esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Different Ratios of Cow Manure and Chemical Fertilizers on Fruit Quality of Gala Apples
## 2 Materials and Methods
### Aroma Substances  
The fruit aroma substances and their contents were determined by headspace extraction combined with gas chromatography-mass spectrometry.  
Headspace extraction combined with gas chromatography-mass spectrometry (GC-MS) was performed on an Agilent 7890A/5975C gas chromatograph-mass spectrometer.  
Sample pretreatment: The fruit samples were taken, cleaned, cut and de-seeded, diced, and sampled by the quadratic method. A total of 200 g of samples were crushed and homogenized with a juicer, 10 g of homogenate was weighed in a 20 mL headspace vial, 1 g of sodium chloride and 5 \(\upmu\)L of 0.4 g/L 3-nonanone were added, sealed in a glander, and stored at \(-80\)\({}^{\circ}\)C for measurement.  
Headspace extraction: The sample vial was equilibrated at a constant temperature of 40 \({}^{\circ}\)C in a water bath for 10 min, and the Supelco 50/30 \(\upmu\)m PDMS/CAR/DVB solid phase microextraction head was inserted into the headspace portion of the sample vial and extracted at 40 \({}^{\circ}\)C in a water bath for 30 min.  
GC-MS measurement conditions: the column was HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m); the carrier gas was high-purity helium with a flow rate of 1.3 mL/min. manual injection, no splitting mode. Inlet temperature was 250 \({}^{\circ}\)C. Column temperature ramp-up procedure: initial 40 \({}^{\circ}\)C, hold for 3 min, ramp up to 140 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min; ramp up to 240 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, hold for 3 min, mass spectrometry interface temperature at 250 \({}^{\circ}\)C, ion source temperature at 230 \({}^{\circ}\)C, full scan mode, and scan range: 40-550 amu.  
The data were analyzed using the chromatographic workstation that comes with the system, and the integration parameters were: initial area truncation 10,000, initial peak width 0.1, shoulder peak detection OFF, and initial threshold 5.0. The spectrum library was selected from the NIST 11 standard library. The internal standard method was used for the quantitative analysis of the aroma components, and the content of the substances was expressed as the amount of three-nonanone.  
Calculation formula (ug/g FW) = sample peak area \(\times\) 0.4 \(\times\) 5 \(\times\) 10\({}^{-6}\)/3-nonanone peak area/sample mass \(\times\) 10\({}^{9}\) (Sample mass is generally 10 g).
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 2 CNNs and Normalization Layers
### From BN to Whitening Features  
Image data may contain correlating data between nearby pixels and channels. For example, the pixels nearby a blurry object in an image correlate spatially. In addition, a grey object in an RGB image results in channel-wise correlation, since the RGB values are close to equal in the grey pixels. Machine vibration data may contain a similar correlation. For example, nearby sensors likely collect similar vibration patterns. Such data can hamper the optimisation process by inducing a correlation between the features learned by the neural network. Moreover, correlated features have been deemed problematic for neural network optimisation in the past [28].  
Although BN has been shown to be effective in numerous state-of-the-art works [35; 36; 37; 38], it merely scales and shifts the activations. BN was not designed to decorrelate the features due to the expense of computing the inverse square root of the activation covariance matrix and the corresponding derivatives. Nevertheless, several normalisation techniques that also decorrelate features have been proposed. These techniques typically seek better optimisation results by whitening the activations directly. Activations can be whitened for example according to the population statistics [39; 40]. However, the estimation of the population statistics is hindered by the computational constraints and the changes to the activation distributions caused by the weight updates during optimisation [27]. Fortunately, these problems can be avoided by whitening the activations based on the mini-batch statistics. Such techniques are, for example, decorrelated batch normalisation (DBN) [30], IterNorm [41] and network deconvolution [31]. These three techniques first estimate the covariance matrix of the activations in the batch, as shown in Equation (8). In Equation (8), \(\mathbf{X}\in\mathbb{R}^{C\times N}\) is the activation matrix with \(C\) channels and \(N\) samples, and \(\mathbf{\mu}\in\mathbb{R}^{C}\) is the mean of the \(N\) activations in the batch. The batch of activations is then whitened with the inverse square root of the covariance matrix, i.e., the whitening matrix, and the batch mean, as shown in Equation (9). DBN has a slight disadvantage since it computes the whitening matrix with eigenvalue decomposition. IterNorm was proposed to solve the computational problems of DBN. IterNorm approximates the inverse square root of the whitening matrix with [PERSON]'s iteration. Network Deconvolution is similar to IterNorm. However, it employs coupled Newton-Schulz iteration, which was shown both a quick and stable approximation technique for the whitening matrix [31].  
\[\mathbf{Cov}=\frac{1}{N}(\mathbf{X}-\mathbf{\mu})^{T}(\mathbf{X}-\mathbf{\mu}) \tag{8}\]  
\[\mathbf{\widehat{X}}=(\mathbf{X}-\mathbf{\mu})\mathbf{Cov}^{-\frac{1}{2}} \tag{9}\]
###### Abstract  
In electrical power systems, from generation power stations down to distribution substations, power transformers play a key role in ensuring reliable electricity transfer in the correct range from the generating source to the end-users. Over time, due to their operational demands and other various factors, transformers become susceptible to failures which threaten their reliability and life span. To address this issue, various transformer fault diagnosis methods are employed to detect and monitor the state of transformers, such as the dissolved gas analysis (DGA) method. In this paper, a systematic literature review (SLR) is conducted using the Preferred Reporting Items for Systematic Reviews (PRISMA) framework to record and screen current research work pertaining to the application of machine learning algorithms for DGA-based transformer fault classification. This study intends to assess and identify potential literature and methodology gaps that must be explored in this research field. In the assessment of the literature, a total of 124 screened papers published between 2014 and 2024 were surveyed using the developed PRISMA framework. The survey results show that the majority of the research conducted for transformer fault classification using DGA employs the support vector machine (32%), artificial neural network (17%), and k-Nearest Neighbor (12%) algorithms. The survey also reveals the countries at the forefront of transformer fault diagnosis and a classification based on DGA using machine learning algorithms. Furthermore, the survey shows that the majority of research conducted revolves around fault diagnosis with an emphasis on improving the accuracy of techniques such as SVM and ANN. At the same time, limited effort is put into other key metrics such as precision, Mean Squared Error, and R-Squared, and also, current works surveyed do not explore regularization techniques for preventing overfitting and underfitting of the proposed models.  
Keywords:transformers; dissolved gas analysis; machine learning; system literature review
# An Improved Genetic Algorithm for Solving the Semi-Soft Clustered Vehicle Routing Problem
## 3 Problem Description and Mathematical Modeling
### Mathematical Modeling  
For convenience in modeling, the following notations are introduced: Let \(S\) denote any non-empty subset of \(V\). Let \(\delta^{+}(S)\) denote the set of arcs \((i,j)\in S\times V\backslash S\). Let \(\delta^{-}(S)\) denote the set of arcs \((i,j)\in V\backslash S\times S\). Additionally, the following two decision variables are defined:  
\[x_{ijk}=\left\{\begin{array}{ll}1,\text{ if vehicle }k\text{ travels from node }i\text{ to node }j\\ 0,\text{ otherwise}\end{array}\right.\]  
\[y_{ik}=\left\{\begin{array}{ll}1,\text{ if customer }i\text{ is served by vehicle }k\\ 0,\text{ otherwise}\end{array}\right.\]  
The mathematical programming model for the SemiSoftCluVRP is as follows:  
\[\min\sum_{(i,j)\in E}\sum_{k\in K}d_{ij}x_{ijk} \tag{1}\]  
\[\sum_{k\in K}y_{ik}=1\;\forall i\in N \tag{2}\]  
\[\sum_{k\in K}y_{0k}\leq m \tag{3}\]  
\[y_{0k}\geq y_{ik}\;\;\forall i\in N,\forall k\in K \tag{4}\]  
\[\sum_{j\in N}x_{ijk}=\sum_{j\in N}x_{jik}=y_{ik}\;\forall k\in K,\forall i\in V \tag{5}\]  
\[\sum_{i\in V}q_{i}y_{ik}\leq Q\;\;\forall k\in K \tag{6}\]  
\[\sum_{i\in S}\sum_{j\
otin S}x_{ijk}\geq y_{hk}\;\;\forall S\subseteq N,\forall h \in S,\forall k\in K \tag{7}\]  
\[\sum_{(i,j)\in\delta^{+}(V_{i})}\sum_{k\in K}x_{ijk}=\sum_{(i,j)\in\delta^{-} (V_{i})}\sum_{k\in K}x_{ijk}=1\;\forall V_{r}\in R_{1} \tag{8}\]  
Figure 1: Schematic diagram of feasible solutions for the CluVRP, SoftCluVRP, and SemiSoftCluVRP.  
\[\sum_{(i,j)\in\delta^{+}(V_{r})}\sum_{k\in K}x_{ijk}=\sum_{(i,j)\in\delta^{-}(V_{r} )}\sum_{k\in K}x_{ijk}\geq 1\ \forall V_{r}\in R_{2} \tag{9}\]  
\[y_{ik}=y_{jk}\ \ \ \forall i,j\in V_{r},\forall k\in K \tag{10}\]  
\[x_{ijk}\in\{0,1\}\ \ \forall(i,j)\in E,\forall k\in K \tag{11}\]  
\[y_{ik}\in\{0,1\}\ \ \forall i\in V,k\in K \tag{12}\]  
In the above mathematical model, the objective function (1) minimizes the total distance traveled by all vehicles. Constraint (2) ensures that each customer is visited exactly once. Constraint (3) indicates that the number of vehicles does not exceed the number of clusters. Constraint (4) guarantees that all the vehicles used should visit the depot. Constraint (5) ensures that the same vehicle that arrives at a customer also leaves from that customer. Constraints (6) and (7) are the vehicle capacity constraint and the subtour elimination constraint, respectively. Constraints (8) and (9) impose the service restrictions for hard and soft clusters, respectively. Constraint (10) ensures that the customers in each cluster are served by the same vehicle. Finally, constraints (11) and (12) define the domain of the decision variables.
# Causality in Control Systems Based on Data-Driven Oscillation Identification
## 3 Research Case Studies
### Simulation Data  
To generate data for analysis, an artificial multi-loop simulation model is created in MATLAB Simulink(r) environment. The model scheme is presented in Figure 2. This model consists of five control loops--four PID controllers (\(R_{1}\), \(R_{2}\), \(R_{3}\), and \(R_{4}\)) and one PI (\(R_{5}\)) controller. The control errors from the loops, marked in the scheme as \(\epsilon_{1}\), \(\epsilon_{2}\), \(\epsilon_{3}\), \(\epsilon_{4}\), and \(\epsilon_{5}\), respectively, are the basis for the problem analysis. The model incorporates the possibility to apply noises. In this case, two variants are given--first that simulation data is distorted by Gaussian noise (represented by variable \(\sigma_{i}\)) and second that simulation data is distorted by both Gaussian and Cauchy (represented by variable \(\gamma_{i}\)) noises. A sinusoidal signal is added to the Cauchy disturbance to simulate potential loop oscillations of known frequency and their propagation.  
Simulated process transfer functions are in form of the following linear models:  
\[\begin{split} G_{1}(s)&=\frac{1}{0.15s+1},\quad G_{2 }(s)=\frac{0.25(-s+1)}{s(2s+1)},\quad G_{3}(s)=\frac{1}{\left(s+1\right)\left(0.04s^{2}+0.04s+1\right)},\\ G_{d}(s)&=\frac{-0.25(-s+1)}{s(s+1)(2s+1)},\quad G_{4 }(s)=\frac{1}{\left(s+1\right)^{4}},\quad G_{5}(s)=\frac{1}{\left(0.2s+1\right) ^{2}}e^{-s}.\end{split} \tag{7}\]  
Feedforward filters are defined as  
\[\begin{split} F_{3}(s)&=\frac{1}{4s^{2}+3.2s+1}, \quad F_{4}(s)=\frac{1}{s^{2}+1.2s+1},\quad F_{5}(s)=\frac{1}{4s^{2}+2s+1},\\ F_{d3}(s)&=\frac{1}{2s+1},\quad F_{d4}(s)=\frac{0. 1}{s+1},\quad F_{d5}(s)=\frac{4}{2s+1}.\end{split} \tag{8}\]  
Figure 2: Simulation environment presenting multi-loop PID-based control layout.  
Figure 5: Control errors time series for the simulation data impacted by the Gaussian noise and Cauchy disturbance.  
Figure 6: Control errors histograms for the simulation data impacted by the Gaussian noise with PDF fitting. (**a**) Control error \(\epsilon_{1}\), (**b**) Control error \(\epsilon_{2}\), (**c**) Control error \(\epsilon_{3}\), (**d**) Control error \(\epsilon_{4}\), (**e**) Control error \(\epsilon_{5}\).
## 4 Results and Discussion  
This section discusses the models developed in the previous section. As mentioned earlier, the seepage equations in earth dams and embankments were first implemented and then coded, then the different conditions of these equations were investigated. Finally, a dataset containing 24,000 datasets evaluating different aspects was created. Then, neural and hybrid models were developed to provide a new solution for seepage rate estimation. Different models of the neural network were developed and finally the best model was improved by the IWO algorithm in order to increase its capacities in the simulation. This final model was used to evaluate the seepage rate as a new solution for all data. The results of the two sections of training and testing are presented in Figures 10 and 11. As can be seen, the new solution has been able to offer high potential as an alternative to the equations governing for seepage rates in the defined condition.  
Figure 14: The upstream slope effects on seepage rate based on the newly developed method.  
Figure 13: The freeboard effects on seepage rate based on the newly developed method.  
Figure 16: The width crest effects on seepage rate based on the newly developed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Parameters** & **Equation** & \(\mathbf{R^{2}}\) \\ \hline Water height & Y = \(9\times 10^{-5}\) e\({}^{0.0491\mathrm{x}}\) & 0.9944 \\ Freeboard & Y = \(-8\times 10^{-6}\) x + 0.0002 & 0.9994 \\ Upstream slope & Y = \(2\times 10^{-6}\) x + 0.0001 & 0.9997 \\ Downstream slope & Y = \(8\times 10^{-6}\) e\({}^{0.0425\mathrm{x}}\) & 0.9998 \\ Width crest & Y = \(-3\times 10^{-6}\) x + 0.0002 & 0.9996 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The equations between the parameters and seepage rate (x and y indicate the parameters and seepage rate in the equations).  
Figure 15: The downstream slope effects on seepage rate based on the newly developed method.
# Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System
## 3 Proposed Methodology  
The proposed system shown in Figure 1 is designed to be compact, portable, and fitting on the tip of a finger. Equipped with a digital camera, it is capable of capturing images of the braille dots for processing. The dimensions of each braille dot are determined based on the tactile resolution of a person's fingertips. The dot's height measures approximately 0.5 mm (0.02 inches), with a vertical and horizontal spacing of 2.5 mm (0.1 inches) between dot centers and a spacing of 3.75 mm (0.15 inches) between adjacent cells. A standard braille document measures 11 \(\times\) 11.5 inches with each line having between 40 and 43 cells.  
Figure 1 shows a detailed workflow of the proposed system. During the AI software processing phase, the captured image with the help of a button will be segmented to exclude the region of the image that does not contain braille dots. The IoT system follows a series of image processing steps, including edge detection, binary conversion, hole fitting, and image filtering. Preprocessing methods are used to reduce noise and enhance the visibility of the dots. The system also performs segmentation to allow for individual identification of the letters. During the next step, the image is resized to 16 \(\times\) 16 pixels. In the braille system, each letter is represented by a single cell consisting of six dots arranged in two columns and three rows. Once the image has been extracted, the system undergoes training to classify the braille characters based on their corresponding classes. Each letter or number is associated with a specific class, allowing for accurate mapping. The performance of the algorithms used to train the models is evaluated in terms of accuracy, positive and negative predicted values, and other relevant metrics. It is important to note that misclassification errors may arise due to challenges encountered during noise removal, variations in braille dot sizes, and the process of segmentation.
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 3 Results
### Rheological Characterization of Modified Binders  
#### 3.2.1 Superpave Grading  
Base binder, S1 (PG 64-22), was modified to PG 58-28 using modifiers provided in Table 5. All the modifiers were able to convert the base binder to the acceptable limits of PG 58-28 except modifier K (ReOB). Figure 4a shows the continuous PG for base binder, unmodified PG 58-28 (S5), and modified binders. The selection of modifier dosage was based on (i) achieving similar high temperature true-grades (0.6 \({}^{\circ}\)C standard deviation) and (ii) obtaining similar true-grade results to S5's (\(\pm\)1.2 \({}^{\circ}\)C).  
For modifier K, none of the dosages from 6% to 12% were able to produce a PG 58-28 binder. It was observed that increase in the addition of modifier K had a softening effect on high and intermediate temperature grades (Figure 4b,c). In case of low temperature PG, the stiffness was reduced with  
Figure 4: (**a**) Continuous PG of binders; for modifier K, effect of dosage on (**b**) High-temperature PG; (**c**) intermediate-temperature PG; and (**d**) Low-temperature PG Stiffness and PG\({}_{\text{m-value}}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Binder ID** & **High PG** & **Low PG** & **Continuous PG** & **Superpave PG** \\ \hline S1 & 66.4 & \(-\)23.7 & 66.4-23.7 & 64-22 \\ S5 & 61.1 & \(-\)29.5 & 61.1-29.5 & 58-28 \\ S1-A-3.5 & 61.6 & \(-\)30.0 & 61.6-30.0 & 58-28 \\ S1-C-3.1 & 61.4 & \(-\)30.1 & 61.4-30.1 & 58-28 \\ S1-D-3.1 & 62.1 & \(-\)28.3 & 62.1-28.3 & 58-28 \\ S1-E-3.1 & 62.3 & \(-\)30.7 & 62.3-30.7 & 58-28 \\ S1-G-6.5 & 60.8 & \(-\)28.5 & 60.8-28.5 & 58-28 \\ S1-K-10 & 59.4 & \(-\)27.3 & 59.4-27.3 & 58-22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Continuous and Superpave Performance Grade (PG) of base binder, unmodified binder and modified binders.  
increasing modifier dosage whereas m-value was not affected (as shown in Figure 4d). Moreover, the low temperature PG for K was controlled by the m-value which failed to meet required criteria. The 10% dosage for K was chosen to obtain the continuous PG closest to PG 58-28.
## 3 Pseudo-Dynamic Tests and Numerical Analysis of Model Structure
### Experimental and Numerical Results
#### 3.3.2 Lateral Displacement Responses  
Table 4 summarizes the test results of maximum lateral displacements and drift ratios on the top of A-shaped frame. Figure 11 presents the comparison of time-history lateral displacement responses between the FE simulation and test results. It can be found that there was a good agreement between the numerical and experimental results. Both the peak values and phases of the lateral displacements exhibited the reasonable accuracy. This indicated that the proposed FE simulation approaches could reasonably simulate the seismic behavior of this hybrid structure. In addition, it can be seen that the occurring moments of peak displacements were delayed with increase of PGA, as shown in Figure 11. This was due to the fact that the accumulated damage of the specimen with increase of PGA, resulting in the stiffness deterioration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Forward Loading Direction (+)**} & \multicolumn{2}{c}{**Backward Loading Direction (-)**} \\ \cline{2-5}
**PGA (g)** & \multicolumn{1}{c}{**Maximum Lateral**} & \multicolumn{1}{c}{**Maximum Load**} & \multicolumn{1}{c}{**Maximum Lateral**} & \multicolumn{1}{c}{**Maximum Roof**} \\  & **Displacement (mm)** & **Drift Ratio (\%)** & **Displacement (mm)** & **Drift Ratio (\%)** \\ \hline
0.05 & 1.28 & 0.02 & 0.97 & 0.01 \\
0.10 & 2.05 & 0.03 & 1.52 & 0.02 \\
0.20 & 8.23 & 0.10 & 8.07 & 0.10 \\
0.30 & 10.70 & 0.13 & 9.77 & 0.12 \\
0.40 & 12.72 & 0.16 & 11.73 & 0.14 \\
0.60 & 17.99 & 0.22 & 16.66 & 0.21 \\
0.80 & 29.07 & 0.36 & 32.29 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of maximum lateral displacements and drift ratios on the top of A-shaped frame.  
Figure 11: _Cont._  
Figure 10: Numerical results of tensile damage patterns of tubular columns: (**a**) At the end of loading case with PGA (peak ground acceleration) of 0.20 g; (**b**) At the end of loading case with PGA of 0.60 g.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Most Common Scenarios Used to Evaluate the Integration between 5G and Deep Learning?  
Evaluating the works found in this systematic review, we noted that most of the works (40 of them) considered a generic scenario in their evaluations, and that only 16 articles considered specific ones.  
The urban environment tops the studies as the most common scenario presented in the works [6, 34, 41, 44, 45, 46, 48, 66, 67, 72, 75]. This is justifiable as urban scenarios are very dynamic, very challenging and heterogeneous, with the presence of different obstacles (persons, vehicles, and buildings). They reflect extreme conditions that could not be easily handled by the previous cellular generations and where 5G requires special solutions such as the use of milli-meter waves, advanced beamforming, NOMA, etc., to deliver its promises. Notably, efficient usage of the frequency spectrum and the high energy consumption are two big challenges present in these scenarios [96].  
Two recent works [2, 17] considered vehicular networks as use case to evaluate their solutions. These demonstrate the increased research and interest in the domain of autonomous and connected vehicles, where 5G networks play a important role, providing a low latency with high availability [97]. Vehicular networks present unprecedented challenges that are not present in traditional wireless networks, such as fast-varying wireless propagation channels and ever-changing network topology [98]. Therefore, many researchers see the use of deep learning as a promising venue to solve some the stringent 5G problems.  
In [2], the authors considered two vehicular network configurations while varying the device battery capacity and the available bandwidth; and also scenarios with a smartphone transmission of high definition (720p) real-time video conferencing signals.  
Three different scenarios were evaluated in [5], namely, a video medical consultation (full duplex two direction live stream uplink/downlink), a virtual treatment (propagating a single direction video live stream (downlink)) and a simple Data Submit (Single direction data exchange over the uplink).  
Cellular networks can also differ in terms of device location such as when operating indoor or outdoor. Indoor environments (homes and offices) have different characteristics that outdoor ones (road intersections, squares, stadiums, etc.). Evaluations carried out in outdoor scenarios are more common in the works found in this systematic review. Additionally to the works that considered urban cities and vehicular network, the work presented in [35] considered the users location problem within the New York University campus. A hybrid setup was considered in [7]. Here two outdoor and two indoor scenarios were examined: The two outdoor scenarios were parking lots situated outside a building, while the two indoor scenarios were a workroom and an aisle inside a building.