esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Video Frame Interpolation Module Evaluation
#### 4.1.4 High-Dynamic Analysis of Melt Pool Temperature Distribution  
To gain a deeper understanding of the dynamic behavior of the melt pool and to verify the effectiveness of the video frame interpolation module, we conducted a regional analysis of the melt pool motion in the interpolated infrared video. As shown in Figure 9, during the 1380-1420 ms time interval, melting occurred on both sides of the front end of the melt pool, while the central part did not fully melt. The pre-trained MTVFI model accurately captured the motion state of the melt pool, where the under-melted front end rapidly heated up and melted as the melt pool moved forward, and the melted parts on both sides converged under the influence of surface tension, causing the front sides to narrow.  
A low-temperature area exists in the center of the melt pool, which is due to the violent convection and heat accumulation in the melt pool as the metal powder melts and merges into it, forming melt pool oscillations that cause the liquid metal to fragment and form high-temperature slags, thereby creating a low-temperature area in the center. The motion state of this low-temperature area is relatively random, posing a challenge for model learning; hence, there is a certain deviation between the center area in the generated intermediate frames and the actual observed values. The edge part of the melt pool's central region has distinct motion characteristics and shows an overall stable trend of change, which the pre-trained MTVFI model has also learned.  
During the solidification process in the tail area of the melt pool, the uneven temperature distribution of the melt pool leads to uneven solidification in this area. As shown in Figure 9, during the 2000-2040 ms period, the uneven temperature distribution at the tail of the melt pool is alleviated. This is due to the laser's motion direction rotating by 90\({}^{\circ}\), causing temperature accumulation on the right side of the melt pool and an increase in temperature on the right side. The MTVFI model learned this temperature variation trend and generated the correct temperature gradient. This indicates that the proposed video frame interpolation module can correctly learn the motion characteristics of the melt pool and can generate interpolated frames that include an accurate distribution of the melt pool's temperature.  
In summary, the proposed method effectively addresses the issue of frame-rate discrepancies in multi-source data and enhances the utilization of multi-source data for the  
\begin{table}
\begin{tabular}{l|l l} \hline
**Model** & **PSNR** & **SSIM** \\ \hline PreTrain-MTVFI & 38.908 & 0.992 \\ MTVFI & 32.490 & 0.966 \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison of training strategies based on pre-training.  
Figure 9: High-dynamic analysis of melt pool temperature distribution.  
melt pool. This provides robust data support for the monitoring of the high-dynamic thermal history of the melt pool.
## 3 Materials and Methods
### Study Cases
#### 3.1.3 Lake Banyoles (NE Iberia)  
Lake Banyoles (Girona, Spain) (Figure 1) is a karst lake associated with a large karst aquifer system located in a tectonic depression, fed by underground water. The lake isapproximately 2100 m long and 750 m wide with an average depth of 15 m that in several places reaches up to 46 m [35; 36]. Two pollen records are dated in Mid-Holocene chronologies. Firstly, the Lake Banyoles core (LB) [37] was obtained from the eastern shore of the lake. This 3310 cm long core provided a continuous pollen record for the last 30,000 years. However, this paper focuses on the first 410 cm, dated in the Early to Middle Holocene transition (9.0-6.0 kyr cal BP). Secondly, the SB2 core was obtained from the western shore of Lake Banyoles, 160 m away from the present lakeshore. This core is 370 cm long, with a continuous analysed pollen record between 174 and 281 cm depth, providing data for the period 9.0-3.3 kyr cal BP [38; 39].
# High Performance Receiver Design for RX Carrier Aggregation
## 3 LNTA Topologies and Simulation Results  
In this section, several LNTA topologies are investigated to support both inter-band and intra-band CA. LNTA needs to be in the form of single input and multi-output (SIMO) in order to provide separate signal paths for different carriers. Although two CA carriers are assumed in the implementation, the architecture can be extended to N carriers.  
Figure 5 shows the simulated receiver front-end architecture to evaluate different LNTA topologies. LTE band 7 (2620-2690 MHz) is chosen for the implementation and simulation of the receiver. L-section off-chip matching lumped elements are configured with RLC equivalent circuit parameters adopted from Murata Chip S-parameters & Impedance Library. CA-capable LNTA is used as a single-\(g_{m}\) of the receiver front-end. The main transistor (\(g_{m}\)) is biased in the sub-threshold region to improve the current efficiency (\(g_{m}/I_{d}\)). The degradation in the unity-gain frequency (\(f_{t}\)) due to sub-threshold operation is not a problem per se, since the resonance behavior of the impedance matching network (IMN) cancels out the parasitic effect of the main transistor. The cascade-transistor is half the size of the main transistor, and its gate bias is supplied from the regulated loop to make \(V_{DS}\) of the main transistor at the same potential of the current mirror transistor's \(V_{DS}\). A conventional single input, single output (SISO) LNTA is designed for band 7, and is simulated with the test-bench without CA. The gain of the single-carrier receiver front-end as a reference is set at 56.6 dB. NF and current consumption of the single-carrier receiver is 1.33 dB and 7.4 mA, respectively.  
Figure 4: Single-\(g_{m}\) zero-IF receiver architecture.
## 3 Results
### Experimental Considerations
#### 3.3.4 Standing Waves  
Standing waves are an important consideration in measurements of beam profiles, because they may cause errors and artefacts in the data. Standing waves are formed between the emitter and detector when their surfaces act as a resonator, supporting multiple reflections. The presence of standing waves causes significant variations in the detected signal arising from wavelength-scale changes in the relative distance between the emitter and detector. In order to understand the effect of standing waves, the beam profile at 600 GHz was mapped for both orientations over the distance equal to half-wavelength (0.25 mm). The results are shown in Figure 9a,b below. It is seen that standing waves have a negligible effect on the observed beam profile.  
Figure 8: Simulations of model beam profiles as they would appear at various acceptance angles, revealing the effects of angular resolution. The initial profile has 0.2\({}^{\circ}\) resolution. (**a**) single Gaussian, (**b**) skewed Gaussian, (**c**) Gaussian with shoulders, (**d–f**) Gaussian with additional 3\({}^{\circ}\) wide feature of differing amplitudes and separation.  
Figure 7: Comparison of the profile at 600 GHz measured at 35 mm from the emitter with an acceptance angle of 17.9\({}^{\circ}\) with the profile extrapolated from a measurement at 70 mm from the emitter with an acceptance angle of 9.0\({}^{\circ}\): (**a**) E-plane orientation and (**b**) H-plane orientation.
## 5 Conclusions  
We proposed a joint fractional Doppler offset and channel estimation iterative algorithm for OTFS systems with non-integer Doppler offsets in LEO satellite communications. The full channel information can be reconstructed by the estimated fractional Doppler offsets and coefficients of paths. The estimation algorithm can greatly improve the BER performance of OTFS systems with slight complexity and be suitable for paths with arbitrary Doppler shifts in the channel for OTFS systems in other high-mobility scenarios.  
Conceptualization, X.L.; methodology, X.L.; software, X.L.; validation, L.S., X.L., and G.R.; formal analysis, L.S.; investigation, L.S.; resources, G.R.; data curation, G.R.; writing--original draft preparation, X.L.; writing--review and editing, G.R.; supervision, G.R.; project administration, L.S. All authors have read and agreed to the published version of the manuscript.  
Figure 5: BER performance vs. SNR for the proposed algorithm, the embedded pilot algorithm [8], the two-stage basis function correlation algorithm [13], two-step modified OMPFR algorithm [16], and tensor decomposition-based channel estimation [18].  
**Funding:** This research was funded by the National Natural Science Foundation of China grant number 62371363. The authors gratefully acknowledge the financial support from the foundation.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
In this review, we probe into the intricate realms of integrated photonics, exploring material platforms, fabrication methods, applications, and the associated challenges in the development of PICs. Furthermore, a forward-looking perspective is offered on the future of integrated photonics, shedding light on potential advancements and avenues for \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Aspect**} & **PICs** & **EICs** \\ \hline \multirow{2}{*}{Operational mechanism} & Utilizes photons (light) for signal transmission & Relies on electrons for signal transmission \\ \hline \multirow{2}{*}{Medium} & Optical fibers, WGs, and photonic crystals & Si or other semiconductor materials \\ \hline \multirow{2}{*}{Speed} & Usually, higher speed & Limited by electron mobility and resistance \\ \hline \multirow{2}{*}{Energy efficiency} & Generally, more energy-efficient & Subject to resistive losses and heat dissipation \\ \hline \multirow{2}{*}{Bandwidth} & Higher bandwidth capabilities & Bandwidth limited by material properties \\ \hline \multirow{2}{*}{Signal loss} & Lower signal loss over long distances & Signal loss over distance in conductors \\ \hline \multirow{2}{*}{Interference} & Less susceptible to EM interference & Susceptible to EM interference \\ \hline \multirow{2}{*}{Integration density} & Limited by photonic component sizes & Can achieve high integration densities \\ \hline \multirow{2}{*}{Overall cost} & Typically, higher costs due to fabrication complexity & Lower cost due to mature manufacturing processes \\ \hline \multirow{2}{*}{Applications} & Optical communication, computing, sensing, imaging & Computing, data processing, control systems \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PICs versus EICs. innovation in this rapidly evolving field. Figure 1 serves as the focal point of this dialogue, elucidating its core concepts and providing a visual anchor for the debate.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 2 Materials and Methods
### Scenario Description
The complete topological and morphological characteristics of this scenario were taken into account, prior visual inspection and measurement of dimensions within the tunnels. A schematic view of the employed simulation scenario is shown in Figure 4.  
One of the particular aspects of this scenario is the fact that the tunnel is not a conventional tunnel (i.e., free hallway with walls that can be smooth or can present a certain degree of surface roughness and, hence, diffuse scattering), due to the fact that there is a set of service trays placed inside the tunnel. This leads to new interactions with the electromagnetic waves launched by the source, with strong multipath component generation given the large ratio between the length of the tunnel in comparison with the width of the tunnels, tray dimension and inter-tray distance. This condition implies the need for including more precise topological information of the scenario in order to consider the interaction, specifically for launched rays within a RL algorithm, such as the one employed and described in next subsection.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Data-Assimilation Algorithms
#### 4.2.1 Overview of Assimilation Data  
In 1949, [PERSON] [130] introduced the polynomial interpolation method, primarily based on least squares fitting of regional polynomials that include observation points. In 1955, [PERSON] et al. [131] presented the background field analysis method, arguing that initial values should be incorporated in objective analysis to address the problem of insufficient observations. Building on background field analysis, two variations of interpolation methods were developed: the successive correction method (SCM) and the optimal interpolation method (OI). During the 1980s and 1990s, variational assimilation methods, such as three-dimensional variational assimilation (3D-VAR) and four-dimensional variational assimilation (4D-VAR), effectively tackled the nonlinearity issues in data assimilation [132]. [PERSON] et al. [133] categorized these approaches into three stages: simple analysis methods, statistical or OI methods, and variational analysis.  
After the 1990s, sequential assimilation algorithms, notably the Kalman filter (KF) and its derivatives, became predominant. Recently, various particle filter (PF) algorithms derived from the ensemble approach have rapidly evolved and gained widespread use. Traditional KF algorithms are mainly suited for linear system models, while many sequential assimilation algorithms have been adapted for complex, nonlinear models, such as the extended Kalman filter (EKF) [134], unscented Kalman filter (UKF) [135], and ensemble Kalman filter (EnKF) [136]. Although the ensemble Kalman filter offers advantages over earlier algorithms, it faces challenges like rank-deficient inverses in the gain matrix and filter divergence. To address these issues, several ensemble Kalman filter-based algorithms have been improved and optimized, including the ensemble transform Kalman filter (ETKF) [137], ensemble square root filter (EnSRF) [138], ensemble adjustment Kalman filter (EAKF) [139], local ensemble Kalman filter (LEKF) [140], and ensemble steady-state Kalman filter (EnSSKF) [141].  
Additionally, the particle filter, a novel sequential assimilation algorithm based on Monte Carlo simulation and Bayesian sampling, is particularly effective for complex nonlinear and non-Gaussian system models. With a sufficient number of particles, it can achieve near-optimal accuracy and deliver robust results, demonstrating considerable potential for various applications. The field of data assimilation is also evolving with the integration of artificial intelligence technologies, including genetic algorithms, neural networks, and simulated annealing, leading to the development of modern intelligent assimilation algorithms.
## 2 Materials and Methods
### Hyperspectral Data Acquisition and Processing
#### 2.2.1 Non-Imaging Canopy Hyperspectral Data for Winter Wheat  
The ASD Field Spec3 field portable hyper spectrometer (Analytical Spectral Devices Inc., Boulder, CO, USA) was used to determine the non-imaging hyperspectral data of the winter wheat canopy. Prior to collecting spectral data, it is necessary to turn on the power supply 30 min in advance to allow for warming up. The instrument has a spectral wavelength range of 350 to 2500 nm. Within this range, the sampling interval is 1.4 nm for the range of 350 to 1000 nm, and 2 nm for the range of 1000 to 2500 nm. The resampling interval is 1 nm, and the field of view is 25\({}^{\circ}\). To mitigate the impact of forward shadows and minimize soil background noise, the ASD external probe was positioned vertically downwards at a height of approximately 80 cm from the top of the canopy. Spectral values were collected from a representative plant canopy, with 10 measurements taken for each experimental plot. The average spectra were then calculated, excluding any abnormal values. Additionally, a white board was utilized to correct for variations in light conditions before and after each measurement in every plot. A whiteboard was employed in each plot both prior to and subsequent to the measurement to rectify the mistakes induced by the prevailing light condition.
# Airborne and Dermal Collection Methods of Gunshot Residue for Toxicity Studies
## 3 Results/Discussion
### Elemental Analysis (All Samples)
#### 3.4.3 Hand Swabs/De-Leading Wipes  
The hand swab and de-leading wipe elemental data (Table 1) had concentrations for some of the GSR-related elements that were elevated compared to the airborne samples. The hand swab average and SEM for single and triple shots were Ba (45.52 \(\pm\) 27.16 ppb and 61.25 \(\pm\) 8.26 ppb) and Pb (261.11 \(\pm\) 143.20 ppb and 446.24 \(\pm\) 82.67 ppb), respectively (Figure 5C). Following use of de-leading wipes, the concentrations on the hand swabs after one or three shots were below the limit of detection for Ba and substantially reduced for Pb (15.57 \(\pm\) 12.99 ppb and 3.13 \(\pm\) 4.95 ppb), respectively. Prior to use of the de-leading wipe, the hand swab samples had the highest concentration of combined characteristic GSR elements when compared to the airborne samples. The use of de-leading wipe decreased the hand swab characteristic GSR (Ba, Pb) concentrations by over 175% for the single and triple shot scenarios, demonstrating the effectiveness of the wipes in removing heavy metals from the hands of the shooter (Figure 5C). This was further confirmed by measuring the elements present on the used de-leading wipes which had the highest total elemental concentrations of all samples analyzed. The high levels of Ba, Pb on the de-leading wipes further suggest the transfer of metals from the hands of the shooter to the de-leading wipe. The chemicals present in the de-leading wipe allow for the effective removal of metals, including ethylenediaminetetraacetic acid (EDTAA), a known chelator [58], along with other chemical compounds present in the wipes. The use of de-leading wipes helped reduce the amount of Ba and Pb on the hands of the shooter after both scenarios and may be a more effective collection method for inorganic GSR than hand swabs.  
The above table shows the concentration mean values and standard error mean (SEM) in ppb for all four of the sample types. All the samples were blank corrected with respective sample blanks except for the de-leading wipe samples. The GSR elemental is the average combined concentration of Pb and Ba from all of the sample runs.
## 3 Research Findings and Discussion
### Four Point Bending Test
In the first studies, it was shown that the resistance also increased as its sediment increased. As is known, the AZ91 Mg alloy contains the \(\delta\)-Mg17 Al12 precipitate phase in addition to the \(\alpha\)-Mg phase, which is the matrix phase. In their study, [PERSON] et al. (2023) investigated the effects on the structural, mechanical, and bio corrosion properties of a solid-soluted AZ91 alloy by adding bioactive glass (BG) to the AZ91 alloy with the friction-stir backward extrusion method (FSBE). They concluded that the formation of a discontinuous \(\delta\)-Mg17 Al12 phase within the \(\alpha\)-Mg matrix and the presence of bioactive glass particles would contribute to higher corrosion resistance of the AZ91-bioactive glass composite wire [56]. In addition, many researchers have investigated physical and chemical modification methods for better charge transfer from mineral matrices to CFs. In addition to adapting the interphase region between polymer impregnations and temperature-resistant mineral suspensions, CF wicks, and surrounding matrices, surface modification approaches such as oxidation, electrophoretic deposition, plasma, and inoculation treatments have also been applied to improve fiber-matrix interactions [57]. In their carbon fiber modification study (2023), [PERSON] et al. proposed a long fiber modification method in which carbon fiber filaments were first modified and then easily dispersed by dividing them into short fibers, and modified carbon fiber reinforced cemented composites could have good properties [58]. It is predicted that the improved AZ91 and its carbon fiber microstructure may be effective in adhesive material and material  
Figure 12: AZ91-A291; (**a**) bonded, (**b**) bolted and (**c**) bolted-bonded, CF-CF; (**d**) bonded, (**e**) bolted and (**f**) bolted-bonded, AZ91-CF, (**g**) glued, (**h**) bolted and (**i**) bolted-bonded single lap plate joints. interface, adhesion and strength in the single-lap joint types obtained by bonding the AZ91 Mg alloy and the carbon fiber composite plate with adhesive.
## 3 Proposed Method
### Influence Score (I-Score)  
Assume that we have a binary response variable \(Y\) (taking values of 0 and 1) and all explanatory variables are discrete. Consider the partition \(\mathcal{P}_{k}\) generated by a subset of \(k\) explanatory variables \(\{X_{b_{1}},\ldots,X_{b_{k}}\}\). Assume all variables in this subset are binary. Then, we have \(2^{k}\) partition elements; see the first paragraph of Section 3 in [PERSON] et al. [24]. Let \(n_{1}(j)\) be the number of observations with \(Y=1\) in partition element \(j\). Let \(\bar{n}(j)=n_{j}\times\pi_{1}\) be the expected number of \(Y=1\) in element \(j\). Under the null hypothesis, the subset of explanatory variables has no association with \(Y\), where \(n_{j}\) is the total number of observations in element \(j\) and \(\pi_{1}\) is the proportion of \(Y=1\) observations in the sample. In [PERSON] and [PERSON] [25], the influence score is defined as  
\[I(X_{b_{1}},\ldots,X_{b_{k}})=\sum_{j\in\mathcal{P}_{k}}[n_{1}(j)-\bar{n}_{1}( j)]^{2}. \tag{1}\]  
The \(I\) statistic is the summation of the squared deviations of the frequency of \(Y\) from what is expected under the null hypothesis. There are two properties associated with the \(I\) statistic. First, the measure \(I\) is nonparametric, which does not require the specification of a model for the joint effect of \(\{X_{b_{1}},\ldots,X_{b_{k}}\}\) on \(Y\). This measure \(I\) is created to describe the discrepancy between the conditional means of \(Y\) on \(\{X_{b_{1}},\ldots,X_{b_{k}}\}\), disregarding the form of the conditional distribution. Secondly, under the null hypothesis that the subset has no influence on \(Y\), the expectation of \(I\) remains nonincreasing when dropping variables from the subset. The second property makes \(I\) fundamentally different from [PERSON]'s \(\chi^{2}\) statistic, the expectation of which is dependent on the degrees of freedom and, hence, on the number of variables selected to define the partition. We can rewrite the \(I\) statistic in its general form when \(Y\) is not necessarily discrete:  
\[I=\sum_{j\in\mathcal{P}}n_{j}^{2}(\tilde{Y}_{j}-\tilde{Y})^{2}, \tag{2}\]  
where \(\tilde{Y}_{j}\) is the average of \(Y\) observations over the \(j\)th partition element (local average), and \(\tilde{Y}\) is the global average. Under the same null hypothesis, [PERSON] et al. [24] showed that the normalized \(I\), \(I/n\sigma^{2}\) (where \(\sigma^{2}\) is the variance of \(Y\)), is asymptotically distributed as a weighted sum of independent \(\chi^{2}\) random variables of one degree of freedom each such that the total weight is less than one. It is precisely this property that serves as the theoretical foundation for the following algorithm.