esa-sceva/satcom-chunk-collection huggingface dataset:
# Conditional Dependencies and Position Fixing
## 3 Random Data Processing  
The distributions of the two-dimensional indications of the approximately observed locations are subjected to processing to obtain knowledge on the accuracy of the available navigational aids. Such processing is rarely performed in computer-based applications. Currently, seafarers exploit the standard deviations of Gaussian functions as satisfactory substitutes that fulfil the popular practical nautical requirement with regard to the un certainty evaluation, and the statistical dispersions are important factors. Seafarers only consider the delivered indication to be complete when it is accompanied by a specification of the associated uncertainty, which is usually understood as the circular or elliptic deflection. The values reflect the quality of the delivered data.  
I present the steps of the proposed random two-dimensional data processing in Figure 2. In the left part, I present the raw experimental data as the output of the various recorded experiments. I further upgraded the x- and y-axis histograms, and I evaluated the empirical data structures according to the embedded uncertainty. The doubtfulness is the main factor to consider when obtaining the converted forms of stepwise density functions. To achieve unique solutions, the density functions should be locally injective ones.  
The goal of the illustrated processing scheme is the creation of a function, converted histogram. The locally injective diagram enables the establishment of a conditional relationship (\(P(X|O)\)). The latter is a measure of the support for a given hypothesis, which is an element of the set (\(X\)) that is embedded within a particular observation that belongs to the collection (\(O\)).
## 5 Critical Aspects of FSS Technology
### The Limitations of Research on Frequency-Selective Surfaces (FSSs)  
Although significant progress has been made in the development and application of FSSs, several limitations persist in the current body of research. One notable limitation is the lack of comprehensive studies on the real-world performance of FSSs under diverse environmental conditions. Most designs are tested in controlled environments, as anechoic chambers, or laboratories, but variables such as temperature, humidity, or mechanical stress can impact the reliability and effectiveness of FSSs in practical settings. Additionally, many FSS designs are tailored to specific frequency bands, which makes it challenging to create broadly adaptable solutions. Finally, the need for multi-band or reconfigurable FSSs for modern applications (e.g., 5G, IoT, and satellite communication) remains underexplored.
## 3 Chemistry Applications under Ambient Conditions
### Potential for Enhanced Sensitivity and Resolution of WAXS  
One unique selling point for femtosecond solution WAXS at the FXE instrument lies in the high photon energies of up to 20 keV, which cannot yet be delivered as fundamental radiation at other FEL sources. Already in the Fall of 2019 the European XFEL machine has demonstrated stable lasing with 16.5 GeV electrons resulting in 20 keV X-ray photons with up to 900 \(\upmu\)J energy per pulse in the FXE branch. With such high photon energies and the high energy detection capability of LPD, it is possible to considerably extend the range of recorded momentum transfer in the WAXS patterns. As an example, for 20 keV with the large LPD detection area one can expect the maximum momentum transfer (\(q_{\rm max}\)) up to 12 A\({}^{-1}\). In this \(q\)-range, high resolution solute structural determination is possible together with a non-correlated extraction of the solvent observables, and all this during the very same measurement. This has been recently demonstrated in the proof-of-principle synchrotron pump-probe WAXS studies with 25.2 keV photons and 100 ps time resolution [50; 51]. Figure 6 shows an example of such high-resolution scattering measurement on the aqueous [Fe(bpy)\({}_{3}\)]\({}^{2+}\) performed at the ESRF synchrotron. The extended \(q\)-range allows to distinguish very similar potential molecular structures (here, the AIMD-calculated structure and the \({}^{5}\)A state structure in D\({}_{3}\) symmetry from DFT calculations) as well as substantially reduces the correlation of the LCF parameters for the extracted solvent (here the solvent temperature rise \(\Delta\)T) and the solute (here the excited state fraction) components.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 1 Introduction
In this scope, to recommend the right situation rule in the right context for the right person, a context-aware semantic model for rule-based recommendation system needs to be considered. Since situations are correlated with the user's context, it can be considered in designing a recommendation approach to enhance relevancy and reduce the execution time. This paper proposes a new de-facto classification of situation rules that reflect the different types of user's situations rather than dealing with each situation separately, which we believe provides an alternative as a complement to the standard semantic models. More precisely, we define four probability-based-context items (user's location, user's role, user's preferences and time) in their classification. Underlying these selections of such dimensions are three main situations rules classes that should be sufficient to derive other specific situations according to user's role, specific points of days and smart domain. Thus, summary of the contributions of this work are:  
* Enhance semantic description of user's context profile and improve classification of situations rule based on four probability-based-context items (user's location and time, user's role, user's preferences and user's experiences). * Apply multidimensional (_user's context_, _device capability and rule content_) recommendation space for smart environments. * Combine semantic classification techniques and Bayesian-classifier to improve recommendation of situation rules and a high accuracy rate by Bayesian-classifier. The weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user's situations. * Compare performance between the proposed approach and four other recommendation approaches among the most common algorithms learning-recommendation for various performance metrics. The remaining paper is organized as follows. Section 2 reviews the existing related works. Section 3 presents and details the proposed recommendation approach. Section 4 presents in detail the proposed methodology. Section 5 discusses some potential results and conclusions. Finally, Section 6 presents the conclusions of our paper and the future research directions.
# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
## 2 Related Work
### Method Based on Deep Learning  
In recent years, with the proposal of residual neural network and gated cyclic network, the depth of neural network has been greatly expanded. Larger network layers and more network parameters enable the model to adapt to more complex scenarios; thus, deep learning begins to shine in various fields, including intelligent operation and maintenance.  
[PERSON] et al. [14] proposed an early fault prediction method for circuits. This method uses fast Fourier transform (FFT) to obtain fault frequency characteristics, principal component analysis (PCA) to obtain dimensionality reduction data, and convolutional neural network (CNN) to learn and classify faults. [PERSON] et al. [15] proposed a new CNN model based on LeNet-5 for fault diagnosis. By converting the signal into a two-dimensional (2D) image, this method can extract the features of the converted two-dimensional image and eliminate the influence of manual features.  
In [16], a combination of a deep belief network, an autoencoder, and LSTM were introduced into the field of renewable energy power forecasting. Compared to the standard MLP and physical prediction models, the results of the deep-learning algorithm showed excellent prediction performance. In [17], a framework based on a long-term short-term memory (LSTM) recurrent neural network was proposed, which can adapt to the high volatility and uncertainty in time series to accurately predict the power load of a single user. [PERSON] et al. [18] proposed a short-term power load forecasting model based on the deep residual network, which can integrate domain knowledge and researchers' understandings of tasks with the help of different neural network building blocks. Several test cases and comparisons with existing models show that the proposed model provides accurate load forecasting results and has high generalization ability.  
To summarize, the traditional machine-learning model is difficult to deal with unstructured data and adapt to massive big-data scenarios. The neural network model needs a deeper number of layers to obtain the full picture of the data, needs to consider the balance between fitting and receptive field, and the deep neural network model depends on more system resources. Therefore, considering the above problems, this paper proposes a TCAG combination model.
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
## 2 Discussion of Orbital Parameters, Sampling Process, and SpOCK  
### Constellation Design Baseline Assumptions  
The achievable coverage of a constellation of remote sensing satellites is determined by both the orbit placements of the satellites within the constellation as well as the capability of the instruments carried on each satellite making the observations. Each individual satellite's orbit is defined by the traditional six Keplarian elements: eccentricity, semi-major axis, inclination, right ascension of the ascending node (RAAN), argument of periapsis (AoP) and true anomaly [12]. For purposes of remote sensing efficacy, the key design factors of the instrument itself include antenna configuration, number of parallel observations, and minimum observation signal strength. The principle design considerations for the constellation are the total number of satellites and their orbits relative to each other. Analyzing all the possible combinations of satellites, their orbits, and instrument configurations is not impractical. Therefore, we have made several assumptions and simplifications in order to isolate and evaluate the primary parameters driving the overall measurement coverage performance. This reduces the number of constellation design variables to a manageable and useful subset. The simulations below all include the following orbit and instrument configuration constraints and assumptions:  
1. All satellites are in circular orbits (eccentricy = 0, AoP not relevant), all satellites within an orbital plane are equally spaced (true anomalies fixed), all satellites in the constellation are at the same altitude (semi-major axis fixed). The number of satellites, the angular spacing of the orbit planes and the inclinations of the orbit planes are the three remaining variables considered in the constellation design;
2. The instruments on each satellite are constrained as follows: a dual antenna configuration of either (a) the NASA CYGNSS mission antennas or (b) a larger dual antenna design for the higher altitude orbit simulations. The instruments are all capable of tracking up to 16 specular reflection measurements in parallel, based on the current estimated capability of the next generation of GNSS-R instruments [13]. The signal strength required for viable land and ocean observations is based on CYGNSS retrievals vs. range corrected gain (RCG), which considers both antenna gain and path losses for individual surface measurements [14; 15].  
Using the above assumptions and constraints on the GNSS-R constellation configuration, the design parameters considered have been reduced to a manageable number: number of planes (incl. number of satellites per plane), inclinations of the planes, and minimum observation RCG. Each is explored individually in the sections below. The results and analysis are intended to examine the impacts of the design parameters on sampling and Earth coverage properties for possible future constellation missions.
## 4 Results
### n-Alkanes and Isoprenoids, Light Hydrocarbons, and Diamondoids
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Well** & **OEP** & **CPI** & **Ph/nC\({}_{18}\)** & **Pr/nC\({}_{17}\)** & **Pr/Ph** & **DBT/P** & **C\({}_{5}\)TT/** & **MAI** & **MDI** & **DNR** & **MDR** & **DMDBr** & **ReL/\%** & **ReL/\%** & **ReL/\%** \\ \hline S1-3 & 0.99 & 1.07 & 0.37 & 0.32 & 1.00 & 1.25 & 1.18 & 0.73 & 0.46 & 10.92 & 26.88 & 10.36 & 1.56 & 1.47 & 1.53 \\ S1-18 & 1.00 & 1.00 & 0.37 & 0.33 & 1.09 & 0.34 & 1.38 & 0.74 & 0.46 & 6.33 & 8.51 & 3.34 & 1.55 & 1.06 & 0.87 \\ S1-7 & 0.99 & 1.01 & 0.37 & 0.32 & 1.07 & 1.22 & 1.43 & 0.73 & 0.47 & 11.13 & 27.15 & 10.25 & 1.59 & 1.49 & 1.54 \\ S1-20 & 1.00 & 1.04 & 0.37 & 0.32 & 0.99 & 1.21 & 1.24 & 0.73 & 0.46 & 11.12 & 26.32 & 10.56 & 1.55 & 1.49 & 1.51 \\ S1-1 & 1.00 & 1.04 & 0.36 & 0.32 & 1.00 & 1.09 & 1.22 & 0.72 & 0.48 & 11.34 & 28.11 & 10.65 & 1.61 & 1.51 & 1.57 \\ S1-5 & 1.00 & 1.04 & 0.38 & 0.32 & 1.00 & 0.92 & 1.14 & 0.71 & 0.45 & 11.03 & 27.26 & 11.83 & 1.54 & 1.48 & 1.56 \\ S1-11 & 1.00 & 1.11 & 0.39 & 0.33 & 1.10 & 0.85 & 1.71 & 0.71 & 0.48 & 13.01 & 31.07 & 13.37 & 1.61 & 1.66 & 1.68 \\ S1-14 & 1.00 & 1.09 & 0.42 & 0.36 & 1.00 & 1.05 & 1.43 & 0.69 & 0.49 & 12.86 & 35.56 & 13.64 & 1.63 & 1.65 & 1.84 \\ S1-15 & 1.00 & 1.16 & 0.39 & 0.33 & 0.98 & 0.76 & 1.00 & 0.69 & 0.49 & 13.09 & 33.59 & 14.59 & 1.63 & 1.67 & 1.77 \\ S1-16 & 0.99 & 1.03 & 0.39 & 0.33 & 0.98 & 0.74 & 1.40 & 0.68 & 0.45 & 13.08 & 34.63 & 14.95 & 1.54 & 1.67 & 1.81 \\ S1-9 & 0.99 & 1.03 & 0.36 & 0.32 & 1.01 & 1.25 & 1.20 & 0.73 & 0.45 & 10.48 & 23.63 & 9.00 & 1.52 & 1.43 & 1.41 \\ S1-9 & 0.99 & 1.00 & 0.35 & 0.31 & 1.02 & 1.02 & 1.02 & 0.72 & 0.47 & 9.65 & 21.81 & 8.38 & 1.58 & 1.36 & 1.35 \\ S1-1 & 0.99 & 1.02 & 0.40 & 0.34 & 0.98 & 0.52 & 0.5 & 0.70 & 0.48 & 8.49 & 19.78 & 6.83 & 1.61 & 1.25 & 1.27 \\ \hline \end{tabular} Note: DBT/P = dibenzothiophene/phenanthrene; TT = tricyclic terpene; MAI = 1-MA/(1-MA + 2-MA); MDI = 4-MD/(1-MD+3-MD+ 4-MD); DNB = (2,6-DMN + 2,7-DMN)/1,5-DMN; MDB = 4-1-MDB; DMDBr = 4.6-DMDBT/(1,4-MDMD+1,6-DMDB); ReL(7) = 2.4389 MD+ 0.4364 [35]; R2(%) = 0.49 + 0.09 * DNR [36]; Rc3(%) = 0.036 * MDR + 0.56 [37].  
\end{table} 
Table 3: Geochemical parameters of the studied oil samples. Figure 2: (a) Representative whole oil gas chromatograms, \(m/z\) 191; (b) (3) \(m/z\) 217; (c) chromatograms showing n-alkane, terpene, and sterane distributions in the studied oils. Abbreviations: Pr = pristane; Ph = phytane; TT = tricyclic terpene.
###### Abstract  
Signal transduction pathways enable organisms to monitor their external environment and adjust gene regulation to appropriately modify their cellular processes. Second messenger nucleotides including cyclic adenosine monophosphate (c-AMP), cyclic guanosine monophosphate (c-GMP), cyclic di-guanosine monophosphate (c-di-GMP), and cyclic di-adenosine monophosphate (c-di-AMP) play key roles in many signal transduction pathways used by prokaryotes and/or eukaryotes. Among the various second messenger nucleotides molecules, c-di-AMP was discovered recently and has since been shown to be involved in cell growth, survival, and regulation of virulence, primarily within Gram-positive bacteria. The cellular level of c-di-AMP is maintained by a family of c-di-AMP synthesizing enzymes, diadenylate cyclases (DACs), and degradation enzymes, phosphodiesterase (PDEs). Genetic manipulation of DACs and PDEs have demonstrated that alteration of c-di-AMP levels impacts both growth and virulence of microorganisms. Unlike other second messenger molecules, c-di-AMP is essential for growth in several bacterial species as many basic cellular functions are regulated by c-di-AMP including cell wall maintenance, potassium ion homeostasis, DNA damage repair, etc. c-di-AMP follows a typical second messenger signaling pathway, beginning with binding to receptor molecules to subsequent regulation of downstream cellular processes. While c-di-AMP binds to specific proteins that regulate pathways in bacterial cells, c-di-AMP also binds to regulatory RNA molecules that control potassium ion channel expression in _Bacillus subtilis_. c-di-AMP signaling also occurs in eukaryotes, as bacterially produced c-di-AMP stimulates host immune responses during infection through binding of innate immune surveillance proteins. Due to its existence in diverse microorganisms, its involvement in crucial cellular activities, and its stimulating activity in host immune responses, c-di-AMP signaling pathway has become an attractive antimicrobial drug target and therefore has been the focus of intensive study in several important pathogens.  
c-di-AMP; Gram-positive bacteria; c-di-AMP synthesis and degradation; c-di-AMP-binding proteins +
Footnote †: journal: Journal of the Royal Society B: Biological and Physical Biology  
0000-0002-4000-0002-3899][PERSON] 00000-0002-4000-0002-3899][PERSON] 0000-0002-4000-0002-3899][PERSON] 0000-0002-0002-3899-3300]C.-H.A.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 4 Discussion
### Comparison with Other State-of-the-Art SITS Analysis Methods
Therefore, this event is not marked as a separate cluster of spatio-temporal evolutions on the resulting maps shown in Figure 14. On the contrary, the proposed query-by-example retrieval method also shows its potential in this use-case scenario by clearly delimiting the affected area and allowing estimation of the damaged surface.
## 2 Materials and Methods
### Quality Assurance and Quality Control  
The PhACs were identified by retention time and HPLC-MS/MS MRM mode, and the two ion transition pairs with the highest products were selected. The concentrations of the PhACs were computed using the internal standard method. A quality control sample (5 mg/L standards) was collected every five samples, followed by matrix spiked and blank samples. Calibration curves for the target analytes were drawn with eight points (0.5, 1, 5, 10, 20, 50, 100, and 200 \(\upmu\)g/L), and the determination coefficients (R\({}^{2}\)) of the regression curves were \(>\)0.99 for each compound.  
Recovery tests were performed by spiking with 1 \(\upmu\)g/L PhACs standard to six select seawater samples. The recoveries ranged from 69% to 112%, and the RSD values ranged from 0.59% to 5.03%. The limits of detection (LODs) and limits of quantification (LOQs) for the instrument were defined as 3 and 10 times the signal-to-noise (S/N) ratios, respectively. The LOQs ranged from 0.06 to 2.3 ng/L, and all instrumental and procedural blanks were below the LOQs.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 1 Introduction
Research in [20] presents a state estimation technique for pre-filter signal states estimate for the ultra-tight GPS/INS navigation. A fuzzy logic system is selected to improve the UKF performance and to prevent the filter divergence problem in the high dynamic environments and periods of the partial GPS outages. An unscented particle filter (UPF) is also added to the system as a pre-filtering technique to deal with the non-linear, non-Gaussian disturbances and leads to the significant accuracy improvement. A comprehensive comparison between tightly coupled integration and ultra-tight integration is given in [21]. The paper presents a fair analysis of tight coupling and deep integration as using same grade IMUs, satellite constellations, and GPS signal processing techniques. Authors in [22] presented an overview of various methods used to mitigate the impact on GNSS receivers caused by both unintentional and intentional jamming. The paper concluded that all the methods are helpful for jamming mitigation when applied individually but, also two or more methods can be integrated in one system. Authors in [23] proposed a method for tracking dual-frequency BeiDou B1/B2 signals simultaneously in a vector tracking architecture to construct an ultra-tightly coupled GNSS/INS system. The article reported that the proposed method can considerably enhance the tracking and navigation performance with respect to scalar tracking systems, but without showing any details about the type/characteristics of the used jamming signals. The main objective of this paper is to introduce an efficient ultra-tight GPS/3D Reduced Inertial Sensor System (RISS) integrated system that enhances the GPS receiver tracking robustness and sensitivity in challenging scenarios and, consequently, improves navigation availability.
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 5 Discussion
```
1:input \(a=256\)integer, maximal value of radiometric resolution, divisor
2:input \(k=9\)integer, number of spectral bands (raster layers)
3:input PX #integer, data fusion pixel final value
4:Output prx(k)#integer, original recalculated (reconstructed) values
5:for i in range (\(k\))
6:prx(i) = 0 freset current pixels' values in a stack of raster layers
7:for i in range (\(k\))
8:prx(i) = PX%a faculatecn reminder, original reconstructed pixel value for certain raster layer
9:PX = PX%a (faculatec dividend for next raster layer
10:for i in range (\(k\))
11:print prx(i)
```  
**Algorithm 2** Reed-Solomon pixel-level reverse algorithm for remote sensing data fusion \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & \(a=256\) **k = 9** & **Divisor = \(a\)** & **Reconstruction from Outcome** \\ \hline
**No of Spectral Band** & \(x\)**Pixel Value (Random)** & **Dividend** & **Modulo Division** \\ \hline
1 & 150 & 6.79246 \(\times\) 10\({}^{20}\) & 150 \\ \hline
2 & 75 & 2.65330 \(\times\) 10\({}^{18}\) & 75 \\ \hline
3 & 56 & 1.03645 \(\times\) 10\({}^{16}\) & 56 \\ \hline
4 & 188 & 4.04862 \(\times\) 10\({}^{13}\) & 188 \\ \hline
5 & 173 & 1.58149 \(\times\) 10\({}^{11}\) & 173 \\ \hline
6 & 204 & 617,770,444 & 204 \\ \hline
7 & 109 & 2,413,165 & 109 \\ \hline
8 & 210 & 9426 & 210 \\ \hline
9 & 36 & 36 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reverse procedure of the RS algorithm resulting pixel value from the random input pixel value for 256 levels of greyscale and 9 spectral bands.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 13 Associated Technologies
### Optical Amplifiers  
A suitable long-wavelength fiber amplifier, known as TDFA, was demonstrated decade ago [128], with a performance comparable to (gain, noise figure) or better (110 nm of bandwidth: 1910-202 nm) than that of EDFA. TDFA can be modified to operate in several additional bands down to 1660 nm by co-doping active fiber with thulium and aluminum. Both amplifiers may cover almost all of the low-loss 1530-2050 nm range [134], but an increase in output power to 10s of watts (\(\geq\)+40 dBm) is necessary to make full use of highly linear NANF fibers.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 5 Discussion and Future Perspectives
Although the resulting architecture is neither compliant with CANopen safety, nor with other safety protocols, its performance figures are satisfactory in that the network provides effective security countermeasures against some types of attacks, as well as safety countermeasures similar to those of the traditional functional safety protocols.