esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Increasing photovoltaic (PV) generation in the world's power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) Skylmager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The Skylmager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The Skylmager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two Skylmagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. \(R^{2}\) values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The Skylmager in a distributed environment with cloud-computing will be an integral part of the command and control for today's SmartGrid and Internet of Things.  
**Keywords:** distributed PV generation; microgrid; irradiance forecasting; all-sky imager; Raspberry Pi; optical flow; machine learning; cloud-computing; SmartGrid; Internet of Things (IoT)
## 3 Results
### Total Phenolic Content and Antioxidant Activity
 \\ \hline May 2018 & Redcurrant leaves & 31 952.06 \(\pm\) 257.48 \({}^{\text{n}}\) & 4 327.28 \(\pm\) 68.24 \({}^{\text{ef}}\) & 5 175.42 \(\pm\) 160.30 \({}^{\text{e}}\) & 9 356.12 \(\pm\) 73.35 \({}^{\text{k}}\) \\  & Whitecurrant leaves & 20 365.50 \(\pm\) 133.75 \({}^{\text{c}}\) & 3 593.43 \(\pm\) 297.49 \({}^{\text{d}}\) & 5 060.14 \(\pm\) 35.37 \({}^{\text{de}}\) & 7 389.15 \(\pm\) 97.19 \({}^{\text{h}}\) \\  & Blackcurrant leaves & 22 101.61 \(\pm\) 170.57 \({}^{\text{d}}\) & 1 800.10 \(\pm\) 232.49 \({}^{\text{a}}\) & 4 316.47 \(\pm\) 96.67 \({}^{\text{c}}\) & 4 669.14 \(\pm\) 54.54 \({}^{\text{c}}\) \\ \hline June/July 2018 & Redcurrant leaves & 23 054.90 \(\pm\) 137.38 \({}^{\text{e}}\) & 3 031.80 \(\pm\) 43.69 \({}^{\text{c}}\) & 4 323.85 \(\pm\) 72.67 \({}^{\text{c}}\) & 8 017.53 \(\pm\) 71.16 \({}^{\text{i}}\) \\  & Whitecurrant leaves & 20 105.07 \(\pm\) 175.78 \({}^{\text{c}}\) & 2 197.94 \(\pm\) 153.74 \({}^{\text{d}}\) & 3 002.98 \(\pm\) 17.43 \({}^{\text{a}}\) & 3 969.21 \(\pm\) 128.06 \({}^{\text{b}}\) \\  & Blackcurrant leaves & 25 616.54 \(\pm\) 320.22 \({}^{\text{h}}\) & 2 378.48 \(\pm\) 155.19 \({}^{\text{b}}\) & 7 213.48 \(\pm\) 120.98 \({}^{\text{i}}\) & 7 248.21 \(\pm\) 154.29 \({}^{\text{h}}\) \\ \hline August 2018 & Redcurrant leaves & 24 180.80 \(\pm\) 89.29 \({}^{\text{f}}\) & 4 401.35 \(\pm\) 42.9 \({}^{\text{f}}\) & 5 492.61 \(\pm\) 35.42 \({}^{\text{f}}\) & 6 068.05 \(\pm\) 153.11 \({}^{\text{f}}\) \\  & Whitecurrant leaves & 17 249.76 \(\pm\) 66.35 \({}^{\text{a}}\) & 1 884.06 \(\pm\) 28.14 \({}^{\text{a}}\) & 323.538 \(\pm\) 99.27 \({}^{\text{a}}\) & 3 517.63 \(\pm\) 85.54 \({}^{\text{a}}\) \\  & Blackcurrant leaves & 30 151.01 \(\pm\) 66.18 \({}^{\text{m}}\) & 4 597.92 \(\pm\) 54.20 \({}^{\text{f}}\) & 6 428.35 \(\pm\) 25.00 \({}^{\text{h}}\) & 7 411.46 \(\pm\) 92.97 \({}^{\text{h}}\) \\ \hline May 2019 & redcurrant leaves & 29 512.52 \(\pm\) 177.98 \({}^{\text{l}}\) & 4 246.76 \(\pm\) 42.45 \({}^{\text{ef}}\) & 5 162.47 \(\pm\) 52.96 \({}^{\text{e}}\) & 8 862.36 \(\pm\) 15.21 \({}^{\text{i}}\) \\  & whitecurrant leaves & 24 304.23 \(\pm\) 199.70 \({}^{\text{f}}\) & 3 946.17 \(\pm\) 87.01 \({}^{\text{de}}\) & 4 065.84 \(\pm\) 35.21 \({}^{\text{c}}\) & 8 763.98 \(\pm\) 42.91 \({}^{\text{i}}\) \\  & blackcurrant leaves & 32 374.29 \(\pm\) 112.05 \({}^{\text{o}}\) & 6 989.44 \(\pm\) 81.46 \({}^{\text{i}}\) & 7 877.82 \(\pm\) 101.60 \({}^{\text{k}}\) & 6 749.45 \(\pm\) 30.64 \({}^{\text{g}}\) \\ \hline June/July 2019 & Redcurrant leaves & 18 699.40 \(\pm\) 66.61 \({}^{\text{b}}\) & 4 8 627.60 \(\pm\) 202.71 \({}^{\text{h}}\) & 3 744.63 \(\pm\) 140.94 \({}^{\text{b}}\) & 5 291.90 \(\pm\) 56.80 \({}^{\text{d}}\) \\  & Whitecurrant leaves & 26 005.09 \(\pm\) 135.54 \({}^{\text{a}}\) & 2 859.15 \(\pm\) 402.33 \({}^{\text{c}}\) & 5 799.54 \(\pm\) 179.8 \({}^{\text{g}}\) & 6 628.90 \(\pm\August 2019, while the whitecurrant leaves from the same harvests of 2018 had the lowest antioxidant activity.
## 1 Introduction
Another critical performance metric, especially for high-speed communication systems is the outage capacity, which denotes the maximum data transmission rate of reliable communications under a specific outage probability [41; 42]. However, to the best of our knowledge, although outage capacity has been extensively utilized for the wider free air communication systems [16; 41; 42; 43; 44; 45], it has not yet been reported in the TOW technical literature. Motivated by the above, in the current contribution, the outage capacity for typical TOW link configurations is estimated, in the presence of skin-induced attenuation and stochastic pointing errors. Under these circumstances, analytical outage capacity expressions are derived with their corresponding results verifying the accuracy of our proposed analysis over a wide average electrical SNR range.
# Mapping the Most Susceptible Regions to Fire in Portugal
###### Abstract  
Mediterranean European countries, including Portugal, are considered fire-prone regions, being affected by fire events every summer. Nonetheless, Portugal has been recording large burned areas over the last 20 years, which are not only strongly associated with hot and dry conditions, but also with high fuel availability in the ecosystems. Due to recent catastrophic fire seasons, Portugal has been implementing preventive policies during the pre-fire season, which, in turn, can optimize combat strategies during the fire season. In this context, our study contributes to fire prevention by identifying the regions with the highest potential to burn. The application of a Principal Component Analysis (PCA) to a range of climatological, ecological, and biophysical variables, either provided by remote sensing or reanalysis products, and known to be linked with diverse fire-vulnerability factors, allows the objective identification of the regions with the highest susceptibility to burn. The central and southernmost areas of Portugal present a stronger signal in the PCA, suggesting a likely high exposure to future fire events. The fuel accumulation over several months, in conjunction with elevation and fire weather conditions, are the terms out of the retained PCs that can explain most of the variability. The quality assessment performed for the burned areas in 2022 showed that they occurred in highly susceptible areas, highlighting the usefulness of the proposed methodology.  
fire susceptibility; fuel management; PCAs; fuel accumulation; vegetation productivity; land cover +
Footnote †: journal:  
0000-0002-1091-5098]T.T.E.Ermitao
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 3 Methods
### The Disparity Field Initialization and Aggregation Build upon the MUD
#### 3.2.2 The MGA Module  
Attention weights in vision transformers [46; 47] are used to aggregate features by assigning higher importance to those with relatively high dot-product scores. This process involves computing the dot product between different features. The dot-product scores are then normalized using the SoftMax function to produce attention weights. In GMA [48], attention weights \(A^{\text{ctx}}\in[0,1]^{h^{2}\times w^{2}}\) guided by contextual feature are reused during iteration for the global aggregation of flow field feature, thus improving the occlusion handling in optical flow task. Inspired by GMA, we propose a new MGA module, where the disparity field's hidden state is globally aggregated to introduce long-range dependencies of the hidden state. The attention weights are computed using the contextual feature encoded with 2D rotary positional embedding (RoPE) [49] to inject relative positional information:  
\[A^{\text{ctx}}=\zeta(\text{rope}(f_{\theta\text{avg}}^{\text{qry}}(F^{\text {ctx}}))\otimes\text{rope}(f_{\theta\text{avg}}^{\text{key}}(F^{\text{ctx}}))), \tag{11}\]  
Figure 6: The proportion of disparity outliers after each iteration. “Bad1.0” is the metric that indicates the percentage of pixels with an error greater than 1 pixel.  
where \(f^{\text{qty}}_{\theta\theta^{\text{ty}}}\), \(f^{key}_{\theta\theta^{\text{ty}}}\) denote convolutional networks with parameters \(\theta^{\text{qty}}\) and \(\theta^{\text{key}}\), and \(\zeta(\cdot)\) denotes the SoftMax function. The iterative update of disparity field with the MGA module is denoted as:  
\[\hat{m}_{t}^{\text{hm}} =f^{\text{hm}}_{\theta\text{hm}}(\text{H}_{t-1},\text{C}^{\text{ mtc}}(\hat{D}_{1}),\hat{d}_{t-1},\hat{m}_{t-1}) \tag{12}\] \[H_{t}^{\text{gb}} =\text{MGA}(A^{\text{ctx}},H_{t-1},\hat{m}_{t}^{\text{hm}})=A^{ \text{ctx}}\otimes(f^{\text{vel}}_{\theta\text{vel}}(H_{t-1})\odot\hat{m}_{t}^ {\text{hm}}),\] \[H_{t} =\text{ConvGRU}(H_{t-1},H_{t}^{\text{gb}},F_{t}^{\text{tf}},F^{ \text{ctx}})\]  
where \(f^{\text{hm}}_{\theta\text{hm}},f^{\text{vel}}_{\theta\text{vel}}\) denote the convolutional network with parameters \(\theta^{\text{hm}}\) and \(\theta^{\text{vel}}\). The adaptive mask \(\hat{m}_{t}^{\text{hm}}\) is used to dynamically adjust the attention weights during iteration, thus suppressing the propagation of unreliable hidden states, thus ensuring the effectiveness of aggregation.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 4 Critical Auxiliary Data Necessary to Support Detecting SCE from SAR Data  
In addition to the sensor types and algorithms, other critical and commonly employed datasets help improve the accuracy of the obtained snow cover classification, including DEM, land cover maps and meteorological information. As shown in Figure 1, these parameters directly affect snow cover distribution and condition, and it is therefore advisable to include these auxiliary datasets to the workflow. They may not be necessary to operate the SAR-based snow cover detection; they, however, can improve the quality of the results.
## 3 Results and Discussion
### Compressive Strength  
Compressive strength was assessed on uncorroded specimens at the age of 60 days. The average values of 3 specimens for each composite were 75.5, 76.5, and 73.6 MPa for composites F80 ZP, F40 ZP, and F40 RC, respectively. The compressive strength of all composites was similar, indicating that fibers do not have a significant influence on compressive strength [9]. Still, it is worth noticing that the decrease in compressive strength with the fiber content was also observed in [4].
## 3 Proposed Model  
In this section, we describe our proposed model. Do keep in mind that we build it upon the concepts and terminology detailed in the previous section. In addition, we must make clear that our proposal stems from the idea of 'unfolding' a metaheuristic into a heuristic sequence found by solving the MCOP with a selection perturbative hyper-heuristic. What does 'unfolding' mean? Please take a look at Figure 1 where a metaheuristic is depicted in terms of simple heuristics (cf. Definition 8). Notice the feedback path (dashed stroke) in the metaheuristic diagram. The finaliser controls this path (cf. Definition 4). For each step, it either returns to the search operator (\(h_{o}\)) or concludes the procedure, leading to \(h_{e}\). The signal-flow representation of metaheuristics is detailed in [7].  
Now, consider a problem with a limited computational budget, which is common in real-world applications. In this case, the finaliser only anticipates the loop exit when an additional condition is met, e.g., a fitness value threshold. Nevertheless, if the finaliser role is delegated to a superior controller (say, an automated designer), we can unfold the metaheuristic scheme as a heuristic sequence. The reason: said controller not only decides when to stop applying a search operator but also which one (from a pool of search operators or heuristic space) to apply next. Figure 2 illustrates this idea in a simple chronological plot from top to bottom. This figure also renders the signal-flow diagram of the MH with an additional axis: the time or number of iterations. We included a dummy \(z\) axis for illustrative purposes; thus, the diagram is presented with a three-dimensional perspective (check the thumbnail cube with faces coloured according to the curves). Thus, Figure 2 depicts the unfolding timeline where the signal-flow diagram of a standard metaheuristic (bluish curves) is transformed into a homogeneous heuristic sequence (greenish curves). Note that this intermediate step in the timeline corresponds to a sequence of \(t\) implementations of the same search operator (\(h_{o}\)) over the population. This can also be considered as a heuristic sequence, although a trivial one. For that reason, we labelled it as a homogeneous heuristic sequence to avoid confusion. Subsequently, it is quite natural to think about not using the same operator over and over, so we can move to a more general sequence of different (non-exclusive) search operators. This signal-flow diagram at the bottom of Figure 2 displays a heterogeneous heuristic sequence (orangish box). We called it an unfolded metaheuristic (uMH). It is worth remarking that shorter sequences are desirable, as long as they preserve the performance level. Therefore, our proposed unfolded metaheuristic model can be framed into the MCOP described in Section 2.3. Beware that, in this case, the MCOP is targeted at solving continuous optimisation problems at the lowest level of abstraction.  
Figure 1: Signal-flow representation of a conventional metaheuristic comprising an initialiser \(h_{i}\), a search operator \(h_{o}\), and a finaliser \(h_{f}\). \(h_{e}\) is the identity heuristic.
# Honey-Based Medicinal Formulations: A Critical Review
## 3 Honey-Based Medicinal Formulations
### Honey-Based Eye Drops  
The only commercially available honey-based eye drop is 'Optimel Manuka+ Dry Eye Drops' (patent pending) manufactured by Melcare. It contains 98% _Leptosperm_ honey, sodium chloride, and benzoic acid. Benzoic acid is a commonly used food preservative that is quickly metabolized by the liver and is present naturally at high levels in honeys derived from various _Leptosperm_ species. The manufacturer notes that benzoic acid is selected as the preferred preservative for this formulation because of its natural existence in honey, as well as its effectiveness in low pH solutions. Optimel eye drops are claimed to be suitable for chronic dry eye and blebaritis, as well as the treatment of sore, irritated eyes and eyelids by creating an environment that assists healing and stops additional damage. Upon application to the surface of the eye, the eye drops create a low pH environment which inhibits the growth of bacteria that otherwise may potentially colonize the traumatized surface. The overgrowth of ocular flora resulting from tear deficiency and meibomian gland disease can contribute to tear film instability and ocular surface damage [47]. Chronic lid disease can be treated with both topical and systemic antibiotics; however, with an increase in resistance among ocular pathogens, the efficacy of antibiotics has been considerably reduced [48]. The manufacturer claims that thrice daily applications of Optimel Manuka+ dry eye drops to the inferior conjunctival forn of patients with tear deficiency and/or meibomian gland disease can notably reduce the colony-forming units (CFUs) in the eyelids after 1 month of treatment. The reduction in CFUs decreases the susceptibility to bacterial conjunctivitis and keratitis, as well as improves the clinical symptoms of dry eye disease, by decreasing the production of certain bacterial biases that may hydrolyze the meibomian gland lipids [49]. In a similar vein, [PERSON] and [PERSON] demonstrated that the application of _Leptosperm_ neat honey is associated with significant improvements in dry eye and meibomian gland dysfunction [47; 50]. The benefits of a conjunctival application of honey (30-100%) was also studied by [PERSON] using _E. coli_, _Proteus_ sp., _S. aureus_, _Klebsiella_ sp., and _P. aeruginosa_ as bacterial species and adult Wistar Sprague-Dawley albino rats as experimental animals [51]. They found the treatment tobe effective for bacterial conjunctivitis induced in rats by different human pathogens (i.e., _E. coli_, _Proteus_ sp., _S. aureus_, _Klebsiella_ sp., and _P. aeruginosa_) with no significant side-effects.
## 2 System Model
### Full-Duplex UAV Relay  
In contrast to the half-duplex relaying scheme, the signals are transmitted and received simultaneously in the existing full-duplex UAV relaying scheme. Therefore, the full-duplex UAV relay suffers from residual self-interference as the signal leakage from the transmitter's output to its input. The end-to-end SNR for the full-duplex DF, \(\gamma_{\text{{\it{fd\_df}}}}\), and AF UAV relays, \(\gamma_{\text{{\it{fd\_df}}}}\), are defined as:  
\[\gamma_{\text{{\it{fd\_df}}}}=\text{min}\left\{\frac{P_{bs}^{\text{{\it{fd}}} }\mid h_{sr}\mid^{2}}{R_{S1}+\sigma_{r}^{2}},\frac{P_{r}^{\text{{\it{fd}}}} \mid h_{rd}\mid^{2}}{\sigma^{2}}\right\}, \tag{30}\]  
\[\gamma_{\text{{\it{fd\_af}}}}=\frac{P_{bs}^{\text{{\it{fd}}}}\mid h_{sr}\beta_ {hrd}\mid^{2}}{\mid\beta_{f}h_{rd}\mid^{2}\left(R_{S1}+\sigma_{r}^{2}\right)+ \sigma^{2}}, \tag{31}\]where \(P_{bs}^{fd}\) and \(P_{r}^{fd}\) are the transmit power of the BS and the full-duplex UAV relay, respectively, and \(R_{SI}\) is the residual self-interference experienced by the full-duplex UAV relays. The amplification factor of the full-duplex AF UAV relay, \(\beta_{f}\) is expressed as:  
\[\beta_{f}=\sqrt{\frac{P_{f}^{fd}}{P_{bs}^{fd}\mid h_{sr}\mid^{2}+R_{SI}+\sigma^{ 2}}}. \tag{32}\]
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
###### Abstract  
The time to first fix (TTFF) measures the time elapsed by a global navigation satellite system (GNSS) receiver from switch-on to provision of a navigation solution. This parameter is crucial for applications where a position, within an acceptable error, is needed as soon as possible after turning the device on. The quality of the TTFF depends mainly on the receiver, the environment, and the GNSS satellites employed. Although all four available GNSSs (BeiDou, Galileo, GLONASS, and GPS) are complementary, their constellations and signals differ, providing different TTFF performances. This becomes even more relevant in hostile environments, where the TTFF degrades from nominal results. In this work, the robustness of the signals of the four GNSSs against different levels of harshness and its influence on the TTFF performance are evaluated in a comparative way. For this purpose, a typical scenario for mass-market GNSS applications, involving cold-start conditions, single-frequency signals, and a low-cost receiver, is considered. The results indicate that GPS provides the most robust TTFF, followed by GLONASS (although at the expense of positioning accuracy), BeiDou, and Galileo, in that order.  
c pacs: 25, 25, 1599 +
Footnote †: journal:  
Academic Editors: [PERSON],  
[PERSON] and [PERSON]  
Received: 24 January 2025  
Revised: 24 February 2025  
Accepted: 2 March 2025  
Published: 5 March 2025  
**Citation**: [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Time to First Fix Robustness of Global Navigation Satellite Systems:  
Comparison Study. _Sensors_**2025**, 25, 1999. [[https://doi.org/10.3390/s25051599](https://doi.org/10.3390/s25051599)]([https://doi.org/10.3390/s25051599](https://doi.org/10.3390/s25051599))  
**Copyright**: (c) 2025 by the authors.  
Uiconsec MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 1 Introduction
Since the start of the GIC monitoring system, more than 16,000 files have been created, containing data of currents in the autotransformers' neutrals. The total size of these files was about 27 GB at the beginning of 2023. The constantly growing volume of recorded neutral current data cannot be analyzed manually within a reasonable amount of time. The purpose of this study is to develop a convolutional neural network (CNN) model for the automatic analysis of the 12 years of data accumulated by the GIC monitoring system in northwestern Russia. To train the CNN model, a dataset containing 800 scalogram images was created. The dataset includes images of two classes: GICs and geomagnetically quiet hours. To select the most suitable neural network for solving the problem, the performance of four models with different configurations was compared. The structure of this paper is as follows: Section 2 provides an overview of existing research that applies continuous wavelet transform (CWT) and CNNs to analyze nonstationary signals in power systems, including GICs. Section 3 is dedicated to the methodology and the created dataset. Section 3.1 introduces the basic concepts of CNNs, describes the layers required to build one, and explains the choice of performance metrics for binary classification. Section 3.2 presents a description of the CWT method. Section 3.3 details the dataset specially created for this study, consisting of two-dimensional images of wavelet spectra (scalograms) of the current in autotransformer neutrals. Section 4 presents the results of a performance comparison of four CNN models and a detailed description of the architecture of the chosen model. The conclusions are presented in Section 5.
## 4 Results and Analyses of the Numerical Application of Global Cost and Carbon Impact of the PVCS
### Analyses of Action Levers to Reduce the Carbon Impact of the PVCS
#### 4.3.3 Scenario 3: Reduction of the PV Panels Emission Coefficient to 0.012 kgCO2,eq/kWh  
A study published in 2017 [29] demonstrates that the carbon emission factor of PV modules in 2050 will vary between 3.5 and 0.012 kgCO2,eq/kWh.  
By taking PV panels with a carbon emission coefficient of 0.012 kgCO2,eq/kWh, the carbon impact of the panels drops from 37,996 kgCO2,eq to 11,399 kgCO2,eq.  
As depicted in Figure 10, the carbon impact of the PVCS decreases from 85,961 kgCO2,eq to 59,364 kgCO2,eq, a 31% reduction compared to the initially considered charging station.  
Figure 8: Result of scenario 1.  
In comparison with the PGCS, the variation rate becomes \(-23.3\%\), which means that the carbon impact of the PVCS is lower than the PGCS' one.