esa-sceva/satcom-chunk-collection huggingface dataset:
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 2 Literature Review  
### Research on the Operation and Risk of CR Express  
The rapid expansion of the CR Express under the \"Belt and Road\" initiative has spurred a growing body of research on its operation and associated risks. [PERSON] (2019) noted that the CR Express, characterized as a dynamically evolving scale-free network, develops annual consolidation networks through specific evolution rules, utilizing genetic algorithms to optimize the location of consolidation centers [3]. [PERSON] (2020) used cluster analysis and correspondence analysis to identify ten cities, including Tianjin, Guangzhou, and Chongqing, as consolidation centers [4]. [PERSON] (2020) examined the consolidation issues of the CR Express in Europe, identifying Berlin, Budapest, Duisburg, and Lille as primary consolidation hubs [5]. [PERSON] (2021) addressed environmental concerns under the \"Belt and Road\" initiative, designating Harbin, Changchun, and Qingdao as crucial consolidation centers [6]. Additionally, [PERSON] (2022) applied SWOT analysis to evaluate the strengths and weaknesses of the Guangxi CR Express, proposing policy recommendations [7]. [PERSON] (2022) analyzed the CR Express's reliance on government subsidies, advocating for their removal to promote development [8]. In a subsequent study, [PERSON] (2023) used game theory to explore integrated pricing mechanisms, finding that vertical integration alliance decision-making maximizes gross profit [9].  
While these studies offer significant insights into the operational optimization of the CR Express, there is a notable lack of focus on network risk analysis. [PERSON] (2022) investigated the power-law distribution characteristics of the CR Express multimodal transport network, finding that transshipment coupling enhances the network's dynamic flexibility and its ability to mitigate cascading risks [1]. [PERSON] (2023) explored the binary supply-demand relationships at transportation hubs and their effects on network vulnerability, introducing the concept of the \"ripple effect\" and emphasizing that innovative transportation organization can improve resilience [2]. However, most studies mainly concentrate on risk propagation between nodes, with limited exploration of the impact of sudden events on node failures, edge disruptions, and cargo flow reallocation within the network.
## 2 Materials and Methods
### Additional \"Masonry Test\"--Influence of joints  
The water test on \"whole masonry\" specimens was a logical next step after successful results of the previous testing on separate bricks (Section 2.1). It is necessary to analyse the impact of the joints in masonry on the resulting waterproofing effect of selected screeds.  
For these purposes, special test specimens--sets consisting of two bricks connected by mortar--were created. The lime-cement (mixture of cement clinker, cement dust, inorganic fillers and refining additives) mortar was used for joining the bricks. Each specimen was made up of two halves of the brick--145 \(\times\) 140 \(\times\) 65 mm (the original size of the brick had been modified with respect to the dimensions of the test equipment). The overall dimensions of the test specimens were thus 145 \(\times\) 140 \(\times\) 145 mm. For further testing, it was important to create a perfectly flat surface on which the waterproofing exceeds would be further applied. That is why wooden formwork was created (Figure 3a).  
Figure 3: (**a**) Test specimen—time-sand bricks and lime-cement mortal in wooden formwork; (**b**) test specimens with applied waterproof screeds.  
Figure 2: Water pressure test.  
A total of 12 test sets were created--3 with the applied bitumen screed, 3 with the applied polymer screed, 3 with the applied silicate (mineral) screeds, and 3 without any treatment (reference specimen). Each of the waterproofing materials was applied to three test specimens to verify the functionality of the experiment. Another three specimens were left without a screed or screeds as reference specimens (Figure 3b).  
The method of waterproofing testing (Figure 4) was almost identical with a previous case studying the individual bricks described in Section 2.1, however the maximum water pressure applied was reduced. In this case the loading regime consisted of 5 h of water pressure of 0.06 MPa and following 7 h water pressure of 0.12 MPa.  
The tested sets of specimens were not designed for watertightness testing at a water pressure higher than 0.12 MPa. The reason was the method of applying stress (shear load) in the proposed test. The limitation was in the ultimate shear strength of the mortar in the joint. The common shear strength values of masonry (lime-sand bricks) with common cement mortar reach the value of approximately 0.15 MPa.  
There is no need for the resistance to higher water pressures on real in situ masonry. In the case of a higher demand for resistance to pressurised water (deeper substructure), there is an obvious use of a reinforced concrete structure (the White Tank principle-in English-speaking countries and Die Weisse Wanne-in German-speaking countries).
## 1 Introduction
### Verification of Method Accuracy  
To verify the correctness and accuracy of the method presented in this paper, a vehicle-bridge coupling vibration calculation is performed. A two-axle half-vehicle model traveling at a constant speed over a simply supported beam is used, based on the data from [44]. The vehicle-bridge coupling model is shown in Figure 6. The parameter values of the vehicle and bridge model are provided in Table 1. The mesh size of the bridge model is 0.1 m.  
The dynamic responses of the vehicle are calculated when traveling at speeds of 120 km/h and 160 km/h across the bridge. The vertical displacement at the center span of the bridge is shown in Figure 7a,b. The accuracy is calculated by dividing the difference between the displacement results from this paper and the reference case by the displacement results from the reference case. The results show that the degree of agreement exceeds 95%, indicating that the method presented in this paper has good accuracy.  
Figure 5: The flowchart of the contact constraint method in ANSYS.
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 2 The Development History of CT Technology
### CT Image Processing and Analysis
The evaluation results showed that the first five techniques were consistent with theoretical values in the PSD analysis of pore volume, while DTM and DFS-FIJI exhibited instability when analyzing complex soil samples. [PERSON] et al. [93] employed mathematical morphology techniques for the segmentation of CT images. Utilizing the granulometry method within mathematical morphology, they assessed the shape and size of pores by varying the size of the spherical elements. Furthermore, they verified the accuracy of area measurements and, through the concept of the representative elementary volume (REV), determined the optimal image size for evaluating the geometric properties of sand. [PERSON] et al. [94] proposed a novel method that combined Euler number X and \(\Gamma\)-curves to study pore diameter connectivity; it was also suitable for analyzing pore diameters spanning three orders of magnitude. Data regarding pore distribution, fractal dimension, and pore connectivity can be extracted using quantitative image analysis software (e.g., AVIZO, www.vsg3d.com; VSGtudio-MAX, www.volumegraphics.com; and Image J, imagej.net, all accessed on 17 July 2024) or semi-automatic image processing plugins [95] (e.g., Soil). However, [PERSON] et al. [96] pointed out that many existing forms of integrated software and self-compiled algorithms face challenges in dealing with irregular pores and unclear boundaries, recommending an adaptive fuzzy C-means clustering algorithm (AFCM) with fuzzy decision-making capabilities. [PERSON] et al. [97] created an open soil structure database aimed at facilitating image analysis and the modeling of soil structure and pore-related data. Here, this paper also summarizes some CT image reconstruction processes and segmentation methods. Figure 1 illustrates an example workflow for reconstructing three -dimensional pore structures in CT soil images, and Figure 2 briefly describes the 3D microstructure reconstruction process of loess CT images.
## 2 Materials and Methods
### Hard Limits and Linked Variables  
To ensure both biological plausibility and logical consistency, the patient-cohort-generator algorithm enforces two classes of deterministic logic: hard limits and linked parameters. These mechanisms prevent implausible assignments and preserve coherent relationships among variables.  
Hard limits are user-defined logical conditions that restrict the assignment of parameter values based on the values of other variables. These conditions are specified in the input sheet as formula strings using standard logical operators (e.g., AND0, OR(), IF0) and comparison symbols (=, >, <, <). For example, a constraint for the menopause parameter might be entered as AND(Female = 1, Age > 40), indicating that menopause can only be assigned one if the patient is female and older than 40. During simulation, the algorithm evaluates each patient individually by substituting their specific values into the constraint formula. If the result of the expression is false, the parameter value is automatically set to zero. This ensures that invalid or clinically inconsistent characteristics are not assigned.  
Linked parameters are computed deterministically from other variables. If a parameter is marked as linked in the input sheet, its value is calculated using a user-defined expression.  
These linking formulas use standard arithmetic and logical operations and are resolved only after all dependent variables have been finalized. For example, a cardiovascular risk score might be defined as Age \(\times\) 0.1 + SBP \(\times\) 0.2 + Diabetes \(\times\) 1.5. If a patient is 60 years old with a systolic blood pressure (SBP) of 150 and a diabetes indicator of 1, the expression evaluates to 60 \(\times\) 0.1 + 150 \(\times\) 0.2 + 1 \(\times\) 1.5 = 37.5.  
Complex expressions, such as conditional logic (IF(BMI \(<\) 18.5, \"Underweight\",...)) or nested calculations are also supported. The evaluated result is then stored as the final value for the linked parameter. This method guarantees internal consistency, preserves user-defined relationships between variables, and ensures that derived parameters remain logically aligned with the rest of the dataset. The steps for parsing and evaluating user-defined hard limits and linked equations are discussed in detail in Appendix A.  
All parameters and their resolved values are retained in memory throughout the simulation process. This facilitates efficient access, dependency resolution, and the consistent enforcement of both constraints and derivations during cohort generation.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 4 Implementation and Simulation Results of the N78 Band Sub-6 GHz DPA
### Performance Comparison of Three DPA Designs  
In this section, we present and analyze results for three DPA designs in Figure 19, all of whose design parts have been discussed previously. The first design, labeled as (EPD without HS), utilizes an equal Wilkinson power divider but lacks a harmonic suppression network. The results of this design can be seen in Figure 19 as the solid blue line with a square symbol. The second design, labeled as (UPD without HS), is another DPA that employs an unequal [PERSON] power divider and does not include a harmonic suppression network. In Figure 19, the results of this design are represented by the solid green line with a triangle symbol. Lastly, the third design, identified by the labeled (UPD with HS), features an unequal Wilkinson power divider along with a harmonic suppression network. The results of this design can be seen in Figure 19 as the solid red line with a circle symbol.  
This paper proposes adopting the third design of the DPA, which incorporates a post-harmonic suppression network on the output in addition to an unequal Wilkinson power divider on the input. The performance of the proposed amplifier in the third design was compared to the previous two designs, and the results are summarized in Table 2. The large-signal gain of the amplifier in the proposed design reaches 12 dB, representing a 15% improvement over the first design and a 4% improvement over the second design, as shown in Figure 19a. There is also an improvement in the output power of the amplifier, which reaches 41.85 dBm, an increase of 4.6% over the first design and 1.6% over the second design, as shown in Figure 19b. Although the improvement in gain and output power values is not substantial due to the harmonic suppression network being considered a load on the output of the amplifier, its impact is evident in the improved drain efficiency and power-added efficiency, which reached 75.4% and 70.6%, respectively, representing a 64.6% and 69.5% improvement over the first design, and a 33.2% and 34.2% improvement over the second design, as shown in Figure 19c,d.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Design**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Gain (dB)**} & \multicolumn{2}{c}{**Pout (dBm)**} & \multicolumn{2}{c}{**DE (\%)**} & \multicolumn{2}{c}{**PAE (\%)**} \\ \cline{2-9}  & **Value** & **Improvement** & **Value** & **Improvement** & **Value** & **Improvement** & **Value** & **Improvement** \\ \hline EPD without HS & 10.43 & - & 40 & - & 45.8 & - & 41.7 & - \\ \hline UPD without HS & 11.54 & 10\% & 41.2 & 3\% & 56.57 & 23.5\% & 52.6 & 27\% \\ \hline UPD with HS & 12 & 15\% & 41.85 & 4.6\% & 75.4 & 64.6\% & 70.6 & 69.5\% \\ \hline \end{tabular}
\end{table}
Table 2: Summary of the performance of the three DPA designs.  
Figure 18: Simulated time-domain curve of intrinsic drain voltage and current of the designed DPA.
# A New Modified Non-Approximative Method for Dynamic Systems Direct Calculation
## 3 Problem Solution and Simulation Example
### Case of the Nonharmonic Periodical Excitation Function  
Regarding time domain investigation, the Laplace-Carson operatory method has been used [5; 6] when the input exciting function is known. Function \(u_{1}(t)\) on the right side of the Equation (14a,b) can be any nonharmonic one, but it must be the periodical function, e.g., the single-phase-rectified output voltage  
\[u_{1}(t)=U\mathrm{abs}\left(\sin\frac{2\pi}{T}t\right). \tag{17}\]  
For simplicity, consider the excitation \(u_{1}(t)\) rectangular waveform derived by the z-transform in the form:  
\[u_{1}(t)=U\sqrt{2}\sin\left[\mathrm{integer}\left(\frac{4}{T}t\right)\frac{ \pi}{2}+\frac{\pi}{4}\right]\] (17a) or simply algebraically: \[u_{1}(t)=U\mathrm{sign}\left(\sin\frac{2\pi}{T}t\right). \tag{17b}\]  
Now, the notation in the Laplace-Carson form is:  
\[U_{1}(p)=\frac{U_{1}^{T/2}(p)}{1+\mathrm{e}^{-pT/2}},\] (18a) where \[U_{1}^{T/2}(p)=U\left(1-\mathrm{e}^{-p\frac{T}{2}}\right). \tag{18b}\]  
The next solution is for the time range 0 to \(T/2\):  
\[X_{1}(p)=\frac{a_{12}X_{2}(p)+b_{11}U_{1}^{T/2}(p)}{p-a_{11}}=U\frac{A(p)}{B(p )}, \tag{19a}\] \[X_{2}(p)=\frac{a_{21}\left[a_{12}X_{2}(p)+b_{11}U_{1}^{T/2}(p)\right]}{(p-a_{ 11})(p-a_{22})}=U\frac{C(p)}{D(p)}. \tag{19b}\]  
Because \(U_{1}^{T/2}(p)\) is a constant \(U\), the time waveforms of variables \(x_{1}(t)\) and \(x_{2}(t)\) are the same as those in Equation (14a,b) in the range \(\left\langle 0-T/2\right\rangle\).  
In a similar way, the transient component of \(x_{1}(t)\), \(x_{2}(t)\) can be calculated:  
\[x_{1_{trans}}(t)=U\left[\frac{A(0)}{B(0)}+\sum_{k=1}^{N}\frac{1-\mathrm{e}^{-p_ {k}T/2}}{1+\mathrm{e}^{-p_{k}T/2}}\frac{A(p_{k})}{p_{k}B^{\prime}(p_{k})} \mathrm{e}^{p_{k}t}\right], \tag{20a}\]\[x_{2_{tams}}(t)=U\left[\frac{A(0)}{B(0)}+\sum_{k=1}^{N}\frac{1-\mathrm{e}^{-\frac{ P_{k}T}{2}}}{1+\mathrm{e}^{-\frac{P_{k}T}{2}}}\frac{C(p_{k})}{p_{k}D^{\prime}(p_{k})} \mathrm{e}^{p_{k}t}\right]. \tag{20b}\]  
along with the steady-state component for one half-period:  
\[x_{1}2_{steady}^{0-T/2}(t)=x_{1,2}^{0-T/2}(t)-x_{1,2_{tams}^{0-\frac{T}{2}}}(t). \tag{21}\]  
Finally, the total transient waveforms in the entire time range can be calculated:  
\[x_{1,2}(t)=x_{1,2_{tams}}(t)\pm\Big{[}x_{1}2_{steady}^{0-T/2}(t)\Big{]}. \tag{22}\]  
The results of the simulation of \(x_{1}(t)\) and \(x_{2}(t)\) for three half-periods are shown in Figure 2.  
The numerical values of variables at the discrete time instant \(n.t\) are listed in Table 2 when the time constant \(\tau=RC\) equals 10 ms.
## 3 Results
### The Computational Complexity Analysis  
Computational complexity is an important factor for measuring the performance of the scheme. So we calculate and compare the complexities of our scheme with that of CMA+BPS, the result is shown in Table 1. The calculation includes four categories: comparison, real multiplications, real additions, and look-up table (LUT) operations. M is the number of taps, and N is the block length. B is the number of test phases. When M is 11, N is 20, and B is 32. The CMA+BPS method needs 4112 real multiplications, 4030 real additions, and 32 comparisons, whereas the proposed joint scheme only needs 585 real multiplications, 459 real additions and 32 LUT operations. It should also be noted that the complexity of LUT is much less than that of real multiplication or addition. We can also conclude that for each symbol, the computational complexity of the proposed joint scheme is lower than that of CMA+BPS.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method
### Phase 1--Definition of the \"What's\"
#### 4.1.5 Comparison of the Acceptance among a Set of Tasks/Technologies  
TAM application in this type of study is not very diffused. Only three papers were found to analyze the TAM to understand users' preferences of specific techniques, technologies, or tasks over others. Broadly speaking, these studies evaluated how the TAM constructs, such as perceived usefulness and perceived ease of use, tilted the balance in favor of some innovations. In the agriculture field, [PERSON] et al. [93] contrasted three environment-friendly production techniques: Agroforestry, Alternative protein source, and Prolonged maternal feeding, and validated the predictive power of perceived usefulness over the intention to use. [PERSON] et al. [94] tried to understand the factors responsible for the scarce adoption of smart farming technologies (SFTs). The authors clustered some SFTs into two groups: drones, sensors for data acquisition and automatic download, and agricultural apps belonged to the first type. Agricultural robots and autonomous machines formed the second one. They also investigated the effect of exposure to different forms of interpersonal communication on perceived usefulness and perceived ease of use. As for the e-learning sector, [PERSON] [95] examined teachers' intention to use a learning management system for eighteen instructional tasks. The results showed that using the LMS is preferred for some activities, mainly due to task importance, LMS' usefulness for performing the job, and the ease of use.
## 2 System Model and Signal Design  
### System Model  
The application scenario we consider is an integrated MIMO-OFDM system with \(N_{t}\) transmitting antennas and \(N_{r}\) receiving antennas, which simultaneously transmit integrated signals to multiple targets and multiple communication users. Each antenna transmits integrated signals in the form of pulses, and each pulse contains multiple OFDM symbols. This transmission mode improves the communication data rate under the same bandwidth. The model of the proposed integrated platform is shown in Figure 1.  
The transmitted signals of the different antennas in Figure 1 are separated in the frequency domain and only occupy the pre-set continuous frequency spectrum of the whole system's working frequency band. Therefore, the waveforms transmitted by each antenna are orthogonal, which meets the requirements of the MIMO radar for the orthogonality of transmitted waveforms. It is worth mentioning that in order to obtain the subcarrier complex valued weight sequence with the nonoverlapping frequency domain on each  
Figure 1: Schematic diagram of the proposed MIMO-OFDM integrated system model.  
transmitting antenna, we design the subcarrier complex weight sequence by interleaving the structure [39; 40].  
Next, we apply IM technology to the above integrated MIMO-OFDM system and explore the integrated MIMO-OFDM-IM signal design method. At the transmitter side, the integrated signal design scheme is divided into two stages: the first is bit and subcarrier allocation and the second is the integrated signal design, which is shown in Figure 2.
## 2 Data and Methods  
### Experiment Site and Instruments  
The data used in this study are from the Integrated LAND surface-Atmosphere Boundary Layer observation experiment (LANDABL-Hebei) conducted in an onshore wind farm located in the Northwest of Zhangjiakou City, Hebei Province, China (Figure 1).  
The study period is from 4 April to 30 April 2020, when data from all the instruments are relatively complete. The study area is at a height of 1400 m to 1600 m above sea level (ASL), mainly consisting of cultivated land and agriculture. The annual average wind speed is 7.60 m/s, with prevailing relatively higher northwesterly winds in the spring (March-May) and lower southerly winds in the summer (July-September) [23].  
The wind farm has 217 wind turbines with rated powers of 1.50-2.0 MW and blade diameters of 82-105 m. The hub height is 65-80 m, and the cut-in and cut-out wind speeds are 3 m/s and 20-25 m/s, respectively. During the experiment, a 10 m meteorological tower was installed at the upwind side of the wind farm (S1) (Figure 1). The tower was equipped with two levels of integrated Automatic Weather Stations (MetPak II) (2 m and 10 m), one level of eddy covariance system (Campbell CSAT3 + Li-COR Li7500) (5 m), one level of four-component radiometers (Kipp & Zonen CNR1) (1.5 m), and precipitation observation (1.5 m).  
There are also two scanning Doppler Lidars (produced by French company Leosphere) installed at S1 (Windcube 200 s) and S3 (Windcube 100 s), respectively. The Lidar emits laser pulses at a wavelength of 1.54 \(\upmu\)m and has a measurement range of 50-3000 m. Horizontal resolution can be set between 25-100 m, and radial wind speed can be measured in the range of 0-30 m/s, with an accuracy of 0.5 m/s. There is a 70 m wind tower at S3, which has five levels of wind speed (10 m, 30 m, 50 m, 60 m, and 70 m), two levels of wind direction (10 m and 70 m), and two levels of temperature (8 m and 10 m) observations. The sampling frequency for wind direction and wind speed is 1 Hz, with an accuracy of 1\({}^{\circ}\) and 0.1 m/s, respectively. During the experiment, the two scanning Lidars used the same scanning strategy. Every half hour, they performed VAD scanning with 24 azimuths at elevation angles of 10\({}^{\circ}\) and 30\({}^{\circ}\), Doppler beam-swinging (DBS) scanning, range-height-indicator (RHI) scanning, plan-position-indicator (PPI) scanning at six different elevation angles, and Fixed Zenith Angle (Fixed) scan for about 15 min in sequence were carried out to acquire radial wind speed with a spatial resolution of 25 m. Due to the effects of clouds, aerosols, and precipitation, the maximum detection range of the Lidars is 1000-2000 m. In this study, we only used radial wind speed from the VAD and DBS scanning, as well as wind direction, wind speed, and radiation data from the meteorological tower at S1 and the 70 m wind tower at S3. Due to the malfunction of the measurement equipment at 30 m, 50 m, and 60 m during the experiment, only data from 10 m and 70 m on the wind tower are used in this study.  
Figure 1: Location of observation towers: S1, S3; 70 m wind tower; and wind turbines: dots.