esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Significant ice thickness and ice impingement length reductions have been recorded in the tests aimed to evaluate the airspeed and the exposure time. In particular, the application of the SHC allows the ice thickness to be reduced to values of \(-27\%\) and \(-14\%\), at 95 m/s and at 420 s of exposure time, respectively. The impingement length reductions were \(-9.6\%\) and \(-7.6\%\), respectively. The repeatability tests performed at higher airspeed show a good agreement between them, corroborating the overall quality of the carried-out measurements. Finally, the test aimed to assess the LWC effect presents a moderate reduction in the ice thickness, i.e., \(-8\%\), matched to an increase in the impingement length, i.e., \(3.7\%\). Nevertheless, the relevant finding at a higher LWC is the reduced length and number of the ice rivulets observed for the coated configuration. Ultimately, the lesson learned from the two IWT test campaigns is that the low wettability of the SHC reduces the permanence time of water droplets on surfaces and the probability of icing on the coated surfaces with respect to the uncoated ones, but the thickness and the impingement length of the accreted ice largely depend on the geometrical configuration of the test article. As a consequence, it was found that the ice thickness variations observed in the previous work [11] for tests carried out at temperatures ranging between \(-3\)\({}^{\circ}\)C and \(-12\)\({}^{\circ}\)C varied from \(-3\)% to \(-3\)% and from \(+5\)% to \(-5\)% for A1 and A2 coated configurations, respectively, whereas in the present work, the ice thickness reduction is more pronounced, ranging between \(-8\)% and \(-49\)%. On the other hand, the impingement length reduction with respect to the uncoated surfaces in the previous work [11] ranged between \(-35\)% and \(-100\)% for A1, and \(-12\)% and \(-76\)% for A2, which becomes less pronounced in the present work, ranging between \(+3.7\)% and \(-10\)%.
## 3 Objectives  
The central objective of this paper is to evaluate ductility demands of steel buildings; local, story and global ductility as well as relationship among them are considered. To this aim, the nonlinear seismic responses of low- and medium-rise steel building models with typical welded connections, idealized as complex plane MDOF systems, are calculated. The ductility values are compared with those resulting from pushover analysis. The specific objectives are:  
1. Calculate local ductility for individual structural elements (beams and columns) associated to the maximum inelastic curvatures and axial elongations according to nonlinear seismic analysis. Several intensities of the strong motions are considered. The larger intensity of the earthquakes will correspond to a deformation state close to a structural collapse mechanism (maximum drifts of about 5%).
2. Calculate story and global ductility for the same cases above mentioned.
3. Compare the ductility values obtained from nonlinear dynamic analysis with those of pushover analysis.
4. Propose a relationship between local and global ductility.
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 4 Experimental Data Set
### HFSWR Data  
The SeaSonde HF radar system can obtain wide-area ocean observations. The experiment data are acquired by the \"COADS Seasone\" HF radar, which can continuously measure current. The HFSWR operational frequency is 24.5 MHz. The resolution of the measurements ranges from 500 m to 3 km, and the spatial angular resolution ranges from 1\({}^{\circ}\) to 5\({}^{\circ}\). The RMSE of the measured radial velocity RMSE is lower than 7 cm/s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Radar Parameter** & \\ \hline ATI mode & Full transmission, dual reception \\ Polarization mode & HH, HV \\ Incident angle & 15–50\({}^{\circ}\) \\ Radar operating frequency & 5.4 GHz \\ Pulse repetition frequency & 2202–2606 Hz \\ Satellite speed & 7500 m/s \\ Effective ATI baseline length & 3.75 m \\ Transmit signal bandwidth & 66 MHz \\ Incident angle & 15–50\({}^{\circ}\) \\ Antenna length & 15 m \\ NESZ & \(-\)20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: The radar parameters of the GF\(-\)3 satellite.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Parameters** & & \\ \hline Quantization digit & & 16 bit \\ Image size & Azimuth & 23,000 pixel \\ Range & Range & 16,000 pixel \\ Spatial resolution & Azimuth & 5 m \\ Range & & 5 m \\ Pixel size & Azimuth & 2.59 m \\ Range & & 2.25 m \\ Image size & Azimuth & 60 km \\ Range & & 37 km \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of SLC images.  
Figure 2: The schematic diagram of the GF\(-\)3 satellite SAR-ATI mode.  
In this experiment, the HFSWR data were recorded at 21:00 (UTC), 29 January 2022 on the coast of Qingdao. The range of the measured area was from 119.25\({}^{\circ}\) E, 34.89\({}^{\circ}\) N to 122.86\({}^{\circ}\) E, 36.10\({}^{\circ}\) N. Note that the HFSWR product data can generate due east and due north ocean flow field velocity data with latitude and longitude correspondence. Therefore, for the following comparison, it was necessary to convert the flow field velocity in both directions of the HFSWR data to the radial velocity of the SAR image, which is explained in detail in Section 5.3.
# Evaluation of NOx Reduction Effect and Impact on Asphalt
## 3 Experimental Results and Analysis  
### Evaluation of NOx Removal Performance by Reactor Type  
#### 3.1.1 Bed Flow Photo Reactors Test Result  
To verify the NOx removal efficiency of the bed flow photoreceptors (ISO standard) and mixed tank photoreceptors, the NOx removal performance of each reactor was evaluated using TiO\({}_{2}\) powder. Bed flow photoreactor tests were performed at a flow rate of 1 L/min, UV intensity of 10 W/m\({}^{2}\), temperature of 25 \(\pm\) 5 \({}^{\circ}\)C, and humidity of 45 \(\pm\) 5%. Figure 7 show the change in NOx concentration in the bed flow photoreactor experiments for TiO\({}_{2}\). Before turning on the UV light, the initial NOx concentration in the chamber was equilibrated to 1 ppm. After turning on the UV light, the concentrations of NOx and NO were reduced, and NO\({}_{2}\) was produced owing to the oxidation of NO. After 5 h of experimentation, the UV light was turned off, and the concentration showed a tendency to increase again. Table 6 show the NO removal efficiencies by TiO\({}_{2}\) type. In the case of Type-1, the NO removal rate was 26.06%, the Type-2 NO removal efficiency was 18.52%, and the Type-3 NO removal efficiency was 21.82%.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 6 Conclusions  
This study proposes an IMM-MCCKF filter for vehicle state parameter estimation and compares the performance of the IMM-MCCKF, MCCKF, and CKF filters. Simulation experiments were conducted under low-speed double lane-change and high-speed sinusoidal steering scenarios. In the double-lane-change condition, CKF showed significantly decreased estimation performance, whereas IMM-MCCKF and MCCKF maintained high accuracy. According to Table 2, the IMM-MCCKF algorithm reduced errors in longitudinal and lateral speed estimation by 15.3% and 67.0%, respectively, compared to MCCKF. Under the sinusoidal steering scenario, the CKF algorithm exhibited significant estimation fluctuations and a diverging trend. Both IMM-MCCKF and MCCKF maintained high accuracy and stability in their estimates.  
The IMM-MCCKF uses the MCC to optimize a nonlinear cost function, effectively suppressing non-Gaussian noise and capturing and processing outlier data in signals. Furthermore, it incorporates various state and measurement noise submodels that dynamically adapt to environmental changes, thereby mitigating the impact of unknown noise statistical properties. The study conducted simulation tests under two different driving scenarios: double lane-change and sinusoidal steering. The obtained results were contrasted and analyzed quantitatively using RMSE. The results demonstrate that the IMM-MCCKF filter, as proposed in this study, delivers superior estimation performance under heavy-tailed non-Gaussian noise conditions compared to the MCCKF and CKF algorithms. Additionally, under extreme conditions, IMM-MCCKF effectively counters the interference of noise variations, maintaining minimal fluctuations in the estimation results, thereby providing accurate vehicle state parameters essential for implementing autonomous driving systems and active safety in intelligent vehicles.  
In future work, more sophisticated vehicle models will be applied to parameter estimation modeling, and the algorithms detailed in this study will be validated using public datasets.  
Conceptualization, Q.C., F.Z. and L.S.; methodology, Q.C. and F.Z.; software, L.S. and B.L.; validation, F.Z., S.C. and Y.Z.; formal analysis, Q.C.; investigation, L.S., B.L. and S.C.; resources, Q.C., F.Z. and Y.Z.; data curation, Q.C. and F.Z.; writing--original draft preparation, Q.C.; writing--review and editing, Q.C., F.Z. and L.S.; visualization, Q.C. and B.L.; supervision, F.Z. and S.C.; project administration, F.Z. and L.S.; funding acquisition, F.Z. and B.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2021 YFB2500704).
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Determination of the Capacitance Value  
For a CCC, capacitors are inserted between the converter transformer and thyristor valves. Thus, the determination of the capacitance value is important for the main circuit parameter design. To analyze the characteristics of the CCC, a six-pulse CCC equivalent model at the inverter side is arranged in Figure 2.  
The grid side voltage is converted to the valve side voltage with the help of converter transformers. Considering the smoothing reactor at the DC side, it is assumed that the DC current remains unchanged for the steady-state calculation. The valve side voltage and the capacitor voltage are vector superimposed to obtain the commutation voltage, whose amplitude is greater than that of the conventional LCC under the same conditions.
## 2 Materials and Methods
### Feature Selection  
Feature selection was then performed on the original features and the created golden features using permutation-based feature importance, a robust statistical method that assesses the importance of individual features within predictive models [45; 46]. The calculation of feature 'importance' score is performed by assessing the contribution of a feature to the prediction error, Root Mean Squared Error (RMSE) score for the model. Permutation testing involves systematically shuffling the values of each feature across the dataset to break the original association between the features and the target outcomes. The performance of the model is then re-evaluated with the permuted data, and the change in model accuracy is measured. This process is repeated multiple times to obtain a distribution of performance scores for each feature, thereby estimating the significance of each feature's contribution to the model's predictive power. The importance of permutation testing lies in its non-parametric nature, offering a model-agnostic and reliable measure of feature relevance without assuming an underlying distribution. This method is crucial for our analysis as it helps in identifying 'golden features' that hold the most predictive value for understanding touch discrimination outcomes post-stroke, ensuring that our findings are not artefacts of random chance but are truly indicative of underlying neural processes [47]. By employing permutation testing, we ensure the robustness and reliability of the features identified by our AutoML approach, providing a solid foundation for the subsequent interpretation of these features within the context of stroke rehabilitation.
## 3 FEM Analysis and Results  
### Methods and Materials  
To make an estimation of the average location error of the linear location technique using the computational approach [22; 23; 24], an FE model of the receiving transducer was developed. Moreover, the frequency response of the modeled transducers was fully identical to the parameters of AE sensors used in the experiments.  
Figure 5 depicts a finite element model of a typical acoustic emission transducer. The software \"Comsol Multiphysics\" was applied to prepare a FE model during this study.  
A typical model of the AE sensor included an aluminum damper (1), three plates of a lead zirconate titanate piezoelectric element (3), four layers of epoxy resin (2) and a damping projector (4). The FE model was done in accordance with the boundary conditions and parameters of the electric circuit to create the inverse piezoelectric effect. The end faces of the test object were fixed to minimize repeated reflections of the acoustic waves from the side faces of the plate. The elastic displacements were assigned to the upper and lower surfaces of the steel plate. It allowed generating multimode AE signals. In addition to this, a grid of tetrahedral elements was applied, the facet size of which was 250 \(\upmu\)m. An  
Figure 4: Dependence of the relative attenuation \(\delta\) of high-frequency spectral components on the distance \(L\) between the AE sensors and AE source.  
Figure 5: Typical model of an AE sensor: (1)—damper, (2)—bonding layer, (3)—piezoelectric element, (4)—damping protector.  
electrical signal of a rectangular form with an amplitude of 100-300 V was utilized as an exciting signal. In order to simulate Lamb waves in the object of the study, some restrictions were imposed on the displacement of the AE sensors in respect to their fixed place on the steel plate.
## 2 Overview of AC Analysis Methods
### Discrete Fourier-Transform-Based Methods
#### 2.1.2 Goertzel Filter  
Goertzel filter [25] implements the Discrete-Fourier Transform in the form of a second-order Infinite Impulse Response Filter [26; 27]. Unlike FFT, each filter operates on a single frequency bin \(k\). The principle of the algorithm runs on the computation of an intermediate sequence \(y[k]\) as shown in Algorithm 1, from which the real and imaginary DFT components are extracted when the number of iterations reaches the number of samples \(N\). The Goertzel filter is known to not only speed up computations but also reduce memory consumption. The determination of amplitude and phases is done according to (2) and (3).  
```
Input :k: Frequency Bin Index, N: Number of samples Output :X: Complex DFT Analysis at frequency bin k \(\text{a}\gets cos(\frac{2 nk}{N})\) ; \(\text{b}\gets sin(\frac{2 nk}{N})\) ; \(\text{y}[\text{k}]\leftarrow\text{s}+\text{2a}\text{-y}[\text{k}-1]-\text{y}[ \text{k}-2]\) ; \(\text{X}\leftarrow\text{Complex(a}\text{-y}[\text{k}-1]-\text{y}[\text{k}-2]\), b.y[\text{k}-1])\) ;
```  
**Algorithm 1**Goertzel Filter for DFT
## 4 Numerical Results
Figure 4 shows AN power according to the minimum harvested energy by the SUs and the EH users when the minimum SINR of the SUs is given as \(\gamma_{l}=5\) dB and \(\gamma_{l}=10\) dB, respectively. From Figure 4, we can see that AN power decreases for all the considered schemes as the minimum EH requirement increases. The reason is because as the minimum EH increases, more transmission power should be allocated to the users to satisfy their EH requirements. Subsequently, less power is assigned for the AN. Furthermore, we can see that the proposed scheme outperforms SDMA and ZF, since NOMA can achieve higher values of AN power due to the SIC application to nullify interference with other users while causing interference with the eavesdropper to improve network security. Similar to Figure 2, the ZF condition of canceling the interference makes it necessary to transmit more power to satisfy the EH requirements at SUs. Figure 3: SSR performance comparison between the proposed approach and the scheme without AN, in accordance with the minimum harvested energy at the SU and EH users, and the minimum SINR at the SUs. Figure 2: AN power according to the minimum SINR required at the SUs. Figure 5 shows the AN power according to the interference threshold when the minimum EH requirements of the SUs and EH users are given as \(\psi_{l}=\xi_{m}=-25\) dBm, and the minimum SINR for the SUs are \(\gamma_{l}=5\) dB and \(\gamma_{l}=10\) dB. From Figure 5, we can see that the AN power increases along with increasing interference threshold values for the PUs. As the interference threshold increases, the SU-Tx can transmit more power to the SUs and EH users that includes AN power to tackle the eavesdropper's wiretap. Moreover, Figure 5 also verifies that the proposed scheme outperforms the other baseline schemes. This can be explained as follows: Unlikely to SDMA and ZF, NOMA can perform SIC to decode part of the interference caused by other users, which allows transmitting less power to satisfy the QoS requirements of the users and more AN power which results in better PLS in the network. The results obtained by ZF are due to the condition of designing the beamforming vectors to null the interference at the PUs, where the constraint (21f) of maximum interference allowed to the PUs only depends on the beamforming vector of the AN. Figure 4: AN power versus the minimum harvested energy at the EH users and SUs. Figure 5: AN power versus the interference threshold, \(\phi_{PU,k}\).
# Pitting Corrosion in AISI 304 Rolled Stainless Steel Welding at Different Deformation Levels
## 2 Materials and Experimental Procedures
### Tests Description
#### 2.2.4 Corrosion Tests  
Five samples from each batch were selected for corrosion tests (Figure 1). These tests, in accordance with ASTM G48-92 [31], consisted in submerging the samples during 72 h in a 10% mass (0.387 M) solution of iron(III) chloride hexahydrate (FeCl\({}_{3}\):6H\({}_{2}\)O) electrolyte and the subsequent evaluation of the pitting corrosion in the weld and HAZ zones. The FeCl\({}_{3}\) provide the Cl\({}^{-}\) ions needed to accelerate the corrosion rate as described in the introduction.  
Figure 2: Samples preparation for micrographs and hardness measurement. The dashed triple line represents the rolling direction.  
Figure 8 shows the presence of deformation induced martensite at the samples after deformation. Although the tests were carried out at room temperature and martensite formation is favored by lower temperatures, the cold work level is high enough to compensate for the higher temperatures of the rolling. As stated by [32], the presence of this martensite has a great influence on the performance of the protective layer that covers the deformed samples.  
Figure 8: Deformation twins in one of the 33% cold rolled samples. Martensite grows along the deformation twin boundaries.  
Figure 6: Transversal micrography at the weld bead (right)/HAZ (left) interface after 50% cold rolling.  
Figure 7: Transversal micrography at the weld bead (right)/HAZ (left) interface after 57% cold rolling.  
The high levels of cold work also generated small cracks at the surface of the welded samples (Figure 9). This leads to a decrease in the performance of the protective layer as its continuity is broken and a slight reduction of the mechanical properties when compared to a sample with no cracks.  
The results of hardness tests are shown in Table 2, in which the figures given are the average values obtained from the five samples of each batch with their standard deviations. As can be seen, a 50% cold work doubles the hardness of the steel, both in the joint and the base metal, which have similar hardness values in all cases.
## 3 Discussion
MarR-type regulators typically bind to a palindromic 16-20 bp target site that overlaps with the -10 or -35 promoter regions for steric inhibition of RNA polymerase binding [34]. Previously, a 19 bp DNA sequence with a central TTAA motif was shown to be essential for the binding of CrtR from _C. glutamicum_ to its DNA target site [23]. Here, we showed that CrtR from _C. glutamicum_ bound to DNA sequences upstream of _crtR_ from other actinobacteria, and inspection of the DNA sequences revealed a conserved 15 bp binding motif, including the central TTAA base pairs (see Figure 3). The binding of CrtR\({}_{Cg}\) decreased with increasing deviation from the consensus motif (see Figure 3). The CrtR binding motif is typical for the MarR-type family of transcriptional regulators [34]. The observed graded effect of CrtR\({}_{Cg}\) binding to promoter sequences with increasing deviation from the consensus motif in vitro was congruent with the in vivo finding that different CrtR orthologs affected the pigmentation and expression of the Prt\(E\_\)_gfruv_ transcriptional fusion more weakly when their phylogenetic distance to _C. glutamicum_ was greater (see Figure 4). This is in line with the phylogenetic analysis of CrtR orthologs [23]. The application of biosensors has become a prominent tool in strain development over the last few years [5; 70]. In this study, CrtR from _C. glutamicum_ was demonstrated to be the first genetically encoded biosensor for the detection of GGPP that allows one to distinguish between _C. glutamicum_ strains that have accumulated GGPP to different intracellular concentrations (Figure 6), and to monitor GGPP accumulation over time during growth (Figure 7). Since the CrtR-based biosensor system was suitable for the detection of intracellular GGPP concentrations between 0.1 and at least 4 mM (Figure 6), it is plausible that the described system is applicable to the screening of mutants accumulating GGPP well above wild type levels, and their enrichment/isolation, by flow cytometry. As an alternative application, the on-demand expression control of GGPP converting enzymes in response to intracellular GGPP concentration can be envisioned for strain optimization with respect to the production of GGPP-derived triterpenoids and/or carotenoids. On-demand production may be established by transcriptional fusion of the P_CrtE_ to the gene of interest, e.g., a diterpenoid synthase. This approach may improve production as the terminal biosynthesis pathway is initiated only in the presence of high concentrations of the precursor GGPP, which may prevent the accumulation of toxic GGPP concentrations. Biosensor approaches to on-demand expression control have been successfully applied, e.g., to improve lysine production by _C. glutamicum_[71,72]. The central role of GGPP as a terpenoid and carotenoid precursor suggests a wide application range for the GGPP-based biosensor developed here [10], since the tens of thousands of terpenoids derived from GGPP represent one of the biggest sources of valuable natural products for human use [73].