esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Passivation of Crystalline Silicon Wafer Using H\({}_{2}\)[PERSON]
## 2 Materials and Methods  
N-type flat c-silicon wafers (100) with 1-10 ohm\(\cdot\)cm, 200 um were used for the experiments. Before the experiments, the wafers were cleaned in a 3-step process: saw damage removal, organic cleaning, and ionic cleaning. For saw damage removal, the wafers were soaked in 45% KOH solution at 70 \({}^{\circ}\)C for 20 min. For organic and ionic cleaning, the wafers were cleaned in standard RCA-1 and RCA-2 procedures in sequence [16; 17]. After cleaning, the oxide layer induced by H\({}_{2}\)O\({}_{2}\) was removed by 1% HF for 30 s. Afterward, the silicon wafers were blow-dried quickly to prevent reoxidation.  
H\({}_{2}\)S annealing treatments were conducted in a 2-zone quartz tube furnace. The quartz tube was pumped to a pressure of 5 mTorr before being charged with H\({}_{2}\)S gas. To thoroughly evacuate the air from the quartz tube, the tube was charged with H\({}_{2}\)S gas twice before annealing treatment was begun. Then, the quartz tube was heated to the desired temperature. The annealing processing time was 20 min. When the processing time ran out, the H\({}_{2}\)S gas was pumped out of the tube, and the heater was turned off. During the cooling time, argon gas was introduced into the tube at a flow rate of 20 sccm. The silicon wafers were unloaded when the temperature dropped below 50 \({}^{\circ}\)C. For reproducibility, each condition was tested three times, and the average value was taken, as shown in Figure 1 and Table 1.  
The surface morphology of the CuI films was characterized using a scanning electron microscope (JSM-7610F, JEOL, Tokyo, Japan). The surface reactions were monitored by transmission-mode Fourier-transform infrared (FTIR) spectroscopy. The composition of the silicon wafers was analyzed using XPS (k-alpha, Thermo Fisher Scientific, Waltham, MA, USA). Lifetime measurements were done with a WCT-120 lifetime tester from Sinton instruments.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**H\({}_{2}\)S Annealing Temperature (\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**500**} & \multicolumn{1}{c}{**525**} & \multicolumn{1}{c}{**550**} & \multicolumn{1}{c}{**575**} & \multicolumn{1}{c}{**600**} & \multicolumn{1}{c}{**625**} & \multicolumn{1}{c}{**650**} & \multicolumn{1}{c}{**675**} & \multicolumn{1}{c}{**700**} \\ \hline Minority carrier lifetime & \multirow{2}{*}{13.2/} & \multirow{2}{*}{16.1/} & \multirow{2}{*}{12.2/} & \multirow{2}{*}{15.9/} & \multirow{2}{*}{20.3/} & \multirow{2}{*}{13.1/} & \multirow{2}{*}{5.9/} & \multirow{2}{*}{10.8/} & \multirow{2}{*}{17.2/} \\ gain (\(\times\)100\%) & & & & & & & & \\ /standard deviation & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of minority carrier lifetime gains vs. annealing temperature for H\({}_{2}\)S annealing (initial minority carrier lifetime: 6.97 \(\mu\)s).  
Figure 1: The plot of minority carrier lifetime gains vs. annealing temperature for H\({}_{2}\)S annealing.
## 5 Numerical Examples
### Effect of Random 2D Slices  
Next, we examine the impact of the polycrystalline microstructure on the computed mechanical fields when different 2D slices are selected from the same 3D microstructure. First, five 2D slices were randomly selected from a 3D microstructure reconstructed using DREAM.3D. Note that while the statistics of different 2D slices may not be identical to the 3D model, they are relatively similar. The grain orientations in each slice were chosen at random and the number of grains in each case may vary slightly. Figure 27a,c illustrates the microstructure and the conforming meshes generated using PolyCISAMR for three of the five slices analyzed in this section, which consist of 1.8 to 2 million elements. The maximum aspect ratio of elements in all five meshes is lower than 3.9.  
First, we present the macroscale stress-strain curve for various slices in Figure 28. All of the slices exhibit similar trends for the initial yield point and the shape of the curve. As shown in the inset of this figure, even at the saturation point, the curves are still very similar. Next, Figure 29 illustrates the axial stress contour plot at a 0.02 strain for three slices. Although the contour values at a specific point \((x,y,z)\) vary greatly between slices, the overall distribution of low and high stresses appears similar, with roughly two to four grains having a low (blue) value and another two to four having a high (red) value. Additionally, stress gradients are mostly found along grain boundaries, with only two to four grains displaying diagonal bands of color gradients on the interior.  
Figure 27: Polycrystalline microstructures and conforming meshes generated using PolyCISAMR with \(h=5.0\)\(\upmu\)m for three of the five slices analyzed in Section 5.3: (**a**) Slice 2; (**b**) Slice 4; (**c**) Slice 5.
## 3 Results
### Outcomes and Estimation
#### 3.2.1. Pretest to Test Assumptions about Negligible Carry-Over Effects  
The analysis was carried out with the Mann-Whitney U test for the sum of the results from the first and second measurements of parameters without stabilization and with stabilization. The analysis showed that higher results were obtained in group B/A than in group A/B, for finger 3 MaxROM for parameters without stabilization (Table 3). It means that the carry-over effect from one measurement to another occurred for one parameter under conditions without stabilization. For measurements in a stabilized position, the carry-over effect did not occur (Table 4).
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### From Reactive to Predictive Maintenance
The main novelty of the article is its holistic approach, which includes all railway systems in order to optimize infrastructure design through a knowledge system based on rules that incorporate expert knowledge. However, the researchers indicate an obvious disadvantage: the tool depends on the information received from the railway companies, which are usually not very collaborative among them. It is expected that this problem will be tackled in part by the European initiative for railway interoperability. Key Findings  
IIoT can increase the safety and efficiency of the rail traffic by providing preventive maintenance. Economic savings can also be obtained through the simplification of processes and making better decisions by using analytics based on sensor fusion of the data collected from trains and other infrastructures. Information concerning the categorization of faults can be analyzed across multiple assets, even multiple operators, to spot trends and identify areas for preventive maintenance. Additionally, data analytics can speed up root cause analyses, reducing labor time. It is also required to increase the level of automation on maintenance procedures and other routine tasks, like ballast replacement, tamping, and track relaying. For example, remote monitoring helps to reduce maintenance time in the train depot. Another example can be train windshield water tanks equipped with a level sensor. Thus, a technician is able to access this information via a web application on a tablet to see whether the water needs topping up. The amount of data that has to be collected requires high-capacity wireless train-to-ground communications. The following are a list of improvements that can be achieved:  
* Increased up-time through a significant reduction of unplanned downtime. * Extension and flexibility of maintenance intervals because the risk is understood. * Improved utilization of assets (e.g., more mileage with fewer cars). * Enhanced planning, with streamlined Supply Chain Management (SCM). * Maintenance can be performed at the least costly location. IIoT will have an important role in applications for dynamic maintenance as a provider of additional sources of data collected by sensors. In this way, in a Computer Integrated Manufacturing (CIM) context, an Enterprise Resource Planning (ERP) will act as an ad-hoc software extension that will manage the collected data. * Uptime guarantees can be provided. * Increased service contract capture rate, recurring revenues, and higher percentage of the total service revenue.
## 4 Discussion
### Limitations and Implications of This Study  
From the perspective of urbanized areas, this study examines the urban expansion of the Lanxi urban cluster and its impact on the environment. However, owing to the wide-ranging nature of urban expansion and ecological security concerns, coupled with the continual evolution of landscape types over time and the frequent occurrence of human activities, several aspects warrant further improvement and exploration. First, the raster imagery utilized in this study has a uniform sampling resolution of 250 m, limiting its ability to detect urban expansion effectively on a larger scale; consequently, in future studies, higher-resolution imagery could be employed to analyze the Lanxi urban cluster more comprehensively. Moreover, the vegetation examined in this study is confined to the area of urban expansion, without considering the vegetation surrounding the built-up areas or at greater distances. To delve deeper into vegetation indirectly affected by urban expansion, future research could incorporate buffer zone analysis or gradient analysis within the study area.  
Currently, China's arid regions are experiencing rapid urbanization, which will continue in the future. [PERSON] [82] et al. found that compared with the urban land area of 21,000 km\({}^{2}\) in 2015, by 2050, the urban land in the drylands of northern China will further expand to 31,000 km\({}^{2}\). However, the ecological environment of drylands is fragile and vulnerable [83]. Such rapid and large-scale urban expansion has led to a series of ecological issues, such as the degradation of natural habitats and the loss of biodiversity, having significant adverse consequences on the well-being and sustainable development of the region [81]. The extensive urban expansion in drylands in the future will also have a severe impact on regional vegetation, threatening the sustainable development of cities in drylands.  
Therefore, we suggest considering the loss of cropland and grassland caused by urban expansion in drylands to mitigate the negative impacts on vegetation. On the one hand, improving urban land use efficiency and developing high-density cities is imperative, thus encouraging smart urban growth and reducing urban sprawl [62]. On the other hand, rational urban development planning should be adopted to avoid regions with concentrated biodiversity and reduce the encroachment on cropland and grassland [81]. In addition, it is also necessary to expand urban green spaces through the construction of parks, forests, green roofs, lawns, and community gardens. Such measures can increase the positive impacts on vegetation and further offset the negative impacts [17].
# A Fast Method for Calculation of Marine Gravity Anomaly
## 2 Method
### The Proposed Fast Method
Figure 1: The transformation of the data dimension. Here, implementing the fast method includes the following steps:  
1. Initialize MATLAB. 2. Input the gridded data of global geoid height undulation and the corresponding parameters _dia_ (the grid interval along the latitude direction) and _dlo_ (the grid interval along the longitude direction). Transform the gridded data into the vector _global_N_, and set the corresponding vectors _global_la_ (the latitude coordinate vector of global geoid height undulation) and _global_lo_ (the longitude coordinate vector of global geoid height undulation). 3. Input the gridded data of geoid height undulation of the study area. Extract the data in the sea from the gridded data, transform the data in the sea into the vector _study_N_, and compute the number of elements in the vector _study_N_ (_study_number_). Set the corresponding vectors _study_la_ (the latitude coordinate vector of geoid height undulation of the study area) and _study_lo_ (the longitude coordinate vector of geoid height undulation of the study area). Compute the index vector _pos_, which represents the location (index) of the elements of the vector _study_N_ in the vector _global_N_. 4. Set the _average_G_ (the average gravity of the Earth) and \(R\) (the average radius of the Earth). Then, use the code as shown in Figure 2 to compute the vector _study_Grav_ (the marine gravity anomaly of the study area). 5. Transform the vector _study_Grav_ into gridded data. 6. Output the results.
# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
###### Abstract  
In order to improve the resolution and accuracy of seismic inversion, this study constructs a multi-scale super-asymmetric network (Cycle-JNet). In this model, wavelet analysis is used to capture the multi-scale data characteristics of well-seismic data, thereby improving the machine's ability to learn details. Using the UNet neural network from Convolutional Neural Network (CNN), we modified the network structure by adding several convolution kernel layers at the output end to expand generated data, solving the problem of mismatched resolutions in well-seismic data, thus improving the resolution of seismic inversion and achieving the purpose of accurately identifying thin sandstone layers. Meanwhile, a cycle structure of Recurrent Neural Network (RNN) was designed for the secondary learning of the seismic data generated by JNet. By comparing the data transformed through inverse wavelet transform with the original data again, the accuracy of machine learning can be improved. After optimization, the Cycle-JNet model significantly outperforms traditional seismic inversion methods in terms of resolution and accuracy. This indicates that this method can provide more precise inversion results in more complex data environments, providing stronger support for seismic analysis.  
seismic; inversion; wavelet transform; deep learning; Cycle-JNet +
Footnote †: journal: Applied sciences
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 2 Structure and Methods  
### Tracking Loop Structure  
Figure 1 shows the principles of the scalar loop and the vector loop, respectively. In Figure 1a, signal processing and information processing are two separate parts, and each channel processes its own signal. In Figure 1b, the current state of the receiver (receiver reference oscillation frequency drift, user dynamics) can be utilized by every channel. The demodulation results, I and Q values, of each tracking channel are directly used in positioning with the KF. Then, the numerically controlled oscillator (NCO) of each channel  
is controlled by the feedback of positioning results to achieve the joint tracking of multiple channels. According to this information fusion, different channels can complement each other, and the tracking performance can be improved.
## 4 Results and Discussion
### Cases When Our Approach Does Not Work  
It would be naive to assume that this method would work in all the cases. We found that when the object's surface has many superficial details, which mainly look like a texture, our method may generate the transformation corresponding to substandard results; see Figure 5, top and bottom rows. The issue, likely, is in a large number of components at the surface of the object. The number of degrees \(L_{\max}=10\) used for reconstruction is probably not enough to catch the correct alignment of a large number of similar components. In support of this claim, we can refer to Figure 5, a bottom left cell with a stress ball, where the alignment is acceptable, likely due to the not-so-big number of repeated components.
# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 3 Materials and Methods
### Implementation of the Proposed C-MWCAR  
#### 3.2.1. Workflow of C-MWCAR  
As shown in Figure 1, C-MWCAR mainly contains three components, i.e., DOCMA, BCSA, and MWCC. DOCMA extracts the complete set of CARs from the discretized features. With the optimized connection strategy, optimized pruning strategy, and pre-pruning strategy, the speed of CAR extraction is promoted significantly. BCSA then divides the CAR set into several branches, from each of which the most representative CARs are selected. Afterwards, BCSA further select a more concise set of CARs from the above-mentioned representative CARs by simultaneously measuring the coverage and redundancy of the CARs. When classifying an unknown instance, the features of the unknown instance are extracted and then formalized in advance by using the method introduced in Section 3.1, then the MWCC selects the most similar CARs compared with the formalized unknown instance from the concise set of CARs for each class (in this step, the similarity of each of the selected CARs to the unknown instance is computed), and further computes the weighted importance of each of the selected CARs. Finally, the class label of the unknown instance is determined by simultaneously considering the similarities and the weighted importance of the selected similar CARs. The details of the implementation of the proposed DOCMA, BCSA, and MWCC are described in the following sections.
## 4 Results and Discussion
### Instructional Technology Experts' Perceptions of the Proposed Platform
In Section 5, an open-ended question on the opinions of instructional technology experts regarding the proposed platform ETBP was answered by 8 out of 14 respondents (57.1%). The open-ended question was analyzed using the qualitative data coding (inductive coding) approach. This analysis aims to identify the instructional technology experts' favorable and unfavorable opinions of the ETBP. They additionally suggested features for its improvement. The analysis experts' responses coding included three categories (entirely positive comments towards the ETBP, positive comments with suggestions for improving the ETBP, and negative comments towards the ETBP). The analysis shows that three experts (21.4%) from the total sample commented positively on the proposed ETBP, including \"great and successful effort\". In addition, three experts (21.4%) gave both positive comments and suggestions for improving the proposed ETBP: One of the experts said \"The platform is easy to use\" and suggested that \"a hyperlink must be made to the title Educational Talking Books Platform so that when clicking on it, it is transferred to the main interface\", another expert stated \"The platform supports a category for which there are not many assistive technologies\" and suggested to \"add an Excel file insert for student data and names, the expected time to record the definition for the chapter or introduction, Linking the design data for the scenario and design to add data and not repeat the entry, in the report, add a performance analysis. The chapter or page has been completed to follow up on performance. The chapter or page has been completed to monitor performance, the visual book lacks interaction with the content and its parts, and the control panel is called \"return to the main menu\", and another expert stated \"The user interface is easy and familiar, an innovative design for designing talking books, this platform is concerned with an important group, namely the visually impaired\" and suggested that it \"needs a voice speaker, in addition to adding sub-chapters to the main chapter\". Lastly, two experts (14.3%) negatively commented on some of the tools of the proposed ETBP, especially the quizzes and users' tools. One of the experts said \"Questions must be diverse, and choices need more clarity, instructions need more clarity--To some extent,the platform does not prevent design errors due to ambiguity\", and another expert stated \"Modifying instructors' accounts so that each instructor can control the talking books created by him only and not all books\".
## 5 Performance Analysis
### Numerical Simulation
#### 5.2.4 Anti-Narrowband Interference Performance  
GNSS signals are vulnerable to interference, resulting in performance degradation [32; 33]. A modulation scheme with better anti-interference performance could improve signal tracking performance. We compare the anti-narrowband interference performance for the example signals.  
Figure 14: NELP code tracking errors for the example signals.  
Figure 13: Probabilities of false locking on ACF side peaks for the example signals.  
Processing gain is an important index to reflect anti-interference performance for a spread spectrum signal. A larger processing gain means better anti-interference performance. The processing gain for ACE-BOC(\(\alpha\), \(\beta\), \(p\)) can be expressed as  
\[G_{\rm ACE-BOC}\approx 10\log(\beta\times\frac{f_{0}}{f_{D}})+3 \tag{28}\]  
where \(f_{D}\) is the data rate. The processing gain for ACE-FHBOC(\(\alpha_{M-1}\):1:\(\alpha_{0}\), \(\beta\), \(p\)) is  
\[G_{\rm ACE-FHBOC}\approx 10\log\left(\beta\times\frac{f_{0}}{f_{D}}\right)+10 \log\left(\frac{\alpha_{M-1}-\alpha_{0}}{\beta}+1\right)+3 \tag{29}\]  
The processing gain for ACE-FHBOC(\(\alpha_{M-1}\):1:\(\alpha_{0}\), \(\beta\), \(p\)) is \(10\log[(\alpha_{M-1}-\alpha_{0})/\beta+1]\) dB higher than that for ACE-BOC(\(\alpha\), \(\beta\), \(p\)). The processing gains for the example signals are listed in Table 5. ACE-FHBOC(24:1:1,1) has the largest processing gain, and ACE-BOC(12,1) has the smallest. The processing gain for ACE-FHBOC(24:1:6,1) is 2.8 dB larger than that for ACE-BOC(15,10).  
To verify the anti-narrowband interference performance for the example signals, we conduct a simulation. The interference signal is assumed to be a periodic chirp signal, defined as follows:  
\[I(t)=A\cos(2\pi f_{I}t\pm\pi\frac{B_{I}}{T_{I}}t^{2}+\varphi_{0}),\ -\frac{T_{I}}{2}<t<\frac{T_{I}}{2} \tag{30}\]  
where \(A\) is the amplitude, and \(f_{I}\) is the center frequency of the chirp, \(T_{I}\) is the chirp duration, \(B_{I}\) is the sweeping bandwidth. It is assumed that the \(B_{I}\) of the chirp is 1 MHz and \(T_{I}\) is 0.1 ms in the simulation.  
For an early-late spacing of 10.2 ns for ACE-BOC(12,1), 8.15 ns for ACE-BOC(15,10) and ACE-FHBOC(24:1:6,1), 9.8 ns for ACE-FHBOC(24:1:1,1), a coherent integration time of 1 ms, a one-side equivalent rectangular bandwidth of the code tracking loop of 1 Hz, \(C/N_{0}\) of 43 dB, and the chirp interference whose center frequency located at the center of the main lobes of the signal, the simulated tracking errors versus the ratio of interference power to signal power for the example signals are shown in Figure 15. The differences in the processing gains for the example signals are evident. ACEBOC(24:1:1,1) is the most resistant to interference among the example signals. ACE-FHBOC(24:1:6,1) is more resistant to interference than ACE-BOC(15,10).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **(24:1:1,1)** & **(24:1:6,1)** & **(15,10)** & **(12,1)** \\ \hline \(G\) (dB) & 59.9 & 58.9 & 56.1 & 46.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Processing gain for the example signals.