esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 4 Discussion
### MIE of Driving Warnings Reveals a Positive Trend with the Number of Modalities
#### 4.3.1 Comparisons Between Unimodal and Bimodal Warnings  
Upon comparing the auditory-tactile bimodal warnings with auditory warnings and tactile warnings respectively, a significantly shorter drivers' response time under the bimodal warning condition was observed, supporting the hypotheses based on the multi-sensory RSE. The current observation supported MIE and guides us to further analyze the extent of the facilitatory effect of multisensory integration on drivers' performance.  
However, when visual-auditory and visual-tactile bimodal warnings were used to compare with the unimodal warnings (i.e., auditory or tactile warnings), the expected MIE was not found. The potential cause to this outcome might go back to the nature of visual warnings. With the visual field constantly filled with changes and stimuli, bimodal warning conditions with visual modality do not appear to be as effective as auditory-tactile bimodal warnings. As observed in the currently obtainable data, it was concluded that only auditory-tactile bimodal warning systems could benefit drivers in a higher degree than unimodal warning systems.
## 2 Materials and Methods
### Codon Usage and Repetitive DNA Sequences  
Codon usage indicates the actual ratio of synonymous codons for evolutionary studies. The relative synonymous codon usage (RSCU) index is used mainly to evaluate mutational bias between synonymous codons [19]. According to variations in RSCU values, RSCU > 1 codon has a positive bias compared to other synonymous codons, RSCU = 1 codon indicates no bias between the two codons, and RSCU < 1 codon has a negative bias compared to other codons [20]. The RSCU values were calculated using MEGA 11 (version 11) [21].  
Repetitive DNA sequences (repeats) have short- and long-type patterns with multi-copy sequences. Simple sequence repeats (SSRs) have repeat units of less than 7 bp, while long tandem repeats have repeat units of 7 bp or more. SSRs (repeat units <7 bp) were detected by online MISA [22] set at unit size-minimum number of repeats of nucleotides: 10-mononucleotides, 6-dinucleotides, 5-trinucleotides, and 4-multitrinucleotides. Long tandem repeats (repeat unit \(\geq\)7 bp) were identified by Tandem Repeats Finder (version 4.04) set at a maximum size of 500, an alignment score of 50, match numbers of 2, mismatch numbers of 7, and a matching ratio of 80% [23].
# Network Attack Classification in IoT Using Support Vector Machines +
## 4 SVM Models Training and Evaluation
### Evaluation of the SVM Models
#### 4.2.1 Selective Forward (SF)  
The C-SVM had some difficulty in correctly classifying node activity with the Selective Forward attacks. In both SF-FR and SF-BN, the Precision of the C-SVM model is 75%. However, this is expected as the edge nodes of the network are never relay nodes, thus no packets were forwarded to them and in turn the node's local activity was never changed to detect the attack. The C-SVM achieved 100% Recall for the SF-BN attack, showing that all alarms raised by the model were indeed caused by malicious activity. For the SF-FR attack, C-SVM had one false negative alarm, dropping the Recall to 93.8%. The MCC value indicates that the C-SVM model for the SF-FR and SF-BN provides good prediction, as it achieved values of 85.72% and 88.73% respectively.  
Unlike C-SVM, OC-SVM did not perform as well, having accuracy rates of 60.8% for the SF-FR and 41.7% for the SF-BN. The MCC value indicates that the OC-SVM is not a good detection model, having values of 48.81% for the SF-FR and 40.3% for the SF-BN.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 3 Mesoscale Modeling of Concrete by FEM
### Mesoscale Modeling Policy
#### 3.1.2 Material Properties and Cracking Model  
Various researchers have proposed mesoscale models for concrete, with a static modulus of elasticity of coarse aggregate ranging from 40 to 75 GPa and that of mortar from 11 to 35 GPa [14]. The present model does not directly assume the material property parameters, but rather, identifies them by comparing and matching analytical and experimental results based on the results of previous uniaxial compression tests of sound and ASR-impaired cylindrical specimens. Details are given in Section 3. Based on previous papers [15; 16; 17], the Poisson's ratio was set to 0.23 for the coarse aggregate and 0.2 for the mortar. Therefore, the model assumes that the coarse aggregate and mortar are elastoplastic, subject to the linear Drucker-Prager yield condition. The concrete was assumed to follow a linear softening law with a slope depending on the element size, based on Equations (1)-(3), proposed by [PERSON] [18] after the tensile strength.  
\[E_{c}=\left(\frac{1}{E}-\frac{2G_{f}}{f_{t}^{2}h}\right)^{-1} \tag{1}\]  
\[G_{f}=\frac{1}{100}(d_{max})^{\frac{1}{3}}f^{t\frac{1}{3}}_{ck} \tag{2}\]  
\[h=\sqrt[3]{V} \tag{3}\]  
Figure 8: Coarse aggregate distribution.  
Figure 9: Cylinder specimen based on mesoscale modeling.  
where \(E\) is the static modulus of elasticity, \(E_{c}\) is the slope after softening, \(f_{t}\) is the tensile strength, \(G_{f}\) is the fracture energy, \(d_{max}\) is the maximum diameter of coarse aggregate, \(f_{ck}^{\prime}\) is the compressive strength of concrete, \(h\) is the average element length, and \(V\) is the element volume. In this analysis, the maximum diameter of the coarse aggregate is set to 25 mm. The model does not consider crushing on the compressive side.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### Algorithmic Framework  
To effectively address the problem of image classification, this study developed the ITACIE-GP method. ITACIE-GP automates the construction of efficient image classification solutions by utilizing predefined program structure, function set, and terminal set. Figure 1 displays the training and testing process of ITACIE-GP. ITACIE-GP takes the training dataset as its input and subsequently initializes the population based on the newly defined function set and terminal set. Then, individuals/solutions in the population are evaluated for fitness using a fitness function. In each generation, an elitism strategy copies several excellent individuals directly to the next generation from the current population. The algorithm employs a tournament selection method to choose individuals with superior fitness values and stores the first type of accompanying individuals according to the storage strategy in the accompanying evolution strategy. During the iterative process, if the fitness of the optimal individuals stagnates and reaches the predetermined threshold, the replacement strategy within the accompanying evolution strategy will be activated. This approach breaks the stagnation in fitness, thereby assisting the algorithm in searching for more superior individuals. After this, the algorithm performs genetic operators (i.e., crossover and mutation operators) to generate a new population. If the population has already been updated through the accompanying evolution strategy, the algorithm stores the successfully updated individuals as the second type of accompanying individuals. This process continues until predefined termination conditions are met. Finally, GP individual set are selected for individual ensemble based on the multi-generational individual ensemble strategy, returning the ensembled individual/solution.  
To validate the performance of the ensembled individual/solution obtained by the ITACIE-GP method, a test set is used as the input for the ensembled individual/solution. Then, the predicted class labels of the test set are output, and finally, the performance of this solution is evaluated based on the actual labels of the test set.  
Figure 1: The training process and testing process of the proposed ITACIE-GP method.
## 2 Materials and Methods
### Methods
#### 2.2.3 Accuracy Evaluation  
In this study, experimentally obtained vegetation classification binary images and FVC data were used to comprehensively evaluate and analyze the impact of the double-exposure algorithm on the accuracy of vegetation extraction from shadow images. This evaluation was conducted from both semantic segmentation and statistical analysis perspectives. The ground truth labels of the vegetation images in the experimental data were obtained through manual visual interpretation and annotation. This process allowed for the calculation of the true FVC for each image, providing a reliable baseline for our analysis. The confusion matrix consists of four parts: TP, FN, FP, and TN [48]. TP represents the portion where both the actual and predicted values are vegetation. FN represents the portion where the actual value is vegetation but is predicted as non-vegetation. FP represents the portion where the actual value is non-vegetation but is predicted as vegetation. TN represents the portion where both the actual and predicted values are non-vegetation. Based on the confusion matrix, the above indices can be calculated as follows:  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}} \tag{12}\]  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{13}\]  
\[\text{a}=\frac{\left(\text{TP}+\text{FN}\right)*\left(\text{TP}+\text{FP} \right)+\left(\text{FP}+\text{TN}\right)*\left(\text{FN}+\text{TN}\right)}{ \left(\text{TP}+\text{FN}+\text{FP}+\text{TN}\right)^{2}} \tag{14}\]\[\text{Kappa}=\frac{\text{accuracy}-\text{a}}{1-\text{accuracy}} \tag{15}\]  
\[\text{IOU}=\frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP}} \tag{16}\]  
\[\text{mIOU}=\frac{\text{TP}}{2(\text{TP}+\text{FN}+\text{FP})}+\frac{\text{TN} }{2(\text{FP}+\text{TN}+\text{FN})} \tag{17}\]  
Recall is the proportion of vegetation instances correctly predicted as vegetation among all actual vegetation instances. Accuracy denotes the proportion of correct predictions overall. Precision is defined as the proportion of correctly predicted vegetation instances. In this study, the intersection over union (IOU) of the vegetation class denotes the ratio of the intersection to the union of the predicted and ground truth values. Both the kappa coefficient and the mean intersection over union (mIOU) are used to evaluate the overall classification performance of vegetation and non-vegetation classes. The closer the values of these six metrics are to 1, the higher the image classification performance and accuracy. To evaluate the accuracy of FVC prediction, a statistical analysis approach was adopted in this study. A univariate linear regression model was constructed to analyze the relationship between the experimental FVC values and the ground truth values obtained through manual visual interpretation. The regression equation, the R\({}^{2}\), and the root mean square error (RMSE) of the model were obtained. These metrics were utilized to assess the accuracy of shadow images FVC using the double-exposure algorithm.
## 3 LCI System Design Framework
### Dispersion Compensation  
Many manufacturing conditions do not offer an ideal environment to operate within, with machining processes including various liquids for lubrication and for flushing of debris. OCT has already shown that measurement errors due to chromatic dispersion in optically dense materials occur, resulting from the frequency dependence of propagation constants for different mediums [59]. The result of unbalanced dispersion within an LCI system is a reduction in signal peak intensity and a broadening of its Point Spread Function (PSF) shown in literature to be dependent on refractive index, central wavelength, bandwidth and propagation distance [59; 60; 61]. LCI is very tolerant of chromatic dispersion if the amount of dispersion occurring in the reference and sample arms are equal. This can be achieved through insertion of artefacts of varying thickness and refractive index into the reference arm of the spectrometer [60] or through using a common-path setup. Software based compensation is also achievable offering a more flexible method of compensation [59; 62; 63].  
The propagation constant for a material can be described by a Taylor series expansion shown by Equation (24) with the expansion made around the light sources central frequency \(k_{0}\)[63]:  
\[\theta(k)=\theta(k_{0})+\frac{\partial\theta(k)}{\partial k}(k_{0}-k)+\frac{1 }{2!}\cdot\frac{\partial^{2}\theta(k)}{\partial k^{2}}(k_{0}-k)^{2}+...+\frac {1}{n!}\cdot\frac{\partial^{n}\theta(k)}{\partial k^{n}}(k_{0}-k)^{n}. \tag{24}\]  
The terms in Equation (24) relate to the zeroth, first and second orders of dispersion which respectively represent: constant offset, group velocity, both of which are not related to dispersive broadening and group-delay dispersion per unit length, though higher order terms may be present.  
In practice, the frequency dependent dispersion variation can be compensated for by multiplying the obtained interferometric signal by a phase correction term such that \(I_{corrected}(k)=I(k)\times e^{(-i\theta(k))}\) and can be obtained during post-processing [46]. This is achieved through isolation of the coherence function of a strong reflector, followed by performing an inverse Fourier transform on the masked signal and then taking the arctangent of the imaginary component divided by the real component. By performing a linear fit and then identifying the difference through subtraction, an error term can be achieved indicating how much wavenumbers are out of phase with one another. This can then be used to form a phase adjustment value \(e^{(-i\theta(k))}\). Using the knowledge that, for a properly dispersion compensated system the signal peak intensity will be at its maximum [60], an iterative procedure can be carried out until the maximum peak value is achieved.
###### Abstract  
This work presents a comprehensive technological study of dielectric resonator-based microstrip filters (DRMFs), encompassing the design, fabrication, and rigorous characterization of the \(TE_{01\delta}\) mode. Through systematic coupling analysis, we demonstrate filters featuring novel input-output coupling techniques and innovative implementations of both transmission zeros (4-2-0 configuration) and equalization zeros (4-0-2 configuration), specifically designed for demanding space and radar receiver applications, while the loaded quality factor (\(Q_{L}\)) and insertion loss do not match those of dielectric resonator cavity filters (DRCFs), our solution significantly surpasses conventional microstrip filters (MFs), achieving \(Q_{L}>3000\) compared to typical \(Q_{L}\approx 200\) for coupled-line MFs in X-band. The fabricated filters exhibit exceptional performance as follows: input reflection (\(S_{11}\)) below \(-18\) dB (4-2-0) and \(-16.5\) dB (4-0-2), flat transmission response (\(S_{21}\)), and out-of-band rejection exceeding \(-30\) dB. Mechanical tuning enables precise control of input-output coupling, inter-resonator coupling, cross-coupling, and frequency synthesis, while equalization zeros provide tailored group delay characteristics. This study positions DRMFs as a viable intermediate technology for high-performance RF systems, bridging the gap between conventional solutions.  
aspect ratio (AR); band-pass filter (BPF); coupling matrix (CM); curved transmission line (C-TL); dielectric resonator (DR); dielectric resonator based-microstrip filter (DRMF); equalization zeros (E2); input multiplexer (IMUX); input-output coupling; inter-resonator coupling; group delay; quality factor (Q); TE\({}_{01\delta}\); transmission zeros (TZ)
## 2 Experimental Details  
A commercial CuSe\({}_{2}\) alloy target (TASCO, 99.99% purity, 2-inch diameter) was used for preparing Cu\({}_{2}\),Se thin film on 2 \(\times\) 2 cm\({}^{2}\) Corning glass substrates. The thin films were deposited by radio frequency (RF) magnetron sputtering (IDT Engineering Co.) at room temperature using the following set of process parameters after pre-sputtering for 5 min [15]. The Ar gas flux was 50 sccm; the base pressure was 1.0 \(\times\) 10\({}^{-6}\) Torr; the substrate-to-target distance was 5.0 cm; the frequency was 13.56 MHz; the RF sputtering power was 35 W; and the vacuum pressure was 7.5 \(\times\) 10\({}^{-3}\) Torr during sputtering. After the growth process, the films were annealed using rapid thermal annealing (GD-Tech Co., GRT-100) at a temperature of 300 \({}^{\circ}\)C for 20 min under 10 sccm N\({}_{2}\) gas ambient. The thickness of the films was 850-870 nm.  
The films were treated with RF high-density low-pressure inductively coupled plasma (ICP) at an RF of 13.56 MHz using an RF ICP reactor [16]. The experiments were performed with the following parameters. The inductive RF power P was 800 W; the argon gas flow was 10 sccm; the operating argon gas pressure was 0.07 Pa; and the RF bias powers to the substrate holder P\({}_{\rm sb}\) was 0 and 300 W. The ion energy was controlled by the RF bias power P\({}_{\rm sb}\) applied on the substrate. At RF bias powers of 0 and 300 W, the average ion energies were 25 and 200 eV, respectively. The ion current density was 7.5 mA cm\({}^{-2}\). The plasma treatment was conducted for durations of 30, 60, and 90 s. The plasma treatment of the sample involved bombarding the surface with normal incident argon ions accompanied by the heating of the surface and ultraviolet illumination of the argon plasma. The sample temperature during plasma treatment can increase by 100 \({}^{\circ}\)C at P\({}_{\rm sb}\) = 0 W and a processing duration of 60 s [12] as well as by 230 \({}^{\circ}\)C at P\({}_{\rm sb}\) = 200 W and a processing duration of 30 s [16].  
The surface morphology of the films was studied via scanning electron microscopy (SEM) using a Supra 40 microscope (Carl Zeiss, Germany). The chemical analysis of the samples was performed by energy-dispersive X-ray (EDX) microanalysis with the aid of an INCA Energy attachment (Oxford Instruments). An accelerating voltage of 20 kV was used in the research. The studies were carried out on an area of 20 \(\times\) 15 \(\upmu\)m\({}^{2}\) with a normal incidence of the electron beam. This provided the collection of information from the entire thickness of the film. The reproducibility of the results on different parts of the film surface was high, and the deviations in the determined Cu/Se ratio did not exceed 2%. The X-ray diffraction (XRD) studies were performed using an ARL X\({}^{\prime}\)tra (Switzerland) diffractometer for Cu K\({}_{\rm x}\) radiation. The step-scan mode was used with a step size of 0.02\({}^{\circ}\) and counting time of 1.5 s per step.
## 7 Architecture Validation
### Resource Consumption
#### 7.1.1 Testing Strategy  
To measure the memory and CPU consumption under load, a series of test cases were conducted. The tests aim to mimic the behavior of several users performing tasks on the system over a period as follows:  
1. Ten min of interactions between 20 users;
2. Thirty min of interactions between 50 users;
3. One h of interactions between 100 users.  
The first test case will be further referenced as a \"normal testing scenario\" due to the balanced proportion between timing and the number of users doing continuous interactions with the system.  
As testing scenarios, the capabilities of the system are divided in two, based on the communication means:  
* Synchronously representing the read, create, update, and delete operations;
* Asynchronously representing the long-lived connections exchanging actions between the client and the server.  
The synchronous testing mimics real-world user interactions like creating accounts, logging in, receiving document data and content (the testing document content has 50,000 characters, equivalent to 20 single-spaced pages), and receiving and deleting notifications. To have a realistic scenario, the databases are populated with records simulating a real-world quantity of entities stored. Since the system does not involve caching, it will keep under load the database. Each test was conducted three times, and the values were averaged out among all the test runs of the same feature.  
In terms of system scaling capabilities, two test runs are conducted: one with a fixed number of instances for each feature and one with horizontal scaling capabilities enabled. The scaled versions of the system were subject to the most intensive test case only. The target was to observe the evolution of running instances over time.  
Apache JMeter (Version 5.5, The Apache Software Foundation, Wakefield, MA, USA, 2023) was used as a testing tool for generating the synthetic load tests for the synchronous and asynchronous cases. As tooling for monitoring and extracting microservice resource consumption data Prometheus was used. Prometheus is an open-source plug-and-play monitoring system that facilitates the extraction of process-related performance metrics. To visualize the extracted data, Grafana was utilized due to its native compatibility with Prometheus. Grafana can use Prometheus as a real-time data source, facilitating real-time observation of the resources consumed by each microservice.
# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 4 Discussion
### Channel Detection Using U-Net (Binary Case)  
Figure 9a portrays two inline sections representing spectral decomposition volumes after validating the best frequency range to be blended between low, medium, and high volumes, highlighting I-27 to I-35 Lower Reservoir. Log pattern analysis from nearby key wells was observed in both inline 980 and 1070. We display the seismic attribute dataset (inset are shown the sweetness attributes in Figure 9b as part of the input data. Theoretically, sweetness attributes can be generated based on instantaneous amplitude divided by the square root of instantaneous frequency and defined as the trace envelope a(t) divided by the square root of the average frequency f\({}_{\text{a}}\)(t) [26].  
In the lens of prospect identification and understanding the reservoir in fluvial settings, sweetness is capable of highlighting hydrocarbon-bearing reservoirs and lithofacies discrimination, given that seismic data quality is conditioned and pre-processed. We chose two different time slices penetrating the studied channelized reservoir interval to identify different fluvial structures, namely, meandering channel, point bars, distributary channels, and mud-filled channels (MFC). Figure 9c depicts the ground truth built using the labeling tool. In order to test the effectiveness for binary classification of fluvial channels, hold-out layers were applied to highlight only on channel and non-channel features during the test  
Figure 9: The results for binary facies classification in A Field. (**a**) Two random sections of blended spectral decomposition cubes passing through three key wells showing log pattern analysis in the channelized reservoir interval. Two different time slices in (**b**) portray comparisons between multi-attributes as the input (inset are the sweetness attributes). (**c**) The labeled data prepared by a human interpreter. Results from binary classification (**d**) show that the trained neural network is still capable of capturing Class III facies and distributary channels, as indicated in the ellipse, despite dealing with the challenge of imbalanced facies.  
stage. It was observed that the network is capable of capturing distributary channels that were missed by sweetness attributes (Figure 9d). Meanwhile, penetrating toward deeper slices shows that MFC are able to be captured by the network even with a small training dataset in Class III.