esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Combining First-Principle Models with Experimental Observations  
The process of development of a mechatronic system usually consists of several subsequent phases. Preliminary analysis and machine design are usually followed by a detailed elaboration of mechanical construction, today with the aid of computer software. Control-related questions may arise in this phase by analysing basic dynamic properties using FEA methods. Adjustment of the design choices may be needed to deliver favourable dynamic properties to fulfil formulated control objectives. Assembly of a prototype is often necessary for validation of the machine design. Data acquired from experiments with a real machine may provide invaluable information relevant both for the purpose of control and for improvement of the fidelity of virtual models. The Digital Twin concept extends this approach by proposing to instance the virtual model according to specific machine samples which may differ considerably due to construction tolerances, assembly, differences in installation site, mechanical wear, and ageing of individual components. The virtual models can live parallel lives with their physical counterparts, being continuously updated with available observation data and providing improved predictions of machine performance and remaining lifetime. A fundamental question is which data may be used for improving the fidelity of physics-based models from the perspective of control-relevant predictions. This section provides some insights regarding this topic.  
Figure 15 shows the model-measurement loop. Deviations between modelled and measured responses could indicate that either the real system does not function as designed or the model is not accurate enough. The first scenario may be relevant for the case of long-term operation of a machine that starts showing signs of wear or malfunction. However, we are now interested more in the early phases of the design cycle, which deals with validation of geometric models using data acquired from first experiments with assembled prototypes. Both the model and the actual system can be improved by completing this loop a few times. How we used this loop for stiffness, damping, and machine geometry in our flexible manipulator benchmark problem is discussed below.  
Figure 14: Comparison of physics-based models vs. experimental measurements: amplitude frequency response acquired from experimental identification (black), analytical equation-based model (red) and FEM model (blue).
# A Wideband Terahertz Transmissive Polarization Manipulator Based on Metasurfaces
## 3 Results and Discussion
### Transmit Reject Filter Design and Analysis  
When an incident x-polarized or y-polarized wave strikes the proposed polarization manipulator, the transmitted wave is divided into two orthogonal components, one \(x\) polarized and the other y-polarized. These two waves, when having phases 90 degrees apart, give rise to circularly polarized waves. This concept was used to design a filter. Instead of \(x\) or \(y\) polarized waves, a slant (xy-polarized) wave was applied to the designed metasurface-based structure. It was observed that the waves passed through the structure at a resonance frequency within a certain bandwidth (decided by the conductor patch layout of the unit cell). It was confirmed that the same structure behaves as a bandpass filter, allowing 0.713 THz to 1.4346 THz waves to pass through, while rejecting other frequencies lying outside the band. This structure works over 67% fractional bandwidth and gives a sharp transmission zero at 1.505 THz (rejection \(>\) 30 dB). The loss at the receive frequency is as low as 0.3 dB. Since the rejection dip is at a higher frequency than the operating band, it can be used as a transmit reject filter in receivers to reject any spurious signal in the transmission frequency of the THz communication system. Figure 11 shows the transmission characteristics of the filter.  
To obtain physical insight into the transmission behavior of the proposed polarization manipulator, we considered electric field distribution for the filter within the passband. Figure 12 shows that electric field at 0.91 THz is concentrated at the outer layer of the inner patch.
## 2 Background Theory and Methods
### Temperature Distribution and Phase Delay Analysis
To determine an unsteady heat conduction equation with periodic heat flux to a steady state, complex transformation is applied to Equation (8) using Equation (17), thereby converting it into Equations (18)-(20). \[Q_{1}(x,y,z,t)=\frac{\lambda_{1}P_{0}(1-R_{1})}{4\pi a^{2}}e^{-\frac{(x^{2}+y^{ 2})}{a^{2}}-\lambda_{1}z}[1+\cos(\omega t)] \tag{4}\]6  
\[Q_{1}(x,y,z,t)=\frac{\lambda_{1}P_{0}(1-R_{1})}{4\pi a^{2}}e^{-\frac{(x^{2}+y^{ 2})}{a^{2}}-\lambda_{1}z}[1+\cos(\omega t)] \tag{4}\]7  
\[Q_{1}(x,y,z,t)=\frac{\lambda_{1}P_{0}(1-R_{1})}{4\pi a^{2}}e^{-\frac{(x^{2}+y^{ 2})}{a^{2}}-\lambda_{1}z}[1+\cos(\omega t)] \tag{4}\]8  
\[Q_{1}(x,y,z,t)=\frac{\lambda_{1}P_{0}(1-R_{1})}{4\pi a^{2}}e^{-\frac{(x^{2}+y^{ 2})}{a^{2}}-\lambda_{1}z}[1+\cos(\omega t)] \tag{4}\]9  
Figure 2:0  
The deflection analysis of the probe beam was performed using the temperature gradient for the path of the probe beam, as described in Equations (1) and (2). Since it is difficult to understand exactly the main crystal orientation of the anisotropic material, a conversion process is needed to make the crystalline coordinates of the anisotropic material consistent with those of an experimental apparatus. Equation (21) was employed to match the crystalline coordinates used in the numerical analysis and the coordinates of the experimental device. When the main crystal orientation of the anisotropic material is known, the effective thermal conductivity coefficient is derived as shown in Figure 4b and Equation (22). Figure 2:1 Figure 2:2 Figure 2:3  
Figure 2:4  
where \(k_{11}\), \(k_{22}\) and \(k_{33}\) are the main thermal conductivity for each main crystal direction, and \(\theta\), \(\varphi\), and \(\sigma\) are the direction cosines for the \(x\)-, \(y\)-, and \(z\)-axis, respectively. The deflection analysis of the probe beam was conducted using Equation (23), with the beam oriented perpendicular to the specimen and using the temperature gradient derived from numerical analysis after matching the coordinates. The change in the refractive index was generated due to the temperature gradient and refractive index of air. Figure 2:5  
Figure 2:6 (**a**) Crystalline coordinates of anisotropic material and coordinates of experimental apparatus; (**b**) effective thermal conductivity of anisotropic material. Since the deflection analysis result contains complex numbers, the phase delay (\(\psi\)) between the probe and pump beam can be expressed as in Equation (24). Figure 2:7
# Sustainable Management of Water Resources in Supplementary Irrigation Management
## 5 Conclusions  
Overall, the results of this study showed that the sensor performance considerably varied across different soil water content and monitoring soil depths. Regression equations indicated slow response of Watermark and Tensiometer in a lower range of soil water content, which is especially important in areas where irrigation is a supplementary source of water for the crops. That was another factor that was examined in this study, and the result indicated that TDR showed better responsiveness to drying and wetting cycles regardless of the monitoring soil depth. The Watermark and Tensiometer responses to rapid wetting and drying cycles were slow or even non-existant in deeper soil layers. All tested sensors performed best at 20 cm soil depth, even though the soil profile was homogenous in terms of physical properties. This means that if the effects of soil depth are ignored, the error in estimated soil water content will be greater. Results of the study indicate that in growing conditions where irrigation is a supplementary source of water, laboratory testing is necessary to improve sensor performance for accurate measurement in a wider soil water range. Several factors have contributed to differences among sensor performances, including the lack of adequate contact of the sensor with dry cracking soil as well as the loss of the soil suction in low soil water content conditions. Because some of the sensors did not capture changes in SWC at a certain soil depth, it is suggested that the effect of sensor installation depth might not be negligible in certain cases, especially in growing conditions when frequent changes in soil water content are present.  
Conceptualization, M.M. and G.K.; methodology, M.M. and G.K.; formal analysis, A.B. and A.A.; investigation, M.M. and B.J.-P.; writing--original draft preparation, M.M., D.P. and Z.B.; writing--review and editing, M.M., B.J.-P., A.B., D.P. and Z.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 2 Materials and Methods
### Experimental Design  
We selected 316 L stainless steel powder (Changsha TIJO Metal Materials, Changsha, China) as the raw material. Its element composition and particle size distribution are shown in Table 1. Two sets of full factorial experiments with different build layer counts were conducted by varying the laser power and scanning speed. The process parameters are shown in Tables 2 and 3, and the surface energy density (SED) and volumetric energy density (VED) are calculated using the following formulas [23]:  
\[E_{S}=\frac{P}{VL} \tag{1}\]  
\[E_{V}=\frac{P}{VLh} \tag{2}\]  
in which \(P\), \(V\), \(L\), and \(h\) represent the laser power, scanning speed, hatch spacing, and powder layer thickness, respectively. The experiment adjusted the energy input by altering the laser power and scanning speed to determine the effects of process parameters on melt pool characteristics, temperature distribution, and surface morphology during the printing process. Furthermore, it investigated the impact of the surface quality of different preceding layers on the surface morphology of subsequent layers and the cumulative patterns of surface defects based on multi-layer printing.  
The distribution of samples on the substrate is shown in Figure 2. The experiment used a bidirectional serpentine scanning strategy with a hatch spacing (\(L\)) of 0.05 mm and a powder layer thickness (\(h\)) of 50 \(\upmu\)m. The base plate material used was 316 L stainless steel, which was the same as the metal powder material. Prior to the experiment, the base plate (Empower, Shanghai, China) was preheated to 200 \({}^{\circ}\)C to reduce thermal stress caused  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Element** & **C** & **Si** & **Mn** & **Mo** & **Ni** & **Cr** & **Fe** \\ \hline Content (\%) & 0.02 & 0.74 & 1.88 & 2.54 & 12.48 & 17.53 & Balance \\ Powder distribution & D10 & & & D50 & & & D90 \\ Powder size (\(\upmu\)m) & 28.5 & & & 44.6 & & & 69.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental composition and particle size distribution of 316 L powder.  
Figure 1: (**a**) Schematic and optical configuration of LPBF system. The red arrow indicates the 1080 nm laser beam, the green arrow indicates the 808 nm auxiliary laser beam, and the blue arrow indicates the optical emission of melt pool; (**b**) photograph of actual setup.  
by temperature gradients [24], and nitrogen gas (Nanning Air Separation Gas, Nanning, China) was introduced to maintain the working oxygen content below 100 ppm.
## 4 Discussion
[53] showed that with an increased concentration of TiO\({}_{2}\) nanoparticles, the germination process of parsley seeds (_Petroselinum crispum_) was stimulated in in vitro cultures. Similarly, [PERSON] et al. [54] found the stimulation of germination of corn seeds (_Zea mays_) caused by the presence of nanoparticles in culture. Therefore, it can be said that nanoparticles can be potential biostimulators in theprocess of plant germination and the applied in the present experiment concentrations of silver nanoparticles had no toxic effect and did not reduce tomato seed germination, and in some cases even stimulated germination. One of the new ideas for human, animal or plant pathogens prevention is the composition of new materials with antibacterial or antifungal properties, with long-acting times and without negative environmental impact. One of the potential solutions are metallic nanoparticles (NPs), especially silver nanoparticles. Their antimicrobial effect results from the cell membrane and DNA damages or interaction with enzymes from thiol groups [55,56]. It should be emphasized that the morphology, stability, type, aggregation, and concentration of nanoparticles are the main properties that determine their toxic effect on biological systems [57]. The results of our study showed that for both non-sterile and sterile seeds in vitro cultures, the lowest value of the infection rate was observed for the concentration of 75 ppm of AgNPs. The worst effect of AgNPs was observed at a concentration of 15 ppm. Therefore, our research confirmed the influence of the concentration of metal nanoparticles on antimicrobial properties, which was described by other authors--higher concentration of NPs increases their antimicrobial properties [58, 59, 60, 61]. However, the concentrations of AgNPs, used in our research, were too low to completely inhibit the growth of microorganisms, because in all experimental trials, the presence of microbes was found. The results of our research showed the growth of Gram-positive bacilli as well as _Alternaria_, _Rhizopus_ and _Aspergillus_ fungi (in all samples). All these microorganisms are common in the natural environment. Although previous biological studies reveal potential inhibition of growth of Gram-positive and Gram-negative bacteria as well as microscopic fungi as a result of interactions of NPs [62, 63, 64, 65, 66], the nanoctixity towards Gram-positive cells was significantly less, possibly due to the presence of a thicker peptidoglycan layer [67]. Antifungal properties of AgNPs usually concern the study of various types of yeast, there are merely a few studies concerning the interaction of molds with AgNPs. The research by [PERSON] et al. [68] showed that the sensitivity of mold fungi to AgNPs depends on the mould species. The study also showed that, for example, concentration 2.5 mg/L of AgNPs was sufficient for the inhibition of _Aureobasidium pullulans_, but 10 times higher concentration was needed for the _Aspergillus niger_ growth inhibition [69].
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 4 Simulation Results Evaluation
### Simulation Result
Epidemic uses a flooding mechanism to forward messages, so when message lifetimes are extended, the number of message copies increases, and they reach the destination node faster, outperforming Prophet, which relies solely on encounter information for decision-making. Both MDDI and SDDQ apply reward values that consider average delay when calculating Q-values, effectively reducing network delay. However, when message lifetimes are shorter, MDDI and DDNR perform better because they both use dynamic attributes of nodes and messages to make decisions, resulting in less time for messages to reach their destination nodes. As message lifetimes increase, MDDI, through effective learning of the network environment, can find more accurate next hops, resulting in the lowest average delay. In Figure 8c, when message generation intervals are short, there are many messages in the network due to node congestion, resulting in higher average delays. However, when message generation intervals are small, both DDNR and MDDI maintain good performance, as they consider average delay when deciding whether to forward messages. Thus, they can maintain lower average delays even with a high number of messages. As the generation interval increases and the number of messages decreases, with sufficient buffer space,  
Figure 8: Performance of protocols with different Generation interval. SDDQ and MDDI can fully learn the network environment, while DDNR excels due to its dynamic decision-making capabilities for selecting relay nodes, resulting in the best delay performance.
###### Abstract  
Aiming at the problem that the accuracy of flight parameters calculated by the traditional flush air data sensing (FADS) and inertial navigation system (INS) complementary filter cannot meet the fine real-time control of the aircraft, a data fusion algorithm based on distributed inertial network and FADS is proposed. This method converts the inertial navigation parameters calculated by the distributed inertial network into flight parameters, and using the least squares fitting theory, the flight parameters are obtained from the pressure data measured by the FADS pressure hole. Then, by adjusting the filter constant of the complementary filtering algorithm, the flight parameters calculated by the inertial network are fused with the flight parameters calculated by the air data sensing to obtain high-precision flight parameters. Finally, the simulation results show that the proposed filtering algorithm can keep the flight parameter estimation error less than 0.01 degrees throughout the flight phase. Compared with the traditional complementary filter, the estimation error of the proposed algorithm is smaller.  
data fusion; inertial network; flush air data sensing Article
## 2 Materials and Methods
### Synthesis of Nd\({}^{3+}\)- and Yb\({}^{3+}\)-Doped NaYF\({}_{4}\)  
NaYF\({}_{4}\) co-doped with Nd\({}^{3+}\) and Yb\({}^{3+}\) (NaYF\({}_{4}\): Nd\({}^{3+}\), Yb\({}^{3+}\)) was synthesized using the standard co-precipitation method [27]. Y(NO\({}_{3}\))\({}_{3}\)-6H\({}_{2}\)O (6.0 mmol), Nd(NO\({}_{3}\))\({}_{3}\)-6H\({}_{2}\)O (3.0 mmol), and Yb(NO\({}_{3}\))\({}_{3}\)-6H\({}_{2}\)O (1.0 mmol) were dissolved in 10 mL of distilled water. The mixture solution was dropped into 40 mL of an aqueous solution of NaF (60 mmol) and stirred for 1 h at 75 \({}^{\circ}\)C. After stirring, the precipitate was collected by centrifugation (20,000\(\times\)\(g\), 10 min, \(\times\)3). The samples were then dried at 80 \({}^{\circ}\)C for 24 h. Subsequently, the samples were treated with NH\({}_{4}\)F (800 mg) for 1 h at 550 \({}^{\circ}\)C to yield NaYF\({}_{4}\) co-doped with 30 mol\(\%\) Nd\({}^{3+}\) and 10 mol\(\%\) Yb\({}^{3+}\).
## 3 Results and Discussion
### Variations of Agro-Morphological Traits
#### 3.1.2 Quantitative Agronomical Traits
TKW ranging from 30.5 to 58.8 g, and from 23.2 to 52.0 g were also reported, each being a much lower range compared to our findings [21; 22]. In general, these reports along with our results indicate the wide-ranging values of yield and growth-related traits in barley. Once again, differences in environmental, genetic, and post-harvest handling processes could cause such variations [10; 28; 42; 43]. The performance of the barley accessions was also compared to that of the control cultivars. Among the control cultivars, Betaone simultaneously displayed the longest CL and AL, while Heuknuri had the longest SL. In contrast, Saessalchalbori simultaneously displayed the shortest CL and SL and the lowest GPP and TGW, while Saessalbori had the shortest AL and the highest GPP. Heukdahyang displayed the highest GPP. Compared to all the control cultivars, the majority of the accessions (\(n=223\)) had higher CL (\(>\)72 cm). In particular, 11 accessions were found to have a much longer CL (\(>\)100 cm). Likewise, 225 accessions displayed higher SL than all the control cultivars (\(>\)5.9 cm), out of which 25 had much higher SL (\(>\)10 cm). Interestingly, four accessions, including GRA2256 from Tajikistan, GRA1015 from Ukraine, GRA2621 from Greece, and PI 573706 from Georgia, simultaneously displayed long CL and SL (Table S2). On the other hand, 86 accessions had longer AL than all the control cultivars (\(>\)13.7 mm), out of which four (HVS 355, HV S366, and PI 466252 from Lebanon, and CI 6222 from Turkey) had much longer awns (\(>\)20 mm). Similarly, 43 accessions displayed a higher GPP than all control cultivars, out of which 10 had a much larger GPP (\(>\)60). In terms of TGW, a large population of barley accessions (\(n=178\)) outperformed all the cultivars. Out of these, 12 accessions were found to have a much higher TGW (\(\geq\)62.00 g). Apart from their relation to yield, GPP, TGW, and cum and spike-related characters are important traits that determine the various favorable characteristics of barley genotypes. For instance, shorter culms are found to have increased support and reduced lodging [47]. Likewise, awns are also found to improve seed size and TGW, although they reduce GPP and total yield [48; 49]. Therefore, those accessions with favorable properties and superior performances to the cultivars could be utilized in different ways to reduce lodging, maximize grain size, and improve yield (Table S4). Moreover, the identification of genes that are involved in the regulation of grain size, grain weight, and grain number is a research focus, and hence, the observation of wide-ranging values in this study could be a great input for future genomic studies [32].
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 6 Discussion of Findings and Consideration for Processing Approaches
### Common Time-Scale Analysis Implementation Techniques for Batched Schemes
This method can however not be applied to compensate range migration of processed high instantaneous time-bandwidth product waveforms.
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees
#### 7.2.1 Session Key Confidentiality  
The shared session key is derived independently by both parties as  
\[K_{\mathcal{A}}=[\kappa_{\mathcal{A}}]\Psi_{1}=[\kappa_{\mathcal{A}}][\kappa_ {B}]\Phi,\qquad K_{B}=[\kappa_{B}]\Gamma_{1}=[\kappa_{B}][\kappa_{\mathcal{A}}]\Phi\]  
Under the ECDLP assumption, and provided that \(\kappa_{\mathcal{A}}\) and \(\kappa_{B}\) remain secret, the adversary cannot compute \(K_{\mathcal{A}}=K_{B}\), even if \(\Phi\), \(\Gamma_{1}\), and \(\Psi_{1}\) are transmitted in clear.