esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We report self- and air collisional broadening coefficients for the H\({}_{2}\)O line at 7299.43 cm\({}^{-1}\) and corresponding temperature coefficients for a temperature range spanning 293-573 K. New laser spectroscopic setups specifically designed for this purpose have been developed and are described. The line parameters reported here are in good agreement with those values reported in the HITRAN 2020 database, but the uncertainties have been reduced by factors of about 4, 1.3 and 4.4 for the self-broadening coefficient, air broadening coefficient and the temperature exponent of air broadening, respectively. Further, we combined our measurement approach with metrological data quality objectives, addressing the traceability of the results to the international system of units (SI) and evaluated the uncertainties following the guide to the expression of uncertainty in measurement (GUM).  
laser spectroscopy; gas metrology; water vapor; collisional broadening; broadening temperature dependence
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 4 Experiments
### Parallelized Implementation of G-SP2  
We also assess the quality of the CH-partitions by measuring the speed-up when parallelizing the G-SP2 algorithm and applying it to real physical systems. To do this, the implementation of the G-SP2 algorithm of [9] was modified to incorporate the graph partitioning step.  
In particular, to measure the speed-up, we run _METIS_ and _METIS+SA_ on the _polyalanine 259_ protein system of Table 1 and record the obtained CH-partitions. Computations were carried out with the Wolf IC cluster of Los Alamos National Laboratory, whose computing nodes have 2 sockets each containing an 8-core Intel Xeon SandyBridge E5-2670, amounting to a total of 16 cores per computing node. A total of 32 GB of RAM is shared between the 16 cores on each node, which are connected using a Qlogic Infiniband (IB) Quad Data Rate (QDR) network in a fat tree topology using a 7300 Series switch. Parallelization across nodes was done with OpenMPI (version 1.8.5), and parallelization across cores within a node was done with OpenMP 4.  
The sum of cubes measure and the computing time are displayed in Figure 7 as a function of the number of CH-partitions for the _polyalanine 259_ protein system. We observe in Figure 7 (top) that the total effort of G-SP2, measured with the sum of cubes criterion, decreases monotonically as a function of the number of blocks and parallelized subproblems.  
Figure 7 (bottom) displays the computing time for the graph partitioning step alone. We observe that the partitioning effort increases with the number of blocks. The steps occurring at 65, 129, 257 etc.  
Figure 7: Sum of cubes measure (**top**) and runtime to find the partitions (**bottom**) as a function of the number of blocks. Figure taken from [12]. Copyright ©2016 Society for Industrial and Applied Mathematics. Reprinted with permission. All rights reserved.  
partitions in the plot can easily be explained: Every time the number of blocks surpasses a power of two, the multilevel algorithm which _METIS_ is based on bisects the partitioning problem into one more (recursive) layer. Figure 7 (bottom) also displays that employing the SA post-processing step only adds a minimal additional effort to the overall computation (when compared to the graph partitioning step alone). The usage of SA thus seems very sensible in light of the improvements it achieves when applied to the edge cut optimized partitions computed by a conventional algorithm. To summarize, Figure 7 demonstrates that the total effort of the G-SP2 algorithm decreases when applied in parallel despite the increasing effort to compute a partitioning.
## 4 Experiments and Results  
### Laboratory Testing  
Ongoing testing was performed throughout the entire system development process, starting in the laboratory and ending in the field. First, bench testing in the laboratory confirmed that the high-accuracy frequency standards could be used to synchronize the transmitter and receiver precisely enough to allow a high-precision measurement to be made with the transmitter and receiver operating  
Figure 6: Images of the prototype transmitter (**Left**) and receiver (**Right**) pair.  
remote from each other. This was done in an electrical loopback setup, where the transmitter electrical modulation signals were routed to the receiver analog-to-digital converter. With high-accuracy clock generators to synchronize the transmitter and receiver, the system proved to be time-stable at integration times of more than one hour.  
The electrical loopback configuration was replaced with laser diodes, modulators, optics, and a detector and amplifier, using in-line fiber attenuators to reduce the transmitted power to the low levels expected in the field. In the absence of atmospheric interference, the system was able to achieve measurement SNRs of \(>\)1,000 at optical powers of tens of picoWatts and a lock-in averaging time of 60 s.  
The next stage of testing used a gas cell and high-purity CO\({}_{2}\) gas to measure the accuracy of the LAnTeRN proof-of-concept system. Due to equipment limitations, we were unable to measure the cell pressure, and consequently, the expected differential transmission, to the level of accuracy desired, but we were able to show ratio measurement accuracy to within a few percent of the expected ratio. The 22 m gas cell was filled with 99.996% pure CO\({}_{2}\) gas at a variety of pressures ranging from 0 to 0.16 atmospheres. The combined-wavelength laser signal from the Tx was propagated through the cell and collected at the cell output by the Rx. Two pressure gauges were used to measure the CO\({}_{2}\) gas pressure--one at the cell and one at the pump. The pump gauge is most accurate at very low pressures, and the cell gauge is most accurate at higher pressures, so the theoretical (\"truth\") ratios at intermediate pressures are the least accurate. The results of the gas cell test are shown in Table 2 and Figure 7.  
The plots in Figure 7 show the error at the intermediate pressures that can likely be attributed to errors in the gas cell pressure readings. The limitations of the pressure gauges can be overcome by using a scientific-grade gas mix with a CO\({}_{2}\) gas dry air mole fraction known to a very high precision. This gas can be used to fill the gas cell to near-atmospheric pressure where its pressure gauge is most accurate without complete absorption of the online laser signal. An alternative to gas cell validation is to take the system to a well-instrumented site with extensive _in situ_ measurements of the atmospheric state to validate against previously calibrated instrumentation.
# Ammonia Gas Adsorption in Fixed Bed and Fluidized Bed Using Bentonite Particles
###### Abstract  
The present paper investigates the ammonia adsorption kinetic from air on sodium bentonite particles and on aluminum pillared bentonite particles in fixed bed and fluidized bed. The sodium bentonite is used as adsorbents and as raw material for chemically modified bentonite with aluminum polyhydroxocations. The aluminum pillared bentonite is prepared by a classical pillaring process to create high porosity and to increase the ammonia particle surface contact. Adsorbents used were characterized by the following analysis: granulometric distribution, acid-base character determination by Thermal Programmed Desorption (TPD), elemental microanalysis by Energy Dispersive X-Ray coupled with scanning electron microscopy (EDX-SEM), X-Ray diffractograms, adsorption-desorption isotherms by Brunauer-Emmett-Teller method and distribution of pore sizes and pore volume calculation by Barrett-Joyner-Halenda method. The variable parameters used in ammonia adsorption capacity on bentonite particle determination are particles size, gas velocity and total gas flow rate. The parameters kept constant during the ammonia adsorption process on bentonite particles are geometric ratio, adsorbent mass and initial ammonia gas concentration. The ammonia adsorption capacity on sodium bentonite particles and on aluminum pillared bentonite particles was measured until bed saturation as a function of the gas-particle contact technique. The best results are obtained with homogeneous fluidization with small gas bubbles for the aluminum pillared bentonite particles after 100 s bed saturation with ammonia adsorption capacity of 0.945 mmol NH\({}_{3}\)/g. To complete the study, ammonia desorption determination was carried out by a thermo-desorption process in order to recover the used particles. The adsorbent particles studied proved to be high-performance materials in order to use them in ammonia air depollution. Fluidized bed adsorption can be an efficient technique to accelerate mass transfer between ammonia from air and adsorbent particles.  
ammonia adsorption; sodium bentonite; aluminum pillared bentonite; fixed bed; fluidized bed; particle regeneration Article
# Polarization Influence on Algorithms of TFBG Sensors Data Analysis for Bending Application
## 3 Analysis of Measurement Data
### Analysis of the Resolution and Accuracy of Demodulation Methods  
A calibration curve is a quantitative relationship between the response of a measurement method and the values of the quantities we are interested in. This curve is then used to determine the value of the quantity being measured based on data from the measuring instrument. In the case of spectral measurements, the response of the measuring instrument is a specific spectral parameter, such as the area between the mode envelopes or the length of the spectral contour. The calibration curve is most often approximated by polynomials of a certain degree with a possible prior transformation of the variables. However, if the calibration curve is non-linear, it is better to approximate its inverse, i.e  
The next parameter considered is the length of the contour (Figure 4a), which can be regarded the simplest parameter in the calculations. The resolution values are slightly higher than for the area method (Figure 4b).  
The last amplitude parameter is the mean deviation from the local mean parameter which is presented on Figure 5a. It has slightly lower resolution values than the previous methods (Figure 5b).  
Local parameter changes shift towards shorter wavelengths as the curvature increases (bending radius decreases). The movement of these changes can be used as a parameter to determine the value of the curvature (radius). The simplest indicator of the characteristic wavelength may be the derivative of the transformed transmission spectrum. To highlight the differences in individual spectra, the parameter function of the absolute value of the spectrum length versus wavelength was used. To smooth the spectrum processed in this way, a smoothing filter with a rectangular shape of the impulse response was used. The  
Figure 4: Characteristics of normalized spectrum length method: (**a**) curvature versus normalized spectrum length parameter; (**b**) resolution and accuracy calculated for spectrum length method.  
Figure 5: Characteristics of normalized mean deviation method: (**a**) curvature versus normalized mean deviation from the local mean parameter; (**b**) resolution and accuracy calculated for mean deviation from the local mean method.  
characteristic wavelength is the extremum of the derivative of the spectrum processed in this way. The dependence of the curvature on the normalized wavelength is shown in Figure 6a. It is characterized by greater linearity than the previously presented amplitude methods. The resolution and accuracy of determining the bending radius is better than for amplitude methods. The resolution and accuracy values are the same for the entire range of the bending radii considered (Figure 6b).
## 3 Results
### Box-Jenkins Transfer Function Modelling  
Based on the full model fits, YIC values and NRMSE values (see below and Discussion), a first order system model with a [2 0] noise model order was selected and used for comparing kinetic feature values between exponential and BJ-TF models in the next section. An example of applying this BJ-TF model is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Value Mean (SD)** \\ \hline Age (years) & 63 (7) \\ Body mass index (kg/m\({}^{2}\)) & 26 (5) \\ Forced expiratory volume in 1 second (FEV1) \% predicted & 48 (14) \\ Forced vital capacity (FVC) \% predicted & 105 (18) \\ FEV1/FVC (\%) & 37 (13) \\ Transfer factor for carbon monoxide \% predicted & 55 (15) \\ Partial pressure of oxygen (kPa) & 8.8 (1.8) \\ Partial pressure of carbon dioxide (kPa) & 5.3 (0.7) \\ Modified Medical Research Council dyspnoea scores (scores 1-2-3) & 2-14-2 \\ CPET maximal cycling load \% predicted [18] & 50 (23) \\ CPET maximal oxygen uptake (ml/min) & 1133 (231) \\ CPET maximal oxygen uptake \% predicted [18] & 55 (21) \\ CWRT cycling load (W) & 57 (16) \\ CWRT endurance time (s) & 243 (81) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient characteristics.  
Figure 2: Example plot of VO\({}_{2}\) data and the three different exponential modelling methods. The estimated time delay (TD) values for methods B and C were 8.3 s and 20.3 s, respectively.  
BJ-TF models with first order noise models (i.e., first order in D(z\({}^{-1}\))) resulted in visually inappropriate model fits. Noise models of order [1 0] and [1 1] led to the full model (i.e., system + noise model) lagging behind the measured VO\({}_{2}\) data. This lag was solved in the noise models of order [1 2] and [1 3]. However, for both these noise model structures the full model fits were completely off for some patients. Similarly, noise model orders [2 3] and [3 3] led to bad fits for some patients. YIC and NRMSE values of the remaining noise model orders are summarised in Table 2. Second order noise models had more negative (i.e., better) YIC values, but slightly higher residual values compared to third order models. Example residual and autocorrelation plots for [2 0] and [3 2] noise models are shown in Figure 3b,c, respectively. These noise model orders were chosen as examples because they had the lowest YIC value of all second and third order noise models, respectively. All second order noise models exhibited similar residual and autocorrelation plots, whereas some third order noise models still had a significant autocorrelation coefficient at lag 1, opposite to the example in Figure 3c.  
Figure 3: Residual analyses of the residuals from (**a**) exponential method C (representative for methods A and B) and Box-Jenkins transfer function (BJ-TF) models with noise model orders (**b**) [2 0] and (**c**) [3 2]. Left: residual values divided by the model parameter Amp, plotted in function of time. Right: autocorrelation coefficients up to lag 10.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 3 Vessel Data Preprocessing
### Data Preprocessing  
#### 3.1.1 Removing Invalid Values  
The absence of latitude and longitude information in ship trajectory data can lead to track discontinuity and result in incorrect interpretations of ship behavior. Therefore, it is necessary to remove these data points when traversing the data to ensure the integrity and continuity of the trajectory data.
## 3 Identification and Quantification of Volatile and Semi-Volatile Compounds in Sparkling Wines
### Sample Preparation
#### 3.1.5 Thin-Film Solid-Phase Microextraction (TF-SPME)  
Following the evolution of sorptive extraction techniques and to address some of the inherent limitations of conventional SPME, thin-film solid-phase microextraction (TF-SPME) has emerged. By employing a planar sorbent-coated surface (carbon mesh or metallic strip), TF-SPME offers a considerably greater surface-to-sample ratio and sorbent volume, resulting in enhanced extraction capacity, faster kinetics, and improved sensitivity for trace-level volatile and semi-volatile compounds [76; 98]. Four types of TF-SPME are commercially available: HLB/PDMS (hydrophilic-lipophilic balance), DVB/PDMS, CAR/PDMS, and pure PDMS). The advantage of TF-SPME devices over SBSE is the presence of the solid sorbent, which enhances the extraction of a wide range of analytes.  
Even though there are not many studies regarding sparkling wines, in recent years, [PERSON], [PERSON] et al. [99] optimized TF-SPME conditions for the analysis of volatile compounds in grape musts, evaluating two commercially available coatings--PDMS/CAR and PDMS/DVB. Through a multifactorial design combining variables such as extraction mode, temperature, stirring speed, and duration, it was determined that PDMS/CAR in direct immersion mode (500 rpm, 6 h, 20 \({}^{\circ}\)C) yielded the most efficient extraction [99]. [PERSON], [PERSON] [76] demonstrated the potential of HLB-TF-SPME to comprehensively capture volatile profiles in challenging matrices and, [PERSON], [PERSON] et al. [98] employed sequential TF-SPME to investigate the aroma profile of sparkling wine, showing that longer extraction times could minimize competitive displacement effects among polar analytes and improve calibration linearity compared to conventional microextraction formats [76; 98].  
As well as SBSE, TF-SPME represents a significant advancement in sample preparation for volatile compound analysis in sparkling wine, offering improved performance in sensitivity, selectivity, and reproducibility over traditional fiber-based approaches.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 3 Evaluation Parameters of Mutual Coupling
### S-Parameters  
Viewing an array of \(n\) antenna elements as a network with n-port. Assuming that the incident waves of the \(n\)-port network are \(a_{1},a_{2},\ldots,a_{n}\), and the outgoing waves are \(b_{1},b_{2},\ldots,b_{n}\), the relationship between the outgoing and incoming waves can be expressed by Equation (1):  
\[\left\{\begin{array}{l}b_{1}=S_{11}a_{1}+S_{12}a_{2}+\ldots+S_{1n}a_{n}\\ b_{2}=S_{21}a_{1}+S_{22}a_{2}+\ldots+S_{2n}a_{n}\\ \ldots\\ b_{n}=S_{n1}a_{1}+S_{n2}a_{2}+\ldots+S_{nn}a_{n}\end{array}\right., \tag{1}\]  
Expressed in matrix form, Equation (1) is as follows:  
\[b=Sa, \tag{2}\]  
Of which:  
\[a=[a_{1},a_{2},\ldots,a_{n}]^{T}, \tag{3}\]  
\[b=[b_{1},b_{2},\ldots,b_{n}]^{T}, \tag{4}\]  
\[S=\left[\begin{array}{cccc}S_{11}&S_{12}&\ldots&S_{1n}\\ S_{21}&S_{22}&\ldots&S_{2n}\\ \ldots&\ldots&\ldots&\ldots\\ S_{n1}&S_{n1}&\ldots&S_{nn}\end{array}\right], \tag{5}\]  
Among them, \(S_{ii}\) is called \"reflection coefficient\". It indicates the voltage reflection of the port i when other ports are connected to the matching load. As we all know, the operation bandwidth of a MIMO system is decided by its impedance matching situation. Adding any decoupled structures will change the radiation impedance of the proposed antenna arrays. Thus, \(S_{ii}\) needs to be referenced when we consider decoupling design because it represents impedance match indirectly. By adjusting \(S_{ii}\), desired operation bandwidth can be attained. \(S_{ij}\) is called \"transmission coefficient\". It represents the voltage transmission from port j to port i when the i element is connected to the matching load. The mutual coupling degree of two ports is measured by \(S_{ij}\). Additionally, the preferred isolation is achieved by modifying \(S_{ij}\). The unit of these parameters is usually expressed in \"dB\". To ensure that the RF element is in a normal working state, it is generally required \(S_{ii}\) be less than \(-6\) dB and \(S_{ij}\) be less than \(-10\) dB.  
Taking the two-port network as an example, assume that 1 is the feed port and 2 is the coupling port. The \(S\)-parameters of the two-port network are given in Equation (6):  
\[S=\left[\begin{array}{cc}S_{11}&S_{12}\\ S_{21}&S_{22}\end{array}\right], \tag{6}\]  
where \(S_{11}\) is the input reflection coefficient, or input return loss; \(S_{22}\) is the output reflection coefficient, or output return loss; \(S_{12}\) is the reverse transmission coefficient or isolation; and \(S_{21}\) is the forward transmission coefficient, or gain.  
Assuming that the power of port 1 is \(P_{1}\) and the power of port 2 is \(P_{2}\), the isolation of the ports can be given by Equation (7):  
\[S_{12}(dB)=20 lg|S_{21}|=10 lg\frac{P_{2}}{P_{1}}, \tag{7}\]  
The degree of mutual coupling of the antenna element is inversely related to the absolute value of the transmission coefficient. Therefore, the larger the absolute value of the transmission coefficient, the smaller the degree of mutual coupling of the antenna.
# Data-Driven Health Assessment in Flight Control System
## 2 Literature Review  
Various system health condition estimation or state assessment based on measured data or information in different fields has been a topic of considerable interest. NASA [1; 2] has done a lot of work on theoretical research and engineering application of aircraft system health management. At present, for both the military and civil aircraft, health monitoring functions or equipmenthave become essential for mainstream aircraft, such as Boeing 787, A350, and F35 [3; 4]. Theoretically, there are two main methods for health assessment/monitoring: model-based and data-driven method. The model-based method can be divided into parameter estimation, states estimation, and equivalent space method [5]. All these model driven methods use the system mathematical model to complete the measured data processing, which constructs the observer to estimate the system output and then compares it with the measured value of the output (such as residual) to obtain information [6]. The data-driven method is to establish a health assessment model based on the acquired monitoring data and define the health state of the system by the failure threshold or health level [7]. Common data-driven methods mainly include Artificial Neural Networks [8], Support Vector Machine [9], Principal Component Analysis [10], etc. [PERSON] [11] used Gated Recurrent Unit (GRU) neural network to evaluate flap position sensor's health state; however, due to the small number of fault samples used in neural network training, it was difficult to identify the fault state so that the assessment accuracy was not high. [PERSON] [12] proposed a state monitoring method for flight control system based on Bayesian network and converted thefault signals, whichare difficult to detect, into deviation information which is easy to detect; however, the selection of deviation proportional coefficient was not determined by the distribution characteristics' variation of eigenvectors. [PERSON] [13] combined the parameters'weights of the hydraulic system by entropy weight and Analytic Hierarchy Process (AHP) and used Grey Correlation and Fuzzy Clustering method to evaluate its health state, but there were obvious subjective factors in the weight selection. [PERSON] [14] established a health assessment model with independent and nonindependent performance parameters to achieve health assessment based on multiple performance parameters degradation. [PERSON] [15] used the TadaBoost algorithm to evaluate the bearing's health state, but the training data noise increased the classifier training difficulty, which affects each parameter's variable weight. In general, the small number of fault samples in practical engineering will lead to inaccurate fault classification, and such subjective factors and multisource noise will also affect the weight distribution of assessment index.