esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Desorption

Table 4: Experimental and calculated values of the objective functions during desorption from Amberlite XAD16 HP resin. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Exp.** & \(\mathbf{Y_{1 exp}}\) & \(\mathbf{Y_{1 cale}}\) & \(\mathbf{Y_{2 exp}}\) & \(\mathbf{Y_{2 cale}}\) & \(\mathbf{Y_{3 exp}}\) & \(\mathbf{Y_{3 cale}}\) & \(\mathbf{Y_{4 exp}}\) & \(\mathbf{Y_{4 cale}}\) & \(\mathbf{Y_{5 exp}}\) & \(\mathbf{Y_{5 cale}}\) \\ \hline
1 & 52.7 & 51.7 & 23.1 & 26.1 & 0.471 & 0.438 & 0.291 & 0.301 & 12.2 & 12.2 \\
2 & 58.7 & 56.8 & 25.1 & 26.8 & 0.454 & 0.423 & 0.272 & 0.275 & 11.7 & 11.2 \\
3 & 55.9 & 56.3 & 24.6 & 22.6 & 0.475 & 0.475 & 0.284 & 0.265 & 12.6 & 12.5 \\
4 & 59.5 & 61.2 & 24.6 & 24.5 & 0.488 & 0.482 & 0.268 & 0.260 & 11.4 & 12.0 \\
5 & 49.8 & 51.4 & 31.2 & 28.4 & 0.349 & 0.396 & 0.308 & 0.304 & 11.2 & 11.4 \\
6 & 58.3 & 58.9 & 23.2 & 23.9 & 0.511 & 0.458 & 0.285 & 0.262 & 12.4 & 12.7 \\
7 & 58.9 & 58.7 & 21.7 & 22.2 & 0.425 & 0.462 & 0.225 & 0.249 & 12.5 & 12.4 \\
8 & 61.6 & 60.5 & 25.1 & 24.2 & 0.446 & 0.487 & 0.251 & 0.267 & 13.3 & 12.7 \\
9 & 59.5 & 57.6 & 25.8 & 24.8 & 0.400 & 0.400 & 0.244 & 0.240 & 11.6 & 11.7 \\
10 & 58.3 & 57.6 & 24.4 & 24.8 & 0.423 & 0.400 & 0.254 & 0.240 & 11.2 & 11.7 \\
11 & 58.2 & 57.6 & 24.6 & 24.8 & 0.401 & 0.400 & 0.234 & 0.240 & 11.8 & 11.7 \\
12 & 55.9 & 57.6 & 25.2 & 24.8 & 0.372 & 0.400 & 0.236 & 0.240 & 12.0 & 11.7 \\
13 & 56.1 & 57.6 & 23.9 & 24.8 & 0.405 & 0.400 & 0.233 & 0.240 & 11.9 & 11.7 \\ \hline \end{tabular} Y\({}_{1}\): Phenolic desorption yield (%); Y\({}_{2}\): Sugars desorption yield (%); Y\({}_{3}\): Total phenolic content (g GAE/g extract); Y\({}_{4}\): Total sugar content (g D-glucose/g extract); Y\({}_{5}\): Radical scavenging activity (mM Trolox).  
\end{table} 
Table 3: Experimental and calculated values of the objective functions during desorption from Sepabeads SP207 resin.
## 5 Empirical Studies: Industrial Cases  
We explored whether our model-based prioritization could be applied to real industrial UML models for the automotive industry (specifically: models of chassis parameters, body electronics, and engine controls). Most prior empirical studies have focused on automotive electronics, many of which are simple, with outputs determined by the combinations and the timings of input signals. In Section 4, we showed how to generate mutations using mutation operators that seeded the state  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**(I) Method** & **(J) Method** & **Average Difference (I \(-\) J)** & \(p\) \\ \hline \multirow{4}{*}{Random} & StTotal & 0.003824646490 & \(<\)0.05 \\  & StAddtl & 0.004647020250 & \(<\)0.05 \\  & PepTotal & \(-\)0.0515400500 & \(<\)0.05 \\  & PepAddtl & \(-\)0.060530909030 & \(<\)0.05 \\  & AVM & \(-\)0.060551111040 & \(<\)0.05 \\ \hline \multirow{4}{*}{StTotal} & Random & \(-\)0.003824646490 & \(<\)0.05 \\  & StAddtl & 0.000822373760 & \(\geq\)0.05 \\  & FeTotal & \(-\)0.055364696990 & \(<\)0.05 \\  & PepAddtl & \(-\)0.064355555520 & \(<\)0.05 \\  & AVM & \(-\)0.064375757530 & \(<\)0.05 \\ \hline \multirow{4}{*}{StAddtl} & Random & \(-\)0.004647020250 & \(<\)0.05 \\  & StTotal & \(-\)0.000822373760 & \(\geq\)0.05 \\  & PepTotal & \(-\)0.056187070750 & \(<\)0.05 \\  & PepAddtl & \(-\)0.065177929280 & \(<\)0.05 \\  & AVM & \(-\)0.065198131290 & \(<\)0.05 \\ \hline \multirow{4}{*}{FepTotal} & Random & 0.051540050500 & \(<\)0.05 \\  & StTotal & 0.055364696990 & \(<\)0.05 \\  & StAddtl & 0.056187070750 & \(<\)0.05 \\  & PepAddtl & \(-\)0.008990858530 & \(<\)0.05 \\  & AVM & \(-\)0.009011060540 & \(<\)0.05 \\ \hline \multirow{4}{*}{FepAddtl} & Random & 0.060530909030 & \(<\)0.05 \\  & StTotal & 0.06435555520 & \(<\)0.05 \\ \cline{1-1}  & StAddtl & 0.065177929280 & \(<\)0.05 \\ \cline{1-1}  & PepTotal & 0.008990858530 & \(<\)0.05 \\ \cline{1-1}  & AVM & \(-\)0.000020202010 & \(\geq\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The outcomes of test case prioritizations.  
Figure 3: A comparison of test case prioritization methods.  
charts of model-based tests in a manner unlike code-based TCP. This Section seeks answers to RQ2 and RQ3 (Which prioritization technique allows optimal fault detection? Is this better than the code-based approach?; Is the optimal search algorithm for model-based TCP the same as the code-based algorithm? Does this work for real industrial cases?) via empirical studies. Section 5.4 analyzes threats to the validity of such results. We prioritized test cases for body electronic components: the power-window switches on the driver and front passenger doors; the wiper, interior, and exterior lamps; central door controls; seat belt controls; the burglar alarm; and the smart key (passive entry/start) system. A mutant kill table explored whether a new test case killed mutants automatically generated by mutant operators. Using this table, the TCP satisfying a fitness function was statistically analyzed.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### Particle Size Distribution  
Particle size distribution (PSD) is a crucial parameter describing particle size distribution patterns, significantly affecting soil's physical and mechanical behavior. Based on previous unconfined compressive strength test results, PSD tests were conducted on the specimens (R20-C10, R20-C15, R20-C20, R40-C20, R60-C20), and the results were analyzed and discussed. Figure 10 shows the PSD curves and changes in silt-clay content ratios for different proportions of stabilizer and recycled aggregate contents after 40 days of water curing.  
PSD test curves for the R20-C10, R20-C15, and R20-C20 specimens show that with fixed recycled aggregate content, PSD curves shift rightward as stabilizer content increases. This indicates strengthening aggregation effects due to hydration reactions, leading to increased coarse particle content. Specifically, the R20-C10 specimens contained 24.08% clay, 19.33% silt, and 56.59% sand particles; while the R20-C15 and R20-C20 specimens showed decreased or stable clay and silt contents, with sand content increasing by 16.94% and 27.58%, respectively. This leftward to rightward curve shift indicates the formation of large particle aggregates after stabilizer mixing, enhancing specimen density.  
For the R20-C20, R40-C20, and R60-C20 specimens' PSD test curves, with fixed stabilizer content, PSD curves showed different trends as recycled aggregate content increased. R20-C20 and R60-C20 curves appeared more rightward compared to R40-C20. Specifically,  
Figure 10: Distribution curves of grain sizes of specimens with different ratios and the proportion of powdery and viscous sand at 40 d of immersion conditioning.  
Figure 9: Schematic diagram of particle skeleton changes.  
the R40-C20 specimens contained 63.20% sand particles, while R20-C20 and R60-C20 contained 84.43% and 87.48%, respectively. R40-C20's lower sand content was due to stabilizer hydration binding more silt-clay particles, reducing silt content and relative sand content. Additionally, increased recycled aggregate content in the R60-C20 specimens led to higher coarse particle content, affecting mechanical properties.
# Addressing the Algorithm Selection Problem through an Attention-Based Meta-Learner Approach
###### Abstract  
In the algorithm selection problem, where the task is to identify the most suitable solving technique for a particular situation, most methods used as performance mapping mechanisms have been relatively simple models such as logistic regression or neural networks. In the latter case, most implementations tend to have a shallow and straightforward architecture and, thus, exhibit a limited ability to extract relevant patterns. This research explores the use of attention-based neural networks as meta-learners to improve the performance mapping mechanism in the algorithm selection problem and fully take advantage of the model's capabilities for pattern extraction. We compare the proposed use of an attention-based meta-learner method as a performance mapping mechanism against five models from the literature: multi-layer perceptron, \(k\)-nearest neighbors, softmax regression, support vector machines, and decision trees. We used a meta-data dataset obtained by solving the vehicle routing problem with time window (VRPTW) instances contained in the Solomon benchmark with three different configurations of the simulated annealing meta-heuristic for testing purposes. Overall, the attention-based meta-learner model yields better results when compared to the other benchmark methods in consistently selecting the algorithm that best solves a given VRPTW instance. Moreover, by significantly outperforming the multi-layer perceptron, our findings suggest promising potential in exploring more recent and novel advancements in neural network architectures.  
offline algorithm selection; meta-learning; attention mechanism; neural network; combinatorial optimization; vehicle routing problem with time windows; simulated annealing; machine learning +
Footnote †: journal: Applied  
Article  
Addressing the Algorithm Selection Problem through Attention-Based Meta-Learner Approach  
[PERSON]  
Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico;  
[EMAIL_ADDRESS] (E.D.A.L.-H.); [EMAIL_ADDRESS] (J.C.O.-B.); [EMAIL_ADDRESS] (H.T.-M.)
# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 3 LocFiND--Localization Using Fingerprinting and Novelty Detection
Thus, we cannot simply start ignoring any information that might be unreliable. The question that remains is how to define, in the online phase, if a set of RSS's received can be considered reliable for correct classification. We refer to this step as Evaluation of Confidence in our LocFiND architecture. As mentioned before, we propose three different techniques in LocFiND to evaluation the confidence for estimated positions:  
1. Prediction Probability: uses the estimated prediction probability to decide whether to accept the estimated class. 2. Novelty Detection: uses the novelty detection technique to identify the reliability of the classified sample. 3. Hybrid Approach: uses both probability and novelty detection to improve on the isolated solutions. These three techniques are explained and detailed in the next sections.
## 3 Results
### Comparison of C5 and C6 CI Products
#### 3.1.2 Spatial Coverage of the Main Algorithm
The rate of backup algorithm retrieval of the C6 CI is lower than that of the C5 CI, except for pixels south of the Sahara in July. Most CIs retrieved by the main algorithm are larger than 1 in south of the Sahara from the C6 CI data in July, and the backup algorithm is adopted to generate the corresponding reasonable CI values for these pixels according to an averaged BRDF shape using the AFX. The retrieved CIs from the backup algorithm of the C6 data are generally consistent with the corresponding C5 CIs. The C5 BRDF data of these backup algorithm CIs consist of more full inversions and missing values than the C6 data, which is considered to be the main reason for the different performances of these two versions of CIs. The C6 BRDF/Albedo products use all available clear-sky observations for retrievals and result in fewer missing values. The updated thresholds for high-quality observations may lead to fewer full inversions at low latitudes with high aerosol or cloud contamination [35]. Previous studies reported that the accuracy of CI retrievals from the main algorithm is higher than that of the backup algorithm for snow-free surfaces [26; 100]. Considering the higher rate of the main algorithm retrieval of the C6 CI mentioned above, the overall inversion quality of the C6 CI is higher than that of the C5 CI. Figure 4: Global distribution of CI quality assurance (QA) data of the MODIS C5 and C6 CI products in January and July 2015. Panels (**a–d**) are global maps of the CI quality flags corresponding to the CI maps in Figure 2 (see Section 3.1.2 for more details). Panels (**e**,**f**) show the latitudinal distribution of the retrieval rates of different algorithm paths in January (**e**) and July (**f**) 2015. Possible algorithm paths include the main algorithm (green), backup algorithm (red), and missing value (not retrieved, gray). These statistics exclude non-vegetation areas (15-Snow and Ice, 16-Barren) according to the MODIS IGBP land cover data.
# Partial Oscillation Flow Control on Airfoil at Low Reynolds Numbers
## 4 Results and Discussion
### Influence of the Oscillation Position
The pressure distribution on the upper surface of both the rigid airfoil and airfoils with surface oscillation at different locations is depicted in Figure 17. As illustrated by the distribution curves of pressure coefficients, remarkable fluctuations in pressure coefficients are observed over the separation bubble region on the upper surface of the rigid airfoil, a phenomenon attributed to the presence of multiple vortex structures in this area. However, when oscillation occurs at positions \(0.1c\) and \(0.6c\), the pressure fluctuations within the separation bubble region are notably mitigated. This further suggests that oscillation at these two specific locations exerts a certain degree of control over the laminar separation bubble. The mechanism underlying the aforementioned phenomena is as follows: When the oscillation position is at \(0.1c\), proximate to the airfoil's leading edge, the oscillation serves to efficiently infuse energy into the boundary layer, influencing a broader downstream area, thus effectively managing the LSB. In contrast, when the oscillation occurs at \(0.6c\), situated within the LSB itself, it directly supplies energy into the low-velocity airflow within the separation bubble, thereby efficaciously controlling laminar separation. At \(P=0.1c\) and \(P=0.6c\), Figure 18 depicts the peak displacement of the oscillating surface, denoted as \(\bigtriangleup y_{m}\), alongside the aerodynamic forces recorded at distinct instants across two complete oscillation cycles. This figure demonstrates that as the airfoil undergoes partial skin deformation, both its lift and drag coefficients exhibit periodic fluctuations. Over the course of one oscillation cycle, the lift coefficient of the airfoil surpasses that of its rigid counterpart, and, concurrently, the drag coefficient is found to be less than that of the rigid airfoil. Therefore, the introduction of partial oscillation enhances the aerodynamic performance of the airfoil at low Reynolds numbers. The magnitude of the aerodynamic coefficients when the position \(P=0.6c\) exceeds that observed at \(P=0.1c\), suggesting that the influence of oscillation on the LSB is more pronounced at the former position. Consequently, the unsteady nature of the aerodynamic forces is more evident when \(P\) is \(0.6c\). Figure 17: Pressure distributions on the upper surface of airfoils. Figure 18: Variation of aerodynamic force of airfoil with time. (**a**) Lift coefficient; (**b**) drag coefficient. Illustrated in Figure 19 are the varying shapes of the LSB on the upper surface of the airfoil at distinct moments within a single oscillation cycle when \(P=0.6c\). From the figure, it becomes evident that the LSB assumes diverse configurations at different instants during the oscillation cycle. This cyclical transformation of LSB shapes directly contributes to the periodic fluctuations in the aerodynamic forces exerted upon the airfoil.
## 5 Numerical Validation of the Model
### Dynamic Analysis of a Masonry House
#### 5.2.2. Shake Table and Ground Acceleration  
The experiment was conducted using the single-axis shake table at the Institut Technologique FCBA in Bordeaux [36]. This shake table is designed to support a maximum dead load of 15 tons. Movement is driven by a servo-hydraulic jack capable of exerting up to 250 kN of force, and it is guided by roller bearings. The shake table's motion is aligned along the X-axis, as illustrated in Figure 16.  
Figure 16: Schemes of the reduced scale house.  
Figure 17: The tested masonry house [31]: (**a**) house construction; (**b**) the roof truss and its connection with masonry house.  
The seismic signal used for the experiment is a customized version of the natural acceleration signal recorded during the 2003 Miyagi earthquake in Japan, as measured at the K-Net station. This ground acceleration was modified to reflect the characteristics of a distant subduction earthquake as might be experienced in Guadeloupe, French Caribbean, based on the methodology proposed by [PERSON] et al. [37]. This methodology modifies the ground acceleration of a known signal in order to obtain a response spectrum featured by the type of soil, earthquake depth, source type, peak ground acceleration, and moment magnitude. For the considered scenario, the peak ground acceleration was 0.35 g.  
The adjusted signal corresponded to a reference return period of 475 years on a Type B soil in line with Eurocode 8 standards [10], with a 10% probability of exceeding the reference peak ground acceleration over a span of 50 years. This scenario aligns with the \"no collapse\" condition specified in Eurocode 8. The response spectrum of the accelerogram and the ground acceleration, as shown in Figure 18, were further scaled using [PERSON]'s similitude law, yielding a maximum ground acceleration of 0.7 g.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 2 Products and Related Work
### Related Work and Problems
Users cannot replace the current algorithms with their own methods. Furthermore, because the volumes of ocean remote sensing data have been increasing drastically, it is impossible for users to download and process them on their premises, especially for time-series analysis. More and more users are asking for an interaction platform to browse the data, design their own processing procedures and view results on the fly. Apparently, the existing ground application system cannot meet this kind of requirement.
## 2 Materials and Methods
### Remote-Sensing Data  
MODIS is an optical sensor on the satellite of the Earth Observation System (EOS). It contains 36 optical channels and realizes full spectral coverage ranging from 0.4 \(\upmu\)m (visible light) to 14.4 \(\upmu\)m (thermal infrared). The sun-synchronous near-polar orbit satellites Terra and Aqua carrying MODIS transit twice a day to achieve global observation every two days. The MODIS data have three resolutions: 1000 m, 500 m, and 250 m. Among these, the 250 m product covers red and near-infrared bands that can be used to discriminate clouds, plants, and land. These characteristics of the MODIS data are helpful for obtaining information about _U. prolifera_ and monitoring its outbreaks over a large continuous area, as has been confirmed in previous studies [2; 14].  
The re-entry period of the MODIS images is one day, which is shorter than that of other high-resolution images. For example, the Landsat-8 re-entry period is 16 days, and that of HJ-1 CCD is 2 days. With the advantages of comprehensive time coverage and simple processing, MODIS is suitable for real-time, large-area monitoring of _U. prolifera_.  
The data used in this study were all downloaded from the National Aeronautics and Space Administration (NASA) data sharing website [24]. MODIS Level-1B 250 m class data (MOD02 QKM) and MODIS geolocation data (MOD03, Geographic Location File) were selected as data sources from May to August during 2018. MOD03 was involved in the geometric correction of MODIS02 QKM.  
Figure 1: Map of the study area and major cities along the coast.
## 2 Literature Review
### Simulations and Human-Computer Interaction (HCI)  
Simulation and Human-Computer Interaction (HCI) are two closely related fields that intersect in several ways. Simulation is the process of creating a computer model or representation of a system or process to gain insights into its behavior or to evaluate its performance under different conditions [25]. Simulations can be used to test various scenarios, optimize designs, and make predictions about how a system might behave in the real world. Human-Computer Interaction (HCI), on the other hand, is the study of how people interact with computers and other digital technologies. HCI researchers aim to understand the cognitive and social processes involved in human-computer interaction and to design user interfaces that are intuitive, efficient, and effective.  
The connection between simulation and HCI lies in the fact that simulations can be used as a tool for studying and evaluating user interfaces [26]. For example, simulations can be used to test different interface designs and evaluate their effectiveness in supporting user tasks. They can also be used to study the cognitive processes involved in human-computer interaction, such as attention, memory, and decision-making. In addition to studying user interfaces, simulations can also be used to create virtual environments that support immersive and interactive teaching and learning experiences. For example, virtual reality and augmented reality technologies, use simulation to create digital environments that users can interact with in a natural and intuitive way. Particularly, if the computer simulations are to be used for learning by students independently (without the support of a teacher), it is required that the interface design of these systems meet the principles HCI, such as user-centeredness, feedback, learnability, and error prevention. This is to ensure that the learners have an interactive learning system that is easy to use, efficient, and satisfying to interact with comfortably on their own [27].
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### Optimized Coordinate Descent Detector  
The OCD algorithm enhances coordinate descent by integrating preprocessing and channel-aware step sizes. The update for the \(k\)-th symbol is  
\[\hat{\mathbf{s}_{i}}=\left(\|\mathbf{G}_{i}\|^{2}+\mathbf{w}_{0}\right)^{-1} \mathbf{G}_{i}^{T}\left(\mathbf{r}-\sum_{j\
eq i}\mathbf{G}_{j}\mathbf{s}_{j} \right), \tag{17}\]  
where \(\mathbf{G}_{i}\) is the \(i\)-th column of the channel matrix, and \(\mathbf{w}_{0}\) is the noise power. OCD achieves near-optimal performance with low per-iteration complexity, making it suitable for hardware implementations. While offering near-optimal performance with low complexity, OCD's sequential updates can lead to latency, suggesting a need for parallelization strategies in future high-throughput applications [32].
# View Synthesis with Scene Recognition for Cross-View Image Localization
## 3 System Design
### Framework Overview  
Figure 2 illustrates the proposed framework, which is composed of the following components: patch image generator, reference image, style transfer, attribute extractor, and comparison. The fundamental idea was to generate a synthesis view for the reference images, such as Google Street View, with lower updating frequency by transferring the style from patch images, which contain the latest weather/season information from an external data source, such as Flickr [30]. Flickr is an online photo management and sharing application that covers various images with different lighting and weather from anywhere in the world. Most importantly, the large number of active users ensures that the image database is updated at a very high frequency. Once we obtain the qualified patch images by providing a time frame and a geo-location near the desired reference images, we can pass patch images and reference images to the style transfer component to produce an image that contains the features from both images. The auxiliary deep learning model contains an attribute extractor, which can recognize the scenes when the model is given images. The attribute extractor then produces a set of attributes to best describe the images. The set of attributes is then passed to the comparison component with the performance evaluator to determine the results of the attributes extracted and transferred.