esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Uncertainty Analysis  
Firstly, in order to explore the identification ability of the LSST and LSPL methods in the SINDy framework for vibrations, a simple vibration system was chosen for the validation. For this paper we use an oscillator with one DoF. These two methods were numerically compared in Monte Carlo Simulation (MCS) with known parameters of the oscillator. In each MCS the two methods identified \(10,000\) randomly generated oscillators  
Figure 1: Flowchart of (**a**) LSST and (**b**) LSPL.  
that have the normal distributed parameters mass, \(m\), damping coefficient, \(d\), and stiffness, \(k\), as shown in Table 1. The movement of an oscillator with the DoF \(q\) is governed by the following equation:  
\[\begin{bmatrix}\dot{q}\\ \dot{q}\end{bmatrix}=\begin{bmatrix}0&1\\ -\frac{k}{m}&-\frac{d}{m}\end{bmatrix}\begin{bmatrix}q\\ \dot{q}\end{bmatrix}. \tag{7}\]  
In this simulation, the minimum absolute value of all coefficients was approximately \(|-\frac{d}{m}|=1\frac{1}{s}\). Accordingly, the threshold 0.5 was selected as the hyperparameter \(\lambda\) in the method LSPL, so that no real non-zero parameter is falsely filtered out. Of course, the predetermination of the value of \(\lambda\) is impossible for a real identification case. In this study, interest lies in the performance of the algorithm and not the process of finding a value for \(\lambda\). A plausible threshold value can be found using the optimization method detailed in other research [1].
## 3 Results and Discussion
### Carbon-Oxygen Vibrations  
In the C=O bond, the lone pair of electrons in the oxygen determine the nature of the carbonyl group. The C=O bond functionality and the carbonyl stretching vibration have been previously studied by infrared spectroscopy [30]. This multiple bonded group is highly polar and therefore gives rise to an intense infrared absorption band. Carboxylic acids are identified by the C=O vibration, which gives rise to a strong absorption between 1700 and 1800 cm\({}^{-1}\). In general, the C=O stretching modes of acids are more intense than those for ketonic stretching modes. The characteristic ketonic C=O band in the current study appear at 1620 cm\({}^{-1}\) in the FT-Raman spectrum, and this peak is very intense [31]. The \(\pi\)-\(\pi\) bonding between the carbon and oxygen results in the formation of a double bond, and intermolecular hydrogen bonding reduces the wavenumbers of the C=O stretching absorption to agreater degree than the presence of the molecular H. This C=O vibrational mode of the title molecule appears in the expected range, which indicated that it was not strongly affected by other vibrational modes. The computed C=O frequency, which was calculated at the B3 LYP/6-311G(d,p) level of theory, was 1622 cm\({}^{-1}\), which strongly supports the assignment of the experimental data.
## 2 Cooperative V2X Traffic Surveillance: Background and Related Work
The cooperative traffic surveillance solutions currently available in the literature can be classified according to several parameters: their architecture may be centralized, when all collected data from participating vehicles are sent to a central elaboration point, or decentralized, when monitoring data are distributed among the nodes; the protocols can use direct communication between vehicles, or can rely on groups of nodes with similar characteristics/features; the collected information may be either used locally or shared globally within the whole monitored area; finally, the type of collected data may be used as a classification criterion, from the simple visibility of the concise indicator of vehicle density in the targeted area, to more detailed monitoring information, e.g., including position, speed, acceleration, break usage, capable of discriminating the different roads and vehicle directions. As stated above, traffic surveillance systems can collect different types of information about the flow of vehicles depending on the particular type of sensors used, such as number of vehicles, their density inside a specific area, or the average speed on a specific street. Classic induction loops and pressure sensors installed on the road surface provide only a limited subset of the potential information of interest, for example, to optimize traffic light management. The use of wireless transmission interfaces that can share the large amount of data collected by the sensors already installed inside the single vehicles can enable the collection of more detailed information, which can be used todevelop new and more accurate traffic control algorithms. However, up to now, this type of sensors has suffered from the significant drawback of low penetration rate in the current vehicle population. Although this problem should gradually become less relevant, it is nevertheless important to focus on the collection of traffic surveillance information that has a low dependence on the number of vehicles actively cooperating to their collection. Indeed, let us note that (as it will be shown also by our reported experimental results in this paper) the precision and accuracy of some types of collected data, such as the number of vehicles, are more sensitive on the penetration rate, while others, like the average speed, are less conditioned; also this element could represent a significant design/selection choice when considering cooperative traffic surveillance systems.
## 3 Results and Discussion  
### Characterization of the Resins  
#### 3.1.1 X-ray Diffraction (XRD)  
Figure 5A depicts the XRD patterns of the various resins, the H-form (activated and inactivated) and the Na\({}^{+}\)-form. All patterns exhibit a substantial amorphous area at angles above 40\({}^{\circ}\). Inactivated and activated H-form resins show two large and broad crystalline zones at angles of 16.8\({}^{\circ}\) and 26.5\({}^{\circ}\), although with differences in the relative intensities for the angles. The inactivated form has a greater intensity at 16.8\({}^{\circ}\), whereas the activated H-form has a greater intensity at 26.5\({}^{\circ}\). This observation suggests that saturation/activation with H\({}^{+}\) somewhat modifies the crystalline structure of activated versus non-activated resin. At an angle of 5.4\({}^{\circ}\), a new sharper peak is visible for the Na\({}^{+}\)-resin. Notably, the broad peak previously seen at an angle of 16.8\({}^{\circ}\) in resins of the H-type (activated or inactivated) is slightly shifted to an angle of 17.3\({}^{\circ}\). The peak at 26.5\({}^{\circ}\) is still detectable, although residual. The obtained results with the Na\({}^{+}\)-form resin indicate that the crystalline structure of ion-exchange resins is highly dependent on the cation with which they interact. To the best of our knowledge, the characterization of these resins by XRD has not yet been described in the literature.  
Figure 5: (**A**) XRD patterns and (**B**) FT-IR spectra of resins: (**a**) inactivated H-form, (**b**) activated H\({}^{+}\)-form, and (**c**) Na\({}^{+}\)-form.
## 4 Conclusions  
The influence of equivalence ratio and temperature on olive cake gasification in a bench-scale plant using air as a gasification agent was studied. An increase in temperature generally increased the efficiency of cold gas. The ER of 0.3 showed higher cold gas efficiency for all temperatures studied. The percentage of CO in the gas ranged from 6.9 to 20.2 mol %, with H\({}_{2}\) and CH\({}_{4}\) remaining constant with the operating temperature. The production of CO, H\({}_{2}\), and CH\({}_{4}\) was estimated to be higher for tests conducted with an ER of 0.3, with averages of 393.06 g of CO per kg of olive cake, 9.07 g of H\({}_{2}\) per kg, and 51.55 g of CH\({}_{4}\) per kg.  
Conceptualization, A.P. and G.B.; methodology, A.G.-P., A.P. and G.B.; software, M.A.M.-L.; validation, M.A.M.-L.; formal analysis, G.B., M.C. and M.A.M.-L.; investigation, A.G.-P. and G.B.; resources, G.B. and M.C.; data curation, A.G.-P., G.B. and A.P.; writing--original draft preparation, M.A.M.-L.; writing--review and editing, M.A.M.-L. and M.C.; supervision, A.P. and M.C.; project administration, M.C. and M.A.M.-L.; funding acquisition, M.C. and M.A.M.-L. All authors have read and agreed to the published version of the manuscript.  
All authors are grateful to the Spanish Ministry of Economy, Industry and Competitiveness for the financial support received (Project CTM2016-75977-R).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**T**, \({}^{\circ}\)**C**} & \multirow{2}{*}{**ER**} & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\ \cline{3-8}  & & & **Gas, g/kg** & **Solid, g/kg** & **Total, g/kg** & **Gas, g/kg** & **Liquid, g/kg** & **Solid, g/kg** & **Total, g/kg** \\ \hline
750 & 0.1 & 712 & 1000 & 1712 & 1227 & 211 & 144 & 1582 \\ \hline
750 & 0.3 & 1965 & 1000 & 2965 & 3023 & 163 & 133 & 3319 \\ \hline
750 & 0.5 & 3190 & 1000 & 4190 & 4559 & 127 & 125 & 4811 \\ \hline
800 & 0.1 & 655 & 1000 & 1655 & 1120 & 187 & 132 & 1439 \\ \hline
800 & 0.3 & 1965 & 1000 & 2965 & 2857 & 149 & 114 & 3120 \\ \hline
800 & 0.5 & 3190 & 1000 & 4190 & 4318 & 111 & 117 & 4546 \\ \hline
850 & 0.1 & 712 & 1000 & 1712 & 1343 & 191 & 126 & 1671 \\ \hline
850 & 0.3 & 1965 & 1000 & 2965 & 2880 & 135 & 121 & 3136 \\ \hline
850 & 0.5 & 3219 & 1000 & 4219 & 4163 & 100 & 112 & 4375 \\ \hline
900 & 0.1 & 741 & 1000 & 1741 & 1284 & 163 & 133 & 1580 \\ \hline
900 & 0.3 & 1994 & 1000 & 2994 & 2867 & 128 & 109 & 3104 \\ \hline
900 & 0.5 & 3190 & 1000 & 4190 & 4087 & 79 & 106 & 4272 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Yield of solid and liquid residues and mass balance data (data in g per kg of olive cake).
## 3 Materials and Methods
### Total Phenolics and Antioxidant Activity  
Total phenolics content (TPC) was determined by using the Folin-Ciocalteu reagent based on colorimetric reduction [33]. The phenolics compounds were oxidized to phenolates using the reagent at alkaline pH in a saturated solution of sodium carbonate resulting in a blue complex. About 1 mL of Folin-Ciocalteau (10%, \(w/v\)) was added to 0.2 mL of prepared pomeranate beverage, followed by the addition of 1.2 mL of aqueous Na\({}_{2}\)CO\({}_{3}\) (7.5%, \(w/v\)). The mixture was left in the dark for 90 min. The absorbance of the blue color solution was monitored at 760 nm on a UV visible spectrophotometer (Shimadzu, Kyoto, Japan), against blank (distilled water). The Total Phenolics Content (TPC) was assessed by plotting the gallic acid calibration curve and expressed as milligrams of gallic acid equivalents per liter of sample.  
Total phenolics concentration (mg/mL) of the samples were analyzed in triplicates and extrapolated from a standard curve constructed by using Gallic acid as a standard. Results were expressed as mg Gallic acid equivalents (GAE)/100 mL of pomeranate beverage.  
The antioxidant activity (AA) of pomeranate beverages was evaluated applying the 2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS) radical cation decolorization assay [34]. ABTS\(\bullet\)+ was prepared by reacting of ABTS with potassium persulfate. Samples were analyzed at five different dilutions, within the linearity range of the assay, as previous described [35]. TAA was expressed as mg Trolox equivalent (TE)/100 mL of pomeranate beverage. All measurements were repeated three times.
# Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
## 5 Conclusions  
This paper presents a novel signal-processing scheme for extracting fault-related information from vibration signals, leveraging cluster-based segmentation as a key component. This method utilizes the inherent capability of clustering algorithms to automatically divide the signal into a predetermined number of distinct segments.  
The identification of the segment containing the most valuable fault-related information is based on two crucial criteria: maximum L-kurtosis and minimum entropy. L-kurtosis, a measure of impulsiveness, highlights segments characterized by sharp, transient events, often indicative of incipient faults. Conversely, minimum entropy identifies segments exhibiting a high degree of cyclo-stationarity, suggesting a strong periodic pattern related to the machine's operating conditions. By focusing on the segment exhibiting both maximum L-kurtosis and minimum entropy, the proposed scheme pinpoints the frequency range containing the most relevant fault-related information.  
To further refine the signal and extract this crucial information, a passband filter is specifically designed for the identified frequency range. This filtering process effectively isolates the desired information, enriching the signal with impulsive and cyclo-stationary components directly related to the fault.  
The proposed scheme has been successfully validated through fault detection experiments on various gearbox types. Its ability to automatically segment signals, identify the most informative frequency range, and enhance the signal with fault-related components demonstrates its robustness and versatility as a tool for condition monitoring and fault diagnosis in mechanical systems.  
S.K. ([PERSON]): Data curation, Writing--original draft; S.C.: Data curation, Writing--original draft; G.V.: Data curation, Software, Writing--original draft, Methodology; S.K. ([PERSON]): Data curation, Software, Writing--original draft; R.K.: Writing--review and editing, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
Data Availability Statement: Data can be made available upon reasonable request.  
The authors declare no conflicts of interest.
## Results and Discussions  
### Standard Gas Stability Check  
The stability of standard gas generated by the standard gas generation system is shown in Figure 2. For the purpose of checking stability, the standard gas was simultaneously measured with exposed and \"retracted\" SPME fibers and sorbent tubes several times per day for three consecutive days.  
The result of daily extractions with exposed and \"retracted\" SPME fibers and sorbent tubes shows that the standard gas generation system was successful in generating a continuous supply of acetic acid. As can be seen in Figure 2, the exposed SPME fiber responses were more variable (relative standard deviation, RSD 5.6%) than the \"retracted\" SPME fiber (RSD 3.2%) in terms of extracted mass. Because the exposed SPME fiber was fully in contact with the moving gas, it resulted in more than two orders  
Figure 2: Standard gas stability needed for simulating steady-state conditions for TWA SPME sampling. Extraction conditions: two 85-\(\mu\)m Car/PDMS SPME fibers (one was a standard exposed fiber, and the other was an exposed fiber that was kept inside of a glass liner). Both were exposed to the standard gas (acetic acid, \(C_{\mathrm{gas}}\) = 617 \(\mu\)g\(\cdot\)m\({}^{-3}\)). Retraction depth was 17.5 mm. Gas sampling was performed every hour for three consecutive days. Sampling times were 20 s for the exposed SPME fiber and 1 h for the retracted SPME fiber. The dashed lines on the graph indicate a \(\pm\)7.5% band from the average. The concentration of acetic acid was verified with sorbent tubes. The concentration of acetic acid in the system obtained by sorbent tubes is shown on the right \(y\)-axis. Selected ion monitoring (SIM) mode at \(m/z\) 60.0 was used for acetic acid detection and quantification.  
of magnitude higher extraction rates than the \"retracted\" SPME fiber. These results are consistent with the findings from [PERSON] et al. [30]. The limits of detection (LOD, signal-to-noise ratio (S/N) = 3) and limits of quantification (LOQ, S/N = 5) were 10 and 18 \(\upmu\)g\(\cdot\)m\({}^{-3}\) (4.3 and 7.2 ppbV), respectively.
## 2 Science Data Products, Calibration/Validation, and Algorithm Management
### Science Data Products Calibration and Validation (Cal/Val)  
For each JPSS satellite mission (S-NPP, NOAA-20, and future missions), the JSTAR SDR/EDR Cal/Val activities are planned and executed for all Cal/Val phases: pre-launch, EOC, ICV, and LTM [2]. The major objective is to evaluate and ensure that the operational SDR/EDRs meet Beta, Provisional, and Validated maturity requirements [4]. Beta maturity designates an early release product that is minimally validated and may have significant errors. The beta product is made available to users to gain familiarity with the data format. Provisional maturity indicates that the product is deemed ready for operational evaluation. Versioning control commences as the product continues to undergo improvements, and user participation in quality assurance is encouraged. Validated maturity occurs when the product performance is well-defined over a range of representative conditions using a widely distributed set of statistically robust independent measurements (from many locations and time periods) representing global conditions. Each product's Cal/Val maturity is determined by validating the algorithm's performance with truth data sets and comparing the performance with the requirements. Details of various Cal/Val tasks for each xDR product, as well as the roles and responsibilities of team members are discussed in xDR-specific Cal/Val plan documents [4].  
Product validation maturity reviews are performed by review panels comprised of JPSS Program and Project Scientists, NOAA customers (National Weather Service, NWS; National Ocean Service, NOS; National Marine Fisheries Service, NMFS; Office of Oceanic and Atmospheric Research, OAR), external users, Low-earth Orbiting Working Group (LORWG) advisors, product development managers, and the JSTAR program manager. Following the Cal/Val maturity science reviews presented by the Cal/Val teams, the review board assesses the product performance, makes an overall assessment of each algorithm, and approves the maturity status according to the criteria for Beta, Provisional, and Validated Maturity. Currently, most S-NPP data products have passed the Cal/Val validation maturity stage, and are in the LTM phase. The NOAA-20 data products are progressing through the Cal/Val maturity according to the Cal/Val plans.  
The next section provides comprehensive details of the S-NPP product performance. The preliminary results of NOAA-20 product performance are discussed in Section 4.
# Preface: Land Surface Processes and Interactions--From HCMM to Sentinel Missions and Beyond
## 2 Recent Advances in Monitoring Land Surfaces and Interactions
### Drought Detection
[10] investigated the complementarity and interoperability of optical and synthetic aperture radar (SAR) data for crop parameter retrieval and crop type classification, with a focus on crop water requirements. The crop parameter includes the basal crop coefficient \(K_{cb}\) values and the length of the crop's development stages. The combined optical and microwave images provide useful information on qualitative and quantitative land cover changes, supporting land cover monitoring in different agriculture scenarios, with enhanced temporal resolution for agricultural applications. The determined \(K_{cb}\) was used to compute the crop's evapotranspiration and to subsequently estimate the crop irrigation requirements based on a soil water balance model. They concluded that optical data can be replaced by microwavedata in the presence of cloud cover. Unfortunately, lack of data for a part of the growing season made it impossible to properly identify each stage of the crop cycle.
## 4 Simulation Comparison of Different Penetration Modes
### Influence of the Number of Target Layers on the Residual Velocity  
Due to the particularity of the forest environment, the penetration factors in the target area are affected by the tree parameters. The most significant of these is the large number of trees on the fragment penetration trajectory caused by the large forest density, which makes the fragments penetrate multi-layer spaced targets. It can be seen from the above inference that the penetration type in the forest environment is generally interval target, and the relationship between the thickness of interval target and the overall target needs to be further calculated [17; 18; 19; 20]. In order to analyze the influence of the number of spaced target layers on the residual velocity under the same target plate thickness, the initial velocity of spherical fragments is set to be 1000 m/s, the fragment diameter is 10 mm, and the total thickness of wood target plates is 500 mm. The target plate layers are divided into six types: 1, 3, 5, 7, 9, and 11. The residual velocity of fragments after penetration is counted separately.  
It can be seen from the analysis results in Table 6 that with the increase in the number of target layers, the residual velocity also increases, that is, the number of wood target plates weakens the impact of the thickness of the target plate, and the smaller the thickness of the target plate, the smaller the thickness of the target plate, the smaller the impact resistance, so that the residual velocity of fragments increases continuously [21; 22]. When the number of target plates is 11, the difference ratio reaches 23.89%.
## 5 Numerical Experiments  
In this section, we validate and evaluate the proposed MILP model and the improved LNS algorithm through extensive numerical experiments. All the experiments are carried out on a personal computer (Intel Xeon W-2775 Processor @3.30 GHz with 64 GB of RAM). The linearized MILP model is coded in Gurobi 10.0.2. The LNS is implemented in Matlab R2022a using the C++ language.
## 3 Results
### Comparison of Independent Detection Accuracy of Traffic Flow Parameters under Light Interference  
In addition to the interference factor of noise, light was also an important factor affecting the accuracy of independent detection of traffic flow parameters. Therefore, the accuracy of independent detection of traffic flow parameters by different methods under different light intensity was compared. The light intensity is represented by a value of 1-5. The larger the value, the lower the light intensity. Figure 8 shows the comparison results.  
Figure 8: Comparison of the accuracy of traffic flow parameter self-detection under light interference.  
Figure 7: Comparison of the accuracy of traffic flow parameter self-detection under noise interference.  
According to Figure 8, under the interference of different intensities, the accuracy of independent detection of traffic flow parameters of different methods was different. On the whole, the higher the light intensity, the lower the accuracy of different methods. However, the accuracy of this method was still higher than that of the traditional method, which shows that the degree of light interference of this method was low and it can get reliable detection results.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps Generated Using GIS-Based Methods
#### 3.1.2 A Map of Living Forest Biomass and Carbon Stock in Europe  
Using a similar approach to [PERSON] and [PERSON] [49], [PERSON] et al. [51] provided a forest biomass dataset for Europe at 1 km spatial resolution for the year 2010. They first estimated forest biomass from the CORINE Land Cover 2006 map, then the average biomass value of each ecological region from the Food and Agriculture Organization (FAO) Global Ecological Zone (GEZ) map, and then post-adjusted the estimates at pixel level for each country by applying adjustment ratios to match the national values reported in the FRA 2010 [52]. The generated map corresponded with biomass and carbon reported in FRA at the country level and therefore at a continental level. However, due to a lack of field data, the map was not validated at the pixel level.