esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Cognitive and Motor Abilities in the Aging Workforce: An Information-Based Model
## 4 An Information-Based Model for Age- and Sex-Dependent Performance Time of Workers
### Motor Tasks
This value has been adopted to evaluate I\({}_{\text{m,MTM}}\) using Equation (8b) and then I\({}_{\text{m}}\) from Equation (8a) for different values of n. For each subject observed during PPT (s, A), by using Equation (12) (MT equal to 30 (s)), the corresponding T\({}_{\text{p,m}}\) (s, A) value has been obtained. Finally, the mean of the T\({}_{\text{p,m}}\) (s, A) values has been compared with initial assumed value T\({}_{\text{p,m,0}}^{*}\). At the first iteration a relative error of 9% on the initial approximate value of T\({}_{\text{p,m}}^{*}\) has been obtained. The initial value of the second iteration has been set equal to the mean value obtained by the first iteration. After four iterations, the calculation converged on T\({}_{\text{p,m}}^{*}\) = 180 ms/bit. Evaluating I\({}_{\text{m}}\) by Equation (8a), the T\({}_{\text{p,m}}\) (s, A) average values for all pairs (s, A) can be calculated by Equation (12). T\({}_{\text{p,m}}\) (s, A) values are shown in Table 4.  
As in the case of cognitive task, linear trends are observed (Figure 4), with large R\({}^{2}\) values for both male and female (\(\sim\)0.9) and small differences (\(\sim\)6.5%) in the T\({}_{\text{p,m}}\) values between male and female for each age class. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Age Class** & **18** & **23** & **28** & **36** \\
**Age Range** & **15–20** & **21–25** & **26–30** & **31–40** \\
**Sex** & **M** & **F** & **M** & **F** & **M** & **F** & **M** & **F** \\ \hline Performance (\#) & 15.56 & 16.69 & 15.54 & 16.64 & 16.22 & 17.25 & 15.35 & 15.94 \\ T\({}_{\text{p,m}}\) (ms/bit) & 274 & 253 & 275 & 254 & 261 & 244 & 278 & 265 \\ \hline
**Age Class** & **45** & **55** & **65** & **75** & **75** & **70–79** \\
**Age Range** & **41–49** & **50–59** & **60–69** & **70–79** & **70–79** & **F** \\
**Sex** & **M** & **F** & **M** & **F** & **M** & **F** & **M** & **F** \\ \hline Performance (\#) & 14.6 & 15.9 & 14.4 & 15 & 13.6 & 14.6 & 13 & 13.8 \\ T\({}_{\text{p,m}}\) (ms/bit) & 295 & 267 & 299 & 285 & 319 & 295 & 336 & 314 \\ \hline \end{tabular}
\end{table} 
Table 4: Performances (average number of placed pegs (\#)) and motor processing times (T\({}_{\text{p,m}}\)) of the Purdue Pegboard Test (PPT) (M for male, F for female). Source: Authors’ elaboration of data from [57; 58]. The linear trend observed for \(T_{p,m}\) (s, A) is as follows:  
\[T_{p,m}(s,A)=\alpha_{m}(s)+\beta_{m}(s)\times A\left[\frac{s}{bit}\right] \tag{14}\]  
Values of \(\alpha_{m}\) and \(\beta_{m}\) parameters are in Table 5:  
The information processing rate in case of motor tasks with low cognitive content (IPR\({}_{m}\)) can be evaluated as the inverse of \(T_{p,m}\) (s,A) for differently age classes of male and female subjects (Equation (13)); decreasing values of IPR\({}_{m}\) are observed with age for male and female.
## 6 Experiments
### Selecting Background Knowledge Configurations with Low-Entropy Feature Frequency
#### 6.5.3 Foursquare
Table 11 demonstrates that the **Low-Entropy Percentile** approach significantly reduces execution times for all attack types compared to the **Original** method. For location visit attacks (\(k=1\)), times drop from 6 days to between 15 h and 3 days 7 h. For visit attacks (\(k=2\)), times decrease from an estimated 51,240 days to between 10,094 and 39,641 days. For time visit attacks (\(k=1\)), times reduce from 5 days 12 h to between 13 h and 2 days 16 h. For visit attacks (\(k=2\)), times drop from an estimated 27,423 days to between 7681 \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Attack Type** & **Feature** & **k** & \multicolumn{4}{c}{**Percentage**} \\  & & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline
**Location Sequence** & Location & 1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
**Visit** & Location & 1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
**Location Sequence** & Location & 2 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
**Visit** & Location & 2 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
**Visit** & Time & 1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
**Visit** & Time & 2 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Foursquare dataset: Ks-statistics for different attacks using location and time, comparing approaches. Figure 6: Comparison of uniqueness of each feature in Foursquare dataset. and 30,354 days. Location sequence attacks (\(k=1\)) show reductions from 1 day 13 h to between 3 and 19 h, and, for \(k=2\), from an estimated 30,023 days to between 5898 and 23,059 days. Table 12 demonstrates that the **Low-Entropy Percentile** approach significantly reduces execution times for all attack types compared to the **Break** method. For location visit attacks (\(k=1\)), times drop from 3 h 51 min to between 1 h 10 min and 2 h 8 min. For visit attacks (\(k=2\)), times decrease from 12 days 8 h to around 3 days 19 h. For time visit attacks (\(k=1\)), times reduce from 9 h 14 min to around 1 h. For visit attacks (\(k=2\)), times drop from 25 days 3 h to less than 3 days. Location sequence attacks (\(k=1\)) show reductions from 1 h 29 min to between 18 min and 1 h 3 min, and, for \(k=2\), from 7 days 7 h to around 2 days 7 h.  
In summary, as the percentage increases, the accuracy of the risk assessment improves correspondingly. A similar trend is observed with the \(k\) value, where larger \(k\) values yield more accurate risk estimates. Regarding the types of attacks, the visit attack produced more accurate results compared to the location sequence attack. Additionally, the use of entropy on location or time showed varying results depending on the dataset's unique characteristics. These variations are closely linked to the uniqueness inherent in the data.
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 3 Proposed MPPT Algorithm and Irradiance Estimator  
Here we propose a low-complexity MPPT algorithm and an irradiance estimator with the aim of improving the MPP voltage prediction accuracy. Unlike the available NN-based algorithms that directly determine the MPP voltage from the measurements, the proposed algorithm relies on the NN model of the PV module, described in Section 2.2. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the MPP voltage and irradiance estimation. The proposed solution has smaller computational complexity compared to the available NN-based algorithms that use the same input information. While other solutions use one multilayer NN or two single-layer NNs, the proposed technique relies on the NN-model that can be trained accurately with 4 neurons in the hidden layer. The proposed algorithm cannot instantaneously determine the optimal voltage, but it has a tunable parameter that can be used to control the trade-off between the tracking speed and computational complexity.
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### RS Data Used in Wetland Studies
#### 3.4.5 Single Date Versus Multi-Date  
Multi-date and time series RS data has been widely conducted with many sensors to monitor the status of wetlands and detect land cover changes globally [6; 7; 109; 133]. There are a number of satellite missions, both optical and SAR systems, which provide long time-series and ongoing multi-temporal imageries with global coverage for LULC mapping, wetland monitoring, and CD efforts [7; 40]. Our systematic review provides insights into the available data types for investigating wetland covers in a multi-date manner. For example, of the optical sensors, Landsat archive, MODIS, and Sentinel-2, and among SAR missions, Sentinel-1 and RADARSAT-2 are suitable for large spatial and long time-series wetland studies. In some cases, such as the MODIS sensor, which provides a historical observation of the earth's surface, the coarse resolution of such imagery cannot precisely identify the details of the wetlands and smaller wetland sites. Looking at Figure 14, about two-thirds of the studies have applied multi-date RS data for wetland studies.
## 3 High-\(k\) Dielectric Nanoparticles  
Many researchers have reported works on silicon dioxide nanoparticles in combination with other metal oxide nanoparticles and their polymer nanocomposites for sustainable energy storage and related applications [106; 107]. In the construction of thin-film transistors (TFTs), zinc oxide (ZnO) nanoparticles are predominantly used. In the recent past, different kinds of oxide nanoparticles were explored, whereas perovskites show unique characteristics. Since then, lots of active studies have been done in constructing organic/inorganic structures to optimize the optical, dielectric, piezoelectric, electronic, catalytic or magnetic properties [108; 109]. Nowadays, most studies have focused their attention on designing multi-metal-oxide hybrid nanoparticles because of their remarkable dielectric properties. [PERSON], et al. prepared ultrafine strontium hafnium oxide (SrHfO\({}_{3}\)) nanoparticles of 2.5 nm in size and demonstrated its potential as high-\(k\) gate dielectrics [110]. These perovskite-types, strontium-doped or mixed hafnium oxides with other metal oxides recently gained much interest because of exhibiting ferroelectric behavior and ultimately utilized in optoelectronic device applications [111; 112; 113; 114]. The dielectric constant reported by [PERSON], et al. was 17.0 and a relatively large capacitance value of 9.5 nF cm\({}^{-2}\)[110]. Thus, these ultra-fine nanoparticles are readily useful in gate dielectrics for capacitors and in MOSFET technology [115; 116]. However, hybrid CNTs decorated by ultrafine silver nanoparticles demonstrate the conducting behavior and show negative permittivity [116]. [PERSON], et al. studied the temperature-dependent dielectric behavior of nanostructured ferrite material, tetramanganese oxides (Mn\({}_{3}\)O\({}_{4}\)). The authors presented a convincing theory that the nano-dipole behavior of Mn\({}_{3}\)O\({}_{4}\) under an applied electric field resulted in obtaining a high dielectric constant. This is due to the increasing dipole moment of nano-sized Mn\({}_{3}\)O\({}_{4}\) particles per unit volume [117]. In addition, similar dielectric responses were observed in ZnO nanotubes and ZnO nanoparticles synthesized on a bio-template [118; 119]. Some of the selected nanoparticles and its relative dielectric constants at ambient conditions are listed in Table 2.  
Currently, core-shell nanoparticle structures are gaining prominent attention due to their versatile architectures and wide applicability in electronic and optoelectronic devices [131]. Various materials have been synthesized with numerous nano architectures to understand the properties of organic polymers and its hybrid structures on loading inorganic nanoparticles [132]. [PERSON], et al. investigated the aluminum (core)-aluminum oxide (shell) nanoparticles by coating nylon-6 polymer by a vacuum deposition technique. The fabricated core-shell nanostructures showed varied dielectric constants which were directly proportional to the thickness of the aluminum (Al) layer, with a relative permittivity of 28.0 reported for the 20 nm thickness of the Al layer [133]. By adopting a surface-coating
## 5 Experiment
### Experimental Setup
#### 5.1.1 Parameters Settings  
In Equation (6), \(\alpha\) represents the redundancy of defect foreground, \(\beta\) represents the uniformity of defect foreground, \(\gamma\) represents the sparsity of background. We conduct some experiments to study the detection performance variation with respect to different \(\alpha\), \(\beta\) and \(\gamma\), which shows that the detection performance can achieve a high level at \(\alpha\in(0.2,\ 0.4)\), \(\beta\in(0.9,\ 1.3)\) and \(\gamma\in(0.05,\ 0.25)\). In order to achieve the better segmentation results, \(\alpha\), \(\beta\) and \(\gamma\) are set to 0.35, 1.2 and 0.1, respectively. For other methods in our comparison, we use the source codes provided by the authors with default parameters.
## 3 Proposed Framework
### Feature Selection
#### 3.4.1 Wrapper Method  
The wrapper method identifies the most suitable feature for machine-learning algorithms, and the main goal is to enhance the learning process. A machine-learning algorithm is needed in a wrapper method, and its performance is used as an evaluation criterion. Unfortunately, wrapper methods [24] are over-complicated, though they produce the most reliable outcomes based on the explicit-learning technique [25].  
RFE is a common approach that combines other machine-learning techniques. Lately, RFE has emerged as a method employed in numerous biomedical fields such as protein classification, selection of genes [26; 27], areas of expression analysis, cancer diagnosis, and many others [28]. These methods are created iteratively and determine each feature's best or worst traits. Once all elements are explored, these methods rank the models according to how features have been eliminated. The importance of specific attributes increases until it reaches an appropriate level [26].  
The GB algorithm is used in the GB-RFE method to train the classifier. Boosting applies a sequential-training approach, where every learner tries to change the previous one. Loss functions are calculated as gradients, indicating how well the model coefficients fit the data. This research estimates feature importance with a frequency-based measure of algorithm performance called the Gini index, per Equation (2). The Gini-index value is high for an  
Figure 3: Balanced data.  
Figure 2: Imbalanced data.  
important feature. Class variable c denotes the number of observations in the class; it indicates the ratio of both instances where each node has its particular type.  
\[W_{G}=\sum_{j=1}^{c}p^{2}j \tag{2}\]  
An ensemble method such as RF bagging becomes an excellent method in prediction accuracy, performing a great representative for bagging algorithms. RF algorithms use two methods to calculate feature importance from a training model and measure variable significance. Equation (3) computes the relevant ranking of the features. The mean reduction in accuracy shows that the model accuracy reduces when the variable values are adjusted [29; 30]. This metric quantifies how observation lowers node impurity over time, weighted by the proportion of data reaching that node, with a mean drop in the Gini coefficient.  
\[W_{R}(X_{i})=\frac{\sum_{\text{c\in B}}\text{VI}^{\text{T}}X_{i}}{ntree} \tag{3}\]  
In this work, we have used n_estimators = 100, random_state = 10, and n_features_to select = 1-30, which are chosen as the optimal hyperparameters of RFE-RF that help in selecting the most important features.
# Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
## 4 Case Study
### Comparative Experiments
The transportation costs calculated from the deterministic models are significantly higher than those from the two-stage stochastic programming model. Specifically, the cost based on the minimum demand is HKD 205,181, which is 38% higher than that of the proposed model; the cost based on the average demand is HKD 159,997, and the cost based on the maximum demand is HKD 193,320, which are 7% and 30% higher, respectively. Figure 2 also illustrates deterministic models' specific rental plans for transporting modules under the same scenario in Section 4.2. [DM3] determines the number of ships based on the maximum number of modules needed over 20 weeks. Consequently, no temporarily rented ships were required, and therefore, it is not included in Figure 2.  
From the comparison results, balancing between the utilization of booked and temporarily rented barges for module transportation is the key point to minimize total transportation costs. Essentially, it involves making robust initial decisions about booking barges to accommodate uncertainties in subsequent construction schedules. [DM1], which opts not to book any barges in advance, ends up renting barges nearly every week (as shown in Figure 2), leading to higher costs. Consequently, the performance of [DM1] is worse than both [M1] and [DM2]. From the comparisons, we can understand that booking barges in advance has the potential for cost savings. However, overbooking or significantly booking more capacity than needed is also not an economical solution. This is because the contractor must still pay for the unused barge capacities, leading to wasted resources and higher overall expenses. This is exemplified by the higher costs incurred in [DM3] compared to [DM1], which demonstrates the inefficiency of overbooking. The planning based on average demand, as seen in [DM2], yields the best outcome among the three deterministic models. However, compared to the two-stage stochastic programming model [M1], [DM2] is less proficient at managing the uncertainties inherent in real-world projects. In summary, the two-stage stochastic programming model shows great superiority over other models.
## 4 Discussion
### Impacts of Unilateral ICA Revascularization on ACoA Flow Patterns  
Our results showed that in the sever stenotic case, the skewed inflow jet from the healthy side impinged the contralateral aneurysm neck due to the asymmetrical blood supply and formed a single vortex that rotated towards the posterior of the ACoA. Along with the relief of ICA stenosis, the flow pattern in the ACoA became more complex. As the blood supply from the ACA-A1 of the affected side restored, the inflow jet moved towards the tip of the ACoA. Meanwhile, the confluence of bilateral ACA-A1 flow formed two recirculation zones that vary with the cardiac cycle.  
Several investigations have shown that such complex flow patterns play critical roles in aneurysm rupture via inflammatory remodeling of the aneurysm wall [38; 40; 51; 52; 53]. [PERSON] et al. analyzed 119 aneurysms and found that complex flow patterns appeared in more than 60% of ruptured aneurysms, which were featured by multiple vortices [51]. In contrast, most of the UIAs only presented a single vortex [51]. Similar observations were reported by some recent studies [54; 55]. These phenomena indicate a potential increase in rupture risk of ACoA after revascularization.
## 3 Results and Discussion
### Effects of Storage Conditions on the Overall Perceived Aroma of Blueberries  
The R-index by ranking (degree of aroma similarity) sensory evaluation was conducted to determine if changes in the concentration of aroma compounds found in S1, S2, and S3, relative to SC, could be perceived by the panelists. It should be acknowledged that the number of panelists and the imbalance in gender are not optimal for this type of sensory testing. The results (Table 4) indicated that only S1 significantly differed from SC (R-index value = 81), whereas S2 and S3 were not significantly different than SC (R-index = 57 and 62, respectively). The dissimilarity in aroma perception between S1 and SC could be due to the significant reduction in (_E,Z_)-2,6-nonadienal, 1-octen-3-ol, 1-octen-3-one, and linalool, considering that these key odorants declined from SC to S1 and had the highest OAVs (Table 3). Additionally, the increase in eucalypt in S1 (OAV = 50) may have played some role in the altered aroma since minty perception was commented on in relation to off-flavor in 'Patrecia' blueberries [12]. However, [PERSON] et al. (2020) did not observe an objectionable off-flavor in blueberries stored at 0 \({}^{\circ}\)C for 60 d but did note a change in freshness compared to 15 d storage [6]. In comparison to S1, the similarity in S2 and S3 to the control is likely due to the restoration of key odorants that declined in S1.
## 5 Experimental Results  
In order to test and evaluate the proposed algorithm for training FNNs, experiments are commonly performed over synthetic and real (benchmark) problem sets. In the following experiments, we used two examples to compare the performances of PSO, CS, and PSOCS algorithms in training FNNs.  
For FNN trained by PSO (hereinafter, we refer to as PSO-FNN), \\(c_{1}\\) and \\(c_{2}\\) were set to 2, \\(w\\) decreased linearly from 0.9 to 0.4, and the initial velocities of particles were randomly generated in [0, 1].  
For FNN trained by CS (hereinafter, we refer to as CS-FNN), the step size (\\(\\alpha\\)) was 0.25; the net discovery rate (\\(p_{a}\\)) was 0.1; and \\(\\lambda\\) was set to 1.5.  
For FNN trained by PSOCS (hereinafter, we refer to as PSOCS-FNN), \\(\\alpha_{\\min}\\) and \\(\\alpha_{\\max}\\) were 0.01 and 0.5, respectively; \\(p_{\\text{amin}}\\) and \\(p_{\\text{amax}}\\) were 0.05 and 0.5, respectively; \\(\\lambda\\) was set to 1.5; \\(c^{\\prime}_{1}\\) and \\(c^{\\prime}_{2}\\) were set to 2; \\(w\\) decreased linearly from 0.9 to 0.4; and the initial velocities of particles were randomly generated in [0,1].  
Figure 3: The vector of training parameters.  
The population sizes of PSO-FNN, CS-FNN, and PSOCS-FNN for all problems were 50. The criterion for finishing the training process is to complete the maximum number of iterations (equal to 500 in this study). The best results are signified in bold type.  
A popular solution to the overfitting problem is stopping early. In our work, the number of iterations has been chosen as an early stopping condition.
## 3 Results and Discussion
### Transmission Electron Microscopy (TEM)  
The TEM micrographs of the samples obtained by the sol-gel route are shown in Figure 8 and indicate agglomerated particles that form clusters. Crystals in unmodified samples aggregate into large particle sizes, where crystals can merge. Apparently, the LTA-Pt sample does not change its morphology after impregnation with platinum chloride.  
There is no apparent porous structure in the large particles. In Figure 9, a uniform distribution of platinum particles across the entire surface of the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) particle agglomerates (sample LTA-Pt) can be observed.  
Particle size distributions were determined from TEM micrographs for both samples. The mean particle sizes were comparable, measuring approximately 70 nm (69.54 \(\pm\) 19 nm for the LTA sample and 68.64 \(\pm\) 22 nm for the LTA-Pt sample). The corresponding particle size distribution histograms are presented in Figure 10.