esa-sceva/satcom-chunk-collection huggingface dataset:
## Experimental and Kinetic Investigation on NO Reduction by Rice Husk Char and Catalytically with CO
###### Abstract  
Biomass char is accepted as an environmentally friendly energy resource, and is promising for NO removal in industrial furnaces. NO reduction with biomass char (rice husk) was investigated in a fixed-bed quartz reactor. The influence of CO concentration, reaction temperature, initial NO concentration, and char loading on the NO reduction were evaluated. The results showed that the overall NO reduction reaction was dominated by the direct reaction of NO with char without CO. Increasing CO concentration resulted in an enhanced catalytic reaction between NO and CO over the char surface and a decreased carbon consumption of char. With decreasing temperature, the influence of CO concentration on NO reduction was strengthened. Over the test temperature range, char provided a surface to catalyze the NO-CO reaction at excess CO/NO ratio. Increasing the initial NO concentration led to an increasingly significant influence on NO reduction with the enhanced CO/NO ratio. With increased char loading, the enhancement of NO reduction became unobservable for excess CO/NO ratio. Reaction orders of NO and activation energy for NO reduction by char were 0.71 and 60.21 kJ-mol\({}^{-1}\), respectively, against 0.82 and 34.60 kJ-mol\({}^{-1}\), respectively, for NO reduction by CO, catalyzed by char.  
NO; biomass char; CO; kinetics +
Footnote †: journal: Applied Physics Letters
## 3 Numerical Simulations and Discussion
### Adjustment Point
#### 3.3.1 Results
Figure 17: Statistical results of the number of AFPs of rotor unbalance for each disc in the simulation of g1.4 using #60 point as one of the required measuring points and using a 0.1 nm resolution: (**a**) results for the unbalance amplitude; (**b**) results for the rotor unbalance angle. Figure 18: Statistical results of the number of AFPs of rotor unbalance for each disc in the simulation of g1.4 using #80 point as one of the required measuring points and using a 0.1 nm resolution: (**a**) results for the unbalance amplitude; (**b**) results for the unbalance angle. 4. According to Figure 18, for the rotor unbalance amplitude of #1 disc, #2 disc, #3 disc, and #4 disc, there are 917, 929, 897, and 1001 AFPs, respectively. For the angle, there are 861, 966, 881, and 999 AFPs, respectively. This indicates that the identification results for rotor unbalance for #4 disc are best when #80 point is used as one of the required measuring points. For TODA, the same conclusion can be obtained according to Figures 15-18, although the number of AFPs obtained by TODA is obviously lesser than that of SDA. When #20 point is used as one of the required measuring points, the numbers of AFPs of rotor unbalance amplitude for #1-#4 discs are 867, 163, 29, and 12, respectively. For the rotor unbalance angle, there are 896, 117, 57, and 186 AFPs, respectively. Apparently, #1 disc has the most AFPs. When #40 point is used, the numbers of AFPs of rotor unbalance amplitude for #1-#4 discs are 587, 662, 107, and 38, respectively. For the rotor unbalance angle, there are 541, 720, 137, and 158 AFPs, respectively. Apparently, #2 disc has the most AFPs. When #60 point is used, the number of AFPs of rotor unbalance amplitude of #1-#4 discs are 408, 297, 461, and 234, respectively. For the rotor unbalance angle, there are 352, 271, 421, and 201 AFPs, respectively. Obviously, #3 disc has the most AFPs. When #80 point is used, the number of AFPs of rotor unbalance amplitude of #1-#4 discs are 78, 48, 39, and 707, respectively. For the rotor unbalance angle, there are 166, 128, 97, and 675 AFPs, respectively. Obviously, #4 disc has the most AFPs. Similarly results can be found in the third kind of calculation simulation example for g4.4, h1.4, and h4.4, according to Equations (A1)-(A12) and Figures A9-A20 in Appendix C. However, for h4.4, no result can be obtained based on SDA and TODA from 1 Hz to 20 Hz.
## 4 Proposed Method
### Overview of the Proposed Approach  
The architecture of the automatic approach to generate the CTI records based on multi-type OSTIPs (GCO) is as in Figure 3. The architecture includes article collector, article classifier, CSI extractor, and CTI records generator. Firstly, the article collector crawls articles from multi-type OSTIPs and preprocesses the collected articles. Secondly, for the articles collected by the article collector, the article classifier is trained based on features selected by an experiment to filter out non-threat-articles. Thirdly, the CSI extractor extracts all the cybersecurity intelligence details (CSIs) from threat-articles with the HBKSL  
Figure 2: A cybersecurity intelligence detail (CSI) group example.  
method. The HBKSL is designed to locate the key sentence for a CSI-candidate or a CSI-candidate group. A CSI-candidate group means CSI-candidate data in list format, which cannot be extracted by existing methods, and we defined the key sentence as the sentence that can determine whether the CSI-candidates are valid or not. Finally, the CTI records generator converts the extracted CSI details into STIX 2.0 records with a set of mapping rules. To conveniently analyze the generated STIX 2.0 records, a CTI database is constructed by mapping the STIX 2.0 records into a Neo4j database and the interconnections among STIX 2.0 are revealed by a set of correlation analyses. The details about the proposed approach are described in the rest of this section.
## 1 Introduction  
Over the past few decades, the worldwide semiconductor industry has maintained an average growth of 15% per year. This steady growth is the result of a continuous reduction in the cost per function of 25-30% per year [1, 2, 3].  
Semiconductor manufacturing involves a highly complex and lengthy wafer fabrication process, with 300-500 process steps and a large number of interrelated variables [4, 5, 6]. To obtain substantial benefits, the semiconductor industry focuses on features related to manufacturing technology that are required in order to maintain the high reliability and effectiveness of the manufacturing process, such as reducing gate length, increasing wafer size and improving yield [7, 8, 9]. However, manufacturing technology has already reached its limit. Thus, it has been recognized that factory productivity should also be improved in order to maintain growth while reducing the production cost [10, 11]. In semiconductor manufacturing, some process steps need to be performed continuously, without any interruption in the process flow, such as etching, ashing, deposition, and lithography. The sequence of continuous steps is called a \"run\" and is performed on individual manufacturing equipment. Even though a fault may occur during any step of the run, it can only be detected after the entire run has been completed by performing wafer metrology [12, 13, 14].  
As a result, data-driven fault detection has become a significant topic in the semiconductor industry and is expected to provide an efficient method for predictive maintenance that allows companies to save time and money [15, 16, 17, 18, 19, 20]. Vacuum leakage is considered to be one of the major drawbacks in semiconductor processing and its final yield. An unnecessary O2 and N2 mixture, a major component of the atmosphere, creates unanticipated process outcomes and, consequently, drops in yield [21; 22; 23].  
In this article, a new chamber vacuum leak detection method named index regression and correction is presented, utilizing common data gathered during normal chamber operation. This method was developed by analyzing a simple list of data, such as pressure, the temperature of the chamber body, and the position of the APC, to detect any changes in the sustained vacuum of the vacuum chamber system.  
The remainder of this paper is organized as follows. Section 2 introduces a review of related work in real-word leak detection methods and data-driven methods. Section 3 presents the experimental setting and data acquisition from a real semiconductor facility. Section 4 presents the vacuum leak modeling by applying the ideal gas equation. Section 5 presents proof of the Section 4 modeling with experimental results. Section 6 introduces the proposed new leak detection method named IRC. Section 7 presents the experimental results using the actual process data, comparing our proposed method with the Auto Regressive Integrated Moving Average (ARIMA) model. Lastly, Section 8 concludes the paper by listing its contributions and discussing future research directions.
# Inferring Urban Social Networks from Publicly Available Data
## Appendix B Social Contact Data
### Data Preprocessing  
In general, the matrix \(\Gamma\) may be not symmetric because \(\gamma_{ij,i}\) depends upon the habits of the individuals of group \(i\), whereas \(\gamma_{j,i}\) on the habits of group \(j\). Using graph terminology, in the bipartite graph composed of individuals of groups \(i\) and \(j\), \(\gamma_{ij,i}\) and \(\gamma_{j,i}\) are an estimate of the average degree of vertices of type \(i\) and \(j\), respectively. Even if asymmetric, the matrix should be consistent, in the sense that its entries should guarantee the _reciprocity_ of contacts. At the population level, reciprocity means that, in the bipartite graph, the total number of edges that \"exit\" group \(i\) must be equal to the total number of edges that \"enter\" group \(j\), i.e., that \(|V_{i}|\cdot\gamma_{i,j}=|V_{i}|\cdot\gamma_{j,i}\). Since survey data rarely meet this requirement, it is standard practice to introduce a reciprocity correction [52; 77] by taking the arithmetic mean \(\frac{1}{2}\big{(}\gamma_{ij,i}\cdot|V_{i}|+\gamma_{j,i}\cdot|V_{i}|\big{)}\) as an estimate of the total number of edges in the bipartite graph. When \(i=j\), instead, the total number of intra-group contacts for group \(i\) is given by \(\frac{1}{2}\big{(}\gamma_{ij,i}\cdot|V_{i}|\big{)}\) because the graph is _not_ bipartite. This leads to a group-adjacency matrix \(A=\{\alpha_{i,j}\}\) defined as follows:  
\[\alpha_{i,j}=\begin{cases}\frac{1}{2}\big{(}\gamma_{i,j}\cdot|V_{i}|+\gamma_{ j,i}\cdot|V_{j}|\big{)}&\text{if }i\
eq j\\ \frac{1}{2}\big{(}\gamma_{ij,i}\cdot|V_{i}|\big{)}&\text{if }i=j\end{cases}. \tag{1}\]  
Finally, we divide each \(\alpha_{i,j}\) by the total number of potential edges \(m_{i,j}\) (see (1) in Section 2.3) to obtain the matrix \(S=\{s_{i,j}\}\) of inter-group edge frequencies:  
\[s_{i,j}=\frac{\alpha_{i,j}}{m_{i,j}}=\begin{cases}\frac{1}{2}\Big{(}\frac{ \gamma_{ij}}{|V_{j}|}+\frac{\gamma_{ij,i}}{|V_{i}|}\Big{)}&\text{if }i\
eq j\\ \frac{\gamma_{ij}}{|V_{i}|-1}&\text{if }i=j\end{cases}.\]  
It is worth underlining that different sub-territories of the same country may have different age-group ratios, therefore requiring different corrections. The reciprocity correction, implicit in (1), therefore, is necessary, regardless of whether the matrix \(\Gamma\) had already been corrected beforehand. For instance, the SOCRATES tool implements the reciprocity correction but with national age-group statistics. If the local and national age-group statistics are identical, it is easy to verify that a double correction is useless yet harmless.
## 2 Materials and Methods
### Oil Spill Behavior and Calculation Principles
#### 2.2.2 Weathering Process
The rate at which the oil droplets return to the oil film is:  
\[\frac{\mathrm{d}\mathrm{V_{oil}}}{\mathrm{dt}}=\mathrm{D_{a}}\cdot(1-\mathrm{ D_{b}}) \tag{9}\]  
Formation Process of Water-In-Oil Emulsions  
The changes of water content in oil can be expressed by the following equilibrium equation:  
\[\frac{\mathrm{d}\mathrm{y_{w}}}{\mathrm{dt}}=\mathrm{R_{1}}-\mathrm{R_{2}} \tag{10}\]  
where \(\mathrm{R_{1}}\) and \(\mathrm{R_{2}}\) are the absorption rate and release rate of water, respectively, as calculated in the following equations:  
\[\mathrm{R_{1}}=\mathrm{K_{1}}\cdot\frac{\left(1+\mathrm{U_{w}}\right)^{2}}{ \mu_{oil}}\cdot\left(\mathrm{y_{w}^{max}-y_{w}}\right) \tag{11}\]  
\[\mathrm{R_{2}}=\mathrm{K_{2}}\cdot\frac{1}{\mathrm{As\cdot\mathrm{Wax}\cdot \mu_{oil}}}\cdot\mathrm{y_{w}} \tag{12}\]  
where \(\mathrm{y_{w}^{max}}\) is the maximum moisture content; \(\mathrm{y_{w}}\) is the actual water content; As is the bitumen content (weight ratio) in the oil; Wax is the content of paraffin in the oil (weight ratio); and \(\mathrm{K_{1}},\ \mathrm{K_{2}}\) are the absorption and release coefficients, respectively. Dissolution  
Oil has a very weak tendency to dissolve into water, and its dissolving has little influence on the material balance calculation in the oil spill dynamic simulation, so it can be ignored in most cases [10]. However, in light of the ecological and social security significance of simulating the dissolution process of oil spills and predicting oil concentrations in water, the dissolution amount is generally calculated in oil spill models. The dissolved oil in water usually peaks within 8 to 12 h of the accident, and then the amount of dissolved oil decreases exponentially. The dissolution rate Is expressed by the following equation:  
\[\frac{\mathrm{d}\mathrm{V_{ds_{i}}}}{\mathrm{dt}}=\mathrm{K_{Si}}\cdot \mathrm{C_{i}^{sat}}\cdot\mathrm{X_{mol_{i}}}\cdot\mathrm{M_{i}}\cdot\mathrm{ A_{oil}} \tag{13}\]where \(\rm C_{i}^{sat}\) is the solubility of component i; \(\rm X_{mol_{i}}\) is the mole fraction of component i; \(\rm M_{i}\) is the molar weight of component i; and \(\rm K_{Si}\) is the dissolution mass transfer coefficient, which can be estimated from the following equation:  
\[\mathrm{k}_{\mathrm{ei}}=\mathrm{k}\cdot\mathrm{A}_{\mathrm{oil}}^{0.045}\cdot \mathrm{S}_{\mathrm{ci}}^{-2/3}\cdot\mathrm{U}_{\mathrm{w}}^{0.78} \tag{5}\]0  
\[\rm e_{i}=\left\{ \begin{array}{rl}1.4&\rm alkane\\ 2.2&\rm aromatic~{}hydrocarbons\\ 1.8&\rm refined~{}oils\end{array} \right. \tag{15}\]
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 3 Results
### Spatial Frequency of Typhoon-Induced Rice Damage  
In Vietnam, Nghe An (17 times), Khanh Hoa, Phu Yen, Quang Nam, Quang Tri, and Thanh Hoa (all 13 times) had the most reports of typhoon-induced rice damage (Figure 2). At a regional level, these provinces were evenly split between the North Central Coast (Nghe An, Quang Nam, and Quang Tri) and the South-Central Coast (Khanh Hoa, Phu Yen, and Quang Nam). In the Philippines, typhoon-induced rice damage was most frequently experienced in Cagayan, Tarlac (both 16 times), and Nueva Ecija (15). The provinces of Albay, Aurora, Bulacan, Isabela, Pangasinan, Zambales, Camarines Sur, Ifugao, and  
Figure 1: (**a**) Number of tropical cyclones that came within 500 km of Vietnam from 1970 to 2018. Out of this, the number of tropical cyclones that caused rice damage was reported. (**b**) Number of tropical cyclones that came within 500 km of the Philippines from 1970 to 2018. Similarly, the number of tropical cyclones that damaged rice was shown. Data for rice damage were only available after 1979 for both countries. For the Philippines, information was generally sparse before 2006. For each landfall category, the number of typhoons with and without information on rice damage was shown for (**c**) Vietnam and (**d**) the Philippines.  
Pamapanga were affected 13 to 14 times (Figure 3). Rice fields in Cagayan Valley and Central Luzon in northern Philippines and Bicol Region in the east were predisposed to typhoon damage.
## 5 Conclusions  
In this study, the GLCM texture measures were applied to improve the supervised classification of SAR intensity images for urban areas. For this purpose, Tehran was selected as the study area because of its rapid expansion, which has resulted in land cover changes and the appearance of new urban regions. Due to the similarity of the spectral signatures of soil and roof material in built-up regions, classification from multi-spectral optical images seems very difficult. Alternatively, SAR images may be a better option because the backscattering depends on the geometrical features of the objects within the recorded area. Dual-polarized data from L-band ALOS-2 (HH, HV) and C-band Sentinel-1 (VV, VH) were employed. In addition, the texture properties were calculated by applying a  
Figure 13: A close-up of the SVM classification results from the ALOS-2 (**a**); Sentinel-1 (**b**); ALOS-2 PCT (**c**); and Sentinel-1 PCT (**d**).  
gray-level co-occurrence matrix (GLCM). Thus, eight texture features were obtained for each intensity element. Furthermore, a principal component analysis was applied to each set of texture measures, and the first three components were selected based on the greatest covariance. Then, maximum likelihood and support vector machine algorithms were used for the three datasets: optical images and the SAR intensity data without and with texture measures. The results of the supervised classification were compared with the truth data obtained by visual inspection of the Landsat 8 image.  
The supervised classification results with texture measures were found to be superior to the results without texture in two main aspects: the highest accuracy and least noise. The support vector machine for both the optical and SAR sensors produced a higher accuracy than the maximum likelihood. Moreover, the classification of ALOS-2 with the SVM methodology using a window size of 13 \(\times\) 13 obtained the highest overall accuracy. Besides, Sentinel-1 gained the best accuracy in window size 11 \(\times\) 11 with SVM classification. Although the intermediate window sizes of 13 \(\times\) 13 and 11 \(\times\) 11 worked well in this study area, the best window size could change based on the different study areas and truth data using for assessing the accuracy.  
The ALOS-2 PALSAR-2 data used in this study are owned by the Japan Aerospace Exploration Agency (JAXA) and were provided through the JAXA's ALOS-2 research program (RA4, PI No. 1503). The Pleiades images are owned by Airbus Defense and Space and licensed to Chiba University.  
This research was conducted by [PERSON] for her Doctoral studies under the supervision of [PERSON] and guidance of [PERSON]. All authors read and reviewed the article.  
The authors declare no conflict of interest.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 2 Theoretical Background  
### Overview of Spectral Kurtosis and Fast Kurtogram  
To overcome the shortcomings of the power spectral density (PSD), which is not sensitive to the statistical nature of the signals, the frequency domain kurtosis (FDK) was first introduced by [PERSON]. It can highlight the frequency harmonic that is smeared because of random variation in the periodicity [5]. Inspired by this development, [PERSON] proposed spectral kurtosis (SK) in [6]. Different from FDK, which calculates the kurtosis of a particular frequency's amplitude, SK calculates the kurtosis of the complex envelope of filtered signals [17].  
According to the Wold-Cramer decomposition theorem, a zero-mean non-stationary signal \(x(n)\) can be expressed as [7; 25]:  
\[x(n)=\int_{-l/2}^{+l/2}H(n,f)e^{j2\pi fn}dX(f) \tag{1}\]  
where \(dX(f)\) is a spectral increment and \(H(n,f)\) is the complex envelope of \(x(n)\) at frequency \(f\). The SK can be defined as the fourth-order normalized cumulant [7]:  
\[K_{x}(f)=\frac{\left\langle\left|H(n,f)\right|^{4}\right\rangle}{\left\langle \left|H(n,f)\right|^{2}\right\rangle^{2}}-2 \tag{2}\]  
where the symbol \(\left\langle\ \right\rangle\) denotes the temporal average operator. The constant \(-2\) is used here because \(H(n,f)\) is complex. Considering the presence of added noise, the SK of the non-stationary process \(x(n)\) is described by:  
\[K_{y}(f)=\frac{K_{x}(f)}{\left[1+\rho(f)\right]^{2}} \tag{3}\]  
where \(\rho(f)\) is the noise-to-signal ratio at frequency \(f\). The transients in signals increase the spectral kurtosis value. Therefore, SK possesses the ability to detect and localize the presence of transients from a signal. However, to detect a narrow-band transient buried in noise, SK depends both on frequency and frequency resolution. Although this can be performed by computing the SK of each combination of different central frequencies \(f_{c}\) and bandwidths \(\left(\Delta f\right)_{m}\), this process is time consuming. To improve the calculation efficiency, [PERSON] utilized \(1/3\) binary tree algorithms to split the frequency band; then, the SK of each level \(m\) and bandwidth \(\left(\Delta f\right)_{m}\) were calculated to construct a 3D map-fast kurtogram, as shown in Figure 1. The horizontal axis represents the frequency, the vertical axis represents the level of the split frequency band, and the third dimension represents the color band, which is the kurtosis value of the filtered signal's envelope spectrum for each frequency bandwidth. The node with the largest kurtosis is chosen as the optimal band. However, the fast kurtogram calculates each kurtosis value of the filtered signal without source identification, which is sometimes incorrect.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.7. Maps of Canada's Forest Attributes for 2001 and 2011  
[PERSON] et al. [68] produced forest biomass maps for Canada at 250 m resolution for 2001 using k-nearest neighbours (kNN) method. The predictors included MODIS reflectance data, climatic variables, topographic variables and land cover information. Validation of estimated forest biomass with the independent field data at the pixel level showed that the R\({}^{2}\) was 0.69 and the relative RMSE was 69.2%, but that accuracy could be improved if the pixels were spatially aggregated to 1 km resolution. A biomass map for 2011 was similarly generated. To infer biomass dynamics from the sequential biomass maps, [PERSON] et al. [69] updated the 2001 and 2011 forest biomass estimates [68]. The new forest biomass datasets were generated using an improved reference dataset sourced from Canada's National Forest Inventory (NFI) and a refined kNN method. Evaluation results were in better agreement than that of [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [68], and showed that changes in aboveground biomass matched expectations between 2001 and 2011 where fire, harvest, or post-disturbance regrowth occurred.