esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network
## 1 Introduction  
Accurate prediction of elastic and reservoir properties is crucial in getting a more precise image of the subsurface. This will lead to the correct placement of a well, reducing risks and uncertainties and thus increasing the chance of success in finding hydrocarbon. Velocity model building (VMB) as well as seismic inversion and rock physics study have been used to achieve these objectives. Although significant advancement has been made in these methods, they remain an open topic with a lot of ongoing research. There are several approaches to VMB [1]. The most conventional method is tomography, an iterative process involving several steps, such as sorting the data into common image points (CIP) and picking the reflections to flatten the image gathered prior to producing the final velocity model. Another widely used approach is Full Waveform Inversion (FWI) [2], which matches calculated data with observed data by considering amplitude and travel-times [3, 4]. Traditional FWI is depth-limited as the diving wave does not penetrate deep enough in the subsurface, hence the reflection FWI (RFWI) is proposed [5]. RFWI suffers from a highly non-linear coupling of density and velocity. The problem faced by RFWI can be solved by Joint Migration Inversion (JMI) [6, 7], which manages to reduce the non-linearity of the inversion. However, JMI is based on a one-way wave equation, which is inferior to FWI. In 2018, a hybrid FWI-JMI was proposed [8]. Seismic inversion and rock physics study is another method used in reservoir characterization. Unlike VMB, this method is much more focused on the reservoir itself than on the entire seismic volume [9, 10]. In a multi-step inversion approach, elastic properties such as P-impedance, S-impedance,and density are produced by stochastic or deterministic inversion on seismic data. Next, at a well, a rock physics model is developed connecting these elastic properties to reservoir properties. High-resolution models of the reservoir's properties are created in a single-loop manner before rock physics transformations are used to create the elastic properties volume. After that, calculated synthetic seismic traces are compared to actual seismic data. In order to combine these multi-step and single-loop approaches, [PERSON], [PERSON] [9] suggested using the output from the first approach as an a priori for the second approach. As was previously established, inversion and rock physics are reservoir-centric, and accuracy decreases as reservoir depth increases. A regional rock physics template (RPT), which is based on the rock physics inclusion model, has been proposed to address these issues [11].
# Study on Nanosecond Laser Ablation of 40 Cr13 Die Steel Based on ANOVA and BP Neural Network
## 3 Effects of Process Parameters on Ablation Results
### Effects of Process Parameters on Material Removal Rate  
Figure 8 shows the effect of laser power and scan speed on the MRR and cross-sectional area for a pulse frequency of 50 kHz. The cross-sectional area clearly changes with the laser power and scan speed; the cross-sectional area increases with an increase in laser power, and it decreases with an increase in scan speed. However, the change rule of the MRR based on laser power and scan speed is unclear because the MRR is the result of the combined effect of the cross-sectional area and scan speed.  
Figure 9 shows the effect of laser power and pulse frequency on the MRR and cross-sectional area for a scan speed of 150 mm/s. The cross-sectional area increases with an increase in laser power or pulse frequency. The change rule of the MRR with the pulse frequency is unclear. Table 7 shows the ANOVA results of the MRR. Laser power has the highest degree of influence, at 70.4%, followed by pulse frequency and scan speed, at 27.4% and 2.1%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Process Parameter** & \begin{tabular}{c} **Degree of** \\ **Freedom** \\ \end{tabular} & \begin{tabular}{c} **Sum of** \\ **Square** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & \begin{tabular}{c} **F-Value** \\ \end{tabular} & \
\begin{tabular}{c} **\(\rho\)-Value** \\ \end{tabular} &
\begin{tabular}{c} **Influence** \\ **Level** \\ \end{tabular} \\ \hline Laser Power & 2 & 733.699 & 366.850 & 117.493 & 0.000 & 60.9\% \\ \hline Pulse Frequency & 3 & 153.075 & 51.025 & 16.342 & 0.000 & 8.5\% \\ \hline Scan Speed & 3 & 553.007 & 184.336 & 59.038 & 0.000 & 30.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANOVA results of W.  
Figure 8: Effect of laser power and scan speed on the MRR.  
Figure 7: Effect of laser power and pulse frequency on W.
## 4 Model Development and Simulation  
### The Conventional VW Crafter Development  
The conventional VW Crafter based on the model-in-the-loop (MIL) approach was simulated in MATLAB software (R2024a version). The model comprises various subsystems such as the drive cycle, the vehicle controller, the engine, the torque/speed sensor, the transmission, the gearbox controller, the tire and vehicle dynamics subsystems. The proposed engine in this research made in 2012 and complied with the euro 6d emission standard, is based on the 2.0 TDI CR diesel engine, which has replaced a 2.5 TDI CR diesel engine. The study in [45] reaffirmed that the 2.5 TDI CR diesel engine had a turbo failure problem, necessitating its redesign in 2010 and an updated version that used diesel exhaust fluid (DEF) combined with DPF, which complies with the euro V emission standard. The 2.0 TDI was chosen due to its higher output torque, reduced consumption, and lower emission compared to the 2.5 L TDI engine. The proposed engine features a displacement of 1968 cc, an approximately 8-900 km range, and a tank capacity of 70 L. The conventional VW Crafter vehicle's powertrain consists of a mechanical drivetrain comprising a transmission and driveline. The transmission includes a disc friction clutch and a six-speed manual gearbox, while the driveline consists of the driveshaft and final drive. Real-world driving scenarios based on WLTP were simulated to assess the vehicle's fuel consumption, serving as a benchmark for the hybrid powertrain design. Figure 19 shows the MATLAB (R2024a) model of the conventional VW Crafter with modeling approach adapted from [68].
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method
### Model Training for Noise-Assessment-Based Pulse Signal Screening  
#### 3.4.1 Random Forest  
The random forest technique, an ensemble method of decision trees [19], is employed to achieve accurate predictions. This approach involves generating multiple training datasets through random sampling, training decision trees on these datasets, and aggregating the predictions of each decision tree to obtain the final prediction [23].  
To create a random forest model, we utilized the 'RandomForestClassifier' class. In order to mitigate the risk of overfitting, we set the maximum depth of each decision tree to 2 using the max_depth parameter. Additionally, considering the characteristic of random forest where it learns randomly from each decision tree, we set the random_state to 0 to  
Figure 8: Region of interest for signal-to-noise ratio extraction.  
ensure consistent learning results across the model. Random forest is particularly effective for handling large amounts of data and mitigating overfitting issues that can arise from excessive noise within the model, thereby improving the predictive performance.
## 2 Materials and Methods
### Analysis of Bacterial Communities Colonizing the Substrate Surface
#### 2.4.2 Dgge  
A DCodeTM Universal Mutation Detection System (Bio-Rad Laboratories, Hercules, CA, USA) was used for DGGE analysis. Samples containing approximately 800 ng of nested-PCR amplicons were loaded onto 8% (_w/v_) polyacrylamide gels (37.5:1, acrylamide/bis-acrylamide) in 1\(\times\) TAE buffer using a denaturing gradient ranging from 35 to 60% (100% denaturant solution contained 7 M urea and 40% (_v/v_) formamide). Electrophoresis was performed at 60 \({}^{\circ}\)C in 1\(\times\) TAE buffer, initially at 20 V (15 min) and then at 75 V (960 min). Every gel contained a commercial standard marker (Hyperladder IV, Bioline, London, UK) for internal and external normalization of the gel. Gels were staining using 5\(\times\) GelGreen in 0.1 M NaCl solution (Biotium, Fremont, CA, USA). DGGE images were acquired using a Safe ImagerTM Blue-Light Transilluminator (Invitrogen, Waltham, MA, USA) and a Micro DOCTM gel documentation system (Cleaver Scientific Ltd., Warwickshire, UK).  
DGGE profiles, concerning the presence and intensity of the bands, were analyzed using Bionumeric's software (Applied Maths, St-Martens-Laten, Belgium). Sample profiles were compared using Jaccard similarity coefficient (1% band matching position tolerance) and clustered according to unweight pair group mean average (UPGMA) method. Species richness (S) was calculated based on the total number of distinct bands in a lane, while the Shannon-Wiener index (H') was determined by using the intensity of the DGGE bands given by peak heights in the densitometric curve, as follows (1):  
\[H=-\sum\left(\frac{\text{ni}}{\text{N}}\right)\,\text{log}\left(\frac{\text{ni }}{\text{N}}\right) \tag{1}\]  
where ni is the height of the peak and N is the sum of all peak heights of the densitometric curve for all bands in each sample. The influence of sampling zones (inlet/outlet), season (cold/hot) and year (1, 2, 3) on the composition of bacterial communities was evaluated through permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. PERMANOVA and Principal Coordinate Analysis (PCO) plot were performed based on Bray-Curtis distances matrixes constructed from the DGGE dataset using the PRIMER 6 and PERMANOVA+ software (PRIMER-E Ltd., Plymouth Marine Laboratory, Plymouth, UK).
## 5 Instrumental Analysis
### Separation Techniques
[32] coupled a DB-5 MS primary column (30 m \(\times\) 0.25 mm) with a DB-17 MS secondary column (1 m \(\times\) 0.25 mm) in a GC \(\times\) GC-HRMS workflow, detecting 267 semi-volatile organic compounds (SVOCs) in rPET using orthogonal column selectivity. Similarly, [PERSON] et al. [39] coupled a semi-non-polar HP-5 MS primary column (30 m \(\times\) 0.25 mm) with a semi-polar DB-17 MS secondary column (1 m \(\times\) 0.25 mm), resulting in 1247 tentatively identified VOCs, including hydrocarbons, benzenoids, organic oxygen compounds, lipids, and lipid-like compounds, demonstrating the applicability of GC \(\times\) GC in NTA. The reviewed studies highlight the adaptability of LC and GC methods to diverse NIAS profiles, with UHPLC being the preferred technique for polar/non-volatile analytes and GC\(\times\)GC being able to address the increased complexity of recycled materials. Column chemistry, gradient design, and temperature optimization remain important in balancing resolution, analysis time, and sensitivity, underscoring the need for method optimization. New separation dimensions, such as IMS, further enhance identification capabilities, driving the adoption of multidimensional and hyphenated techniques.
## 3 Experimental Setup
### Validation Results  
Figure 11 compares the validation accuracy and loss to evaluate each encoding method's generalization performance on unseen data.  
Among the three-phase encoding methods, DWT achieved the highest average validation accuracy of 98.9%, while RGB and STFT recorded similar accuracies of 97.6%. These values were averaged over the final 10 epochs (epochs 41-50).  
However, both 3 Phase-RGB and 3 Phase-STFT exhibited fluctuations in their validation curves during the early and middle training stages. Notably, 3 Phase-STFT struggled to  
Figure 10: Comparison of training accuracy and loss by encoding method: (**a**) accuracy; (**b**) loss.  
converge even in later epochs. In contrast, 3 Phase-RGB eventually stabilized and delivered a relatively consistent performance.  
For the Id-based encodings, Id-STFT achieved a 99.6% average accuracy, while Id-Linear reached the highest at 99.7%. Conversely, Id-DWT had the lowest performance at 88.8%, indicating that a DWT image derived from a single time series may be insufficient to capture the complexity of fault patterns.  
These findings show that single-channel (Id) inputs, when transformed using STFT or linear mapping, can achieve a classification performance and training stability comparable to those of multi-channel, three-phase methods.  
Figure 12 compares the best-performing three-phase method (3 Phase-DWT) and the best-performing Id-based method (Id-STFT).  
Both methods exhibit fast learning and rapid convergence in validation performance, achieving a high accuracy.  
This confirms that meaningful feature extraction in the time-frequency domain, as in STFT, enables single time-series inputs to perform comparably to three-channel, three-phase data.  
Particularly, Id-STFT benefits from using grayscale input images, which reduces computational and memory demands compared to RGB-based methods--ultimately enabling a faster and more efficient fault diagnosis.  
Figure 11: Comparison of validation accuracy and loss by encoding methods: (**a**) accuracy; (**b**) loss.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.4 Load Model  
The ideal load-balancing state in a network is one in which equal load is assigned to all controllers. In this section, we consider the average flow request volume of switches within a controller's management scope to be the controller's load. Through the allocation of switches, the load difference among all controllers is defined as the load deviation rate, denoted as \(L\). The smaller the index, the better the network load-balancing performance after correctly placing controllers. The load deviation rate of the controller is calculated by Equation (10):  
\[L=\sqrt{\frac{1}{k}\sum_{v_{c}\in C}\left(l_{c}-l_{opt}\right)^{2}}, \tag{10}\]  
where \(l_{c}\) is the actual load of the controller \(c\), and \(l_{opt}=\lambda_{i}N/k\) is the load of the controller in the ideal state.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 4 Collaborative Filtering for Content Prediction
### Content Prediction  
In this subsection, we formulate the CF based content request distribution prediction algorithm. The prediction is based on the idea that the people who have agreed on something in the past may also agree in the future. It means that the people who have similar tastes may get best recommendations from one another. We can apply this idea to our multi-tier cellular caching network. If we find the similarity between the two BSs then, we can predict the popularity of the contents on the BSs that they have not been requested by now but will most probably be requested in the future.  
Let us consider \(A^{t}(mi)\) as the set of the content requested by the users of the \(\text{BS}_{t,\text{mi}}\) and \(A(m\underline{j})\) be the set content requested by the \(\text{BS}_{t}\text{mj}\). \(A^{t}(f)\) be the set of the BSs on which file f is requested. Then to find the similarity between two BSs we have to find the weighted matrix \(W_{ij}\) which is given as:  
\[\text{W}^{\text{T}}=\left(\begin{array}{cccc}1&W_{12}^{t}&\cdots&W_{1n}^{t} \\ \vdots&\ddots&\vdots\\ W_{1n}^{t}&\cdots&1\end{array}\right)\]  
The matrix is obtained from the following Equation [26]:  
\[W_{mi,mj}^{t}=\sum_{f^{\prime}\in A^{t}(mi)\cap\ A^{t}(m\underline{j})}.\frac {\frac{1}{\log(1+\left\|TA^{t}(f)\right\|}}{\sqrt{A^{t}(mi)\left\|A^{t}\ (m \underline{j})\right\|}} \tag{8}\]  
Now, the probability that the content f is requested by the \(\text{BS}_{\text{mi}}\) is obtained as:  
\[\text{P}^{\text{t}}\ (\text{mi}_{\underline{j}})\ =\sum_{m^{t}_{j}\in Z(mi,G)\cap\ A^{t}(f)}W_{mi,mj}^{t}P_{m,j,f}^{t} \tag{9}\]  
where, \(Z(mi,G)\) contains G SBSs which are the most popular set with mi. \(\text{P}_{\text{mj},\underline{i}}\) is the entry of the matrix P. After calculating \(\text{P}_{\text{(mi,f)}}\) of each content for the SBS mi, we can cache the requested content on the BS based on p(mi,f) in decreasing order.
## 3 Bearing Fault Diagnosis Based on Deep Learning
### Network Model of Bearing Fault Diagnosis Based on Deep Learning  
[PERSON] [42] of Harbin University of Technology designed a model known as \"Deep Convolutional Neural Networks with Wide First layer kernel\" (WDCNN) based on the characteristics of one-dimensional signal vibration.  
Its structural feature is that the first layer is a wide convolution kernel. In the WDCNN network model, 64 \(\times\) 1 feature extraction is used for the first layer of the large convolution kernel, and a 3 \(\times\) 1 small convolution kernel and a pooling layer are used for the rest for further feature extraction.  
In the WDCNN network model, the first layer of a large convolutional kernel using a single 64 \(\times\) 1 convolutional kernel inevitably loses information in downsampling, so the first layer of the large convolutional kernel of different sizes is used in the original paper to verify the reliability of the model.  
To address this feature, this paper introduces multi-scale large convolutional kernels for the first layer of large convolutional kernels based on the WDCNN model. In this paper, four different sizes of convolutional kernels, 16 \(\times\) 1, 32 \(\times\) 1, 64 \(\times\) 1, and 96 \(\times\) 1, are introduced in the first layer of a 64 \(\times\) 1 large convolutional kernel to perform further feature extraction for sample information of different lengths. The structure of the first layer of the main network is shown in Figure 6.  
Figure 5: FFT of bearing vibration signal: (**a**) Vibration signal; (**b**) FFT transform.
# Towards Mirror-Less Graphene-Based Perfect Absorbers
## 2 Graphene Perfect Absorbers with External Mirror
### Performance Improvement of Absorbers  
#### 2.2.1 Angle-Insensitive Absorption  
For certain graphene-based optoelectronic devices, achieving broadband graphene absorption with incident angle independence is highly desirable. In particular, GPAs designed by coupling the graphene with metal nanocavity can effectively provide angle-insensitive absorption due to the magnetic dipole resonance based on plasmon in metals [23; 31]. In 2015, [PERSON] et al. [23] proposed an electrically tunable absorber based on epsilon-near-zero (ENZ) effect of graphene embedded in a nanocavity, which is composed of metal grating and metal reflector (Figure 5a). When _hf_ is close to 2\(E\), graphene can be an ENZ material with vanishingly small permittivity [23; 79] at certain wavelengths under the proper contributions of interband and intraband transitions. Due to the strong surface-normal electric field confined in ENZ graphene, greatly enhanced absorption is achieved. Moreover, owing to the ENZ effect and the magnetic dipole resonance, it has a unique feature of incident angle insensitiveness. In 2018, [PERSON] et al. [31] also demonstrated the GPA based on a deep subwavelength 2D square plate, where graphene has a dielectric property because undoped graphene is applied (Figure 5b). Similarly to the structure described in ref. [23], the electric field is mostly concentrated around the bottom corners of the silver plates, and the magnetic field is highly confined in the dielectric layer between the silver plates and silver reflection mirror. The direction of the magnetic field of the TM incident wave remains unchanged with varying incident angles, which can effectively drive a current loop circulating the cavity, and thus, the angle dependence of its resonant wavelength is significantly weak.  
Figure 4: GPAs using unpatterned graphene and periodically patterned dielectric structures. (**a**) Schematics of a GPAs based on photonic crystal slab backed by DBR [24]. (**b**) Schematics of a GPAs based on PMMA grating on top of back gold mirror [29]. (**c**) Left: schematic of a GPA based on low-contrast grating (LCG) and DBR. Right: absorption map as a function of _FF_ and _d_G_nt at \(\lambda\) = 1550 nm [33]. Reproduced with permission from [24; 29; 33], © 2023 American Chemical Society; © 2023 WILEY-VCH; © 2023 Springer Nature.  
Compared to GPAs using the patterned dielectric structures of Figure 4, a wider absorption bandwidth is obtained at the cost of high ohmic loss in noble metals.
## 3 Materials and Methods
### Methods for Checking the Integrity of the Watermarked Audio Recordings
#### 3.4.2 Identifying a Cut Smaller Than One Second  
In this scenario, the watermark property that is investigated is the temporal distance between consecutive ticking sounds, monitored as it was explained in Section 3.2.2. If a value for \(\widetilde{\Delta t}\) is smaller than 0.975 s, and the entire extracted ENF sequence matches the corresponding part of the reference, it indicates that a cut operation was made in this time interval. The value of 0.975 s is used as inferior limit because of the classification tolerance explained in Section 3.3.2. It was shown in [35] that the minimum voice activity duration can be considered 60 ms. The duration of the cut can be found using the auxiliary pseudorandom binary sequence. An approach similar to Section 3.4.1 is done. The aim is to find the value of the reference PRBS that was expected for the interval where extracted \(\widetilde{\Delta t}\) was smaller than 0.975 s. The extracted PRBS is compared with the reference PRBS using the RSE. After finding \(k_{\text{min}}\), \(N\) vectors are computed using a customized version of (9) by replacing the ENF with the values of the PRBS. From the \(N\) vectors, the one with the most consecutive trailing zeros is selected, denoted with \(\mathbf{D}_{PRBS,L_{u},N_{\text{start}}}\), for consistency with Section 3.4.1. The number of consecutive zeros in the selected vector is stored in \(z_{N}\). Finally, the value of the cut duration can be found using two consecutive samples of the reference PRBS sequence, as shown in Table 2.