esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Satellite-Based Evapotranspiration
## 1 Introduction
The resulting \(\mathrm{ET_{a}}\) retrievals can be directly validated using site-level in situ measurements of evapotranspiration from a range of field methods (e.g., Bowen ratio system, eddy covariance, scintillometry, etc.) to provide an accurate operational monitoring at larger regional scales for optimal water crop irrigation [31; 32]  
SEB models could provide an efficient approach to estimate and monitor water consumption of pastures in southern Chile to face the prospects of increasing water scarcity and uncertain sustainability of current pasture production systems. Here, we evaluate the performance of four SEB models (SEBS, SEBAL, METRIC and SSEBop) to estimate regional \(\mathrm{ET_{a}}\) from remote sensing observations. Their performance is compared to that of the readily available Global Land Evaporation Amsterdam Model (GLEAM 3.2b; [33; 34] and Penman-Monteith-Leuning (PML_V2; [35; 36; 37] satellite-based global products. The structure of the paper is detailed as follows: Section 2 presents the data used and the study area, Section 3 describes the methodology, Section 4 presents the results and analysis, Section 5 presents the discussion and Conclusions.
## 2 Related Works
### Threats in Satellite Networks  
Traditional satellite communication systems (SCSs) are vulnerable to security threats and highly susceptible to various attack vectors [28]. [PERSON] et al. [3] critically evaluated the characteristics, vulnerabilities, and security and reliability issues of LEO communication systems, discussing potential security attacks and reliability risks while proposing design guidelines and solutions. [PERSON] et al. [5] conducted a comprehensive study on the security threats, solutions, and challenges of SCSs, categorizing the existing literature into two main areas--physical-layer security and cryptographic schemes--and identifying specific research domains within each category. [PERSON] et al. [1] focused on the critical security vulnerabilities that have emerged alongside the rapid advancement of SCSs. This study criticized the limitations of previous research and emphasized the need for a systematic classification of attacks and defenses based on confidentiality, integrity, and availability (CIA) through a comprehensive investigation of the SCS security landscape. [PERSON] et al. [12] examined the security risks associated with the growing reliance on commercial hardware and software. [PERSON] et al. [29] analyzed the cybersecurity vulnerabilities of end-of-life (EOL) satellites. That study highlighted the increased risk of cyberattacks during periods when operators lower their security vigilance after mission completion and discussed how inactive satellites negatively impact space sustainability. [PERSON] et al. [30] investigated the susceptibility of SCSs to distributed denial-of-service (DDoS) attacks.  
Given the crucial role of SCSs in global connectivity, that study assessed their potential vulnerabilities to cyberthreats and suggested strategies for mitigating these risks.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 5 Frameworks for TE
### Classification Based on Optimization Techniques
#### 5.3.1 Game Theory  
Energy trading algorithms are becoming increasingly important in the development of the smart grid due to the need to meet energy demand considering the intermittent supply of DER. Recently, game theory has emerged as an analytical tool for smart MG energy trading. This is because it provides an analytical and conceptual framework with a set of mathematical tools to analyse optimization problems with several objective functions [83]. The study in [84] provides a comprehensive overview, discussion and future applications of game theory in smart grid (SG) and SMGs. While [21; 27; 47; 85] also proposed a game theory approach to energy trading and sharing, in particular, [PERSON]. [22; 86; 87] used a game theory approach for cost optimization in DERs. Figure 4 shows the game theory approach and some optimization solution techniques used in the literature for energy trading and sharing.  
There are two basic types of game theory: cooperative and non-cooperative games [84]. Players in non-cooperative game theory make decisions independently by using several frameworks to optimize and devise pricing strategies that adapt to the nature of their requirements, while players in cooperative game collaborate to achieve a common goal. Some form of incentive is provided to aid participation in the game. Based on an energy pricing model, the grid optimization problem was formulated in [21] in terms of cooperative and non-cooperative games. This also involved iterative and distributed algorithms to optimize the energy production and storage capabilities of users to reduce their monetary expenses.  
From the literature studied, classical game theory can be represented as either a non-cooperative (strategy) or cooperative (coalition) game to find a solution of equilibrium or incentive (Nash equilibrium, Shapley value). Then, the solution can be analysed for efficiency or uniqueness. Finally, a solution algorithm (best response dynamic) can be proposed to solve the initial problem (Figure 4).  
Another important aspect of game theory is auction theory. Auction theory is an analytical framework to study the interaction between a number of sellers and buyers to optimize their objectives. Outcomes of the auction theory model are the prices at which a trade takes place and goods are exchanged with each buyer. A generic auction mechanism is presented by [89] for energy trading in local markets. The works in [29; 31; 85] presented a double auction mechanism for energy trading  
Figure 4: Taxonomy of game theory and solution concepts for the transaction-based energy (TE) model (adapted from [88]).  
amongst distributed energy storage units. More specifically, [31] formulated a double auction mechanism for energy trading among EV and the main grid.
# Demosaicing by Differentiable Deep Restoration
## 2 Related Work
### Joint Optimization of CFA and Demosaicing  
Recently, with the help of end-to-end learning of deep neural networks, pioneering works with joint optimization of CFA and demosaicing have been proposed, where the CFA is learnable parameters jointly optimized with the demosaicing network. [PERSON] et al. [2] proposed the first joint optimization framework, in which the design of CFA was modeled as a color channel selection at each pixel from the predefined color set. In [3], the jointly learned CFA pattern was regressed directly which is able to run throughout the whole color space. While CFA is jointly optimized with demosaicing network in these two methods, the demosaicing network only takes the raw data as input to directly regress the color image. In contrast, our method is a combination of machine learning and physically based optimization, that the demosaicing network not only takes the raw image as input, but takes the degradation model and the adopted CFA into account as well. The prediction is constrained to satisfy the degradation model by a closed-form solver, which is differentiable that our method can also follow the joint optimization paradigm.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 2 Overview of Existing KF RAIM
### Protection Levels (PLs)  
The SKF estimates error in the a priori user position and velocity in the Earth Centered Earth Fixed (ECEF) frame, and the receiver clock offsets and drifts for GPS and NavIC from respective truths. Furthermore, the time correlated error of each carrier-smoothed pseudorange measurement is modeled as an FOGM process but not estimated by the SKF. First, mean position error bounds are discussed, and then PL equations are provided.
###### Abstract  
In order to meet the increasing capacity requirements, network operators are extending their optical infrastructure closer to the end-user while making more efficient use of the resources. In this context, long reach passive optical networks (LR-PONs) are attracting increasing attention.Coherent LR-PONs based on high speed digital signal processors represent a high potential alternative because, alongside with the inherent mixing gain and the possibility of amplitude and phase diversity formats, they pave the way to compensate linear impairments in a more efficient way than in traditional direct detection systems. The performance of coherent LR-PONs is then limited by the combined effect of noise and nonlinear distortion. The noise is particularly critical in single channel systems where, in addition to the the elevated fibre loss, the splitting losses should be considered. In such systems, Kerr induced self-phase modulation emerges as the main limitation to the maximum capacity. In this work, we propose a novel clustering algorithm, denominated histogram based clustering (HBC), that employs the spatial density of the points of a 2D histogram to identify the borders of high density areas to classify nonlinearly distorted noisy constellations. Simulation results reveal that for a 100 km long LR-PON with a 1:64 splitting ratio, at optimum power levels, HBC presents a Q-factor 0.57 dB higher than maximum likelihood and 0.21 dB higher than k-means. In terms of nonlinear tolerance, at a BER of 2\(\times\)10\({}^{-3}\), our method achieves a gain of \(\sim\)2.5 dB and \(\sim\)1.25 dB over maximum likelihood and k-means, respectively. Numerical results also show that the proposed method can operate over blocks as small as 2500 symbols.  
p 2019 232019 2019 202019 202019 Article Histogram Based Clustering for Nonlinear Compensation in Long Reach Coherent Passive Optical Networks
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 5 Algorithm Verification and Simulation Results
### Detection and Exclusion for a Multiple Outlier Mode  
Outliers were inserted into the pseudo-ranges and the success rates of the outlier test and 3D positioning errors after outlier exclusion were recorded. Based on the comparison of the pseudo-ranges excluded and the pseudo-ranges that contained simulated outliers, the estimated parameters were categorized as shown in Table 1. The scenarios were:  
1. None of the outliers were excluded and some normal pseudo-ranges were excluded;
2. Some of the outliers were excluded;
3. Some of the outliers were excluded and some normal pseudo-ranges were also excluded;
4. All of the outliers were excluded and some normal pseudo-ranges were also excluded; and
5. All of the outliers were excluded and none of the normal pseudo-ranges were excluded.  
For Case 3, a bias of 10 m was randomly added into one to four outlier combinations at each epoch during simulation. The detection results of the four algorithms are shown in Figure 12 and the statistical results are provided in Table 5.  
Figure 11: 3D positioning errors of the four detectors for the worst combination in Case 2: (**a**) detector 1, (**b**) detector 2, (**c**) detector 3 and (**d**) detector 4.  
The root mean squared error (RMSE) of the four algorithms are shown in Figure 13. The results of detector 4 were similar to those of detector 3 especially when the number of outliers was less than four.  
As the number of outliers increased, the amounts of categories (b) and (c) increased while categories (d) and (e) of the correct exclusion decreased for all detectors and especially for detector 1. For detector 2, the probability of category (d) decreased rapidly with the increase in outliers so that many normal observations were excluded although outliers were excluded. However, for detectors 3 and 4, the probability of categories (d) and (e) were still over 90% although the probability of category (e) also decreased with the increase in outliers.  
Category (d) could be also an acceptable result when there were enough available pseudo-ranges compared with the misidentification of any outlier.  
Figure 12: Identification result of four detectors in Case 3: (**a**) one, (**b**) two, (**c**) three and (**d**) four outliers.  
Figure 13: Root mean squared error (RMSE) of four detectors in Case 3.
## 4 Evaluation
### Comparison to Standard SDN  
The previous section analysed the benefits of the elements that comprise the devised MAB agent for end-to-end path delay minimisation, avoiding the negative impact of congested network devices in the quality of top system applications or services. This section compares the benefits of using an MAB agent for this purpose compared to a standard SDN solution without the support of any intelligent agent.  
In a standard SDN solution, all links need to be periodically probed to determine their current delay. Without this periodic and global probing process, the SDN controller is unable to determine the optimal path for packet exchange. The periodic and global nature of the traffic imposed by these probing messages results in a considerable overhead on the network. Figure 3 presents the number of messages that need to be exchanged by a classic SDN-based solution, when the network is composed of four, seven, and ten switches, at both network and control levels. Analyzing these results, it is clear that the higher the number of switches, the higher the number of probing messages that flow through the network.  
The MAB-based solution for end-to-end path delay minimisation proposed in this article aims to reduce the number of probing messages by properly handling the exploration-exploitation trade-off. Figure 3 shows that the number of messages exchanged at both the network and control levels of the MAB agent is systematically lower than the number of messages exchanged by a standard SDN solution. Interestingly, the growth rate in the number of messages, as a function of the number of switches, is lower with the MAB-based solution than with the standard SDN solution. Hence, the MAB-based solution offers higher scalability than the standard SDN solution as the network increases in size.
# Polarimetry for Photonic Integrated Circuits
## 2 Measurement Methodology
### Device Measurement  
With the setup calibrated, it can be used to characterize actual PICs in transmission experiments. The goal of this section is to show the path towards the measurement of the Muller matrix of a given DUT as well as the interpretation of the results.  
With the knowledge of the entire setup polarization behavior, the SOP exiting a DUT when measuring \(\overset{\rightarrow}{s}_{\mathit{meas}}\) at the PM is  
\[\overset{\rightarrow}{s}_{\mathit{out}}=\alpha_{\mathit{FZF}}^{-1}\underline {M}_{\mathit{fiber},\mathit{out}}^{-1}\overset{\rightarrow}{s}_{\mathit{meas}} \tag{6}\]  
The SOP incident to the DUT \(\overset{\rightarrow}{s}_{\mathit{in}}\) can be set arbitrarily via the PC angles (\(\theta_{LP}\), \(\theta_{\lambda/4}\), \(\theta_{\lambda/2}\)). To achieve this, we numerically solve Equations (2) and (3) for (\(\theta_{LP}\), \(\theta_{\lambda/4}\), \(\theta_{\lambda/2}\)) to yield the desired \(\overset{\rightarrow}{s}_{\mathit{in}}\).  
With arbitrary control over \(\overset{\rightarrow}{s}_{\mathit{in}}\) and complete knowledge of the corresponding \(\overset{\rightarrow}{s}_{\mathit{out}}\), the DUT is characterized in terms of its Muller matrix via  
\[\overset{\rightarrow}{s}_{\mathit{out}}=\underline{M}_{\mathit{PIC}}\overset{ \rightarrow}{s}_{\mathit{in}} \tag{7}\]  
To obtain \(\underline{M}_{\mathit{PIC}}\), we follow the solution to the polarimetric measurement equation (PME) as in [11]. We use a special case of the PME here (see Equations (A7) and (A8) in Appendix B) that directly gives the elements of the Muller matrix. We call \(\overset{\rightarrow}{s}_{\mathit{out},\,0}\), \(\overset{\rightarrow}{s}_{\mathit{out},\,1}\), \(\overset{\rightarrow}{s}_{\mathit{out},\,2}\), and \(\overset{\rightarrow}{s}_{\mathit{out},\,3}\) the SOPs exiting the DUT when setting \(\overset{\rightarrow}{s}_{\mathit{in}}\) to TE-, +45\({}^{\circ}\) linearly-, right-hand circularly, and TM, respectively. Then it can be shown that the elements \(m_{i,j}\) of \(\underline{M}_{\mathit{DUT}}\) are:  
\[\left(\begin{array}{c}m_{0,0}\\ m_{1,0}\\ m_{2,0}\\ m_{3,0}\\ m_{0,1}\\ m_{1,1}\\ m_{2,1}\\ m_{3,1}\\ m_{0,2}\\ m_{1,2}\\ m_{2,2
# Blockchain-Based Distributed Patient-Centric Image Management System
## 4 Overview of the PCIM System
### IPFS Network  
IPFS is used to store encrypted medical images that contain the encrypted patient information in an open distributed storage system, in which images can be exchanged using a hash string path. The paths work similarly to the traditional uniform resource locator used in the web. Therefore, all patient images are always accessible through their hash.  
The radiologist uploads medical images of the patient to the system and uses a patient public key to encrypt the images: thereafter, only the patient can decrypt them. Medical image contents are signed by Ethereum private keys of the patient and then, are stored in the blockchain. Therefore, other entities can check the authenticity and integrity of the image ownership using the content hash and digital signature in the blockchain. In IPFS, files can be accessed even if the host node is offline, as they are within the healthcare consortium. Thus, at least more than one node in the network cached the medical images and can provide access to medical images, even if the host node is offline. SC enforces access control only to the on-chain content stored on blockchain. It controls access to the medical image in the IPFS network in terms of image file attributes, that help in tracking all the on-chain activities of participants. Moreover, The PCIM system protects the off-chain patient data using the security and privacy feature. Therefore, combining IPFS and the blockchain allows building a permanently addressable on-chain and off-chain data storage that can be linked securely to other significant systems or databases in the world, thereby, forming a global healthcare network.
## 2 Materials and Methods
### Analyses of the Chemical Composition
#### 2.4.7 Sds-Page  
The sample was mixed with a 4X electrophoresis sample buffer (0.125 M Tris-HCl; pH 6.8; 20% \(v/v\) glycerol, 4% SDS, 0.2 M dithiothreitol, 0.01% bromophenol blue). Electrophoresis was performed in 10% polyacrylamide gels using a BioRad vertical electrophoresis system (Hercules, CA, USA) according to [PERSON]'s method [17] at 120 V for the stacking gel and 180 V for the resolving gel. Following the electrophoresis, the proteins were visualised using Coomassie Brilliant Blue staining (Thermo Fisher Scientific, Inc., Waltham, MA, USA). Perfect(tm) Colour Protein Ladder (EurX, Gdarsk, Poland) was used to assess the molecular weight of the protein in a range from 7 kDa to 240 kDa.  
4.8 Identification of Protein in Cpl-Rx--Liquid Chromatography Mass Spectrometry and Analysis of Mass Spectrometry Data  
Proteomic analysis was preceded by a sample preparation procedure. The protein band revealed during the SDS-PAGE analysis was excised from the gel and analysed by liquid chromatography mass spectrometry (LC-MS). The sample was subjected to trypsin digestion, during which proteins were reduced (using DTT 10 mM; 30 min at 56 \({}^{\circ}\)C), alkylated with iodoacetamide (for 45 min at room temperature in darkness), and digested overnight with 10 ng/\(\upmu\)L trypsin. The obtained peptide mixture was concentrated anddesalted on a RP-C18 pre-column (Waters, Milford, MA, USA). Peptide separation was carried out on a nano-Ultra Performance Liquid Chromatography (UPLC) RP-C18 column (Waters, Milford, MA, USA, BEH130 C18, 75 \(\upmu\)m i.d., 250 mm long), using a linear gradient of acetonitrile with 0.1% formic acid (45 min). The analysis of the sample was preceded by a blank run to ensure the absence of cross contaminations. LC was coupled to the ion source of the Orbitrap Velos mass spectrometer (Thermo Electron Corp., San Jose, CA, USA) working in the regime of the data-dependent MS to MS/MS switch.  
The analysis of the mass spectrometry data was performed using Mascot Distiller software (v. 2.6, MatrixScience, London, UK) and the Mascot Search Engine (MatrixScience, London, UK, Mascot Server 2.5) against the NCBInr database (155,200,242 sequences; 56,856,634,127 residues) with a Rosaceae filter (304,414 sequences). The mass tolerance settings for the peptide and fragment were determined separately for individual LC-MS/MS runs after a measured mass recalibration to reduce mass errors. The remaining search parameters were as follows: enzyme, trypsin; missed cleavages, 1; fixed modifications, carbamidomethyl (C); variable modifications, oxidation (M); and instrument, HCD. Analyte identification was carried out using the Mascot search engine with the probability-based algorithm. The expected value threshold of 0.05 was used (all peptide identifications had a less than 1 in 20 chance of being a random match) [18].
## 2 Material and Methods
### Laboratory Analysis of PAHs  
Out of the 20 dust samples that were collected, 3 were destroyed during the analytical process. Therefore, the results that were obtained from 17 residences are subsequently presented. The dust extracts were analyzed for 25 PAHs using gas chromatography/mass spectrometry (GC/MS), including naphthalene (Nap), 2-methylnaphthalene (2M-Nap), 1-methylnaphthalene (1M-Nap), 1,2-dimethylnaphthalene (1,2 DM-Nap), 2,6- dimethylnaphthalene (2,6 DM-Nap), acenapththenene (Ace), 2,3,5-trimethylnaphthalene (2,3,5-TM-Nap), acenphthylene (Acy), anthracene (Ant), benzo[a] anthracene (B[a]A), benzo[a]pyrene (B[a]P), benzo[e]pyrene (B[e]P), benzo[b]fluoranthene (B[b]F), benzo[k]fluoranthene (B[k]F),benzo[g,h,i]perylene (B[ghi]Per), chrysene (Chry), dibenzo[a,h]anthracene (dBaAnt), fluoranthene (Fla), fluorene (Fl), perylene (Per), indeno (1,2,3-c,d) pyrene (IndP), phenanthrene (Phe), dibenzothiophene (dbt) pyrene (Pyr), 3,6-dimethylphenanthrene (3,6-dMePhe), 1-methylphenanthrene (1-MePhe). The analysis of the polyaromatic hydrocarbons was performed according to ISO12884 with the use of the certified material NIST Urban Dust (1649b). The main steps of the analytical procedure are the following. Samples were extracted in a Soxhlet extractor for 24 h at a reflux rate of about 4 cycles per hour. Before the extraction, deuterated PAHs (d8-Nap, d10-A, d10-Phe, d10-Chr, d10-Pyr, d12-B[ghi]P and d12-Perylene) were added as internal standards to monitor recovery. Subsequently, the extracts were concentrated in a rotary evaporator, loaded onto activated silica gel column chromatography, and eluted with n-hexane and n-hexane/dichloromethane (3:2). PAHs fraction was concentrated under a gentle stream of nitrogen, and an aliquot was analyzed by GC/MS (Agilent Technologies 7890A GC). Twenty-five PAHs were detected, including a group of suspected carcinogens PAHs.
## 1 Introduction
While these designs have achieved significant improvements, they often rely on additional decoupling structures such as EBG, stubs, or neutralization lines, parasitic elements, Split Ring Resonators (SRR), and Inder Digital Capacitors (IDC) which can complicate fabrication, increase design complexity, and sometimes limit further miniaturization. This underlines a crucial need for antenna designs that can deliver wide bandwidth, low envelope correlation coefficient (ECC), high diversity performance, and strong isolation without relying on any decoupling structures between the radiating elements. In response, this study introduces novel miniaturized dual port and quad port MIMO antenna variants that achieve enhanced diversity performance across the 5G mid-band and UWB spectrum without including decoupling structures in between the radiators. The antenna designs leverage intrinsic structural symmetry, orthogonal mode excitation, and careful spatial arrangement of the elements to naturally suppress mutual coupling while maintaining compactness and simplicity. The proposed antennas demonstrate wide impedance bandwidth, low ECC, high diversity gain, and stable time-domain performance, offering a streamlined solution well-suited for next-generation wireless communication systems.