esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### NIRv and GPP Calculations  
A long-term dataset of the Advanced Very-High Resolution Radiometer (AVHRR) reflectance at the red band (R) and near-infrared band (NIR) was applied to calculate the monthly NIRv over the YRB from 2001 to 2018. The dataset was derived from the Land Long-Term Data Record (LTDR) product (v4) at a spatial resolution of 0.05\({}^{\circ}\). The LTDR product performs well in the sensor post-launch calibration, as well as for bias corrections for systematic orbit shifts, cloud screening, sensor degradations, and atmospheric corrections [47].  
The cloudy and cloud shadow pixels were excluded in this work, and the median value of the reflectance of all the available pixels was obtained by month for each of the grid cells of interest. Then, monthly NIRv was calculated by the following equation:  
\[\mathrm{NIRv}=\mathrm{NIR}\times(\mathrm{NDVI}-0.08) \tag{1}\]  
\[\mathrm{NDVI}=(\mathrm{NIR}-\mathrm{R})/(\mathrm{NIR}+\mathrm{R}) \tag{2}\]  
A constant of 0.08 was subtracted from all NDVI values to minimize the effect of bare soil [16; 22]. Moreover, the NIRv values that were less than or equal to zero were excluded in the following analysis [16].  
The NIRv-GPP relationships proposed by [PERSON] et al. were used to estimate GPP for the different land cover types in this study [22]. For the percentage of covered PFTs, we calculated the weighted average of GPP. The relationships were established using the GPP estimations and satellite NIRv at 104 eddy-covariance (EC) flux sites. The linear regression between GPP and NIRv was calibrated and validated for each land cover type (Table 2). The models performed well across all of the types, with an average R\({}^{2}\), bias, and RMSE of 0.71, 0.02, and 1.95 gC m\({}^{-2}\) d\({}^{-1}\), respectively. More details of the methods and evaluations are described in [PERSON] et al. [22]. Notably, large differences in the parameters of the linear models were observed across the vegetation types, which indicated the relationships between GPP and NIRv were likely to be biome-specific [21]. That is why the schemes of land cover change and the discrepancy in multisource products proposed in this study should be considered in NIRv-based GPP estimation.
###### Abstract  
Recently, deep learning technology has been applied to medical images. This study aimed to create a detector able to automatically detect an anatomical structure presented in a brain magnetic resonance imaging (MRI) scan to draw a standard line. A total of 1200 brain sagittal MRI scans were used for training and validation. Two sizes of regions of interest (ROIs) were drawn on each anatomical structure measuring \(64\times 64\) pixels and \(32\times 32\) pixels, respectively. Data augmentation was applied to these ROIs. The faster region-based convolutional neural network was used as the network model for training. The detectors created were validated to evaluate the precision of detection. Anatomical structures detected by the model created were processed to draw the standard line. The average precision of anatomical detection, detection rate of the standard line, and accuracy rate of achieving a correct drawing were evaluated. For the \(64\times 64\)-pixel ROI, the mean average precision achieved a result of \(0.76\pm 0.04\), which was higher than the outcome achieved with the \(32\times 32\)-pixel ROI. Moreover, the detection and accuracy rates of the angle of difference at 10 degrees for the orbitomeatal line were \(93.3\pm 5.2\) and \(76.7\pm 11.0\), respectively. The automatic detection of a reference line for brain MRI can help technologists improve this examination.  
Keywords:object detection; standard line for brain; faster R-CNN; medical image analysis; magnetic resonance imaging +
Footnote †: journal: Medical Imaging
# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 2 Emotion Estimation Approaches and Environmental Factors
### Facial Expressions  
Emotion estimation methods using facial images primarily identify emotions by analyzing changes in facial expressions. In this approach, facial feature points are first detected, and then features related to a given expression are extracted. Based on the extracted data, emotions are classified using machine learning and deep learning techniques [26; 27]. Emotion estimation through facial images enables real-time emotion analysis and facilitates noncontact emotion estimation. This method is utilized in the field of robotics; for example, robots such as Pepper recognize users' facial expressions, estimate their emotions, and respond accordingly, leading to more natural communication. However, this method can only analyze emotions expressed through facial expressions, which means that it cannot read underlying emotions, raising concerns about privacy invasion.
# Research on Students' Action Behavior Recognition Method Based on Classroom Time-Series Images
## 3 Proposed Method
### Detection of Students' Action Behavior
#### 3.1.6 Equalized Focal Loss Function  
The student action behavior dataset used in this paper suffers from a category imbalance problem, whereby most of the students show action behaviors such as listening and reading during the class period while a few students have action behaviors such as using mobile phones and lying on the table. The loss function used in the original AIA network is Focal Loss [37], but [PERSON] et al. [12] proved that Focal Loss does not deal with the foreground category imbalance problem; so, in this paper, we use the EFL proposed by [PERSON] et al. [12] as the loss function of the action classifier in the AIA network to improve the category imbalance problem, specifically shown by Equation (19):  
\[EFL(p_{t})=-\sum_{j=1}^{C}\alpha_{t}\Bigg{(}\frac{\gamma_{b}+\gamma_{v}^{j}}{ \gamma_{b}}\Bigg{)}(1-p_{t})^{\gamma_{b}+\gamma_{v}^{j}}\log(p_{t}) \tag{19}\]  
where \(\alpha_{t}\) is used to regulate the weights of positive and negative samples during training and \(p_{t}\) is the prediction score. \(\bigg{(}\frac{\gamma_{b}+\gamma_{v}^{j}}{\gamma_{b}}\bigg{)}\) is the weight factor associated with the category, \(\gamma_{b}+\gamma_{v}^{j}\) is the focusing factor for the \(j\)th category, \(\gamma_{b}\) is a constant, and \(\gamma_{v}^{j}\) can be expressed as  
\[\gamma_{v}^{j}=s\Big{(}1-g^{j}\Big{)} \tag{20}\]  
where the hyper-parameter \(s\) is the scaling factor that determines the upper limit of \(\gamma_{v}^{j}\) in EFL, and the parameter \(g^{j}\) indicates the accumulated gradient ratio of positive samples to negative samples of the \(j\)th category during the training process, with the range of \(g^{j}\) set to [0,1].  
Since \(g^{j}\) indicates the accumulated gradient ratio of positive samples and negative samples of the \(j\)th category during the training process, a larger value of \(g^{j}\) indicates that the category is trained to be balanced and a smaller value of \(g^{j}\) indicates that the category is trained to be unbalanced; so, the focusing factor \(\gamma_{b}+\gamma_{v}^{j}\) and the prediction score \(p_{t}\) in the EFL, composed of \((1-p_{t})^{\gamma_{b}+\gamma_{v}^{j}}\) as the category-related weighting factor in the loss, can dynamically regulate the weight of the loss based on the cumulative gradient ratio of positive and negative samples in each category during the training process and the prediction score \(p_{t}\) to handle the problem of category imbalance.
## 1 Introduction
All the above studies take advantage of broadcast routing and consider how to reduce the transmission energy cost. However, compared with fixed routing, such as tree routing and cluster routing, broadcasting data consumes more reception energy because neighboring nodes receive broadcast data whether they need it or not. For example, in ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018), the neighboring nodes first receive the broadcasting data and then determine whether to merge the data based on certain conditions. Consequently, broadcasting data consumes large amount of reception energy, although the received data are rarely merged. Furthermore, none of these studies consider the problem of packet loss; instead, they make the unrealistic assumption that the wireless links are completely reliable. Tackling the abovementioned consideration, two challenges must be resolved. The first involves how to effectively reduce the quantity of data disseminated (transmissions and receptions), especially the number of receptions rather than the number of fusions. The second problem is related to reducing the impact of lossy links (namely, the packet loss rate) on data reconstruction. To solve these two challenges, a distributed data storage and gathering algorithm based on compressive sensing (CS-DDSG) is proposed utilizing CS and DDS. Relying on collected data, the mobile sink generates a sparse measurement matrix aimed at reducing communication energy consumption. Furthermore, it is proven that the measurement matrix satisfies the restricted isometry property (RIP) ([PERSON] et al., 2016). Based on random geometric graph theory, an expression of the total number of transmissions and receptions is formulated to analyze the energy consumption of CS-DDSG. The reminder of this paper is organized as follows. In Section 2, we commence by reviewing the CS theory and introduce the network model. In Section 3, we present the proposed CS-DDSG algorithm, describe the formulation of the measurement matrix and provide a proof that this matrix can satisfy RIP. Based on the proposed scheme, we formulate the expression of the total number of transmissions and receptions in Section 4. We present our simulations and their results and investigate the performance of CS-DDSC in Section 5. Finally, concluding remarks are provided in Section 6.
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## Appendix A Blur Measure Operators
### Transform-Based Operators  
\([TRA01]\)_Power Spectrum_  
In the power spectrum of a blurred image, in contrast to sharp image, high frequency components posses lesser energy as compared to the energy of low frequency components. Thus, a blurred image has its average power located at a lower frequency \(\omega\), and [1] utilized it as a measure:  
\[M_{PS}(x,y)=\frac{1}{n}\sum_{\omega}\sum_{\theta}J(\omega,\theta), \tag{22}\]  
where \(J(\omega,\theta)\) is the squared magnitude of DFT of image neighborhood window in polar coordinates and \(n\) is the number of quantizations of \(\theta\).  
\([TRA02]\)_High-Frequency Multiscale Fusion and Sort Transform_  
By taking various sized neighborhood windows around a central pixel, [PERSON] et al. [31] computes high frequency DCT coefficients of gradient magnitudes, groups them in a number of layers after sorting and then normalizes them between [0, 1]. The blur measure for a pixel \(p\) is given by  
\[M_{HiFST}(x,y)=\max\{\hat{L}_{p:t}:t=1,\ldots,\sum_{r=1}^{m}M_{r}\}, \tag{23}\]  
where \(\hat{L}_{p:t}\) represents its \(t\)th DCT coefficient which has been normalized among the \(t\)th DCT coefficients of all image pixels, and \(M_{r}\) represents the size of a square neighborhood window.  
\([TRA03]\)_Sum of Wavelet Coefficients_  
In the first level of discrete wavelet transform (DWT), the image is decomposed into four sub images such as \(|W_{LH1}(i,j)|\), \(|W_{HL1}(i,j)|\), \(|W_{HH1}(i,j)|\) and \(|W_{LL1}(i,j)|\) representing the three detail sub bands and one coarse sub band, respectively. Iteratively, coarse sub band is further divided into three detail sub bands and one coarse sub band. [PERSON] and [PERSON] [70] combined the sub-bands to propose a focus operator which can be treated as a blur measure and is given by  
\[M_{SWAV}(x,y)=\sum_{(i,j)\in\Omega_{D}}|W_{LH1}(i,j)|+|W_{HL1}(i,j)|+|W_{HH1}(i, j)|, \tag{100}\]  
where \(\Omega_{D}(x,y)\) is the corresponding window of \(\Omega(x,y)\) in DWT sub bands.  
\([TRA04]\) _Variance of Wavelet Coefficients_  
The variance of wavelet coefficients within \(\Omega_{D}\) for focus measurement as proposed in [70] can also be used as blur measure  
\[M_{VWAV}(x,y)=\sum_{(i,j)\in\Omega_{D}}(W_{LH1}(i,j)-\mu_{LH1})^{2}+\sum_{(i,j )\in\Omega_{D}}(W_{HL1}(i,j)-\mu_{HL1})^{2}+\sum_{(i,j)\in\Omega_{D}}(W_{HH1} (i,j)-\mu_{HH1})^{2}, \tag{101}\]  
where \(\mu_{LH1}\), \(\mu_{HL1}\) and \(\mu_{HH1}\) are the mean value of the respective sub bands within \(\Omega_{D}\).  
\([TRA05]\) _Ratio of Wavelet Coefficients_  
[PERSON] et al.'s operator [71] that involves the high and low frequency coefficients, can be used as the blur measure  
\[M_{RWAV}(x,y)=\frac{M_{H}^{2}}{M_{L}^{2}}, \tag{102}\]  
where \(M_{H}^{2}=\sum_{k}\sum_{(i,j)\in\mathcal{P}_{D}}W_{LHk}(i,j)^{2}+W_{HLk}(i,j)^ {2}\) and \(M_{L}^{2}=\sum_{k}\sum_{(i,j)\in\mathcal{P}_{D}}W_{LLk}(i,j)^{2}\) and \(k\) represents the \(k\)-th level wavelet.
## 2 Frame Beam with ECC Protective Layer
### Structural Construction of ECC Protective Layer  
(1) Distribution point key  
The distribution point key is located on the main contact surface between the ECC protective layer and RC, which is arranged in a plum-blossom shape. A large number of distributed point keys can significantly improve the bonding force between the ECC protective layer and the internal concrete and still maintain a close fit after a large deformation of the frame beam. The distribution of concave points on the inner side of the occupying plate is shown in Figure 1.  
(2) Stirrup key  
The stirrup key is located between the ECC protective layer and the contact surface of the RC. By setting a groove with the same diameter and spacing as the stirrup of the frame beam in the occupation plate, the stirrup part (not more than half) protrudes from the surface of the RC when the RC is poured first. Through the mechanical interlocking between the ECC protective layer and the stirrup, the force transmission capacity between the ECC protective layer and the internal concrete is enhanced. The stirrup keys on the inner side of the occupying plate are shown in Figure 2.  
(3) Beam end T-shaped key  
The joint surface between the ECC protective layer and the frame column is located at the maximum negative moment at the beam end. By bending the ECC protective layer inward and outward on the column surface, the T-shaped key at the beam end is formed to enhance the anchorage performance of the ECC protective layer at the beam end and delay the occurrence of cracks at the end of the ECC protective layer. The inner anchorage depth of the T-shaped key at the beam end is the same as the thickness of the ECC protective layer, and the width of the outer extension section is 3 to 5 times the thickness of the  
Figure 1: Distribution point keys on the inner side of the ECC cover. (**a**) Distribution point key section. (**b**) 3D schematic of distribution point key.  
(4) Long round key  
The long round key is located at the interface between the ECC protective layer and the surrounding RC, forming an oblong bulge on the side of the RC through the occupying plate to enhance the bonding force between the ECC part of the secondary construction and the RC. And it can restrain the gap between the two materials. The long round key around the placeholder is shown in Figure 4.
## 2 Related Work
### Single Object Tracking Based on Regularized Correlation Filters  
Correlation filters (CF) have been actively adopted in single object tracking to improve robustness and efficiency. Initially, since CF needs training, the work of online tracking seems inappropriate. In the later years, with the development of the minimum output of sum of squared error (MOSSE) filter [28], which introduces efficient adaptive training, changed the situation. To meet the real-time requirement, a high-speed tracking method using kernelized correlation filters (KCF) [29] has been proposed, using a Gaussian kernel function to reduce computational complexity. However, discriminative correlation filter (DCF) based trackers are limited in their detection range because of the requirement of equal filter and patch size, which leads to a constant failure under occlusion and object deformation. Thus, a larger search region is necessary, which needs incorporation of a measure of regularization due to the degradation of the discriminative power under more complex background. [PERSON] et al. [44] presented spatially regularized DCF (SRDCF) by introducing a spatial regularization in DCF learning. To improve the performance of SRDCF, [PERSON] et al. [5] replaced the Histogram of Oriented Gradient (HOG) features to deep features in a Convolutional Neural Network (CNN). Then, in [30], a multi-resolution feature maps learning, named continuous convolutional operators for tracking (C-COT), was proposed to optimize the estimation of the object position. Finally, the efficient convolution operators (ECO) tracking scheme [3], as an improved version of C-COT, was proposed. ECO picks the most efficient set of filters and discards unnecessary ones, and uses a Gaussian mixture model (GMM) to represent diverse object appearances. These methods led to increasing performance of the tracker and significantly reduced processing time.
## 3 Results
### Analysis of Metabolites in Kimchi
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Groups** & **Metabolites** & _[PERSON]_ & _[PERSON]_ & _[PERSON]_ & _[PERSON]_ & _[PERSON]_ & _[PERSON]_ \\ \hline \multirow{3}{*}{Carbohydrates} & Glucose & 27.51 \(\pm\) 1.99\({}^{\circ}\) & 27.96 \(\pm\) 2.06\({}^{\circ}\) & 36.62 \(\pm\) 0.39\({}^{\circ}\) & 47.62 \(\pm\) 7.26\({}^{\circ}\) & 32.80 \(\pm\) 1.99\({}^{\circ}\) & 38.61 \(\pm\) 1.33\({}^{\rm\,ab}\) \\  & Glucose & 85.79 \(\pm\) 1.22\({}^{\circ}\) & 59.21 \(\pm\) 0.40\({}^{\circ}\) & 70.22 \(\pm\) 1.23\({}^{\circ}\) & 74.09 \(\pm\) 5.62\({}^{\circ}\) & 73.58 \(\pm\) 2.76\({}^{\circ}\) & 67.91 \(\pm\) 0.61\({}^{\circ}\) \\  & Sucrose & 4.28 \(\pm\) 0.86\({}^{\circ}\) & 4.77 \(\pm\) 0.88\({}^{\circ}\) & 41.41 \(\pm\) 0.78\({}^{\circ}\) & 4.01 \(\pm\) 0.81\({}^{\circ}\) & 4.45 \(\pm\) 0.07\({}^{\circ}\) & 4.34 \(\pm\) 0.48\({}^{\circ}\) \\ \hline \multirow{3}{*}{Alcohols} & Ethanol & 16.62 \(\pm\) 1.62\({}^{\rm\,ab}\) & 18.75 \(\pm\) 0.13\({}^{\circ}\) & 16.46 \(\pm\) 1.39\({}^{\rm\,ab}\) & 14.17 \(\pm\) 1.4\({}^{\rm\,b}\) & 14.24 \(\pm\) 1.69\({}^{\rm\,b}\) & 14.21 \(\pm\) 0.95\({}^{\rm\,b}\) \\  & Mannitol & 28.95 \(\pm\) 2.22\({}^{\circ}\) & 29.55 \(\pm\) 2.33\({}^{\circ}\) & 27.79 \(\pm\) 1.20\({}^{\circ}\) & 20.10 \(\pm\) 0.83\({}^{\circ}\) & 23.10 \(\pm\) 1.32\({}^{\rm\,b}\) & 23.20 \(\pm\) 0.59\({}^{\rm\,b}\) \\ \hline \multirow{3}{*}{Organic acids} & 2-Aminobutyrate & 1.80 \(\pm\) 0.33\({}^{\circ}\) & 1.68 \(\pm\) 0.13\({}^{\circ}\) & 1.64 \(\pm\) 0.51\({}^{\circ}\) & 1.26 \(\pm\) 0.83\({}^{\circ}\) & 1.71 \(\pm\) 0.37\({}^{\circ}\) & 1.38 \(\pm\) 0.50\({}^{\circ}\) \\  & 4-Aminobutyrate & 1.93 \(\pm\) 0.20\({}^{\circ}\) & 1.62 \(\pm\) 0.5
## 4 Conclusions  
Three experimental models of the layers with antimicrobial potential were obtained and characterized: two reproducing the properties obtained in the previous research and one improved, with high transparency and higher silver content. The layers' thickness was 30-35 nm, with an Ag content equivalent to a thickness of 3 nm, and approximately 10% of the total and the topographic characteristics of the foil surface are preserved after the deposition of thin layers. Analyzing X-ray diffractometry spectra and spectrophotometric measurements indicates the presence of metallic Ag nanoclusters embedded in the polymer matrix for deposition processes without oxygen and the absence of such structures for the optimized process with the addition of oxygen. The presence of silver within the layer was also proved by EDX measurements, highlighting an increase in its concentration when a sputtering process with added oxygen is used. Using the ready-to-use MC Media Pad E. coli and Coliform culture media is effective for evaluating the antibacterial activity of the layers deposited on the foils and evaluating the antibacterial properties of the samples; the maximum concentration for possible applications (countable colonies) for the PAD media is 10\({}^{4}\) CFU/mL. The highest antibacterial efficiency (expressed in cm--zone of inhibition of development) was recorded for the film deposited with TiO\({}_{2}\)+Ag+O\({}_{2}\). Comparing the three types of foils, an efficiency of TiO\({}_{2}\)+Ag+O\({}_{2}\) > TiO\({}_{2}\)+Ag > SiO\({}_{2}\)+Ag was recorded for the tests performed on the PAD media.  
Conceptualization, C.-S.G., M.B. and C.V.; methodology, C.-S.G., M.B., M.I., A.C.P., A.E.K. and M.V.; software, C.-S.G., M.B., C.A.I., C.M. and I.P.; validation, C.-S.G., M.B., M.I, L.R.C. and M.D.; formal analysis, C.-S.G., M.B., M.V., C., A.I. and C.M.; investigation, C.-S.G., M.B., M.I., M.D., I.P., A.C.P. and C.M.; resources, C.-S.G., M.B. and A.V.; data curation, C.-S.G., M.B., A.C.P. and C.A.I.; writing--original draft preparation, C.-S.G., M.B., C.V., C.M. and C.A.I.; writing--review and editing, C.-S.G., M.B., E.L.G., M.A.M., A.V. and M.V.; visualization, all authors; supervision, M.B. and C.V.; project administration, C.-S.G., M.B., C.V. and L.R.C.; funding acquisition, C.-S.G., M.B., M.A.M. and L.R.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Romanian Ministry of Research, Innovation and Digitization, CCCDI-UEFISCDI, project number PN-III-P2-2.1-PED-2019-4966, within PNCDI III, Core Program of the National Institute for R&D in Optoelectronics INOE 2000 Project No. PN 23 05 (PN11N-03-01-2023). The Transilvania University of Brasov partially supported the cost for publication of this paper.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.