esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 2 Analysis of Influencing Factors and Feature Selection of TBM Performance
### Feature Selection of Influencing Factors of TBM Net Driving Speed Based on Random Forest
#### 2.2.2 Feature Selection of Influencing Factors of TBM Penetration Rate Based on Random Forest  
After the parameters of the random forest are determined, the weight of each parameter is generated on the whole sample set by using the random forest. The weight ranking table of each parameter is shown in Table 3. It can be seen from Table 3 that cutterhead thrust TF is the most important factor affecting the TBM penetration rate, and its weight proportion is 23.86%. The weight of the angle between joint surface and tunnel axis \(\alpha\) is 7.97%. However, with the specific work of chiseling a rock abrasion resistance index \(A_{\text{b}}\) and quartz content \(q\), the influence weights of these three rock mass parameters on the TBM penetration rate are relatively small, and are 3.35%, 1.11% and 0.96%, respectively. According to the weight table analysis, the total weight of the four parameters TF, \(J_{\text{v}}\), UCS and RPM is 75.99%, and they are the most important parameters affecting the penetration rate of the TBM.  
Therefore, based on the random forest algorithm, the nine influencing factors of the TBM penetration rate are selected. Finally, four parameters are selected as the input parameters of the TBM penetration rate prediction model based on machine learning,  
Figure 1: Variation trend of mean square error of RF model under various _n_estimators_ quantity (_n_estimators_ is the number of regression trees in the random forest. The red part represents the range of _n_estimators_ when mean square error is minimum).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameter** & **Weight/\%** \\ \hline Cutterhead thrust TF (kN) & 23.86 \\ Number of unit volumetric joints \(J_{\text{v}}\) (pieces/m\({}^{3}\)) & 19.71 \\ Uniaxial compressive strength UCS (MPa) & 17.89 \\ Rotational speed RPM (r/min) & 14.53 \\ Torque \(T\) (kN-M) & 10.62 \\ Angle between joint surface and tunnel axis \(\alpha\) (\({}^{\circ}\)) & 7.97 \\ Specific work of chiseling \(a\) (J/cm\({}^{3}\)) & 3.35 \\ Rock abrasion resistance index \(A_{\text{b}}\) (10\({}^{-1}\).mm\({}^{-1}\)) & 1.11 \\ Quartz content \(q\) (\%) & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: TBM penetration rate influencing factors’ weight ranking.  
including cutterhead thrust TF, number of rock volume joints \(J_{\rm v}\), uniaxial compressive strength UCS and cutterhead speed RPM.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 1 Introduction
This process requires significant expertise and experience and is long and expensive in terms of time and labor. The lack of a systematic approach to fully evaluate the whole design space might lead to a sub-optimal solution or worse an intractable solution. Such an approach can also miss innovative designs not thought of by the human design team. Evolutionary design techniques however benefit from the exponential rise in computational speed and can overcome human team limitations, including number of experts available, time available to perform a design task, and time available to meet together and work continuously. A machine-automated approach can augment a human design team by being able to review a larger candidate of designs than would otherwise be possible and thus find better designs. Our approach to the spacecraft design problem is modelled after the knapsack problem (KP). Optimization of the knapsack problem is considered an NP (non-deterministic polynomial-time) hard problem. NP is the set of decision problems in computational complexity theory that are solvable by a non-deterministic Turing machine in polynomial time. NP hard are the problems that are at least as hard as the hardest problems in NP and they are not necessarily decision problems [17]. In the knapsack problem, the goal is filling a knapsack with as many items so that the value of the items is maximized without exceeding the capacity of the knapsack. For a spacecraft, thanks to high launch costs, mass and volume are still a premium. In our spacecraft design problem, the focus is to effectively package components from a COTS inventory within a specified mass and volume constraint satisfying the standard CubeSat form factor. In our approach, the design of the spacecraft is specified by a gene that determines what components are to be packaged inside the spacecraft. This method of describing the design with genes keeps the process simple and produces results fast using desktop computers. In the following sections, we provide a brief description of the knapsack problem and the multidimensional multiple-choice knapsack problem which is the basis for our CubeSat design problem through trade space selection. Next, we present the optimization problem to design a 3U nadir-pointing, earth-observing CubeSat in low Earth orbit (LEO) followed by the models used for simulation. This is followed by the results and conclusions.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Machine Vision Methods
#### 2.4.3 Implementation on Ground Vehicles
However, imaging of every row represents over sampling for the estimation of orchard yield. Workload can be reduced by driving every ith row, where i depends on tree load variability across the orchard.
## 4 Discussion
stated that when the resilience of EVA reached 63%, the insole was softer compared with 17% that was stiffer [91]. Furthermore, the Australian Diabetic Association suggested that DM people wear soft but sufficiently resilient insole because it redistributes plantar pressure and increases contact time between the foot and insole [22]. It means that EVA materials might be excellent in reusing mechanical energy by adjusting greater resilience [92]. Aside from EVA, PU is a good insole material for pressure reduction in the forefoot [43]. This study discovered that the mechanical properties of the PU insole have almost the same PPP reduction as EVA (37%) (Figure 4B). The mechanical properties of the PU insole reduced the pressure by around 30% at 14 Shore A and about 40% in 10-16% 3D printing infill density. Cushioning and stiffness optimization of the PU insole has been approved for decreasing the PPP in the forefoot [58; 62]. Some researchers argued that PU has some advantages in its mechanical properties [93; 94]. Nonetheless, PU is more expensive than EVA [93; 95; 96]. Thus, PU could be the multi-insole in part of the plantar region combined with another insole material [41; 97]. This study has two limitations. First, our study was limited to discussing EVA and PU in pressure reduction but not in Plastazote and Poron as the second and third most commonly used insole materials. Only a few researchers examined Plastazote and Poron because they need longer walking duration to induce pressure reduction. For example, after a month of use, a single Plastazote has the pressure reduction result [98], and Poron has the pressure reduction after 50,000 steps [43]. If four insole materials are compared in the same procedure, the results may show different PPP reductions. Thus, the walking duration effect to induce pressure reduction should be conducted in further studies. Second, our study focused on the three regions of PPP reduction in walking speeds and insole materials--forefoot, midfoot, and rearfoot. Since the first and second metatarsal heads in the forefoot medial region change pronated positions, alongside the three regions in this study, DFUs may occur in other smaller regions. Due to other smaller regions being at high risk for DFUs, future work may be arranged to investigate the other foot regions such as the first toe.
## 6 Conclusions
This would require adjustments in the optimisation algorithms and prioritisation criteria, tailoring them to the specificities of onshore environments. Additionally, implementing specific simulations for different types of onshore installations would help validate and adjust the model in these contexts. Finally, integrating advanced machine learning techniques would enable the system to continuously adapt and improve using collected data, optimising maintenance schedules more precisely and efficiently. These enhancements would extend the system's applicability and increase its effectiveness and robustness across various industrial environments. **Author Contributions:** Conceptualisation, M.P.R. and R.G.G.C. ; methodology, R.G.G.C. and M.P.R. ; software, M.P.R., P.I.N.S. and Y.R.V. ; validation, P.I.N.S., M.P.R., Y.R.V. and E.T.C. ; formal analysis, R.G.G.C. and M.P.R. ; investigation, M.P.R. and Y.R.V. ; resources, R.G.G.C. and E.T.C. ; data curation, M.P.R. and Y.R.V. ; writing--original draft preparation, R.G.G.C. and M.P.R. ; writing--review and editing, R.G.G.C., M.P.R., Y.R.V. and E.T.C. ; visualisation, M.P.R. and Y.R.V. ; supervision, R.G.G.C. and E.T.C. ; project administration, M.P.R., R.G.G.C. and P.I.N.S. ; funding acquisition, P.I.N.S., R.G.G.C. and E.T.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Agencia Nacional do Petroleo, Gas Natural e Biochomusi ves--ANP (Grant Numbers: 2018/00203-2 and 2022/00/0078-9), the National Council for Scientific and Tech-nological Development (CNPq) (grant numbers: 307173/2022-4, 405734/2023-9, 442384/2023-8), Carlos Chagas Filho Foundation for Research Support of Rio de Janeiro State (FAPERJ) (Grant numbers: E-26/201.363/2021 (260426), E-26/204.408/2024 (297945)), and Coordination of Higher Education Personnel Improvement (CAPES) (Grant numbers: Finance Code 001). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are unavailable due to the privacy of the company being studied. **Acknowledgments:** We thank the Tecgraf Institute of Technical-Scientific Software Development of PUC-Rio (Tecgraf/PUC-Rio). **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the study's design, data collection, analysis, interpretation, manuscript writing, or the decision to publish the results.
## 4 Discussion
### Morphological Malformations of the Roots  
Analyses were performed on the seminal roots, and the main visible difference in their morphology was a decrease in the formation of root hairs after treatment with the higher concentration of (-) AuNP, which confirms our earlier results [31]. A similar reaction to NP was described for _Lolium multiform_, which did not develop root hairs when exposed to silver NP (AgNP) [34], and in Arabidopsis that was exposed to AgNP and titanium dioxide NP (TiO\({}_{2}\) NP) [35; 36]. Seedlings of _Fagopyrum esculentum_ that were exposed to zinc oxide NP (ZnO NP) and copper oxide NP (CuO NP) responded with a decreased number of root hair cells [37]. In NP-exposed Arabidopsis plants, the transcriptional analysis revealed the repression of the genes that are directly involved in root hair development [35]. The opposite reaction was described for wheat (_Triticum_ L.) roots that were exposed to CuO NP, in which there was an increase in the formation of the root hair cells [38]. In tomato (_Solanum lycopeneum_ L.) plants treated with cerium oxide NPs, there was also no inhibition of the formation of root hair cells [39].  
However, it should be remembered that the above-mentioned examples are only small fragments of our current knowledge. It is difficult to make generalisations about the effect of NP on plant organisms at this time. When undertaking research on this topic, one should always remember the enormous differences in this interaction depending on the factors mentioned above.  
This also indicates that further research is needed to understand how crops react to NP. It was postulated that the reduction in plant growth after NP treatment is connected with phosphate starvation. Such a reaction was documented for _Arabidopsis_ seedlings that were treated with TiO\({}_{2}\) NP and AgNP [35] and _Nicotiana tabacum_ roots that were treated with aluminum oxide NP (Al\({}_{2}\)O\({}_{3}\) NP) [40]. Studies with tobacco (_Nicotiana tabacum_) also indicated that NP might influence the availability of copper for plants [40]. The above examples show that the reduction in plant growth of NP-treated crops might result from the lack of the basic elements that are necessary for the proper functioning of the biochemical processes. In the case of crops, preventing such effects is extremely important.
## 4 Results
### Run-off Risk Assessment (FFPI)  
#### 4.3.1 Flash-Flood Predictor Selection  
It should be mentioned that the predictive ability was estimated for the years of the analysis. For 1989, the results of linear support vector machine (LSVM), in average merit (AM) values (Table 3), show that the slope had the highest predictive ability (AM = 0.87), followed by MFI\({}_{1989}\) (AM = 0.68), TWI (AM = 0.61), lithology (AM = 0.52), land use/land cover\({}_{1989}\) (AM = 0.47), profile curvature (AM = 0.41), convergence index (AM = 0.35), TPI (AM = 0.28), hydrological soil groups (AM = 0.23), and aspect (AM = 0.17). In terms of 2019, the highest predictive ability was also assigned to slope (AM = 0.91), followed by land use/land cover\({}_{2019}\) (AM = 0.73), lithology (AM = 0.59), MFI\({}_{2018}\) (AM = 0.55), TWI (AM = 0.46), TPI (AM = 0.39), convergence index (AM = 0.32), hydrological soil group (AM = 0.26), profile curvature (AM = 0.22), and aspect (AM = 0.13).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**1989\(\backslash\)2019** & **Agricultural** & **Transitional Areas** & **Build-Up Woodland** & **Equity Areas** & **Forests** & **Pastures** & **Water Bodies** & **Fruit Trees** & **Losses (ha)** \\ \hline Agricultural & - & 2.91 & 12.06 & 55.92 & 318.61 & 0 & 0 & 389.5 \\ areas & & & & & & & & \\ Transitional & 21.99 & - & 2.62 & 1554.83 & 97.87 & 0.27 & 12.43 & 1677.58 \\ woodland & & & & & & & & \\ Built-up areas & 564.13 & 0 & - & 34.39 & 3293.4 & 17.49 & 42.42 & 3909.41 \\ Forests & 53.73 & 4088.73 & 104.8 & - & 4088.73 & 23.3 & 0 & 8359.29 \\ Pastures & 906.6 & 666.66 & 220.01 & 1090.5 & - & 16.91 & 100.32 & 3000.9 \\ Water bodies & 7.45 & 16.58 & 51.37 & 48.75 & 32.91 & - & 0 & 157.06 \\ Fruit trees & 0 & 4.51 & 12.54 & 0 & 4.23 & 0 & - & 21.28 \\ Gain (ha) & 1553.9 & 4779.39 & 403.4 & 2784.39 & 7835.75 & 57.97 & 155.17 & 17,569.97 \\ \hline \end{tabular}
\end{table}
Table 2: Markov matrix for the period 1989–2019.
# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
## 2 Data and Methods
### Tropomi No\({}_{2}\)  
The TropPOMI NO\({}_{2}\) product has been updated and validated since its release [23]. In this paper, the V2.2 version product is used [24] to cross-validate with the EMI product. The fitting method for the TROPOMI NO\({}_{2}\) data follows that used in the study by [PERSON] et al. in 2011 [25] and the studies by [PERSON] et al. in 2020 [23] and 2022 [24], with a fitting error of approximately 0.5-0.6 \(\times\) 10\({}^{15}\) molec./cm\({}^{2}\). The NO\({}_{2}\)-v2.2 data showed a better performance when compared to that of the previous v1.x, especially over highly polluted areas. This improvement benefits from the improved calibration of TROPOMI version-2 level-1b (ir) radiance spectra and updated FRESCO cloud product, as well as the optimization of input parameters such as surface albedo, snow/ice information, and adjustment of the quality control scheme [24]. The ground-based validation results demonstrated the TROPOMI V2.2 NO\({}_{2}\) VCD has a 5% negative bias [24].
## 2 Materials and Methods
The filter radius \(r_{\min}\) is typically set to 2-3 times the element size to ensure numerical stability and mitigate mesh dependency. However, in engineering practice, increasing \(r_{\min}\) may be necessary to avoid generating excessively fine structural features that complicate manufacturing. To further enhance convergence and algorithmic stability, a historical sensitivity averaging scheme [11] is introduced, which integrates current and historical sensitivities to mitigate oscillations during the iterative process, as expressed in Formula (6):  
\[\alpha_{i}^{(l)}\leftarrow\left(\alpha_{i}^{(l)}+\alpha_{i}^{(l-1)}\right)/2 \tag{6}\]  
where \(l\) is the number of iterations. The design variable update scheme of the BESO method is formulated as follows in Formula (7):  
\[x_{i}^{(l+1)}= \begin{cases}0&\text{if }\alpha_{i}\leq\alpha_{\text{del}}^{th} \ x_{i}^{(l)}=1\\ 1&\text{if }\alpha_{\text{add}}^{th}\leq\alpha_{i}x_{i}^{(l)}=0\\ x_{i}^{(l)}&\text{otherwise}\end{cases}  \tag{7}\]  
where \(\alpha_{add}^{th}\) and \(\alpha_{del}^{th}\) denote the sensitivity thresholds for material addition and deletion, respectively. Solid elements are removed when their sensitivity values fall below \(\alpha_{del}^{th}\), while void elements are added if their sensitivity values exceed \(\alpha_{add}^{th}\), thereby updating the design variables. In stiffness optimization problems, it is common practice to set \(\alpha_{add}^{th}=\alpha_{del}^{th}=\alpha_{th}\), thereby requiring only a single threshold \(\alpha_{th}\)[11]. The iterative process terminates when the convergence criterion in Formula (8) is satisfied:  
\[error=\frac{\left|\sum\limits_{i=1}^{N}\left(C_{k-i+1}-C_{k-N-i+1}\right) \right|}{\sum\limits_{i=1}^{N}C_{k-i+1}}\leq\tau \tag{8}\]  
where \(C_{k}\) denotes the objective function value at the \(k^{th}\) iteration, and \(\tau\) is the convergence tolerance (typically \(\tau=0.001\)). With \(N=5\), the algorithm terminates when the relative difference between the sum of the objective function values over the last \(N\) iterations and the sum over the preceding \(N\) iterations within a continuous 10-iteration window is sufficiently small.
###### Abstract  
The complexity of object tracking models among hardware applications has become a more in-demand task to accomplish with multifunctional algorithm skills in various indeterminable environment tracking conditions. Experimenting with the virtual realistic simulator brings new dependencies and requirements, which may cause problems while experimenting with runtime processing. The goal of this paper is to present an object tracking framework that differs from the most advanced tracking models by experimenting with virtual environment simulation (Aerial Informatics and Robotics Simulation--AirSim, City Environ) using one of the Deep Reinforcement Learning Models named as Deep Q-Learning algorithms. Our proposed network examines the environment using a deep reinforcement learning model to regulate activities in the virtual simulation environment and utilizes sequential pictures from the realistic VCE (Virtual City Environ) model as inputs. Subsequently, the deep reinforcement network model was pretrained using multiple sequential training image sets and fine-tuned for adaptability during runtime tracking. The experimental results were outstanding in terms of speed and accuracy. Moreover, we were unable to identify any results that could be compared to the state-of-the-art methods that use deep network-based trackers in runtime simulation platforms, since this testing experiment was conducted on the two public datasets VisDrone2019 and OTB-100, and achieved better performance among compared conventional methods.  
object tracking; object detection; deep learning; deep reinforcement learning; deep Q-network; DQN; AirSim; virtual simulation environment; virtual drone
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 5 Numerical Simulation of VVSDSI Bearing  
### Element Type and Material Constitutive Model  
This section conducts a numerical simulation analysis on the VVSDSI bearing using ABAOUS finite element software. The disc spring material is 60 Si2 Mn with an elastic modulus \(E=2.06\times 10^{5}\) MPa and Poisson's ratio \(\mu=0.3\). During the modeling process, the disc spring is simulated using an 8-node incompatible mode hexahedral element (C3D8l). The influence of guide rods is neglected, while the upper connecting plate, middle steel plate, and lower connecting plate are all modeled as rigid materials. The finite element model of the VVSDSI bearing is shown in Figure 14.
## 3 Intelligent Integrated Navigation Q-Learning FEKF Algorithm
### Design of the \(Q\)-Learning-Based FKF for Intelligent Integrated Navigation  
The \(Q\)-learning-based FEKF navigation algorithm is designed based on the dynamics and measurement model established in Sections 2.1 and 2.2, and the updated cumulative reward criteria of the \(Q\)-learning shown in Equation (21). The \(Q\)-learning and federated EKF are deeply integrated, and the X-ray pulsar and Doppler velocity measurement sub-filter parameters are trial-and-error and fine-tuned to improve the PVSE precision of the intelligent integrated navigation for the cruise phase. The state process and observation noise error covariance matrices related to the dynamic and measurement model are both crucial filtering parameters that affect the state estimation precision and performance for  
Figure 4: Collections of states and corresponding collections for actions for the QLFEF. The shaded areas denote various combinations of the state and observation noise error covariance matrices \(\mathbf{Q}_{k}\) and \(\mathbf{R}_{k}\). The arrows represent the transitions between different states, and it means choosing different actions. the intelligent integrated FEKF navigation system. Thus, the QLFEKF algorithm is adopted to adjust the state and observation noise error covariance matrices in real-time to accurately assess the PVSE precision of the in-orbit operation probe during the cruise phase. According to the above description, the implementation process of the QLFEKF is given in Algorithm 1.