esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Data-Assimilation Algorithms
#### 4.2.3 Summary of Data-Assimilation Algorithms  
In the field of traditional data assimilation, each method has its strengths and limitations, depending on specific applications and resources. The Kalman Filter (KF) optimizes state estimates based on linear models and Gaussian noise assumptions but struggles with nonlinearity and non-Gaussian scenarios. The Ensemble Kalman Filter (EnKF) captures nonlinearity and broader uncertainties using multiple samples, though its performance is influenced by sample size. The Particle Filter (PF) addresses complex nonlinearity and non-Gaussian issues through weighted particles, albeit with high computational costs. Variational Methods optimize a cost function to integrate observations and model predictions, effectively managing uncertainty but requiring accurate error covariances and significant computational resources.  
Emerging Machine Learning Methods tackle uncertainty by learning complex patterns from data, though their efficacy depends on data quality and model training. While 4D-VAR and the ensemble Kalman filter series are well-established and widely used, intelligent assimilation algorithms, including particle filters, Bayesian methods, and neural networks, represent promising directions for future development. The choice of assimilation algorithms and models in practical research largely depends on the specific characteristics of the problem (such as model structure, observation data type, and error manifestations) and available computational resources. Researchers may opt for a single suitable method or combine various methods to leverage their strengths and mitigate their weaknesses.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 1 Introduction
Our proposed method leverages the characteristics of angle estimation, which depends on target detection results and the selectivity and latency of input data. Instead of precomputing and storing the entire data set's Doppler FFT in advance, this method dynamically extracts data corresponding to specific target ranges and performs real-time Doppler FFT computation after target detection. Subsequently, a secondary filtering process is applied based on velocity information, reducing storage resource consumption while enhancing flexibility in data storage and management. Additionally, a data retrieval and reordering mechanism is introduced to improve adaptability to different antenna array configurations. Experimental results demonstrate that the efficient dynamic MIMO radar engineering system outperforms conventional systems in storage resource consumption and real-time processing performance. Specifically, under the same antenna channel data volume, the proposed efficient dynamic engineering implementation architecture achieves a 50% reduction in storage resource consumption and a 15% improvement in processing speed. Furthermore, it exhibits enhanced MIMO antenna array compatibility, where, given a fixed number of virtual antenna channels, the system can adapt to changes in antenna array configurations simply by reconfiguring the array information. Notably, the overall processing time remains stable regardless of variations in the antenna array layout, ensuring system robustness and efficiency. The structure of this paper is organized as follows: Section 2 discusses the fundamental principles of MIMO radar and analyzes the architecture of traditional MIMO radar engineering implementation systems and their limitations. Section 3 proposes a new efficient dynamic MIMO radar engineering implementation architecture and describes the process implemented on an FPGA platform for this efficient dynamic MIMO radar engineering system. Section 4 constructs two types of radio frequency (RF) boards, each equipped with different antenna arrays, to assess the effectiveness of the dynamic MIMO radar engineering implementation system. Section 5 analyzes the performance of the efficient dynamic MIMO radar engineering implementation system through practical experiments. Section 6 concludes the work presented in this paper.
## 3 Results
### Iodine Content in Lettuce  
The highest concentrations of iodine were seen in lettuce subjected to the SDA treatments (25 mg I kg\({}^{-1}\) KI or KIO\({}_{3}\)), regardless of Cs (Figure 1). SDA of KI and Cs-KI increased lettuce iodine concentrations to levels 77.9- and 63.7-times, respectively, above the corresponding controls. Similarly, SDA of KIO\({}_{3}\) and Cs-KIO\({}_{3}\) increased concentrations by factors of 97.4 and 36.4, respectively. The highest recorded concentration was 103.16 mg I kg\({}^{-1}\) dry weight (DW), following SDA of KIO\({}_{3}\) (25 mg I kg\({}^{-1}\)). Apart from those results, single applications of Cs- KIO\({}_{3}\) and KIO\({}_{3}\) (25 mg I kg\({}^{-1}\)) also led to improvements in iodine concentrations (17.4- and 18.2-times above control concentrations, respectively). Single applications of KI and Cs-KI (25 mg I kg\({}^{-1}\)) increased iodine concentrations by factors of 14.4 and 24.4, respectively. However, these improvements were not significant. According to the analysis of variance the iodine species did not have a significant impact on the results, rather the concentrations applied, and the presence of chitosan were the statistically important factors. The only interaction with significant differences that the iodine species factor had was with the chitosan factor (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatments** & **Variable 1** & **Variable 2** & **Spearman** & \(p\)**-value** \\ \hline \multirow{6}{*}{General treatment analysis} & \multicolumn{2}{c}{Zinc} & \(-\)0.04 & 0.7056 \\  & \multicolumn{2}{c}{Manganese} & 0.4 & 0.0001 \\  & \multicolumn{2}{c}{Iron} & \(-\)0.06 & 0.5316 \\  & \multicolumn{2}{c}{Copper} & \(-\)0.03 & 0.7894 \\  & \multicolumn{2}{c}{Sodium} & \(-\)0.16 & 0.1275 \\  & \multicolumn{2}{c}{Potassium} & \(-\)0.31 & 0.0018 \\  & \multicolumn{2}{c}{Nitrogen} & 0.05 & 0.6013 \\  & \multicolumn{2}{c}{Magnesium} & \(-\)0.18 & 0.0827 \\  & \multicolumn{2}{c}{Phosphorus} & \(-\)0.17 & 0.0951 \\  & \multicolumn{2}{c}{Calcium} & \(-\)0.22 & 0.0301 \\ \hline \hline \end{tabular} Values of \(\alpha\leq 0.05\) are significant. Spearman correlation coefficients greater than +0.5 indicate positive, moderately strong correlations or less than \(-0.5\) indicate negative, moderately strong correlations, according to a previously described scale [40].  
\end{table}
Table 4: Correlation analysis of iodine and minerals.  
Figure 1: Iodine concentrations in mixed samples of lettuce leaves and heads following iodine salt treatments with and without chitosan. Means with the same letter are statistically identical (LSD, \(p\leq 0.05\)); the bars represent the standard error (n = 6).
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 2 GNSS Processing
### Post-Fit Residuals  
The mean RMS values for the station-specific post-fit residuals of ionosphere-free linear combination of B1 and B3 code and phase observations from the POD parameter estimation are shown in Figure 2. The station THAT (Table 2) cannot observe BDS-3 IGSO (C31 and C32) satellites. The mean phase residuals for all stations are about 1.01 cm, 1.13 cm, 0.99 cm, and 1.01 cm for BDS-2 IGSO, BDS-2 MEO, BDS-3 IGSO, and BDS-3 MEO satellites, respectively. The mean code residuals for all stations are about 1.66 m, 1.86 m, 1.94 m, and 1.69 m for BDS-2 IGSO, BDS-2 MEO, BDS-3 IGSO, and BDS-3 MEO satellites, respectively. We can see that the phase and code post-fit residuals of BDS-2 and BDS-3 are nearly the same level. It is noted that these errors are amplified by a factor of about three when forming the ionosphere-free linear combination, compared to the single-frequency measurement. In the next section, we will further analyze pseudo-range errors (multipath and noise) by the multipath combination.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Models** \\ \hline Basic observable & B1 and B3 code and phase observations of BDS-2 and BDS-3 \\ Modeled observable & Ionospheric-free linear combination \\ Sampling rate & 300 s \\ Elevation cutoff & 10\({}^{\circ}\) \\ Weighting & A priori precision of 0.002 m and 2.0 m for raw phase and code observables, respectively, and elevation-dependent data weighting \\ Phase wind up & Phase polarization effects applied [26] \\ Tropospheric delay & Saastamoinen model [27], global mapping function [28], two-hourly ZTD without gradients \\ Tide displacement & Solid Earth tide, pole tide, ocean tide loading; according to IERS Conventions 2003 [29] \\ Relativity effect & Considered according to IERS Conventions 2003 [29] \\ Geopotential & EIGEN\_GL04C up to 12 \(\times\) 12 degree \\ N-body gravitation & Sun, Moon, and other planets; JPL DE405 ephemeris used \\ Solar radiation & ECOM 5-parameter model [30] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Important options of the GNSS processing for POD.
## Materials and Methods
### Sample Preparation and Volatile Compound Analysis  
Volatile compounds were analyzed using headspace solid phase microextraction (HS-SPME). Before use, chicken breasts were thawed at room temperature by immersion of the frozen vacuum-packed samples in water, for 10 min. White tendons were removed and samples were minced by means of a mini chopper (DS Produkte GmbH, Gallin, Germany). Next, 4.5 \(\pm\) 0.05 g of meat were weighed and transferred into 20 mL glass headspace vials together with 5 \(\mu\)L of a methanolic solution of 5 ppm 1,2-dichlorobenzene (Sigma-Aldrich, Munich, Germany), as internal standard. Vials were sealed with polytetrafluoroethylene (PTFE)-faced silicone septum (Macherey-Nagel, Duren, Germany). Meat samples were heated at 70 \({}^{\circ}\)C for 10 min in a TriPlus RSH\({}^{\mathrm{TM}}\) autosampler (Thermo Fisher Scientific, Waltham, MA, USA) for equilibration, before exposing the 30/50 \(\upmu\)Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Bellefonte, PA, USA) into the headspace, where it was held for 20 min under constant stirring. Before its first use, the solid phase microextraction (SPME) fiber was thermally pre-conditioned at 270 \({}^{\circ}\)C for 1 h in accordance with the manufacturer recommendation.
## 2 Literature Review
### Contractors' BNB Decision Factors  
A tentative list of 31 factors frequently cited in the related literature is shown in Table 1. These factors are restructured in line with [PERSON]'s [25] proposal, which offers generic criteria for categorizing the factors into three groups: project-related, firm-related, and market-related factors. As Table 1 indicates, the diversity of these factors suggests that contractors' BNB decisions are not uniform. First, project-to-bid is one of the most crucial decision factors. A contractor's determination to bid is likely due to the attractiveness of a project's location [4], type [4], size [26], complexity [27], and profit track record for similar projects [28]. Second, clients' requirements are another aspect that contractors should pay attention to. Contractors have to evaluate, in detail, clients' reputations and financial resources [3], the contract type [10], the criteria for bid selection [8], the original price estimated by the client [7], the project duration [4], required subcontractors [3], the quality of bidding documents (e.g., drawings and specifications) [3], security requirements (e.g., bid security and performance security) [3], terms of payment [26], and time for bid preparation [8]. Third, contractors' situations are not exclusive and include the needs of the work (i.e., labor force, materials, equipment, and funds) [8], current workload [2], contracting experience [3], financial situation [26], relationship with the client [29], and compliance with strategies [23]. Fourth, competitor analysis is a key element of BNB decisions. The elements of competitor analysis include the availability of other projects [2], the competence of the expected competitors [23], the current workloads of the expected significant competitors [3], and the number of expected competitors [4].
# Do Pores Exist?--Foundational Issues in Pore Structural Characterisation
## 1 Introduction
\begin{table}
\begin{tabular}{p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt}} \hline
**Pore Characterisation Method** & **Pore Size Range** & **Most Appropriate Porous Materials** & **Advantages** & **Disadvantages** \\ \hline Gas sorption (with overcondensation) & 0.3 nm–10 \(\upmu\)m & Sol–gel silica, alumina, carbons, MOFs, zeolites, shales. & Wide pore size range within single experiment. & Indirect—requires model of interpretation. Sample needs pre-preparation (e.g., drying and evacuation) \\ \hline Mercury porosimetry & 3.5 nm–100 \(\upmu\)m & Sol–gel silica, alumina, monoliths, rocks. & Wide pore size range within single experiment. & Indirect—requires model of interpretation. Sample needs pre-preparation (e.g., drying and evacuation). \\ \hline Thermoporometry & 2 nm–100 nm & Wet samples, e.g., gels. & Can run wet samples using pore fluid as probe. & The Gibbs–Thomson parameter limits pore size range for many probe fluids. \\ \hline Small-angle X-ray scattering (SAXS) & 0.3 nm–200 nm & Samples with closed porosity, e.g., shales. & Can probe closed porosity. & Strugles with deconvolution of partially ordered systems. \\ \hline NMR relaxometry & 1 nm–10 \(\upmu\)m & Wet samples. & Can use on already wet samples using fluid as probe. & The presence of paramagnetic impurities can complicate interpretation. \\ \hline  & & & & Small sample size (microns). Slice thickness is finite. Beam can cause sample damage. & Slice thickness is finite. Beam can cause sample damage. \\ \hline FIB-SEM & 10 nm–1000 nm & Silicas, aluminas, carbons, rocks. & Direct characterisation. & Small sample size (microns). Slice thickness is finite. Beam can cause sample damage. & Small sample size (microns). Slice thickness is finite. Beam can cause sample damage. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the main pore characterisation methods.
## 2 Review of Existing Studies
### Local Scale Studies
#### Digital image classification
These studies also indicate that multi-temporal satellite remote sensing is a viable approach for identifying and monitoring irrigation. Methods based on multi-temporal NDVI features derived from Landsat data were particularly successful in distinguishing healthy irrigated lands from uncultivated and nonproductive areas.
# Hardware Model Checking Algorithms and Techniques
## 5 Interpolation-Based Model Checking  
[PERSON]'s _interpolation theorem_ is a fundamental result in mathematical logic taking into account the relationship between model theory and proof theory. Interpolation, despite its heavy theoretical foundation, has found many practical applications in different areas of computer science. Originally, the formulation of the theorem introduced by [PERSON] [40] was given in the context of first-order logic. Variants of the theorem hold for other logical systems as well, including propositional logic, which is the context we will focus on hereinafter, as it is the one typically encountered in the context of model checking.  
**Theorem 6** (Propositional Craig's Interpolation Theorem).: _Given two propositional formulas \(A\) and \(B\), if \(A\wedge B\) is unsatisfiable then there is a propositional formula 1, called interpolant between \(A\) and \(B\), such that_  
* \(A\to I\) _is valid;_
* \(I\wedge B\) _is unsatisfiable;_
* \(\operatorname{Vars}(I)\subseteq\operatorname{Vars}(A)\cap\operatorname{Vars }(B)\)_._  
Intuitively, \(I\) is an abstraction of \(A\) from the standpoint of \(B\). The interpolant summarizes and translates in the common language between the two formulas the reasons why \(A\) and \(B\) are inconsistent. For ease of notation, we denote with \(I=\textsc{Itp}(A,B)\) the procedure that derives a [PERSON]'s interpolant starting from a pair of inconsistent formulas \(A\) and \(B\).  
Interpolants can be derived from refutation proofs of unsatisfiable SAT-solving runs. Given an unsatisfiable formula \(A\wedge B\), most modern SAT solvers are capable of generating a refutation proof, as described in Section 2.6, either in resolution-based or clausal form. Given a resolution proof, an interpolant \(I=\textsc{Itp}(A,B)\) can be derived in the form of an AND/OR combinational circuit in polynomial time and space with respect to the size of the proof. Different algorithms for generating interpolants from resolution proofs have been proposed, such as the ones by [PERSON] [41], [PERSON] [42], [PERSON] [43] and [PERSON] [44]. We refer to those algorithms as _interpolation systems_. Interpolants can also be derived as CNF formulas, instead of circuits, from either resolution proofs [45] or clausal proofs [46]. Although interpolants may be quite large in size, different approaches exist to compact them, also taking into account the relation between the size and strength of the interpolants themselves [47].  
In the context of model checking, if \(A\) represents a set of reachable states and \(B\) represents a set of bad states, then the interpolant \(I=\textsc{Itp}(A,B)\) can be deemed as a safe overapproximation of \(A\) with respect to \(B\). In turn, such overapproximations can be used to detect a reachability fix-point.
## 2 Application Areas  
In this section, we briefly explain some major application areas of privacy-preserving data aggregation (PPDA) protocols where the leakage of sensor data is a critical issue for WSN users. People might not agree to allow an application to intrude on their personal domain if the privacy of the collected information is not protected. Some common application areas of the PPDA protocol are health monitoring, military surveillance and private households.  
_(a) Health Monitoring:_ There are two main health monitoring applications for WSNs. First, there is athletic performance monitoring such as tracking a person's pulse and respiration rate via wearable sensors. Secondly, using health sensors, we can monitor the health of patients, e.g., personal weight, blood sugar level, blood pressure, etc. These sensor measurements of people's health data should be kept private and hidden from other people during transmission with aggregation to the sink node.  
_(b) Military Surveillance:_ In military communications, we can use WSNs to replace guards and sentries around defensive perimeters, keeping soldiers out of harm's way, to locate and identify targets for potential attacks and to support attacks by locating friendly troops and unmanned vehicles. Therefore, the privacy of the sensor data is always critical and it should be preserved during aggregation.  
_(c) Private Households:_ As mentioned in PDA [17], wireless sensors could be placed in houses in order to collect statistics about water, gas and electricity consumption within a large neighborhood. The aggregated population statistics may be useful for individuals, businesses and government agencies for resource planning purposes and usage advice. However, the individual sensor readings could reveal the daily activities of a household, such as when all family members are absent or when someone is taking a shower; _i.e.,_ different water appliances have distinct signatures of consumption that can reveal their identity. Hence, we need a way to collect the aggregated sensor readings while preserving data privacy.
# A Sentiment Analysis Model Based on User Experiences of Dubrovnik on the Tripadvisor Platform
## 2 Background and Related Work
### Sentiment Analysis
#### 2.1.2 Limitations of Sentiment Analyses in Tourism  
The author of [35] points out that an important limitation of any form of a sentiment analysis in social networks is that the individuals who actively participate in these platforms represent only a portion of the total visitor population. In addition, these users may have strong biases. Visitors who have had a positive or negative experience are more likely to share their experiences on social networks, perhaps even most likely if the experience was bad [38]. Older people and children may also be less likely to share their experiences on social networks, because they are not active users. Conversely, busy parents may not be able to post reviews regularly. There may also be more subtle biases in user demographics, for example, those in favor of certain ethnic groups or social classes. Some reviews may be fake, possibly malicious reviews from competitors or paid positive reviews with the aim of promoting a new or lesser-known destination. There is no adequate substitute for social networks, nor is there an effective online solution for those groups of people who are not adequately represented. Therefore, researchers should be cautious in identifying sources of bias, analyzing their influence, and interpreting results in light of these factors [35].  
It is important to note that no information about the user, such as their name, age, etc., was used in our study. It should also be emphasized that the individuals were not interviewed, but the employed method was conducted with publicly available data; however, this does not mean that biases did not influence this research study's outcomes. There are some similar approaches in the literature [39; 40; 41; 42], but, to the best of our knowledge, we have not found a single approach that is fully comparable to our model presented in this paper.  
Dubrovnik is a popular tourist destination, i.e., a tourist hotspot like Venice, Barcelona, and Amsterdam, etc. These cities have common problems with overtourism, so Dubrovnik can be considered as a use case in our study, providing an informal insight into opinions about tourist attractions. However, we cannot generalize, as these cities do not have the same attractions; thus, we cannot normalize the dataset to make a direct comparison. Nevertheless, our approach can be extended to other cases. First, the reviews collected from Tripadvisor for the attractions of these cities should be collected. After that, our original model should be modified following the characteristic of newly acquired attractions. After that, modifications of the analysis process should be applied. A detailed elaboration is out of the scope of this paper. We conducted an initial feasibility study with promising results, even with different domains outside of tourism.
## 2 Results
### Identification of GJ-7 VOCs through SMPE-GC-MS Analysis  
VOCs of the GJ-7 strain were identified based on the comparison of GC-MS results (Figure 2) with the NIST Mass Spectra database with a similarity index (SI) > 95. We identified six characteristic VOCs in the GJ-7 strain (Table 1), all of which included a diverse collection of alcohols, ketones, and pyrazines. The VOCs were selected for time durations of 0-25 min. The numbers represent peaks for different VOCs, and they are, respectively: 3-methyl-1-butanol for peak 2, 3-methyl-2-pentanone for peak 3, 5-methyl-2-hexanone for peak 5, 2-heptanone for peak 6, 2,5-dimethylpyrazine for peak 8, and 6-methyl-2-heptanone for peak 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **RT (min)** & **SI** & **Molecular Weight** & **Peak Area (\%)** & **Peak Number** \\ \hline
3-methyl-1-butanol & 4.29 & 96 & 88.15 & 4.44 & 2 \\
3-methyl-2-pentanone & 4.665 & 97 & 100.16 & 3.56 & 3 \\
5-methyl-2-hexanone & 7.565 & 96 & 114.19 & 2.46 & 5 \\
2-heptanone & 8.65 & 98 & 114.19 & 14.49 & 6 \\
2,5-dimethylpyrazine & 9.375 & 95 & 108.14 & 16.28 & 8 \\
6-methyl-2-heptanone & 10.95 & 96 & 128.21 & 15.56 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GC/MS analysis of strain GJ-7 fermentation broth.  
Figure 2: The volatiles were extracted by SPME and analyzed by GC-MS. LB medium (**A**); the GJ-7 strain fermentation broth (**B**). Peaks: (1) dimethyl-silanediol, (2) 3-methyl-1-butanol, (3) 3-methyl-2-pentanone, (4) hexamethyl-cyclotrisiloxane, (5) 5-methyl-2-hexanone, (6) 2-heptanone, (7) Oxime-,methoxy-phenyl-, (8) 2,5-dimethylpyrazine, (9) 6-methyl-2-heptanone, (10) benzaldehyde.