esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Attitude Control Strategy of TBM Digging with V-Shaped Propulsion System
## 6 Conclusions  
By analyzing the attitude characteristics of the V-TBM propulsion system and applying the Newton-Raphson method, a mathematical model is established to describe the relationship between the attitude of the front shield platform and the displacement of the electro-hydraulic proportional speed control valve spool in the propulsion system. Based on the joint simulation results from AMEsim and Simulink, the proposed digging attitude control strategy, utilizing a nonlinear controller, outperforms traditional PID control in  
Figure 11: Horizontal error of circular curve excavation posture.  
Figure 12: Vertical error of circular curve excavation posture.  
attitude control performance for the complex, nonlinear, and load-interfered V-TBM propulsion system. In a project experiment conducted by a company, the proposed tunneling attitude control strategy is validated using V-TBM. The results demonstrate that the average horizontal error in circular curve tunneling is less than 20 mm, and the average vertical error is less than 22 mm, successfully achieving rapid tracking and adjustment of the tunneling axis, thus meeting the requirements for curve tunneling with a small turning radius.  
H.W.: investigation, formal analysis, writing--original draft, visualization. L.X.: conceptualization, validation, formal analysis, writing--review and editing, project administration. X.Z.: methodology, software, validation, funding acquisition. B.G.: formal analysis, resources, data curation, supervision. L.C.: formal analysis, resources, data curation, supervision. All authors have read and agreed to the published version of the manuscript.  
The research is supported by Henan Province Science and Technology Research Project (242102220116).  
Not applicable.  
The detailed data are available upon request. The data are not publicly available due to privacy.  
Author [PERSON] and [PERSON] was employed by the company Henan China Railway Engineering Equipment Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiments
There is, however, an obvious advantage of excluding transitive overlaps, which is that the problem size is reduced and requires less computational resources. So, our recommendation is to exclude transitive overlaps. Comparing our results so far to Edena, we find out that we obtained fewer correct bases and more _contigs_ that are not in the genome, but the length of our _contigs_ is better. \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Problem**} \\ \hline
**Fragments** & 22,448 & 89,718 & 298,194 & 730,201 & 2,278,504 \\
**Contigs Obtained** & 82 & 114 & 163 & 224 & 512 \\
**Total Number of Bases** & 18,879 & 84,646 & 240,314 & 551,600 & 1,333,881 \\ \hline
**Contigs Found** & 74 & 102 & 149 & 204 & 481 \\
**\%** & 90.2 & 89.5 & 91.4 & 91.1 & 93.9 \\
**Bases Found** & 15,806 & 71,652 & 206,826 & 462,774 & 1,103,587 \\
**Contigs** & **\%** & 83.7 & 84.6 & 86.1 & 83.9 & 82.7 \\
**Found** & **N50** & 360 & 1527 & 3326 & 4937 & 4499 \\
**Average Length** & 213.6 & 702.5 & 1388.10 & 2268.50 & 2294.40 \\
**Minimum Length** & 54 & 58 & 54 & 62 & 53 \\
**Maximum Length** & 882 & 7261 & 10,943 & 22,745 & 24,396 \\ \hline
**Contigs not Found** & 8 & 12 & 14 & 20 & 31 \\
**\%** & 9.8 & 10.5 & 8.6 & 8.9 & 6.1 \\
**Bases not Found** & 3073 & 12,994 & 33,488 & 88,826 & 230,294 \\
**Contigs** & **\%** & 16.3 & 15.4 & 13.9 & 16.1 & 17.3 \\
**not Found** & **N50** & 539 & 2308 & 3954 & 7258 & 11,657 \\
**Average Length** & 384.1 & 1082.80 & 2392.00 & 4441.30 & 7428.80 \\
**Minimum Length** & 95 & 95 & 95 & 181 & 255 \\
**Maximum Length** & 826 & 3703 & 6809 & 9542 & 23,614 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Algorithm 1, \(F_{1}\) objective function, no transitive overlaps. \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline  & \multicolumn{5}{c}{**Problem**} \\ \hline
**Fragments** & 22,448 & 89,718 & 298,194 & 730,201 & 2,278,504 \\
**Contigs Obtained** & 334 & 1883 & 6080 & 15,618 & 48,263 \\
**Total Number of Bases** & 40,076 & 265,584 & 831,449 & 2,109,070 & 6,613,747 \\ \hline  & **Contigs Found** & 304 & 1827 & 5980 & 15,443 & 46,187 \\  & **\%** & 91 & 97 & 98.4 & 98.9 & 95.7 \\  & **Bases Found** & 35,980 & 256,641 & 815,994 & 2,081,306 & 6,208,258 \\
**Contigs** & **\%** & 89.8 & 96.6 & 98.1 & 98.7 & 93.9 \\
**Found** & **N50** & 135 & 165 & 154 & 153 & 153 \\  & **Average Length** & 118.4 & 140.5 & 136.5 & 134.8 & 134.4 \\  & **Minimum Length** & 62 & 62 & 61 & 57 & 57 \\  & **Maximum Length** & 446 & 660 & 747 & 894 & 1152 \\ \hline  & **Contigs not Found** & 30 & 56 & 100 & 175 & 2076 \\  & **\%** & 9 & 3 & 1.6 & 1.1 & 4.3 \\  & **Bases not Found** & 4096 & 8943 & 15,455 & 27,764 & 405,489 \\
**Contigs** & **\%** & 10.2 & 3.4 & 1.9 & 1.3 & 6.1 \\
**not Fund** & **N50** & 154 & 199 & 198 & 194 & 245 \\  & **Average Length** & 136.5 & 159.7 & 154.6 & 158.7 & 195.3 \\  & **Minimum Length** & 64 & 65 & 65 & 64 & 63 \\  & **Maximum Length** & 362 & 481 & 481 & 493 & 1130 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Algorithm 1, \(F_{1}\) objective function, Minimum Spanning Tree (MST).
## 3 PFR - A Probabilistic Forwarding Protocol
### The Correctness of \(Pfr\)  
Without loss of generality, we assume each square of the fictitious lattice \(G\) to have side length 1.  
In [9] the authors prove the correctness of the PFR protocol, by using a geometric analysis. We below sketch their proof.  
Consider any square \(\Sigma\) intersecting the \(ES\) line. By the occupancy argument above, there is with high probability a particle in this square. Clearly, the worst case is when the particle is located in one of the corners of \(\Sigma\) (since the two corners located most far away from the \(ES\) line have the smallest \(\phi\)-angle among all positions in \(\Sigma\)).  
By some geometric calculations, [9] finally proves that the angle \(\phi\) of this particle is \(\phi>134^{o}\). But the initial square (i.e. that containing \(E\)) always broadcasts and any intermediate intersecting square will be notified (by induction) and thus broadcast because of the argument above. Thus the sink will be reached if the whole network is operational.  
**Lemma 4** ([9]): \(\mathsf{PFR}\) _succeeds with probability 1_ in sending the information from \(E\) to \(S\) given the event \(F\).
## 3 Beams Retrofitting  
The beams DB1 and DB2 were strengthened in bending by the addition of new bottom longitudinal ribbed rebars and in shear by EB-CFRP reinforcement (U-jacketing) and were labeled as DB1-R and DB2-R beams. The retrofitting interventions consist of (Figure 4a-d): 1. the mechanical removal of the concrete cover from each beam side; 2. the cleaning of the original rebars surface and the application of anticorrosive protection; 3. the addition of 3 bottom longitudinal steel rebars with diameter \(\phi\)20 mm (SS2.2.2); 4. the concrete cover restoration by a thixotropic hydraulic anti-shrinkage mortar with synthetic fibers (mean cubic compressive strength R\({}_{\text{m}}\) = 35 MPa); 5. the construction of a concrete groove with transverse section 20 mm \(\times\) 20 mm along the entire beam length to anchor the CFRP strips; and 6. the application of epoxy mortar on beam surface to fix the CFRP strips.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Specimens** & \(\Phi\) & \(\mathbf{f_{0.2\%}}\) & \(\mathbf{f_{0.2\%}}\) & \(\mathbf{f_{su}}\) & \(\mathbf{f_{su}}\) \\  & & **(Mean)** & **(CoV)** & **(Mean)** & **(CoV)** \\ \hline
8 & 6 & 392.43 & 0.28 & 627.25 & 0.32 \\
6 & 10 & 424.85 & 0.14 & 675.93 & 0.05 \\
4 & 20 & 496.67 & 0.02 & 607.22 & 0.00 \\ \hline \hline \end{tabular} Note: mean value (Mean); Coefficient of Variation (CoV).  
\end{table}
Table 2: Beams DB1 and DB2: existing and new strengthening bars diameter (\(\phi\)), yield stress at strain of 0.2% (f\({}_{0.2\%}\)); maximum stress (f\({}_{\text{su}}\)) [mm, MPa].  
The application of the EB-CFRP reinforcement was performed on the two beam sides and on the intrados of the beam (U-jacketing). The CFRP shear reinforcement was realized by 3 U-strips for each beam end (Figure 4c). Each U-strip was built by two strips of CFRP fabric, one for beam side at 45\({}^{\circ}\) orientation, with spacing of 200 mm that crossed on the beam intrados (Figure 4d). In addition, 3 U-strips built by one single strip of CFRP fabric were applied at midspan vertically oriented, with spacing of 200 mm to restrain the new bottom longitudinal rebar (Figure 4c). The strips were realized by unidirectional CFRP fabric with weight equal to 300 g/m\({}^{2}\), tensile modulus of 240 GPa, tensile strength of 3500 MPa, thickness of 0.167 mm and width of 100 mm. Each U-strip end was anchored to the beam surface by an improved anchorage system. This anchorage system was realized for the following steps: 1. bending the CFRP fabric in the groove realized the beam web; 2. inserting a 150 mm long composite bar in the groove on the bended fabric; and 3. fixing the bar in the groove by an epoxy mortar that fills the groove (Figure 4d).
## 2 Data and Methods
### Methods
#### 2.2.2 Bagging  
Bagging is an ensemble model that outputs based on multiple decision trees [44]. It directly relies on the bootstrap sampling method [45]. Given a dataset containing m samples, a sample is randomly selected and placed into the sampling set, then returned to the original dataset, allowing it to be potentially selected again in subsequent sampling iterations. After m random sampling operations, a sampling set containing m samples is obtained. This process is repeated to obtain T sampling sets, each containing m training samples. Then, a base learner is trained based on each sampling set, and finally, the predictions of each base learner are averaged to obtain the final prediction result. By combining multiple base learners, the predictive performance of the model is improved. In this study, the scikit-learn library ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)) (accessed on 24 October 2024)) was used to construct a bagging model.  
\begin{table}
\begin{tabular}{c c} \hline
**Band** & **Wavelength Range (nm)** \\ \hline Band1-Coastal aerosol & 430.4–457.4 \\ Band2-Blue & 447.6–545.6 \\ Band3-Green & 537.5–582.5 \\ Band4-Red & 645.5–683.5 \\ Band5-Vegetation Red edge & 694.4–713.5 \\ Band6-Vegetation Red edge & 731.2–749.2 \\ Band7-Vegetation Red edge & 768.2–796.5 \\ Band8-NIR & 762.6–907.6 \\ Band8a-Vegetation Red edge & 848.3–881.3 \\ Band9\_water vapour & 932.0–958.0 \\ Band11-SWIR & 1542.2–1756.7 \\ Band12-SWIR & 2081.4–2323.4 \\ \hline \end{tabular}
\end{table}
Table 1: Spectral band information of Sentinel-2 remote sensing data used in this study.
## 2 Method
### Life Cycle Cost Calculation Method
#### 2.1.1 Model Boundary Conditions  
Boundary conditions in LCC refer to the calculation period and financial input parameters determined on a national level. In Denmark, the Ministry of Finance enforces discount rates (DR) and price developments (PD) for discounting future to present values. Guidance describing different DR types and their respective applications was published on 12 November 2018 and updated on 7 July 2021 [31]. The main difference in the letter was its lowered DR with 0.5% point in the updated 2021 guidance compared to 2018 values [32].  
The guideline enforces a stepwise decrease in the real DR for public clients, as illustrated in Figure 2 (left). The applied LCCByg software provides two additional sets of assumptions: fixed real DR and fixed nominal DR. The respective values for each DR type are illustrated in Figure 2 (left), whereas a general description of the applicable building projects is outlined below.  
* Decreasing real DR--This DR type is applied to public buildings where a 1% decrease is applied after years 36 and 75. This DR type applies fixed prices without inflation [32].
* Fixed real DR--Applied to projects concerning social housing organizations. As the name suggests, the DR and possible PD are fixed and exclude inflation.
* Fixed Nominal DR--Prices and DR are stated in current prices, including inflation. This DR type is applied to projects requiring DGNB certification.  
Optionally, a specific PD can be applied for different cost types considered in the calculation. Those can be observed in Figure 2 (right). If applied, the individual PD is used instead of the general PD (inflation). The baseline models in [18] apply PD to energy costs for building operation, underlined in Figure 2 (right), to the respective energy supply system. For example, PD for gas and electricity in a scenario with natural gas boiler and only PD for electricity in the HP scenario.  
To investigate the impact of the three DR types on the resulting NPV, they are applied to the baseline model, using the OAT approach. Additionally, the impact of applying the optional PD for DH and electricity is investigated by computing the DH baseline model with and without PD for each of the DR types.
# Bench Test and Analysis of Cleaning Parameter Optimization of 4 L-2.5 Wheat Combine Harvester
## 4 Discussion  
Our bench test and analysis of cleaning parameter optimization of a 4 L-2.5 wheat combine harvester are an extension and improvement on previous research on theoptimization of cleaning parameters of wheat combine harvesters and their influence on cleaning quality. To complete the research in this paper, a bench test under closed conditions was used, i.e., excluding the influence of complex working conditions such as weather, environmental conditions, and the driver's level of experience. Such an approach provides more realistic results than a simulation test. The cleaning parameters investigated included all five adjustable parameters that influence the cleaning quality of wheat combine harvesters. Notably, we investigated how to effectively reduce the loss rate and impurity content of wheat harvester cleaning, as well as how to effectively improve the accuracy and timeliness of cleaning parameter setting and adjustment during field operation.  
The cleaning parameters of the six groups of experiments (see Table 4) with test numbers 7, 14, 18, 22, 29, and 38 were the same. These six groups of experiments revealed average cleaning loss and impurity rates of 1.59% and 2.15%, respectively. The cleaning loss and impurity rates obtained using the optimum cleaning parameter combination were reduced by 0.12% and 0.19%, respectively, when compared to the experimental data of the optimum cleaning parameter combination.  
The apparatus studied in this paper was an air screen cleaning device, similar to those discussed in the literature [11; 12; 13; 14; 15]. The cleaning parameters studied in the literature have included shale shaker crank speed and centrifugal fan speed (fan speed) [11], cleaning screen frequency (crank speed of shale shaker), fan wind speed (throttle opening) [12], feed quantity (operation speed), throttle opening, and fan speed [13], feed quantity (operation speed), throttle opening, and fan speed [14], and fan speed and the included angle of the fish scale screen  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Serial Number** & **Cleaning Loss Rate (\%)** & **Impurity Rate (\%)** \\ \hline
1 & 1.25 & 1.89 \\
2 & 1.54 & 1.98 \\
3 & 1.61 & 2.01 \\ Average value & 1.47 & 1.96 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimum cleaning parameter combination verification experiment data.  
(opening of chaffer) [16]. Given that in each of these papers, only two or three parameters were studied, it may be stated that the optimization of cleaning parameters in this paper and the study of their impact on cleaning quality are more comprehensive.
## 5 Experimental Results
### Time Overhead Analysis and Optimization  
Since the time required for the test directly increases the production cost, it is one of the key challenges to minimize test time for the successful mass production process of NAND flash memory. To this end, we introduce two approaches to optimize time overhead in our proposed test procedure. One is eliminating redundant test steps, denoted as _Optimized-1_. In our test procedure, to distinguish the Hard and Soft defects, we performed two function check steps, which are totally identical, before and after the test steps of applying stress. Therefore, the test procedure can be optimized by performing the function check steps only once after the test steps of applying stress. The other is that only defect-prone WLs are tested considering the type of channel hole defects, denoted as _Optimized-2_. For example, the Bowing defects are more likely to occur in the WLs near the top layer, whereas the Bending defects are concentrated in the bottom WLs. If we select the upper 24 WLs for the Bowing defect screen and the bottom 24 WLs for the Bending defect screen, respectively, only 48 WLs, which is 1/4 of the total 192 WLs, will require program or read operations  
Figure 14: The effect of electrical stress on flash reliability.  
during the function check. (Note that our tested flash block has 48 layers, and each layer has different four WLs that consist of three pages). As a result, with additional optimization, test time can be improved by 72.2% on average without damaging detection accuracy.  
Table 1 shows measurement results for test time for a single flash block. Three-dimensional TLC NAND flash memory performs an erase operation at _block_ granularity, which causes erase latency tBERS to be significantly higher than program latency tPROG and read latency tR (e.g., tBERS = 3.5 ms, tPROG = 400 \(\upmu\)s, and \(\tt R=40\) \(\upmu\)s [35]). Although the test time in Table 1 seems insignificant (less than 10 sec), it can cause serious issues in advanced modern 3D TLC/QLC NAND flash memory. First, since the capacity of 3D NAND flash memory depends on the number of vertical layers in a flash chip, as the capacity of 3D NAND flash memory increases, the number of WLs tends to increase as well. For example, a flash block of modern 3D TLC NAND flash memory has more than 200 vertical layers [26] that consist of four different WLs, and the number of pages in a single block has increased by 5.3 times in 4 years [12]. Since reading and programming all pages in a block takes up most of the function check time, as the number of pages increases, the function check time also increases linearly. (Note that tERS has a fixed value of 3.5 ms, regardless of the number of pages in a block.) Second, the test time is proportional to the number of blocks in a single flash chip. For example, if there are 2000 blocks in a flash chip, the total test time exceeds 5000 s. Therefore, to overcome the time overhead of the test procedure, a more optimized technique, such as aggressive sampling for WLs or blocks, is required in the future.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 2 Data Sources and Preprocessing
### Data Sources
#### 2.1.2 Sea Ice Concentration Data from NSIDC  
The National Oceanic and Atmospheric Administration/National Snow and Ice Data Center (NOAA/NSIDC) climate data record (CDR) of passive microwave sea ice concentration (SIC) dataset from the NSIDC will be used to train our algorithm and validate the results [28]. The CDR SIC is based on two well-established algorithms for sea ice concentration estimation: the NASA Team (NT) algorithm and the NASA Bootstrap (BT) algorithm. The CDR algorithm mixes the NT and BT outputs by selecting higher SIC values for each grid cell. The NSIDC SIC data are provided in a polar stereographic projection at a grid cell size of 25 km \(\times\) 25 km for the northern and southern hemispheres separately.
# Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
## 5 Conclusions  
In this work, we introduced a stochastic feature selection approach, called SFS, to support bearing fault diagnosis and severity evaluation from vibration signals. Our method comprises the following stages: (i) multi-domain feature estimation using time, frequency, and time-frequency parameters, and (ii) supervised relevance analysis under stochastic modeling that includes feature ranking based on a relief-f algorithm and HMM-based classification. So, our approach gathers a subset of multi-domain parameters under a windowing scheme to favor HMM discrimination. We test our SFS over a public bearing fault database under different conditions. According to the obtained results, the time-frequency-based features are the most relevant for separating the type of fault and severity levels. Achieved classification results demonstrated that our proposal is competitive and even exceeded the state-of-the-art works concerning both discrimination accuracy and the number of required features. Our strategy extracts a relevant subset of features that favors further learning stages by coding complex data relationships related to hidden interactions between fault type and severity levels. Besides, the statistical parameters computed by the SFS approach represent a significant advantage since experts can interpret them in the field. As future work, the authors plan to test the introduced feature relevance analysis on different vibration-based fault diagnosis tasks, e.g., gearbox fault diagnosis and combustion engine monitoring. Furthermore, tests under non-stationary velocity conditions provide an exciting research line. Moreover, improving the feature relevance by using information theory and nonlinear mapping functions would be an appealing task.  
Conceptualization: A.M.A.-M.; Data curation: M.H.-L.; Methodology: J.A.H.-M. and J.B.B.-U.; Project administration: A.A.O.-G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Vicerrectoria de Investigaciones, Innovacion y Extension and Maestria en Ingenieria Electrica both from Universidad Tecnologica de Pereira.  
The authors declare no conflict of interest.