esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 5 Influence of Linear Movement within the Signal Bandwidth due to Doppler
### Impact of a Constant Speed on the Achievable Coherent Integration Gain \(G_{\mathrm{int}}\)  
Another direct consequence of the spread over multiple range-Doppler cells is that the energy of single confined and point-like scattering points becomes equally distributed over a surface. The target return will start to decorrelate. A kind of integration loss is therefore already given by Equation (21)  
\[Loss_{\mathrm{rw}}=\left\lceil\#,\#_{\mathrm{d}}\right\rceil\approx 4\,v_{ \mathrm{eff}}^{2}\,T_{\mathrm{cov}}^{2}\,f_{\mathrm{s}}\,B_{\mathrm{eff}}/c^{2}.\]  
Because this range walk related spread depends only on the effective bandwidth \(B_{\mathrm{eff}}\), the sampling rate \(f_{\mathrm{s}}\), the effectively measurable velocity \(v_{\mathrm{eff}}\) and the covered timespan \(T_{\mathrm{cov}}\), this impact will solely relate to the bandwidth-dependent part of Equation (17) independently of the actual RF carrier frequency \(f_{\mathrm{c}}\). The stationary and linearly uncorrelated noise components are still suppressed by the matched filter gain but the decorrelation leads to a decreased target level--in presence of the stationary clutter and remaining noise levels. In the special case of a high instantaneous effective bandwidth \(B_{\mathrm{eff}}\) and a long covered timespan \(T_{\mathrm{cov}}\), this extension as it had already been illustrated in Figure 7 can severely degrade the effective coherent integration gain even in the case of a constant effective velocity \(v_{\mathrm{eff}}\).  
Whenever \(Loss_{\mathrm{rw}}\) becomes larger 1, high time-bandwidth products \(T_{\mathrm{cov}}\,B_{\mathrm{eff}}\) cause large integration losses according to Equation (21) in classical range-Doppler processing. This is illustrated for exemplary but typical passive radar parameters in Figure 10a,b. Considering the previous example of \(v_{\mathrm{eff}}=700\,\mathrm{km}/\mathrm{h}\) in Section 2.5, a desired gain of \(T_{\mathrm{cov}}\,B_{\mathrm{eff}}=65\,\mathrm{dB}\) is far too high for classical range-Doppler processing. An introduced loss of 12 dB would give a detectable 'narrowband' RCS that is by \(\Delta 6\,\mathrm{dB}\) worse than if 59 dB had been used and the maximal effective \(G_{\mathrm{int}}\) would reduce to 53 dB. This is expressed under the consideration that \(T_{\mathrm{int}}\leq T_{\mathrm{cov}}\) and an approximated \(f_{\mathrm{s}}\approx B_{\mathrm{eff}}\). Equally, the minimal detectable RCS would increase from e.g., 0 dBm\({}^{2}\) to over \(+12\,\mathrm{dB}\mathrm{m}^{2}\) or, respectively, the maximum covered range would decrease in this effective velocity span. Even in the case of \(v_{\mathrm{eff}}=300\,\mathrm{km}/\mathrm{h}\) a loss of already 5 dB would be present. Multiple objects could therefore be missed if just this effect is not considered. It is important that this consideration does not yet include any performance degradation like the Gross Doppler frequency sensitivity of a batched implemented scheme (Section 4).
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 3 Implementation Optimization
### Resource Optimization and Throughput Enhancement  
The implementation employs serialization of the parallel X/Y polarization inputs, interleaving the data streams before entering the processing chain. By converting parallel data paths into a single serialized stream, the architecture reuses critical blocks including windowing and FFT processors. This serialization strategy reduces FPGA resource utilization by approximately 50% compared to a fully parallel implementation, as identical operations for both polarizations share the same hardware blocks. The resource optimization becomes particularly relevant in FPGA platforms where DSP blocks and memory represent constrained resources.  
To maintain processing throughput despite serialization, the system implements a 4x overclocking scheme in the FFT processing block. The input data arrives with a 1/4 duty cycle, providing timing margins that enable clock rate multiplication. By operating the FFT at four times the input clock frequency, the system achieves the same effective throughput as a parallel implementation while utilizing fewer hardware resources. This overclocking strategy ensures that the serialized data path can process both X and Y polarization samples within the required time constraints, matching the performance of a dual-path architecture.  
Table 1 provides an estimation of the FPGA resources used by the entire RFI Mitigation block once implemented in the FPGA. The estimation is performed by Vivado with knowledge of the architecture of the FPGA where the block will be implemented in, but is still missing further optimizations that can only be performed once the block is implemented with the rest of the system, instead of isolated.
## 2 Methods
### GBInSAR
The phase values can be affected by ambiguity (unwrapped phase), but, due to the short-elapsed time between two subsequent measurements on Stromboli volcano, the interferometric displacements were usually smaller than half wavelength, so that no unwrapping procedures were needed. The GBInSAR phase data are useful to detect: (i) the inflation/deflation of the summit plumbing system [40,44]; (ii) small-to-large scale slope instability, in response to eruptive (over-steepening and overloading; [14]) and magmatic activity (dike intrusions; [33,56]); (iii) the gravitational re-adjustment of the talus, sometimes evolving into rock-falls [35]; (iv) thermal contraction of the lava field, mainly in areas of lower pre-effusive slope angle [57]; and, (v) persistent flank motion [58]. Averaged power (amplitude squared) images produced by the GBInSAR NE400 were used to quantitatively evaluate the changes in the NEC's morphology, as they were the closest devices to the NEC. Each analyzed image was derived-from-48-images averaged (\(\approx\) 1 every 4 h) in order to increase the signal to noise ratio and, in doing so, facilitate its interpretation. Because the NEC rim produced a shadow zone corresponding to the crater depression, it was possible to calculate the area of the crater itself as it changed over time [14]. The standard deviation was calculated as equal to 80 m\({}^{2}\) (see [14]). A threshold was set at ~50 dB to map the area affected by the NEC widening and narrowing collapse.
## 3 Methodology
### Calibration Methods
#### 3.2.2 Isotonic Regression  
Isotonic Regression addresses calibration through a non-parametric, monotonic transformation of raw model scores \(\hat{p}_{i}\). Unlike parametric methods like Platt Scaling, it makes no assumptions about the functional form of miscalibration, instead learning a piecewise constant calibration function \(\phi_{\text{Iso}}\) that preserves the ordinal relationship between scores. Formally, the calibration function satisfies:  
\[\phi_{\text{Iso}}(\hat{p}_{j})\leq\phi_{\text{Iso}}(\hat{p}_{k})\quad\text{ whenever}\quad\hat{p}_{j}\leq\hat{p}_{k}, \tag{5}\]  
ensuring that higher raw scores never map to lower calibrated probabilities.  
The optimal calibration function minimizes the squared error over \(\mathcal{D}_{\text{cal}}\):  
\[\phi_{\text{Iso}}^{*}=\underset{\phi\in\mathcal{F}_{\text{mono}}}{\arg\min} \sum_{(\mathbf{x}_{i},y_{j})\in\mathcal{D}_{\text{cal}}}\bigl{(}y_{j}-\phi( \hat{p}_{j})\bigr{)}^{2}, \tag{6}\]  
where \(\mathcal{F}_{\text{mono}}\) is the class of all monotonic non-decreasing functions. This optimization is solved via the Pool Adjacent Violators (PAV) algorithm, which iteratively merges adjacent score intervals until monotonicity constraints are satisfied. For a sorted sequence of predictions \(\hat{p}_{1}\leq\hat{p}_{2}\leq\dots\leq\hat{p}_{m}\), the algorithm partitions them into \(K\) bins \(\{B_{1},\dots,B_{K}\}\), assigning each bin a calibrated probability:  
\[\phi_{\text{Iso}}(B_{k})=\frac{1}{|B_{k}|}\sum_{j\in B_{k}}y_{j}. \tag{7}\]
## 2 Materials and Methods
### Instrumental Methods
#### 2.4.5 Dynamic Mechanical Analysis (DMA)  
The researchers performed a dynamic mechanical analysis (DMA) using HITACHI DMS 6100. With regard to the testing method, a stretching mode with 0.5% dynamic stretch was used, and the specimen dimensions were controlled between 40 mm (length) \(\times\) 10 mm (width); the thickness of the specimen was maintained between 0.2 and 0.3 mm. The DMA system temperature was set from \(-\)60 \({}^{\circ}\)C to 110 \({}^{\circ}\)C, and the heating rate was 5 \({}^{\circ}\)C/min; the frequency used was 1 Hz.
# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
###### Abstract  
Keyword extraction is a critical task that enables various applications, including text classification, sentiment analysis, and information retrieval. However, the lack of a suitable dataset for semantic analysis of keyword extraction remains a serious problem that hinders progress in this field. Although some datasets exist for this task, they may not be representative, diverse, or of high quality, leading to suboptimal performance, inaccurate results, and reduced efficiency. To address this issue, we conducted a study to identify a suitable dataset for keyword extraction based on three key factors: dataset structure, complexity, and quality. The structure of a dataset should contain real-time data that is easily accessible and readable. The complexity should also reflect the diversity of sentences and their distribution in real-world scenarios. Finally, the quality of the dataset is a crucial factor in selecting a suitable dataset for keyword extraction. The quality depends on its accuracy, consistency, and completeness. The dataset should be annotated with high-quality labels that accurately reflect the keywords in the text. It should also be complete, with enough examples to accurately evaluate the performance of keyword extraction algorithms. Consistency in annotations is also essential, ensuring that the dataset is reliable and useful for further research.  
keyword extraction; natural language processing; dataset; structure; quality; complexity +
Footnote †: journal: Applied Sciences
## 1 Introduction
The Gaussian process is a machine learning process [13] which is largely used in research areas such as image processing. Recently, it has been used in surface metrology. [PERSON] et al. [14] utilized the Gaussian process to form error assessment for CMMs. With the proposed Gaussian modelling method, the designed geometric form, systematic manufacturing errors and random manufacturing errors were decomposed. Simulation and actual measurement data demonstrated the improvement as compared to the traditional method. [PERSON] et al. [15] developed a Gaussian process-based modelling method and data fusion method for the measurement of complex surfaces. The result showed that the measurement uncertainty of the fused dataset was smaller than the original datasets. However, the systematic error was not considered in the proposed method. [PERSON] et al. [16] extended the one-to-one linkage model to the one-to-many neighbourhood linkage model to improve the misalignment problem. Simulation and actual measurement both exhibited significant improvement. In the multi-sensor CMM scenario, a generic method for data modelling and data fusion method is much needed to meet the development trend of the emerging market for multi-sensor metrology. Moreover, in the presence of measurement noise and form error, there is a need to study the accuracy of the data modelling method and how it is affected by these factors. The performance of the data modelling method should also be verified experimentally. In order to address these issues, this paper proposes a Gaussian process data modelling and maximum likelihood data fusion method for multi-sensor CMM measurement of freeform surfaces. The datasets from different sensors are firstly modelled using the Gaussian process to obtain the mean surfaces and the covariance surfaces, which represent the underlying surfaces and the associated uncertainties. Hence, the mean and covariance surfaces are then fused together with the maximum likelihood principle. The method is verified through a series of simulations and real measurement conducted on a multi-sensor CMM, followed by detailed discussions including some failure situations to which much attention should be paid. The proposed Gaussian process and maximum likelihood-based method establishes a generalized, sensor-independent data fusion framework for multi-sensor CMMs, which provides an indispensable solution for integrating different sensor data for measuring high-precision freeform surfaces. The influence of the form error and measurement noise on the accuracy of the data modelling method is also discussed and demonstrated in a series of experiments, while the performance of the Gaussian process modelling of the measurement uncertainty is also experimentally verified.
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 4 Statistical Analysis of the Occurrence of Plasma Bubbles
### Statistical Analysis of the Monthly Occurrence of Plasma Bubbles  
Using the airglow data from September 2014 to August 2015, we statistically analyzed the monthly occurrence of plasma bubbles. Through the statistics of optical data during this period, we obtained the numbers of EPB days and clear days per month, and the statistical features of EPB occurrence with months were analyzed. In the process of data statistics, the situation that the clear period at night exceeded 1 h was marked as a clear night. Figure 7 shows the statistical distribution histogram of EPB days and clear days in each month.  
Figure 7 gives the statistical results of EPB days and clear days every month. The total numbers of observation days in Fuke are 330 from September 2014 to August 2015, and the optical observation was almost carried out every day in most months of the year. From Figure 7, EPBs occur most frequently in September 2014, with 10 events. The rest of fall in October and November generally have decreasing occurrence than September, with 9 and 6 events, respectively, followed by EPB occurrence in the early-mid winter. Occurrence increases again in late winter and spring, followed by a small peak in early summer. To conclude, EPBs appear more frequently from September to November 2014 and February to April 2015.  
Therefore, the occurrence rate of EPBs in each month can be calculated and obtained. In this study, the occurrence rate is the percentage of clear days with EPB, Figure 8 presents the statistical results of the monthly occurrence rate of EPBs over the period from September 2014 to August 2015.  
Figure 6: Drift velocity of EPB on 2 September 2014.  
Figure 8 shows the statistical distribution histogram of EPB occurrence in each month, the green bar represents the EPB occurrence rate. From Figure 8, EPBs mainly occur from September to November in 2014, and from February to April in 2015. The maximum occurrence rate is 40% in September. Then followed by April, the occurrence rate of EPBs is also relatively higher, reaching 38%. EPBs also show a high occurrence rate in October and February, which are both close to 33%. Figure 8 shows that the EPB occurrence rate is very low from December to January, the occurrence rate is 0 in these months. In addition, EPBs  
Figure 8: Statistical results of EPB occurrence in each month over Fuke (9.9 degrees MLAT).  
Figure 7: Statistical results of EPB days and clear days in each month over Fuke (9.9 degrees MLAT).  
are less observed during the period from June to July 2015, and the calculated occurrence rate is relatively lower, which is less than 15%. In conclusion, EPBs occur more frequently during the months of September to November and February to April, for the twelve months observed. However, the occurrence rate of EPBs is relatively low in other months.
## 3 Results and Discussion
### Relative Performance of LPD Biosorbent  
A comparison of the adsorption performance of LPD biomass in removal of lead ion from contaminated water with those of various biosorbents reported in literature is shown in Table 7. For simplicity of comparison, the maximum adsorption capacity (\(q_{m}\)) of the biosorbents, as well as the corresponding adsorption conditions, were tabulated. As can be seen, the predicted \(q_{m}\) value of LPD is relatively higher than most of the listed values. It is worth mentioning that, despite no modifications having been carried out on the LPD surface characteristic, its potential for removal of lead(II) is noticeable, and modification may further enhance its adsorption capacity [29]. Adsorption enhancement of modified biosorbent is proven by the work of [PERSON] et al. [42], in which the obtained \(q_{m}\) values of untreated-, H\({}_{2}\)SO\({}_{4}\)-, HNO\({}_{3}\)-, and NaOH-oil tree pruning biosorbents were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**T (K)** & **In\(K_{s}\)** & **\(\Delta G^{o}\) (kJ/mol)** & **\(\Delta H^{o}\) (kJ/mol)** & **\(\Delta S^{o}\) (J/mol-K)** & **\(R^{2}\)** \\ \hline
303 & \(-2.547\) & 6.417 & & & \\
308 & \(-2.437\) & 6.239 & & & \\
318 & \(-2.097\) & 5.545 & 38.814 & 106.211 & 0.8871 \\
323 & \(-1.519\) & 4.079 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Thermodynamic parameters for lead(II) ion adsorption onto LPD adsorbent.  
Figure 6: Van’t Hoff plot for the adsorption of Pb\({}^{2+}\) onto _Lavandula pubescens_ Decne adsorbent.  
27.05, 65.62, 85.09, and 121.60 mg/g, respectively. The difference in the adsorption capacities of various biomasses is attributed to the variety in the chemical and textural properties, including their functional groups' type and abundance. Adsorption conditions are also one important influencer in the adsorption performance of the adsorbent, therefore, optimization of the adsorption parameters may enhance the adsorption efficiency.
# Depth Retrieval Procedures in Pulsed Thermography: Remarks in Time and Frequency Domain Analyses
## 2 Materials and Methods
As a result, for known thermal properties of the tested material, the determination of \(t_{peak}\) and \(f_{b}\) can be used for depth quantification purposes. Additionally, the correlation factor \(C\) used in this study (frequency domain analysis) had the value of 1.82, while the thermal diffusivity \(\alpha\) of the inspected component according to its manufacturing characteristics was defined by the literature, having the value of 4.2 \(\times\) 10\({}^{-7}\) m\({}^{2}\) s\({}^{-1}\)[6]. Both the temporal and the frequency domain quantification techniques required the selection of a sound area. An inappropriate sound area selection can be an important factor for error production when depth has to be estimated, making its selection the most crucial parameter which in combination with a non-uniform stimulation process can provide large deviations from the actual depth in which the detected feature is located to. Based on the qualitative assessment of the examined sample presented in a previous study by the authors [40], which showed that temperature data elaboration suffered from uneven heating, depth quantification in the time domain was designed by selecting the sound area next to each defect. In such a manner, it was ensured that the two respective regions received the same amount of heat energy and consequently minimising errors due to uneven heating. In particular, according to the lateral dimensions of the detected Teflon squares, a sound area occupying exact the same pixels as the defective one was selected next to the insertion as illustrated in Figure 2 and their dimensions were defined in terms of pixels as 4 \(\times\) 4, 6 \(\times\) 6, 8 \(\times\) 8, 10 \(\times\) 10 and 12 \(\times\) 12 for correspondent defects No. 1, No. 2, No. 3, No. 4 and No. 5. Figure 2: Selection of the sound area for each detected defect in order to calculate the peak slope time from the respective thermal contrast curve in the time domain analysis. However, as the application of PPT produced enhanced background uniformity, only an area from the background was selected for the calculations of the produced phase contrasts. In other words, contrary to the time domain analysis the same sound area was used in this case for the calculations of all the phase contrast curves and consequently for the depth measurements of the detected features. Additionally, due to the enhanced background uniformity observed on the frequency domain analysis, this depth quantification method was repeated and assessed, considering as well the field of view as the sound area. For the clarity of the readers about the above selections, indicative imaging results from the two domains are presented in Figure 3, justifying the decisions concerning the selection of the sound areas in both the temporal and frequency domain analysis. In particular, the influence of uneven heating can be observed in Figure 3a, contrary to the phase image of Figure 3b, where background uniformity was observed.
## 3 Numerical Simulation
### Prediction Model of Structural Parameters, \(A\), \(S\), and \(L\), to \(\Phi\)  
It can be seen from the above that after knowing the structural dimensions \(D\), \(d\), \(S\), and L of the compact parallel channels (referring to Figures 7 and 9 or via calculation), we can know the primary term coefficient (\(a1\)) and constant term (\(b1\)) of the linear relationship between \(A\) and \(\Phi\). Then, \(A\) can be inserted into the linear relationship to obtain the predicted \(\Phi\). Predicting the flow distribution results in compact parallel channels with different structural parameters is of guiding significance.  
In studying the accuracy of the prediction model in the range of \(0<A<2.5\), \(2<S<5\), and \(2<L<10\), we designed 85 random groups of data with different structural parameters for simulation calculation and analyzed the results. \(\Phi\) is the exact solution of the non-uniformity flow coefficient calculated by the simulation results, \(\Phi_{pr}\) is an approximate solution of the non-uniformity flow coefficient calculated by the prediction model, and the error parameter (\(r\)) is defined to describe the error between the exact solution and the approximate solution, that is:  
\[r=\left|\frac{\Phi-\Phi_{pr}}{\Phi}\right| \tag{14}\]  
Figure 10 revealed the value of the error parameter (\(r\)) between the approximate solution (\(\Phi_{pr}\)) and the exact solution (\(\Phi\)), indicating that the error was mostly maintained within 0.1. Only when \(0<\Phi<0.05\) would a very small number of points produce a large error (\(r>0.1\)). With increasing \(\Phi\), \(r\) gradually decreased, which indicated that the approximate solution was more accurate and \(A\) had no effect on the error. Because \(\Phi\) is the non-uniformity flow coefficient of the compact parallel channels, when \(0<\Phi<0.05\), even if the approximate solution error of a few points is \(0.1<r<0.16\), the error of the prediction results was still within the acceptable range of the prediction model. Therefore, it can be considered that the exact solution (\(\Phi\)) and approximate solution (\(\Phi_{pr}\)) are basically the same with the same structural dimensions (\(A\), \(S\), \(L\)), showing that the prediction model was accurate for the prediction of the non-uniformity flow coefficient (\(\Phi\)).  
This study established a prediction model for flow distribution to simplify the calculation, as demonstrated in Figure 11. Through numerical simulation analysis, we could obtain the flow behaviors of fluids in the compact parallel channels, thereby guiding the design of the structure to meet the needs.