esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applicability of NDT Methods to ABC Closure Joints
### Grouping of Various Defects  
Furthermore, although the focus of this paper was on NDT methods that would be employed to detect damage that is not visible, it was realized that for visible damage and defects, visual inspection always offers the fastest, most economic, and most accurate method of detection. Hence, among the potential defects for closure joints described earlier in this paper, visible defects, such as an abnormal appearance including signs of leakage and efflorescence, surface defects, surface roughness, surface cracks, spalling of concrete cover, and exposure of reinforcing bars and embedment, can be best detected using visual inspection. The potential defects that are not visible can be listed as follows:  
* delamination of wearing surface
* delamination of concrete cover (before cracking and spalling becomes visible)
* reflective cracks (for the extent of cracking inside the joint)
* voids (internal)
* honeycomb (internal)
* debonding at cold joints (for the extent inside the joint)
* concrete material segregation
* corrosion of reinforcing bars
* corrosion of embedded steel.  
According to the bridge damage etiology approach, the following defect types were recognized as the most common defects that may occur in deck closure joints:  
1. delamination
2. cracks
3. voids
4. corrosion of embedded steel.  
Collectively, these four types of defects/damages represent, by type or feature, all expected types of damage and defects in closure joints.
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 4 Numerical Experiments
### Example 1: Simulated AR(1) Processes with No Change-Point  
The null case is considered first with three standard AR(1) processes simulated with no change-point. Three sets of 100 AR(1) sequences are generated with each sequence of length \(T=201\). The autocorrelation coefficients were set to be 0.1, 0.5, and 0.9, respectively, for each set. The variance was set globally to be \(\
u=1\), and the mean was set to be \(\delta=0\).  
Table 1 shows that both strucchange and CE.AR perform well for all values of \(\rho\), while AR1 seg tends to slightly overestimate the number of change-points when the value autocorrelation parameter increases. The best performing method is the CE.AR method.
# The Influence of Different Cleaning Protocols on the Surface Roughness of Orthodontic Retainers
###### Abstract  
Thermoplastic materials are sensitive to humidity, temperature variations, enzyme activities, and cyclic loading. All these factors can cause changes to the mechanical properties of the material. The aim of this study was to determine the influence of different cleaning protocols on the surface roughness of orthodontic retainers. Samples of two brands of polyethylene terephthalate glycol (PET-G) material were exposed to four cleaning protocols: Corega (alkaline peroxide tablets), Toothbrush, Corega + toothbrush, Toothbrush + toothpaste, and Control. Measurement of the surface roughness of the sample on both the top and bottom side was carried out before and after cleaning. There was no statistical difference between the final values of the measured parameters. However, looking at the extent of the change in surface roughness, there was a statistically significant difference in the upper side of the Corega + toothbrush group between Materials A and B. This suggests that there was a greater change in the roughness of material A (Erkodur), given that the mean change in roughness of Material A was Ra 0.047, whereas the mean change in roughness of Material B was Ra 0.022. Almost all the tested cleaning procedures significantly increased the surface roughness of the PET-G retainer material. Of all the methods, the Corega tablets had the lowest influence on surface roughness.  
retainer; PET-G; cleaning; surface roughness; dentistry; orthodontics Article  
The Influence of Different Cleaning Protocols on the Surface Roughness of Orthodontic Retainers
###### Abstract  
As the demand for high data volumes keeps increasing in optical access networks, transmission capacities and distance are becoming bottlenecks for passive optical networks (PONs). To solve this problem, a novel scheme based on multi-twin single sideband (SSB) modulation with direct detection is proposed and investigated in this paper. At the central office, two SSB signals are generated simultaneously with the same digital-to-analog converters (DACs). The twin-SSB signal is not only robust against frequency selected power fading introduced by chromatic dispersion (CD), but also improves the spectral efficiency (SE). By combining a twin-SSB technique with multi-band carrier-less amplitude/phase modulation (multi-CAP), different optical network units (ONUs) can be supported by flexible multi-band allocation based on software-reconfigurable optical transceivers. The Kramers-Kronig (KK) scheme is adopted on the ONU side to effectively mitigate the signal-signal beat interference (SSBI) induced by the square-law detection. The proposed system is extensively studied and validated with four sub-bands using 50 Gbps 16 quadrature amplitude modulation (QAM) modulation for each sub-band using numerical simulations. Digital pre-equalization is introduced at the transmitter-side to balance the performance of different ONUs. After system optimization, a bit error rate (BER) threshold for hard decision forward error correction (HD-FEC) code with 7% redundancy ratio (BER = \(3.8\times 10^{-3}\)) can be reached for all ONUs over 50-km standard single-mode fiber.  
fiber optics and optical communications; fiber optics links and subsystems; modulation +
Footnote †: journal: Applied
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
###### Abstract  
Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient's body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (_Xception_) while extracting the most crucial features from the patient's X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence.  
breast cancer classification; deep learning; CNN; equivocal mammogram; BI-RADS classification; machine learning; artificial intelligence
## 4 Performance Analysis
### SINR Comparison of All Beamformer with Respect to Disparity Angle
#### 4.2.4. SINR Comparison of the NVL-Based Beamformer with Respect to the Disparity Angle  
In Figure 3d, the SINR of the LCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\)and 3\({}^{\circ}\) disparity is 40.30 dB, 40.23 dB, and 40.11 dB, respectively. With 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity, the SINR of the LCPAA beamformer is 40.29 dB, 40.18 dB, and 39.98 dB, respectively. The SINR of the LRAA beamformer is 40.29 dB, 40.18 dB, and 39.99 dB with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\)disparity, respectively. The SINR of the LCCAA beam former with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\)disparity is 40.29 dB, 40.18 dB, and 39.99 dB, respectively. The SINR of all beamformers without disparity is 40.45 dB. The NVL technique shows a better performance. In Table 1, the value of SINR using robust techniques is shown concerning the corresponding beamformer for different methods.
## 2 Results and Discussion
### MazEF
#### 2.2.1 MazEF (I) -MazE-MazF Composite  
MazEF (I) of _G. kaustophilus_ HTA426, _Geobacillus_ sp. ZGt-1, and _G. thermoleevorans_ Gts  
The genes with the locus tags GK1648 and *_19090 in strains HTA426 and Gts, respectively, and the gene encoded on contig 16_162 in ZGt-1 code for a putative MazF domain-harboring toxin, here labeled as MazF (I) (Table 1). In the three strains, each of the genes with the locus tag GK1647 and *_19080, and that encoded on contig 16_161 code for a putative MazE antitoxin, here labeled as MazE (I) (Table 1). Each of these genes is adjacent to and upstream of its cognate toxin-coding gene. The two genes coding for each of the putative TA pairs potentially share the operon in each of the strains and overlap by one nt. The one nt overlap between TA pair genes is common among TA systems [8]. The aa sequences of the three putative toxins are identical among the three strains, and the same applies to the putative antitoxins (Table S1; global alignment Figure S2). In each of the three strains, the NCBI annotation of the putative TA pair supports our results (Table S1). The fourth strain, _G. thermodentirificans_ NG80-2 does not have the MazE-MazF composite.
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results
### Experimental Dataset and Computer Environment  
In the detection stage, the publicly available German traffic sign detection benchmark (GTSDB) dataset and the Chinese traffic sign dataset (CTSD) were adopted for performance evaluation. In both the GTSDB and CTSD, there are three classes of traffic signs: Prohibitory, mandatory and danger, and the shapes of the traffic signs are circular and triangle. Figure 10 shows the examples of the subclasses of GTSDB, and Figure 11 shows the examples of subclasses of CTSD.  
The GTSDB dataset included 900 high-resolution natural scene images of the road environment in Germany. The sizes of those images were 1360 \(\times\) 800 pixels, and the size of the traffic signs varied from 16 \(\times\) 16 pixels to 128 \(\times\) 128 pixels. These 900 pictures were divided into two parts: 600 of which were used for training, and 300 were used for testing. There were 1213 traffic signs in the GTSDB scene pictures, and zero to six traffic signs in one traffic scene. In the test dataset of GTSDB, there were 161 prohibitory traffic signs, 63 danger signs, and 49 mandatory signs, respectively.  
For research into Chinese traffic sign detection, we used the Chinese Traffic Sign Dataset (CTSD) from Reference [10]. In the CTSD, there were 1100 images, which included 400 for testing and 700 for training. The sizes of the images were 1024 \(\times\) 768 or 1280 \(\times\) 720. The traffic signs included prohibitory traffic signs with a circular shape and red color, danger traffic signs with triangular shape and yellow  
Figure 9: The structure of the three convolutional neural network (CNNs).  
color, and mandatory traffic signs with a circular shape and blue color. In the CTSD test dataset, there were 264 prohibitory traffic signs, 129 danger signs, and 139 mandatory signs, respectively.  
Our research into traffic sign recognition was based on the Germany traffic sign recognition benchmark (GTSRB), and the sizes of the traffic signs varied from 15 \(\times\) 15 pixels to 250 \(\times\) 250 pixels. The total number of traffic signs in GTSRB was 51,839: 12,630 images for testing and 39,209 images for training. Consistent with the GTSDB, the traffic signs of GTSRB were divided into three main categories, including 43 subclasses.  
Figure 11: Examples of the subclasses of the Chinese Traffic Sign Dataset (CTSD): (**a**) prohibitory traffic signs; (**b**) danger traffic signs; (**c**) mandatory traffic signs.  
Figure 10: Examples of the subclasses of the German traffic sign detection benchmark (GTSDB): (**a**) prohibitory traffic signs; (**b**) danger traffic signs; (**c**) mandatory traffic signs.  
Traffic sign detection and recognition algorithms were conducted in a Matlab development environment. Additionally, the hardware configuration of the system was Intel(r) CoreTM i5-6300U [EMAIL_ADDRESS] GHz 2.50 GHz, 8 GB DDR3, Windows 10 64-bit, and Intel(r) HD Graphics 520 Graphics.
## 2 Materials and Methods
### Amplification and Analysis of the S and R Genes  
Total plant DNA was extracted from leaf tissue in the laboratory of the Biotechnology Center of the Eduardo Mondlane University of Mozambique (UEM), following the protocol of [PERSON] et al. [23]. For extraction, 150 mg of fresh plant tissue was macerated in liquid nitrogen and 750 uL of CTAB buffer (100 mM Tris-HCl, pH 8.0; 20 mM EDTA; 1.4 M NaCl; 80 mM Na\({}_{2}\)SO\({}_{3}\); 2% PVP-40 and 2% cetyl trimethyl ammonium bromide (CTAB) containing 0.2% g-mercaptoethanol) was added. After homogenization, the 1.5 mL micro-centrifuge tubes containing the sample were transferred to a water bath and incubated at 60 \({}^{\circ}\)C for 30 min, and the tubes were mixed by inversion every 10 min. A volume of 750 uL of chloroform:isoamyl alcohol solution (24:1) was added to the tubes and centrifuged at 12,000 rpm for 10 min at 4 \({}^{\circ}\)C. The aqueous phase (supernatant) was transferred to new Eppendorf tubes, and the DNA was precipitated by adding 0.6 volumes of isopropanol and incubated at \(-\)20 \({}^{\circ}\)C for one hour. After centrifugation at 12,000 rpm for 10 min, the supernatant was discarded, and the DNA was washed with 500 uL of 70% ethanol and resuspended in 100 uL of 1X TE buffer. The tubes containing the extracted DNA were placed on dry ice and transported to Brazil to the Laboratory of Molecular Virology, Department of Plant Pathology, Federal University of Lavras (UFLA), for further studies.  
DNA amplification was performed in a 25 uL reaction consisting of 2.5 uL of 10X PCR buffer containing 15 mM MgCl2, 2.0 uL of 10 mM dNTPs, 1.0 uL of forward primer and 1.0 uL of reverse primer at a concentration of 10 uM, 0.25 uL of Taq DNA polymerase (Sigma, Livonia, MI, USA), 1.0 uL of DNA and enough water for a total reaction volume of 25 uL. Amplification was performed in a Master Cyler Thermocyler (Eppendorf, Hamburg, Germany) with the following program: 94 \({}^{\circ}\)C for 2 min followed by 35 cycles  
Figure 1: Geographic distribution of BBTV isolates collected in the Chökw\(\grave\) district, province of Gaza, Mozambique (indicated with red triangles). (**A**) Map with the territorial demarcations of the provinces of Mozambique. (**B**) Map highlighting the Chökw\(\grave\) district in the province of Gaza. (**C**) Map highlighting the sample collection region in the Chökw\(\grave\) district (red square).  
of 94 \({}^{\circ}\)C for 30 s, 49 \({}^{\circ}\)C (S-DNA) or 52 \({}^{\circ}\)C (R-DNA) for 20 s and 72 \({}^{\circ}\)C for 60 s, with a final extension of 72 \({}^{\circ}\)C for 5 min. The amplified products were analyzed by electrophoresis in a 1% agarose gel and counterstained with Gel Red (Biotin, Fremont, CA, USA). The bands were visualized and documented using an Alpha Imager (Alpha Imnotech Corp., Santa Clara, CA, USA).  
In the DNA amplification reaction, primers specially designed to flank the S gene region and the R gene region were employed, as specified in Table 1.
## 2 Materials and Methods
### 2.5. Physical Parameters of Fresh Bead Samples
#### 2.5.2. Bread Crumb Colour Analysis  
The colour parameters of the bread crumb samples were analysed using a CM-5 spectrophotometer (Konica Minolta Sensing, Inc., Osaka, Japan) equipped with a D65 illuminant and a 10\({}^{\circ}\) observer angle. The colour was determined on 10 different points from a slice of 20 mm thickness, taken from the middle of the loaf. The colour parameters were as follows: lightness (L* = 0 is black and L* = 100 is white), a* (-a = green and +a = red), and b* (-b = blue and +b = yellow). Using the colour coordinates, the following parameters were calculated: chroma (C* = \(\sqrt{\text{a}^{\text{+2}}+\text{b}^{\text{*2}}}\)) and hue angle (h = arctan(b*/a*)). The total colour difference (\(\Delta\)E) between the control bread and the different protein formulations was determined as follows: \(\Delta\text{E}=\sqrt{\left(\text{L}_{\text{C}}^{\ast}-\text{L}_{\text{P}}^{ \ast}\right)^{2}+\left(\text{a}_{\text{C}}^{\ast}-\text{a}_{\text{P}}^{\ast} \right)^{2}+\left(\text{b}_{\text{C}}^{\ast}-\text{b}_{\text{P}}^{\ast} \right)^{2}}\), where subscripts  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Wheat** & \multirow{2}{*}{**PP (g)**} & \multirow{2}{*}{**SP (g)**} & \multirow{2}{*}{**Yeast (g)**} & \multirow{2}{*}{**Salt (g)**} & **Water** \\  & **Flour (g)** & & & & & \\ \hline Control & 1500 & - & - & 45 & 22 & 895.5 \\
5 PP & 1425 & 75 & - & 45 & 22 & 927.0 \\
10 PP & 1350 & 150 & - & 45 & 22 & 984.0 \\
15 PP & 1275 & 225 & & 45 & 22 & 1073.25 \\
5 SP & 1425 & - & 75 & 45 & 22 & 904.5 \\
10 SP & 1350 & - & 150 & 45 & 22 & 919.5 \\
15 SP & 1275 & - & 225 & 45 & 22 & 924.0 \\ \hline \hline \end{tabular}  
\end{table}
Table 1: Bread recipes.  
C and P refer to the control bread and to the different bread formulations with protein concentrates, respectively.
## 1 Introduction
At present, the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT), launched in 2009 by Japan, is the first instrument dedicated to record CO\({}_{2}\) and methane (CH\({}_{4}\)) concentrations (denoted XCO\({}_{2}\) and XCH\({}_{4}\), in ppm. XCO\({}_{2}\) and XCH\({}_{4}\) are column-averaged dry air mole fractions of atmospheric CO\({}_{2}\) and CH\({}_{4}\)) [8, 27]. The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) aboard the ENVIronmental SATellite (ENVISAT), launched in 2002 and lost in 2012 by the European Space Agency (ESA), can also retrieve GHG concentrations, but it is not specially designed for GHG retrieval and is not highly accurate; the single-measurement precision is approximately 1\(\sim\)2%, with an intermonth scatter of 2.3% [28, 29]. Moreover, before 2002, the GHG concentrations could not be measured directly using the remote sensing technique. To obtain CO\({}_{2}\) concentration data using remote sensing data, [30] used CO\({}_{2}\) fluxes with static chamber techniques and the normalized difference vegetation index (NDVI) data from a charged coupled device camera to analyze the relationship between NDVI and CO\({}_{2}\) fluxes in arctic Alaska and found that the NDVI is not sufficient to estimate the carbon flux rate. Using remote sensing data and field data, [31] have developed models to map air-sea CO\({}_{2}\) fluxes. To our knowledge, there is no model that can be used to retrieve CO\({}_{2}\) concentrations from remote sensing data at a regional or global scale. Therefore, new approaches are critically needed to capture regional variations in CO\({}_{2}\) concentration and to bridge a major gap between field and satellite observations [32, 33]. In the present study, we assessed CO\({}_{2}\) concentration using Moderate Resolution Imaging Spectroradiometer (MODIS) products globally from June 2009 to November 2011. The objectives were: (1) to examine the relationship between the MODIS-derived indices and TANSO XCO\({}_{2}\); (2) to develop a monthly CO\({}_{2}\) concentration model for Africa, Australia, Eurasia, North America and South America; and (3) to analyze the model uncertainties. This study explored the remote sensing potential for monitoring CO\({}_{2}\) concentration at regional and global scales.
# Voltage Optimization in PV-Rich Distribution Networks--A Review
## 4 Voltage Optimization in PV-Rich Distribution Networks--Formulation and Solution Methods
### Analytical Methods  
Classical analytical methods are used in multi-field optimization problems. In addition to the basic methods such as LP, QP [69], NLP, MINLP [76], and MILP, some papers deal with problems that reduce to the basic ones (see Table 4). In [65], SLP is developed to solve the optimization problem in real time. To obtain global optima, SQP has been developed in [38]. The iterative gradient projection method is implemented to specify VAR outputs for voltage optimization in [87] and additionally, active power outputs in [84]. A method for solving linear and nonlinear optimization problems was developed in [34; 78]. The alternating direction method of multipliers (ADMM), which is one of the augmented Lagrangian-based methods, was developed as one of the most used methods for a network optimization problem. In ADMM, the optimization problem is decomposed into subproblems to deal with it. The subproblems are coordinated to seek the global optimal solution. The authors in [70; 72; 73] solve the voltage optimization problem using ADMM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Formulation** & **Analytical Methods** & **Reference** \\ \hline \multirow{4}{*}{NLP} & ADMM-based method & [69] \\  & IPM & [36] \\  & SQP & [54] \\  & SLP & [63] \\  & QP & [52; 53; 74] \\  & Gradient projection methods & [59; 82] \\  & ADMM-based method & [70; 71] \\ MILP & MILP & [58] \\  & QP & [74] \\  & IPM & [76] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of formulation and analytical methods.  
Figure 9: Mathematical formulation and solution methods used in OPF solving.