esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Under such a design, data transmission can be switched between the single-path scheme and the multi-path scheme. According to our simulation results and theoretical analysis, the proposed strategy proves that reducing energy consumption on the premise of ensuring time latency is possible. The rest of this paper is organized as follows. Section 2 summarizes the related works. Section 3 describes the system model and detailed design of AUA. The evaluation and result analysis are given in Section 4, and the conclusions are drawn in Section 5. In addition, the notation and operation descriptions of this paper are declared in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Explanation** \\ \hline \(\gamma_{\text{th}}\) & SNR threshold \\ \(\gamma_{\text{UB},i}\) & SNR between \(i\)th UE and BS \\ \(\gamma_{\text{UC},ij}\) & SNR between \(i\)th UE and \(j\)th cooperation node \\ \(\gamma_{\text{CB},j}\) & SNR between \(j\)th cooperation node and BS \\ \(d_{\text{th}}\) & Distance of two devices when SNR between them is equal to threshold \\ \(d_{\text{UB},i}\) & Distance between \(i\)th UE and BS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation and operation descriptions.
## 4 Millisecond Delay Determination
The main shock phases of the blasting vibration velocity waveforms basically overlap when the millisecond delay is 5 ms. With the increase in the differential millisecond delay, the main shock phases generated by different blast holes are gradually separated, and the superposition effect of the blasting vibration gradually weakens. Four peaks are formed without considering the peripheral blast holes. The main shock phases are completely separated when the millisecond delay is 50 ms, and the blasting vibration waveforms caused by the four blast holes can be clearly observed in the Figure 8.  
Figure 8: The blasting vibration velocity of monitoring element A under different millisecond delays. In addition, the peak blasting vibration velocity of each segment obtained by the monitoring element is different. When the millisecond delay is 5 ms, the peak blasting vibration velocity of the particle is the largest, and the blasting vibration velocity is 2.304 cm/s. When the millisecond delay is 20 ms, the peak blasting vibration velocity of the particle is the smallest, and the blasting vibration velocity is 1.483 cm/s. The millisecond delay affects the blasting vibration waveform caused by subsequent blast holes. For example, after the second blast hole is detonated, the peak blasting vibration velocity of the monitoring element is 1.095 and 1.008 cm/s, respectively, when the millisecond delay is 20 and 30 ms. It is significantly lower than the other groups of blast holes in the same segment. After the third and fourth blast holes are detonated, the peak blasting vibration velocity will decrease with the increase in the millisecond delay and finally tend to be a stable value. Therefore, we can find that the influence of the millisecond delay on the peak blasting vibration velocity is not a single positive correlation or negative correlation. But when the millisecond delay is greater than 40 ms, the millisecond delay does not affect the peak blasting vibration velocity. In summary, the blasting vibration velocity effect is the best when the millisecond delay is 20-30 ms, and it meets the requirements of the project that the blasting vibration velocity is less than 2 cm/s. Therefore, the millisecond delay will be set to 20 ms in the subsequent simulation.
## 4 Results and Discussion
### Varying \(C_{\rm mix}\) Coefficient  
Unfortunately, modifying the \(C_{\rm sep}\) parameter alters the modeled eddy-viscosity in the entire flow field, including free shear flow regions (such as wakes). In particular, decreasing \(C_{\rm sep}\) and, thus, increasing the eddy-viscosity, results in higher spreading rates and turbulence levels. To avoid a possible negative impact on free shear flows, the following optimal value of the \"mixing\" coefficient \(C_{\rm mix}\) was suggested in the original report [21]:  
\[C_{\rm mix}^{\rm opt}=0.35\ {\rm sign}(C_{\rm sep}-1)\sqrt{|C_{\rm sep}-1|}\, \tag{6}\]  
which corresponds to the default choice in Table 1. In practice, the above correlation decreases \(C_{\rm mix}\) with decreasing \(C_{\rm sep}\) to maintain the correct spreading rate.  
Herein, to empirically test the effect of varying \(C_{\rm mix}\), two additional calculations were performed, fixing \(C_{\rm sep}=1\) by prescribing either \(C_{\rm mix}=0.3\) or \(1.5\). Figure 12 shows the predicted distributions of sectional pressure and skin-friction coefficients compared to the solution corresponding to the default value that is \(C_{\rm mix}=0\) as well as to SBES. Only the wing section at \(\eta=0.40\) is reported, as the effect of varying \(C_{\rm mix}\) resulted in being practically negligible almost everywhere. The superiority of the suggested default level (6) is however confirmed when looking at the skin-friction distribution.
## 4 Test Results and Discussion
### The Effect of Soil-Caisson Kinematic Interaction  
In the framework of the substructure approach, soil-caisson dynamic interaction can be decoupled into kinematic interaction and inertial interaction. The kinematic interaction arises from the substantial stiffness of the caisson foundation, leading to incompatible deformation between the caisson foundation and the surrounding soil. On the other hand, the inertial interaction is a result of the inertial force of the foundation itself and those transmitted to the foundation by the superstructure.  
The SCDI effects, which modify the incident seismic wave field applied to the caisson foundation, result in a deviation of the caisson foundation motion from the free-field ground motion. This deviation is primarily attributed to the kinematic interaction [3; 4; 10; 29; 30], as the effect of the inertial interaction on the foundation motion is mainly concentrated around the natural frequencies of the system [29; 31].  
Compared to free-field ground motion, the impact of the kinematic interaction on the motion of the caisson foundation can be observed in two distinct aspects: it creates a difference in horizontal acceleration between the caisson foundation and the free-field ground motion, and it introduces a rotational component to the foundation's motion. The effect of the kinematic interaction on the motion of caisson foundations is further explored from two specific aspects mentioned above.
## 1 Introduction
### Related Works
For more details, refer to Table 1. From Table 1, we find that much of the existing literature imposes stringent assumptions on the performance function, for example, an analytically known performance function [2; 12; 20; 22]. In addition, some of the literature that aims to find a robust design point imposes restrictive assumptions on parameter variability [2; 12]. To our knowledge, it seems that little attention has been paid to multi-objective optimization problems aimed at identifying solution spaces based on black-box performance functions.
## 2 System Model  
### Network Model  
The network model considered in this thesis is illustrated in Figure 1. The transmitter is positioned at the upper left, represented by a tethered balloon at an altitude of 500 m. The balloon is tethered to the ground via a cable, which also supplies power. To account for wind effects, the three-dimensional coordinates of the balloon, initially set at \((0,0,500)\), are allowed to vary within a range of \([-50,50]\) m, corresponding to a cubic space of \(100\times 100\times 100\) m. The balloon moves randomly within this range at a velocity of 0 to 5 m/s to simulate typical balloon movements [14].  
The receiver is located at the lower right, representing a ship. The ship travels at a constant speed of \(V_{s}\), and due to ocean waves, it experiences vertical displacement, introducing a vertical velocity component. The wave-induced movement of the ship is modeled as a sinusoidal function to simulate realistic sea conditions. The amplitude of the wave is set to 5 meters, and the frequency is set to 0.1 Hz.
## 2 Structure and Principle  
### A DHPM-Type Vernier Motor  
A previously presented DHPM vernier motor, referred to as the predecessor model, is illustrated in Figure 1. Compared to the typical SPM-type vernier motors, a DHPM-type vernier motor has major structural features. It has one half of the magnet in the rotor and the other half of the magnet in the stator. This architecture lets the magnetic flux flow smoothly and increases the torque constant compared to the conventional SPM-type vernier motor.  
In the DHPM-type vernier motor, the rotor magneto-motive force (MMF) of the PM (10 th order) is modulated by the 12 th-order magnetic flux change in the stator. This modulation generates a secondary magnetic flux density distribution in the air gap. Simultaneously, the stator MMF of the PM (12 th order) is modulated by the 10 th-order permeance change in the rotor to produce a secondary magnetic flux density distribution. Consequently, the modulated rotor and stator are coupled because each side's modulation order is the same.  
A three-phase alternating current is applied to generate a second rotating magnetic field, resulting in the rotation of the rotor. A motor designed with a rotor-half vernier motor reduces the PM by half compared to the conventional SPM-type vernier motor. Similarly, a stator-half vernier motor includes a PM. It is inserted into the electromagnet based on the coil inside the stator slot. Table 1 lists the significant specifications of the DHPM vernier motor.  
A conventional DHPM-type vernier motor consists of a PM-type rotor and stator. A DHPM-type vernier motor has a higher torque density than the conventional SPM-type vernier motor due to the high induced voltage. A DHPM-type vernier motor meets the two conditions given in Equations (1) and (2), as follows:  
\[St-Nr=Np \tag{1}\]  
\[Ns-Sr=Np \tag{2}\]  
where  
\(St\): stator teeth;  
\(Nr\): poles of the PM of the rotor;  
\(Ns\): poles of the PM of the stator;  
\(Nr\): magnetic poles of the rotor;  
\(Np\): number of coil poles.
## 5 Conclusions and Future Work
### Limitations and Future Works  
We acknowledge several limitations in our study. First, our analysis primarily focuses on topological parameters to assess transportation network vulnerability under critical-link attacks, without considering essential travel demand modeling parameters such as route choice, link cost, travel time, origin-destination trip distribution, etc. Second, our study assesses vulnerability using worst-case critical-link attacks on a topologically transformed network but lacks a direct comparative analysis with classical methods for identifying vulnerable road network components. This omission may limit the ability to explicitly highlight the advantages of the proposed approach over traditional methods. Finally, our study focuses solely on the CMC area, which limits the generalizability of our findings. Although CMC shares many characteristics with other Asian cities, our analysis did not include direct comparisons of network topology or disruption patterns with other cities. Consequently, caution should be exercised when applying these results beyond the CMC context.  
Future research will address these limitations by incorporating additional parameters and statistical tests to enhance the model's accuracy and robustness, conducting a detailed comparative analysis to validate the method, and expanding the analysis to similar-sized cities to improve generalizability.  
Conceptualization, K.L. and T.K.; methodology, K.L. and T.K.; software, K.L.; validation, K.L. and T.K.; formal analysis, K.L.; investigation, K.L., T.K. and K.S.; data curation, K.L.; writing--original draft preparation, K.L.; writing--review and editing, K.L. and T.K.; visualization, K.L.; supervision, T.K. and K.S.; project administration, T.K. and K.S.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JSPS KAKENHI Grant Number 24K17369.  
Not applicable.  
Data Availability Statement: Details of all the data used in this research are provided in Section 2.  
The authors gratefully acknowledge the two anonymous reviewers for their valuable comments and suggestions.  
Regarding this paper and the research, the authors state that they have no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCDF & Complementary Cumulative Distribution Function; \\ CDF & Cumulative Distribution Function; \\ CMC & Colombo Municipal Council; \\ UE & User Equilibrium; \\ SC & Street Connectivity; \\ BC & Betweenness Centrality; \\ CC & Closeness Centrality; \\ EC & Eigenvector Centrality; \\ TS & Total Segment; \\ AS & Attacked Segment; \\ CMA & Colombo Metropolitan Area; \\ GDP & Gross Domestic Product; \\ JICA & Japan International Cooperation Agency; \\ DS & Division Secretariat; \\ SLSD & Survey Department of Sri Lanka; \\ AADT & Annual Average Daily Traffic; \\ CDF & Cumulative Distribution Function; \\ VIF & Variance Inflation Factor; \\ MAPE & Mean Absolute Percentage Error. \\ \end{tabular}
## 4 Conclusions  
FeNi-Pd@CN catalyst was synthesized by thermal method, and its physical and chemical structures were fully characterized. In situ generation of H\({}_{2}\)O\({}_{2}\) using formic acid and air was verified, and both \(\bullet\)OH and HO\({}_{\bullet}\bullet\) were generated during the reaction. Additionally, 92.0% of Acid Red 73 (200 mg/L) and 93.2% of tetracycline hydrochloride (10 mg/L) were removed in 150 min without any pH adjustment. Its high activity for organic removal was realized through: (i) the exposed Pd(111) facets with small size due to g-C\({}_{3}\)N\({}_{4}\) support; (ii) the large accumulation of H\({}_{2}\)O\({}_{2}\) due to Ni addition, and (iii) the high Fenton activity of Fe for ROS generation. The process only needs formic acid, catalysts, and air aeration, which is easy to realize in industrialization. It is worth studying further practical use in wastewater treatment.  
The following are available online at [[https://www.mdpi.com/article/10.339](https://www.mdpi.com/article/10.339)]([https://www.mdpi.com/article/10.339](https://www.mdpi.com/article/10.339)) 0/app112210797/s1, Figure S1: Nitrogen adsorption-desorption isotherms and pore size distribution curves, Figure S2: \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) (104) facet in the HRTEM image, Figure S3: Degradation of TC by different catalysts, Figure S4: XRD of fresh catalyst and after the fourth reused catalyst, Figure S5: The comparison of Pd 3d XPS spectra of different samples, Figure S6: O\({}_{2}\)-TPD curves of different catalysts, Table S1: Elements contents of FeNi-Pd@CN calculated from the EDS results.  
Conceptualization, [PERSON] and L.W.; methodology, L.W.; validation, J.M.; formal analysis, L.W., Q.G. and J.S.; investigation, A.S.; resources, J.M. and L.L.; data curation, Q.G. and J.S.; writing--original draft preparation, L.W.; writing--review and editing, J.M.; visualization, L.W.; supervision, J.M.; project administration, L.L.; funding acquisition, J.M. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.52000155), the Zhejiang Provincial Natural Science Foundation of China (No. LQ20E080002), the Public Benefits Projects of Ningbo (No. 202002N3055), and the Major Technological Innovation Projects of Ningbo National High-tech Zone (New Material Technology City, No.20201 CX050018).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China, Zhejiang Provincial Natural Science Foundation of China, Ningbo Science and Technology Bureau, and the Science and Technology Innovation Bureau of Ningbo National High-tech Zone (New Material Technology City) as mentioned in the Funding Part. The authors also thank Shiyanjia Lab (www.shiyanjia.com (accessed on 28 March 2021)) for their participation in catalysts characterization.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Inference of Drowsiness Using HMM  
The result is illustrated in Figure 12 (and a video as a supplementary material), in which the top panel shows how PE-ASM runs, drawn as the warping grid on the driver's face, and the head pose of the subject is indicated as yaw, pitch, and roll direction, respectively, calculated by POSIT algorithm. The second panel from the top shows the subject's eye-blinking, and the third panel the moving head pose, as their graphs in time, respectively. Notice that when his head moves in the left or right direction, the yaw value increase as indicated in arrow (1) and (4) in the figure, whereas when he nodes, the pitch value increases as indicated by the arrow (3). When he looks in the forward direction, three values approach zero as indicated by the arrow (2). The fourth panel shows the corresponding HMM states,  
Figure 11: Illustration of head pose estimation with AAM and PE-ASM using BU DB (**a,b**) and our custom DB (**c,d**), respectively. GT indicates the ground truth. The blue arrow indicates the time when each subject made his head rotation. Note that PE-ASM follows ground truth better than AAM, although it has some noise.  
_i.e._, eye-blinking and head nodding. The bottom panel depicts decision of drowsiness according to the HMM state transition diagram. Here the colors are coded as shown in Figure 6. Note that whenever the eye-blinking and the head nodding are overlapped, the red sign, _i.e._, asleep, is flashed.
# Effect of Different Ratios of Cow Manure and Chemical Fertilizers on Fruit Quality of Gala Apples
## 2 Materials and Methods
### Experimental Design and Field Management  
The long-term fertilization experiment was conducted using a randomized block design, having three replications. Six different treatments were implemented in the present study: (1) control check (CK), (2) 100% chemical fertilizer alone (100% CF), (3) 75% chemical fertilizer with 25% cow manure (75% CF + 25% CM), (4) 50% chemical fertilizer with 50% cow manure (50% CF + 50% CM), (5) 25% chemical fertilizer with 75% cow manure (25% CF + 75% CM), and (6) 100% cow manure alone (100% CM). Every five trees were divided into one block, with three trees in the middle as test trees and two trees at the edge as protection trees. Each block was replicated three times and randomly arranged within the block. Fertilizers included well-rotted cow manure and chemical fertilizer (urea(N-P-K is 46-0-0), potassium sulfate(0-0-50), diammonium phosphate(18-48-0)). N content of cow manure was 1.41% in 2017, 1.83% in 2018, 2.79% in 2019, 1.62% in 2020. According to [PERSON] et al. (2016) [26], the nitrogen dosage was 240 kg/hm\({}^{2}\) at the young tree stage (2017-2018) and 480 kg/hm\({}^{2}\) in 2019-2020.  
The cow manure was calculated based on its N content, and the specific weight of application was calculated. The cow manure was applied at once in autumn. Chemical fertilizer is applied in three periods each year, with one-third of the nitrogen, one-quarter of the phosphorus and one-fifth of the potassium applied in the spring during the slow growing period, one-third of the nitrogen, one-quarter of the phosphorus and three-fifths of the potassium applied during the fruit expansion period, and one-third of the nitrogen, one-half of the phosphorus and one-fifth of the potassium applied in the autumn after the fruit has been harvested. The fertilizer is applied in furrows, in the middle of the canopy projection. The specific amounts (pure nutrients) applied in 2017-2020 are shown in Table 1.  
The sampling of the fruits took place in the second half of August 2020. For each treatment, 45 apples from three trees (15 fruits per tree) were hand-harvested (from the middle part and the entire perimeter area of each tree) at physiological maturity, and immediately transported to the laboratory. Once in the laboratory, a selection of the 30 most homogeneous fruits (size, shape, and colour) was made and 10 of these were separated for the weight, size, colour, total soluble solids (TSS), total titratable acidity (TA) and the other 20 fruits were used for organic acids, sugars, and volatile compounds determinations.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Comparative Analysis of Different Algorithms
To further evaluate the robustness and adaptability of the proposed method under varying operating and algorithmic conditions, a set of sensitivity analyses was conducted at the end of this Section. These tests aim to assess how the model performs under different load fluctuation intensities, and variations in optimization parameters. Load Fluctuation Intensity: A \(\pm\)10% perturbation was introduced to base load profiles across all nodes. The resulting topologies remained feasible, and the overall network performance degradation was under 3%, showing the method's adaptability to short-term demand uncertainty. IEO Parameter Variation: Key parameters in the IEO algorithm, such as mutation factor and crossover rate, were varied within standard ranges. Convergence behavior remained stable, with less than 5% deviation in final objective values, confirming that the proposed algorithm is not overly sensitive to parameter tuning. This insensitivity to parameter variations indicates that the proposed method can be deployed with minimal calibration effort. It enhances the algorithm's usability across different scenarios and ensures stable performance under reasonable configuration changes. These results confirm the robustness and generalizability of the proposed framework under a variety of system- and algorithm-level uncertainties.