esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Spatial-Temporal Characteristics of CCD between ESSI and ESDI
In 2020, the CCD highlighted the spatial disparities between urban areas, agricultural areas, and forest-grassland areas from the perspective of land use, as well as the differences between valleys, basins, and mountains from the perspective of terrain.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 2 Overview of Nanomaterials in Crani Tissue Engineering
### Polymeric Nanomaterials  
Polymeric nanomaterials have nanoparticles composed of compounds such as carbon, hydrogen, oxygen, and nitrogen. These compounds form monomeric chemical structures, which repeat to form longer chains or nanoparticles [13]. Polymeric nanomaterials have vast uses and potential in medicine due to their unique properties; these potential uses include drug delivery, surface coatings, and filtration [14]. Some examples of polymeric nanomaterials used, particularly in dentistry, include polyethylene glycol, solid lipids, nanogels, dendrimers, chitosan, peptides, polylactic acids, and gelatin [13; 15; 16; 17]. Polymeric particles have the ability to encapsulate various molecules, including vaccine antigens, proteins, drugs, various growth factors, and even cells, making polymeric nanomaterials extremely versatile and a powerful tool for transportation and delivery [18]. Additionally, certain polymeric nanomaterials have nanofibers and structural compositions that resemble a human extracellular matrix (ECM) and thus can be used as a scaffold to nurture and facilitate tissue regeneration [15]. The versatility in polymeric nanomaterials highlights its potential to overcome many challenges faced in the field of medicine, specifically in dentistry today.
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 3 Results
### Secrecy Analysis  
In our previous study [13], we have shown that quantified return map analysis is superior to spectral analysis and recurrent analysis, so, in this study, we performed the secrecy analysis using the QRMA method.  
The return maps for SCM and PM modulation for the third experimental case, with noise present and non-present in a communication channel, are shown in Figures 8 and 9, respectively.  
The return maps are obtained for the second variable of the Lorenz system and consist of three clusters of points. It should be noted that, while changing the bifurcation parameter, one can observe a shift in all return map clusters. At the same time, when the symmetry coefficient changes, one can see a shift only in two out of three clusters.  
We performed the quantified return map analysis for different signal-to-noise ratio values and with different ranges of the symmetry coefficient and parameter values to test the secrecy of chaotic communication with SCM and PM. The results are shown in Figure 10. We simulated a noised communication channel with values of signal-to-noise ranging from  
Figure 8: Return maps of the chaotic signal modulated with SCM technique.  
Figure 9: Return maps of the chaotic signal modulated with PM technique.  
25 to 35 dB by using the Gaussian white noise generator. A noise level equal to 40 dB corresponds to the communication channel without the noise generator.  
In all experimental cases, the symmetry coefficient modulation technique outperforms the parameter modulation. As was noted in the previous subsection, the most engaging is the comparison of PM and SCM with sets of parameters where a similar noise resistivity is observed. A lower value of QRMA in cases with a higher value of SNR is caused by signal blurring, which reduces the efficiency of QRMA. Nonetheless, the experimental results from Figure 10, namely the cases with \(\Delta s=4\) and \(\Delta c=0.2\) and with \(\Delta s=5\) and \(\Delta c=0.3\), show the superiority of SCM in secrecy by 10-20% depending on the noise level in the communication channel.
# A Watermarking Protocol Based on Blockchain
## 5 Watermarking Protocol
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & \multicolumn{1}{c}{**Meaning**} \\ \hline \(\mathcal{B}\) & buyer \\ \(\mathcal{CP}\) & content provider or seller \\ \(\mathcal{RA}\) & registration authority \\ \(\mathcal{BC}\) & blockchain \\ \(\mathcal{J}\) & judge \\ \(X\) & digital content purchased by \(\mathcal{B}\) \\ \(X_{d}\) & information used by \(\mathcal{CP}\) to unambiguously identify \(X\) \\ \(T_{X}\) & timestamp referred to the transaction by which \(\mathcal{B}\) buys \(X\) \\ \(B_{id}\) & information used to identify \(\mathcal{B}\) \\ \(B_{ad}\) & destination address provides by \(\mathcal{B}\) \\ \(N\) & nonce used to mark the watermarking transaction \\ \(W\) & watermark \\ \(W_{Ent.}\) & part of the watermark \(W\) generated by the entity \(Ent.\) \\ \(\mathbb{X}\) & watermarked \(X\) \\ \(pk_{Ent.}\) & public key of the entity \(Ent.\) \\ \(sk_{Ent.}\) & secret key of the entity \(Ent.\) \\ \(pk_{Ent.}^{X}\) & one time public key generated by the entity \(Ent.\) in the transaction to watermark \(X\) \\ \(sk_{Ent.}^{X}\) & one time secret key generated by the entity \(Ent.\) in the transaction to watermark \(X\) \\ \(E_{key}(\dots)\) & token encrypted using the key _key_ and a public key cryptosystem \\ \(\mathbb{S}_{key}(\dots)\) & token digitally signed using the secret key _key_ and the SHA-1 secure hash algorithm \\ \(\mathbb{E}_{key}(\dots)\) & token encrypted using the key _key_ and a cryptosystem that is privacy homomorphic with respect to the watermark insertion \\ \(\mathbb{D}_{key}(\dots)\) & decryption function corresponding to the encryption function \(\mathbb{E}_{key}(\dots)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Meanings of the symbols used to describe the proposed protocol.
## 3 Results  
### Morpho-Rheological Properties of Sickle RBCs Are Patient-Specific  
#### 3.1.1 Transit Times through Microapillary-like Constrictions  
The transit time (_tt_) through the 187 deformation-imposing capillary-like constrictions in the MMM is a readout for the stiffness of cells. We measured the _tt_ for 25 prepared blood samples of SCD patients who participated in the study, using three flow rates: 0.9, 9 and 99 \(\mu\)L/h. The lowest flow rate, 0.9 \(\mu\)L/h, enabled the most reliable serial advection of RBCs amenable to _tt_ measurement. As shown in Figure 2, we found significant variation in _tt_ between patients. Notably, data for 4 patients out of the 25 patients measured are shown in Figure 2 because these were the most comparable measurements in terms of sustained smooth flow through the device throughout the duration of measurement in the MMM (about 15 min).  
For the four patients shown, denoted as P1, P2, P3 and P4 (Figure 2), cell numbers (_n_) analyzed and the mean _tt_ at a flow rate of 0.9 \(\mu\)L/h were: P1, \(n\) = 56, _tt_ = 0.38 \(\pm\) 0.01 s; P2, \(n\) = 50, _tt_ = 0.39 \(\pm\) 0.01 s; P3, \(n\) = 56, _tt_ = 0.28 \(\pm\) 0.01 s; and P4, \(n\) = 60, _tt_ = 0.45 \(\pm\) 0.01 s. All given uncertainties are standard errors of the mean. (**b**) Box plots from one-way ANOVA for the transit times data in (**a**). At ***\(p\) < 0.001, there is a statistically significant difference in _tt_ of RBCs from the four patients.  
For the four patients shown, denoted as P1, P2, P3 and P4 (Figure 2), cell numbers (_n_) analyzed and the mean _tt_ at a flow rate of 0.9 \(\mu\)L/h were: P1, \(n\) = 56, _tt_ = 0.38 \(\pm\) 0.01 s; P2, \(n\) = 50, _tt_ = 0.39 \(\pm\) 0.01 s; P3, \(n\) = 56, _tt_ = 0.28 \(\pm\) 0.01 s; and P4, \(n\) = 60, _tt_ = 0.45 \(\pm\) 0.01 s. All given uncertainties are standard errors of the mean. The histogram distribution of transit times or RBCs from these four patients and the normal or Gaussian fits to these histograms illustrate a wide range of differences between patients. To quantify these differences, we carried out analysis of variance (ANOVA), specifically one-way ANOVA, and found that at  
Figure 2: Transit times of RBCs in MMM vary between patients. (**a**) Histogram distribution of transit times from four patients, labeled P1, P2, P3 and P4, with normal (Gaussian) fits. The cell numbers (_n_) and mean transit times (_tt_) at a flow rate of 0.9 \(\mu\)L/h are: P1, \(n\) = 56, _tt_ = 0.38 \(\pm\) 0.01 s; P2, \(n\) = 50, _tt_ = 0.39 \(\pm\) 0.01 s; P3, \(n\) = 56, _tt_ = 0.28 \(\pm\) 0.01 s; and P4, \(n\) = 60, _tt_ = 0.45 \(\pm\) 0.01 s. The 5 \(\mu\)m MMM device was used. All given uncertainties are standard errors of the mean. (**b**) Box plots from one-way ANOVA for the transit times data in (**a**). At ***\(p\) < 0.001, there is a statistically significant difference in _tt_ of RBCs from the four patients.  
*** \(p<0.001\), there is a statistically significant difference in the \(tt\) of RBCs among the four patients, signaling the need for patient-specific monitoring rather than some fixed expected pathological values.
## 3 Results and Discussion  
### Crop Physiological Responses to Irrigation  
The values of soil moisture content, relative water content, canopy temperature, and relative humidity recorded in different irrigation methods and levels over the two years are shown in Figure 4. The average values (\(\pm\)SD) of relative water content (Figure 4a) were 90 \(\pm\) 2.4%, 86 \(\pm\) 4.3%, 75 \(\pm\) 9.0%, and 74 \(\pm\) 7.8% in flood irrigation and 86 \(\pm\) 2.9%, 82 \(\pm\) 1.8%, 75 \(\pm\) 2.9%, and 71 \(\pm\) 2.2% in sprinkler irrigation for 100, 75, 50, and 25% Etc, respectively. The canopy temperatures (Figure 4b) obtained from thermal imagery were higher in sprinkler irrigation treatments compared to corresponding flood irrigation treatments. Over the two years, average (\(\pm\)SD) canopy temperature with 100% Etc was  
Figure 3: Process flow chart for data acquisition, and processing for okra water stress identification from thermal–RGB imagery and deep learning models.  
20 \(\pm\) 2 \({}^{\circ}\)C and 28 \(\pm\) 2.4 \({}^{\circ}\)C in flood and sprinkler irrigation, respectively. At the same time, canopy temperature of 29 \(\pm\) 3 \({}^{\circ}\)C and 33 \(\pm\) 3.4 \({}^{\circ}\)C were recorded in 25% ETc treatment in flood and sprinkler irrigation, respectively. Similarly, average values (\(\pm\)SD) of soil moisture content (Figure 4c) were 26 \(\pm\) 2.2, 25.2 \(\pm\) 2.5, 22 \(\pm\) 3.0, 21 \(\pm\) 3.0% in flood irrigation and 26 \(\pm\) 2.9, 25 \(\pm\) 1.7, 23 \(\pm\) 1.5, 23 \(\pm\) 1.7% in sprinkler irrigation for corresponding levels of crop evapotranspiration. Relative humidity varied from 26 to 55% across the study period. Statistical analysis of the above variables revealed significant effect of irrigation method as well as irrigation level (\(p\) = 0.05). Overall, higher values of canopy temperature and lower values of soil moisture content and relative water content were recorded in stressed plants and vice-versa for non-stressed plants.
## 2 Materials and Methods
### Point Spread Function Assessment  
Images of the point source were employed to estimate the PSF of the system, which was characterized by the standard deviation (\(\sigma\)), assuming a Gaussian profile [7]. The three horizontal (x), vertical (y), and axial (z) profiles were extracted from the central region of the image and a Gaussian fit was performed.
# Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications
## 3 Results
### Biochemical Characterization of Soil Bacteria
#### 3.2.2 Preliminary Assessment of the Enzymatic Activity of Soil Bacteria in Plate Assays  
The 22 selected strains were examined for the production of extracellular hydrolytic enzymes, using the on-plate method with commercial substrates (Figure 2).  
Most of the studied strains (63.6%) exhibited proteolytic activity for the hydrolysis of casein. Approximately 32% were able to degrade carboxymethyl cellulose (CMC). Few strains showed the ability to degrade starch (22.7%). Only one strain, WK-13, showed pectinolytic activity (Figure S1). None of the isolates showed xylanolytic or ligninolytic activity under the screening conditions.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **WK No.** & **Identification (Percentage Compliance, \%)** \\ \hline
13 & WK-13 & _Bacillus pumilus_ (99.9) \({}^{\text{A}}\)_/Bacillus pumilus_ (83.1) \({}^{\text{M}}\) \\ \hline
14 & WK-14 & _Bacillus velezensis_ (99.9) \({}^{\text{S}}\) \\ \hline
15 & WK-15 & _Bacillus subtilis_ (99.9) \({}^{\text{S}}\) \\ \hline
16 & WK-16 & _Bacillus cereus_ (99.6) \({}^{\text{A}}\) \\ \hline
17 & WK-17 & _Bacillus megaterium_ (99.9) \({}^{\text{A}}\)_/Bacillus_ \\  & _ megaterium_/coagulans/amyloliquefaciens_ (92.0) \({}^{\text{M}}\) \\ \hline
18 & WK-18 & _Bacillus lentus_ (99.9) \({}^{\text{A}}\)_/Bacillus_ \\  & _ megaterium_/coagulans/amyloliquefaciens_ (88.0) \({}^{\text{M}}\) \\ \hline
19 & WK-19 & _Bacillus mycoides_ (63.7) \({}^{\text{A}}\) \\ \hline
20 & WK-20 & _Bacillus licheniformis_ (99.9) \({}^{\text{S}}\) \\ \hline
21 & WK-21 & _Bacillus cereus_ (94.3) \({}^{\text{A}}\) \\ \hline
22 & WK-22 & _Lysinibacillus fusiformis_ (55.1) \({}^{\text{M}}\) \\ \hline
23 & WK-23 & _Bacillus megaterium_ (98.6) \({}^{\text{A}}\) \\ \hline
24 & WK-24 & _Bacillus cereus_ (78.9) \({}^{\text{A}}\)_/Bacillus wiehenstephanensis_ (86.4) \({}^{\text{M}}\) \\ \hline \end{tabular} A—based on API test; M—MALDI TOF-MS identification; S—nucleotide sequence of gene 165 rDNA.  
\end{table}
Table 1: Cont.  
On-plate biochemical tests revealed that some of the strains possess rich enzymatic potential that might be useful in the biodegradation of plant residues. Particularly interesting in this regard are the strains WK-13, which showed the ability to hydrolyse casein, starch, CMC, and pectin, and WK-20, which is able to hydrolyse casein, starch, and CMC. The 10 strains with the highest and most diverse activities desirable for the degradation of crop residues were selected for further studies aimed at assessing the biotechnological potential of the bacteria (WK-4, WK-5, WK-7, WK-8, WK-9, WK-12, WK-13, WK-14, WK-15, and WK-20).
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 4 Results
### Temporal Robustness Analysis  
To evaluate the robustness of model performance across varying macroeconomic conditions, we conducted a time-segmented analysis by dividing the test dataset into three distinct economic regimes:  
Pre-Pandemic Period (January 2014-December 2019): Characterized by stable economic growth and moderate interest rates.  
Pandemic Shock Period (January 2020-December 2021): Defined by heightened volatility due to COVID-19 disruptions, aggressive monetary policy interventions, and sharp yield movements.  
Post-Pandemic/Inflation Shock Period (January 2022-December 2023): Marked by rising inflation, supply chain disruptions, and a tightening monetary policy cycle by the Federal Reserve.  
For each period, model performance was re-evaluated using MSE, MAE, and R\({}^{2}\). The random forest (RF) model maintained the highest accuracy and stability across all three  
Figure 4: MLP model structure.  
Figure 5: Training loss trend chart of four models in 50 training cycles.  
regimes. Notably, RF exhibited strong predictive ability during the volatile pandemic period, suggesting resilience to structural shifts and nonlinearities in the data. In contrast, linear regression (LR) and decision tree (DT) models experienced performance degradation during the pandemic phase, likely due to their sensitivity to abrupt regime changes and limited capacity to capture complex interactions.  
This segmented evaluation confirms the temporal robustness of ensemble-based machine learning models and highlights their suitability for real-world applications where economic conditions are dynamic and often nonstationary.
## 3 Results
### Extracted Themes
#### 3.4.5 Different Types of eHealth Tools  
Out of 60 studies, 22 studies investigated mHealth, 19 studies investigated telehealth, 10 studies looked at electronic apps/devices (e.g., digital health home spirometry, electronic mental health screening), three studies used web-based portals, two studies used emerging technologies (e.g., AI, wearables), two studies looked at telemedicine, and two studies used other forms of tech (e.g., health information technology and interactive health technology). A summary of the extracted themes, key indicators, and related references is presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Themes** & **Key Indicators and Factors of Theme** & **References** \\ \hline \multirow{4}{*}{Users of digital health} & Caregivers and families & [30; 37; 40; 73] \\ \cline{2-3}  & Transplant providers, clinicians, & \\  & and pharmacists & [10; 11; 23; 26; 34; 46; 47] \\ \hline \multirow{4}{*}{Barriers of digital health tools} & Access and knowledge barriers (e.g., poor IT & \\  & literacy, poor IT acceptance, demographic, & [22; 24; 39; 44; 70; 75; 76] \\ \cline{2-3}  & and socio-economic factors) & \\ \cline{2-3}  & Usability and implementation challenges & \\  & (manual and continuous data entry, lack of & \\  & professional support, poor dissemination of & \\  & digital intervention, costs) & \\ \cline{2-3}  & User experience and trust issues (privacy and & \\  & security concerns, lack of human connection, & [9; 24; 38] \\  & outdated/inconsistent information) & \\ \hline \multirow{4}{*}{Benefits of digital health tools} & Improving patient care and engagement & [17; 18; 26; 29; 30; 32; 41; 49; 52; 54; 55; 69; 72] \\ \cline{2-3}  & Convenience and accessibility & [17; 22; 35; 56; 59; 61] \\ \cline{2-3}  & Empowerment and safety & [22; 33; 52; 55] \\ \cline{2-3}  & Cost-efficiency and time-saving & [35; 56; 59; 64; 65] \\ \hline \multirow{2}{*}{Process of development of  digital health} & Patient-centered & [12; 37; 70] \\ \cline{2-3}  & Preparation and readiness & [37; 73] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Main Extracted Themes and Related References.  
The analyzed studies showed that telehealth and mHealth applications enhance patient engagement and accessibility, but their effectiveness depends on high-speed internet connectivity, cloud-based health information systems, and secure communication networks. Some studies highlighted that digital health tools, such as virtual consultations and real-time remote monitoring, require robust networking infrastructures to ensure data security, interoperability, and low-latency transmission [15; 16; 26].
# Within and Across-Language Comparison of Vocal Emotions in Mandarin and English
## 3 Results
### Within-Language Comparison of Vocal Emotions  
Table 2 presents the normalized mean values for each emotion in Mandarin and English. To test whether each of the 20 cues discriminated between the five emotions within each language, we conducted a linear mixed-effects model implemented with the _ImerTest_ package [45] in R [46]. These cues were fitted against a model for each language, where emotion was used as a fixed factor and speaker was treated as a random factor. Kolmogorov-Smirnov (K-S) tests were used to estimate the normality of the residuals of the model. Results suggested an acceptance of normality assumption for most of the variables except for the residuals of range (\(p\) = 0.001), rise (\(p\) = 0.003), fall (\(p\) = 0.018), rise slope (\(p\) = 0.001), fall slope (\(p\) = 0.023), PIC (\(p\) = 0.006) and SQ (\(p\) = 0.000) in Mandarin and rise (\(p\) = 0.000), fall (\(p\) = 0.013), rise slope (\(p\) = 0.000) and SQ (\(p\) = 0.007) in English. We attempted to perform a data transformation (e.g., log transformation) to improve the normality of the residuals but more variables were shown to violate the normality assumptions and the interpretation of the results was not as straightforward as before. This allowed us not to employ any data transformations because the assumption of normality is of little importance in the regression model [47]. Here we present the results in the order of prosodic cues, spectral cues and EGG cues for each language.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Measurement**} & \multicolumn{5}{c}{**Emotion**} \\ \cline{3-6}  & & **Anger** & **Fear** & **Happiness** & **Neutral** & **Sadness** \\ \hline Mandarin & Mean pitch & 0.474 & 0.889 & 0.664 & \(-1.111\) & \(-0.952\) \\  & Pitch range & 1.392 & \(-0.228\) & 1.055 & \(-0.050\) & \(-0.656\) \\  & Mean absolute slope & 1.561 & \(-0.023\) & 0.945 & \(-0.165\) & \(-0.864\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Normalized mean values of five emotions in each language.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Construction of Mirroring Central Ciphertext  
As shown in Figure 5, the image owner constructs the MCC by replacing the ciphertexts of the target pixels with the ciphertext of the reference pixel, which is \(C_{t}^{{}^{\prime}}=C_{r}\). In a square grid, all target pixels become mirror of the reference pixel. All target pixels have the same ciphertext and the \(n\) LSBs that have been set to zero as the reference pixels.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 3 Co-Analysis of Rain Rate and C-Band SAR Backscattering
### Scattering by the Melting Layer  
There is no doubt that the rain-induced structures (ring waves and splash products) can enhance the sea surface roughness in low to moderate wind regimes as illustrated by the experiments conducted by [PERSON] et al. (2019), [PERSON] et al. (2019) and [PERSON] (2018). However, for SAR operating on spacecraft, the signal transmission in the atmosphere (not considered in the laboratory experiments) can not be neglected since rain/hydrometeors can scatter and absorb part of the microwave energy. By taking advantage of the observations from multi-sensors and/or at multi-polarizations, [PERSON] et al. (2018), [PERSON] et al. (2018) and [PERSON] et al. (2018) attribute the strong and localized enhancement of NRCS values to radar backscattering hydrometeors in the melting  
Figure 4: The rain rate and NRCS along the transects for the upper case (**a**) and the lower case (**b**) in Figure 3. For each case, the upper seven plots with dark curves: Rain rate measured by the weather radar along the transect inserted in the images depicted in Figure 3 at different times. Lower two plots with blue curves: NRCS values measured along the same transect. T0 is the SAR acquisition time.  
Figure 3: Base reflectivity at different times overlaid on Sentinel-1 VV polarization SAR images. **Upper** panel: SAR image acquired on 25 June 2017 at 23:21 UTC. The orange contours mark base reflectivities larger than 40 dBZ and the blue contours mark base reflectivities between 20 and \(-\)40 dBZ. **Lower** panel: SAR image acquired on 11 June 2015 at 04:30 UTC. The orange contours mark base reflectivities larger than 35 dBZ and blue contours mark base reflectivities between 20 and \(-\)35 dBZ. The overlays (**a3**,**b3**) are coincident with the SAR acquisition time.  
layer, where the falling ice particles undergo phase change from solid to liquid. Here, we provide more evidence for the contribution from hydrometeor in the melting layer in Figure 5. This figure presents the hydrometeor classification corresponding to the three rain events of Figure 2. The product of hydrometeor classification is provided by NEXRAD for an elevation angle of 0.5\({}^{\circ}\) (around 4-5 km where the rain cells are located). It shows that most of the rain-related features in the SAR image correspond to pixels for which the classification indicates light/moderate rain. However, the brightest patches (corresponding to highest NRCS values) in VV and VH seem to be strongly associated with radar backscattering from graupel/hail. Moreover the presence of graupel/hail, big drop rains validated the presence of the melting layer. As investigated by [PERSON] and [PERSON] [33] and [PERSON] [34], the cross-polarized backscatter was found with more contribution from the melting layer than the co-polarized one. These high correlations found between the dual-pol images and the classification further confirm the importance of the melting layer in the strong backscatter observed in C-band SAR images acquired during events.