirow{2}{*}{**Chemical**} & \multicolumn{6}{c}{**Peak Areas 1**} \\ \cline{2-7}  & \multirow{2}{*}{**SDE**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**HS-SPME**} & \multicolumn{3}{c}{**SE**} \\ \cline{4-7}  & & & & **35\%** & **75\%** & **95\%** \\ \hline aldehydes & 3593.07 & 3383.06 & 21,546.27 & 989.18 & 2096.52 & 2266.22 \\ esters & 396.12 & 319.49 & 174.65 & - & 22.45 & 22.25 \\ furans & 16.11 & - 2 & 289.08 & - & - & - \\ monoterenes & 13.19 & - & 24.58 & - & - & - \\ sesquiterpenes & 68.26 & - & 55.22 & - & - & - \\ carboxylic & 3882.94 & - & - & - & 12.68 & 39.62 \\ acids & 164.39 & 425.67 & 934.03 & 14.36 & 33.76 & 46.43 \\ ketones & 137.94 & 627.84 & 204.12 & 210.39 & 110.28 & 365.43 \\ phenols & 2306.55 & 175.39 & 6104.42 & 48.92 & 118.94 & 117.61 \\ hydrocarbons & 779.67 & 28.73 & 30.10 & - & - & - \\ total & 11,391.62 & 4960.18 & 29,362.47 & 1262.85 & 2394.63 & 2857.56 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) Each value is the mean of three replication. 2 undetectable.  
\end{table} 
Table 1: Total peak areas of the chemical groups of vanilla pods using different extraction methods.