esa-sceva/satcom-chunk-collection huggingface dataset:
# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 3 Results and Discussion  
### Comparison of Aroma Compounds between Grasevina Wines and Other Wines from the Same Producers  
We analyzed 36 Grasevina wine samples and 9 other wine samples (Pinot Gris and Chardonnay) in an attempt to identify VOCs that could potentially contribute to the aromatic typicality of Grasevina wines in comparison with wines made from other varieties by the same producers. Wine samples with different vintages (2019, 2020, and 2021) were obtained from 14 producers located in two Croatian sub-regions, namely the Slavonia and Podunavlje sub-regions. The aromatic profile of the analyzed wine samples was determined by quantifying 60 VOCs from five different groups, namely 21 terpenes, 6 norisoprenoids, 6 volatile phenols, 4 C6 alcohols, and 23 esters. Concentration levels of all VOCs are available in Table S2 in the Supplementary Materials.  
Quantification data for each analyzed VOC in each sample (data given in Supplementary Materials, Table S2) showed no distinct differences between Grasevina wine samples and other wine samples (Chardonnay and Pinot gris). The effect of the winemaking protocol could potentially overlap the varietal differences for the targeted aromatic compounds. Interestingly, when comparing Chardonnay and/or Pinot gris wine samples with Grasevina wines made by the same producers as shown in Figure 1, four terpenes, namely \(\alpha\)-terpinen, \(\gamma\)-terpinen, 1,4-cineol and 4-terpineol, stood out, since they were predominantly present in higher concentrations in Grasevina wines. The sensory impact of these monoterpenes on wine aroma is not well documented, except for 1,4-cineole, which has recently been studied [10]. Nevertheless, it seems that the concentrations of these monoterpenes in Grasevina wines were well below their sensory thresholds, considering the case of \(\gamma\)-terpinene in matrices other than wine [11]. However, monoterpenes are known to contribute to wine aroma, even when their levels are lower than their sensory threshold, through complex synergistic effects [12; 13]. Such complex sensory interactions between terpenes and other compounds cannot be excluded and require further investigation.
# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 3 Outdoor Activity Recognition System
### POI Identification Mechanism
#### 3.1.1 First Iteration
However, the data cleaning algorithm was mainly oriented to motion-related GPS drifts. After analysis of results, another more problematic type of GPS drift associated with static points was detected (See Figure 3). Figure 2: Yellow points are static-jumps. Each of those points are really ten points with the same coordinates. 3. Multi-clustering: even though it is directly related to the two previous errors, multi-clustering is so relevant that it has to be addressed in a different way. Sometimes, due to GPS errors, although the user remains in the same place, spatial jumps (in the form of drifts or static-jumps) occur, resulting in false clusters, when in fact all the points belong to the same unique cluster (see Figure 4 where all the points belong to a single cluster). The data cleaning algorithm developed for the first iteration was suitable for GPS drifts that take place when there is motion, so it can detect and clean points that appear anomalously in the current user's trajectory. However, when the GPS drifts take place statically (i.e., when the user is at home), this algorithm is not reliable. These kinds of drifts usually have a zigzag shape (going and returning several times) so the algorithm is unable to determine which of the points is the correct one and which one is the drift. A clear example of this problem can be seen in Figure 5.  
Figure 4: Example of a multi-cluster. All the points on the image are part of the same cluster. Figure 3: Examples of motion-related GPS drift (**left**) and static-related GPS drifts (**right**). Also, the detected multi-clustering problem invalidated the clustering system developed during the first iteration, since all those false clusters generated by GPS errors were considered real clusters, reducing the reliability and accuracy of the algorithm.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 2 Materials and Methods
### Method
#### 2.2.3 Permeability Coefficient Test  
The permeability coefficient was tested in accordance with the specifications of GB/T 50123-2019 [29], and the steps were as follows: The aeolian sand column samples were removed from the temperature maintenance chamber every 12 h, and the aeolian sand in the mold was poured out and mixed well. The appropriate amount of aeolian sand was loaded into the falling head permeameter (height: 40 mm, cross-sectional area: 2.998 \(\times\) 10\({}^{4}\) mm\({}^{2}\)) with a thickness of approximately 10 mm per layer, and the density of sand in the falling head permeameter was consistent with the sand column sample after layered tamping. The head of the permeameter was connected to a water head pipe, water was supplied to the pipe, and the head height was controlled to within 2 m. After opening the outlet drain valve to expel air from the falling head permeameter and saturating the desert sand, the time, outlet temperature, and heights of the initial and final water heads were tested and recorded, with each parameter accurate to 0.1.  
Each experimental group was tested five times in parallel, and the permeability coefficient was calculated according to Equation (2).  
\[k_{T}=2.3\frac{aL}{At}\lg\frac{H_{b1}}{H_{b2}} \tag{2}\]  
where \(k_{T}\) is the permeability coefficient at \(T\)\({}^{\circ}\)C (cm/s), \(a\) is the cross-sectional area of the water head pipe (cm\({}^{2}\)), \(t\) is the permeation time (s), \(H_{b1}\) is the initial water head height (cm), and \(H_{b2}\) is the final water head height (cm).
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 3 Model Description  
### Problem Formulation  
Given a single-microphone noisy signal \(y(t)\), the target of single-channel speech enhancement is to estimate the target speech \(x(t)\). This paper focuses on the additive relationship between the target speech and noise. Therefore, the noisy signal \(y(t)\) can be defined as  
\[y(t)=x(t)+n(t) \tag{1}\]  
where \(y(t)\) is the time-domain noisy signal at time \(t\), \(x(t)\) is the time-domain clean signal at time \(t\), and \(n(t)\) is the time-domain noise signal at time \(t\).
# Integral Seismic Risk Assessment through Fuzzy Models
## 3 A Fuzzy Approach for Integral Assessments  
The objective of this study is to simulate the effects of an earthquake on an urban environment, with a more accurate estimation of how the social dimension may influence the final risk level, once the infrastructure damage has been taken into account.  
Our pre-conception of accuracy regards two general aspects that may weaken the reliability of most of the assertions based on integral risk estimations through composite indices. The first, as we mentioned before, is the assumed preferential independence among indicators and the potential compensability sources coming from the use of weights as importance coefficients. The second aspect is related to the inherent uncertainty and impreciseness embedded in many of the facets that are used to describe a social risk-related feature, many of whom are not entirely reflected in the numerical value associated with its quantitative representation. The treatment of such complexity and impreciseness can be very well handled by fuzzy logic, and this is why this methodology is gaining ground, as it allows the analysis of complex processes from a different perspective. By the use of linguistic variables, fuzzy logic produces results closer to common language, transforming the whole process of risk communication into a two-way bridge built on intuition.  
Fuzzy logic [26] is a theory regarding the logic of imprecision and a mathematical tool capable of performing reasoning processes when available information is uncertain, incomplete, imprecise, vague, partially of truth, or even contradictory [27]. The introduction of the notion of multiple-valued membership degrees to a particular fuzzy set and a gradual transition scheme among system states are crucial elements that make fuzzy logic a suitable theory to handle complexity.  
Fuzzy systems are approximators to algebraic functions since their main structure is based on a plausible (or empirical) relationship between antecedents and consequents. In this sense, the mapping between inputs and outputs represents an isomorphism among fuzzy logic, and abstract and linear algebra. However, one of the most evident advantages of fuzzy logic is that either inputs or outputs can be represented in the form of linguistic prepositions, enclosing in this manner the fuzziness contained in the information and describing a system behavior that could not be represented by analytical functions only.  
The use of linguistic variables, as well as the use of a compositional rule of inference, allows the establishment of what is known as approximate reasoning. An inference rule can be expressed in natural language as: \"IF premise (antecedent) THEN conclusion (consequent)\"; if premises and conclusions are expressed by linguistic fuzzy variables, and fuzzy relations are used, these inference rules are then called fuzzy inference rules. Therefore, any system that infers its conclusion by means of fuzzy rules is called a fuzzy inference rule-based system (FIS).
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 4 Mission Elements
### Validation  
The overall goal of validation is to assess the usefulness of the main MERLIN products for their intended scientific application [90]. In practice, the MERLIN data and products will be comparedto temporally and spatially coincident measurements performed by independent instrumentation from various platforms and from a variety of locations around the globe. To infer the data quality over the mission lifetime, both long-term and campaign-based validation activities are foreseen. For this, the full suite of the existing, high-accuracy and reliable techniques, including flask sampling and remote sensing, will be used (e.g., [3; 12; 24]). The Total Carbon Column Observation Network (TCCON), which has been developed and already successfully deployed for validation of space-based passive GHG sensors, is regarded as a beneficial validation infrastructure, since it consists of various stations distributed over the globe [12]. Of particular importance for validation are balloon-borne and airborne in situ sensors, both providing profile information on CH\({}_{4}\), which can be converted to XCH\({}_{4}\) using Equation (3) (e.g., [8; 11]). In situ sensors provide the most accurate data calibrated against the WMO standard, thus promising the highest level of confidence [24]. Furthermore, aircraft are quite flexible, providing temporally and spatially coincident measurements almost anywhere on the globe (e.g., in data-sparse regions without any information, or far away from ground-based measurements). The validation of the DAOD (L1b) products will benefit from co-located flights with the airborne MERLIN demonstrator CHARM-F introduced in Section 1[66]. This instrument will use identical measurement frequencies and a similar viewing geometry for a direct inter-comparison of the airborne DAOD values to the MERLIN observations, which is particularly helpful for validation over orographically structured terrain and areas with changing surface reflectivity. In the case of the high-flying HALO aircraft (up to 15 km altitude), about 95% of the CH\({}_{4}\) column as seen by MERLIN can be captured by airborne validation. The remaining part of the profile (above the aircraft) will be taken from simulations of state-of-the-art atmospheric chemistry and transport models, as well as from co-located in situ profiles acquired by balloons. In order to fly more frequently, the installation of CHARM-F on the SAFIRE (Service des Avions Francais Insurumentes pour la Recherche en Environnement) Falcon aircraft will be considered. Synergies of validation with other space missions scheduled for the early 2020s are also foreseen (e.g., GOSAT II, Sentinel 5P, MICROCARB for CO\({}_{2}\) and IASI-NG for broader atmospheric composition).
# Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations
## 1 Introduction  
Newton-Raphson, often referred to as [PERSON]'s, fixed-point iteration method has been the gold standard for numerically solving equations for several centuries. To set the symbols and nomenclature, we start by defining a generic problem:  
\[\text{Find }x=x^{*}\text{ such that }r(x)=0 \tag{1}\]  
for a given differentiable function \(r:\mathbb{K}\rightarrow\mathbb{K}\) (\(\mathbb{K}=\mathbb{R}\) or \(\mathbb{C}\)). The condition of differentiability is the same as that for [PERSON]'s method. To solve this problem, fixed-point iterations are commonly used; starting with a guess to the solution \(x_{n}=x_{0}\), the guess is iteratively updated using \(x_{n+1}=\phi(x_{n})\), where \(\phi(x)\) depends on \(r(x)\) and the chosen numerical scheme. It is required that \(x_{n}\to x^{*}\) as \(n\rightarrow\infty\) for the numerical scheme to converge to the true solution, and, in practice, the fastest possible convergence is desired. If we expand \(r(x)\) in finite Taylor's series up to second order about point \(x_{n}\) and evaluate it at \(x=x^{*}\), we get  
\[r(x^{*})=r(x_{n})+\left.\frac{dr}{dx}\right|_{x_{n}}(x^{*}-x_{n})+\left.\frac{1 }{2}\frac{d^{2}r}{dx^{2}}\right|_{\zeta}(x^{*}-x_{n})^{2}=0, \tag{2}\]for some \(\zeta\in[x_{n},x^{*}]\). The last term is called the remainder and can be written in other forms [1]. Since \(\zeta\) is unknown, in the standard [PERSON]'s method, we neglect the second order term and approximate \(x^{*}\) with an updated guess to the solution: \(x_{n+1}\). Thus, we obtain [PERSON]'s fixed-point iteration (using notation \(r^{\prime}=dr/dx\)):  
\[x_{n+1}=x_{n}+\Delta x^{\text{N}}=x_{n}-\frac{r(x_{n})}{r^{\prime}(x_{n})}. \tag{3}\]  
Using (2) and (3), one can rearrange to get the evolution of error \(e_{n}=|x^{*}-x_{n}|\):  
\[e_{n+1}=N(\zeta,x_{n})e_{n}^{2}, \tag{4}\]  
where \(N(\zeta,x_{n}):=\left|\frac{r^{\prime\prime}(\zeta)}{2r^{\prime}(x_{n})}\right|\) is the measure of nonlinearity (This nonlinearity measure \(N\) is similar to the \(\gamma\) number in [PERSON]'s \(\alpha\)-theory[2]). The above step proves (at least) quadratic convergence of [PERSON]'s method _close to the solution_ when \(r(x)\) has only simple roots. This quadratic convergence has led to a wide adoption of [PERSON]'s method in solving problems in all scientific disciplines.  
Despite the aforementioned attractive convergence of [PERSON]'s method, it can be seen from (4) that to achieve quadratic convergence, the current guess \(x_{n}\) must be \"sufficiently close\" to the solution. When the initial guess is not close to the solution, the convergence can be slower than quadratic, or, worse, the iterations may diverge if \(N(\zeta,x_{n})e_{n}>1\). As an example, if we consider \(r(x)=e^{x}-500\) and solve it using (3) with \(x_{0}=0\), iterations diverge until we get numerical overflow (Figure 1a, solid red line). Instead, if we start with \(x_{0}=3\), the error decreases sub-quadratically before achieving quadratic convergence closer to the solution (Figure 1a, dashed red line).
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 4 Impact of the Timing Group Delay Bias
### Signal-In-Space Range Error Assessment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Frequency** & **BDS-2** & **BDS-3** & **BDS** \\ \hline \multirow{3}{*}{S1} & B1I & 0.92 & 0.54 & 0.71 \\  & B3I & 1.03 & 0.63 & 0.81 \\  & B1I/B3I & 1.45 & 1.36 & 1.40 \\ \hline \multirow{3}{*}{S2} & B1I & 0.83 & 0.47 & 0.64 \\  & B3I & 0.85 & 0.49 & 0.66 \\  & B1I/B3I & 0.83 & 0.48 & 0.64 \\ \hline \multirow{3}{*}{S3} & B1I & 0.90 & 0.52 & 0.69 \\  & B3I & 0.94 & 0.59 & 0.75 \\  & B1I/B3I & 0.91 & 0.75 & 0.82 \\ \hline \multirow{3}{*}{S4} & B1I & 0.84 & 0.48 & 0.64 \\  & B3I & 0.89 & 0.52 & 0.69 \\ \cline{1-1}  & B1I/B3I & 0.86 & 0.50 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: BDS signal-in-space range error (SISRE) performance for different schemes (in meters). S1: Normal satellite TGD correction. S2: Use of the satellite-dependent TGD bias calibrated from precise products. S3: Use of the TGD bias between BDS-2 and BDS-3 calibrated from precise products. S4: Use of the satellite-dependent TGD bias calibrated from network solution.
## 3 Results  
### Experiment Setup  
* Experimental platform and computational resources: The experiments were conducted on a Windows personal computer using an Intel(r) Core(tm) i5-10210U processor (Intel, Santa Clara, CA, USA) and 8 GB of memory.
* Compared methods: We compare the experimental results of GBNSS with NSSRF [28] and PathEmb [30] to highlight its effectiveness. These methods were chosen for comparison due to their robustness, efficiency, and widespread use in biological network similarity searches, making them strong benchmarks for evaluating the performance of GBNSS. NSSRF, PathEmb, and GBNSS use the global similarity score to work out the similarity of biological networks. NSSRF2, NSSRF3, and NSSRF4 are different versions of the NSSRF method, characterized by the size of the subgraph signatures they use. Specifically, NSSRF2 uses a two-node signature, NSSRF3 uses a three-node signature, and NSSRF4 uses a four-node signature.  
* Experimental Datasets: GBNSS was examined on all of the available databases (Bos Taurus, Homo Sapiens, Mus Musculus, and Saccharomyces cerevisiae), which were retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAY database. [37]. The pathway was transformed into a network where nodes represent molecules and edges represent interactions between them. The data statistics for the four KEGG datasets are presented in Table 1. We measured GBNSS biological similarity performance using Gene Ontology terms. The GO terms were retrieved from UniProt [38].
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 6 Micro-Constituents Extraction  
The typical methods for extracting milk micro-constituents involve a variety of chemical, physical and biochemical techniques, essential in dairy science and nutrition research. Table 4 lists the methods that have been used in the last ten years, which will be summarized in the following lines.  
One of the main classical and simple extraction methods that suit most of the micro-constituent classes is solvent extraction. Polar or nonpolar solvents can be used to separate vitamins and carotenoids after proteins denaturation [122, 123, 124]. For bovine milk, a proposed protocol for the isolation of D3 vitamins involves the extraction with hexane/butylated hydroxytoluene [125]. Instead, nucleotides are extracted with water as a solvent, under reflux [126]. The same solvent can be used also for minerals extraction. In this case, a preliminary acid digestion under high pressure is requested [127, 128].  
Nevertheless, the most modern protocols demand solvent extraction to be assisted by the use of ultrasound or microwaves [128, 129, 130]. These advanced methods, i.e., ultrasound assisted extraction (UAE) and microwaves assisted extraction (MAE), use energy input (ultrasounds or microwaves, respectively) to enhance the extraction efficiency, speed and selectivity of target compounds from complex matrices and dairy products like milk [131]. Moreover, other works report the use of assisted solvent extraction (ASE), often referred to as pressurized liquid extraction (PLE) for the extraction of fat-soluble vitamins or nucleotides. ASE combines heat and pressure to enhance solvent extraction efficiency, at the same time guaranteeing several unique advantages compared to UAE and MAE, mostly in terms of reproducibility, versatility and compound stability. For vitamins, the extraction is carried out in stainless steel vessels protected by UV-Vis radiation to preserve the compounds oxidation [132, 133].  
When a further purification and pre-concentration phase is required, solid phase extraction (SPE) is used, thus ensuring a minimal use of sample and chemicals [134]. For example, for BAs, C18, cation exchange, or mixed-mode cartridges can be used. Commonly used solvents to pre-conditionate the cartridges and activate the sorbent are methanol followed by water [135]. To help the release from the milk matrix, biogenic amines previously undergo an acid extraction, often using a solution like 0.1 M HCl [136].
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Background Noise Performance  
The classification results for Background Noise augmentation are presented in Table 6. Using Background Noise for data augmentation led to improved recognition performance compared to not using any data augmentation techniques. However, the recognition performance was related to the SNR and decreased as the background noise energy ratio increased. In cases of high SNR (with a SNR of approximately 10 dB), there was not much variation in recognition accuracy, and it could reach 0.92. When combining spectrogram masking with Background Noise augmentation, the improvement in recognition performance compared to using Background Noise alone was not very significant. Recognition performance may degrade in the presence of high background noise.