esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of the Horizontal Mass Ratio of the Seismic Isolation Layer  
Each vibration isolation subframe in the mega-sub isolation structural system is a normal frame structure and a large FM (Frequency Modulation) mass block. This structure has an FM damping effect and a basic seismic isolation function. Under earthquake action, the main frame is subjected to its inertial forces and the inertial forces from the individual damped sub-frames. The inertia forces of the sub-frames are transferred to the main frame through the isolation device and partially offset by the inertia forces of the main frame. As a result, the dynamic response of the main frame is reduced.  
In the ordinary FM mass damping system, the mass block is attached to it, and its weight is generally about 1% to 5% of the total weight of the structure, and the number of arrangements is small. In the mega-sub isolated structure system, the weight of each sub-frame can reach 60% to 80% of the total weight of the structure, which means that the weight of the mass block can reach more than 200% of the main structure, and more vibration isolation subframes can be arranged. Therefore, the research on the dynamic response of a mega-frame structure with varying masses of isolated subframes can help to optimize the design of mega-isolated structures.  
Figure 2: Variation of sub-frame dynamic response: (**a**) Variation in sub-frame acceleration; (**b**) Acceleration absorption factor change; (**c**) Variation in seismic isolator displacement.
# Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning
## 1 Introduction
An arbitrarily large number of training epochs is specified and the training of the model is stopped when it is observed that its performance on the validation dataset does not improve (note that the validation data is part of the learning dataset and thus different from the test dataset used for evaluation of the final model). - Parameter initialization. For non-convex functions, different parameter initialization strategies can lead to different results. As a result, a unique best model does not exist, as can be seen in [44]. This issue was explored by [45]. Thus, parameter initialization is a very important step. - Embedding representation and feature prepossessing. For expository purposes, we consider the problem of predicting the probability that an insurance claim will be filed in a French motor third party liability (MTPL) insurance portfolio consisting of features describing the vehicle, the driver and the geographic area of the car registration and the number of insurance claims observed during one accounting year. We discuss in detail the embedding representation for categorical features and feature preprocessing for the continuous and ordered features in the data. - Hyperparameter tuning. The architectures of the neural network-based models were chosen based on rigorous hyperparameter tuning, i.e., the process of determining the right combination of hyperparameters that allows the model to maximize performance. - Interpretability. As is well known, for insurance claim predictions, it can be of crucial importance to be able to analyze the reasons behind a certain prediction. For this purpose, we will apply a model that _locally_ approximates the function learned by the NN model with a linear function. The coefficients of this linear approximation function will then show the importance of the original features for the output of the advanced NN model. The rest of this paper proceeds as follows: in Section 2, we present the classical logistic regression model and how it generalizes to a neural network model. In particular, the Newton-Raphson and the gradient descent algorithms for the logistic regression model are described and the maximum likelihood (ML) estimation, goodness-of-fit and model evaluation are discussed. Then, we discuss how NNs can be used for predicting claim occurrence in the French MTPL data.In Section 3, a detailed description of insurance claims prediction using the neural network boosting of logistic regression is given and the LIME approach is utilized for providing local model interpretability. Finally, concluding remarks can be found in Section 4.
# Estimating Weibull Parameters Using Mabchour's Method (MMab) for Wind Power at RAWA City, Iraq
## 1 Introduction  
The fast growth of energy technology and the increasing use of fossil energy has led to a dramatic increase in the pollution of the environment. Recently, there has been a strong trend towards using green energy, where the most common forms of renewable energy generation are solar photovoltaic (PV), wind and hydro. In addition, there are many other types of renewable energy such as tidal and biomass, etc. These forms of renewable energy use energy resources that are renewed sustainably and at a faster rate [1]. Wind is one of the most available and fast-growing energy sources in the world [2]. The Weibull distribution is a continuous distribution and is commonly used in failure models which have three parameters. This method is important in the field of reliability and life testing and it also used for drawing and interpreting the behavior and distribution of wind [3]. There are several methods for calculating Weibull parameters [4]. Many studies have demonstrated that finding the optimal statistical distribution can represent change in wind gusts. Using this method, two parameters of the Weibull distribution (shape and scale) are calculated, from which the wind speed frequency curve for the site can be drawn [5]. In most studies concerning wind energy, the mentioned methods have been used to compare between different periods of time in many different ways [6].  
The main objectives of this research paper are to find the most optimal site among different promising locations in Iraq, and in the next step apply the Mabshour method (MMab method) to calculate the error in estimations of wind power density by using the Weibull distribution function (\(P_{W}\)), compared with the measured data (\(P_{M}\)). The results present sequential wind speed data for 10 min, measured at different heights of 10 m and 50 m in the city of RAWA (optimal site), located in western Iraq.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 \(\mu\)mol-m\({}^{-2}\)s\({}^{-1}\); subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.  
SIF; GPP; growing season; meteorological factors +
Footnote †: journal: Remote Sensing
# Predictors of Speed and Agility in Youth Male Basketball Players
## 3 Results  
Table 1 summarizes the body composition, speed, and agility characteristics for the three age categories of basketball players. The U16 players were significantly taller and heavier than their peers from other age groups. Overall, the U16 group showed better performance in all physical fitness tests.  
The results of Pearson product-moment correlations according to age groups are presented in Table 2. In the U14 group, chronological age presented significant positive correlations with CMJ (r = 0.50, \(p\leq 0.01\)) and DJ (r = 0.43, \(p\leq 0.01\)). In the U15 group, chronological age showed significant correlation with 10m speed (r = \(-\)0.30, \(p\leq 0.05\)) and 20 m speed (r = \(-\)0.34, \(p\leq 0.05\)). In the U16 group, chronological age did not correlate with any of analyzed variables. PBF correlated significantly and positively with speed and agility tests in all age groups, but was, in contrast, a negative predic  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**U14**} & \multicolumn{2}{c}{**U15**} & \multicolumn{2}{c}{**U16**} & \multicolumn{2}{c}{**ANOVA**} \\ \cline{2-10}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **\(\mathbf{\eta p^{2}}\)** & **F** & \(\boldsymbol{p}\)**-Value** \\ \hline Chronological age (years) & 13.41 & 0.54 & 14.71 & 0.29 & 15.64 & 0.32 & / & 304.879 & \(\leq\)0.01 \\ Height (cm) & 175.14  & 8.58 & 183.60 & 7.91 & 185.99  & 5.83 & 0.28 & 26.888 & \(\leq\)0.01 \\ Body mass (kg) & 63.46  & 13.70 & 75.20 & 11.87 & 76.25  & 10.29 & 0.19 & 16.044 & \(\leq\)0.01 \\ PBF (\%) & 12.88 & 5.98 & 13.92 & 5.62 & 12.92 & 5.74 & 0.07 & 0.477 & 0.62 \\ CMJ (cm) & 28.83 & 4.36 & 30.39 & 5.62 & 31.44  & 4.88 & 0.04 & 3.255 & 0.04 \\ DJ (cm) & 27.68 & 4.63 & 29.89 & 5.28 & 31.68  & 5.49 & 0.09 & 7.039 & \(\leq\)0.01 \\
15 m sprint (s) & 2.69 & 0.19 & 2.64  & 0.18 & 2.53  & 0.14 & 0.12 & 9.345 & \(\leq\)0.01 \\
20 m sprint (s) & 3.40 & 0.24 & 3.35  & 0.23 & 3.20  & 0.19 & 0.13 & 10.254 & \(\leq\)0.01 \\ \(t\)-test (s) & 11.45 & 0.87 & 11.21  & 0.78 & 10.77  & 0.85 & 0.10 & 8.129 & \(\leq\)0.01 \\ Lane agility (s) & 13.48 & 0.98 & 13.13 & 0.85 & 12.77  & 0.89 & 0.09 & 7.215 & \(\leq\)0.01 \\ \hline \hline \end{tabular}  
* SD = standard deviation; PBF = body fat percentage; CMJ = countermovement jump; DJ = drop jump; \(\eta_{p}\)2 = partial eta squared; \(\epsilon\) = sig. difference U14 vs. U15 \(p<0.01\); \(\xi\) = sig. difference U15 vs. U16 \(p<0.01\); \(\xi\) = sig. difference U14 vs. U16 \(p<0.01\).  
\end{table}
Table 1: Descriptive statistics and post-hoc comparisons for body composition and physical fitness tests.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## Appendix C Tracking Error Distribution  
The ratio in Equation (44) is defined as random variable \(W\),  
\[W:=\frac{Y}{X} \tag{14}\]  
where, \(Y\sim N(\mu_{Y},\,\sigma_{Y}^{2})\) and \(X\sim N(\mu_{X},\,\sigma_{X}^{2})\). Here, \(\mu_{Y}=\phi\) (where \(\phi\) is assumed to be a known constant), \(\mu_{X}=1\), and \(\sigma_{X}^{2}=\sigma_{Y}^{2}=\sigma_{n_{Q}^{2}}^{2}/N\).  
The distribution of \(W\), denoted \(f_{W}(w)\), is given in [21] and repeated here for convenience.  
\[f_{W}(w) =\frac{b(w)d(w)}{\sqrt{2\pi\sigma_{X}\sigma_{Y}a^{3}(w)}}\left[ \Phi\left(\frac{b(w)}{a(w)}\right)-\Phi\left(-\frac{b(w)}{a(w)}\right)\right]\] \[\quad+\frac{1}{a^{2}(w)\pi\sigma_{X}\sigma_{Y}}e^{-c(w)/2} \tag{15}\]  
where \(\Phi\) is the Gaussian cumulative distribution function, and,  
\[a(w) =\sqrt{\frac{w^{2}}{\sigma_{Y}^{2}}+\frac{1}{\sigma_{X}^{2}}} \tag{16}\] \[b(w) =\frac{\mu_{Y}}{\sigma_{Y}^{2}}w+\frac{\mu_{X}}{\sigma_{X}^{2}}\] (17) \[c(w) =\frac{\mu_{Y}^{2}}{\sigma_{Y}^{2}}+\frac{\mu_{X}^{2}}{\sigma_{X} ^{2}}\] (18) \[d(w) =\exp\left[\frac{b(w)^{2}-c(w)a^{2}(w)}{2a^{2}(w)}\right] \tag{19}\]The distribution of \(Z:=\tan^{-1}(W)\) (equivalent to definition of \(Z\) in Equation (44)) may be computed as,  
\[f_{Z}(z) =\frac{1}{|dz/dw|}f_{W}(\tan(z)) \tag{47}\] \[f_{Z}(z) =\sec^{2}(z)\,f_{W}(\tan(z)) \tag{48}\]  
where \(z\) is between \(\pm\pi/2\) (otherwise \(f_{Z}(z)=0\)).
## 3 Results and Discussion
Classifying types of forest by considering NDVI values may not be more accurate than the direct vegetation survey. Moreover, the criteria depend on the temperature changes. Accuracy can be increased by using more satellite image datasets for different areas. We do not insist to replace the direct vegetation survey with the proposed approach. Rather, the automatic method identifies dangerous regions that are further needed for in-depth investigations such as traditional direct vegetation surveys. The proposed method enables the monitoring of a large area; therefore, it also helps construct a plan for mixed forests to prepare for the threat of global warming by reducing the labor needed to perform direct vegetation surveys. Additionally, considering these data with geological information would support the successful afforestation and management of forests. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13010303/s1](https://www.mdpi.com/article/10.3390/app13010303/s1)]([https://www.mdpi.com/article/10.3390/app13010303/s1](https://www.mdpi.com/article/10.3390/app13010303/s1)). Conceptualization, J.A. and G.D.H. ; methodology, Y.S.C. ; software, S.U.Y. ; validation, S.H., Y.S.K. and J.A. ; formal analysis, Y.-H.K. ; investigation, Y.S.C. ; resources, J.A. ; data curation, S.U.Y. ; writing--original draft preparation, S.U.Y. and Y.S.C. ; writing--review and editing, G.D.H. ; visualization, G.D.H. ; supervision, J.A. ; project administration, J.A. ; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript. This study was funded by the Agency for Defense Development of Korea (Project No. 915026201), and Basic Science Research Program supported this research through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2019R1A6A1A11052070). Not applicable. This study was conducted with support from Agency for Defense Development of Korea (Project No. 915026201), and Basic Science Research Program supported this research through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2019R1A6A1A11052070). The authors declare no conflict of interest.
## 2 Methodology
### Calculation Metrics  
The evaluation of how the size and position of the window size can affect the energy savings, the visual performance and circadian entrainment of an ICU cubic necessitated the implementation of two distinct dynamic daylighting metrics: The first is the daylight autonomy (_DA_), and the second is the circadian stimulus autonomy (_CSA_).  
_DA_, or daylight autonomy, can be described as \"the percentage of the year when a minimum illuminance threshold is achieved solely by daylight\". In other words, _DA_ represents the portion of the year when sufficient natural daylight is available, reducing the need for electric lighting (with lighting switched off). This is expressed in Equation (1), from which it can be observed that the lower the _DA_ value, the greater the need for electric lighting, thus resulting in higher associated electrical consumption, as follows:  
\[DA=\frac{\sum_{i}wf_{i}\cdot t_{i}}{\sum_{i}t_{i}}\in[0,1]\quad\quad wf_{i}= \left\{\begin{aligned} 1&\text{ if }E_{D}\geq E_{L}\\ 0&\text{ if }E_{D}<E_{L}\end{aligned}\right. \tag{1}\]  
where \(t_{i}\) is the \"occupied time of the studied room, represented as a time interval per day\", \(w_{\bar{n}}\) defines the \"weighting factor which depends on the illuminance threshold\", \(E_{D}\) represents the \"daylight illuminance defined at a given point\", and \(E_{L}\) defines the \"illuminance threshold defined by lighting requirements\".  
Another important metric used is _CSA_ (circadian stimulus autonomy), which focuses on assessing the expected circadian entrainment due to light effects. As mentioned previously, _CSA_ can be expressed as \"the percentage of days when a threshold for adequate melatonin suppression is met solely by daylight during a specific time of the day, typically in the morning\" [1; 33; 41; 56]. The main factors that affect _CSA_ are the spectral power distribution (_SPD_) of light and the illuminance perceived by the observer in terms of circadian light (_CLA_).  
Figure 4: Average value of sky SPD proposed in both Mediterranean locations.  
The mathematical development of \(CSA\) is outlined through the phototransduction model [21] and is expressed using Equation (2) [1]:  
\[CS=0.7\cdot\left(1-\frac{1}{1+\left(\frac{C_{L_{4}}}{355.7}\right)^{1.1026}}\right) \tag{2}\]  
The \(CS\) (circadian stimulus) value obtained is directly linked to the predicted level of melatonin suppression induced by the perceived light effect, ranging between full suppression (0.0) and the saturation threshold (0.7). This prediction assumes that the average observer is exposed for a one-hour period to the given lighting conditions, considering an eye with a pupil size of 2.3 mm. To achieve an appropriate circadian entrainment, a \(CS\) value of 0.4 is considered adequate [19]. Thus, \(CSA\) must be calculated using this minimum light exposure--a \(CS\) value of 0.4--based on the light flux received and its combined SPD, taking into account the inner reflections in the ICU cubic [41; 56].
# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 3 Experiments and Discussion
### Evaluation Metrics  
In classification tasks, commonly used evaluation metrics include _accuracy_, _precision_, _recall_, and _F1 score_. Among these, _accuracy_ is a key indicator of the overall performance of an algorithm, reflecting the proportion of samples correctly classified to the total number of samples. When dealing with datasets with imbalanced classes, precision and recall become more critical tools for performance assessment. Precision refers to the proportion of samples correctly predicted as positive out of the total samples predicted as positive, while recall is the proportion of samples correctly predicted as positive out of the total actual positive samples. The _F1 score_ is the harmonic mean of precision and recall, providing a comprehensive evaluation of these two metrics. These indicators collectively assist us in thoroughly understanding and evaluating the performance of classification models. The definitions of these metrics are as follows:  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{7}\]  
where _TP_ represents the number of true positive samples, _TN_ denotes the number of true negative samples, _FP_ stands for the number of false positive samples, and _FN_ refers to the number of false negative samples.
# Numerical Simulation of Anchor Pullout and Shear Tests Using a Regularized Damage Model
## 3 Simulation of Single-Stud Tests
### Simulation with Deformable Stud
The failure mode appears to change around a critical embedment length of 100 mm; below 100 mm, the concrete fails in a conical shape, while above this length, the stud fails. It is to be noted that this transition zone is very sensitive, which can explain the higher discrepancies between experimental and numerical results (uncertainties in the material properties, for example). The cone failure (distribution of damage) for an embedded length of 80 mm is illustrated in Figure 5 and is in good agreement with the experimentally observed failure mode. Experimentally, the failure occurs in the form of a concrete cone. The diameter of the simulated crack cone base is approximately 40 cm, resulting in an angle of rupture of approximately 20\({}^{\circ}\). The experimental crack cone [3] measures around 50 cm long with an angle of 25\({}^{\circ}\). The fracture angle is, thus, very similar. Figure 6 demonstrates the sensitivity to the embedment length. A 5 mm difference in embedment length results in a 15% variation in resistance in the simulation. It accounts for the differences between the experimental and numerical results, either from the uncertainties in the material properties or in the reproduction of the experimental conditions. This sensitivity to the embedment length may explain the discrepancies observed between the simulations and the experimental results. Figure 4: Summary of tests and simulations for single studs. Figure 5: Representation of cracking for an embedded length of 80 mm.
## 3 Results and Discussion  
### Effect of the Pulsed Electric Field Treatment of Olive Paste Versus Extractability  
Table 1 includes the extractability and pomace characterization values. Only trace amounts of oil were found in all the analyzed wastewater samples, and therefore, the data are not shown.  
PEF treatments exhibited different behaviors. A positive effect on extractability was observed when olive paste was treated with a PEF device (2 kV/cm, 7.83 kJ/kg). In fact, the extractability value was 79.5% for the control and 85.5% when a PEF device was used. The resulting difference was statistically significant. The PEF treatment of olive paste delivered a significant reduction in pomace oil loss of about 40.5%. This confirmed the results reported in Puertolas and Martinez de Maranon [30], where a PEF treatment (2 kV/cm; 11.25 kJ/kg) was applied to Arroniz-variety olive paste. The authors assert that the PEF treatment significantly increased the extraction yield by 13.3% with respect to a control. The results of the present study are also in accordance with [PERSON] et al. [29], where the authors reported an increase of 13.85% in Arbequina oil yield by combining a PEF treatment (2 kV/cm; 5.22 kJ/kg) with a subsequent, gentle malaxation step of 30 min at 15 \({}^{\circ}\)C. In addition, the authors assert that no significant improvement was observed when the malaxation temperature was increased to 26 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Conditions**} & \multirow{2}{*}{**Variety**} & \multicolumn{2}{c}{**Pomace**} & **Extractability** \\ \cline{3-5}  & & **Moisture (\%)** & **Oil (\% Dry Basis)** & **(\%)** \\ \hline Control & Nocellara del Belice & 60.6 \(\pm\) 1.1 \(a\) & 10.4 \(\pm\) 0.7 \(a\) & 79.5 \(\pm\) 0.8 \(b\) \\ PEF & Nocellara del Belice & 60.2 \(\pm\) 0.5 \(a\) & 7.4 \(\pm\) 0.7 \(b\) & 85.5 \(\pm\) 1.5 \(a\) \\ \hline \hline \end{tabular} Different letters in columns denote statistically significant differences (Tukey’s test, \(p\) \(<\) 0.05).  
\end{table}
Table 1: Quantitative results and process parameters.  
The positive effects of PEF technology on olive oil yield could be explained by a double mechanism: the improvement of oil extraction from olive tissue, and the release of olive oil trapped in oil-water emulsions [30; 37; 38].