esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Models
### Boundary Condition  
In this study, the following fluid pressure boundary condition is applied to solve the average Reynolds equation:  
\[\left\{\begin{array}{l}p_{h}(\theta,0)=p_{h}(\theta,L)=0\\ p_{h}(0,z)=p_{h}(2\pi,z)\\ p_{h}(\theta_{0})=0,\partial p_{h}(\theta_{0})/\partial\theta=0\end{array}\right. \tag{21}\]  
where \(\theta_{0}\) is the interface between the pressured region and the cavitation region.  
For simulating the elastic deformation, the outer surface of the bearing bush, which connects with the bearing house shown in Figure 1, is assumed to be fixed, as shown in Figure 5a, while all of the other boundaries in the calculation of elastic deformation are considered as elastically free. The thermal boundary conditions for the JWR system, shown in Figure 5b, can be classified into two categories.  
1. Internal boundary conditions: B.C.1 and B.C.2 represent the bearing-water and water-journal interfaces; which can be evaluated by the following Equation (22). \[\left\{\begin{array}{l}T_{W}=T_{B},k_{rL}\frac{\partial T_{W}}{\partial r}=k _{rB}\frac{\partial T_{B}}{\partial r}\ (\text{BC1})\\ k_{rI}\frac{\partial T_{I}}{\partial r}=\frac{1}{2\pi}k_{rL}\int_{0}^{2\pi} \frac{\partial T_{W}}{\partial r}\ (\text{BC2})\end{array}\right.\] (22) It should be mentioned that the journal is treated as isothermal along the circumferential direction.
2. External boundary conditions: B.C.3 through B.C.7 are the external boundaries for the system and they must be specified. In the present thermal model, the convective boundary is applied to describe the thermal behavior at B.C.3-B.C.7, which is given by: \[k\frac{\partial T}{\partial n}=-h_{h}(T_{c}-T_{\infty})\quad(r,\theta,z)\subset \Gamma_{3}\] (23) where \(T_{c}\) and \(T_{\infty}\) represent the water inlet and ambient temperatures, respectively, and \(h_{h}\) denotes the convective heat transfer coefficient.  
Figure 5: Boundary conditions for (**a**) elastic deformation and (**b**) the thermal analysis.
# Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer
## 4 MOGWO Exploration for Multi-Robot System
### Mathematical Formulation of MOOPs in the Multi-Robot Exploration  
The process of searching uncertainties by a team of robots can be considered a multi-tasking system. Each robot receives the sensor reading data and upgrades the probabilities of the grid occupancy in the map. One robot should have the same task as another robot in the multi-robot system, wherein the task can be any of the following: scanning the environment using sensors, avoiding obstacles and collisions with other robots, seeking to explore new terrain, and increasing the accuracy of the map. It means that together, each single robot should provide good implementation as a multi-robot system satisfying the multi-objective functions for obtaining the best solutions.  
In this paper, we formulated the objective functions of the exploration as follows:  
\[\text{Maximize}:\ f_{1}\ \to number\ of\ explored\ cells, \tag{6}\]  
\[\text{Minimize}:\ f_{2}\ \to probability\ values\ P\big{(}occ_{x,y}\big{)}. \tag{7}\]  
Subject to:  
\[i\geq(total\ number\ of\ cells\ -\ total\ number\ of\ obstacle\ cells)\big{/}\ number\ of\ robots, \tag{8}\]  
\[wp_{w}\ \geq\ number\ of\ robots^{3}, \tag{9}\]  
\[wp_{w}\ \leq\ total\ number\ of\ cells\ -\ total\ number\ of\ obstacle\ cells, \tag{10}\]  
where,  
\[i-number\ of\ iterations,\]  
\[wp_{w}-number\ of\ waypoints.\]  
The first objective function in Equation (6) tries to maximize the search space by visiting various numbers of cells in the map. In a good scenario, robots should avoid explored cells. The waypoints in MOGWO exploration allow saving the direction to the unexplored part of the map. However, there are some constraints for a successful search. For example, the number of waypoints should not be too small and big. In the case when it is small, robots will stay in one point, because they do not have the tasks to drive next waypoints. If it is bigger than the total number of cells in a map, then a robot will drive around one place longer than it is needed.  
After the map is explored, the second objective function tries to improve the map accuracy by reducing the probability values of the grid cells. It means once a sensor beam touches a grid cell, the cell is marked as explored. However, the signal strength projected on the cell is not identical. In the robot position, the probability value has the lowest value. In frontier cells, the values are higher according to the power of a signal.  
In the subsection below, the MOGWO exploration algorithm is described extensively.
## 4 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{Klimometers/Area} \\
**Compound** & **Urban Area km** & **Laguna Salada** & **Mountains** & **Mountains** & **Total (Ton)** \\  & **0-18** & **km 18-42** & **Area km 42-64** & **Totals (g)** & **Total (Ton)** \\ \hline HC & 535,569.18 & 620,600.50 & 315,859.84 & 1,472,029.52 & 1.47 \\ CO & 3,901,463.07 & 4,336,926.05 & 1,965,043.53 & 10,203,432.64 & 10.20 \\ NOx & 139,277.39 & 1,855,356.39 & 1,243,753.03 & 4,916,865.32 & 4.49 \\ PM & 27,720.09 & 35,998.32 & 322,029.99 & 959,394.0 & 0.10 \\ CO\({}_{2}\) & 95,241,818.04 & 124,295,181.33 & 86,868,295.74 & 306,405,295.11 & 306.41 \\ SO\({}_{2}\) & 14,018.98 & 19,191.13 & 14,578.02 & 47,788.12 & 0.05 \\ Pb & 18,765,391.86 & 20,958,666.62 & 10,052,981.52 & 49,777,040.00 & 49.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: pollutant emissions generated by the operation in the case study. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **km from 0–18** & **km from 18–42** & **km from 42–64** \\ \hline CO\({}_{2}\) & 95,241,818.04 & 124,295,181.33 & 86,868,295.74 \\ Road km & 36 & 48 & 44 \\ Emissions per km & 2,645,606.06 & 2,589,482.94 & 1,974,279.45 \\ AADT & 12,665 & 11,135 & 7,960 \\ Emissions per vehicle & 208.89 & 232.55 & 248.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: CO\({}_{2}\) emissions analysis. section concentrates greater slopes and degrees of curvature in its geometry, in addition to the greatest concentration of truck vehicles. The results described above allow us to understand the behavior of the different variables and their effect on the reduction or increase in emissions. However, a sensitivity analysis of road and traffic conditions is proposed for subsequent studies. On the other hand, a limitation of this study is that k1 factor calibration that is predefined is used, because this calibration involves field studies with a direct measurement of the various characteristics of the vehicles since this factor is closely linked to the IFC for future research desirable to perform these measurements. Another limitation is that it analyzes vehicle emissions entirely during their operation, that is, while being in constant circulation. However, other circumstances can cause pollutant emissions on a road, such as toll booths, vehicle inspections, accidents, or road maintenance work, which are not considered in this analysis. These aspects are proposed as future work, promoting comprehensive emissions models. In a general sense, this research provides an initial approach for quantifying pollutant emissions specifically along road sections in Baja California. However, this initial endeavor should be complemented by solutions or mitigation plans aimed at reducing the number of emissions produced on roads.
## 1 Introduction
Thus, developing an interpolator is a precursor to collision modeling when using predictors like friction that vary over short distances. To bridge the identified research gaps, this study focuses on answering several key questions: Is friction a reliable predictor for winter collision occurrence? Can road friction be accurately interpreted by ML algorithms? How does one determine the optimal road segment length for data aggregation? And what are the expected savings for a collision likelihood model? These questions lay the groundwork for the primary objective of this study, which is to develop a framework that generates spatially comprehensive collision likelihood readings. In particular, we aim to create a winter collision likelihood model and use it to quantify its potential benefits in a connected vehicle (CV) environment. In terms of the research contributions made by this study, they are as follows:  
* This study provides a novel collision likelihood modeling framework that uses interpolators to determine the optimal aggregation length. * This study quantifies the expected savings of the proposed framework in a connected vehicle and intelligent transportation system environment. * This study evaluates the performance of machine learning (ML) models in the interpolation of friction coefficients. * This study compares interpolation performance between ML models, geostatistical methods, and hybrid models that combine ML with geostatistical methods. * This study validates the relationship between friction coefficients and winter collision occurrences through modeling. * This study evaluates the prediction accuracy of a friction-based binary collision likelihood model. Upon completion, this study will have assessed various interpolation methods, validated the friction-collision connection, and presented a collision likelihood model. These concerted efforts are aimed at enhancing winter collision management through improved model accuracy while offering high interpretability for maintenance personnel to understand the logic behind the predictions made. This paper is structured as follows: Methodology, Results and Discussions, and Conclusions. In the Section 2, the techniques used in the proposed framework are explained and an overview of the dataset used is provided. The Section 3 analyzes the performance of the friction interpolator and collision model. Lastly, in the Section 4, the main findings, limitations of the study, and suggestions for future research are summarized.
# Circulating Current Reduction in MMC-HVDC System Using Average Model
## 7 Conclusions  
A comprehensive analysis and modelling of the MMC-HVDC system using a non-linear average model based on the direct modulation scheme is carried out in this work. A vector current control strategy is implemented for all the discussed modulation methods. The direct modulation strategy does not control the circulating current in a MMC.  
A new method is implemented for the reduction of circulating currents using a direct modulation approach. This strategy is based on the fact that the addition of even order harmonics leads to a decrease in arm voltage oscillations by restricting the flow of negative currents, while having smooth DC current components in each arm during part of a period without affecting input and output current waveforms. Less capacitor energy variations are obtained with the proposed method compared to the direct modulation method. The sum of these even harmonics does not contribute to the DC link current of the HVDC system. The analytical and simulation results show the reduction in circulating current amplitude and RMS values of the arm current by the proposed direct modulation method. The detailed analysis of the proposed method on capacitor voltage ripple reduction on MMCs using a detailed model will be addressed in future publications.  
Conceptualization and analytical wave forms (A.V.d.B.); Methodology, software implementation and writing-original draft (K.H.); review, editing and formal analysis (S.A.K.); supervision (Q.U.H.).  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline Apparent Power (\(S\)) & 1000 MVA \\ \hline Active power (\(P\)) & 960 MW \\ \hline Grid side AC voltage & 420 kV \\ \hline Converter side AC voltage & 380 kV \\ \hline DC side voltage & \(\pm\)320 kV \\ \hline DC side current & 1.5 kA \\ \hline Transformer connection & Y/\(\Delta\) \\ \hline Arm capacitor & 28 μF \\ \hline Arm inductance (continuous) & 76 mH \\ \hline Transformer inductance & 15 mH \\ \hline Arm resistance & 0.8 \(\Omega\) \\ \hline DC Line (two conductors) & 400 km \\ \hline Ac side Power Factor (\(Cos\varphi_{L}\)) & 0.96 \\ \hline Angular frequency & 341 d/s \\ \hline \end{tabular}
\end{table}
Table 2: HVDC System Parameters.
# Can Biochar Improve the Sustainability of Animal Production?
## 6 Land Application
### Land Application of Biochar Enhanced Compost  
Current reviews by [PERSON] et al. [125] and [PERSON] et al. [60] provide in-depth details on biochar-amended compost for land application to improve soil quality. [PERSON] et al. [126] investigated the impact of biochar-enhanced poultry manure compost on soil CO\({}_{2}\) and N\({}_{2}\)O emissions. Rice hull biochar was incorporated into the compost at 1 and 5% addition rates for 120 days in lab-scale modules with loamy soils. Compared to regular manure compost, the biochar manure compost reduced the cumulative soil N\({}_{2}\)O emissions by 27%.  
Although possible mechanisms for this include biochar toxicity, increased aeration, and NO\({}_{3}-\) immobilization (suppressing bacterial denitrification), the authors identified the challenge to generalize biochar mitigation mechanisms due to the large variation in biochar characteristics. Biochar enhanced manure compost also reduced the cumulative CO\({}_{2}\) emissions by 35% compared to regular manure compost. This relationship was attributed to higher fungi-to-bacteria ratio, which has previously been associated with lower CO\({}_{2}\) respiration and greater soil carbon storage.  
[PERSON] et al. [127] compared the use of wood biochar alone (10 t ha\({}^{-1}\)) as well as incorporated into chicken-manure compost compared to commercial fertilizer on soil quality, crop yield, and GHG emissions from tropical agricultural soils. Compared to fertilizer alone, the biochar amendments increased the maize yield and total biomass by 29% for biochar alone and 12% for co-composted biochar. Additionally, biochar and biochar amendment compost improved the soil organic C, total N, available P, and CEC by 43-73%, 14-29%, 59-117%, and 20-41%, respectively. Interestingly, CO\({}_{2}\) and N\({}_{2}\)O emissions were higher with biochar amendments than with traditional fertilizer application. The author does not give a reason for this trend but does allude that soil type could play a factor in the emission results.
## 4 Discussion
[28] compared six survival prediction models, including Randomized Survival Forest and XGBoost Survival Embeddings, and found that DeepSurv was the most accurate in predicting the prognosis and survival time of patients with ampullary adenocarcinomaof the jugular abdominal region. The findings of these studies are consistent with the findings of this paper. In conclusion, DeepSurv has good predictive performance. In addition, DeepSurv can automatically extract features and process complex data, which is superior to Cox-PH. Therefore, this study presents a web-based prediction platform ([[http://47.120.75.127:5000/](http://47.120.75.127:5000/)]([http://47.120.75.127:5000/](http://47.120.75.127:5000/))). (The website was built and accessed on 8 April 2024 successfully.) After the clinical staff fills in the corresponding data of the patient in the webpage, the platform will display the predicted survival rate of the patient in the next 1 year, 3 years and 5 years, thus realizing the risk stratification of the patient. Clinical workers can also select different surgical methods, radiation methods, etc. on the webpage to calculate the survival rate of patients under different circumstances, so as to choose a more suitable treatment plan for the patients. It is expected to provide clinicians with powerful tools to help respond to patient counseling, inform treatment strategies, and optimize prognosis based on patient survival, and risk stratification. This study found that chemotherapy or radiotherapy alone could not improve the prognosis of patients. Previous research has demonstrated that a combination of radiotherapy, chemotherapy, and surgery can positively impact the outcomes of patients with PTL [25]. Further research is required to investigate more precise and effective treatment plans for various subgroups of PTL patients. There are some limitations to this study. Firstly, it is a retrospective study based on the SEER database and lacks other populations for external validation. Obtaining data from a different geographic area would have been beneficial as it would have allowed for local factors to be taken into account, such as environmental conditions and access to healthcare. Secondly, as the clinical treatment data of the patients were not included in the SEER database, further conclusions regarding treatment could not be drawn, and we expect that more datasets will be accessible in the future, which will allow further progress in this study. In addition, primary thyroid lymphoma is a rare cancer and the small amount of data may not be sufficient to ensure a high level of generalizability of the model, which could potentially reduce the robustness of the model. This study did not use optimization algorithms to optimize machine learning models. Several biologically inspired algorithms have been developed in recent years, such as the Auricular Fox optimization algorithm, which have limited integration with machine learning. If the optimization algorithm could be combined with the XGBoost model, the accuracy of prediction would be further improved.
## 3 Control Strategies
### Model Structure of CNN-BLSF
#### 3.3.2 Mathematical Model Structure of CNN-BLSF  
To better analyze the relationship between the input and output of the tremor filter, the mathematical model is shown in Figure 5, where \(d(k)\), \(n(k)\) and \(n_{p}(k)\) are a desired signal, a faint signal caused by tremors and a predicted tremor signal, respectively. An actual signal \(S_{1}(k)\) with the tremor can be expressed as  
\[S_{1}(k)=d(k)+n(k). \tag{7}\]  
The input data of the CNN-BLSF is \((S_{1}(k)\), \(n_{p}(k))\), and the output of the CNN-BLSF \(S(k)\) is  
\[S(k)=S_{1}(k)-n_{p}(k)=d(k)+n(k)-n_{p}(k)=d(k)+\Delta n. \tag{8}\]  
According to the Equation (8), when \(n(k)=n_{p}(k)\), the end item \(\Delta n=n(k)-n_{p}(k)\) is zero. We expect the tremor attenuation filter to be designed so that the predicted error \(\Delta n\) is closer to zero.
## Materials and Methods
### Experimental Methods  
Our previous study predicted the adhesion locations by comparing the differentiation of expansion force in terms of time (i.e., expansion force rate) with measurements of the tube's inner groove adhesion, because the sudden increase in the expansion force has a close relation to the occurrence of adhesion in the tube's inner grooves during the expansion [17]. In the present study, a custom expansion experimental device was made with a jig to position the tube, a 1-axis current controller (RCS-6000G, Robostar, South Korea), a 100-kgf load cell (CDFS-100, Bongshin, Gyeonggi-do, Korea), a 12-bit A/D converter (VSAD-101, Visol, Gyeonggi-do, Korea), and signal amplifiers. For the expansion experiments, an expansion ball was inserted into the tube after fastening it to the device, and then the tube was expanded (Figure 1c). The expansion force profiles were measured from the reaction force generated in the expansion ball in the direction of expansion. Then, the expansion force was calculated by the maximum value of each expansion force profile.  
The expansion force rate and its standard deviation were obtained to investigate the effect of the expansion force magnitude on the amount of adhesion. The expansion force profiles were recorded as an analog signal and converted to a digital signal (ADU, 1 ADU = 20/4096 volts) using commercial software ([PERSON], Visol, Gyeonggi-do, Korea). The 100-kgf load cell was calibrated using ADU values acquired with 25 cycles of a sinusoidal load at 10 Hz with a stress ratio of 0.1 (50-500 N). The results were then compared with load values obtained from a 200-kgf capacity load cell (BCA-200, TESTA, Busan, Korea) installed in an MTS machine (MTS 810, MTS Systems Corp., Eden Prairie, MN, USA). The following linear calibration equation was obtained: Load (N) = 1.3078 \(\times\) ADU \(-\) 5.9057 (Figure 1d).  
Next, expansion experiments were done to analyze the effect of the expansion speed, lubricant viscosity, and expansion ball shape on the expansion force. Different process variables and ball shapes were examined. In the notation of the experimental groups, \"G\" represents a grooved ball, \"S\" indicates a smooth ball, \"1\" represents expansion at 1 m/s, and \"05\" indicates 0.5 m/s. \"N\" indicates that no lubricant is used, \"A\" indicates DH173A lubricant (0.9 mPa\(\cdot\)s at 20 \({}^{\circ}\)C), and \"H\" indicates DH23H (2.9 mPa\(\cdot\)s at 20 \({}^{\circ}\)C) (Table 1). The expansion experiment results of the S1H and S05H groups were compared to investigate the effect of the expansion speed on the expansion force and adhesion; the experiments with groups S1H and S1A were done to analyze the effect of the lubricant type; and those with groups G1N and S1N were used to determine the influence of the lubricant groove (Table 1).  
Figure 1: (**a**) Tensile test grips installed with specimens; (**b**) stress–strain curves of Al3003-O and C1220-O specimens; (**c**) schematic of Al tube expansion experiments; and (**d**) calibration of load cell used in the tube expansion experiments by correlating with the load values measured through an MTS device.
## 4 Analysis and Comparison
### Comparison of Algorithms
#### 4.2.2 Synchronization Accuracy  
After detecting the data packet, it is necessary to find the starting position of the packet, so synchronization accuracy is also important. We use the synchronization error time to measure the accuracy. In the experiments, the real starting position of the packet is at the index of 101. We calculate the average value of the error time between the initial position in the experiment and the real position. The result shown in Figure 20 indicates that the synchronization error decreases with the increase in SNR. Signal energy detection has the largest error, which is close to 150 ns, and the improvement is not noticeable as SNR increases. Cross-correlation performs much better than others, and has an almost exact detection position. The synchronization errors of the other two algorithms are close and decrease from 150 ns to 50 ns as SNR increases from 0 to 10.