esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
###### Abstract  
The aim of this study was to evaluate the impact of microwave treatment on the physical properties and bioactive compound content of acorn flour and its subsequent effects on the quality of muffins. Acorn flour, both dry and hydrated (flour-to-water mixture = 1:1, \(w/v\)), was subjected to microwave processing at varying power levels (925, 1295, and 1850 W) and exposure times (0.5, 1.0, 1.5, 2.0, and 3.0 min). The treated flour was incorporated into composite wheat-acorn muffins, which were analyzed for key physical attributes, including volume, porosity, crumb firmness, and color. In addition, the chemical composition of the muffins was assessed, with a particular focus on antioxidant activity and the content of bioactive compounds such as total free phenolics, flavonoids, and tannins. The results indicated that microwave treatment significantly influenced the antioxidant potential and technological properties of acorn flour-based muffins. Microwave treatment at 925 W for 3 min or 1295 W for 1.5 min under moist conditions resulted in muffins with enhanced antioxidant capacity (e.g., an increase in total free phenolics by up to 61.4% and flavonoids by up to 135.9%, and a reduction in tannin content by up to 38.2%), as well as improved volume and sensory acceptance. However, high-power treatment (1850 W) negatively affected muffin volume, porosity, and firmness. These findings emphasize the effectiveness of controlled microwave processing in improving both the nutritional and technological qualities of bakery products enriched with acorn flour.  
acorn; microwave treatment; color; hardness; phenolic compounds; antioxidant capacity; tannins; organoleptic characteristics 2025, 15, 6204
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 5 Simulation Results
### Channel Estimation Performance  
The channel estimation performance can mainly be evaluated in terms of channel estimation normalized MSE (NMSE) and BER performance.  
In Figure 7, we compare the NMSE performance of SPBA and EP methods. The NMSE is defined as  
\[\text{NMSE}=\mathbb{E}\Big{(}\big{\|}\mathbf{\hat{f}}-\mathbf{h}\big{\|}_{2}^{2 }\Big{)}/\mathbb{E}\Big{(}\big{\|}\mathbf{h}\big{\|}_{2}^{2}\Big{)}, \tag{52}\]  
where \(\mathbf{\hat{f}}\) is the vector form of the MMSE channel estimation result \(\bar{h}_{q}\). Additionally, for the SPBA method, the theoretical NMSE is given for reference. We set the power ratio to \(\beta_{\text{opt}}\) for the SPBA method and the pilot SNR to \(\text{SNR}_{p}=40\) dB for the EP method. From Figure 7, it can be seen that the simulated NMSE curve _SPBA(Simu)_ is basically consistent with the theoretical NMSE curve _SPBA(Theo)_, which confirms our analyses in Section 3. Regarding the comparison between the curves _EP(Simu)_ and _SPBA(Simu)_, as the SNR increases, the NMSE performance of the proposed SPBA method approaches that of the EP method. The  
Figure 5: Effects of the optimal power ratio. (**a**) The effect of the optimal power ratio on the SINR. (**b**) The effect of the optimal power ratio on the BER with SNR = 10 dB.  
Figure 6: SE performance versus SNR.  
reason why the NMSE performance of the SPBA method is slightly inferior to that of the EP method can be explained as follows. As the SNR increases, the noise interference \(v_{g,d}\), in the SPBA method is reduced, and the main interference term becomes \(v_{g,d}\), which is the interference between superimposed PBA and transmitted symbols. In the EP method, however, the protected symbols eliminate this interference, at a high cost in terms of SE.  
In Figure 8, we show the BER performance based on the MP detector in [11]. We assume that each transmitted OTFS symbol has a power ratio of \(\beta_{\text{opt}}\) for the SPBA method and \(\text{SNR}_{p}=40\) dB for the EP method. Furthermore, the BER performance of the SPBA method is given with the threshold estimator in Section 3.1 and with the ideal MMSE estimator in Section 3.4, corresponding to the curves labeled _SPBA(Thr)_ and _SPBA(MMSE)_, respectively. It can be seen in Figure 8 that with prior channel information, the MMSE estimator shows better performance than the threshold estimator. Compared with that of the EP method, due to the interference term \(v_{g,d}\) mentioned above, the BER performance of the SPBA method is close but slightly inferior, with a loss of approximately 0.75 dB for the threshold estimator and 0.2 dB for the ideal MMSE estimator. However, as mentioned before, the proposed SPBA method has a significantly higher SE than the EP method.  
Figure 8: BER performance versus SNR.  
Figure 7: Channel estimation NMSE versus SNR.
# Chalcogenide Thin Films--Holographic Media for Augmented Reality Devices
## 4 Experimental Results and Discussion
### Recordings on Common ChG Samples  
Four samples of common As\({}_{2}\)S\({}_{3}\) compositions were used for primary test recordings. These samples had strong absorption in blue spectral range, so the test recordings were made in order to provide red FOV. The samples had the following parameters: substrate refractive index, 1.7; ChG film refractive index, 2.2; ChG film thickness, 1 \(\upmu\)m.  
Holographic recording was made by standard two-beams recording scheme with 457-nm DPSS laser source, and linear polarized 650-nm laser diode was used to control the diffraction efficiency while recording (Figure 5). Recording beams power density and polarization as well as ChG sample orientation could be changed throughout experiments.  
Volume and surface non-slanted gratings were recorded by linear and circular polarized recording beams correspondingly, while the recording laser power density was 20 (for volume) and 120 mW/cm\({}^{2}\) (for surface). Beams polarization was chosen in accordance with [20] to provide high grating DE. The period of the recorded gratings was set to 430 nm to transmit the red part of the FOV through the periscope system symmetrically. The resulted diffraction efficiency was 1% for olume non-slanted gr  
Figure 5: Two-beams holographic recording scheme in ChG. L, DPSS laser; TS, telescope; BS, beam splitter; M, mirror; PP1, PP2, quarter-wave plates; ChG, chalcogenide sample; D, laser diode; DB, diffracted beam; PM, power meter.  
time \(t_{\text{exp}}\) = 3 min and 10% for surface grating with the recording time \(t_{\text{exp}}\) = 12 h. Photo of the surface grating is shown in Figure 6.  
To demonstrate periscope mode of holograms operation in waveguide devices (incoupling HOE-TIR-transmission through the waveguide-out-coupling HOE), the recording of in-coupling/out-coupling HOEs system was carried out. The in-coupling surface relief hologram was recorded by circular polarized beams with total power density of 120 mW/cm\({}^{2}\) and 12-h exposition. Its diffraction efficiency reached 10%. The out-coupling volume hologram with diffraction efficiency of 1% was recorded with linear polarized light of the same power density during 30 s. The red image provided through the periscope system is presented in Figure 7. From Figure 7, it is seen that the image has good resolution and brightness. However, it is to be noted that only red and green colors can be transmitted through CHG glass-based holograms but not blue.
# Optimization Parameters for PLA Through Additive Manufacturing: Taking Mixed Shapes as an Example
## 3 Results and Discussion
### Taguchi Method
#### 3.1.1 Taguchi Method of Dimensional Accuracy  
Five control factors and four levels influencing the quality characteristics are selected. This study selected important parameters as the setting basis based on [9; 10; 11; 12]. findings. These are nozzle gap height (mm), Extruder temperature (\({}^{\circ}\)C), bed temperature (\({}^{\circ}\)C), Infill print head velocity (mm/s), and retraction velocity (mm/s). The processing parameters are determined based on material's characteristics, as shown in Table 2. The printing material used in this study is PLA. After the model is printed, measurements are taken for specific dimensions, and the values are converted into scores based on the measurement deviations. The scoring method for quality characteristics is detailed in Table 3. The recorded scores are used to calculate the S/N ratio for each group using Formula (2), as shown in Table 4. Five measurements were conducted, and the standard error of dimensional accuracy of fraction is \(\pm\)0.0003.  
Figure 5: Universal testing machine.  
From Table 5, It is possible to obtain a response table showing how various factors affect the dimensional accuracy of additive manufacturing. Figure 6 displays the response graph for each factor's impact on the dimensional accuracy of additive manufacturing. In terms of quality characteristics, a larger S/N ratio indicates better quality characteristics. According to Figure 6 and Table 5, the optimal factor level combination is A1 B1 C4 D2 E3, with a layer height of 0.1 mm, nozzle temperature of 185 \({}^{\circ}\)C, print platform temperature of 75 \({}^{\circ}\)C, print speed of 35 mm/s, and retraction speed of 65 mm/s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Exp. & A. & B. & C. & D. & E. & **Dimensional Accuracy of Fraction** & **S/N Ratio** \\ \hline
1 & 1 & 1 & 1 & 1 & 1 & 3.025 & 9.615 \\ \hline
2 & 1 & 2 & 2 & 2 & 2 & 3.050 & 9.686 \\ \hline
3 & 1 & 3 & 3 & 3 & 3.100 & 9.827 \\ \hline
4 & 1 & 4 & 4 & 4 & 4 & 3.550 & 11.005 \\ \hline
5 & 2 & 1 & 2 & 3 & 4 & 2.525 & 8.045 \\ \hline
6 & 2 & 2 & 1 & 4 & 3 & 2.525 & 8.045 \\ \hline
7 & 2 & 3 & 4 & 1 & 2 & 2.475 & 7.872 \\ \hline
8 & 2 & 4 & 3 & 2 & 1 & 2.600 & 8.300 \\ \hline
9 & 3 & 1 & 3 & 4 & 2 & 2.725 & 8.707 \\ \hline
10 & 3 & 2 & 4 & 3 & 1 & 2.700 & 8.627 \\ \hline
11 & 3 & 3 & 1 & 2 & 4 & 2.575 & 8.216 \\ \hline
12 & 3 & 4 & 2 & 1 & 3 & 2.775 & 8.865 \\ \hline
13 & 4 & 1 & 4 & 2 & 3 & 2.025 & 6.129 \\ \hline
14 & 4 & 2 & 3 & 1 & 4 & 1.775 & 4.984 \\ \hline
15 & 4 & 3 & 2 & 4 & 1 & 1.350 & 2.607 \\ \hline
16 & 4 &Through Table 5, we can see the effect of each factor on dimensional accuracy. The greater the factor effect, the larger the impact on the experimental results. From the table, it can be observed that Factor A (layer height) has a significant impact on the dimensional accuracy of additive manufacturing, while Factor D (printing speed) has a relatively smaller effect on dimensional accuracy.
## 2 Pointing Strategy and Model Establishment
### Disturbance Analysis and Descriptions  
#### 2.3.1 Reaction Torque from the Spacecraft on the Telescope Pointing Mechanism  
When the spacecraft adjusts its attitude in orbit, the forces and torques applied by the micro-thrusters will have a certain impact on the telescope pointing mechanism. The influencing torque \(\mathbf{M}_{\mathrm{T}}^{\mathrm{SO}}\) can be expressed as:  
\[\mathbf{M}_{\mathrm{T}}^{\mathrm{SO}}=T_{\mathrm{S}}^{\mathrm{O}j}I_{t}\dot{ \omega}_{\mathrm{SI}}, \tag{7}\]  
where \(\omega_{\mathrm{SI}}\) represents the spacecraft's inertial angular velocity in the SRF coordinate system, which can be obtained through dynamic modeling of the spacecraft. \(T_{S}^{\mathrm{O}j}\) is the transformation matrix from the spacecraft to the pointing mechanism, and according to coordinate definitions, the matrix is a rotation matrix about the \(\mathbf{o}_{zj}\) axis.  
The attitude dynamics equations of a spacecraft:  
\[\dot{\omega}_{\mathrm{SI}}=-J_{\mathrm{S}}^{-1}\omega_{\mathrm{SI }}\times J_{\mathrm{S}}\omega_{\mathrm{SI}}+J_{\mathrm{S}}^{-1}(\mathbf{M}_{ \mathrm{T}}^{\mathrm{S}}+\mathbf{D}_{\mathrm{T}}^{\mathrm{S}}+\mathbf{D}_{ \mathrm{S}\mathrm{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{\mathit{ \mathit{\mathit{\mathit{\mathit{  
where \(\mathbf{F}_{\odot\textit{press}}\) is the solar radiation pressure, and \(l\) is the distance between the center of pressure and the center of mass of the illuminated surface. The solar radiation pressure torque model [21] used in this paper is depicted in Figure 6a. Additionally, in Equation (8), the spacecraft is subject to the thrust noise from micro-propulsion thrusters [21], and its noise model is illustrated in Figure 6b.
## 5 Structures of Alternative \(\sigma\) Factors  
Bacterial RNAP uses a group I \(\sigma\) factor (\(\sigma^{70}\) in _E. coli_ and \(\sigma^{\rm A}\) in other bacteria) for transcribing housekeeping genes required for log-phase growth. Other classes of \(\sigma\) factors, including the group 2 \(\sigma\) factors (\(\sigma^{\rm S}\), \(\sigma^{\rm H}\), and \(\sigma^{\rm F}\) in _E. coli_), the extractoplasmic function (ECF) \(\sigma\) factors (\(\sigma^{\rm E}\) and Fecl in _E. coli_), and \(\sigma^{\rm N}\) (also known as \(\sigma^{54}\)), direct RNAP to genes for induction of stress responses, flagella synthesis, and spore formation in spore-forming bacteria, such as _Bacillus_. Structures of these alternative \(\sigma\) factors were determined as stable domains (Table 1) and provided insights into the mechanism of promoter DNA recognition. Structures of some alternative \(\sigma\) factors have been determined as \(\sigma\)/anti-\(\sigma\) factor complexes, and these structures will be described below (Table 2 and Figure 2b). To date, there has been no high-resolution structure that supports bacterial holoenzyme having any alternative \(\sigma\) factor.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Structure** & **PDB** & **References** & **Source** \\ \hline \(\sigma\)/anti-\(\sigma\) complex & & & \\ \hline \(\sigma^{\rm F}\) domains2, 3, 4/FlgM & 1 SC5\({}^{\rm X}\), 1 RP3\({}^{\rm X}\) & [54] & D, b \\ \(\sigma^{\rm E}\) domains2, 4/ChrR & 2Z2S\({}^{\rm X}\), 2Q1Z\({}^{\rm X}\) & [55] & J, c \\ \(\sigma^{\rm E}\) domains2, 4/RseA & 1 OR7\({}^{\rm X}\) & [56] & A, c \\ \(\sigma^{\rm K}\) domains2, 4/RskA & 4 NQW\({}^{\rm X}\) & [57] & I, c \\ \(\sigma^{\rm F}\) domain3/SpoIIAB & 1 LOO\({}^{\rm X}\) & [58] & C, b \\ \(\sigma^{\rm 70}\) domain4/Rsd & 2P7V\({}^{\rm X}\) & [59] & A, a \\ \(\sigma^{\rm D}\) domain4/RsdA & 3 VEP\({}^{\rm X}\) & [24] & I, c \\ \(\sigma^{\rm L}\) domain4/RslA & 3 HUG\({}^{\rm X}\) & [60] & I, c \\ \hline Transcription factor complex & & & \\ \hline \(\alpha\)CTD–CAP–DNA & 1 LB2\({}^{\rm X}\), 3N4M\({}^{\rm X}\) & [61] & A \\ \(\alpha\)CTD–Spx & 1Z3E\({}^{\rm X}\), 3 GFK\({}^{\rm X}\), 3 IHQ\({}^{\rm X}\) & [62–64] & C \\ \(\alpha\)CTD–NusA (AR2) & 2 JZB\({}^{\rm N}\) & none & H, A \\ \(\beta\) subunit 1/2 domains–CarD & 4 KBM\({}^{\rm X}\) & [12] & I \\ \(\beta\) subunit 1 domain–TRCF & 3 MLQ\({}^{\rm X}\) & [65] & B \\ \(\sigma^{\rm A}\) domain4–\(\lambda\)cl–DNA & 1 RIO\({}^{\rm X}\) & [66] & B \\ \(\sigma^{\rm 70}\) domain4–PhoB–\(\beta\) flap–DNA & 3T72\({}^{\rm X}\) & [67] & A \\ Holoenzyme–CAP–DNA & 3 IYD\({}^{\rm C}\) & [68] & A \\ Core enzyme–GreA/Gfh1 & 4 WQT\({}^{\rm X}\) & [45] & B \\ Elongation complex–Gfh1 & 3 AOH\({}^{\rm X}\), 3 AOI\({}^{\rm X}\) & [69] & B \\ Elongation complex –RapA & 4S20\({}^{\rm X}\) & [53] & A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural information on bacterial RNAP and transcription factor complexes.
## 2 Materials and Methods
### Determination of Cu(II) and Zn(II) Adsorption Efficiency  
Stock solutions (500 mM) of Cu\({}^{2+}\) and Zn\({}^{2+}\) were prepared by dissolving CuSO\({}_{4}\)5H\({}_{2}\)O and ZnSO\({}_{4}\)7H\({}_{2}\)O, respectively, in Milli Q water, and passed through 0.45 \(\upmu\)m pre-sterile syringe filters (Millipore filter paper). A stock solution of EPS (1000 mg L\({}^{-1}\)) was also prepared using milli Q water. The batch adsorption system consisted of a total 30 mL of EPS and heavy metal solutions both at 25 mg L\({}^{-1}\) concentrations. The pH of the solutions was adjusted using 1 N HCl and 1 N NaOH at a range of 5-8 keeping the concentrations of EPS and heavy metal ions constant. All experiments were conducted in triplicates, and a setup without EPS was treated as control. The suspensions were agitated on a shaker at 25 \(\pm\) 2 \({}^{\circ}\)C at 150 rpm for 24 h. An aliquot of 6 mL from the batch suspensions was withdrawn, to which equal volume of ice-cold ethanol was added, and the suspension was centrifuged at 1000\(\times\)\(g\) for 10 min [27]. The supernatant obtained was filtered through 0.45 \(\upmu\)m syringe filter and further acid digested at 100 \({}^{\circ}\)C on hot plate with 1 mL concentrated HCl in order to remove any organic impurities which would otherwise interfere in the measurement of metal ions. Then the amount of residual metal ions remaining in the digested supernatants, both in the EPS treated and EPS free control, was determined using atomic absorption spectroscopy (AAS) (AAnalyst 200, PerkinElmer,Waltham, MA, USA). A calibration was performed using individual standard reference solutions (Fluka Analytical, Buchs St. Gallen, Switzerland) for ensuring the quality control. The standard curve has been provided in (Supplementary information: Figure S2). The concentration range for both the metal ions taken was from 0-2 mg L\({}^{-1}\). The correlation coefficients (R\({}^{2}\)) for Cu(II) and Zn(II) were 0.992 and 0.991 respectively.
## 5 Conclusions and Outlook  
* In the quasistatic experiments, the elevated temperature (60 \({}^{\circ}\)C) leads to a small decrease in the sustained maximum force of the single material without affecting the sustained maximum force of the hybrid specimen significantly. Nonetheless, a difference in occurring damage is observed for the hybrid. While there are abrupt damage events at 23 \({}^{\circ}\)C, there is a smoother damage progression at 60 \({}^{\circ}\)C, which goes along with a significant increase in displacement at failure for the elevated temperature.  
* In the dynamic experiments, the initial dynamical stiffness of all specimen types decreases at elevated temperatures. The sustained cycles of PPA-GF30 and hybrid specimens decrease as well. The elevated temperature worsens the overall fatigue performance of the hybrid significantly.
* The used nondestructive testing techniques support the observations of the mechanical testing and give further information about the underlying damage mechanisms. The abrupt damage events can also be seen and localized in thermography. By using CT, occurring damage in the metal inlay at lower temperatures can be observed.
* The investigated hybrid specimen is a complex system containing of three single materials with two interfaces and interlockings. A temperature change affecting one part of the system has an influence on other parts and change the damage mechanism in a hard-to-calculate way.  
Further investigations including the role of the interface, hybrid samples of different geometries, and more temperature levels must be analysed to obtain a deeper look. The question of whether the transition of the damage mechanism happens at a specific temperature or over a wider temperature range is also not answered yet. Moreover, the influence of further external influences, i.e., humidity should be monitored as well. A shortcoming in this work is the missing observation of the isolated interfaces in a simpler geometry (i.e., single lap joints). Such an investigation could give a deeper understanding of what exactly happens at the interfaces.  
Investigation, [PERSON], [PERSON]; Writing--original draft, [PERSON], [PERSON]; Writing--review & editing, H.-G.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by DFG (Deutsche Forschungsgemeinschaft) project number 237091934.  
We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) and Saarland University within the funding programme Open Access Publishing. The authors kindly thank their research associates from WBK institute at KIT, LKT at TU Dortmund, as well as the German research committee for the project funding.  
The authors declare no conflict of interest.
## 4 Awareness-Prediction Joint Compensation Methods
The awareness latency is small in the case of satellites in the network that are relatively close to the gateway station. The real-time performance of the awareness data at this time is better, and the error between the actual onboard resource status and the awareness data is smaller, which means the compensation weight of the awareness data is larger. For instance, when the resource state undergoes a sudden peak shift due to services, resulting in a significant prediction error, the accuracy of the awareness data be-comes more pronounced. Consequently, the proportion of awareness data dynamically increases. There are also differences in the speed and magnitude of changes in the onboard bandwidth, computation, and storage resources in the same node, leading to variation in the prediction accuracy of the LSTM algorithm for three types of resources. Regarding re-source types exhibiting poor prediction accuracy, the impact of prediction results on compensation data can be mitigated through the application of Equation (18). For example, in the scenario where the resource state changes abruptly, the prediction result error is relatively large. Although there is a delay in the awareness data, the error may be relatively small. Therefore, the impact of the awareness data can be improved by adjusting the compensation weight. The compensation effect of the prediction results on data is strengthened for resource types with better prediction results. For instance, the compensation weight of the prediction results can be elevated to bolster the compensating impact of pre-diction results on the data in scenarios with significant delays and substantial awareness data error.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna
### Mobility Demand Forecast  
We use two different mobility demand forecasts in our computational experiments. The first is based on an Austrian mobility behavior survey (OEU), whereas the second is based on live taxi traffic data within Vienna. Each demand forecast consists of a set of trips that we assume the customers of ELOOP would like to make. A trip consists of an origin sub-district in which it starts, a destination sub-district in which it ends, and a duration.  
First, we analyzed historic trips made by customers of ELOOP within their current area of operation between July and October 2019. While these data could not be used for the planning of expansion strategies (since they do not contain any data outside the current AoO and the time was pre-COVID and therefore with other mobility behavior), it proved useful during data analysis as a benchmark to compare the other two data sets against. We refer to this as the ELOOP data set.