esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Enhanced LMS Algorithm
### Numerical Evaluation of the SM-LMS  
A simple numerical validation was carried out to examine the signal tracking performance of the proposed algorithm under the following conditions: (1) a second-order linear time-invariant (LTI) system with a primary frequency of \(\omega_{n}=30\ Hz\) and damping ratio of \(\zeta=0.2\); (2) a finite impulse response (FIR) filter with three coefficients; and (3) a single-frequency sine wave \(u(t)=A\sin(\omega t+\phi)\) with amplitude \(A=1\), excitation frequency \(\omega=120\ Hz\), and no phase (\(\phi=0\)). The control parameters are selected as \(\alpha=15\) and \(\mu=0.3\). Figure 6a shows the time-domain tracking results with the two algorithms. When the signal has sudden changes in the direction and amount, the conventional LMS algorithm has worse performance than the SM-LMS algorithm, and higher frequency fluctuations are observed compared to the estimation error for both cases, as shown in Figure 6b. It is also important to ensure that the filter weights at steady state approach the same or close values for both algorithms, as shown in Figure 7a. The results show that the sliding mode control only supplements the original LMS algorithm to achieve better optimized convergence without changing its primary characteristics. Figure 5b shows the trajectory of the coefficient plane (the filter weights), and the path to the desired steady state is relatively straight and fast for the SM-LMS. Therefore, changes of the filter weights in the SM-LMS case show superior control without inappropriate system movement.  
Figure 6: Control of a single sinusoid at 120 Hz in the time domain: (**a**) predicted with least mean squares and sliding mode least mean squares (Key:, original signal;, filter output); and (**b**) predicted estimation error (Key:, with least mean squares;, with sliding mode least mean squares).
# Evaluation of 3D Concrete Printing Extrusion Efficiency
## 3 Results
### Extrusion Efficiency Dependence on Nozzle Size  
The influence of nozzle size on extrusion was examined by testing two nozzle diameters: 12 mm and 20 mm. These tests were conducted at two extreme rotational speeds: 37.3 RPM and 30.0 RPM, with a mixture consistency of approximately 150 mm maintained across all tests (Figure 11).  
A significant reduction in extrusion efficiency was observed as a result of decreasing the nozzle size. At 37.3 RPM, reducing the nozzle size resulted in a 14.3% decrease in extrusion rate, while at 30.0 RPM, the decrease reached 17.9%.  
This finding is particularly important, as the nozzle size may frequently vary depending on the dimensions of the printed object. Observations suggest that a smaller nozzle restricts material flow, thereby reducing extrusion efficiency. To achieve consistent parame  
Figure 11: Graphic depictions of printhead efficiency depending on nozzle size.  
Figure 10: Graphic depictions of printhead efficiency as a function of mass of material left in tank.  
ters for printed elements, it is essential to introduce extrusion coefficients that compensate for this observed effect.  
Additionally, based on the graph in Figure 12, it can be observed that the amount of material inside the reservoir has a greater impact on 20 mm nozzles than on smaller ones, regardless of rotational speed. Extrusion efficiency was less constrained for the 12 mm nozzle during the final phase of the test. Again, the end of extrusion is evident, occurring when less than 4 kg of material remains in the reservoir. In this regard, the 20 mm nozzles performed slightly better, allowing approximately 2.5% more material to be extruded compared to the smaller diameter nozzles.
## 5 Numerical Simulation and Verification
### Simulation Investigation and Verification  
The finite element (FE) is widely used to investigate the interaction between guided waves propagating along a structure and features within the structure [22; 23; 24; 25; 26; 27]. In this section, in order to test the functionality and reliability of the proposed method, numerical simulations in sample pipes were conducted. The three-dimensional solid and finite element model of the sample pipes were constructed in a commercial FE software package [28].
# Effect of PEF Treatment on Chosen Properties of Raw and Hot Air- and Freeze-Dried Poultry Meat
## 4 Conclusions  
Dried meat can serve as a protein-rich product suitable for easy transport and storage. Thus, the study focused on assessing the influence of pulsed electric field treatment on the quality of poultry meat during hot air-drying and freeze-drying. The method of drying as well as the pulsed electric field (PEF) pretreatment induced significant physical changes in the poultry meat. The PEF improves drying efficiency in the case of freeze-drying due to electroporation, which affects cell membranes, shortening the drying time and reducing shrinkage, whereas for hot air-drying the PEF-treated samples resulted in prolonged drying, which may have been caused by extensive damage to the muscle structures and high shrinkage. Drying lowered water activity and increased dry matter content, ensuring safety. The freeze-dried material exhibited a higher rehydration rate, hygroscopic properties, and better meat color compared to the hot air-dried material. Furthermore, microscopic analysis revealed distinct differences: hot air-drying led to high shrinkage and a dense structure, while the freeze-dried meat showed higher porosity and a spongy surface. The PEF treatment resulted in loosened structures with cracks and voids, which were more pronounced in the freeze-dried meat. Nevertheless, precise parameter selection for pretreatment and drying is crucial to ensure a high quality of the dried meat product.  
**Author Contributions:** Conceptualization, M.N. and A.W.; methodology, M.N., A.W. and K.R.; software, K.R.; validation, M.N. and K.R.; formal analysis, M.N.; investigation, K.R. and M.N.; resources, D.W.-R.; data curation, I.S., M.K., A.M. and M.S.; writing--original draft preparation, M.N., I.S., K.R., A.M., M.K. and M.S.; writing--review and editing, D.W.-R., M.N. and A.W.; visualization, M.N., I.S., A.M. and M.K.; supervision, M.N.; project administration, M.N.; funding acquisition, D.W.-R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank [PERSON] for his help.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Linear Regression Modeling for Drift Correction  
Quantification and correction for drift during an 8 h exposure period should only require a linear correcting factor based on the aforementioned drift experiments. Equation (5) shows the full multilinear regression, with corresponding constants being found in Table 8. The results are extremely accurate when compared with experimental trials, with an order of magnitude higher resolution than the previously presented calibrations. Based off of these calibrations it is recommended that the  
Figure 3: Unfiltered Readings of Calcium and Chloride ion selective electrodes (ISE’s) for Drift Quantification; showing generally linear drift over an 8 h period.  
electrodes be washed and reset every 8 h. This will ensure accurate results within \(\pm 1\) mV for most readings with these electrodes.  
\[\begin{array}{ll}E=c_{1}+c_{2}x_{1}&+c_{3}x_{2}+c_{4}x_{3}+c_{5}x_{4}+c_{6}x_{ 1}x_{2}+c_{7}x_{1}x_{3}+c_{8}x_{1}x_{4}\\ &+c_{9}x_{2}x_{3}+c_{10}x_{2}x_{4}+c_{11}x_{3}x_{4}+c_{12}x_{1}x_{2}x_{3}+c_{13 }x_{1}x_{2}x_{4}\\ &+c_{14}x_{1}x_{3}x_{4}+c_{15}x_{2}x_{3}x_{4}+c_{16}x_{1}x_{2}x_{3}x_{4}\\ \\ &x_{1}=\ln(I_{i})\\ &x_{2}=\ln\left(\sum_{j;j\
eq i}^{k}I_{j}\right)\\ &x_{3}=\textit{temperature}\left(\mathrm{K}\right)\\ &x_{4}=\textit{time}\left(\mathrm{hr}\right)\end{array} \tag{5}\]
## 4 Results
### The Reliability of the Moonlight Remote Sensing Imagery
#### 4.1.3 Visual Examination of the UAV Moonlight Image  
Finally, the spatial and spectral resolution features of the UAV moonlight remotegensing images, obtained under a waxing gibbous moon and without any further image processing, are very vivid and comparable to the high-resolution optical images (SuperView-1) with a resolution of 1 m (Figure 8). More importantly, it is possible to obtain the same clear spatial distribution and texture characteristics of the ground surface as daytime optical remote sensing imagery, especially for non-self-lighting objects, such as fish ponds, bare ground, farmland, and even greenhouses. From part 1, although the timing of collecting data is different and the texture of the land surface changed, we could still clearly observe that both images clearly captured the spatial distribution and texture characteristics of farmland. Meanwhile, the characterization of water bodies and bare land is well reflected in the UAV moonlight remote sensing mosaic (Part2, Part3).  
In conclusion, it is now feasible to capture a non-self-lighting surface under weak lunar illumination at night.  
Figure 7: The ISS nightlight image and Landsat-7 image of the Komsomsolsk, Russia (above ISS and Landsat-7) and three sub-areas of the ISS nightlight image under faint lunar illumination with reference to the Google Earth images taken in summer daytime (below A, B and C).
## 4 Mathematical Validation
### Validation (10 Hz)
#### 4.2.2 With 10-30 dB Noise  
On the basis of the above research, environmental background noise is further considered. Ambient noise with a signal-to-noise ratio (SNR) of 10, 20, and 30 dB was added to the original signal respectively, as shown in Figure 12.  
Figure 11: PED value.  
Figure 12: Acceleration signals with varying degrees of white noise when excited at 10 Hz.  
As can be seen in Figure 14, no matter how the environmental noise changes within the SNR range of 10-30 dB, the PED values of the locations of sensors 2-4 and 2-5 will increase significantly, and PED values of damage Condition 3 are significantly higher than those of Condition 2. Therefore, even under the interference of environmental noise, WPPE theory can still be as good as that without noise interference. The precise location of the damage is clearly identified (between sensors 2-4 and 2-5), with high anti-noise robustness.
## 1 Introduction
We focused on the reflections and diffractions signals caused by structures, which lead to reduced signal effectiveness and signal loss. The combination of predicting the signal attenuation and performing the correction of the transmission intensities make it possible to maintain a proper signal-to-noise ratio  
Figure 1: The satellite communication scene. (SNR) and prevent transmission interference. For each section, A and B, we developed a separate prediction model using LSTM and an associated Python programming software. We used collected data from the Genesis LEO satellite and corresponding simulated data in the range of 2.4 GHz to 72 GHz. We then executed two LSTM systems, one for each section, to predict the attenuation levels in each part. We reached a less than 2% gap between the predicted and the actual attenuation levels for each piece. We calculated the expected SNR based on this data, the ITU model, and the LSTM neural networks for a second ahead. The signal strength was changed accordingly based on the predicted SNRs, resulting in reduced power consumption. We conducted separate experiments for each frequency. Results show that the predicted measurements for all frequencies were close to the actual value. In summary, we used LSTM, developed software, good training data, and a testbed with a wide range of frequencies to provide a comprehensive prediction accuracy model. The main novelty of this work is the prediction accuracy, comprehensiveness, and the ability to apply it for more cases. The rest of this paper is organized as follows: Section 2 outlines related work, Section 3 presents our model for predicting the signal attenuation level due to climate situations and blocking construction near the ground station, Section 4 outlines the experiment setup and the corresponding results for each route section, and in Section 5 we conclude with conclusions and future work. Where applicable, we separated between sections A and B to emphasize the differences.
## 3 Results and Discussion
The total amount of Mg per plant, and its total nutrient absorption, was found to be significantly increased when the nutrient solution contained 192 mg Mg L\({}^{-1}\), compared to 0-24 mg Mg L\({}^{-1}\) (Table 3). As far as the Mg use efficiency by the sweet orange plants, it decreased significantly and gradually as its concentration in the nutrient solution increased (Table 3). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{6}{c}{**Macronutrients**} & \multicolumn{3}{c}{**Mironutrients**} \\  & **P** & **K** & **Ca** & **Mg** & **Mn** & **Zn** & **Fe** \\ \hline Nutrient concentrations of various plant parts & Leaves & 0.095 & 0.248 & \(-\)0.808 *** & 0.722 *** & \(-\)0.299 & \(-\)0.029 & 0.605 *** \\ \hline \multirow{4}{*}{Total element absorption} & Scion’s stems & \(-\)0.138 & 0.124 & \(-\)0.497 *** & 0.802 *** & 0.098 & \(-\)0.058 & \(-\)0.137 \\  & Rootstock’s stem & \(-\)0.280 & 0.142 & 0.588 *** & 0.579 *** & \(-\)0.547 *** & \(-\)0.118 & 0.487 *** \\  & Roots & 0.213 & 0.584 *** & \(-\)0.612 *** & 0.634 *** & \(-\)0.633 *** & \(-\)0.226 & \(-\)0.544 *** \\ \hline \multicolumn{2}{c}{Total element absorption} & \(-\)0.599 *** & \(-\)0.371 * & \(-\)0.495 ** & 0.511 ** & \(-\)0.700 *** & \(-\)0.175 & \(-\)0.717 *** \\ \hline \multicolumn{2}{c}{Element use efficiency} & 0.239 & \(-\)0.029 & 0.177 & \(-\)0.677 *** & 0.478 *** & \(-\)0.151 & 0.464 *** \\ \hline \multicolumn{2}{c}{\(n=36\); *** \(p<0.001\); *** \(p<0.01\); * \(p<0.05\).} & & & & & \\ \end{tabular}
\end{table} 
Table 2: Correlation coefficient (r) between Mg concentration of the nutrient solution and P, K, Ca, Mg, Mn, Zn, and Fe concentrations of various plant parts, total element absorption, and element use efficiency of sweet orange plants.
###### Abstract  
We address the applicability of quantum key distribution with continuous-variable coherent and squeezed states over long-distance satellite-based links, considering low Earth orbits and taking into account strong varying channel attenuation, atmospheric turbulence and finite data ensemble size effects. We obtain tight security bounds on the untrusted excess noise on the channel output, which suggest that substantial efforts aimed at setup stabilization and reduction of noise and loss are required, or the protocols can be realistically implemented over satellite links once either individual or passive collective attacks are assumed. Furthermore, splitting the satellite pass into discrete segments and extracting the key from each rather than from the overall single pass allows one to effectively improve robustness against the untrusted channel noise and establish a secure key under active collective attacks. We show that feasible amounts of optimized signal squeezing can substantially improve the applicability of the protocols allowing for lower system clock rates and aperture sizes and resulting in higher robustness against channel attenuation and noise compared to the coherent-state protocol.  
quantum cryptography; quantum optics; quantum key distribution; continuous variables; coherent states; squeezed states; satellite; low Earth orbit +
Footnote †: journal: _Entropy_  
_Article_  
**Citation:** [PERSON]; [PERSON].  
Applicability of Squeezed- and Coherent-State Continuous-Variable Quantum Key Distribution over Satellite Links. _Entropy_**2021**, 23, 55.  
[[https://doi.org/10.3390/e23010055](https://doi.org/10.3390/e23010055)]([https://doi.org/10.3390/e23010055](https://doi.org/10.3390/e23010055))  
Received: 13 December 2020  
Accepted: 29 December 2020  
Published: 31 December 2020  
**Published:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 4 Performance Evaluation
### Simulation Setup  
Extensive simulations for evaluating the performance of our CRSSA are conducted by using Matlab (R2022b) [37]. In order to prove the stability and the efficiency of our approach, we test it in different propagation parameters, as well as different numbers of nodes. The general simulation setup is listed in Table 2.  
A random deployment of \(N\) static nodes inside a square region of length \(\sqrt{A}\) in each run is considered. We execute 100 runs, and we make sure that the network is fully connected at each run. Random received power values for each link are generated, following relations explained in Section 3.2. Parameters' values used for simulation such as \(\gamma\), \(\sigma\), and \(m\) are listed in each subsection below. The path loss exponent, the communication range, and the inter-node distances are then estimated using the generated noisy received power. The CRSS approach is studied by fixing two variables and varying the third one.  
In order to test the impact of varying the Nakagami parameter, \(\gamma\) and \(\sigma\) values are fixed and \(m\) is changed. At each variation of the \(m\) value, the approach is tested for 100 runs, and the results are reported in Section 4.2.1. Similarly, to assess the impact of the PLE value on the accuracy and the reliability of the CRSS approach, \(\sigma\) and \(m\) values are fixed, and the \(\gamma\) value is changed; Section 4.2.2 details obtained results. Analogously, Section 4.2.3 elucidates the consequence of varying the shadowing standard deviation on the CRSS approach.
## 2 Materials and Methods
### Net Primary Production (NPP) Estimation Model  
The CASA model is a remote sensing-based process model. It performs well in long-term series and large-scale estimation of NPP [31; 32]. The two indispensable components in the CASA model are absorbed photosynthetically active radiation (APAR) (MJ\(\cdot\)m\({}^{-2}\)) and light use efficiency (LUE) factor \(\varepsilon\) (gC\(\cdot\)MJ\({}^{-1}\)) [33; 34; 35]. For a particular pixel \(x\) at month \(t\), NPP is calculated by the following equation:  
\[NPP(x,t)=APAR(x,t)\times\varepsilon(x,t) \tag{1}\]  
\[APAR(x,t)\ =\ FPAR(x,t)\times SOL(x,t)\times 0.5 \tag{2}\]  
where SOL is the surface solar radiation downwards (MJ\(\cdot\)m\({}^{-2}\)). The constant 0.5 indicates that the solar energy used by vegetation photosynthesis is half of the total incident solar radiation [35].  
The FPAR is a linear expression of NDVI and simple ratio (SR):  
\[FPAR(x,t)=min\bigg{\{}\frac{SR(x,t)-SR_{min}}{SR_{max}-SR_{min}},0.95\bigg{\}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Spatial Resolution** & **Temporal Resolution** & **Source** \\ \hline NDVI & 1 km & monthly & MOD13A3 \\ Air temperature & 0.25 degree & monthly & ERA5 2m temperature \\ Precipitation & 0.25 degree & monthly & ERA5 total precipitation \\ Solar radiation & 0.25 degree & monthly & ERA5 surface solar \\ Land cover & 5.6 km & yearly & radiation downwards \\ Soil attribute & 30 arc-second & - & MCD12C1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the model input data set.  
\[SR=\frac{1+NDVI(x,t)}{1-NDVI(x,t)} \tag{4}\]  
where \(SR_{max}\) and \(SR_{min}\) correspond to the 95% and 5% quantiles of the normal distribution of all pixels from monthly SR, respectively.  
\[\varepsilon(x,\ t)\ =T(x,\ t)\times W(x,\ t)\times\varepsilon_{max} \tag{5}\]  
where \(T(x,t)\) is the temperature stress coefficients, and \(W(x,t)\) is the water stress coefficient. \(\varepsilon_{max}\) is the maximal LUE under ideal conditions with a value of 0.541 [36].
## 5 Medical Imaging-Based Kinematics
### Ultrasound Technology (US)
#### 5.1.2 Speckle Tracking  
Speckle tracking is a relatively novel technique developed during the early 2000s for the measurement of tissue 3D displacement and deformation. Speckles are defined as image features/spots generated by the acoustic response of tissue fibers to ultrasound signals. Single speckles are analyzed in identifiable kernels that are followed along the cardiac cycle. Postprocessing techniques allow the averaging of kernel displacements over several cardiac cycles to reduce the effects of noise [163]. The spatial and temporal resolution of speckle tracking is remarkable, providing hundreds of frames per second for pixels of \(<\)1 mm size [164]. This resolution allows not only the study of the myocardium but also the mechanics of arterial walls and aortic aneurysms [165; 166]. Displacement measurements are limited by kernel size (\(\sim\)1 mm) and show reproducibility issues common to any US-based technology (Figure 8a) [167; 168].  
Figure 8: _Cont._
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 3 Implementation
### Deec-K-nn
#### 3.2.2 Methodology  
The proposed clustering approach enhances the Distributed Energy-Efficient Clustering (DEEC) protocol by integrating a K-Nearest Neighbours (K-NN)-based cluster head selection mechanism. The process begins by evaluating the spatial density of sensor nodes using their positional information. The K-NN algorithm identifies nodes centrally located within dense regions, making them strong candidates for CHs. This spatial analysis is combined with an energy-aware weighting scheme, where each node's residual energy is compared with its initial energy. The number of clusters is not fixed; instead, it dynamically adjusts according to the number of active (alive) nodes in the network. This adaptive clustering maintains an optimal cluster size, which helps balance the energy consumption across the network and prolongs the operational lifetime of the sensor system. The value of \(k\) determines the number of nearest neighbours considered for node assignment and cluster head selection. In our simulations, we set \(k=3\), a commonly used value in WSN clustering literature. This choice balances local neighbourhoods' awareness with computational efficiency. Through preliminary experiments, we found that increasing \(k\) beyond 3 did not yield significant improvements in energy efficiency or network lifetime, while smaller values led to less stable clusters.  
Our experimental results demonstrated significant improvements in the DEEC-K-NN protocol compared with conventional DEEC implementation. These quantitative improve ments validate the effectiveness of the K-NN-based clustering approach in optimising network organisation and resource utilisation compared with traditional DEEC protocols.