esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Topics Covered, Notation, and Figures  
The algorithms surveyed in this paper are evaluated by the analytical asymptotic performance of  
1. the feature extraction step,  
Figure 1: Typical Star-Tracker Image-based Attitude Determination Flow.  
Figure 2: Typical Star-ID Process.  
2. database search, and
3. their utilization of independent pattern features in the star features based on how many stars are used in a pattern.  
For all cases, the number of stars referenced in the star catalog is \(n\), the average number of stars observed in the field of view of the camera is \(f\), and the number of stars in a pattern is \(b\). Although a star pattern containing \(b\) stars has \(\dfrac{b(b-1)}{2}\) inter-star angles which may be measured, the number of independent pattern features for a star pattern of \(b\) stars, is only (\(2b-3\)), since some inter-star angles are dependent on the values of others. By including star magnitude information, which is typically much less precise than inter-star angle information, an additional \(b\) independent pattern features may be used. Throughout this paper, the use of asymptotic notation, \(O(\ldots)\), is used to indicate the highest order term in the running time of an algorithm. Algorithms are analyzed based on the described implementation in the referenced papers. Any assumptions drawn about their performance time, when not directly stated in the paper, is based on the progression and availability of certain algorithms at the time, although alternative running times are discussed if it seems likely they could have been implemented. Furthermore, the common computer science notation, \"\(\lg n\)\" is used to mean \"\(\log_{2}n\)\".  
To preserve the original authors' intent, the figures shown here are duplicated from the referenced papers.
## 3 Data and Methods  
### Input Data  
To investigate calving rate of PMG from 2001 to 2017, we used (i) open-source satellite imagery to derive motion vector fields of the glacier, (ii) the perimeter of the glacier, and (iii) ablation measurements from 1995 to 2003 [27]. We used images from the LANDSAT and SENTINEL2 collections. The images covering the PMG area are positioned at 50\({}^{\circ}\)17\({}^{\prime}\)11\({}^{\prime\prime}\) S, 73\({}^{\circ}\)23\({}^{\prime}\)12\({}^{\prime\prime}\) W (path: 231, row: 95). Both collections are sensible to cloud cover, due to the optical nature of the sensors. Here, we used images in pairs, with a maximum time window of 100 days. In Table 1, we report the pairs used. We used LANDSAT4-5 images (from 1999 to 2017, with 30 m of spatial resolution, which was found too low to be used here after a preliminary screening) and LANDSAT7 (from 1999 to 2003) and LANDSAT8 (from 2003 to 2017) images with a spatial resolution of 15 m (panchromatic band). Eventually, the processed cloud-free images from 1999 to 2017 were 62 for LANDSAT7 and 24 for LANDSAT8. Some images were discarded due to a large acquisition interval or the low quality of the feature tracking. In addition, we considered nine images in natural color from SENTINEL2 (bands 2, 3 and 4 corresponding to BGR, at 10 m of spatial resolution) to map the glacier's front variation. Most of our processed images eventually covered the years 2014-2017, providing a most recent estimation of GPM dynamics. The outline of the glacier was defined by the GLIMS glacier database relative to the year 2012, published by the National Snow and Ice Data Center ([[http://nsidc.org/](http://nsidc.org/)]([http://nsidc.org/](http://nsidc.org/)) (accessed on 21 December 2021). To investigate the topography of the glacier, the ASTER Global Digital Elevation Model (Version 2) was used. This DEM has a spatial resolution of 30 m, and it is delivered by NASA and freely available at [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 21 December 2021). We used topographic features, such as slopes and aspect, to qualitatively validate the estimated fields of velocity on the glacier. Figure 1: Location of the Perito Moreno Glacier. (**a**) Patagonia region (satellite image). (**b**) Profiles where annual ablation was recently measured (in [PERSON] et al. [27]). Albeit meteorological data can be very useful in measuring and modeling glacier dynamics (i.e., snow and ice ablation and accumulation whenever occurring, e.g., [3]), here no such data were available. The nearest weather station is at El Calafate (50\({}^{\circ}\)20\({}^{\prime}\)24\({}^{\prime\prime}\) S, 72\({}^{\circ}\)16\({}^{\prime}\)13\({}^{\prime\prime}\) W, 199 m a.s.l. ), about 60 km from the glacier front and thereby little usable
## 1 Introduction
We also previously demonstrated that PPG could be a useful tool for blood glucose estimation [30]. A preliminary study conducted by [PERSON], [PERSON] et al. proposed a subspace KNN (K-Nearest Neighbors) based method for classifying between diabetic/nondiabetic patients by using a PPG signal acquired from a single smartphone [31]. Using a binary classifier, this study constructed two classes in the (70 mg/dL-130 mg/dL) blood glucose range. However, the reported approach achieved a classification accuracy of 86.2% and used a Butterworth filter for denoising motion artefacts. This study outlines the initial development steps towards a new reliable technique for blood glucose level estimation using a smartphone camera. A smartphone camera is used to record multiple videos from the same position of the fingertips for fifty-two subjects. A Gaussian filter is applied to denoise the raw PPG waveform generated, and the Asymmetric Least Squares (ALSs) method is used to remove baseline wander. Attributes such as systolic and diastolic peaks, the peak to peak time interval (DeIT), first derivative peaks, and second derivative peaks are extracted from the denoised signal. These extracted features are subsequently fed into four statistical learning approaches--Principal Component Regression (PCR), Partial Least Squares Regression (PLS), Support Vector Regression (SVR), and Random Forest Regression (RFR) for the prediction of glucose level in blood. The paper focuses on data acquisition techniques, noise removals which are a big challenge in smartphone-based data acquisition systems, and the development of algorithms to provide increased acquisition capabilities, comforts, and flexibilities in measurements compared to the existing systems.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### Number of Sensor Nodes  
Figure 14 depicts the error distance of each scheme, which decreases as the number of the sensor nodes increases, which implies that if there are more active sensor nodes participating in target tracking, a larger amount of sensed data will be generated. For DCTTP with a larger number of active sensor nodes surrounding the target, the result of the target tracking will be improved. Because DCTTP  
uses centroid tracking, the base station receives the distance estimation of each active sensor node, (_i.e._, the distance between each active sensor node and the target) and then uses the position of the active sensor nodes for target tracking. If the active sensor nodes are closer to the target and uniformly distributed, the performance of target tracking can be improved. On the other hand, if active sensor nodes are distributed in a certain direction, the result of the target tracking will move toward where the distributed active sensor nodes are, not to the target position. Therefore, the error distance of DCTTP may decrease with an increasing sensor density in the network.  
In TCAT, with a higher sensor density (_i.e._, a larger number of active sensor nodes in the cluster), the bounding area for sampling will be smaller, which leads to a lower error distance of TCAT. Comparing the method of target tracking of TCAT to that of DCTTP, TCAT uses multiple sensors with TOA/AOA information and a particle filter, and DCTTP just uses the distance estimation and the position of active sensor nodes. As shown in Figure 14, the error distance of TCAT is much lower than the error distance of DCTTP. In the proposed CTCI, with a larger sensor density, the bounding area for sampling points will be refined to a smaller region compared to TCAT. This is because the location information of active sensors can be used to further refine the sampling area. Therefore, the error distance of CTCI is lower than the error distance of TCAT.  
Figure 15 depicts that the data size of DCTTP rapidly rises as the number of sensor nodes increases. Observe that when the number of sensor nodes is about 230, the data size of DCTTP is larger than that of TCAT. This is because the DCTTP does not have a constraint on the number of active sensors that participate in the tracking task. In contrast, the data size of TCAT and CTCI is nearly constant because of the pre-defined sub-cluster size. As shown in Figure 16, the distribution of the average energy consumption is similar to the performance of the average data size. Observe that with a high network density (e.g., the number of sensor nodes = 500), the energy consumption of CTCI is larger than that of TCAT and lower than that of DCTTP.  
Figure 14: Impact of the number of sensor nodes on average error distance.
## 3 Results and Analysis
### Analysis of Bearing Performance
#### 3.2.2 Force-Bearing Performance  
Please see Table 3 for the split limit, loading limit and converted compressive strength. \(f_{c}\) refers to the split limit of loading, where the first value observed applies. \(f_{u}\) represents the loading limit, the maximum value of which applies;\(f_{m}\) stands for the converted compressive strength, where the loading limit to cross section area ratio is used.  
From Table 3, the following can be concluded: (1) Compared with the specimens of clay bricks, the masonry walls of recycled concrete bricks deliver equally matched compressive strength. (2) The application of square-shaped wood, square-shaped steel pipe and circular steel pipe helps largely to elevate the compressive strength of a structure, with the most preferable choice being the circular steel pipe. Take the last groups of specimens, or the integrated wall structure of recycled bricks, stems and fly ash masonry blocks, as an example; the application of square-shaped wood stem, square-shaped steel pipe and circular steel pipe increases the compressive strength by 41%, 174% and 208%, respectively.
# Machine Learning-Assisted Secure Random Communication System
## 6 Results and Discussion  
Following the results and trends observed in the effective demo transmissions, in this section, we analyze the proposed ML-RCS for a larger number of bits. The generalized method of checking 1000 binary bits for errors [43; 44; 45; 46; 47; 48; 49; 50; 51; 52] was adopted to compute the confusion metrics and BER performance.  
First, the proposed ML-RCS was analyzed from the perspective of the T and LR, termed 'intended communication'. This indicates that the T and LR are synchronized and in harmony with the user and device authentication. This is followed by the perspective of the T and ILR, which is referred to as 'unintended communication'. This indicates that the T and LR are synchronized but not in harmony with the user and device authentication.
## 3 Simulation Study
From Equations (5) and (17), the pressure-induced refractive index change can be indicated as  
\[\Delta n(r,\ t)=\frac{\epsilon\ n_{0}^{3}(r)}{2}\ (\beta\ T(r,\ t)-\
abla\stackrel{{ \rightarrow}}{{u}}(r,\ t)) \tag{25}\]  
The thermally induced refractive index change can be expressed as  
\[\Delta n=\alpha\,\Delta T=\frac{1}{\beta}\,\Delta u \tag{26}\]  
Then, the total refractive indices changes can be given as  
Figure 5.0  
When the displacement on the sample is small enough, the refractive indices change is proportional to the generated displacement. From Equation (20), it derived that  
Figure 5.1  
where  
\[\left\{ \begin{array}{l}a_{1}=\sqrt{A_{S}{}^{2}\ I_{0}\ T_{S}{}^{2}}\\ a_{2}=\sqrt{A_{S}{}^{2}\ I_{0}\ R}\\ a_{3}=\sqrt{A_{R}{}^{2}\ R\ I_{0}}\end{array} \right.. \tag{29}\]  
As the AC term is collected during detection, there is  
\[I_{AC\_PD}=a_{1}{}^{2}\ R_{S}{}^{2}+2\ a_{1}\ (a_{2}+a_{3})\ R_{S} \tag{30}\]  
where  
\[R_{S}=\left|\frac{n_{1}+\Delta n\ -\ n_{2}}{n_{1}+\Delta n+n_{2}}\right|^{2} =\left(1-\frac{2\ n_{2}}{n_{1}+\Delta n+n_{2}}\right)^{2}. \tag{31}\]  
Although refractive index modulation induced by the thermal effect is small, it is potentially noticeable in the scattering medium and would be applied locally in the heating region [18]. Thermal cooling occurs on a scale of microseconds to milliseconds after laser-induced heating. This was proved by simulation on COMSOL and was also verified by the following experiments.
## 3 Results and Discussion
### Photocatalytic Reaction
#### 3.2.4 Identification of Active Species (Scavenger and ESR Studies)  
Different types of active species, such as \({}^{\bullet}\)O\({}_{2}\)-, h+, and/or \({}^{\bullet}\)OH, can be responsible for the photocatalytic degradation of ethiofencarb. These active species were tested using scavenger experiments with isopropanol (IPA) for hydroxyl radicals, ammonium oxalate (AO) for h+, and 1,4-benzquinone (BQ) for \({}^{\bullet}\)O\({}_{2}\)- species [47]. Ethiofencarb degradation experiments with SnIn\({}_{4}\)S\({}_{8}\) catalyst under visible-light irradiation in the presence and absence of these scavengers are summarized in Figure 8. In comparison to absence of scavengers, the addition of 1 mM of BQ (1 mM) strongly inhibited the degradation process, reducing the efficiency from 98% to 58% under 24 h-irradiation. This suggests the important role of the \({}^{\bullet}\)O\({}_{2}\)- species in the reported degradation. Similar results were obtained upon adding 1 mM of IPA, with efficiency decrease to 76% in 24 h, inferring that \({}^{\bullet}\)OH radicals effectively contribute to ethiofencarb degradation. In contrast, inhibition effect was marginally noticed upon adding AO, indicating only an assistant role of the holes in this photodegradation.  
Electron spin resonance (ESR) spin-trap technique was further applied to identify the active radicals using 5,5-dimethylpyridine-\(N\)-oxide (DMPO). The resulting ESR spectra using the SnIn\({}_{4}\)S\({}_{8}\)/visible-light system are presented in Figure 9. In accordance with the scavenger results, the presence of \({}^{\bullet}\)OH and \({}^{\bullet}\)O\({}_{2}\)- species was confirmed by the observed characteristic signals for DMPO-\({}^{\bullet}\)OH (intensity ratios of 1:2:2:1) and DMPO-\({}^{\bullet}\)O\({}_{2}\)- (1:1:1:1) spin adducts [48]. Thus, the ethiofencarb degradation with the proposed photocatalytic system is primarily achieved by \({}^{\bullet}\)O\({}_{2}\)- and \({}^{\bullet}\)OH radicals, with an assistant contribution from photogenerated holes.  
Figure 8: Photocatalytic degradation of ethiofencarb with SnIn\({}_{4}\)S\({}_{8}\) catalyst in the absence and presence of scavengers (IPA, AO, and BQ) under visible-light irradiation. Experimental conditions: ethiofencarb concentration 10 mg L\({}^{-1}\); SnIn\({}_{4}\)S\({}_{8}\) concentration 0.5 g L\({}^{-1}\); scavenger concentration 1 \(\times\) 10\({}^{-3}\) M; pH 3.  
Figure 7: Effect of anions on the photocatalytic degradation rate of ethiofencarb. Experimental conditions: ethiofencarb concentration 10 mg L\({}^{-1}\); SnIn\({}_{4}\)S\({}_{8}\) concentration 0.5 g L\({}^{-1}\); pH 3.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results
### Effect of Spreading Code Length on Robustness  
The 512 \(\times\) 512 Lena image is watermarked with a 16 \(\times\) 16 watermark. For watermarking, three different lengths spreading codes, 512 bits, 256 bits and 128 bits are used. When we use 512 bits spreading code the whole row is used for embedding and for 256 bits and 128 bits only half and quarters of the selected coefficients respectively in a row are used for embedding. The BER performance is shown in Figure 6. Robustness of the algorithm in each of the above cases is noted and tabulated Table 3. It can be seen that the 512 bit spreading code is more resistant to compression (JPEG) compared to others and in the case of detailed attack analysis also 512 bit spreading codes show best robustness against all the attacks and 128 bit code the least.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks/Distortions (Level)** & **DCT Technique (Samee)** & **FWHT Technique** \\ \hline Original & & \\ Scaling (6 times) & & \\ Gaussian Noise (\(\mu=0\), \(\sigma^{2}=0.005\)) & & \\ Gaussian Noise (\(\mu=0.01\), \(\sigma^{2}=0.005\)) & & \\ Gaussian Noise (\(\mu=0\), \(\sigma^{2}=0.05\)) & & \\ Salt \& Pepper Noise (Density—0.01) & \\ Salt \& Pepper Noise (Density—0.1) & \\ Sharpen (\(3\times 3\)) & & \\ Blur (\(2\times 2\)) & & \\ Median filtering (\(3\times 3\)) & & \\ Gaussian filtering (\(5\times 5\)) & & \\ Crop (50\%) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extracted watermark After Different Attacks.
## 4 Equalization Technologies
### Equalization Based on Machine Learning
#### 4.3.1 Support Vector Machine  
The basic SVM classifier is a two-class classifier and its training process can be described as finding the maximum edge hyperplane to define the decision function of the classifier. The concept of basic SVM classifier is shown in Figure 17a, where the margin means the minimum distance of all samples to the hyper-plane [40]. To solve this convex quadratic programming of optimization target in SVM, the sequential minimal optimization (SMO) algorithm [87] can be used.  
Note that PAM4 cannot be de detected directly by the basic SVM classifier. Thus, a proposed complete binary tree (CBT) structure multi-classes SVMs method is utilized as depicted in Figure 17b. It can be seen that two layers are contained in the tree, where the first layer classifies the first bit of PAM4 signal, and the second layer classifies the last bit. Although the wrong decision in the high layer will be retained to the next layers, a significant improvement in performance for PAM4 system can still be expected.  
The BER curves of the 40 Gbps and 50 Gbps PAM4 signal are shown in Figure 18a,b, respectively [42]. Using the proposed CBT-SVMs, significant receiver sensitivity reduction of 2.99 dB and 4.68 dB in optical B2B and 2 km SMF transmission can be obtained for 40 Gbps PAM4 system. Moreover, for 50 Gbps PAM4 modulation, it provides 3.59 dB and 3.63 dB received power tolerance compared with the hard decision. Therefore, the SVM algorithm shows outstanding performance in nonlinear impairment mitigation and could be a potential choice for future PAM4 optical links.  
Figure 16: (**a**) BER vs. received optical power for a back-to-back system. (**b**) BER vs. received optical power for 10 km and 20 km transmission (reprinted from [31] with permission from authors).  
Figure 17: (**a**) Basic SVM classifier. (**b**) Complete binary tree structure multi-classes SVMs and mapping rule for PAM4 signal where the black circle and the red square represent two categories (reprinted from [40] with permission from IEEE).
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
### Principles of Tree-Based Search Power Allocation Algorithm
Figure 2: Propose a tree model and find a final path to allocate power to each CU through the tree-based search method. In each layer the nodes are divided into several groups to pick out the best node for the next layer. As introduced in (3), after using the SIC technology, the target CU is influenced only by the interference coming from the CUs with low power. Different from traditional tree-based search algorithms [43; 44; 45; 46], we arrange the CUs in a descending order of SINR in a tree and allocate power to the CUs in an increasing order. Thereby, we draw the conclusion that the CUs would only be influenced by the signals from CUs in lower layers rather than in higher layers, which can reduce the number of searches.
## 1 Introduction
However, seasonal changes of LUE that are similar to seasonal VI patterns have been observed by studies over northern hemisphere plants (e.g., [17]). Although directly estimating LUE from VI lacks physically-based theoretical basis, some empirical relation may exist between seasonal VI and LUE. [PERSON], et al. [18] reported a significant linear correlation (determination coefficient, \(r^{2}\) = 0.78) between seasonal NDVI and LUE. [PERSON] et al. [19] found a significant linear relationship between VIs (NDVI and enhanced vegetation index, EVI) and LUE over maize and then used VIs as proxies for LUE at seasonal scale. Based on this scope, a VI-based LUE model is developed:  
\[\text{GPP}\ =\ \text{$m\times\text{VI}\times\text{VI}\times\text{PAR}$}, \tag{2}\]  
where VI could be either NDVI or EVI and \(m\) is an empirical adjust parameter which is determined from the calibration dataset. Favorable results have been reported for this model by several studies (e.g., [19; 20]). Most of the applications of this model on GPP estimation are site-based, where in-situ flux-based GPP data that are used to calculate parameter \(m\) are available. However, for phenology applications, it is not necessary to calculate \(m\) from calibration datasets because in these situations we focus on the seasonal fluctuations of the indicators we use rather than their absolute values. If VI is able to be a proxy of LUE at seasonal scale, as is the case reported by previous studies, the VI-based LUE model should be able to improve the VI-based phenology towards tracking photosynthesis dynamics more precisely. Based on this assumption, this study aims to evaluate the performance of VI-based LUE model in tracking the seasonal cycle of GPP and to use this model to improve VI-based vegetation phenology.