esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
###### Abstract  
In the last decade, we have been witnessing a rapid development of the constellations of Global and Regional Navigation Satellite Systems (GNSS/RNSS). Besides the well-known GPS and GLONASS, newly developed systems such as Galileo, BeiDou, QZSS and NAVIC have become increasingly important. All satellites of new GNSS are equipped with laser retroreflector arrays (LRA) dedicated to Satellite Laser Ranging (SLR). SLR allows, e.g., an independent validation of microwave-based orbit products. Therefore, a fully operational online service called the multi-GNSS Orbit Validation Visualizer Using SLR (GOVUS) has been developed allowing for near real-time analysis of the quality of multi-GNSS orbits. The mean offsets of SLR residuals for Center for Orbit Determination in Europe (CODE) orbits in 2016 are at the level of \(-8\), \(-38\), \(-14\), and \(-107\) mm, for BeiDou, Galileo, GLONASS, and QZSS, respectively, with the standard deviations of 66, 36, 29, and 100 mm. Moreover, GOVUS can be used as a database containing information on equipment used at SLR stations and multi-GNSS satellite parameters. This paper includes a comprehensive description of the functionality and the structure of the developed service with exemplary analyses. The paper points out the most critical issues, limitations and challenges of multi-GNSS and SLR tracking network in the context of the SLR orbit validation. The goal of the paper and GOVUS itself is to determine: (1) what is the current quality of multi-GNSS orbits validated using SLR results; (2) what kinds of systematic errors can affect GNSS orbits and SLR observations; and (3) how to provide the online analysis tools to the broadest possible multi-GNSS community. The service has been officially operating since March 2017 as the Associate Analysis Center of the International Laser Ranging Service (ILRS ACC).  
SLR; multi-GNSS; orbit quality; ILRS AAC 2017, 9, 1049; doi:10.3390/rs9101049
## 3 System Design Overview  
### Platform Architecture  
The SPARK-IT platform is designed to foster collaboration between innovators and experts through a secure, decentralized, and modular system. The design heavily leverages blockchain technology, AI-driven expert matching, and a decentralized storage architecture to ensure data security, transparency, and efficient communication between users.  
Figure 1 presents the SPARK-IT platform architecture. The main components illustrated in this figure are as follows:  
* Blockchain: permissioned blockchain and smart contracts for token management, proof of data records, contract execution records, and innovator and expert interaction records.
* Off-chain storage: templates for NDA contracts and the business model canvas, web application, and user data; high performance in-memory caches.
* IPFS (InterPlanetary File System): distributed storage for public and encrypted data.
* API Gateway: API front-end entry point that provides a unified API interface to the system; integrates/orchestrates back-end calls to all system components and provides authentication/authorization mechanisms, as well as support for audit services.
* Web App @innovator: front-end web application for innovators, used to interact with the system for profile and offer management, activity history, tokens, and various statistics; provides the means to interact with the matchmaking module to assist in expert selection.
* Web App @expert: front-end web application for experts, used to interact with the system for profile management, to accept/reject offers, activity history, score, accrued tokens, and various statistics.
* Web scraper: provides data to verify and prefill expert profiles; gathers data from professional social media platforms; can be extended to include other data sources.
* Data extractor: analyzes and extracts expert profile information from web scraper data; can be extended to extract data from different sources; written in a data-source agnostic way by means of pluggable extraction templates.
* Matchmaking module: employs AI and other algorithms used to perform the matchmaking between the innovators and experts; matchmaking data are recorded into blockchain along with reasoning data traces to ensure transparency into the selection process.
## 1 Introduction
Replacing orthogonal functions of \(x\) and \(r\) by their multivariate counterparts, we obtain an estimator that formally covers also multivariate cases, but it is still non-trivial to derive a computationally efficient algorithm and to establish its asymptotic properties. Below, we outline examples of possible applications of the \(\mathcal{F}\)-estimation:  
* The temperature of a jet engine \(x\) depends on the rotating speed \(r\) of its turbine and on many other non controllable factors. It is relatively easy to collect a large number of observations \((\kappa_{l},\,r_{l})\), \(l=1,\,2,\,\ldots d\) from proper and improper operating conditions of the engine. For diagnostic purposes, it is desirable to estimate the continuum set of p.d.f.s of the temperatures for rotating speeds \(r\in[R_{1},\,R_{2}]\). This is our main motivating example that is discussed in detail in Section 7.1. * Consider the fuel consumption \(x\) of a test exemplar of a new car model. The main impact on \(x\) comes from the car speed \(r\), but \(x\) also depends on the road surface, the type of types and many other factors. It would be of interest for users to know the whole family \(\mathcal{F}\) p.d.f. 's in addition to the mean fuel consumption. In a similar vain, it would be of interest to estimate \(\mathcal{F}\) when \(x\) is the braking distance of a car running at speed \(r\). * Cereal crops \(x\) depend on an amount \(r\) of a fertilizer applied to a unit area as well as on soil valuation, weather conditions, etc. Estimating \(\mathcal{F}\) would allow for selecting \(r\) which provides a compromise between a high yield and its robustness against other conditions. Taking into account a rapidly growing amount of available data and increasing computational power, it would be desirable to extend many other examples of nonparametric regression applications to estimating the whole \(\mathcal{F}\). Our starting point for constructing an estimator for \(\mathcal{F}\) is nonparametric estimation of p.d.f. 's by orthogonal series estimators. We refer the reader to the classic results in this field [1; 3; 12; 13; 14; 15]. We shall need also some results on the influence of rounding errors on these estimators (see [16; 17]). The paper is organized as follows: the derivation of the algorithm is presented, a fast method of computation is proposed. Subsequently, tests of synthetic data are preformed and the convergence of the method is shown. Finally, a real world problem regarding jet turbine temperature is presented as well as other possible applications. As an appendix, a detailed proof of convergence is given.
## Results and Discussion
### Antioxidant Activity of F3 and F4 Fractions In Vitro  
To analyse the antioxidant potential of the obtained _D. vermicularis_ fractions (F3 and F4), and to obtain better insight into their antioxidant behaviour, different assays were used. The results for four assays (Folin-Ciocalteu method, ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPPH), and oxygen radical absorbance capacity (ORAC)) are depicted in Figure 6. The results of Folin-Ciocalteu method showed significantly (_p_ < 0.0001) different activity for F3 and F4 fractions, namely, a 50-fold higher activity was obtained for F3. As no polyphenols were found in these fractions (Table 3), this response for F3 could be assigned to the presence of xanthophylls fucoxanthin and zeaxanthin (or lutein) in higher amounts when compared to F4, as it is known that the pigments interact with Folin-Ciocalteu reagent [38]. Contrarily, almost the same activity was obtained using the FRAP assay for both fractions, i.e., 3.1 \(\pm\) 0.2 mmol/g for F3 and 3.2 \(\pm\) 0.07 mmol/g ferrous equivalents for F4. The scavenging activity using DPPH assay when normalised per gram of the fraction revealed also almost the same activity for both fractions, i.e., for F3, it was 286.79 \(\pm\) 14.09 mg AAE/g fraction, while for F4, it was 273.42 \(\pm\) 1.29 mg AAE/g fraction. ORAC assay is based on a fluorescent signal from a probe that is quenched in the presence of reactive oxygen species (ROS). The results shown in Figure 6b revealed a 1.4-fold higher (_p_ > 0.001) activity for F3 than F4.  
Further on, the detailed reduction of radical cations by implementing ABTS assay was studied. Different concentrations of F3 and F4 fractions were prepared ranging from 0.005 to 5 mg/mL to obtain dose-response curves (Figure 7) and calculate IC\({}_{50}\) values (Table 4). As can be seen, two _D. vermicularis_ samples exhibited a similar activity, i.e., IC\({}_{50}\) values were 0.498 and 0.798 mg/mL for F3 and F4 fractions, respectively. A lower IC\({}_{50}\) value for F3 could be ascribed to higher amounts of xanthophylls found in F3. However, one should note that a similar antioxidant behaviour was observed for both F3 and F4 of _D. vermicularis_, which indicate that, although different compounds were identified in each fraction and in different amounts, a synergistic effect leading to a similar activity can be observed. Additionally, in F4, indole derivatives were found (for example, 1'H-5\(\alpha\)-cholest-2-eno [3,2-b] indole) in a higher amount, which probably contributed to the antioxidant responses [39].  
Figure 6: Scavenging radical activity of two fractions of _D. vermicularis_ (F3 and F4) obtained using (**a**) Folin–Ciocalteu method and ferric reducing antioxidant power (FRAP), and (**b**) 2,2-diphenyl-1-picrylhydrazyl (DPPPH) assay and oxygen radical absorbance capacity (ORAC) (mean \(\pm\) SD; \(n\) = 4). An asterisk indicates a significant difference between F3 and F4 (*** \(p\) < 0.001; *** \(p\) < 0.0001).
## 3 System Control Structure
### Control Strategy of the MSC  
The MSC still adopts the vector control method based on the orientation of the rotor flux linkage. In the control loop of the MSC in Figure 3, the cascaded control structure, including the outer power loop and the inner current loop, is employed. Since the DC-link voltage can reflect the dynamics of the grid frequency, an auxiliary control loop based on the differential of the DC-link voltage is introduced to the power reference of the MSC, which can realize the autonomous inertial response function of the wind turbine to the grid. When a short-circuit fault occurs in the grid, i.e., the flag is 1, the output of the switch SM is the limited power reference, P\({}_{\text{mft}}\), aiming to reduce the output power of the MSC. When the short-circuit fault is cleared, i.e., the flag is 0, the output of the switch SM is the normal power reference, and the MSC returns to the normal operation state.  
Figure 3: Control structure of the PMSG-based wind turbine with the auxiliary energy storage device.
## 3 Results and Discussion
### Comparative Analysis  
A comparative analysis between the analytical and numerical models have been carried out. Air mass flow rates of 50 kg s\({}^{-1}\) and \(-\)75 kg s\({}^{-1}\) were also considered in the analytical model to carry out the comparative study. As indicated previously, although  
Figure 12: Numerical model results in the first cycle at 5 MPa (decompression). Distribution of air temperature within the reservoir and temperature detail in the steel and concrete.  
Figure 13: Numerical model results. Distribution of surface heat flux for FRP and steel in the first cycle at a pressure of 8 MPa in the contact surface air-sealing layer.  
both analytical and numerical models use the same model geometry, the air mass flow rates of the numerical model are higher to reduce the computational time. Figure 14 shows a comparative analysis for five cycles between analytical and CFD results using FRP as sealing layer. A comparison between FRP and steel is shown afterwards in Figure 15 for one cycle. The obtained heat transfer coefficient is also indicated in Figure 15b. In general, good agreements have been obtained between both analytical and numerical simulations.  
To justify the accuracy of the simulations, the results obtained in the analytical model have been compared with experimental and numerical models. Figure 16 shows the comparison of results obtained for air temperature (Figure 16a) and pressure (Figure 16b) during the first cycle with an experimental model developed by [PERSON] et al. [24]. Dot points, corresponding to the experimental values, are compared with the results obtained in solid blue lines in Figure 16. In addition, Figure 17 shows the comparison of results obtained for air and wall temperatures (Figure 17a) and air pressure (Figure 17b) during the first cycle with a numerical model conducted by [PERSON] et al. [25]. The analytical model (solid lines)  
Figure 14: Comparative analysis between analytical and numerical models for 5 charging and discharging cycles considering FRP as sealing layer. (**a**) Temperature of air and FRP; (**b**) Air pressure; (**c**) Surface heat flux of FRP and concrete lining.  
Figure 15: Comparative analysis between analytical and numerical models in the first cycle considering FRP and steel as sealing layers. (**a**) Air temperature; (**b**) Heat transfer coefficient; (**c**) Surface heat flux.  
has been employed to reproduce the numerical model considered by [PERSON] et al. (dashed lines), reporting good accuracy as shown in Figure 17.
## 3 Sensor Node Position Estimation
### The Position Packet Validation Algorithm
#### 3.2.1 Position Distance Algorithm  
When executing the position distance algorithm, a sensor node stores the information of the first received position packet. As soon as the next position packets are received, the sensor node calculates the distance between the source position of the newly received packet and the source position of each packet that was already stored. If all of these distances exceed a predefined threshold (i.e., Equation (4) is true), the packet is accepted and the sensor node stores its information to be used in future estimations. If at least one of the calculated distances does not exceed the threshold, the received position packet is considered invalid and it is dropped.  
Figure 6a shows a sensor node that received, validated, and stored two position packets at times \(t1\) and \(t2\). The shaded region represents the area where other position packets will be considered invalid based on the threshold. This is done to avoid the issue depicted in Figure 5b, where all the received position packets belong to the same quadrant, leading to poor position estimation due to GPS error. Any packet received from a position outside this area will be accepted.  
\[\forall p\in rp:d(p,r)>t \tag{4}\]  
where \(rp\) represents the set of received packets, \(r\) is the newly received packet, \(d(p,r)\) is the distance between \(p\) and \(r\), and \(t\) is the predefined threshold.  
Figure 5: (a) Position packets in opposite sides may not intersect due to the GPS error. (b) Poor trilateration due to all position packets laying on the same quadrant.
###### Abstract
Through these methods, it is possible to obtain information about metabolism and cell viability, allowing the elucidation of the concentration-dependent cytotoxic responses of the nanoparticles and providing valuable insights into their biocompatibility for further potential therapeutic efficacy [13; 33]. Therefore, a comprehensive evaluation of the cytotoxicity of these novel Fe\({}_{3}\)O\({}_{4}\)-Ag-decorated nanoparticles on relevant cell lines is vital to guarantee their biocompatibility for being considered suitable nanosystems for biomedical applications. This study is designed to investigate the cytotoxic effects of Fe\({}_{3}\)O\({}_{4}\)-Ag nanoparticles on human fibroblasts (HFF-1) and breast cancer cells (MCF-7), providing crucial information on their biocompatibility. For the toxicological study of nanomaterials, it is essential to investigate not only the cytotoxic effects but also the generation of reactive oxygen species (ROS), in vitro inflammation processes, and hemocompatibility. This approach provides broader knowledge of nanoparticles' impact on cellular processes. This study presents the synthesis and physicochemical characterization of Fe\({}_{3}\)O\({}_{4}\)-decorated Ag nanoparticles and a comprehensive nanotoxicology investigation. The study demonstrates the in vitro biocompatibility of these nanoparticles, showcasing their potential for future biomedical applications as antiproliferative agents. This potential stems from their magnetic properties and the ability to modify their surface with biological ligands and anticancer drugs.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### End-to-End Speech Synthesis
#### 4.6.1 Speech Synthesis Based on WaveNet  
WaveNet [58], which is evolved from the PixelCNN [59] or PixelRNN [60] model applied in image generation field, is a powerful generative model of raw audio waveforms. It was proposed by Deepmind (London, UK) in 2016 and opens the door for end-to-end speech synthesis. It is capable of generating relatively realistic-sounding human-like voices by directly modeling waveforms using a DNN model which is trained with recordings of real speech. It is a complete probabilistic autoregressive model that predicts the probability distribution of the current audio sample based on all samples that have been generated before. As an important component of WaveNet, dilated causal convolutions are used to ensure that WaveNet can only use the sampling points from 0 to \(t-1\) while generating the \(t\)th sampling point.  
The original WaveNet model uses autoregressive connections to synthesize waveforms one sample at a time, with each new sample conditioned on the previous ones. The joint probability of a waveform \(X=\{x_{1},x_{2},...,x_{T}\}\) can be factorised as follows:  
\[p(X)=\prod_{i=0}^{T-1}p(x_{i+1}|x_{1},x_{2},...,x_{i}). \tag{10}\]  
Like other speech synthesis models, WaveNet-based models can be divided into training phase and generation phase. At the training phase, the input sequences are real waveforms recorded from human speakers. At the generation phase, the network is sampled to generate synthetic utterances.  
To generate speech of the specified speaker or the specified text, global and local conditions are usually introduced to control the synthesis contents.  
While the WaveNet model can produce high-quality audios, it still suffers from the following problems: (1) it is too slow because the prediction of each sampling point always depends on the predicted sampling points before; (2) it also depends on linguistic features from an existing TTS front-end and the errors from the front-end text analysis will directly affect the synthesis effect.  
To address these problems, the parallel WaveNet is proposed to improve the sampling efficiency. It is capable of generating high-fidelity speech samples at more than 20 times faster [61]. Another neural model, Deep Voice [62], is also proposed to replace each component including a text analysis front-end, an acoustic model and a speech synthesizer by a corresponding neural network. However, since each component is trained independently, it is not a real end-to-end synthesis.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 5 Results and Discussion
### Cruise Pressure  
In Section 3.2.5, cruise pressure was proposed to represent the condition of the air route. Figure 16 depicts the average cruise pressure and the average delay per day of PEK-HGH flights from 1 January 2019 to 31 January 2020. It can be observed that flight delays and cruise pressure were consistent in most cases. The Pearson correlation coefficient was 0.81, which confirms a strong positive correlation. Additionally, the cruise pressure during 30 July and 5 August was significantly different from the other time frames, while the average delay was higher than in the other time frames. Checking the historical weather data, we interestingly found that there was rainfall, hail, and other severe weather in Beijing at that time. The observation shows that the method used in this paper to represent air-route condition is valid and can be used to predict delay.  
Figure 15 shows that the marginal effect of cruise pressure on delay tended to increase. There was a slight drop in delay before cruise pressure was 0 compared with the first concentrated value. This means that cruise pressure was not the main factor affecting flight delays. As cruise pressure increased, it indicated that the air route was more congested, resulting in increased delays.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 1 Introduction
The deep, low-permeability sandstone reservoirs of the Paleogene Wenchang Formation and Enping Formation contain vast amounts of oil and gas resources, accounting for over 30% of the total natural gas resources in the depression [37]. Currently, the focus of oil and gas exploration and development in the entire Pearl River Mouth Basin's Huizhou Sag is on the deep and ultra-deep layers of the Enping Formation [37]. Previous studies have been conducted on the provenance conditions, sedimentary characteristics, hydrocarbon accumulation, and reservoir prediction of the Enping Formation [37; 38]. It is posited that the sand bodies formed under the braided river sedimentary environment of the Enping Formation are the material basis for the formation of deep, low-permeability reservoirs. The reservoirs exhibit characteristics of being initially tight followed by accumulation, and oil is ubiquitous throughout the region [37; 38; 39]. Although there are many studies on the controlling factors of the deep reservoirs in the Enping Formation, research on these controlling factors is relatively superficial, and the impact of diagenesis on pore development is not clear [38]. Therefore, based on a comprehensive core analysis of test data from nine wells combined with previous research, this paper systematically studies the diagenesis of the deep, low-permeability sandstone reservoirs of the Paleogene Enping Formation in the Huizhou Sag and its impact on the reservoir. This study aims to elucidate the controlling factors of reservoirs' physical properties in the study area, thereby providing valuable geological support for deep oil and gas exploration in this block.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 1 Introduction
### Characterizing Training Data Error  
Due to different disciplinary lineages, terminology associated with the various datasets used to train and evaluate map algorithms is sometimes contradictory or disparate. Here we harmonize terminology by defining four distinct types of data: training, validation, training reference, and map reference. _Training data_ (TD) refers to a sample of observations, typically consisting of points or polygons, that relate image pixels and/or objects to semantic labels. _Validation data_ are typically a random subset of TD that are withheld and used to fit ML model parameters and internally evaluate performance. _Training reference data_ are expert-defined exemplar observations used to assess TD errors during or after data creation. _Map reference data_ are independent observations used to assess final map accuracy; while these may be collected using many of the same procedures as the other three datasets [57], they have more stringent design protocols and can only be used to assess the final map product, rather than used iteratively in model or map improvement [57]. Map reference data are often referred to as the test set in ML literature [77], but we use the former term to align with the terminology commonly used by the EO community.