esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
[21] used social media information to complement physical sensor information to produce more precise real-time intensity maps. They developed four empirical predictive relationships (linear, two-segment linear, three-segment linear, and exponential) that connected the tweet rates in the first 10 min after the earthquake with the Mercallic intensity (MMI) scale in Napa earthquake. Their approach combines data from both social and physical sensors for earthquake intensity prediction. [PERSON] et al. [1] measured the relationship between citizen-sensor data's temporal development pattern and the region of the effect of the earthquake. In addition, they integrated social media data with other auxiliary data. E2 mC was designed by [PERSON] et al. [22] to explain how the region was impacted by the earthquake and to determine the intensity of the damages. The main idea with E2 mC is the integration of automated evaluation of social network information with crowdsourcing. Their main objective was to enhance the quality and reliability of the information given to professional users in the Copernicus system. [PERSON] et al. [23] estimated damages in the Mercallic intensity scale based on social network posts. [PERSON] et al. [6]suggested a method to find construction damage in a disaster image and estimate damage based on convolution neural networks. They investigated the utility of the suggested method for other infrastructure damage classifications, specifically bridge and highway damage. [PERSON] et al. [9] assessed both physical and emotional earthquake damages. Physical damage is associated with damage to infrastructure, people, assets, house, agriculture, and industry. Emotional damage is associated with emotions reported by individuals after the crisis, especially negative emotions. In our previous work ([24]), we used the SVM method for creating earthquake damage map. The present article is a continuation of the mentioned article and has completed it in terms of the number of spatial units and the evaluation of different algorithms. While damage assessment based on social network data has gained considerable academic interest, little attention has been given to the effect of scale on the results. After identifying the impact of the scale on social network performance in damage assessment, decision makers can determine which scale can use social networks and which spatial unit can use other data sources or integrate social networks with other data sources to achieve the best results as quickly as possible. Another issue that has a great impact on the results of the damage assessment is the algorithm for extracting the damage information. Due to the fact that the damage assessment will be based on the damage-related extracted data, the issue is the impact of the classification algorithm on the end result of damage assessment. Therefore, the performance of different classification algorithms must be examined in the damage assessment.
# Optimization of Processing Parameters to Increase Thermal Conductivity of Rice Straw Fiber Film
## 3 Results and Discussion
### Model Validation  
The optimum model was validated by manufacturing the BMF at basis weight 70 g/m\({}^{2}\), wet strength agent content 1.5%, neutral sizing agent content 0.5%, charcoal addition ratio 15%, and beating degree 55 \({}^{\circ}\)SR. Dry tensile strength, wet tensile strength, and thermal conductivity obtained from the validation experiment were 33.4 N, 12.2 N, and 0.0714 W/m-K, respectively, which were highly consistent with the predicted results (dry and wet tensile strengths larger than 32 N and 11N, and thermal conductivity larger than 0.0700 W/m-K). Hence, the optimum condition obtained from RSM design was reliable and applicable, and it indicates that RSM is a powerful tool for system optimization.
## 3 Results and Discussion
### Test Process and Implementation  
To validate the effectiveness of the proposed hybrid control system, we consider a representative tertiary system with time delay, denoted as Process 1. This process is characterized by the following transfer function:  
\[\text{G}(\text{s})=e^{-0.5\text{s}}1/\left[(\text{s}+1)(\text{s}+5)^{2}\right] \tag{18}\]  
Following the model reduction methodology outlined in Section 2, we obtain the reduced model:  
\[\widetilde{G}(s)\text{= }e^{-0.6076s}/(7.7756s^{2}+32.6507\text{s}+25) \tag{19}\]  
The implementation of the hybrid control system comprises two main components: an LSTM network for model identification and a Genetic Algorithm for parameter optimization. The LSTM network architecture is designed with careful consideration of the system's complexity and real-time requirements. The network consists of an input layer with 64 neurons to capture sufficient temporal information, followed by two hidden LSTM layers with 32 neurons each to process the temporal dependencies. The output layer contains three neurons corresponding to the predicted model parameters. Training  
\begin{table}
\begin{tabular}{c c c} \hline
**Criterion** & **Weight** & **Description** \\ \hline Transient Response & \(\alpha_{1}\) & Rise time and overshoot \\ Steady-state Error & \(\alpha_{2}\) & Long-term tracking accuracy \\ Control Effort & \(\alpha_{3}\) & Actuator usage optimization \\ Disturbance Rejection & \(\alpha_{4}\) & External disturbance handling \\ \hline \end{tabular}
\end{table}
Table 2: Performance optimization parameters.  
is conducted over 100 epochs with a batch size of 32, which provides an optimal balance between computational efficiency and model accuracy.  
The Genetic Algorithm component is configured to ensure efficient exploration of the parameter space while maintaining convergence stability. The algorithm operates with a population size of 50 individuals over 100 generations, allowing sufficient evolution of the solution space. The genetic operators are tuned with a mutation rate of 0.1 and a crossover rate of 0.8, values determined through extensive preliminary testing to provide robust optimization performance.  
The complete system configuration is summarized in Table 3.  
These configuration parameters were selected based on extensive experimental validation and represent an optimal trade-off between performance and computational efficiency for the given process characteristics.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Code Execution Performance
#### 4.5.1 Converting Matlab Files to Noise Figure Datasets  
The first segment of the code follows these steps:  
1. Load each _.mat_ EDFA dataset, which contains the input and output spectral power densities of the amplifier as a function of total input power, target gain, and tilt.
2. Execute all the processing steps outlined in Section 3.
3. Store the results in _.parquet_ files to enhance speed and reduce storage requirements.  
The execution time of the program is summarized in Table 1. Each row, representing a unique combination of total input power, gain, tilt, and channel index within a dataset, is processed in approximately \(0.60\,\mathrm{ms}\). This corresponds to a processing rate of approximately \(1667\) rows per second or \(100,000\) rows per minute. While this provides a general estimate of computation time based on dataset size, it is important to note that the actual  
Figure 17: Cumulative distribution of absolute errors and their correlation with gain and tilt range: (**a**) EDFA-17, low-gain range; (**b**) EDFA-35, high-gain range; (**c**) Juniper, low-gain range. Green and red dashed lines represent \(99\%\) and \(90\%\)-threshold values, respectively, with green and red bullets highlighting minimum and maximum values obtained through these curves.  
Figure 18: Correlation between tilt range and error reduction in JUNIPER devices. Green and red dashed lines represent \(99\%\) and \(90\%\)-threshold values, respectively, with green and red bullets highlighting minimum and maximum values obtained through these curves.  
performance is machine-dependent and should be experimentally verified for different hardware configurations.
# Validation of AISI Design of Cold-Formed Steel Beams Using Non-Linear Finite Element Analysis
## 2 Materials and Methods
### Abaqus  
#### 2.3.1 General  
ABAQUS is a comprehensive and powerful suite of finite element analysis (FEA) software applications, widely used for simulating the physical behavior of structures under various conditions. The finite element (FE) software ABAQUS was employed to create a finite element model (FEM) of CFS (cold-formed steel) channel sections. This tool has been effectively used for linear and non-linear analysis in past. The FEM included the measured dimensions of the specimens, initial geometric imperfections, and non-linear material properties. The analysis of the FEM was carried out in two distinct steps. The first step was used for linear perturbation analysis to obtain the buckling modes (Model-1), whereas during the second step, the \"general static\" method was applied for calculation of the moment capacity (Model-2).
# Review of Ka-Band Power Amplifier
## 2 Technology
Reducing the capacitance is helpful to improve the speed of the circuit and reduce the power consumption of the circuit. IBM 45 nm CMOS SOI technology has been widely used in the development of millimeter-wave power amplifiers [38; 39; 40]. It provides SOI CMOS transistors with very high f\({}_{\rm T}\) and f\({}_{\rm MAX}\) (>400 GHz) [41]. The excellent performance of SOI MOSFET makes the SOI CMOS circuit more suitable for working in harsh environments, such as aerospace and high temperature. Figure 4: SOI CMOS structure profile. However, SOI CMOS process technology cannot replace bulk silicon CMOS technology, mainly because the SOI CMOS process is not as mature as the bulk silicon CMOS process. The quality of the silicon film of SOI material is not as good as that of bulk silicon material. In addition, the cost of SOI CMOS is higher than that of bulk silicon CMOS. The development of SiGe devices is mainly promoted by IBM and other companies. The BiCMOS process is adopted, which can be used as an alternative to CMOS. BiCMOS technology is a new technology that integrates bipolar transistor (BJT) and field-effect transistors (FET) on the same chip. Compared with CMOS, under the same scale, the performance and cut-off frequency of the SiGe device are greatly improved, and it is better than the silicon-based CMOS device in withstanding voltage and the current processing ability, so it is also suitable for medium power applications. As shown in Figure 5, SiGe BiCMOS combines SiGe HBT applied to the RF circuit and SiGe CMOS applied to digital circuit, combining the digital and analog on one chip. Compared with traditional Si BiCMOS, SiGe BiCMOS is more conducive to integration. In the past 10 years, SiGe BiCMOS technology has been greatly developed, breaking through the technical barriers of 0.35, 0.18 and 0.13 um. The cut-off frequency f\({}_{\text{T}}\) of SiGe HBT has reached 40-350 GHz, and the maximum operating frequency can almost reach the level of GaAs [42]. It has strong competitiveness and wide application prospects in the RF field. In the author's opinion, GaN and GaAs materials have power advantages for chips with an operating frequency not higher than 100 GHz. If frequency is the primary consideration of the device, the frequency of the power amplifier made of the InP device can be higher than 500 GHz. Of course, for industrial manufacturing, the product cost is also an important factor in the power amplifier design and mass production. Especially for consumer electronics, CMOS is conducive to system-on-chip integration, so it has a cost advantage. In addition, the loss of performance or quality factors of passive components are quite competitive [43].
# Influence of Eddy-Generation Mechanism on the Characteristic of On-Source Fire Whirl
## 3 Results
### Validation  
An accurate description of the fire whirl's inner structure is intimately tied with a proper description of the temperature and the velocity field of the swirling reacting flow. For this reason, the temperature and velocity profile generated by the baseline model is used to compare with the that reported in the literature for validation purpose.  
Due to the constraints related to the physical set up and experimental design, limited information is provided in prior studies. For clarity, herein, temperature measurement at domain centreline at various HABs is selected for comparison. Furthermore, the incoming y-velocity profile through the air gap is also validated against a prior numerical investigation of the similar experimental setup [8]. The result of the validation procedure is presented in Figures 2 and 3.  
It can be seen from Figure 2 that the centreline temperature peaks at about HAB 0.15 m and sharply decreases to ambient as the combustion process develops towards downstream. Similarly, high incoming velocity at the air gap is observed in the low-intermediate region, and its quantity gradually drops with the increase of flame height. Despite the fact that the flame temperature in the intermittent region is slightly overestimated, potentially attributed to the absence of radiation correction, as well as the inevitable flame flickering, the comparison shows good agreement between the numerical simulation and experimental data. As presented in Figure 3, a similar agreement could be reached in the comparison of incoming y-velocity through the air gap with the prior study.  
In addition, the soot volume fraction evaluated from both cases is proved to be negligible, i.e., on an order of magnitude of \(-21\) and \(-18\) in the baseline and comparison cases, respectively. It was further ascertained that with the adoption of the alcohol-based parent fuel, the in-flame soot formation and the ensuring radiation feedback can be safely neglected in this study.  
With the numerical model of the baseline case validated, the model of the two slits cases is constructed accordingly, by implementing the identical numerical setup on the geometry with the introduction of an additional flow channelling slit. The result generated by the two slits model is therefore assumed to be justified. Temperature and velocity profiles, as mentioned, are the primary parameters for characterising the swirling reacting flow involved in the fire whirl. The assorted temperature and velocity data generated numerically by the baseline model are presented alongside those of the comparison model, to assess the effect of the geometrical configuration on the formation of fire whirl.
## 1 Introduction
Based on this question, this paper proposes a novel method to improve the comprehensive assessment of steady-state power quality. In this paper, a comprehensive steady-state power quality assessment system is established based on China's national standards. Then, the traditional best-worst (BW) method is improved by using triangular fuzzy numbers, and a triangular fuzzy BW method is obtained to determine the interval weight of each steady-state power quality index. Next, the value of priority performance index (PPI) of the daily power quality monitoring value is calculated by the interval VIKOR method with the obtained interval weight. Finally, comparing them with the boundary value of each power quality level, the power quality level of the monitoring points is obtained to evaluate the steady-state power quality. The main contributions of this paper are summarized as follows:  
1. Based on the triangular fuzzy theory, we apply triangular fuzzy number theory to the BW method and propose a triangular fuzzy BW method. Then, we use the optimization method to obtain the interval weight of each indicator. The interval weights can reflect the opinions and preferences of decision-makers more accurately than crisp numbers. 2. We combine the triangular fuzzy BW method and the interval VIKOR method to process the experimental monitoring values of power quality indicators. The interval VIKOR method provides an effective approach for handling the data uncertainty present in the interval weights. As the comprehensive evaluation values of power quality are interval numbers, we rank them by a simple yet practical ranking method. 3. By calculating the power quality level boundary value, together with the daily power quality monitoring value, the level of power quality of the monitoring point can be intuitively obtained. The remainder of this paper is organized as follows. Section 2 introduces the steady-state power quality comprehensive assessment system and presents grade classifications. Section 3 introduces the construction of a triangular fuzzy BW method with triangular fuzzy number, and then determines the interval weight of each power quality index. Section 4 illustrates the interval VIKOR method to achieve a comprehensive evaluation of steady-state power quality based on the obtained interval weights. Next, Section 5 provides an example to verify the proposed method. Conclusions are drawn in Section 6.
# The Reproduction and Evaluation of Star Fields with the Milky Way in a Planetarium
## 2 Experiment
### Experimental Stimulus
#### 2.2.2 Additional Pattern  
* Transmittance-based Pattern  
The brightness of the night sky has a significant influence on the perception of stars during actual observation. When the darkness of the night sky is altered by light pollution and zodiacal light, the visible area of the Milky Way is limited. Changes in contrast between the brightness of the surrounding night sky and the faint stars in the Milky Way may increase or decrease visibility and the perception of the brilliance of the star. Therefore, we changed the contrast with the background of the night sky and visibility by preparing the L1 and L2 patterns. For these, the projected luminance of the variable stimulus was relatively increased or decreased using neutral density (ND) filters on the lens barrel of the projector. The transmittance in patterns L1 and L2 was approximately half and twice the transmittance of the standard pattern, respectively. Furthermore, these ND filters (L1 for half transmittance, L2 for double transmittance) were set on other patterns (S1, D1, and D2) to prepare combination patterns with different sizes, densities, and transmitances.
* Size-based Pattern  
For the variable stimulus, we controlled the size of the stars projected on the dome screen by expanding or contracting the hole diameter of the star plate in the standard pattern. This allowed us to investigate the projection size of faint stars that are too small to observe as individual stars, but are appropriate for observing the Milky Way. Compared to the standard pattern, pattern S1 had a half diameter.
* Density-based Pattern  
Figure 2: Examples of projected experimental stimuli. (**a**) Entire stimulus; (**b**) Stable stimulus (bright stars); (**c**) Variable stimulus (the Milky Way, faint stars).  
A star is reproduced by processing a hole in a star plate. The number of processed faint stars in the Milky Way depends on the brightness, which affects visibility, because of the masking area removed in the star plate. In contrast, an increase in the number of processed faint stars also creates a lack of tint in the Milky Way. Therefore, the size of the star field was stable for all projected patterns. To investigate the appropriate number of processed faint stars in the Milky Way, we changed the density to make the star holes relatively dense (pattern D1, 2/3 density) or sparse (pattern D2, 3/2 density) in the star plate of the standard pattern for the variable stimulus.
## 8 Summary  
Both CAD and CBIR have been important research topics in the medical imaging areas for the last decade. Recently, combining CAD and CBIR technology has also been attracting much research interest. CAD schemes using CBIR approaches have been investigated and developed for detecting abnormalities depicted on mammograms, lung CT images, and many other types of radiographic and pathological medical images. In CAD of mammograms, current commercialized CAD schemes achieved very high performance on detecting micro-calcification clusters but relatively lower performance on mass detection (including the lower specificity and cuing majority of subtle masses only on one view). In order to improve CAD performance and increase radiologists' confidence in accepting CAD-cued results, the CAD schemes using CBIR approaches may allow radiologists to query any suspicious mass region (whether initially cued or not cued by the CAD scheme) and provide radiologists both detection/classification scores and a set of similar reference ROIs depicting suspicious masses with verified outcome. Preliminary studies have showed that such \"visual aid\" approach in CAD applications could be helpful to radiologists in interpreting mammograms. However, developing CAD schemes using CBIR approaches faces a number of technical challenges (or issues) including the accurate region segmentation, selection of features that are effective on both target classification and correlation with visual similarity, assembly of an optimal reference database, and improvement of computational efficiency. Despite of the substantial progress made in developing CBIR algorithms used in medical informatics or PACS as well as the CAD schemes using CBIR approaches in recent years, many of these technical challenges remain. Comparing to the much matured TBIR (text-based image retrieval) searching method, CBIR is still in the early development stage. Therefore, to develop highly performed and clinically acceptable CAD schemes using CBIR approaches, much more research work is needed.  
Author's current research work in developing CAD schemes using CBIR approaches is supported in part by the research grants from the National Center for Research Resources (1 UL1 RR024153) and from the National Cancer Institute, National Institutes of Health, USA (CA77850 and CA101733) to the University of Pittsburgh.