esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Streaming Feature Selection via Conditional Independence
## 3 Framework for Streaming Features Filtering
### Notations, Definitions, and Formalizations
#### 3.1.3 Formalization of Online Feature Selection with Streaming Features  
In traditional feature selection, all candidate features are available before learning starts [37, 38]. For streaming features, features are generated dynamically and arrive one by one. Hence, it is not practical to wait until all features have been generated before feature selection begins [16]. In _OSFSF_, the data stream is fixed, whereas features keep arriving, and each feature is evaluated upon arrival. This poses great challenges to traditional feature selection approaches. A sketch of feature stream with a fixed data stream is provided in Figure 1.  
In Figure 1, let \(X_{i}\) represent the data set at time \(t_{i}\), denoted as \(X_{i}=[x_{1},\,x_{2},\,\ldots,\,x_{n}]^{T}\in R^{n\times i}\), where \(n\) is the number of samples, \(i\) is the number of features so far over an \(i\)-dimensional feature space, and \(S=[f_{1}/f_{2},\,\ldots,\,f_{i}]^{T}\in R^{i}\). Let \(C=[c_{1},\,c_{2},\,\ldots,\,c_{m}]^{T}\in R^{m}\) denote the class label vector with m distinct  
Figure 1: A feature stream with a fixed data stream.  
class labels. \(C\) denotes the class attribute, and at each time, \(t_{i}\), we just obtain feature \(f_{i}\) of \(S\) but do not know the exact number of \(i\) in advance. Therefore, the problem is to derive \(x\) to \(c\) mapping at each time step. This is possible using a subset of the features that have arrived so far.
# Assessment of Pesticide Contamination of Groundwater from Titu-Sarata Plain, Romania
## 2 Materials and Methods
### Non-Carcinogenic Risk Assessment  
To determine the chronic daily intake (CDI) of pesticides via oral exposure, we used Equation (3), derived from the methodology established by United States Environmental Protection Agency (USEPA) [17; 31; 32; 33; 34; 35; 36; 37]:  
\[\mathrm{CDI}=\frac{\mathrm{Cw}\times\mathrm{DI}\times\mathrm{ED}\times\mathrm{ EF}}{\mathrm{BW}\times\mathrm{AT}}\;, \tag{3}\]  
where  
* CDI is the chronic daily intake (mg/kg/day);
* Cw is the concentration of pesticides in water samples (mg/L);
* DI is the daily average intake (L/day);
* EF is the exposure frequency (days/year);
* ED is the exposure duration (years);
* AT is the average contact time (days);
* BW is the average body weight (kg) for each age group of consumers.  
Data on water consumption and body weight used in the risk assessment are presented in Supplementary Data (Table S1). For estimating the body weight of children aged 1-5 years, the formula [Weight (kg) = 2 \(\times\) (age in years + 5)] was applied. Based on this, thebody weight for a 2-year-old child was estimated to be 14 kg. For children under 2 years of age, an average body weight of 7 kg was considered [37].  
According to the USEPA guidelines, the health risk associated with each pesticide for non-carcinogenic effects is estimated by calculating the hazard quotient (\(HQ\)), as defined by Equation (4) [38]:  
\[HQ=\frac{CDI}{RfD}, \tag{4}\]  
where \(RfD\) is the oral reference dose via oral ingestion (mg/kg/day).  
The assessment of the total potential for non-carcinogenic health effects resulting from exposure to a pesticide mixture in water can be performed based on the hazard index (\(HI\)). The \(HI\) is calculated based on the EPA guidelines for health risk assessment [38; 39; 40; 41; 42] as the sum of the resulting HQs for each pesticide, according to Equation (5):  
\[HI=\sum_{i=1}^{n}HQi \tag{5}\]  
\(HQ\) or \(HI\) values less than 1 indicate that there are no significant non-carcinogenic risks. \(HQ\) or \(HI\) values greater than or equal to 1 indicate that there are significant non-carcinogenic risks that increase as \(HQ\) or \(HI\) increases [38; 41; 42].
## 2 Materials and Methods  
### Material Preparation  
The fabrication of B4C-reinforced CoCrMo, Ti, and 17-4 PH alloys was accomplished through the implementation of conventional powder metallurgy (PM) techniques. The raw materials included cobalt powder with 99.5% purity, produced by Alfa Aesar, chromium, molybdenum, and titanium powders with purity levels ranging from 99.5% to 99.9% and a particle size of 44 um, produced by Nanokar Nanotechnology. The 17-4 PH stainless steel powder, characterized by a grain size of 22 um and a purity of 99.9%, is produced by (Carpenter Technology, Philadelphia, PA, USA). The boron carbide (B4C) powder employed as the reinforcing phase exhibited a purity of 99%, a density of 2.52 g/cm\({}^{3}\), and a particle size ranging from 100 to 150 um, produced by (Nanokar Nanotechnology, Istanbul, Turkiye).  
As illustrated in Figure 1, the PM process is comprised of three distinct steps: mechanical alloying, uniaxial cold pressing, and vacuum sintering. The addition of a 0.6% PVA aqueous binder solution (1-3 wt%) was added to ensure adequate particle cohesion. The B4C and metal powder mixtures were mechanically alloyed in a ball mill (MSE Technology, BM-S38003, Istanbul, Turkiye) for 20 h at 380 rpm using zirconia balls (3 mm diameter) with a ball-to-powder ratio of 10:1.  
The mixture of powders was subjected to a cold-pressing process employing a uniaxial hydraulic press (MSE Technology, LP-M2S10, Istanbul, Turkiye) at pressures ranging from 400 to 450 MPa. This resulted in the formation of cylindrical green compacts with a diameter of 10 mm. Sintering was carried out in a horizontal tube vacuum furnace (MTI, GLS-1500X, New York, NY, USA) at 1200 \({}^{\circ}\)C for 60 min with a heating rate of 4 \({}^{\circ}\)C/min. This step led to a densification of the specimens, achieved by facilitating interparticle bonding and eliminating residual porosity.
# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
## 2 Materials and Methods
### Signal Processing and Algorithm Development  
The collected data were analyzed using a systematic approach that integrated signal processing and algorithm implementation. The initial phase of signal processing involved filtering and smoothing the raw IMU data (10 Hz) using a moving average filter to minimize noise while retaining key movement patterns. In the second step, local maxima and minima are detected, and then the key features, such as slopes and time intervals between these points, are calculated. Finally, the results are visualized to facilitate interpretation.  
1. Filtering and smoothing The code divides the series into \(n\) equal parts and computes the mean in each subinterval.
2. Detection of Local Maxima and Minima The mean values are analyzed and compared with their neighbors to identify the local maxima and minima (Algorithm 1).  
```
\(j\gets 1\) \(i\gets 1\) For k from 3 to n - 3 do \(M\leftarrow\) [means[\(k-2\)], means[\(k-1\)], means[\(k+1\)], means[\(k+2\)]] If means[\(k\)] \(\geq\) max(M) then maxima[\(i\)] \(\leftarrow\) means[\(k\)] \(x\)max[\(i\)] \(\gets\) k \(i\gets i+1\) End If If means[\(k\)] \(\leq\) min(M) then minima[\(j\)] \(\leftarrow\) means[\(k\)] \(x\)min[\(j\)] \(\leftarrow\) k \(j\gets 1\) End If End For xmin \(\leftarrow\) [1, xmin, n] # Include the endpoints as minima minima \(\leftarrow\) [means [1], minima, means[\(n\)]]
```  
**Algorithm 1** Detection of Local Maxima and Minima  
Calculation of Slopes, Time Intervals, and Intermediate Averages The following calculations are performed (Algorithm 2):  
* Ascending slopes: change in value between a minimum and the next maximum;
* Descending slopes: change in value between a maximum and the next minimum;
* Intermediate averages: Average between a maximum and its preceding minimum. (The coordinates are rescaled beforehand, considering the exercise duration.)
# Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
## 1 Introduction  
Face recognition is a significant research area in image processing, pattern recognition and computer vision [1] with a very wide scope, especially in the context of information security [2; 3]. Face recognition is tremendously challenging due to the influence of various factors such as environment, illumination, expression, and posture when the face image is acquired. In addition, some facial disturbance components, such as wearing hats and glasses, need to be further addressed and improved. Therefore, the accuracy and speed of computer face detection and recognition by the computer are the key elements in current face recognition technology.  
In recent years, several major successful face recognition algorithms have been widely applied. [PERSON] et al. [4] proposed a data-driven unconstrained face recognition method. This method can simultaneously characterize learning and joint registration. [PERSON] et al. [5] proposes a method and program for deep face recognition that traverses the complexity of deep networks by assembling large data sets. [PERSON] et al. [6] proposes a simplified binary single representation method that does not use images. This method proposes a classification strategy for multiple image sets classifying. [PERSON] et al. [7] proposed a multi-column network that uses images as input to obtain feature descriptors. This network implemented a new visual and content-based neural network architecture. [PERSON] et al. [8] applied a mixed-effects regression model to quantify various aspects of the subject and used longitudinal analysis to study the persistence of automatic face recognition. [PERSON] et al. [9] proposed abinary unsupervised feature learning method for face recognition using context-aware localization by shifting numbers, and its experimental results are superior to other facial descriptors.  
Currently, feature extraction and classifier selection are the two most important components in face recognition research. Feature extraction-based algorithms that have been successfully used for face recognition [10; 11; 12; 13]. However, the feature vectors extracted by these methods have a high dimensionality probably leading to causing dimensionality disasters. In order to deal with the problem of high dimensionality, the linear discriminant analysis (LDA) [14] and the principal component analysis (PCA) [15] of the dimensionality reduction method on projecting high-dimensional features onto low-dimensional subspaces are proposed. Nevertheless, due to the face images in the real world are mostly non-linear and the faces have disturbance items, these conventional methods cannot handle the face occlusion problem well. Classifier-based algorithms, such as support vector machine (SVM) [16], AdaBoost [17], nearest subspace (NS) [18] and nearest neighbor (NN) [19], have been successfully adopted to face recognition. However, since the significant differences between testing and training samples, these methods are always subject to universal problems [20].
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 2 Materials and Methods
### CubeSat-Based PNT Payloads  
To comply with the CubeSat specifications, a PNT payload must adhere to strict constraints regarding mass, power consumption, and volume. Over the past decades, research and development efforts have focused on miniaturizing atomic clocks for commercial use, led by DARPA, which recognized the potential for small, battery-powered versions. Today's CSACs take less volume than 17 cm\({}^{3}\) while weighting about 35 g. Moreover, they tend to have extremely low power consumption (<120 mW), and they can function across a broad temperature spectrum ranging from \(-40\) to 85 degrees Celsius. Thus, CSACs are the ideal AFR for a CubeSat platform.  
Since PNT payloads are not popular in the CubeSat industry yet, one could develop a PNT payload by utilizing COTS components as building blocks, which is also the approach of the ATOMIC payload of the AIOTY-CUBE [4]. Finally, in line with the New Space approach, the objective of a CubeSat-based PNT system is to function independently of a ground segment dedicated to timekeeping, like the ones required by MEO GNSSs. This can be achieved by utilizing GNSS observables for precise orbit determination and synchronizing the CubeSat's time reference with GNSS time, thereby eliminating the need for master clocks [4]. Such a payload would consist of a GNSS antenna and a GNSS receiver so that it obtains inputs about time and position from MEO GNSS, an On-Board Computer (OBC), and a CSAC to generate the system's time and ephemeris and finally a transmitter and an antenna to transmit the PNT signals. The architecture of the proposed payload in terms of function and hardware is outlined in Figure 1.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 4 Results  
### Spectral Separability  
The spectral separability allows the ranking of the variables in their ability to discriminate the different land cover classes, and provides an insight of their usefulness in the segmentation and classification procedures. Results show that NDVI plays an important role, being the variable with the highest value of the M-statistic in the summer flight and the second in the spring flight (Table 2). During the summer period, NDVI maximizes the contrast between the evergreen cork oak trees and the senescent herbaceous vegetation. Besides the spectral variables, the separability obtained with the data concerning the vegetation height (DSM) is important to discriminate between trees/shrubs and trees/grass, in particular in the spring flight when the herbaceous layer is green.
## 3 Results and Discussion
### Solar Collector Performance Assessment  
This section describes the results obtained from the different tests carried out throughout this research. The experimental and analytical results of the nanofluid samples, using an LTSC, are presented. Thirty-four tests were carried out, varying the volumetric flow: 0.0111 LT/s, 0.0166 LT/s and 0.0194 LT/s. Two different samples of nanofluids, with concentrations of 0.035% and 0.063% were made. The tests were carried out during the summer and fall of the year 2019, from 12 a.m. to 3 p.m., in intervals of 5 min per trial, performing three to five measurements for each volumetric flow and volume fraction, throughout the week, observing constant radiation. This has been made with the aim to compare using radiation values as similar as possible. Figure 14 shows the useful heat results of the LTSC, with two different concentrations compared to the base fluid. From the figure, it can be seen that at 0.035%, the heat produced by the collector is less than that of the base fluid. This may be because, at low concentrations, nanoparticles not only absorb energy, but also emit heat. From the same figure, it can be seen that at 0.063% concentration, the useful heat of the LTSC increases up to 47.22% with the flow of 0.0194 LT/s. The decrease in useful heat at a low concentration can be explained because the flow regime, represented by the Reynolds number (Re), is lower, which indicates that the effect of viscous forces predominates as the volumetric flow approaches zero and, therefore, less heat exchange is generated between the receiving tube and the flow.  
Figure 15 shows the efficiency results of the LTSC using carbon nanoparticles, compared with the base fluid at two nanofluid flows. From the figure, like the useful heat, the efficiency decreases at a low concentration of 0.035%, while at a concentration of 0.063%, the efficiency increases up to 47.2%, with respect to that of the base fluid. The increase in efficiency at a higher nanoparticle concentration can also be due to the higher saturation energy of the thin films.  
Figure 14: Average useful heat with base fluid and nanoparticles at 0.035% and 0.063% (H\({}_{2}\)O–C).
## 5 Experiments and Results

Table 3: Structure-wise DSC evaluation of proposed architecture and other 3D based architectures in terms of DSC, JI, HD, SD on CT testing dataset for LV, RV, LA, RA, Myo, Ao, and PA. The \(p\)-values have been calculated using a Wilcoxon rank-sum test to show the significant difference of used architectures. Bonferroni correction was used for controlling the family-wise error rate. Figures 8 and 9 show the comparisons and \(p\)-values for CT and MRI testing datasets, respectively. The visual inspection of the obtained segmentations using each network investigated in this work is presented in Figure 10 for the CT dataset, and Figure 11 for the MRI dataset. For example, Figure 11d shows clear improvements regarding LV segmentation that is obtained using FM-Pre-ResNet compared to missed segmentation of LV parts while using Pre-ResNet without a proposed feature merge residual unit as shown in Figure 11b. Moreover, Figure 11e shows a significant reduction in segmentation error compared to all other presented networks. This further highlights the benefits of the proposed FM-Pre-ResNet + VAE approach. Nonetheless, in both modalities, PA and Myo's segmentation results are significantly lower than other substructures due to high shape variations and heterogeneous intensity of blood fluctuations. Figure 12 shows 3D visualization of the best and the worse segmentation cases on the CT and MRI test dataset obtained using the proposed FM-Pre-ResNet +VAE approach.
## 3 Results
### Driving Force Analysis for Internunual GPP Dynamics
#### 3.3.3 The Interannual V\({}_{\rm cmax}\) Change Effect on GPP  
In our previous study, we quantified the impact of spatial and seasonal changes in V\({}_{\rm cmax}\) on GPP and proved that considering changes in V\({}_{\rm cmax}\) could improve GPP estimations [28]. Neglecting spatial and seasonal changes in V\({}_{\rm cmax}\) could lead to an overestimation of both the GPP and cause substantial differences in the spatial and temporal signatures in GPP [28]. On long-term scales, leaf photosynthetic capacity (V\({}_{\rm cmax}\)) can also change with plant adaptation to climate change (e.g., global warming, radiation brightening, and elevated CO\({}_{2}\) concentration) to optimize photosynthesis. Thus, we further quantified interannual V\({}_{\rm cmax}\) change effect on GPP at annual time scales using the All and PC1 simulation scenarios to help us understand whether including interannual dynamics in V\({}_{\rm cmax}\) could lead to substantial difference in GPP.  
From 2001 to 2016, the annual mean V\({}_{\rm cmax}\) across Europe showed a small (but not significant) increasing trend (Figure 7a). If we consider the spatial and seasonal changes in V\({}_{\rm cmax}\) using the V\({}_{\rm cmax}\) derived from the leaf chlorophyll metric in 2001, the average  
Figure 6: Climate change effect on GPP. Interannual dynamics of (**a**) mean downwards solar radiation (DSR) (w m\({}^{-2}\)), (**b**) mean air temperature, and (**c**) vapour pressure deficit (VPD) (Pa). (**d**) Climate change effect on GPP constrained by the “All”, “CLM1”, “CLM2”, “CLM3”, and “CLM4” simulations. Model simulations are shown in Table 1.  
annual total GPP for Europe was 9.42 Pg C yr\({}^{-1}\) with a positive rate of increase of 0.052 Pg C yr\({}^{-2}\), which was almost equivalent to the GPP estimations from the \"All\" simulation (annual GPP: 9.4 Pg C yr\({}^{-1}\); GPP trend: 0.051 Pg C yr\({}^{-2}\)). Surprisingly, we found that the interannual changes in V\({}_{\rm cmax}\) contributed little to the annual GPP trend (\(-\)0.13 Pg C yr\({}^{-2}\), \(p>0.5\)) from 2001 to 2016 (Figure 7b). This suggests that FGM without interannual dynamic information on V\({}_{\rm cmax}\) but constrained by spatial and seasonal dynamic information on V\({}_{\rm cmax}\) may still provide a reasonable estimation of GPP over the limited time period of 16 years. However, interannual changes in V\({}_{\rm cmax}\) may continue to affect GPP with climate change on longer time scales.
## 4 Experimental Results
### Data Preparation and Transformation  
For evaluating the proposed model, this study used a challenging dataset, i.e., ISIC 2019 to verify the proposed framework (the ISIC skin disease dataset is considered one of the most challenging due to its sophisticated features).
# On Sharing an FIB Table in Named Data Networking
## 3 Proposed Work  
Since the volume of name prefixes would be much larger than that of IP prefixes, building an FIB table would consume the significant amount of network bandwidth. The goal of this paper is to reduce the traffic associated with building an FIB table and the memory consumption for storing an FIB table. To reduce the traffic for building the FIB table, this paper proposes sharing the connectivity information using a Bloom filter. Additionally, to reduce the memory consumption for the FIB table, this paper proposes storing the Bloom filter at each face instead of the FIB table and forwarding _Interest_ packets according to the query result of the Bloom filter.
# Multi-Directional Shape Change Analysis of Biotensegrity Model Mimicking Human Spine Curvature
## 3 Shape Change Strategies for Spine Biotensegrity Models
### Formulation of Incremental Equilibrium Equations
\right] \tag{8}\]  
Superimposing Equation (4), rewritten Equation (3) gives the incremental equation for the entire structure as follows:  
\[{}^{t}\dot{\textbf{F}}={}^{t}\textbf{K}^{t}\dot{\textbf{x}}-{}^{t}\textbf{B }^{t}\textbf{C}_{L}{}^{t}\dot{\textbf{i}} \tag{9}\]  
where \({}^{t}\textbf{K}\) is the structure tangent stiffness matrix and \({}^{t}\dot{\textbf{i}}\) is the vector of the incremental force elongation of cables. \({}^{t}\textbf{C}_{L}\) is a \(m\)\(\times\)\(m\) matrix which could be further expressed as follows:  
\[{}^{t}\textbf{C}_{L}=\left[ \begin{array}{cccc}\frac{E_{1}A_{1}}{{}^{t-1}I_{ 1}}&&&\\ &\frac{E_{1}A_{1}}{{}^{t-1}I_{1}}&&\\ &&\ddots&&\\ &&&\frac{E_{m}A_{m}}{{}^{t-1}I_{m}}\end{array} \right]+\left[\begin{array}{cccc} \frac{E_{1}A_{1}}{{}^{t-1}I_{1}}\left(\frac{{}^{t-1}u_{1}}{{}^{t-1}I_{1}} \right)&&&\\ &&\frac{E_{2}A_{2}}{{}^{t-1}I_{2}}\left(\frac{{}^{t-1}u_{2}}{{}^{t-1}I_{2}} \right)&&\\ &&\ddots&\\ &&&\frac{E_{m}A_{m}}{{}^{t-1}I_{m}}\left(\frac{{}^{t-1}u_{m}}{{}^Since a tensegrity under feasible prestress is a stable structure, the inverse of \({}^{t}\mathbf{K}\) exists. The vector of incremental coordinates for the entire structure \({}^{t}\dot{\mathbf{x}}_{G}\) could be obtained by solving Equation (9) with \({}^{t}\dot{\mathbf{F}}=0\):  
\[{}^{t}\dot{\mathbf{x}}_{G}={}^{t}\mathbf{K}^{-1t}\mathbf{B}{}^{t}\mathbf{C}_{L} {}^{t}\dot{\mathbf{i}} \tag{11}\]  
The vector of incremental axial force could be expressed as follows:  
\[{}^{t}\dot{\mathbf{n}}={}^{t}\mathbf{D}_{2}{}^{t}\dot{\mathbf{i}} \tag{12}\] \[{}^{t}\mathbf{D}_{2}=\big{(}{}^{t}\mathbf{C}_{1}{}^{t}\mathbf{B}{} ^{T}\mathbf{K}^{-1t}\mathbf{B}-\mathbf{I}_{m}\big{)}{}^{t}\mathbf{C}_{L}\]