esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hilbert-Huang Transform  
### Empirical Mode Decomposition  
EMD is an approach in which a complicated signal can be expressed as the sum of \(n\) number of intrinsic mode functions (IMF). Identifying the features contained in respective IMFs in an effective manner, EMD demonstrates a high adaptivity in the aspect of nonlinear as well as non-stationary signal analyses [12].  
It is postulated in an EMD analysis that an arbitrary signal \(x(t)\) can be written as the sum of a number of IMFs, each reflecting the physical meaning over respective spectral band. A typical IMF must satisfy two of the underlying conditions. The first condition is that there exist the same number of extremums as zero crossing points, or a maximum difference of unity between such two numbers, while the second is that the upper and the lower envelopes, covering all the local maximum and minimum points, respectively, and exhibit a zero mean.  
An EMD decomposition is made through the following steps. As the first step, all the local extremum points must be located, then concatenated via cubic splines so as to form the upper and the lower envelopes respectively [13; 14]. The mean \(m_{10}\) between such two envelopes is evaluated as  
\[x(t)-m_{10}=h_{10} \tag{1}\]  
In case \(h_{10}\) does satisfy the IMF definition, \(h_{10}\) is treated as the first IMF. If not, the above step is iterated with \(h_{10}\) as the initial value until the maximum shifting number \(k\) is reached or the IMF definition is satisfied.  
\[h_{1(k-1)}-m_{1k}=h_{1k} \tag{2}\]  
Empirically, it is very unlikely to make \(h_{1k}\) satisfy the IMF definition in the course of decomposition, meaning that it takes the maximum number, \(k\), of iterations to terminate the decomposition process. Consequently, a critical issue of the precise determination of the optimal shifting number in each IMF is detailed as follows.  
Subsequently, letting \(c_{1}\)=\(h_{1k}\), \(c_{1}\) is extracted out of \(x(t)\), and \(r_{1}\), the updated signal, is given as  
\[x(t)-c_{1}=r_{1} \tag{3}\]  
Performing \(n\) times of the above iterations leads to \(n\) IMFs, presented as  
\[\begin{array}{ccccc}r_{1}&-&c_{2}&=&r_{2}\\ &\vdots&\\ r_{n-1}&-&c_{n}&=&r_{n}\end{array} \tag{4}\]  
Such iteration terminates in the event that no more IMF can be found for a monotonic function \(r_{n}\). Derived from (3) and (4), the EMD decomposition is represented as  
\[x(t)=\sum_{i=1}^{n}c_{i}+r_{n} \tag{5}\]
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 4 Developments of Computational Chemistry
### Quantum Chemistry
[159] developed and evaluated two Quantitative-Structure-Activity-Relationship (QASR) models to describe the relationship between quantum parameters and removal behaviors that include the reaction rate constants of total nitrogen (k\({}_{\text{TN}}\)) and the temperature at which the total nitrogen removal efficiency is 50% (T\({}_{\text{TN}50}\)). The degradation behaviors of organics in SCWO could be predicted through quantum parameters by QASR analysis, reducing the need for further experiments to gain optimum reaction conditions. For the former developed DCKMs in SCWO [119; 120; 121], many reaction rates have no reliable, experimentally determined parameters since the experimental measurement is limited by the high temperature and high-pressure conditions of SCWO. Some scholars focus on the quantum chemistry calculations to gain accurate reaction rates [160; 161]. Ideally, all reaction rates could be calculated by ab initio transition state theory (TST) calculations and be more accurate than those estimated from general rate estimation methods [160]. [PERSON] et al. [162] used high-accuracy quantum chemical calculations for the OH\(\bullet\)-initiated SCWO reaction of 2-chlorophenol (2-CP) and performed geometry optimizations of the reactants, the product radicals, the pre-reactive and the transitions state complexes with the DFT method. The theoretical calculations showed that OH\(\bullet\) adding to the para-C atom of 2-CP was the easiest to occur due to the lowest energy barrier, and was the most favorable process for forming chlorohydroquinone. Although the theoretical results were consistent with the experimental findingsin product distributions, they were not further applied to dynamics modeling since the reactions were simulated in gas conditions.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 4 Discussion  
We developed a multi-band ARC that can calibrate L-, C-, X- and Ka-band spaceborne SARs, evaluate the image quality, and verify the calibration results. Although the multi-band ARC has many advantages, the system, especially the RF subsystem and antenna, is very complex, and it is rather expensive compared with a single-band ARC. Moreover, any piece of equipment has a given lifetime. Therefore, if the interval between the launch of various band spaceborne SARs is too long, we should consider using a single-band ARC for each SAR for the sake of cost and simplification. Of course, if there are already various band SARs in space, the use of the multi-band ARC will be very meaningful because on the one hand, the cost of a multi-band ARC will be much lower than the total cost of multiple single-band ARCs, and on the other hand, the number of ARC will be reduced a lot by using the multi-band ARC compared with the single-band ARC for each SAR, which will reduce the workload of the calibration campaign.  
In this paper, we presented the results of the multi-band ARC. We found that the gain stability and the gain flatness of Ka-band were slightly worse than the other three bands, which is attributed to the very high frequency of the Ka-band, and the measurement error, which will be large. Furthermore, this paper only includes measurements conducted in the laboratory, and the experimental results for in-orbit calibration will be given after the satellite is launched in our future work.
# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 2 Material and Methods
### Proposed Approach
#### 2.2.2 Machine Learning Methods  
A series of machine learning methods were used as classifiers for the provided dataset:  
1. GENCLASS: GenClass is a promising classification tool based on genetic programming. It utilizes the Grammatical Evolution method [30] to generate classification rules for a Backus-Naur Form. Its experimental evaluation on a number of different datasets demonstrates its superiority in the majority of cases. This technique was initially presented in the work of [PERSON] [12]. Furthermore, a software that implements this method was also published recently [31]. A short description of this method can be found in Section 2.3.
2. MLP: A combination of a standard neural network [32; 33] and Broyden-Fletcher-Goldfarb-Shanno (BFGS), i.e., an iterative optimization algorithm [34]. BFGS is used to optimize the parameters of the MLP model and is characterized by generality and ease of implementation.
3. SVM: a Support Vector Machine method [35; 36] was used as a machine learning model in the conducted experiments.
4. TREE: the well-known method of Decision Trees [37; 38] was also used as a classification method.
5. FC2 (RBF): Represents the method of feature construction with the assistance of Grammatical Evolution, initially presented in the work of [PERSON] et al. [39]. In this approach, \(N_{F}=2\) artificial features were created using Grammatical Evolution and subsequently evaluated using the Radial Basis Function (RBF) network [40; 41]. The creation of new artificial features from the existing ones on the one hand aims to reduce the dimension of the problem and help to optimize the generalization ability of machine learning models and on the other hand to find hidden correlations that may exist between the existing features which would lead to more efficient training machine learning models.
6. FC2 (MLP): Represents the application of feature construction technique to create two artificial features for the provided dataset. These features were evaluated using a neural network. A brief description of the feature construction method can be found in Section 2.4.  
In this work, two techniques based on Grammatical Evolution were selected for comparative study: the construction of artificial features and the construction of classification rules. The first technique aims to reduce, in principle, the number of features required to successfully classify the data but also to discover hidden associations between existing features that could not be discovered otherwise. The second technique, which from the experimental results appeared to be more reliable, creates classification rules from the existing characteristics on the one hand and on the other hand can be used as a selector of those characteristics that contribute more to the correct classification of the patterns.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 3 CDA Adoptability and Aircraft Descent Times
### Estimation of Aircraft Descent Time
#### 3.2.3. Thrust Model  
BADA uses a general formula to calculate the maximum climb thrust, \(Thr_{\max,\text{climb}}\), at a standard atmosphere for three different types of engines: jet, turboprop, and piston engines. For jet engines, the general equation is given as  
\[Thr_{\max,\text{climb}}=~{}C_{Tc,1}\times\left(1-\frac{H_{p}}{C_{Tc,2}}+C_{Tc,3} \times H_{p}^{2}\right) \tag{8}\]  
Since BADA uses this maximum climb thrust for both take-off and climb phases, the descent thrust is then calculated from the maximum climb thrust using adjustment coefficients for cruise, approach, and landing configurations [41], respectively, as follows:  
\[Thr_{\text{des,low}}=~{}C_{Tdes,\text{low}}\times Thr_{\max,\text{climb}} \tag{9}\]  
\[Thr_{\text{des,app}}=~{}C_{Tdes,\text{app}}\times Thr_{\max,\text{climb}} \tag{10}\]  
\[Thr_{\text{des,ld}}=~{}C_{Tdes,\text{ld}}\times Thr_{\max,\text{climb}} \tag{11}\]  
where \(C_{Tc,1}\), \(C_{Tc,2}\), \(C_{Tc,3}\), \(C_{Tdes,\text{low}}\), \(C_{Tdes,\text{app}}\), and \(C_{Tdes,\text{ld}}\) are aircraft-specific coefficients, and \(H_{p}\) is the geo-potential pressure altitude, in feet. The rate, in feet per minute, at which an aircraft's altitude changes with respect to time when descending and approaching the runway for landing is the Rate of Descent (ROD). ROD is given by  
\[ROD=\frac{dh}{dt}=\frac{(Thr_{des}-D)V_{TAS}}{mg}-\frac{V}{g}\frac{dV}{dt} \tag{12}\]  
where \(dV/dt\) is the aircraft's vertical speed, in feet of descent per minute. Given that the typical target of a flight path is about 3 degrees, the flight path angle, \(\gamma\), in degrees for a 3-degrees flight over the descent path is  
\[\gamma=\sin^{-1}(\frac{ROD}{V_{app}}) \tag{13}\]  
where \(V_{app}\) is the aircraft approach speed, in knots. The distance, in nautical miles, that the aircraft covers over the descent path is given as follows:  
\[\text{Distance}= \frac{(\Delta h\div 100)}{\gamma} \tag{14}\]  
where \(\Delta h\) is the difference between the altitude that the aircraft is currently flying at and the altitude that the aircraft will descend to, in feet. Finally, the time, in minutes, that the aircraft takes to descend and land can be estimated by dividing the difference in altitude, in feet, by the rate of descent, in feet per minute, as follows:  
\[\text{Aircraft}\;\text{Landing}\;\text{Time}= \frac{\Delta h}{ROD} \tag{15}\]
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 3 Analysis of Measured Data
In one study [18], the average pit excavation depth was 16 m; the soil to be excavated was a single stratum dominated by miscellaneous fill, vegetative fill, and pulverized clay, and the maximum settlement of a pipeline was 0.083% of the average value of the pit excavation depth. In another study [19], the average depth of pit excavation was 30.2 m; the soil to be excavated was a soft clay stratum consisting of fill, mud clay, and powdery clay; and the maximum settlement of the pipeline was 0.298% of the average depth of pit excavation. In this study, within the composite soil-rock strata, the maximum pipeline settlement was only 0.042% of the average excavation depth. This value is significantly lower than the maximum settlements in the other two cases, being only 50.6% of that in reference [18] and 14.1% of that in reference [19]. This indicates that compared to single-soil strata, relatively less settlement deformation occurs in adjacent pipelines when excavating foundation pits in composite soil-rock strata. Figure 3: Pipelines settlement versus time. (**a**) P1–8; (**b**) P9–16.
### Contributions and Organisation  
The contributions of this paper are thus summarized as follow:  
* By capturing the spatial distributions of users in both primary and secondary networks following two independent Poisson point processes (PPPs), we obtained tractable analysis for the conventional success probability (i.e., service/update delivery rate) in the considered hybrid spectrum access CRN. Since success probability is a location-dependent random variable, the analysis for the distribution of conditional success probability known as Meta distribution was further obtained.
* Using the analysis of the conventional success probability obtained, we derived expressions for the network throughput in the hybrid spectrum access CRN for both primary and secondary networks.
* Finally, based on the analysis of the conditional success probability, the expression for conditional average AoI was presented for the underlay, overlay and hybrid spectrum access CRN. To the best of our knowledge, this is the first work that investigates AoI in the hybrid spectrum access-based CRN.  
In the remainder of this paper, we present the network model in Section 2, while the details of the analysis for the success probability are presented in Section 3. Section 4 presents the analysis of the network throughput and the conditional average AoI, while Section 5 presents the results of the numerical simulations. Finally, Section 6 concludes the paper.
## 2 Materials and Methods
### Description of Gas Analysers
#### 2.1.5 Axetris  
The Axetris LGD F200-A NH3 is a single gas analyser based on LAS. The device uses a narrowband tunable diode laser (TDL) as the light source, and NH\({}_{3}\) concentration is determined based on the attenuation of the incident light according to the Beer-Lambert law. The Axetris was not a stand-alone measuring device and, thus, it was firstly integrated into a compact system as described in [19] prior to field deployment. The measured NH\({}_{3}\) concentrations were logged by a laptop using the LGD F200 Data Logger software provided by the manufacturer.  
Calibration of the Axetris on NH\({}_{3}\) measurement was performed by the authors based on a degree-1 polynomial fitted with ordinary least squares. The reference values were the offered concentrations of four certified cylinders (Air Liquide Benelux, Liege, Belgium), respectively, containing 0, 5, 14.5, and 68.8 ppm\({}_{\rm v}\) NH\({}_{3}\) mixed in dry nitrogen (\(\pm\)3% uncertainty). The gases were supplied around atmospheric pressure. The measurement accuracy of the Axetris was known to depend on the supply gas pressures [19]. Therefore, the measured NH\({}_{3}\) in the field were corrected for pressure by multiplying the raw ppm\({}_{\rm v}\) readings with a correction factor \(\lambda\), with \(\lambda=\left(-4.032\cdot P^{1.242}+\left(1+4.032\right)\cdot P\right)^{-1}\), where \(P\) is the absolute pressure in bar. The manufacturer stated that water vapour could be an interfering species for NH\({}_{3}\), but no quantitative information about the effect or compensation measures were available.
## 5 The Gut Microbiota in Health and IBD  
The human bacterial microbiota is diverse consisting of 5-6 primary phyla in varying ratios that are dependent on both environment and genetics. Among these phyla, _Firmicutes, Actinobacteria, Proteobacteria, Bacteroidetes, Fusobacteria_, and _Verrucomicrobia_ are highly expressed under normal physiological conditions in the human gut [87]. Contrary to popular belief, baseline microbial populations remain relatively stable and unchanging long term, even with diet interventions. The study published by [PERSON] et al. found that after initial microbe population changes in the first three months of diet interventions, participant microbiota reverted to, or close to, its original composition after the year-long study was complete, suggesting a resilience, which might be harder to alter than originally thought. While commensals are generally considered helpful, in disease or conditions suboptimal to the organism, malevolent changes can occur. These organisms are known as \"pathobents,\" and under normal conditions remain symbiotic, yet in response to environmental exposures or genetic predisposition become pathogenic to the host [88]. Unlike externally acquired pathogens, pathologists remain symbiotic and only cause an acute inflammatory response when environmental cues reduce resident protective microbiota allowing pathogenic bacteria to colonize [89].  
UC and CD are two distinct subgroups of IBD including several distinguishing symptoms and genetic variant predispositions. Interestingly, gut microbiota sequencing from UC and CD patients also show population differences [90]. Overall, reduced diversity of microorganisms and disproportionate phyla are associated with both UC and CD pathogenesis, although there have been studies indicating differences in phylum distribution in UC and CD independently [91].
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 3 Biochar Adsorption of Heavy Metals
### Chromium  
Cr exists in many valence states, with Cr(IV) being the most soluble of them. In addition, the toxicity of Cr(VI) has been identified as superior to Cr(III) by around 1000 times [48]. Thus, the studies of Cr removal are mainly focused on Cr(IV). As was proposed by [PERSON] et al. [49], the adsorption mechanism is by electrostatic attraction and/or reduction to Cr(III) by an oxygen-containing functional group.  
Figure 3: Main sorption mechanisms in biochar (**left**) and regeneration processes (**right**).  
In addition, solution pH is recognized as a significant factor in the adsorption process as well as the redox reactions. At pH below the point of zero charge of biochar, the material is positively charged, contributing to the inorganic anions' (HCrO4\({}^{-}\) or Cr2O\(\gamma\)2\({}^{-}\)) negatively charged uptake onto the biochar by electrostatic attraction [50]. However, at low pH, the reduction of Cr(VI) to Cr(III) is favored, thus the electrostatic repulsion can be also generated.  
The used feedstock has a great influence in the Cr adsorption mechanism. In several studies using biochar fabricated from sugar beet [51] or coconut cozit [52], it was determined that the main mechanism is the Cr(VI) reduction and Cr(III) complexation by hydroxyl and carboxyl groups present in the biochar (Figure 3).  
The modification of biochar by metal impregnation can be produced previous to biochar manufacturing. Thus, by hydrothermal carbonization of biomass with metal salts, it is possible to produce a modified hydrochar with a higher surface area, pore volume and aromaticity. Comparison of bamboo sawdust hydrochar modified by ZnCl2 and AlCl3 treatment shows a higher C content, reducing the H/C ratio together with an increase in surface area and pore volume, with biochar modified by ZnCl2 achieving the best result (Cr(VI) uptake 14 mg/g), with levels around three-fold higher in relation unmodified biochar [53]. In addition, cations added on the surface may have enhanced electrostatic interaction between Cr(VI) and the adsorbent. Similarly, [PERSON] et al. [54] prepared several iron-biochar composites with different Fe/biochar ratios, by pyrolysis at 1000 \({}^{\circ}\)C of pre-treated forestry wood waste with FeCl3. The XPS analyses of spent biochar showed that as Fe loading increases, the dominating specie is FeCr2O4 (78.7% at a loading of 2:1), suggesting the reduction of Cr(VI) by Fe\({}^{0}\).  
Recently, porous biochar was successfully improved by chemical activation of KOH [55], increasing the specific surface area, micropore distribution and pore diameters (1-2 nm). This modified biochar presented an excellent adsorption performance with a theoretical monolayer uptake of 116.97 mg/g for Cr(VI), operating at acid pH below the point of zero charge.
## 2 Materials and Methods
### Data  
#### 2.2.1 Tower-Based Carbon Flux Data  
All sites contained CO\({}_{2}\) flux data and incident photosynthetic active radiation (PAR\({}_{\text{in}}\)) measurements during the research period. The carbon flux data at each site during the study period were collected by members of OZflux [60]. The CO\({}_{2}\) flux data at these sites were measured by an Li-7500 (Campbell Scientific Inc., MS, USA), which is an open-path infrared CO\({}_{2}\)/H\({}_{2}\)O gas analyzer, in half-hour time steps. These flux data were described in [PERSON] et al. 2011, 2017, [PERSON] et al. 2016a, 2016b, and [PERSON] et al. 2017 [61; 62; 63; 64; 65]. Data were downloaded from the Australian Terrestrial Ecosystem Research Network (TERN) website ([[http://data.ozflux.org.au/portal/pub/listPubCollections.jspx](http://data.ozflux.org.au/portal/pub/listPubCollections.jspx)]([http://data.ozflux.org.au/portal/pub/listPubCollections.jspx](http://data.ozflux.org.au/portal/pub/listPubCollections.jspx))) and then post-processed using the REddyProc tool [66]. The REddyProc tool was previously used to transform half-hourly CO\({}_{2}\) flux input data into half-hourly GPP data [67; 68]. We used the daytime method, which avoids the CO\({}_{2}\) flux transfer uncertainties resulting from potentially problematic nighttime data [69]. This method was successfully used to evaluate the global carbon flux [1] to partition the CO\({}_{2}\) flux into GPP. Then, we aggregated the half-hourly GPP and PAR\({}_{\text{in}}\) data on a daily scale. The aggregated flux tower-based GPP data are presented as GPP\({}_{\text{EC}}\) in the following sections.