esa-sceva/satcom-chunk-collection huggingface dataset:
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 2 Materials and Methods
### Homogeneity Tests  
Numerous tests can be used for the homogeneity evaluation of hydro-climatological series data. In the present study, both parametric (Standard Normal Homogeneity Test: SNHT) and non-parametric (Pettitt) tests were used to analyze the homogeneity in monthly, seasonal and annual series of minimum (Tmin) and maximum (Tmax) air temperature and precipitation (P).
## Materials and Methods
### Analysis of Volatile Organic Compounds  
VOCs were analyzed with a GCMS-QP2010 SE gas chromatograph-mass spectrometer (GC-MS; Shimadzu Europa GmbH, Duisburg, Germany) on a DB-WAX (Agilent Technologies, Palo Alto, CA, USA) column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). SPME fiber was introduced into the GC injector (set to 250 \({}^{\circ}\)C and fitted with a SPME liner (0.75 mm \(\times\) 5.0 mm \(\times\) 95 mm); Shimadzu GCs; Restek, Bellefonte, PA, USA) for 30 min for VOC desorption and conditioning before next extraction. Helium was used as carrier gas (flow rate: 0.8 mL min\({}^{-1}\)). The following temperature program was used: 35 \({}^{\circ}\)C (hold 1 min), ramp to 110 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\) and ramp to 230 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) (hold 3 min). The mass spectrometer (MS) detector worked in full scan mode (mass-to-charge ratio: 35-250 m/z) and operated in electron impact mode at 70 eV. Measured chromatogram peaks were integrated based on quantifier and qualifier ions of target compound (Table 1) using MSD Chem-Station E.02.01.1177 (Agilent Technologies, Palo Alto, CA, USA). Specific VOCs were identified (1) by NIST v2.0f library (NIST, Gaithersburg, MD, USA) and (2) by retention index (RI) calculated for each compound according to [PERSON] and [PERSON] [27].  
Figure 1: Scheme of the various pre- (short-term hot-water, sHWT, vs. washed) and post-cutting (chemical prevention, cp, vs. no chemical prevention, non-cp) treatments of fresh-cut apples slices stored in sugar syrup at 4 \({}^{\circ}\)C for up to 13 days.  
RIs were determined based on an alkane standard (C7-C30; 1000 ug mL\({}^{-1}\); Supelco, Bellefonte, PA, USA). For this, 5 uL of alkane standard was injected in a 3.9 L glass jar, extracted with SPME after equilibrium time of 1 d and analyzed as described above. The RIs were compared with those given by [PERSON] and [PERSON] [27] by using online NIST Chemistry WebBook [28]. The total integrated peak area of each VOC was used to calculate a semi-quantitative VOC concentration expressed in nL L\({}^{-1}\) based on the external standard 2-methylbutyl 3-methylbutanoate (CAS-number: 2245-77-4). A standard curve of 2-methylbutyl-3-methylbutanoate was created in the concentration range from 5 to 1000 nL L\({}^{-1}\).
# Line-Detection Based on the Sum of Gradient Angle Differences
## 6 Conclusions  
The proposed SGAD-based line-detection method was proved to produce more accurate line-detection results than the SD- and the EC-based methods by means of experiments conducted using simulation images and natural images.  
Implementation of the proposed SGAD-based line-detection method was shown to be simple but less sensitive to noise compared to the other state-of-the-art methods. Thus, the proposed method can  
Figure 10: Line-detection results obtained using SD-, EC-, and SGAD-based methods for images contaminated with noise \(\sigma_{n}=5\). Ridge and valley pixels are denoted by upward-pointing black and downward-pointing white triangles, respectively.  
be applied readily in general line-detection applications without difficulty because of its simplicity. The SGAD-based line-detection method is expected to provide high-quality line features in applications in the fields of photogrammetry and image processing, such as lane detection, building feature detection, and other applications involving the detection of line features from images.  
As the performance of line-detection is dependent on the contrast present in images, a well-designed contrast enhancement process as a pre-processing step [38] can be utilized to improve the quality of the proposed line-detection method.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2016R1D1A1B02011625).  
The author declares no conflict of interest.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 3 Single Damage Location Detection  
### Single Damage Dataset  
In the single damage location detection, the stiffness of column 1, column 6, and column 11 was reduced by 20%. The displacement data of each node was collected and converted into interlayer displacement. The single damage dataset includes four categories: \"0\", \"c1\", \"c6\", and \"c11\". The category \"c0\" means that there are no damaged columns; \"c1\" means that the damaged column is column 1; \"c6\" means that the damaged column is column 6; \"c11\" means that the damaged column is column 11. The data in the dataset were obtained from the finite element model. The specific parameters in the finite element model were set according to the damage types mentioned above. Displacement of each node in the finite element model under environmental excitation was collected. Meanwhile, the displacement of each node was converted into interlayer displacement, i.e., the interlayer displacement of 12 nodes was obtained. The acquisition frequency was set to 100 Hz. Each sample contains 50 data points, which is 0.5 s of acquisition time. Each sample in the dataset is a matrix of 12 \(\times\) 50. Among them, 12 represents 12 nodes and 50 represents 50 data points. The number of samples is 399 in each category, and the dataset contains a total of 1596 samples. After these samples are randomly sorted, 1200 samples were used for training and verification, 396 samples were used as test set.
## 4 Discussion
### Study Limitations  
One limitation of this study is a possible breakdown of the cold chain of reagents, making it difficult to detect inaccurate or even false results. Regarding the transport of the reagents, they are very sensitive to small changes in temperature.  
Another limitation is that all bacterial species could not be identified in peri-implant tissue; that is, other bacteria different from the five studied may be closely related to the appearance of PI, a possibility that is indicated by the results of metagenomics studies.  
It is also possible that the detection threshold of Periopoc(r) may be too high for the actual bacterial load that appears in peri-implant grooves, i.e., the amount of bacteria present is not sufficient to activate the reagents.  
Another limitation is the small sample size, with a total of 21 patients. Despite the power achieved, it was not possible to establish a statistically significant result and demonstrate the validity of Periopoc(r). Therefore, it is necessary to increase the sample size in future relevant studies.  
The significance of the implant prosthetic abutment connection in peri-implantitis development lies in its impact on the stability, hygiene, and susceptibility of the implant site to bacterial colonization. However, our study did not focus on the inclusion of the type of connection on the presence of peri-implantitis, but on the presence or absence of peri-implantitis itself and the test result tested.
## 3 Dataset
### Data Processing
#### 3.2.1 Facial Expression Image Processing  
The video footage captured during the study includes the child's head, upper body, or entire body, together with the surrounding backdrop environment. This has an impact on the precision of emotion detection. Hence, it was necessary to conduct video processing, including video framing, face cropping, and face alignment.  
Initially, the original video was transformed into individual frame images using the OpenCV library (version 4.5.3) in Python 3.8. The video has a frame rate of 25 frames per second, resulting in the automatic capture of one frame every five frames. In addition, to isolate the subjects' facial expressions, facial photos were preserved using a single-shot multibox detector to focus on the fine-grained state of the facial expressions. Finally, the spatial constraints of the facial key points in the Dlib 19.22.0 model were used to align the facial images in different positions with the coordinates of the overall centers of the left and right eyes as the base point. This reduced the impact of sideward, tilted, and lowered head poses on the positioning of facial feature points. After manual screening, 24,793 facial expression images were finally obtained.
## 2 Image Processing in Remote Sensing
### Image Enhancement

Table 1: Image Enhancement methods and their performance metrics. work can be extended such that instead of manually finding the tie points, an automated approach where tie points can be evaluated from different images could be done. Some of the evaluation metrics for assessing the performance of image enhancement include Peak Signal-to-Noise Ratio (_PSNR_) which defines the standard of restructured image quality, Absolute Mean Brightness Error (AMBE) which is the difference between original and enhanced image brightness, Entropy which is the measure of the richness of information in the image. Mean Square Error (_MSE_) is the squared difference between input and the distorted image.
# The Impact of Fine-Layering of Tailings Dam on the Variation Pattern of Infiltration Lines
## 2 Layering Models and Experimental Methods
### Principles and Scheme of Stratification  
According to the field investigation data, the soil layers of the dam can be primarily divided into three layers: surface fill soil, tailings deposition layer, and bedrock layer. The surface-fill soil consists of silty soil and gravel. The tailings deposition layer consists of fine tailings, silty tailings, sandy tailings, and sticky sandy tailings. The bedrock consists of highly weathered sandstone in the Great Wall system and weathered sandstone in the Great Wall system. According to the \"Code for Geotechnical Investigation of Rock and Soil Engineering\" GB50021-2001 (2009 edition) [29], the compactness classification of sandy soil can be divided into dense, medium dense, slightly dense, and loose categories. The compactness of silty soil can be classified into dense, medium dense, and slightly dense.  
This paper proposes three different stratification schemes to investigate the impact of stratification based on soil layer types and compactness on the infiltration line variation of tailing dams. Scheme 1 divides the tailings dam into initial embankment, deposition layer, and bedrock. The deposition embankment consists solely of fine tailings, while the bedrock is composed of heavily weathered sandstone and moderately weathered sandstone.  
Scheme 2 further subdivides the deposition embankment into silty fill soil, fragmented stone fill soil, fine tailings, silty tailings, sandy tailings, and clayey silt tailings based on soil layer types. Scheme 3, based on scheme 2, further divides the fine tailings and silt tailings based on differences in compactness. The detailed stratification schemes are presented in Table 1, and the stratification models for the three schemes are illustrated in Figures 1-3.  
Constant head strategy: set the initial embankment head and reservoir boundary head, no drainage boundary, comparing the differences in infiltration line inclination and height between each measuring line of the three schemes; Rainfall strategy: set the rainfall boundary, no drainage boundary, comparing the percentage increase of the infiltration line at each measuring line, as well as analyzing the variability of the infiltration line changes in the three schemes; Drainage strategy: set the drainage boundary, compare the changes in the height of the infiltration line at each measurement line, and calculate the fitting percentages of the infiltration line height compared to the constant head strategy.
## 3 Materials and Methods
### GC-MS Analysis  
The extracted volatile components trapped by the SPME fiber were desorbed in the GC injection port for 3 min at 250 \({}^{\circ}\)C in the splitless mode. GC-MS analysis was performed using an Agilent 7890B gas chromatograph system with a 5977B mass spectrometer (Agilent Technologies, CA, USA). A HP-5 ms column (30 m, 250 \(\upmu\)m, 0.25 \(\upmu\)m) (Agilent Technologies, CA, USA) and a constant helium flow of 1 mL min\({}^{-1}\) were used for chromatographic separation. The oven temperature program was initially 35 \({}^{\circ}\)C for 2 min, 5 \({}^{\circ}\)C min\({}^{-1}\) ramp to 190 \({}^{\circ}\)C held for 1 min, 20 \({}^{\circ}\)C min\({}^{-1}\) ramp to 250 \({}^{\circ}\)C held for 2 min, solvent delay of 1 min. The detector was operated in electron impact (EI) ionization mode at 70 eV in a full scan mode with a mass/charge ratio (m z\({}^{-1}\)) range from 35 to 450.  
Chromotograms and mass spectra were analyzed using the Enhanced ChemStation software (Agilent Technologies, CA, USA). Identification of tentative volatile compounds was achieved by matching the mass spectra with the data system library (NIST 2017) and linear retention index (RI) sourced from the NIST Standard Reference Database. Aroma compounds in sweet potato were semi-quantitated using 1-pentanol solution (5 \(\upmu\)0.4055 mg mL\({}^{-1}\)) as an internal standard. The concentrations of aroma compounds were calculated on the basis of the ratio between peak area and the concentration of 1-pentanol. All the analyses were repeated in duplicate or triplicate.
# Waveguide Manufacturing Technologies for Next-Generation Millimeter-Wave Antennas
## 5 Discussion  
In this work, some manufacturing methods applied to microwave antennas and devices in waveguide technology were analyzed. Table 5 summarizes the main advantages and limitations of every methodology discussed in the previous sections. It should be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **[53]** & **[58]** & **[59]** & **[60]** & **[48]** \\ \hline Description & Conformal slotted & Linear slotted & Linear slotted & Planar slotted & Corrugated Plate \\  & array & array & array & array & Antenna \\ Polarization & Dual & Dual & Single & Single & Single \\ Frequency (GHz) & 38.5 & 23.3 & 21.7 & 70.0 & 11.3 \\ Efficiency (\%) & 85/89 & 87/92 & 95 & 71/89 & 56.6 \\ Peak gain (dBi) & 13.5/14 & 17.8/18.2 & 29.5 & 20.0 & 18.7 \\ Beam shape & Conical & Pencil & Pencil & Pencil & Pencil \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of high-gain antenna designs manufactured by SLA technology.  
Figure 14: Conformal array antenna normalized radiation patterns: (**a**) simulated and measured \(\phi=0^{\circ}\) patterns at 38.5 GHz; (**b**) simulated and measured \(\phi=90^{\circ}\) patterns at 38.5 GHz; and (**c**) measured \(\phi=90^{\circ}\) patterns at 37 and 40 GHz.  
noted that the surface finish of SLA technology is usually quite good, while the DMLS technology provides a significant roughness. Although this roughness will be progressively reduced as the technology evolves, an effective conductivity reduction can be found for some high-frequency devices. On the other hand, high-precision machining will offer very high levels of conductivity and surface finish but will not allow the fabrication of devices with internal channels in a monolithic piece. This is a very important advantage of additive manufacturing technologies because the implementation of a prototype in several parts can produce some leakage effects (especially at higher frequencies). The average weight of the devices manufactured by conventional machining and DMLS technology is quite similar, but average weight is drastically reduced by SLA technology, as the prototypes are usually manufactured in plastic material. For the same reason, the cost of SLA technology is usually lower than that of alternatives. Regarding the level of technology maturity, it is obvious that high-precision machining is the most developed manufacturing method, while DMLS and SLA are under continuing evaluation. However, both are undergoing rapid evolution in recent years, due to the growth in the microwave field, so their performance is expected to improve in the coming years.
## 3 Results
### Model Verification Results Using Additional Data Points  
The MRA, ML, and ANN models developed in this study were validated using additional data points not utilized during the training step. Table 4 summarizes the MAPE and R\({}^{2}\) values to ensure the accuracy of the models during the validation process. The verification results of the previously developed prediction models further confirmed that the ANN model with a 10 \(\times\) 5 \(\times\) 1 structure exhibited the lowest MAPE of 3.06% and the highest R\({}^{2}\) value of 0.8644 compared to other models. While the Matern 5/2 GPR model exhibited the lowest MAPE among the ML models during the model training step, it recorded the highest MAPE, i.e., 4.78%, and the lowest R\({}^{2}\) value, i.e., 0.6097, during the model-verification step. Interestingly, the multiple regression model, which had a higher mean absolute MAPE than the ML models during model training, demonstrated a higher prediction accuracy during model validation. Figure 7 compares the measured yield strength of the additional dataset with that predicted by the developed models.  
Analyzing the correlation between the measured and predicted yield strengths based on Figure 7, we see that the Matern 5/2 GPR model exhibits the lowest R\({}^{2}\) value, indicating relatively large discrepancies between the measured and predicted values at certain data points, reaching up to 95 MPa. The coefficient of determination and MAPE of machine learning-based prediction models may be influenced by the attributes of the model and the quantity of data points. The ANN model displayed a maximum difference of 35 MPa between the measured yield strength and the predicted value, the lowest among the compared models. Moreover, the ANN model has relatively fewer data points that deviate significantly from the trend line of the graph compared to the other models, thus ensuring the highest accuracy.  
These results confirm that the ANN models overcame the predictive performance limitations observed in the models developed through MRA and ML.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAPE (\%)** & **R\({}^{2}\)** \\ \hline Multiple & & \\ regression—Equation (1) & 3.25 & 0.8227 \\ Linear regression & 4.02 & 0.7330 \\ Robust linear regression & 3.88 & 0.7484 \\ Linear SVM & 3.81 & 0.7483 \\ Matern 5/2 GPR & 4.78 & 0.6097 \\ ANN—10 \(\times\) 5 \(\times\) 1 & 3.06 & 0.8644 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of models for model validation.  
Figure 6: Type of round tensile test specimen.
# Proteomics Analysis in Dairy Products: Cheese, a Review
## 7 Starters and Proteomics  
Cheese, as mentioned above, is a complex biological ecosystem, containing different microbial communities, originating from starter and adjunct cultures, raw milk and adventitious microorganisms from the production environment [55].  
Functions and activity of these bacteria can be easily determined through analysis of genes and transcripts. The identity and quantity of proteins produced by the starters can be revealed using proteomics' methods providing data for the post-translational level of the activity and function of microorganisms and the associated enzymes. This information is critical for understanding how microorganisms function and the interactions created between the different strains and the remaining cheese compounds under challenging conditions. That kind of data can be described by means of metaproteomics, since it is the study of the protein amount produced by microbial communities in every environment in a specific time [56]. The challenge with metaproteomics analysis in animal food products is the presence of milk proteins and those produced by different microbiota in cheese.
## 6 General Limitations of Current Anomaly Detection Models and Open Issues  
### The Limitations of Currents Anomaly Detection Models  
After exploring the various anomaly detection models, a summary of their limitations is given in the following points:  
* Most current WSN applications are designed to deal with multivariate data whereas some existing models dealt with univariate data. In multivariate data, the features together may pose an anomaly whereas individually they may not. Even though some models dealt with multivariate data, they have not considered data dimension reduction.
* Although recent proposed models were designed to operate online, the computation cost of their detection methods is a major drawback that causes an increase of energy consumption.
* Most current models adopt the distributed structure for anomaly detection. Nonetheless, these models have some drawbacks related to the size of reference model that should be communicated between nodes. Besides, most existing distributed models have not clarified the method of combining the local normal models in a global normal model. Finally, these models have not determined the suitable threshold for updating the global normal model because of data dynamic changes.
* The adaptability with dynamic data changes that was incorporated in some recent detection models produced additional computational costs that affect their suitability for real time detection and update.
* Many proposed anomaly detection models ignore a special feature of WSN data which is its spatial and temporal correlations. This feature helps in improving detection accuracy. Additionally, attribute/feature correlations which show the dependencies among features were ignored by most of the existing works. Exploiting such feature correlations through feature reduction helps sustain sensors' energy.
* Some of models reviewed, especially statistical models and classification-based models suffered from parameter selection issues. Some parameters need to be tuned for the detection model prior to the detection process. The performance of some classification methods such as SVM is changed significantly by the changes of some user parameters. In real WSN applications, it is not easy to determine a suitable parameter value for each application. In addition, it is not suitable to assume fixed values if the aim is to consider the dynamic change of the WSNs data.