esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Segmentation Methods
### Methodology
#### 2.2.4 Implementation Details  
Complete source code used in this paper is available at [23]. All neural networks architectures were trained using Keras framework [21] using Tensorflow backend. Training was done on Nvidia GTX 1080 ti graphics card with 11 GB of memory using CUDA 8.0. As an inspiration for the first U-Net model we used [24] open-source project. Our basic U-Net architecture uses 3D data processing layers. All networks use a binary cross-entropy as a loss function and Adam optimizer with parameters learning_rate equal to \(10^{-5}\), _beta_1 to 0.9, _beta_2 to 0.999, _epsilon_ to \(10^{-8}\) and _decay_ to \(1.99\times 10^{-7}\). Using decay parameter we lower the learning rate parameter each epoch by constant value, which helps to fine-tune the network.  
Residual-U-Net and Dense-U-Net architectures were designed by adding interconnections to the basic 3D U-Net architecture. Residual-U-Net uses only interconnection over whole down or up-sampling blocks as can be seen in Figure 6 and residual interconnection is denoted as green links. Dense-U-Net uses both residual and dense interconnections (blue in Figure 6) which pass unprocessed information to the middle layer of down and up-sampling blocks.  
Network uses as input data values which are in range between 0 and 1. For this reason the input pixel or voxel values, which were encoded as 8-bit monochromatic images in png format, i.e., in range [0,255], were normalized to the range 0.0 to 1.0.  
As we used sigmoid activation on output network layer, the output of the network is not labeled by just discrete values but with continuous values in range from 0 to 1. For brain dataset we used as only post-processing on predicted data thresholding. All pixels with value lesser than 0.5 were labelled as 0 and with value greater than 1. However the results on spine dataset required thresholding level to be set to 0.9 and above labelled positive, under the level negative. This has to be done to ensure least amount of artifacts in the output segmentation. Even after thresholding usually one middle sized artifact in the upper thoracic region stayed in the segmented image. To ensure clean segmentation suitable for 3D modeling we had to threshold the model to remove all stand alone objects smaller than 7500 voxels. This ensured the quality output without any artifacts in the segmented image.
# A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities
## 6 Methodology
### Cost-Based Measurement  
From the point of view of companies, since fraud is mostly a financial problem, the success criteria based on the number of transactions may not always be consistent. When the developed fraud-detection systems are analyzed according to the number of correctly detected transactions, they may seem successful, but the financial loss faced by the company might be much greater. In many fraud-detection studies in the literature, success is calculated based on only the number of transactions, and the monetary costs of the transactions are ignored [63; 64; 65]. Therefore, in this study, besides the success criteria based on the number of transactions, cost-based success criteria were also utilized. Accuracy, precision, recall, and most importantly F1 score metrics, which are widely used in fraud detection, are calculated on a cost basis. The cost-based metrics we propose in this study are named C-Accuracy, C-Precision, C-Recall, and C-F1 Score.  
\[\begin{split} c=currencyUSDRatio\times amount\\ cScore=(c_{i}-min(c))/(max(c)-min(c))\end{split} \tag{1}\]  
While calculating these proposed metrics, first of all, the transaction amounts were standardized by converting them according to currency conversion rates. Afterward, min-max normalization was applied to the amount column within the 0-1 interval. Finally, while creating the cost-based confusion matrix, each transaction was weighted with its c-score and added to the matrix. All metrics calculated using a cost-based confusion matrix have become cost-sensitive. In order to create a cost-sensitive confusion matrix, the operations for calculating the c-score are given in Equation (1).
## 4 Composite Formation Techniques
### Drawbacks in composite formation
The influence of the surface polarity on unsaturated polyester-nanosized SiO\({}_{2}\)-composites was characterized by shear viscosity measurements [106]. While the addition of hydrophilic SiO\({}_{2}\) (primary particle size 12-20 nm) caused a pronounced viscosity increase and small accessible maximum filler load \(<\)3 vol %, a hydrophobic SiO\({}_{2}\) (Aerosil\({}^{\circledR}\) R8200, primary particle size 12 nm) yielded a moderate viscosity rise up to a solid content of 8 vol %. Quite interesting is the influence on the flow activation energy. With increasing R8200 concentration a pronounced reduction of the flow activation energy can be calculated from temperature dependent viscosity data, which means a reduced temperature influence on the composite viscosity. This phenomenon can be explained by the improved attractive interaction of the hydrophobic filler (specific surface area of 142 m\({}^{2}\)/g) with the hydrophobic polymer matrix and the resulting reduced polymer chain mobility. A similar behavior was observed for a hydrophobic TiO\({}_{2}\)[107]. Again the large hydrophobic surface area caused an improved attractive particle-matrix interaction. Summarizing the mentioned literature, the following aspects influence significantly the resulting polymer-nanoparticle composite properties, mainly attributed to the pronounced nanoparticle agglomeration and extreme large specific surface area:  
* shear forces during compounding
* particle surface chemistry and polarity
* interaction between bulk polymer and interfacial-polymer layer as well as interaction between interfacial-polymer layer and ceramic nanoparticles.
## 1 Introduction  
Fruit quality is a complex trait. Consumers are known to initially evaluate fruit at the point-of-sale by its appearance and later by its flavor and mouth-feel [1]. The appearance will determine the initial purchase, while tasting would define the return buy. Appearance is mostly based on the fruit size and color, while tasting encompasses a combination of factors such as aroma, texture, and sugar/acid ratios [1]. Aroma, fruit firmness, and color are among the most important components that contribute to peach fruit quality [2].  
Fruit aroma is produced by emissions of volatile or gaseous compounds and is characterized by the volatile organic compounds' (VOCs) composition. Overall fruit flavor results from the combination of VOCs, sugars, and organic acids. The relative contributions of specific aroma VOCs to the flavor of peaches have been studied by several authors with over 100 VOCs identified [3; 4; 5]. Among these volatiles, aldehydes, alcohols, terpenoids, ketones, lactones, carboxylic acids, and esters have been reported [6]. [PERSON] and [PERSON] [7] identified \(\gamma\)-decalactone to be a major contributor of \"peach\" aroma. Other lactones such as \(\gamma\)-octalactone, \(\gamma\)-dodecalactone, \(\delta\)-decalactone, and \(6\)-pently-\(\alpha\)-pyrone have been also reported to be part of peach aroma, exhibiting a \"fruity\" or \"coconut\"-like aroma [7].  
Terpenoids and aldehydes were identified to be minor contributors [8]. [PERSON] et al. [9] studied the relationship between the relative VOCs' concentration and sensory attributes in peach. The authors reported a higher concentration of monoterenes and esters as compared to lactones. However, when considering the detection thresholds of volatiles, lactones were a major contributor to peach aroma.  
VOCs' composition changes with fruit ripening. In peach, immature fruit have been reported to be abundant in C6 compounds such as alcohols and aldehydes. At full maturity, the concentrations of lactones, linalool, and benzaldehyde are known to increase as the C6 compounds decrease [10; 11]. Similarly, other studies have been carried out to investigate how different cultural management practices such as fertilization, bagging, and postharvest treatments influence the VOC composition in peaches [12]. Flavor volatile composition is also known to be dependent on the cultivar analyzed. In a study conducted by [PERSON] et al. [13], the amount of linalool found in white-fleshed peaches was more than that of yellow-fleshed peaches.  
The aim of this study was to survey and characterize the VOCs' composition of 42 peach cultivars grown in Georgia, USA. Our hypothesis was that each cultivar has different volatile profiles and their VOCs' composition will change as the ripening season changes. The study was focused on the volatile composition among the peach cultivars grown in Georgia, which were minimally characterized for flavor volatiles.
# MFCC Selection by LASSO for Honey Bee Classification
## 3 Results
### Confusion Matrix  
To evaluate the performance of the classification task for different models, we additionally used another common tool, the confusion matrix. It is a table that summarizes the predictions made by the model and compares them with the actual labels or classes of the data. The confusion matrices for the original coefficients sets **MFCC**, **MFCC + Delta** and **MFCC + Delta + DeltaDelta** and for the analyzed feature selection cases are presented in Figures 13 and 13 in Appendix A.  
It can be noted that for the cases with 60, 69 and 80 features selected by LASSO, confusing real worker bees by labeling them with the drone label occurred the most rarely, with the lowest false positives (see almost black lower left square in the confusion matrices). The overall best results were obtained for those three cases with false positive rates (also called the false alarm rate) equal to around 0.6%, which translates to only around 60, on average, for every 10,000 worker bees being labeled as drones.On the other hand, the false negative rate (so-called miss rate) equals to around 14.5% in the same cases, what means that that percentage of drones on average were confused for being worker bees. The fact leaves space for future improvements, especially for imbalanced data sets such as ours, where the majority of recordings contain worker bee sounds.  
The worst case in terms of correct drone bee detection was obtained for the original set of **MFCC**, without derivatives and without LASSO feature selection. The confusion matrix, presented in Figure 13 in Appendix A, shows that only 81.39% of drones were correctly classified as drones, and 18.61% of them were wrongly labeled as worker bees. The original **MFCC** set produces the lowest recall value and the highest precision, which means that that coefficient set is overfitted to the worker bee recordings class, and does not perform well in the drone detection task. The experiments results show that the LASSO selection of MFCC features can improve the drone detection quality, but still the overall performance is highly dependent on the collected data.
## 3 Description of the Investigated Problem
### Measurement of BR Characteristics--Configuration B  
The bearing reducer (BR) subjected to measurement is located on a common axis with the driven and loading servomotor (Figure 6). The loading drive forms a connection between the planetary reduction mechanism and the loading servomotor. In this configuration, the measuring set includes an external position sensor and an external torque sensor. A bellows coupling was used to compensate for the angular misalignments, radial misalignments and axial misalignments of the connected shafts, as well as to ensure accurate transmission of angular motion and torque. Figure 7 shows a realistic view of the measuring stand.  
Figure 4: BR connection arrangement in configuration A.  
Figure 5: View of the stand in configuration A.  
Figure 6: BR connection arrangement in configuration B.  
The following analyses were performed to assess the change in the characteristics of the bearing reducers during their trial run:  
* Lubricant analysis,
* changes in surface microgeometry, wear or abrasive (adhesive) abrasion. Contamination of the oil fill with solid particles of an abrasive nature, which cause an increase in the rate of wear and accelerate the increase in the BR backlash;
* Analysis of temperature changes;
* Analysis of changes in starting torque--fluctuations;
* Analysis of H, LM changes;
* Analysis of changes in the system rigidity;
* Assessment of vibrations in the low frequency range;
* Assessment of vibrations in the high frequency range.
## 4 Stability Analysis of VSC-HVDC with Improved Virtual Impedance Control Strategy
### Impact of DC Cable Length  
Figure 11 shows the Nichols plots of the open-loop transfer function \(T_{m}\) under different lengths of DC cable when the transmission power is \(-500\) MW. The control parameters are \(k_{pudc}\) = 15, \(k_{intdc}\) = 100, \(k_{i}\) = 10, \(R_{eq}\) = 0.5 \(\Omega\), and \(L_{eq}\) = 0.002 \(\Omega\). The blue, red and yellow lines in Figure 11 represent the Nichols plots of \(T_{m}\) at DC cable lengths of 50, 100, and 150 km, respectively. As shown in Figure 11, the Nichols plot gradually approaches (\(-180^{\circ}\), 0), with a decrease in DC cable length. The Nichols plot encircles (\(-180^{\circ}\), 0), and the system is predicted to be unstable when the DC cable length is reduced to 50 km.  
Figure 10: Nichols plots of \(T_{m}\) under different equivalent virtual impedance values (**a**) \(R_{eq}\); (**b**) \(L_{eq}\).
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Research Methodology
#### 3.2.2 CFD Simulations and Numerical Methods  
Numerical simulations were the core of the research methodology of many works. In Ref. [3], CFD simulations were used to analyze the flow and temperature distribution in the furnace, and then supplemented with simplified models built with the use of an ANN and GAs. It was indicated that the synergistic adjustment of the fuel composition and operating conditions allowed for low NOx and soot emissions to be obtained over a wide range of loads. In Refs. [10, 64, 65, 66], CFD played a key role in the analysis of injection, fuel blending, and combustion in engines powered by different blends (diesel, natural gas, hydrogen, methanol). In each case, the actual operating conditions were taken into account, and the models were verified in terms of combustion, emissions, and efficiency. In Ref. [67], data from the RANS simulation was used to train ML models predicting the hydrogen mixing efficiency in a scramjet engine. In Ref. [68], a simplified CFD model was integrated with a neural network for predicting phenomena in blast furnaces. Although the approach significantly reduced the calculation time, it was pointed out that the model needed to be adapted when boundary conditions changed. In Ref. [69], the order reduction of the CFD model by orthogonal decomposition (POD) was used, and then combustion parameters (temperature and oxygen distribution) were predicted using SVM. The slagging index was assessed using the fuzzy comprehensive assessment (FCE) method, and optimization was carried out with both classic GA and its simulated annealing (SAGA) version, resulting in a significant reduction in slagging levels. In Refs. [50, 70], CFD was applied to the modeling of combustion in a coal-fired boiler, where optimization was carried out using a GA. The authors emphasized that the results must be confirmed in real industrial conditions. Ref. [71] combined CFD and machine learning to optimize the geometry and injection parameters of the GCI engine, achieving up to a 10.7% reduction in fuel consumption under partial-load conditions. In Ref. [72], a simple linear regression model was developed to predict the three-dimensional structure of fuel spray under Spray G conditions of the ECN network, using experimental data and tomography. The model showed very good compliance with real measurements and exceeded the accuracy of classic CFD simulations with less computational effort. Ref. [73] presents the current state of development of CFD simulations of high-fidelity combustion of pulverized coal, with particular emphasis on DNS, LES, and combinations of the two. The authors pointed out that further advances require more efficient algorithms, model calibration, and greater use of machine learning to improve prediction accuracy and reduce computational costs. In Ref. [74], a micro-genetic optimization algorithm and CFD were used to improve efficiency and reduce emissions in a dual-fuel heavy fuel engine running on natural gas and diesel.
## 3 Experiments and Discussions
### On-Machine Measurement
In addition, the residuals of the fiducials after best fitting to those calibrated in the CMM are illustrated in Table 3. The results show that the maximum residual of the sphere diameters is up to 1 \(\upmu\)m.
## 2 Calibration Status of Landsat 8 Operational Land Imager (OLI)
### Prelaunch Radiometric Characterization and Calibration  
The radiometric calibration and characterization of the OLI instrument, both in the pre-launch and on-orbit realms, has been well documented ([PERSON] et al., 2018; [PERSON] et al., 2018). The OLI, as indicated, has extensive on-board radiometric calibration capabilities that allow tracking its performance on-orbit. These capabilities  
Figure 1: (**a**) Spectral responses of each focal plane module in the OLI red band; (**b**) effects of spectral response differences in uniformity across the focal plane when flat fielded on the solar diffuser (2018).  
by themselves do not ensure low uncertainty in the radiometric calibration of the instrument; key pre-launch characterizations also required include instrument stability, linearity and non-uniformity, and the calibrators' radiometric and geometric properties.  
The OLI was radiometrically calibrated prior to launch relative to radiance standards traceable to the National Institute of Standards and Technology (NIST) [5]. This calibration is available within the data products. However, uncertainty calculations indicate that a more accurate radiometric calibration can be obtained using the reflectance based approach. This approach relies on the knowledge of the reflectance of the on-board diffuser along with knowledge of the illumination and viewing geometry of this diffuser. The University of Arizona measured the diffuser reflectance at multiple-view angles, illumination angles and locations to reflect the geometric conditions of use.  
The estimated uncertainty in the reflectance-based calibration (Table 3) is about 2%, with the dominant effects being the reflectance measurement uncertainty itself, non-linearity, geometric uncertainty (contributes to illumination angle and view angle uncertainties), and stray light within the diffuser assembly.
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
###### Abstract  
Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.  
air quality; machine learning; linear models; Random Forest; LSTM Article
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
###### Abstract  
This paper comprehensively reviews structural vibration control systems for earthquake mitigation in civil engineering structures. Structural vibration control is vital for enhancing the resilience and safety of infrastructure subjected to seismic activity. This study examines various control strategies, including passive, active, and hybrid methods, with a focus on the advantages of semi-active systems, which offer a balance of energy efficiency and adaptive capabilities. Semi-active devices, such as magnetorheological dampers, are highlighted for their ability to offer adaptive control without the high energy demands of fully active systems. The review discusses challenges like time delays, sensor placement, and model uncertainties that can impact the practical implementation of these systems. Experimental studies and real-world applications demonstrate the effectiveness of semi-active systems in reducing seismic responses. This paper emphasizes the need for further research into optimizing control algorithms and addressing practical challenges to enhance the reliability and robustness of these systems. It concludes that semi-active control systems are a promising solution for enhancing structural resilience in earthquake-prone areas, offering a practical alternative that strikes a balance between performance and energy requirements.  
structural control; semi-active control; seismic control; earthquakes; dampers; control algorithms 2020