esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## Materials and Methods
### Net Ecosystem \(CO_{2}\) Exchange Using Eddy Covariance  
Net ecosystem \(CO_{2}\) exchange (NEE; \(\mu\)mol \(CO_{2}\) m\({}^{-2}\) s\({}^{-1}\)/g \(C\) m\({}^{-2}\) s\({}^{-1}\)) was measured at all sites with open-path eddy covariance (EC) techniques, measuring at 10 Hz [42] using LI-COR CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzers (Li-7500, LI-COR, Lincoln, NE, USA) accompanied by three-dimensional ultrasonic anemometers (CSAT-3, Campbell Scientific Instruments, Logan, UT, USA). Each EC measurement system was installed approximately 4 m above mean canopy height [42]. Tower heights were 34.4, 37.5 and 34.9 m, for the mesic, intermediate, and xeric sites, respectively, resulting in a ~600 m upwind fetch of the EC systems at all sites [41].  
Micrometeorological data, including photosynthetically active radiation (PAR; LI-190, LI-COR Inc., Lincoln, NE, USA), global radiation (LI-200 SZ, LI-COR Inc., Lincoln, NE, USA), incident and outgoing shortwave and longwave radiation to calculate Rn (NR01, Hukseflux Thermal Sensors, Delft, The Netherlands), precipitation (TE525 Tipping Bucket Rain Gauge, Texas Electronics, Dallas, TX, USA), and air temperature (\(T_{a}\)) and relative humidity (HMP45C, Campbell Scientific Instruments, Logan, UT, USA) were collected above the canopy and stored on CR-5000 dataloggers (Campbell Scientific Instruments, Logan, UT, USA).
# Refined Ptychographic Reconstruction of Attosecond Pulses
## 5 Conclusions  
In conclusion, we showed that the CMA in FROG-CRAB reconstructions based on a ptychographic algorithm mainly affects the reconstructed IR femtosecond pulse. The lower the photoelectron kinetic energies (or XUV photon energies), the stronger the effect on both reconstructed IR and attosecond pulses. The error introduced by the CMA can be avoided if more advanced reconstruction algorithms like VTGPA are used. Nevertheless, this approach is very demanding from the point of view of computational costs and seems to suffer from numerical artefacts when the ionization potential of the target lies within the XUV spectrum. In this work, we show that an opportune combination of the two algorithms, called rePIE, can maintain their individual strengths, giving an accurate and fast reconstruction of both IR and XUV pulse. We found that this approach is particularly useful for short attosecond pulses, characterized by a broad spectrum in the VUV-XUV region.  
[PERSON] wrote the rePIE code, performed the simulations and reconstructions. All the authors participated the interpretation of the results and wrote the paper.  
This research received no external funding.  
The authors wish to thank [PERSON] and [PERSON] for their help in implementing the VTGPA code.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} XUV & extreme-ultraviolet \\ VUV & vacuum-ultraviolet \\ IR & infrared \\ CMA & central momentum approximation \\ FROG-CRAB & Frequency Resolved Optical Gating for the Complete Reconstruction of Attosecond Bursts \\ ePIE & extended Ptychograpic Iterative Engine \\ VTGPA & Volkov Transform Generalized Projection Algorithm \\ rePIE & refined extended Ptychograpic Iterative Engine \\ \end{tabular}  
Figure 5: Attosecond pulses with spectrum centred around the 15 th harmonic. (**a**) Temporal amplitude profile of the XUV pulse. (**b,c**) Associated spectra amplitude and phase. (**d**) Reconstructed IR femtosecond pulse. (**e**) Residual error between the simulated and reconstructed IR pulse. (**f**) Reconstruction error of the last 100 iterations for VTGPA and rePIE algorithms as a function of iteration number. In all the figures, orange full dots represent the ePIE results, green squared marks show the VTGPA results while the rePIE output is represented by the blue open circles.
## 3 Materials and Methods
### Anomaly Detection
#### 3.4.1 Data Processing  
To address the challenge posed by the small dimensions of defects found in the dataset, which could potentially disappear when resizing images, a strategy of dividing the images into patches was employed. In this procedure, different sizes were explored, namely by diving images in patches of 256 \(\times\) 256 and 512 \(\times\) 512 pixels. The overlap between patches was also set to 50% and to 10%. Nevertheless, we verified that when patches of 256 \(\times\) 256 or an overlap of 50% were considered, the number of patches without defects was substantially higher, so it was decided to proceed with patches of 512 \(\times\) 512 pixels, using an overlap of 10%.  
Furthermore, to train the anomaly detection framework, only patches without defects (belonging to batches 1, 2, 3, and 4 of images) have been selected in order to teach the models the different variations that a normal sample may exhibit. Therefore, 70% of the patches without defects from set 1 were used to train the algorithm and the remaining 30%, together with all the defects (from 13 different classes) contained in the dataset, were used  
Figure 5: Examples of zones annotated by specialists. The most saturated purple color to the less saturated regions correspond, respectively, to the center top zone, lateral top zone, and border. Similar for the bottom part of the bowl, the annotated zones are the center bottom zone, foot-ring, and lateral bottom zone, respectively, from the center of the bowl to the outside part of the bowl. (**a**) Cereal bowl front zones annotated; (**b**) Cereal bowl back zones annotated.  
to test the framework, forming test set 1. The evaluation comprised yet another test set (test set 2) where a completely new set of images collected in a different time frame (batches 5 and 6) was introduced in order to evaluate the robustness of the developed anomaly detector. Test set 2 then includes all patches from test set 1 and the new dataset that is composed of patches without defects (normal samples) and with defects belonging to 15 different classes.
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 5 Conclusions  
This study proposes a new method for joint multi-pairing and resource allocation in a single cell D2D scenario. Our method is based on fuzzy pairing criteria for selecting the best transmitter in a D2D receiver search radius. The set of transmitters is considered as a fuzzy set with respect to the data rates and battery levels of those transmitters. This fuzzy membership function can be seen as a stability index. The proposed method is compared with three other pairing methods: MSP, CP, and RP methods. The three parameters of stability, fairness, and sum rate in different modes, such as changing the D2D search radius and increasing the number of D2D transmitters and receivers for all four methods, are investigated. As a result, the fairness and stability of the fuzzy method work better than the other methods in different modes, and the FP method sum rate follows the MSP method sum rate.  
Investigation, A.P. and S.R.; Methodology, A.P., S.R., M.S. and M.F.; Supervision, A.P. and S.R.; Writing original draft, A.P. and S.R.; Writing review and editing, M.S. and M.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Hospital equipped with a decentralized network.
## Materials and Methods
### FTIR Analyses  
Determination of the selected chemical parameters (alcohol content, total acids, tartaric, malic, lactic acids glucose and fructose) in wine samples was performed using the ALPHA Bruker Optik GMBH analyser. Fourier transform infrared spectroscopy (FT-IR) with the attenuated total reflect (ATR) measurement procedure was used. The method allows simultaneous analysis of different parameters within one measurement. The analyser was calibrated for wine with use of calibration data from wine measurements, what was performed by technical support of OK Servis BioPro, Ltd. Company. The calibration data consisted of over 2000 red and white wines. The calibration data containing measurement of calibration spectra and the calibration settings according to the reference values were provided by the accredited (DAkkS) Institute Heidger ([PERSON], Germany). The root mean square error of prediction (RMSEP) has been determined. The ranges of calibration curves and RMSEP (in the brackets) were as follows: alcohol 0.12-20.48% (0.27); fructose 0.1-111.5 g L\({}^{-1}\) (0.71); glucose 0.2-125.6 g L\({}^{-1}\) (0.87); total acids 2.9-13.5 g L\({}^{-1}\) (0.31); malic acid 0.1-4.7 g L\({}^{-1}\) (0.40); tartaric acid 0.5-5.4 g L\({}^{-1}\) (0.43); lactic acid 0.0-4.3 g L\({}^{-1}\) (0.31), for Fourier-transform infrared (FTIR) spectroscopy. Before starting the measurement, the flow-through cell of the ALPHA analyser was rinsed with deionized water and a blank (deionized water) was measured. Approximately 20 mL of wine sample was injected into the flow-through cell, where the sample was heated to 40 \({}^{\circ}\)C and then measured. The spectrum was scanned in the wavenumber range between 4000 and 400 cm\({}^{-1}\). The instrument evaluated the sample within 70-100 s and the results for all five parameters were recorded. Precision of the method was checked by analysing the standard wine for analysis (Bruker Optic GmbH, Ettlingen, Germany) with reference values of individual parameters that were tested by reference methods (high performance liquid chromatography, pH electrode, titration, density meter).
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 4 Discussion  
### Comparison of Different Operators  
For edge detection operators for different images, there are differences in edge detection. It is the premise of accurate shoulder line extraction to select an operator that is suitable for the study sample area from among many operators. Therefore, we chose sample area a to discuss the effects of common operators on the shoulder line extraction results, and the experimental results are shown in Figure 14. In this study, the line-related parameters of the shoulder line were used to further compare the detection results of each edge detection operator. These parameters were mainly used to describe the fineness of the line segment extracted by the operator. As shown in Table 4, the overall effect of the line segment extracted by the Canny operator was better than the other operators. We found that the Prewitt operator and the Sobel operator both performed differential and filtering operations on the image and only had some differences in the selection of weights for smoothing. However, the image was blurred to a certain extent, and some edges could not be detected. Therefore, the detection accuracy was relatively low, and this type of operator was deemed more suitable for situations where the gray value of an image edge is relatively obvious. The detection accuracy of the Roberts operator was relatively high,  
Figure 13: Results of improved shoulder line: **(a–d)** represent 4 sample areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Area** & **Method** & **CPA** & **PA** & **Dice** & **IOU** \\ \hline \multirow{4}{*}{a} & Edge detection & 0.814 & 0.791 & 0.818 & 0.734 \\  & Regional growing algorithm & 0.829 & 0.807 & 0.820 & 0.813 \\  & Method in this study & 0.885 & 0.864 & 0.877 & 0.907 \\  & Edge detection & 0.837 & 0  
but it was easy to lose part of the edge, which made the detection results incomplete. At the same time, the image was not smoothed, and the noise could not be suppressed; thus, this operator had the best response to images with steep, low noise. The Laplace operator smoothed the image through the Gaussian function and had a relatively obvious effect on noise suppression. However, the original edge could also be smoothed during processing, resulting in some edges that could not be detected. In addition, the noise had a great influence on it; the detected image details were very rich, but at the same time, false edges could appear. If the false edges were reduced, the detection accuracy could also be reduced; many true edges could be lost, and different parameters should be selected for different images. The Canny operator was more accurate than the Laplace operator in detecting edges. Although some edge information could be lost, this operator had the best effect among the above-mentioned edge detection operators and could identify small edges more clearly.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 2 Related Work
### Main Contributions of the Paper  
Since WSN networks are highly vulnerable, due to the wireless communication channel and the extremely low resources, their security aspects represent an important field of research. Many studies analyze the behavior of different MAC protocols in the context of security. In our previous contribution [12], we have proposed a comparison between two different MAC protocols belonging to different categories, scheduling based (TDMA) or contention based (CSMA): Lightweight MAC (LMAC) [27; 28] and Berkeley MAC (BMAC) [29; 30] respectively. We have provided some details on both protocol operations showing the performance evaluation and results on the basis of simulation performed in a well-know software simulator: OMNeT++. Throughout the tests, we have proved the greater efficiency of LMAC protocol in respect of BMAC in terms of received packets and energy consumption. Starting from this contribution [12] we continue the study of MAC protocols also regarding security aspects.  
Unfortunately, traditional safety mechanisms are not usable for sensors, as they introduce high overload. Several researchers have proposed various optimized security schemes that respect, in fact, the constraints in terms of resources and memory imposed by the WSNs. So, they need light encryption to achieve a high level of security.  
In this paper we present encryption approaches based on key distribution using advanced encryption standard (AES), invest shamir adleman (RSA) [31] and elliptic curve cryptography (ECC) [32] techniques. Then, we show a comparison between these three approaches in a WSN using BMAC and LMAC protocols considering two different types of attacks: energy drain and impersonation.
## 1 Introduction
In this paper, a novel scheme based on a health indicator (HI) and hybrid deep neural network (DNN) model is proposed to predict equipment RUL. Firstly, redundant features are removed by preprocessing. Secondly, true RUL of the equipment is substituted with a piece-wise function and preselected data are transformed into an HI by polynomial regression. Thirdly, the spatial-temporal features are sequentially extracted by a convolutional neural network and multilayer LSTM from the preselected data and the HI. Furthermore, RUL is predicted by a multilayer fully connected neural network. Finally, the effectiveness and accuracy of the proposed scheme are validated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset [12; 44] of equipment degradation. The main contributions are summarized as follows:  
(1) We firstly utilize variance threshold detection methods to select valid features from the raw sensor data in the field of RUL prognostics. (2) We expertly design an input strategy for the coordination of preselected sensor data and HI, which depicts equipment degradation. (3) We propose a novel scheme based on a HI and hybrid DNN to efficaciously predict equipment RUL collected in Turbofan Engine Degradation Simulation. (4) We achieve higher performance in accuracy compared with the four existing models in various metrics. The remainder of the paper is organized as follows. Section 2 defines the prognostics problem. In Section 3, we introduce the methodology, including the health indicator, hybrid deep neural networks, data preprocessing methods, and evaluation metrics. In Section 4, we describe the implementation of the proposed scheme in detail. Then in Section 5, we describe the dataset and present the test results thoroughly by conducting groups of experiments. The last section concludes this paper.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design  
Figure 3 illustrates the design of the proposed protocol. The proposed protocol establishes a secure communication mechanism between server and client nodes by integrating multiple cryptographic techniques. In the proposed protocol, we have used ECDSA for authentication instead of traditional methods like RSA. The primary reasons for this choice are efficiency and security with smaller key sizes: RSA requires significantly larger key sizes. For example, a 2048-bit RSA key provides equivalent security to a 256-bit ECDSA key. In contrast, ECDSA offers strong security with shorter key lengths, which reduces processing time and memory usage while maintaining robust protection against attacks. The steps of our proposed protocol are outlined as follows:  
Figure 3: Proposed server–client secure communication protocol.
# Bayesian Binary Search
## 2 Methodology
### Case 2: Observation \(x>p_{k}\)  
The likelihood function for this observation is  
\[P(\mathcal{O}_{k}=\{x>p_{k}\}|x)=\begin{cases}0&\text{if }x\leq p_{k}\\ 1&\text{if }x>p_{k}\end{cases}\]  
The posterior PDF after this observation is  
\[P(x|\mathcal{O}_{k}=\{x>p_{k}\},\mathcal{D}_{k})\propto P(\mathcal{O}_{k}=\{x >p_{k}\}|x)P(x|\mathcal{D}_{k})\]  
This implies that the posterior PDF is non-zero only for \(p_{k}<x\leq p_{k}\):  
\[P(x|x>p_{k},\mathcal{D}_{k})=\begin{cases}\frac{P(x|\mathcal{D}_{k})}{\int_{a _{k}}^{p_{k}}P(t|\mathcal{D}_{k})dt}&\text{if }p_{k}<x\leq b_{k}\\ 0&\text{otherwise}\end{cases}\]  
Since \(\int_{a_{k}}^{p_{k}}P(t|\mathcal{D}_{k})dt=\frac{1}{2}\) and \(\int_{a_{k}}^{b_{k}}P(t|\mathcal{D}_{k})dt=1\), it follows that \(\int_{p_{k}}^{b_{k}}P(t|\mathcal{D}_{k})dt=1-\frac{1}{2}=\frac{1}{2}\). Thus, the posterior PDF is now concentrated in the interval \([p_{k},b_{k}]\), which also contained exactly half of the total probability mass before the observation.  
In Bayesian Binary Search, the probe location \(p_{k}\) is strategically chosen at each step to bisect the probability mass of the current posterior distribution. Regardless of whether the target is found to be less than or greater than \(p_{k}\), the subsequent posterior distribution is concentrated over an interval that contained exactly half of the probability mass of the previous distribution. This demonstrates that the posterior update step in BBS, even with a non-uniform prior, is mathematically equivalent to a bisection in the probability density space, where the \"bisection\" refers to the halving of the probability mass rather than necessarily the physical midpoint of the search interval.
## 4 Experiments and Discussion
### Datasets  
#### The ISPRS Vaihingen dataset  
is provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and includes multi-category semantic segmentation satellite images of the German town of Vaihingen. It provides 33 images with various sizes ranging from 1996 \(\times\) 1995 pixels to 3816 \(\times\) 2550 pixels, and the ground sampling distance (GSD) is 9 cm.  
The Vaihingen dataset has six categories marked with different colors, and these six categories and their corresponding colors are impervious surfaces (white), buildings (blue), low vegetation (light blue), trees (green), cars (yellow), and background (red). Near-infrared (IR), red (R), and green (G) channels, together with the digital surface model (DSM), are provided in the dataset. In our experiments, we followed the dataset split  
Figure 7: Illustration of the architecture of the 16-piece local channel spatial attention module.  
setting. Sixteen images were used in the training/validation phase and seventeen images in the testing. We cropped them to a size of \(512\times 512\) pixels. After cutting, the total number of training and validation samples was 2550, and the number of test samples was 2400.  
**The ISPRS Potsdam dataset** is also provided by the ISPRS and has multi-category semantic segmentation satellite images of the German town of Potsdam. It provides 38 images of \(6000\times 6000\) pixels and the GSD is 5 cm. The Postdam dataset provides six categories marked with different colors, and these six categories and their corresponding colors are impervious surfaces (white), buildings (blue), low vegetation (light blue), trees (green), cars (yellow), and background (red). IR, R, and G channels, together with the DSM, are provided in the dataset. In the experiments, we also followed the dataset split setting. Twenty-four images were used in training/validation and fourteen images in testing. We also cropped them to a size of \(512\times 512\) pixels. After cropping, the total number of training and validation samples was 3042, and the number of test samples was 1989.  
The reason for choosing the ISPRS Vaihingen and Potsdam datasets for our experiments was that they are more challenging because their use of urban telemetry images can increase the difficulty of the segmentation task. This challenging characteristic make them suitable for the research. However, the bias in these two datasets is that the background area is relatively large compared to other categories.  
The ISPRS Vaihingen and Potsdam datasets are both available online at [[https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx](https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx)]([https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx](https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx)) (accessed on 4 June 2023).