esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison with the Literature  
Figure 10 compares the \(C_{c}\) values predicted by different models with the actual observed values. The models include GP-XGBoost, [PERSON] and [PERSON] [1], [PERSON] et al. [8], [PERSON] [9], and [PERSON] and [PERSON] [11]. The GP-XGBoost model closely matches actual values, especially for lower \(C_{c}\) ranges. In contrast, the empirical models show larger deviations. [PERSON] and [PERSON] [1] often overestimate \(C_{c}\), while [PERSON] et al. [8] significantly  
Figure 9: Feature importance comparison for predicting \(C_{c}\) across MLR, GP, XGBoost, and GP-XGBoost models.  
underestimates it. [PERSON] [9] and [PERSON] and [PERSON] [11] provide more consistent results but lack the precision of GP-XGBoost.  
These results highlight the strength of machine learning models like GP-XGBoost in capturing complex relationships in the data. They outperform empirical models, which are limited by their assumptions and simplified formulas. The graph also shows the variability in soil behavior and the difficulty that empirical methods have in generalizing across different conditions. While traditional models are useful for initial estimates, GP-XGBoost offers higher reliability and accuracy. This makes it a valuable tool for geotechnical engineering and emphasizes the importance of modern, data-driven techniques.  
The results in Table 14 show that the GP-XGBoost hybrid model performs better than empirical models in predicting C\({}_{c}\). The GP-XGBoost model achieved a high R\({}^{2}\) of 0.927 and a low MSE of 0.027. This shows it has strong accuracy and minimal error. In comparison, empirical models had much lower R\({}^{2}\) values. [PERSON] and [PERSON] [1] had the highest R\({}^{2}\) at 0.149, while others had R\({}^{2}\) < 0.1 values that show a poor fit or overestimation.  
MAE values for the empirical models were also much higher. They ranged from 0.090 for [PERSON] and [PERSON] [1] to 0.189 for [PERSON] et al. [8]. These results show that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & \(R^{2}\) & **MSE** \\ \hline GP and XGBoost & 0.927383 & 0.027343 \\ [PERSON] and [PERSON] [1] & 0.149181 & 0.09012 \\ [PERSON] et al. [8] & 0.091134 & 0.189196 \\ [PERSON] [9] & 0.029736 & 0.106369 \\ [PERSON] and [PERSON] [11] & 0.093919 & 0.131074 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Performance comparison of GP and XGBoost with empirical models for predicting C\({}_{c}\).  
Figure 10: Scatter matrix plot of actual and predicted \(C_{c}\) for testing data using different models [1; 8; 9; 11].  
GP-XGBoost is effective at capturing complex patterns in the data. Traditional empirical models fail to represent these relationships accurately.
###### Abstract  
The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adelie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked \"without adaptation\" and \"with adaptation\" on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational \"image-to-assessment pipeline\" that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.  
Antarctica; penguins; guano; GEOBIA; VHSR imagery; census Article
## 3 Results
### SMCI Model Adaptations
#### 3.2.5 Evaluation--BDAT Model  
The SMCI model using physiological signals from participants with SMCIs was combined with the EI model to identify a word that represented an emotion. This was validated against the word provided by the participants with SMCIs. The accuracy of identifying an emotion from six potential outcomes was 14.81% (less than chance, one-sixth or 16%).  
The most likely explanation for the poor outcomes is the data collection issues. Of the 10 participants (9 SMCIs and 1 TD), only 5 had valid (continuously recorded data throughout the trial) GSR and HR data values for at least 1 image stimulus (out of a potential 30 images). SMCI01 and SMCI07 each had valid data for only 1 image, while SMCI08 had valid data for 13 images, and SMCI09 had valid data for 9 images. The typically developing participant (TDM01) had valid data for two images. Therefore, 84.62% of the valid data came from two participants (SMCI08 and SMCI09). The model only predicted the correct emotions for two participants: TDM01 and SMCI08. The accuracy of predicting the emotion was 50% for TDM01 and 23.08% for SMCI08. The small amount of data available for validation may have led to overfitting, though the DEAP dataset was used for training while the BDAT set was used for the validation phase.  
There were 20 different IAPS image stimuli that had valid GSR and HR data values that could be used to predict emotions out of the 300 IAPS image stimuli that were collected from the participants with SMCIs. While the protocol had been tested and checked, it became evident that flexibility in data collection was required when working with participants with disabilities. From the twenty images with usable data, six were viewed by two participants, while fourteen were viewed by only one. Of those, two image stimuli resulted in physiological signals that were mapped onto the intended emotion with 100% accuracy, while another two resulted in 50% accuracy. The other sixteen image stimuli resulted in physiological signals that could not be correctly mapped onto an emotion.  
The model was further tested on only the \"good data\". If the model could not predict the emotions of a specific participant, that participant's data were removed. The accuracy of the model improved from 14.81% to 33.33%. It was evident that the limited number of image stimuli that had valid data and the small number of participants with valid GSR and HR data were the main factors contributing to the lower accuracy of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**DEAP Model (Level 8 Wavelets,**} & \multicolumn{2}{c}{**SMCI Model with Changes**} \\  & \multicolumn{2}{c}{**60 s Duration)**} & \multicolumn{2}{c}{**(Level 5 Wavelets, 12 s Duration)**} \\ \hline  & **Model** & **Accuracy** & **Model** & **Accuracy** \\ \hline
**Pleasure** & Random Forest & 20\% & SVM-rbf & 20\% \\
**Arousal** & SVM-rbf & 21\% & SVM-poly & 19\% \\
**Dominance** & SVM-rbf & 22\% & SVM-rbf & 22\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of models with different durations.
## 4 Discussion
[33] proposed the deep neural network for predicting risk factors of coronary heart disease and compared machine learning techniques such as naive Bayes, k-nearest neighbor algorithm, decision tree, and support vector machine. The proposed method presented the highest AUC value among the compared techniques (AUC = 0.866 [0.852-0.880]. The risk scoring system was developed for the estimation of multiwessel disease (MVD) using the univariate Cox regression approach in ST-segment elevation myocardial infarction by [PERSON] et al. [34]. CVD (HR = 2.19) was a significant factor by multivariate analysis for predicting 1-year mortality in MVD. [PERSON] et al. [35] proposed a prediction model for the association of hypertension and prehypertension with risk factors using the Waikato Environment for Knowledge Analysis data mining tool and evaluated 8212 selected patients. The prediction models were developed using logistic regression, naive Bayes, and decision tree. In prehypertension, BMI showed a high association in men (OR = 1.429 [1.304-1.462]) and women (OR = 1.428 [1.204-1.453]). The present study yielded results similar to those of previous studies [19-28] with regard to the association of hypertension with anthropometric indices, fat mass, and BMD. Specifically, WHtR exhibited the strongest association among the anthropometric variables in both men and women. With regard to fat mass, FT exhibited a significant association in both men and women, whereas for BMD, PlvBMD and TSBMD showed a high association in men and women, respectively. Our results supported the previous findings of sex-based differences in the association of hypertension with risk factors. In addition, building upon previous findings of correlations between hypertension and stroke [8-18], our results of risk factor analysis in hypertension + stroke were as follows. Among all variables except age, WHtR showed the strongest association in both men and women. However, FT showed an association in men, whereas factors related to leg fat mass were indicated as significantly involved in women. For BMD, as opposed to fat mass, arm and leg-related factors were shown to be associated with hypertension + stroke in men, with PlvBMD exhibiting the strongest association in women. Overall, the results of this study showed that body composition, fat mass, and BMD had a significant effect on hypertension and stroke. Specifically, we found that increased fat mass and decreased BMD were associated with hypertension or hypertension + stroke. Clear differences for men and women were also associated with each risk factor. To our knowledge, this is the first report of the association of anthropometric indices, fat mass, and BMD in patients with hypertension + stroke. However, this study had several limitations. First, cause and effect associations were difficult to determine because of the cross-sectional design. Second, our results were limited to Korean adults because the study was based on KNHANES data.
## 4 Second Application: Three-Point Bending Test and Comparison to Experimental Data
### Results
We confirmed this conclusion by generating the corresponding histograms for the new coordinate system and observing that most AE events had aligned with the \"tilted\" \(y\)-axis, i.e., along the fracture's path. Figure 14: Damage evolution of the DEM for \(\zeta=0\%\), and \(40\%\) for 3 moments during the simulation. Figure 15: Event localization and corresponding normalized AE energy histogram for the centered fissure. (**a**) LDEM simulation result, (**b**) Experimental results. Overall, the localization of events and the energy distribution yielded by the simulated models agreed with the experimental results in [41], indicating that the proposed LDEM approach was a valuable tool for analyzing damage in quasi-brittle structures. However, a few relevant discrepancies were noted:  
* A significant part of the experimental AE events occurred below the pre-fissure's head, whereas almost none appeared in the LDEM simulation. That difference was probably due to unintended damage in the pre-fissure region during the specimen preparation. * The simulations indicated noticeable AE activities in regions other than along the main crack, such as in the vicinity of the load application (both tests) and in the horizontal direction crossing the top of the main fissure's head (non-centered case). No such activity occurred in the corresponding regions during the experiments. These discrepancies were probably derived from the determination methods for identifying AE activity: The events in the numerical data were calculated from the kinetic energy produced inside the model, free from the attenuation that affected the signals captured by the sensors.
# A Study of Applying Fuzzy Theory in Simulation-Based Education
## 2 Literature Review
### Aeronautical Decision Making  
The USA FAA defines pilot judgment to mean processes to recognize and analyze available data regarding selfness, aircraft, aviation environment and flight purposes with subsequent reasonable assessment using other methods. It also meant timely decision making to ensure safety. Therefore, pilot judgment includes assessing attitude and appraisal capabilities for team members dealing with crises, as well as their decision making based on personal knowledge, skills and experience. [PERSON] and [PERSON] [27] proposed that flight decisions in the cockpit involved analyzing and comparing all possible available alternatives to respond correctly in a timely manner when flight safety was threatened.  
Pilot decision making was usually based on uncertain messages or performed in dangerous situations, where often thoughtful and logical decisions could not achieve the desired results. Therefore, many flight safety accidents were due decision making errors. Making decisions in-flight is a complex cognitive process deeply affected by flight conditions and operational environment factors [28]. The particular situations forced pilots to make decisions while unable to confirm misjudged conditions or improper operational responses [29; 30]. Pilots often used simple rules and procedures to implement flight tasks, but when facing complex emergencies, they must decide from multiple alternatives or solutions with considerable time pressure [31]. Most pilot reactions were related to flight decisions about establishing or changing flight postures, as a direct response to operating aircraft including understanding and judging instrument accuracy within the cockpit and outer environment. Pilot decision making required external signals, such as aviation control instructions, inspection card procedures, aircraft postures, or meter displays to ensure correct judgment or operation. Generally, this information needed to be free from conflict to provide a complete explanation quickly. If misunderstanding occurs in these situations, further understanding could help reduce human error likelihood [32].  
Pilot judgment and decision making involve different judgment modes, which can be broadly divided into perceptive and cognitive judgment. Decision making and judgment often includes perceived elements (visual, auditory), such as distance or proximity to the ground, which can be very important for pilots to safely operate aircraft. Cognitive judgment occurs when perceptive elements are less clear, and usually requires more time for consideration. Generally, where there are more than two choices and the risk of either option was difficult to assess. Therefore, junior pilot judgement tends to be more cognitive judgment, whereas senior pilot judgement tends more to perceptive judgment. Changing from cognitive to rapid and effective perceptive judgment requires long learning/training times. Many decision making training systems help pilots to clearly understand the decision making process and potential mistakes for each step.
## 2 Materials and Methods  
### Study Area  
The Quaternary Azrou volcanic plain in the Middle Atlas of Morocco covers more than 400 km\({}^{2}\)[21], and well-preserved cones and calderas are still clearly visible (Figure 2) [22]. [PERSON] surveyed over 100 large collapses in the 70-80s on the basalt plain using 1:100,000 maps and some aerial photographs [23]. Since the basalts cover the Jurassic limestones, it is not well known if these voids are karstic in origin, or if they are parts of lava tube systems. [PERSON] defined them as \"caprock collapse sinkholes\" in his chapter on dolines in the Encyclopedia of Caves and Karst [24], thus suggesting the presence of karstic voids underneath. However, some small lava tube segments are known in this area [23], and some of the collapses are clearly aligned, or located at the center of the lava flows. This plateau thus offers the unique opportunity of studying collapses formed by the supposed presence of lava tubes and of karst voids (ancient doline fields or collapsed caves). Recent studies have shown a good agreement between remote-sensing derived lineaments and karst features [25; 26]. These studies used LANDSAT 7 ETM+ (spatial resolution of 30 m, 15 m in panchromatic), Sentinel-2 (spatial resolution greater than 10 m), and ASTER GDEM data (spatial resolution of 30 m). The possible presence of well-developed karst systems, covered by basalts but reactivated after the emplacement of these volcanic rocks, should be taken into account when considering the vulnerability of this significant regional aquifer, with important karst springs used for drinking water purposes (e.g., Oum Er-Rbia) [26; 27]. However, currently, none of the above-mentioned studies in the area used high resolution images (e.g., Pleiades).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Morphometric Parameters** & **Processing** \\ \hline Minimum elevation (**Z\_Min**) & Measured on the topographic surface surrounded by the shape \\ Maximum elevation (**Z\_Max**) & ” \\ Minimum slope & Measured on the sinkhole slope raster \\ Maximum slope & ” \\ Depth (m) & Difference between maximum and minimum elevation \\ Maximum axis (m) & Measured using the bounding box of the shape \\ Minimum axis (m) & ” \\ Azimuth of the maximum axis (deg) & Angular value in degrees from geographic North \\ Perimeter (m) & Linear measure of the shape \\
2D Surface (m\({}^{2}\)) & Area of the shape \\
3D Surface (m\({}^{2}\)) & Area of the topographic surface of the sinkhole \\ Volume (m\({}^{3}\)) & Measured from the topographic surface and the shape \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the morphometric parameters generally used to characterize sinkholes, which can be automatically extracted from DEMs processing.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Data Loading and Exploration
* Heart rate, pulse rate (bpm), respiratory rate (bar/min), and temperature (\({}^{\circ}\)C): Vital signs reflect the patient's immediate physiological state, with abnormal values often seen in AAS due to pain, shock, or cardiovascular strain. * Height, weight, and body mass index (BMI): Obesity and body composition affect cardiovascular health and aortic structure. * Pain score: Chest or back pain is a hallmark symptom of AAS, often described as sudden and severe. * Pulse Ox (oxygen saturation%): May decrease in cases where AAS compromises circulation or causes secondary cardiac issues. These features were chosen not only for their statistical contribution but also for their pathophysiological relevance to AAS. The combination of these indicators provides a comprehensive profile that supports robust model performance and clinical applicability.
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 4 Result and Discussion  
### Comparison of Global Precipitation Products at Temporal Scale  
This section presents a comparison of three global precipitation products and their downscaled versions vs. station data measurements according to the essential subject of gauge representativeness to identify the most reliable products for water resource assessment, climate studies, and hydrological applications across the data-scare WSRB at different temporal scales for the period from 1983 to 2014.
# Structure Control Using Bioderived Solvents in Electrochemical Metal-Organic Framework Synthesis
###### Abstract
However, toxic [18; 19] and fossil fuel derived [20] solvents such as DMF are still currently required. Replacing these with more benign, bio-derived solvents would therefore be a significant step in improving the green credentials of this synthesis method. Very little research has been conducted to combine electrochemical synthesis and bio-derived solvents, and none focusing on the electrochemical synthesis of MOFs. Despite this, some promising early work describes the use of such solvents in traditional solvothermal MOF synthesis. Dihydrolevogluconene (commercially known as CyreneTM) is a bio-derived solvent made from waste cellulose [21; 22] which possesses similar properties to that of DMF and has previously proven effective as a solvent for the solvothermal synthesis of a variety of MOFs [23; 24]. \(\gamma\)-Valerolactone (GVL) has also shown similar promise, particularly for the synthesis of ZIFs [25; 26]. In this work we demonstrate the feasibility of replacing the traditional, toxic, fossil fuel derived solvents in electrochemical MOF synthesis with safer, greener and cleaner alternatives. Specifically, we utilize GVL and CyreneTM to electrochemically synthesize a wide range of MOFs with either Cu, Zn or Co as the metal component and either 1,3,5-benzetricarboxylic acid (BTC), imidazole (IM), benzimidazole (bIM), 2-methylimidazole (mIM) or 2-ethylimidazole (elM) as the organic linker component. The MOFs formed are characterized using X-ray diffraction (XRD) to confirm their identity and scanning electron microscopy (SEM) to compare their morphology and crystal quality. Excitingly, we also demonstrate that less stable, higher porosity MOF structures are obtained when utilizing these novel, bio-derived solvents instead of DMF, indicating that the larger bio solvent molecules may also play a templating role during the synthesis.
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 2 The Generation Mechanism of Wideband Harmonics
### Analysis of Dead Zone Influence  
Due to the influence of conductivity modulation and current tailing, a dead zone delay is usually added to IGBT signals to avoid short circuits. The inverter's output voltage contains low-order odd harmonic components, which increase the distortion of the network connection current and directly affect the power quality. Figure 5 shows a state schematic of the A-phase IGBT turn-on and turn-off in one control cycle.  
From Figure 5a,b, without consideration of IGBT conductive pressure drop and loss in the line, when \(I_{\text{a}}>0\) and \(Q_{\text{1}}\) turn on, the output current in phase A passes through \(Q_{\text{1}}\), midpoint voltage \(V_{\text{A}}=U_{\text{dc}}\). When \(Q_{\text{1}}\) turns off but \(Q_{\text{2}}\) has not turned on, the output current passes through the inverse parallel diode, midpoint voltage \(V_{\text{A}}=0\). Since the output current of the bridge arm does not pass through \(Q_{\text{2}}\), the dead zone of \(Q_{\text{2}}\) does not affect the midpoint voltage. Therefore, the error voltage amplitude of the inverter is positive during the half-cycle in which the upper bridge arm opens.  
Similarly, in Figure 5c,d, if \(I_{\text{a}}<0\), the dead zone of \(Q_{\text{1}}\) also does not make any difference to the grid-connected voltage. When \(I_{\text{a}}<0\) and \(Q_{\text{2}}\) turns on, midpoint voltage \(V_{\text{A}}=-U_{\text{dc}}\)  
Figure 4: Bode diagram of the LCL.  
Figure 5: Current flow path of bridge arm in phase A.  
When \(Q_{2}\) turns off but \(Q_{1}\) has not turned on, midpoint voltage \(V_{A}=0\). The error voltage amplitude of the inverter is negative during the half-cycle in which the lower bridge arm opens.  
During an entire control cycle, IGBT turns on and off once, respectively. The inverter modulation voltage contains an error whose amplitude value is \(U_{\text{dc}}\) and its length is \(T_{d}\). The PV output current's direction determines the error voltage amplitude. Therefore, the error voltage in any phase caused by the dead zone is modeled as a square wave, and its manifestation in a whole control cycle is shown in Figure 6, where \(T_{\text{s}}\) represents the control and sample period, \(T_{\text{on}}\) represents the ideal switch time during the current PWM control cycle, \(T_{\text{d}}\) represents the time of dead zone, and \(U_{\text{dc}}\) represents the DC bus voltage of the PV inverter.