esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Image Texture as a Proxy for DEM Quality
#### 4.3.2 Where Image Correlation Fails  
Beyond visual assessments of hillshades and comparisons with local PAN variance, the holes associated with no-data values in the output SPOT6 DEMs provide insight into the performance of the algorithms and parameters. In Figure 5, we compare the PAN variance in pixels of no data to pixels with elevation values, and we compare the number of output holes with respect to both the stereo algorithms and output DEM resolution.  
In the comparison of 5 \(\times\) 5 pixel window PAN variance in holes versus non-holes in the output AC BM-ck25 3 m DEM (Figure 5A), we note significant differences in the histograms: the distribution of PAN variance in the holes is skewed to lower values compared to non-holes. We remind the reader that holes in the SPOT6 DEM occur anywhere that the intersection error produced during stereo correlation exceeds the SPOT6 map-projected PAN resolution of 1.5 m. In other words, holes occur in the areas of highest stereo-correlation uncertainty, which Figure 5A shows is predominantly in the areas of lower PAN variance.  
Figure 4: (**A**) Map–projected SPOT6 PAN B–scene. The white color to the west is the edge of the salar (white is from salt patches), and there is an alluvial fan in the center of the view, which emanates from the steep mountains to the east; (**B**) the PAN variance calculated on (**A**) in a 5 \(\times\) 5 window, and logarithmically scaled; (**C**) resulting SPOT6 DEM from pairwise correlation of the AC scenes and for the AB neighboring scenes (**D**); (**E**) Intersection error output by the ASP for the AC DEM and for the AB DEM (**F**); See Figure 2A for location in study area.  
The differences in holes with respect to stereo algorithm and CK size are highlighted in Figure 5B. Most importantly, we see that as the CK size is increased, the number of holes decreases, which confirms the qualitative results in Figure 3. Additionally, increasing output resolution - as performed in the point2 dem function of the ASP following stereo correlation and triangulation - significantly decreases the number of holes (Figure 5C). Of interest to the question of neighboring versus widest view-angle pairs, Figure S5 in the Supplemental Material depicts the decrease in holes in the AB and BC DEMs compared to the AC output. This is because the neighboring images are more similar and have fewer occlusions; thus, they can match more pixels, but, as previously noted, the parallax is smaller, and triangulation suffers.
## 2 Results
### VOC Emission Profiles Vary across Different Flowering Stages in P. lacitiflora cv. Zifengyu  
Floral volatiles are restricted to specific flower organs/tissues and vary in emission levels across different flowering stages. In this study, the budding, half-, and fully open flower developmental stages of _P. lacitiflora_ cv. Zifengyu were used to assess the VOC emission profiles, due to its widespread cultivation, economic significance, dominance in the garden, and unique floral VOC composition, particularly its abundance of geraniol and citronellol but absence of 2-phenylethanol (Figure 3A). Twenty-two quantifiable volatile terpenids were identified across the three flowering phases (Figure 3B; Table S2), confirming the prevailing terpenids among all the floral VOCs of _P. lacitiflora_ cv. Zifengyu. Moreover, _P. lacitiflora_ cv. Zifengyu produced more monoterpenes than sesquiterpenes, implying an unbalanced metabolic flux between MEP and MVA pathways. In detail, at the budding stage, the floral scent profile predominantly included fatty acid derivatives such as hexenyl acetate, monoterpenes (+)-limonene and 1,8-cineole, and sesquiterpenes \(\alpha\)-/\(\beta\)-caryophyllene. Citronellol, (\(Z\))-\(\beta\)-cocimene, and \(\beta\)-caryophyllene were prevailing terpenids at the half-open flowering stage. Citronellol, (\(Z\))-\(\beta\)-cocimene, geraniol, 1,8-cineole, and \(\alpha\)-/\(\beta\)-caryophyllene were abundantly detected in fully opened flowers. Generally, these findings suggest dynamic variations in the composition and abundance of VOCs across distinct flowering phases of peony flowers, shedding light on the intricate pathways underlying floral scent synthesis and regulatory web in _Paeonia_ taxa.  
Figure 3: Volatile emission patterns during three flowering stages of _P. lacitiflora_ cv. Zifengyu. (**A**) Mass spectra of VOCs. Numbers above the peaks represented different VOCs marked in subfigure (B). (**B**) Relative contents of the VOCs from budding, half-open, and fully open flowering stages. The terpenids (black) and fatty acid derivative acids (blue) represented differently.
## 3 Results and Discussion  
### Farinograph Properties of Doug  
The farinographic evaluation of dough with partial or full substitution of water by cereal-based plant beverages is presented in Table 1. The results confirm that both the type of beverage and the substitution level significantly influenced the mixing behavior and the quality of wheat dough. Water absorption (WA) increased significantly (\(p\leq 0.05\)) in all tested variants as the proportion of plant-based beverage increased. The control dough (CON) exhibited the lowest WA (56.9%), while the highest values were recorded in BO100 (64.5%), BS100 (63.8%), and BM100 (63.9%). This increase is likely attributed to the presence of dry matter, particularly dietary fiber, which enhances the dough's water-binding capacity. Similar trends were observed by [PERSON] et al. [10], who reported increased water absorption in doughs supplemented with oat beverage residue flour. Their results showed that water absorption rose proportionally with both the particle size and the inclusion level of the additive. In the present study, oat and spelt beverages produced the most substantial increases in WA, particularly at substitution levels above 50%, which aligns with the differences in fiber content across the beverages. The effect of fiber on water absorption, regardless of its origin, has been widely documented in the literature [13; 19; 20; 21]. However, some studies have shown the opposite trend. For example, [PERSON] et al. [22] and [PERSON] et al. [23] found that the addition of ground whole flaxseed led to a decrease in WA. This was attributed to gluten dilution and the high fat content of flaxseed, which may coat gluten and starch particles, reducing their ability to absorb water. DDT, indicating the time required to reach maximum dough consistency, also increased with substitution level. The longest development times were recorded for BS100 (5.9 min) and BO100 (5.8 min), suggesting that higher concentrations of cereal-based beverages delay
## 3 Results
As the retention time increases, the mass of detected parent ions increases (from 469 to 1396 \(m/z\)). Figure 3: Chromatographic profiles obtained during the HPLC-MS SCAN ESI (\(-\)) analysis for the tannic acid (**A**) standard solution and gall nuts samples _C. quercusfolii_ (**B**) and _A. infectorius_ (**C**). GA, gallic acid, QA, quinic acid. The above is confirmed in more detail by the chromatograms of the gall nut samples obtained during the HPLC-DAD analysis. By analyzing the chromatograms presented in Figure 4, it can be concluded that _A. infectorius_ (A, B, C) and _C. quercusfolii_ (C, D) gall nuts differ significantly in their profile, i.e., composition and amount of tannic compounds: gallic acid content (tr = 4 min) and gallotannins (tr = 5-15 min), while within one forest habitat type and also between the three studied habitat types of forests, the differences are small. Additionally, taking into account the sum of the areas of all peaks in the chromatograms for individual samples of _C. quercusfolii_ gall nuts from a given forest habitat type, it can be seen that the average content of tannins in Polish gall nuts from a moist broadleaved forest is the highest. On the other hand, within _A. infectorius_ gall nuts, a greater total tannin content was observed in the sample from the second harvest, which confirms the results obtained with the hide powder method. On the basis of the available values of the areas on the chromatograms for individual peaks (compounds), the percentage share of gallic acid in the water extracts of gall nuts was also calculated in relation to the total amount of determined compounds (total area of all peaks) (HPLC-DAD). The average content of gallic acid in Polish gall nuts is 3.30% (ranges from 2.85% to 3.97%), while Turkish gall nuts contain 5.04% and 3.85% for the first and second harvest, respectively. Interestingly, HPLC-DAD chromatograms (Figures 3 and 4A-C) and the mass spectra (Figure 2B) demonstrate that Polish _C. quercusfolii_ gall nuts are rich in quinic acid (QA, quinic acid, rt approx. 3 min, 191 \(m/z\)), the average content of which is 9% of all compounds present in the chromatogram. Turkish gall nuts contain only about 1% of this acid (Figures 2C, 3 and 4D,E). Figure 4: \(Cont.\)
# Multifunction RF Systems for Naval Platforms
## 2 Requirements for Naval RF Functions
### ES Function Requirements
However, for a weak signal, the antenna gain associated with the formation of a narrow receive beam may be needed to improve its SNR such that it can even be detected, with the AoA measurement in that case simply corresponding to the direction of the beam in which the detection is made. Since duty cycle by definition is a feature of transmitted waveforms, there is no duty cycle requirement specified in Table 1 for the passive ES function. In general, the ES function must be able to detect RF signals of all polarizations, due to the diversity of emitters that may represent potential threats. For example, many types of military communication systems use circular polarization. On the other hand, naval radars typically use vertical polarization for reasons discussed previously, while airborne radars usually employ horizontal polarization because sea clutter returns are significantly lower, compared to the vertical polarization case, at the larger radar grazing angles experienced by aircraft. The requirement to detect all polarization states dictates the use of dual orthogonally-polarized radiating elements in the AESA, which also gives the ES function the capability to measure the polarization characteristics of emitters as another identification attribute. Due to its critical mission role, a unique requirement of the ES function is that it must always be fully operational, in the sense that it must continuously monitor the RF environment over the entire duration of the mission, without suffering any interruptions or performance degradation due to interference from the other onboard RF functions. In contrast, the other RF functions may be inactive at times, depending upon the scenario. Even the radar may be shut off during periods of covert operation.
## 3 Experimental Setup  
Figure 5 shows the composition of the 200 km key distribution experiment system. The signal sampling rate of Arbitrary waveform generator (AWG) is set at 1 Gbit/s. The radio-frequency amplifier (RFA) has an amplification gain of 19 dB. The intensity modulator converts the electrical signal into optical signal. The output power of the intensity modulator is 5 dBm. The optical signal reaches the receiver after passing through the variable optical attenuator (VOA) and 200 km standard single-mode fiber (SSMF). The output power of EDFA is 0 dBm. The photoelectric detector converts optical signals into electrical signals. An oscilloscope with a sampling rate of 5G Sa/s samples the electrical signals. The figure also shows the eye patterns of optical signals collected by oscilloscope before and after 200 km SSMF.  
DA is the original data before [PERSON] sends it. The experiment in this paper is an equivalent experiment. A complete system--[PERSON] to [PERSON], [PERSON] to [PERSON]--is a loop. The equivalent is transmitter to receiver, which means [PERSON] to [PERSON] and [PERSON] to [PERSON]. Simulate the loop system of Figure 5 through the data transmission experiment twice.  
The two physical layer signal processing technologies used in this key distribution experiment are frame synchronization and resampling. The random data sent and received is digitally processed by these two techniques.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 3 Discussion
The SQI calculation worked very well on recordings with easily discernible noise, being particularly good at separating non-HR periods from HR periods, but struggled a bit more with noisy HRs vs. clean HRs. Understanding and automatically detecting which types of noise cause a poorer performance can make it more robust in future studies. The specific parameters used in the SQI are dependent on how much noise is tolerable for a given research problem, meaning it will likely have to be double-checked if the parameters are altered. The proposed pipeline was very computationally efficient. The ECG pipeline was applied to all of Dataset A (\(n=97\), sampling rate = 500 Hz, M\({}_{\text{recording}}\) = 32 min, 94,089,683 data points) and took 111.33 s in total (1.15 s per ECG). The total preprocessing and peak labelling time was 20.75 s (0.21 s per ECG) and the local peak correction took 90.59 s (0.93 s per ECG). The HR pipeline is also computationally efficient, as a combination of median filters and difference functions provide the backbone for both the SQI and the beat-cleaning algorithms. By applying the SQI to the heart rate, the size of the vectors processed are greatly reduced compared to an ECG (1-3 Hz sample density for HR compared to 250-1000 Hz for ECG). A separate \(n=63\) set of noisy HR signals collectively covering 92 h (comprising 559,612 beats in total) took only a 6.68 s total processing time. This included both the time to filter the heart rate with a moving-median filter and the time to create the SQI vector. All calculations were performed consecutively using an 11 th Gen Intel(R) Core (TM) i5-1145G7 @ 2.60 GHz and did not include the loading times required to import the data into Python initially.
## 4 Results
### FPCS of the Landsat-8 Data  
Generally, granite (feldspar + quartz) has major absorption features in the TIR region [61]. In PC3 of the stretched Landsat-8 data, the TIR bands showed significantly low emissivity, i.e., negative loadings for the TIR1 band (\(-0.792\)) and minutely higher emissivity for the TIR2 band (\(-0.496\)). At the same time, it has positive loadings in the SWIR1 (0.247) and SWIR2 (0.258) bands, showing a mild reflection in this region due to the presence of associated quartz and plagioclase. Granite is shown in dark pixels due to the indicative absorption characteristics in TIR1 and TIR2 bands (Figure 7). PC3 of the raw Landsat-8 dataset had major contributions from the SWIR1 (\(-0.675\)) and SWIR2 (\(-0.707\)) bands and showed marble in bright pixels due to the diagnostic absorption features of calcite associated with this band. Vegetation cover and granite were not distinctive, as the NIR (0.127) and TIR1 (0.053) bands had low contributions. PC4 of the raw Landsat-8 dataset showed granite regions in mild, bright pixels due to the low emissivity indicated by negative loadings in the TIR1 band (\(-0.358\)). In contrast, marble showed higher absorption in the SWIR2 band (\(-0.643\)) and was dark due to the absorption of calcite in the SWIR2 band. Vegetation was reported as bright pixels due to the reflection of the NIR band (0.329). In PC3 of the stretched Landsat-8 data, the TIR bands showed significantly low emissivity for the TIR1 band (\(-0.792\)) and relatively higher in the TIR2 band (\(-0.496\)), while positive loadings in the SWIR1 (0.247) and SWIR2 (0.258) bands were observed due to the reflection of quartz and orthoclase. PC3 of the raw Landsat-8 dataset had significant contributions from the SWIR2 band (\(-0.707\)) and showed marble in bright pixels due to the diagnostic absorption features of calcite associated with marble. Granite and vegetation cover were not distinctive, as the NIR (0.127), and TIR1 (0.053) bands had low contributions. PC4 of the raw Landsat-8 dataset showed granite regions in mild, bright pixels due to the low emissivity indicated by negative loadings in the TIR1 band (\(-0.358\)), while marble showed higher absorption in the SWIR2 band (\(-0.643\)) and was dark due to the diagnostic absorption features of calcite in the SWIR2 band. Vegetation was reported as bright pixels due to the reflection of the NIR band (0.329).
## 4 Simulation and Results
### Second Proposed Model  
The results in Figures 10-12 indicate that the proposed model with the neural network can improve the BER performance only when the SNR is lower. To explain this issue, we considered the learning ability of the neural network. In the training process, if we train the neural network at a low SNR, the neural network is not suitable for predicting the noise at a higher SNR. Meanwhile, if we train the neural network at a high SNR, the signal \(e[j,k]\) is small. Consequently, the label in the training process is close to zero. Therefore, the neural network is overfitting to almost zero noise and cannot predict the noise when the SNR is large. To solve this problem, we propose the second model shown in Figure 13.  
Figure 12: The BER performance of serial detection with three hidden layers neural network.  
Figure 13: The noise power compensation model.  
In Figure 13, the neural network is trained only at a constant SNR value to avoid the error signal \(e[j,k]\) being too small. We multiply a parameter \(\alpha\) to compensate for the noise power at other SNR values in the testing process. Although the parameter is unknown, we need it to reduce the noise to increase the SNR. Therefore, the optimal value \(\alpha\) is determined at each SNR value ranging from 0 to 1 in the testing process. Based on the results in Figures 10-12, we chose the best case, which is the neural network trained at 11 dB. The noise \(e[j,k]\) is not too small, and thus, it achieves the gain. In Figure 14, we used the two hidden layer neural networks trained at 11 dB while changing the parameter \(\alpha\) from 0 to 1 with step size of 0.1, and tested it from 10 to 15 dB. The optimal values of \(\alpha\) for each SNR are listed in Table 6; the values of \(\alpha\) decrease as the SNR increases. As the noise power must be decreased for a high SNR, the optimal values of \(\alpha\) also decrease.  
By applying the optimal parameter \(\alpha\), we achieve the BER performance of the second proposed model with two hidden layers, as shown in Figure 15. The results indicate that the second model achieves a gain of 1 dB at a BER of \(10^{-6}\) compared to the serial detection in [19]. In addition, this method was easier to implement.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & **10 dB** & **11 dB** & **12 dB** & **13 dB** & **14 dB** & **15 dB** \\ \hline \(\alpha\) & 0.8 & 0.9 & 0.6 & 0.3 & 0.3 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The optimal value \(\alpha\) corresponding to SNR.  
Figure 14: The BER performance versus \(\alpha\) for the second model.
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 3 Model Description
### DCT-OFDM System Model  
The DCT based OFDM system is analogous to FFT based OFDM system, but the IFFT and FFT modules are swapped by inverse discrete cosine transform (IDCT) and DCT modules. DCT utilizes the real arithmetic instead of the complex arithmetic being utilized in the FFT, which results in the reduction of signal processing complication as well as in-phase/quadrature imbalance. DCT reduces the ISI and also makes efficient utilization of transmitted samples close to zero by its outstanding spectral energy compaction property. The transmitted signal for the DCT based OFDM is depicted in Equation (2). \(\rm X_{k}\) represents the input signal, and \(\rm X_{n}\) represents the \(\rm n^{th}\) OFDM symbols obtained after DCT transform.  
\[\rm X_{n}=\sqrt{\frac{2}{N}}\sum\
olimits_{k=0}^{N-1}\rm X_{k}\beta(k)\,\cos \left(\frac{\rm\pi k(2n+1)}{2N}\right)0\ \leq\ n\ \leq\ N-1,\ 0\ \leq\ k\ \leq\ N-1 \tag{2}\]  
\[\rm\beta(k)=\left\{\begin{array}{c}\frac{1}{\sqrt{2}}\ k=0\\ 1\ k=1,2,\ldots N-1\end{array}\right\}\]
## 4 Conclusions  
Based on the inherent invariant distribution characteristic of chaotic systems, configuring the probability distribution of chaotic signals is often challenging. To overcome this limitation, this paper proposes a strategy using graphical language to flexibly configure the probability distribution of chaotic signals. Using the realization of a uniformly distributed chaotic signal as an example, the effectiveness of this strategy is verified. Furthermore, the remarkable performance of utilizing these uniformly distributed chaotic signals in suppressing high-frequency converter common-mode conducted EMI highlights the potential application value of this strategy. The main conclusions are summarized as follows:  
1. The issue of obtaining smooth probability density functions for high-dimensional chaotic signals is addressed through the use of kernel density estimation algorithms.
2. The 2A transformation algorithm based on graphical language is introduced, allowing the transformation of intrinsic chaotic signals into chaotic signals with arbitrary probability distributions. This enables the flexible configuration of chaotic signal probability distribution types, offering feasible solutions for obtaining chaotic signals with specific probability distributions.
3. Applying the obtained uniformly distributed chaotic signals for chaotic spread spectrum modulation achieves uniform shaping of the power spectrum. Experimental results demonstrate that compared to traditional Chua's chaotic spread spectrum modulation techniques, the proposed strategy exhibits superior performance in suppressing high-frequency converter common-mode conducted EMI.  
Conceptualization, Z.Y. and Y.C.; methodology, Z.Y.; software, Z.Y.; validation, Z.Y., Q.L. and Y.Z.; formal analysis, Z.Y.; investigation, Z.Y. and Y.C.; resources, Z.Y.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y., Y.C. and B.L.; visualization, Z.Y.; supervision, B.C.; project administration, Y.C. and B.C.; funding acquisition, B.L., Y.C. and B.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation General Project of China, grant number 52377169; and National Natural Science Foundation of China, grant number 52377172.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: Author [PERSON] was employed by the company Jiangsu Electric Power Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.