esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 2 Materials and Methods
### Signal Processing
#### 2.3.1 Pulse Compression  
A ringing component (time sidelobes) trailing the radiated wavelet can commonly be observed due to the antenna resonance, which extends the impulse response of the antenna, and degrades the range resolution. To suppress the antenna ringing and compress the transmitted pulse, we apply a deconvolution technique using a Wiener filter, which is an optimal tradeoff between an inverse filter and a matched filter. The Wiener filter is executed in the frequency domain [23], and expressed as  
\[Y(\omega)=\frac{X(\omega)H^{*}(\omega)}{\left|H(\omega)\right|^{2}+\beta}, \tag{1}\]  
where \(H(\omega)\), \(X(\omega)\), \(Y(\omega)\), \(\beta\), and \(\omega\) are respectively the reference wavelet, the input signal, the output signal, a regularization parameter and the angular frequency, and the superscript (\({}^{*}\)) denotes the complex conjugate. The value of the regularization parameter determines the performance of the Wiener filter, which becomes an inverse filter when \(\beta\) is zero, and changes to a matched filter when \(\beta\) is infinity.  
The reference wavelet is obtained from a time-gated transmission signal, which is recorded with two Vivaldi antennas placed face-to-face at a separation of 0.5 m. A tapered time gating is applied to exclude the reflected and/or diffracted signal from the surroundings. The regularization parameter is determined by the signal-to-noise ratio. By testing, we determined the regularization parameter in our data processing as 10 percent of the mean power of the whole spectrum of the recorded reference wavelet. By applying the Wiener filtering, we can clearly discern that the ringing component of the transmitted wavelet has been suppressed and the source spectrum is broadened (data not shown).
# On the Security of Offloading Post-Processing
## 3 Outsourcing Information Reconciliation
### Linear One-Way Information Reconciliation  
Before presenting our scheme for offloading, we first explain one-way IR in the context of DV-QKD in more detail and informally define the concept of secure outsourcing for IR. Traditional error-correcting block codes consist of sets of codewords that contain redundant information. Before sending data over a noisy channel, the data are encoded into codewords. The contained redundancy can then be used by the receiver to correct the introduced errors.  
One-way reconciliation (i.e., source coding with side information) has been studied since the 1970s [23; 24]. While related to error-correcting codes, the idea here is that the data are transmitted over a noisy channel without adding any redundancy. Rather, the source additionally sends a syndrome of the data (computed with a parity check matrix of an error-correcting code) over a _noise-free_ channel. The receiver then uses the syndrome together with the noisy data (side information) to decode the original data.  
More concretely, after sifting, [PERSON] has obtained a noisy version of [PERSON]'s sifted key (i.e., \(\mathbf{k}_{B}=\mathbf{k}_{A}+\mathbf{e}\), where \(\mathbf{e}\) denotes the error vector). [PERSON] and [PERSON] then use a linear block code with parity check matrix \(\mathbf{H}\). [PERSON] computes the syndrome of her sifted key \(\mathbf{k}_{A}\) with the help of \(\mathbf{H}\) by computing  
\[\mathbf{s}_{A}:=\mathbf{k}_{A}\mathbf{H}^{\top}.\]  
[PERSON] sends \(\mathbf{s}_{A}\) over the noise-free classical channel to [PERSON]. [PERSON] corrects his sifted key by (approximately) solving the problem of finding a vector \(\hat{\mathbf{k}}_{A}\) which, among all vectors with syndrome \(\mathbf{s}_{A}\), has the smallest Hamming distance to \(\mathbf{k}_{B}\).  
Searching for this vector is computationally hard and is equivalent to solving the standard syndrome-decoding problem (NP-complete) which, given \(\mathbf{H}\) and a vector \(\mathbf{s}\), requires finding a vector \(\mathbf{e}\) of a minimal weight satisfying \(\mathbf{e}\mathbf{H}^{\top}=\mathbf{s}\). The equivalence can easily be seen considering that in our case, the syndrome decoder is employed for \(\mathbf{e}\mathbf{H}^{\top}=\mathbf{k}_{B}\mathbf{H}^{\top}-\mathbf{k}_{A} \mathbf{H}^{\top}=\mathbf{k}_{B}\mathbf{H}^{\top}-\mathbf{s}_{A}\).
## 5 Conclusion  
In this study, an HS-ANN hybrid model was developed to predict the stability number of breakwater armor stones based on the experimental data of [PERSON] [26]. The HS algorithm was used to find the near-global optimal initial weights, which were then used in the BP training to find the true global minimum of the error function by the Levenberg-Marquardt algorithm. The stratified sampling was used to sample the training data. A total of 25 HS-ANN models were tested with five different values for both HMCR and PAR varying from 0.1 to 0.9 at intervals of 0.2. The HS-ANN models were compared with the conventional ANN model which uses a Monte Carlo simulation to determine the initial weights. The correlation coefficient and index of agreement were calculated to evaluate the performance of the models. Each model was run 50 times and the statistical analyses were conducted for the model results. The major findings are as follows:  
1. The correlation coefficients of the present study were greater than those of previous studies probably because of the use of stratified sampling.
2. In terms of the index of agreement, the HS-ANN model gave the most excellent predictive ability and stability with HMCR = 0.7 and PAR = 0.5 or HMCR = 0.9 and PAR = 0.1, which correspond to [PERSON] [33] who suggested the optimal ranges of HMCR = 0.7-0.95 and PAR = 0.1-0.5 for the HS algorithm.
3. The statistical analyses showed that the HS-ANN model with proper values of HMCR and PAR can give much better and more stable prediction than the conventional ANN model.
4. The HS algorithm was found to be excellent in finding the global minimum of an error function. Therefore, the HS-ANN hybrid model would solve the local minimization problem of the conventional ANN model using a Monte Carlo simulation, and thus could be used as a robust and reliable ANN model not only in coastal engineering but also other research areas.  
In the future, the present HS-ANN model could be compared with other hybrid ANN models using different heuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and Cuckoo Search (CS). Not only GA [18; 35; 36] but also PSO [37; 38] and CS [39] have been applied for neural network training. Analyzing and comparing those hybrid ANN models would provide a way to find the most suitable heuristic algorithm for determining the optimal initial weights for ANN.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014R1A2A2A01007921). The Institute of Engineering Research and Entrepreneurship at Seoul National University provided research facilities for this work.  
[PERSON] conducted the research and wrote the paper; [PERSON] advised on the use of HS algorithm; [PERSON] conceived, designed, and directed the research.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
In this section we are going to present three case studies, that we have chosen to emphasise particular features and highlight the potential of UAV based GNSS-R for monitoring surface water. The first case describes detecting the Avigliana lakes which are two adjacent lakes in Northern Italy. We took them as reference for calibrating both the GNSS-R sensor and the post-processing algorithms. The other two cases relate to smaller and narrower water surfaces, where the performance of the technique is challenged in detecting unexpected water contents on ground and in recognizing narrow river streams.
## 4 Results
### Brooksville, Mississippi  
UAV remote sensing was conducted in 2018 and 2019 at Brooksville, Mississippi in a field of 9K m\({}^{2}\) within the farm area, although the data are limited, for the irrigation study of a soybean field. In 2018, five days were successful for UAV imaging flyovers, 7 June, 27 June, 13 July, 27 July, and 13 August. In 2019, only two days were successful, 3 June  
Figure 5: Cotton yield estimation based on CUC quantified through thresholding of LoG of UAV RGB images (the square shapes represent data points, and the solid line is the trend of the data points with the fitted regression equation as shown on the top).  
Figure 6: Satellite multispectral imagery of the research farm area at Stoneville, Mississippi: Landsat 8 (12 September 2016), Sentinel 2A (3 September 2016), and PlanetScope 4-band (3 September 2016).  
and 1 August. Figure 7 shows the UAV NDVI images of 2018 for the soybean field in the research farm area at Brooksville, Mississippi, calculated from the acquired multispectral images. These time series of NDVI images indicate the phenology of the soybean growth from vegetative to reproductive stages, with highest vegetation vigor indicated on 27 July.  
Landsat 8 imagery is typically limited for cloudless data. In 2018, only four days of images, 7 June, 16 June, 2 July, and 20 September, were cloudless to cover the research farm area. However, the 30 m spatial resolution of the images was not sufficient to clearly delineate the boundary of the field. Further, the imagery of Sentinel-2 is typically more available for cloudless data, with more frequent revisit times. For example, in 2018, there were 24 days of cloudless images from January to September to cover the research farm area. With the 10 m spatial resolution of the Sentinel-2 images, the boundaries of the fields within the farm area can be clearly delineated, but not the rows within the fields. Lastly, there are even more PlanetScope images available to clearly cover the fields in the research farm area. In 2018, 118 days of cloudless images from January to September were found to cover the farm area. With 3 m spatial resolution, the images can delineate the boundaries of the fields and identify features and variations over the fields--although the rows still cannot be differentiated within the field--which may be good enough to discriminate the plot treatments of the experiment in conjunction with the high-resolution UAV images.  
Figure 7: UAV NDVI images of the soybean field in the research farm area at Brooksville, Mississippi in 2018.  
In the concerned soybean field, an irrigation study was conducted with two treatments, rainfed and irrigated, replicated with four plots for each treatment in 2018. Figure 8 shows the multispectral imagery of Landsat 8, Sentinel 2A, and PlanetScope over the research farm area in 2018.
## 3 Results
### Comparison with G-Realm  
At a sub-basin level, RadWet-L inundation extent was highly correlated with water levels recorded by satellite altimetry with Pearson correlation R values ranging from 0.83 to 0.91 (\(p\) value < 0.01) at five out of the six sub-basins examined shown in Figure 13. The correlation for the Rio Marau sub-basin was comparatively low (R = 0.65, \(p\) value < 0.01).  
Figure 11: Comparison between NASA-GFZ GRACE-FO Gravity Anomaly (Water Equivalent Thickness) [57] from January 2019 to June 2023 (**top**) and the classified total wetted extent (Inundated Forest and Open Water) between January 2019 and 1 st October 2023 as measured by RadWet-L (**bottom**).  
Figure 12: Pearson-R correlation coefficient calculated between GRACE-FO and RadWet-L mapped total wetted area at 3\({}^{\circ}\)\(\times\) 3\({}^{\circ}\) native GRACE-FO resolution across the Amazon Basin [57]. RadWet-L 2019–2023 maximum total inundation extent is overlaid to show main areas of inundation within the basin.
## 4 Discussion  
Despite the encouraging results demonstrated in both case studies and the comprehensive statistical analyses, discrepancies persist between the SAR-retrieved radial OSC velocities and those obtained from the SMOC product and drifting buoy measurements. The primary source of these differences stems from the imperfect correction of the non-geophysical Doppler components, which include errors related to the satellite orbit and attitude variations, scalloping artifacts, residual biases, and antenna pointing inaccuracies. Furthermore, the accuracy of the DPDop model's output is inherently dependent on the  
Figure 8: Comparisons of SAR-retrieved radial OSC velocities with collocated drifting buoy measurements: (**a**) after removing wave-induced Doppler shifts using the DPDop model, and (**b**) after removing wave-induced Doppler shifts using the CDOP model.  
precision of its input parameters, including the wind speed, wind direction, and wave field parameters. Errors in the SAR-derived wind retrievals, misalignments in the ECMWF reanalysis wind data, or inaccuracies in the wave modeling from the WW3 could each have introduced uncertainty into the final velocity estimates. Additionally, the uncertainties inherent in the reference currents from the SMOC product and drifting buoy data could have also contributed to the observed discrepancies.  
The applicability of the DPDop model is limited to wind speeds below 30 m/s, due to its dependence on an empirical formulation of wave breaking derived from dual-co-polarization SAR backscatter [28; 42]. Extending this capability to more extreme wind conditions would require additional SAR observations under such environments. A notable example is the near-simultaneous acquisition of quad-polarization SAR imagery during Hurricane Epsilon in 2020 by both RADARSAT-2 and the RADARSAT Constellation Mission [43]. These observations demonstrated that the wave-breaking contributions to radar backscatter are substantially greater in extreme sea states than in low and moderate wind regimes [44]. Future work will prioritize acquiring and analyzing quad-polarization SAR datasets to develop an enhanced Doppler velocity model that can reliably retrieve radial OSC velocities under hurricane-force winds.
## 3 Results and Discussion
### Sensory Analysis
The second group contained samples characterized by the attributes herbaceous, astringent, and a high color intensity. We can also observe that of the esters, only the ethyl dodecanoate, attributing tropical fruit notes, characterized this group. By performing the Pearson's correlation test (\(p<0.05\)) to assess for correlations between the sensory attributes and the volatile compounds, we revealed a high correlation (r = 0.753) between benzaldehyde and prune odor attributes, and between the content of 2-phenylethanol and odor intensity (r = 0.734). It is interesting to emphasize the role of 2-methyl-1-butanol in the sensory perception of wine in the mouth, as it showed high coefficient correlations with taste intensity, bitterness, body, cherry flavor, persistence, and aftertaste (with r ranging from 0.73 to 0.86). This molecule is known to be a compound that increases wine complexity [50]. A similar result was also observed for 2-phenylethanol, which increased taste intensity, astringency, cherry flavor, persistence, and aftertaste (with r ranging from 0.71 to 0.89). Finally, PLSR analysis showed, for this set of wines, according to the loading weights and the regression coefficients, that 2-methyl-1-butanol and 2-phenylethanol can be con  
Figure 2: Principal component analysis (PCA) biplot for volatile organic compounds (VOCs) detected by GC-MS and sensory attributes. NS1-5 wines = north Sardinia; CS1-2 wines = central Sardinia; SSI wine = south Sardinia. Sensory attributes followed by OD represent olfactory attributes, while sensory attributes followed by FL represent flavor attributes. sidered the best predictors of wine sensory characteristics like taste intensity, bitterness, cherry flavor, body, persistence and aftertaste. The positive correlation of 2-phenylethylanol and 3-methyl-1-butanol with the aromatic intensity of red wines and ripe fruit descriptors was also observed in other papers [51].
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 5 Discussion

Table 4: Comparison between Ratio Method and Progressive Flow Method applied on the test rig. Figure 14: Behavior of flow rate variation vs. valve opening in a real system at different authority percentages. greater numbers of users per local manifold. The proposed method turns out to be entirely non-iterative and performs balancing directly at the nominal flow rate without requiring iterations or a final adjusting step. In addition, with presence of the appropriate line valves, the PFM overcomes the critical limitations shown by the other non-iterative method, i.e., the CM. Lastly, the listed advantages are characteristic of a method that can be applied regardless of the presence of flow meters on all terminals; pressure taps can more easily be provided across the terminals to be used as a reference control and this can be a key factor in balancing existing buildings that are difficult to work on. The simplifications on which the evolution of the PFM originally developed for air systems has been based have essentially been made possible by the opportunity to adopt methods of adjusting the compensation flow rate by means of real-time control, which replaces the time-consuming adjustment phases of iterative methods. The assumption, which is always verifiable, of perfect tightness of the hydronic piping has a great influence on the method's operating mode because it allows all flow rate measurements to be reported to a single measuring station located at the pump. However, the full applicability of the method requires specific attention on the part of the designer and, in particular, the ability to determine at the design stage which positions will require the presence of line-balancing valves.
## 2 Materials and Methods
### F-V Profile Analyses  
Further F-V profile analyses were conducted by calculations used in previous studies [15; 27]. The variables we obtained were maximum estimated force (F\({}_{0}\)), maximum estimated velocity (V\({}_{0}\)) and maximum estimated power (P\({}_{\text{max}}\)). F\({}_{0}\) is defined as the value of the force when V\({}_{0}\) = 0 and vice versa in the case of V\({}_{0}\). P\({}_{\text{max}}\) is estimated using the formula: P\({}_{\text{max}}\) = \(\frac{\text{P}_{\text{V}}_{0}}{4}\)[19]. All F-V profiles reported R\({}^{2}\) scores above 0.95. The mean R\({}^{2}\) in this study was 0.971 \(\pm\) 0.017.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Age (y)** & **Height (m)** & **Body Mass (kg)** & **WA Points** &
\begin{tabular}{c} **VO\({}_{\text{2 max}}\)** \\ **(mL/kg/min)** \\ \end{tabular} \\ \hline Men & Middle-distance athletes. n = 6 & 25.7 \(\pm\) 8.6 & 1.76 \(\pm\) 0.06 & 63.6 \(\pm\) 5.0 & 821 \(\pm\) 156 & 64.3 \(\pm\) 6.7 \\ n = 18 & Long-distance athletes. n = 12 & 27.7 \(\pm\) 5.7 & 1.74 \(\pm\) 0.06 & 60.5 \(\pm\) 4.7 & 889 \(\pm\) 61.8 & 64.4 \(\pm\) 6.2 \\ \hline Women & Middle-distance athletes. n = 7 & 23.4 \(\pm\) 5.4 & 1.64 \(\pm\) 0.05 & 52.6 \(\pm\) 6.7 & 824 \(\pm\)164 & 56.4 \(\pm\) 5.4 \\ n = 21 & Long-distance athletes. n = 14 & 24.9 \(\pm\) 4.6 & 1.67 \(\pm\) 0.07 & 52.8 \(\pm\) 4.4 & 848 \(\pm\) 220 & 52.6 \(\pm\) 6.2 \\ \hline \end{tabular} WA, World Athletics.  
\end{table}
Table 1: Participant’s anthropometric and performance data.
## 5 Approaches to Improve Biofilm-Based Wastewater Treatment
### Enhanced Biofilm Performance by Exploiting Quorum Sensing  
Quorum sensing has also been shown to be an essential component of biofilms, and it can be exploited in several ways to enhance biological wastewater treatment. Quorum sensing has been said to enhance the attachment and production of EPSs, which are essential aspects in the protection of the biofilm [67]. Quorum sensing is a form of communication among bacteria that occurs through the production of chemical signals and autoinducers (AIs). Some of the AIs responsible for the biofilm communication include acylated homoserine lactones (AHLs), autoinducing peptides (AIPs), autoinducers-2 (AI-2), pseudomonas quinolone signal (PQS), and autoinducers-3 (AI-3). However, the most common AIs are AHLs, which are controlled by Luxl/LuxR systems. These AIs are produced by a wide range of bacteria and have been reported in over 100 species of _Proteobacteria_, including 25 Gram-negative bacterial species [68]. [PERSON] et al. [69] investigated the role of AHLs in biofilm adhesion and found that AHLs significantly improve bacterial adhesion as well as biomass production, with maximum increases of 2.26, 2.36, 2.52, and 2.80 times in four different biofilms. The author concluded that the observed increases were due to the long-chain AHLs, which resulted in stronger hydrophobicity and hydrolysis resistance.  
Quorum auto-inducer AHLs have been shown to play an important role in biological bioreactor systems. AHLs have been reported in different wastewater treatment bioreactors with large quantities of microbial consortia growing as flocs, granules, or biofilms. The dosing of AHLs has been known to affect the performance of the reactor, as well as the properties of the activated sludge. AHLs have been said to help in preventing biofouling of the bioreactors. The presence of AHLs induces acylase, which is one of the many AHL-hydrolyzing enzymes that degrade AHLs. This hydrolysis process of AHLs by acylase is exploited for the prevention of biofouling in bioreactors [70]. [PERSON] et al. [71] successfully proved the biofouling prevention of MBRs via the activation of AHLs with porcine kidney acylase I. The author further noted that quorum sensing could potentially be an innovative solution to control reactor biofouling in WWTPs. It has been noted that the addition of AHLs to activated sludge increases the rate of ammonia oxidation as well as the expression of amoA genes by ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB). Although quorum sensing is beneficial to the biological wastewater treatment, more studies need to be conducted to understand the impacts of the different toxic chemical compounds present in WWTPs. AIPs have not received much attention in the context of wastewater treatment, although they have been shown to enhance attachment through accessory gene regulators [72].
###### Abstract  
Vibration isolators with quasi-zero stiffness (QZS) perform well for low- or ultra-low-frequency vibration isolation. This paper proposes a novel dual-parallelogram passive rocking vibration isolator with QZS that could effectively attenuate in-plane disturbances with low-frequency vibration. First, a kinematic model of the proposed vibration isolator was established and four linear spring configuration schemes were developed to implement the QZS. Next, an optimal scheme with good high-static-low-dynamic stiffness (HSLDS) performance was obtained through comparison and analysis, and used as a focus for the QZS model. Subsequently, a dynamic model-based Lagrangian equation that considered the spring stiffness and damping and the influence of the payload gravity center on the vibration isolation system was developed, and an average approach was used to analyze the vibration transmissibility. Finally, the prototype and test system were constructed. A comparison of the simulation and experimental results showed that this novel passive rocking vibration isolator could bolster a heavy payload. Experimentally, the vibration amplitude decreased by 53% and 86% under harmonic disturbances of 0.08 Hz and 0.35 Hz, respectively, suggesting the great practical applicability of this presented vibration isolator.  
quasi-zero stiffness; high-static-low-dynamic stiffness; rocking vibration; passive vibration isolation +
Footnote †: journal: Applied Physics Letters