esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Calcareous Tufa Deposition/Decline during Holocene  
As shown by investigations carried out in different parts of the world, widespread deposition of tufa occurred in the early-middle Holocene from 10,000 to 4600 yr B.P. and declined or ceased entirely in the late Holocene [8; 9; 20; 33; 34; 37; 40; 45].  
According to [PERSON] [50], the tufa deposition rates in Belgium during the Holocene were 12 mm/yr in the Pre-Boreal, 26 mm/yr in the Boreal, 6 mm/yr in the Atlantic and 1 mm/yr in the Sub-Boreal. Moreover, the Holocene formation of speleothems shows a similar trend [43; 51].  
Two explanation models for the late Holocene decline of tufa deposition have been proposed: a \"climatic\" model [50; 52] and a \"human impact\" model [8; 9; 40; 45].  
The first model stresses the role of the progressive cooling and wetting of climate that should have affected middle-high latitudes, as indicated by the reduction of thermophilic vegetation species and the southward migration of the boreal forest [53]. In addition, these new conditions would have induced a general deepening of river incision and a related lowering of water tables, resulting in a widespread modification of water regimes and tufa depositional systems [52]. However, more recent research [54] indicates a general shift of climate towards cooler and drier conditions in both middle-high and low latitudes, where tufa formation also declined [20; 33; 34].  
The second model points to the effects of human impact on slopes for agricultural or pastoral use. In particular, the widespread forest clearing started locally in the Early Holocene and widely developed after 5000 yr B.P. would have drastically lowered the biogenic CO\({}_{2}\) in soils, the CaCO\({}_{3}\) dissolution in the limestone aquifers, and the deposition rates of tufa from spring groundwaters. Other possible human-induced factors unfavorable to tufa deposition would have been changes in the chemical characteristics of ground/surface water, changes in stream hydrology, increasing water turbidity, and water pollution [39]. However, even if the above factors could have been effective in reducing/preventing tufa deposition, it seems difficult to explain all the cases of tufa deposition decline observed in different parts of the world as being due to the impact of human activities [50]. Moreover, the \"human impact\" theory cannot explain the cases of tufa deposition decline which repeatedly occurred before the Holocene [10; 11; 52; 55].
## 2 Materials and Methods  
### Materials  
The WFT and WFR utilized in this project are sourced from \"Synergie Mer Et Littoral\" (SMEL), a proficient fishing industry organization located in the Normandie region of France. Figure 1 presents the visual representations of WFT and WFR.  
Table 2 illustrates the physical as well as mechanical properties of WFT (polyethylene) and WFR (polypropylene) fibers. Density or volume mass was calculated following NF EN ISO 18753, 2017, while R\({}_{\text{it}}\) and Young's modulus was calculated following international standard ASTM C1557-03, 2013.  
The mortar was prepared using the CEM V/A 32.5 N cement, which is a type of low-clinker cement, in combination with quarry sand. The composition of the oxides of cement and mortar formulation are provided in Tables 3 and 4, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Cement**} & \multicolumn{1}{c}{**Sand**} & \multicolumn{1}{c}{**Water**} & \multicolumn{1}{c}{**Superplasticizer**} & \multicolumn{1}{c}{**Fibers**} & \multicolumn{1}{c}{**Mortar Reference**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{**Proportion (\%)**} & \multicolumn{2}{c}{**Average Length (mm)**} \\ \hline \(1\) & 1.5 & 0.40 & 0.001 & 0 & 0 & MC \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{**WFT**} & \multicolumn{1}{c}{**WFR**} & \multicolumn{1}{c}{**WFT**} & \multicolumn{1}{c}{**WFR**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{0.3, 0.5 or \(1\)} & 3.67 & 3.81 & MWFT4 & MWFR4 \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{0.3, 0.5 or \(1\)} & 8.67 & 7.22 & MWFT8 & MWFR8 \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{0.3, 0.5 or \(1\)} & 9.81 & 10.44 & MWFT10 & MWFR10 \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{0.3, 0.5 or \(1\)} & 15.81 & 14.98 & MWFT20 & MWFR20 \\ \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{0.3, 0.5 or \(1\)} & 19.70 & 20.57 & MWFT-WO & MWFR-WO \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mortar formulations utilizing different fiber proportions and fiber lengths.  
Figure 1: The visuals of waste fishing trawl (**left**) and waste fishing rope (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Properties**} & \multicolumn{1}{c}{**WFT**} & \multicolumn{1}{c}{**WFR**} \\ \hline Volume Mass (g/cm\({}^{3}\)) & 1.09 & 0.94 \\ \hline Tensile strength (MPa) & 203 & 148 \\ \hline Young’s modulus (GPa) & 3.20 & 1.89 \\ \hline Average length (mm) & 3.67, 8.67, 9.81, 15.81, 19.70 & 3.81, 7.22, 10.44, 14.98, 20.57 \\ \hline Diameter (mm) & 100–300 & 60–200 \\ \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical properties of WFT and WFR fibers.  
The sand used in the study is Granular Class 0/4, as per Norm NF P18-545, 2021. The sieve analysis curve for this sand is given in Figure 2.
## 5 Simulation Results
As shown in Figure 10, the average energy consumption in each round decreases with the increase of node density. Since the node number is fixed, the initial energy is fixed. Hence lower average energy consumption in each round means larger round cycles. This is due to the fact that we shrink the volume so that the average distances from CHs to sink node are shortened. From Figure 10, it can also be observed that compared with other two schemes, our algorithm consumes less average energy each round in the whole range of node density. To measure the delay of relevant schemes, we calculate the average hop numbers in the case of two different node densities. Average hop number is obtained by \(\sum\limits_{i=1}^{M}hop_{i}/M\), where \(hop_{i}\) measures the hop numbers from the \(i\)-th CH to the sink node. Obviously, since the total node number is fixed and the volume is decreased, average hop numbers, i.e., delay is lower with higher node density as shown in Figure 11. On the other hand, our algorithm always presents the lowest delay compared with relevant schemes. Figure 9: The comparison results in terms of remaining living nodes. Another performance we compare is the sensing quality which is measured by MSE between the original signal and reconstructed error. It can be observed from Figure 12 jointly with Figure 8 that our proposed algorithm tends to build a matrix \(\mathbf{\Phi}\) that leads to the smallest sensing error and meanwhile the energy consumed by our algorithm is the least in each round. Besides the previous experiments, we vary the measurements ratio \(M/N\) as well to observe the MSE with varying ratio. We test the average MSE for first 20 rounds. It can be observed that the MSE decreases with the increase of measurement ratio for each algorithm. Figure 13 validates that our algorithm presents significant advantage when the measurement ratio is less than 0.5. The reason is that we add the factor \(B_{r}\), which considers the reconstruction quality, into the fitness function while performing PSO for routing. Our proposed algorithm works well to reduce the MSE when the measurement ratio is low. While the measurement ratio goes up, the superiority of our proposed algorithm becomes less significant. Figure 11: The comparison results in terms of average hop numbers with two different node densities. Figure 10: The comparison results in terms of average energy consumption with different node densities.
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 1 Introduction

Table 1: A comparison of diabetes-based eye diseases methods. In the presented work, we present a deep learning (DL) approach that detects the bounding box (bboxes) of disease regions. Finally, instead of segmenting the whole image, we segment the localized regions. Experimentation proved that this approach is efficient to both complexity and time consumption. The proposed method is based on FRCNN and FKM clustering. Our proposed method deals with the inadequate sample problem using unsupervised pre-training and supervised fine-tuning regions for localization. FRCNN is a single-stage training technique that classifies region proposals and refines their localization. In the test phase, FRCNN produces some class-independent region proposals of images and gets a fixed-size feature descriptor form each proposal using a CNN; afterward, the softmax layer classifies the regions accurately. Finally, FKM clustering precisely extracts the regions from localized regions. The proposed method detects the abnormalities of DR, DME, and Glaucoma regions simultaneously using FRCNN. The contributions of our work are given below:  
* Early and automated detection of diabetes-based eye diseases regions using machine learning-based segmentation is a complex task. In the presented methodology, we used the FRCNN-based method for localization of disease regions. Our findings conclude that the combination of FRCNN with FKM clustering results in accurate localization of the affected areas, which ensure the precise recognition of the disease in an automated manner. * To accomplish the human-level performance over the challenging dataset i.e., ORIGA and MESSIDOR, the retinal images are represented by the FRCNN deep features, that are then segmented through the FKM clustering. * The proposed method can detect the signs of disease including early signs simultaneously and has no issue in learning to detect an image of a healthy eye. * The available datasets do not have bbox ground truths, so first, we developed the bbox annotations from given ground truths of the dataset which are necessary for the training of FRCNN. The rest of our paper is organized as follows: Section 2 briefly explains the proposed architecture i.e., localization and segmentation of localized regions. In Section 3, the experimental results and their discussion are presented to highlight the significance of findings. Section 4 presents the general discussion and the last section follows with the conclusions and future work suggestions.
###### Abstract  
Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR\({}_{705}\)), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60\({}^{\circ}\). Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR\({}_{705}\) were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.  
LAI; leaf inclination angle; vegetation indices; imaging spectroscopy; field crops 201820
## 2 Related Work
### Satellite-Based Edge Computing
Research into multi-satellite collaborative inference investigates how to harness the resources of multiple LEO satellites to execute deep-learning inference tasks in a distributed manner across multiple satellites, thereby improving inference performance. Our research is dedicated to filling this research gap.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
###### Abstract  
One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.  
Global Navigation Satellite System; error analysis; Urban Air Mobility; UAS Traffic Management +
Footnote †: This paper is an extension of conference paper “GNSS Performance Modelling for Positioning and Navigation in Urban Environments” published in 2018 5 th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), Rome, Italy, 20–22 June 2018.
## 2 Background
### NDN-IoT Architecture  
A high level of NDN-IoT architecture was devised in References [22; 23]. The NDN-IoT architecture consists of three layers: the things layer, the NDN layer, and the application layer. Figure 5 shows the NDN-IoT architecture. The things layer represents the multitude of devices in the IoT system. The NDN layer is the networking layer that hides the complexity and diversity of IoT applications from the users. The NDN layer in NDN-IoT architecture consists of two main components: management and the control plane and data plane. The control layer handles the operational functionalities of both interest packets and data packets, including security, naming, caching, and the strategies that adequately meet the IoT features.
## 4 Ionospheric Channels Analysis
Figure 15 presents the BER study performed in our link, specifically the fourth-order modulations with a transmitting power of 50 W. A clear improvement can be observed if we compare the characteristic waves individually with the combining of these techniques. The O and X (ordinary and extraordinary) modes had a 75% to 80% probability to achieve a BER lower than 10\({}^{-4}\) when using the 4 PSK and 4 QAM modulations, respectively. If we used selection combining (SC), this probability improved up to 96% and 85%, respectively. On the other hand, if we used equal-gain combining (EGC), the probabilities to receive a BER lower than 10\({}^{-4}\) improved to 82% for both modulations. The 4 FSK modulation was the modulation with the worst performance. For the O and X waves, the 4 FSK had a  
Figure 14: Received bit energy-to-noise power spectral density (E\({}_{\rm b}\)/N\({}_{0}\)) for the 4 QAM modulation at 50 W throughout the day. 55% to 57% of probability to achieve a BER lower than \(10^{-4}\), respectively. Using diversity combining, this probability improved up to 88% with SC and 59% with EGC.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 7 Data Reduction Techniques
### Lossless Data Reduction Algorithms
The Burrows-Wheeler Transform rearranges the input data to increase redundancy and make it more amenable to subsequent compression techniques. Run-length encoding is then applied to compress repeated sequences. Finally, Huffman coding assigns variable-length codes to different symbols based on their frequencies. Known for its high compression ratios, BZip2 is often used to compress large files or archives, providing a balance between compression speed and efficiency. The primary use cases and applications for Delta Encoding and BZip2 algorithms are listed in Table 5.
## 5 Experimental Results
### Performance Measures  
Six metrics were used to evaluate the performance of the tuned classifiers (i.e., accuracy, balanced accuracy (BA), F1-score, recall, precision, and time in seconds). The balanced accuracy and F1-score are used to report the accuracy of results for imbalanced datasets, as introduced in Equations (26) and (29), respectively. The average results of these metrics are reported in the following section. The whole optimizer had an extra metric (i.e., features) that reported the number of features selected by a specific optimizer.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{25}\]  
\[BalancedAccuracy(BA)=\frac{1}{2}\times\left[\frac{TP}{TP+FN}+\frac{TN}{FP+TN}\right] \tag{26}\]  
\[Recall=\frac{TP}{TP+FN} \tag{27}\]  
\[Precision=\frac{TP}{TP+FP} \tag{28}\]  
\[F1\text{-}Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{29}\]
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 5 Results
### Semantic Segmentation mIOU Results  
Promising mIOU values were exhibited by the proposed method on the three datasets, particularly with the application of the NLF technique. Figure 7 shows sample results from the three datasets with the predicted and filtered (NLF Seg) segmentation results. On the **PASCAL VOC2012** benchmark, DTS-Net and DTS-Net-Lite attained mIOU values of 91.1% and 83.8% on the validation set, respectively. On the **NYUV2** test benchmark, DTS-Net attained high accuracy with a pixel accuracy (Pix.acc.) of 80.72%, while DTS-Net-Lite attained a pixel accuracy (Pix.acc.) of 73.7%. On the **CITYSCAPES** validation benchmark, DTS-Net  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Extractor** & **Params (M)** & **MACs (G)** & **Pix.acc. \%** \\ \hline VGG16 & 138.3 M & 62.1 G & 42.1 \\ DenseNet121 & 8 M & 11.1 G & 51.3 \\ ResNet50 & 25.6 M & 15.4 G & 62.5 \\ MobileNetV2 & **3.5 M** & **1.2 G** & 66.7 \\ Xception & 22.4 M & 18.5 G & **79.4** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of VGG16, ResNet50, DenseNet121, MobileNetV2, and Xception as feature extractors for DTS-Net without filtration in terms of MACs, the number of parameters (Params), and Pix.acc. on the NYUV2 dataset.  
and DTS-Net-Lite attained mIOU values of 80.72% and 61.5%, respectively. These mIOU and Pix.acc. values were superior to those of all SOTA methods for semantic segmentation, even though the proposed architecture is far simpler than the SOTA-method architectures.  
The results in Figures 7 and 8 show the quality of the segmentation results, with fine and sharp edges. Table 2 lists the mIOU values for both DTS-Net and DTS-Net-Lite on the three benchmarks; these results highlight the unique performance of the proposed method. Note that the predicted segmentation masks contained many mispredicted pixel values that had neighboring labels to the true label. Most of those pixels could be corrected easily by the NLF technique. The main reason for the high mIOU and Pix.acc. values obtained by our method is the NLF algorithm which improves the mIOU by 63.4% on PASCAL VOC2012, improves the mIOU by 28.8% on NYUV2, and improves the mIOU by 33.0% on CITYSCAPES. The pixel accuracy (Pix acc.) values also improved when applying NLF but not with a big margin due to the nature of the pixel accuracy metric that reports the number of correctly classified pixels regardless of the ratio of the overlap between the predicted and the ground truth masks which is used in the mIOU metric. Those high improvements in the mIOU and Pix acc. prove the important role of the NLF algorithm in getting more accurate segmentation results than those without NLF.  
Figure 7: Sample results of predicted and NLF-filtered semantic segmentation (NLF Seg) results that were obtained using DTS-Net on PASCAL VOC2012 (rows 1-3), NYUV2 (rows 4–6), and CITYSCAPES (rows 7–9), with input images and ground-truth segmentation masks (Seg).
## 8 Potential of Quasicrystals as Thermoelectric Materials
Therefore, the high-temperature ZT value could be improved if the pseudogap width was widened in order to reduce the unfavorable contribution related to bipolar transport effects. Quite remarkably; however, although it has been reported that the increase of the thermal conductivity of QCs with temperature reduces their potential as competitive TEMs at both moderate and high temperature regimes, this unusual feature makes them attractive for the design of thermal rectifiers [99]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **\(\sigma\) (\(\Omega\)cm)\({}^{-1}\)** & **S (\(\mu\)VK\({}^{-1}\))** & **\(\kappa\) (Wm\({}^{-1}\)K\({}^{-1}\))** & **ZT** & **Reference** \\ \hline Zn\({}_{57}\)Mg\({}_{34}\)Er\({}_{9}\) & 6170 & +7 & 4.5 & 0.002 & [93] \\ Al\({}_{65}\)Cu\({}_{20}\)Ru\({}_{15}\) & 250 & +27 & 1.8 \({}^{2}\) & 0.003 & [76] \\ Cd\({}_{84}\)Yb\({}_{16}\) & 5560 & +14 & 9.4 & 0.004 & [94] \\ Ag\({}_{42.5}\)In\({}_{42.5}\)Yb\({}_{15}\) & 5140 & +12 & 4.8 & 0.005 & [95] \\ Al\({}_{62.5}\)Cu\({}_{24.5}\)Fe\({}_{13}\) & 310 & +44 & 1.8 \({}^{1}\) & 0.010 & [76] \\ Al\({}_{64}\)Cu\({}_{20}\)Ru\({}_{15}\)Si\({}_{1}\) & 390 & +50 & 1.8 \({}^{2}\) & 0.020 & [76] \\ Al\({}_{71}\)Pd\({}_{20}\)Re\({}_{9}\) & 450 & +80 & 1.3 & 0.070 & [91] \\ Al\({}_{71}\)Pd\({}_{20}\)Mn\({}_{9}\) & 714 & +90 & 1.5 & 0.120 & [91] \\ \hline \hline \end{tabular} \({}^{1}\) From reference [96]. \({}^{2}\) Estimated upper limit.  
\end{table} 
Table 5: Room temperature values of the electrical conductivity (\(\sigma\)), Seebeck (S), thermal conductivity (\(\kappa\)) transport coefficients and figure of merit (ZT) for i-QCs belonging to different alloy systems.