esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cantilever Beam  
For the cantilever beam depicted in Figure 9, design areas of 30 \(\times\) 20 and 90 \(\times\) 60 were employed, akin to the previous example. A maximum of 300 iterations were set for all cases. In this solution, only the sensitivity filter was employed, as it consistently delivered  
Figure 8: RBTO design of MBB beam using interior-point and AM filter; overhang regions exceeding 45\({}^{\circ}\) are marked with red lines.  
stable results. The penalty factor was set at 3, the minimum radius (\(r_{min}\)) at 1.2, the volume ratio at 0.5, and the target beta value at 3.  
Figure 10 presents the results obtained from DTO, RBTO, and RBTO with the AM filter. The optimal solutions with dimensions of 90 \(\times\) 60 were produced using a 3D printer and scrutinized. In alignment with the previous example, the west direction was chosen as the printing orientation.  
Figure 10: TO design of cantilever beam using interior-point and AM filter with sensitivity filter; overhang regions exceeding 45\({}^{\circ}\) are marked with red lines.  
Figure 9: Cantilever beam boundary conditions.  
In the given example concerning the cantilever beam problem, a comparative analysis was conducted across various optimization algorithms and structure sizes. Specifically, DTO, RBTO, and AM filter with RBTO algorithms were evaluated with respect to iteration counts, objective function values, volume fractions, and solution durations for two distinct structure sizes. From the examination of the results, it was discerned that the AM-RBTO/W algorithm achieved the most favorable objective function value, thereby rendering the best structural performance with a volume fraction of 52%. While the DTO algorithm yielded quicker results compared to RBTO, the RBTO algorithm demanded the most time to converge, averaging 15.6 s. Furthermore, the implementation of the AM filter, which eliminated the need for support structures, facilitated a reduction in printing time, diminishing it to as low as 21 min.  
Upon inspection of the geometric structures derived from the solution, it was observed that alterations in material distributions occurred when the mesh size was increased. This phenomenon, known as mesh dependency, remained unaffected when the AM filter was employed.
# The Design of a High-Intensity Deuteron Radio Frequency Quadrupole Accelerator
## 5 Electromagnetic (EM) Analysis of RFQ
### Power Loss  
With the RF structure in mind, the water-cooling design and thermal response of the cavity were analyzed to ensure stable operation in CW mode at high power levels. Initially, we investigated the power loss distribution within the RFQ cavity to determine the best water-cooling scheme. As shown in the heat density map in Figure 22, a strong magnetic field was present in the undercut sections, leading to large power losses. The waterway should thus be constructed as close to the undercut as possible.  
The power-loss values are summarized in Table 8. We calculated a cavity power consumption of 60.86 kW, beam power consumption of 20.6 kW, total power consumption of 81.46 kW, and Q value of 1.54 \(\times\) 10\({}^{4}\). As the surface heat loss mostly concentrates around the vane and wall areas of the cavity, the water-cooling system should be designed to accommodate these areas. At the exit of the undercut, the maximum surface power density of the cavity was 14.6 W/cm\({}^{2}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mode** & **Frequency (MHz)** \\ \hline TE210 & 162.38 \\ TE110 & 158.34/158.40 \\ TE111 & 165.67 \\ TE211 & 167.28 \\ TE212 & 181.09 \\ TE213 & 202.06 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mode frequencies.  
Figure 21: Magnetic field (H field) distribution of 2D RFQ section: quadrupole modes (**left**) and dipole modes (**right**).  
Figure 22: Power-loss distribution on CW mode.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 2 Geolocating GNSS Reflections from Land
### Summary of Geolocation Estimation Criteria  
The three geolocation parameter checks described above are computed over the local surface grid around the initial estimate of the specular reflection point. If each criteria check is within limits for a given grid point, it is likely that this location is contributing to the received GNSS-R observation. The limits are determined by the geolocation requirements for a given application. Table 1 summarizes the three criteria and the limits used during the analysis that follows.  
Figure 1: (**left**) Surface terrain view of point on local surface grid showing incident and reflection angles with respect to the surface normal. (**right**) Overhead view with incident and reflected vectors projected to a local two dimensional surface with forward scattering azimuth angles shown.  
The limits in Table 1 were selected to achieve an approximate geolocation accuracy of 1 km or less (although results will vary depending on the amplitude of the terrain variations). The algorithm also assigns a confidence level to each geolocation, as specified in Table 2 based on whether any points can be identified within the local surface grid that satisfy the three conditions and based on the DDM SNR.
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods
### Macromolecular Composition  
The macromolecular composition of the biomass was determined using a combination of analytic data of the samples in this work and literature values. Lipid content was calculated based on measurements of CDW and lipid concentrations. Total protein content was calculated from nitrogen content using a correlation factor of 4.4 [26]. The ash content of _M. salina_ biomass cultivated in a nutrient-replete medium under the same conditions as in this work was measured to be 10.0% in a previous study [12]. The remaining portion of the biomass is assumed to be carbohydrates.  
The total lipid concentration of a sample was determined using the sulfo-phospho-vanillin (SPV) assay described by [PERSON] et al. [27]. To prepare the phospho-vanillin reagent, 0.3 g of vanillin was dissolved in 50 mL of 10% (\(v/v\)) ethanol in water mixture and mixed with 200 mL of phosphoric acid. A total of 50 \(\upmu\)L from each sample was incubated in 1 mL sulfuric acid (98% (\(v/v\))) for 10 min at 90 \({}^{\circ}\)C in glass vials and cooled at -4 \({}^{\circ}\)C for 5 min. Then, 2.5 mL phospho-vanillin reagent was added into each vial, and the mixture was incubated in a thermomixer at 37 \({}^{\circ}\)C and 900 rpm for 15 min (Thermomixer basic, CellMedia, Elsteraue, Germany). After incubation, the absorption at 530 nm wavelength against air was measured with a UV-vis spectrophotometer (Genesys 10S UV-VIS, Thermo Fisher Scientific Inc., Waltham, MA, USA). Blank values were measured separately and subtracted from the measured absorption values manually. Each sample was measured in triplicate. An external standard of rapeseed oil was used to create a correlation line between absorption and total lipid concentration. For this, different amounts of an oil in hexane solution with a known concentration (1 g L\({}^{-1}\)) were transferred into glass vials. After the hexane was evaporated, 50 \(\upmu\)L of demineralized water (same as the sample volume) was added, and the standard assay procedure described above was applied. The resulting correlation line covered a range of 0.0-2.0 g L\({}^{-1}\) lipids. Cellular lipid content was calculated by dividing the total lipid concentration by CDW concentration.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Existing Energy-Saving Techniques
#### 2.3.2 Software Energy-Saving Techniques  
* _Optimized operating systems_. IoT devices often utilize specifically designed lightweight operating systems (e.g., Contiki and TinyOS) to enhance the operation efficiency and reduce system overhead [28], satisfying the needs of resource-constrained environments. For instance, an optimized operating system can intelligently switch an IoT device between sleep and active modes to reduce the power consumption when it sits in an idle state for a certain period, thereby effectively improving the energy efficiency of the device.
* _Dynamic Network Configuration_. Dynamic network configuration can improve the energy efficiency of IoT devices through communication optimization [29]. It allows IoT devices to make adjustments according to changing network conditions, such as the signal strength, network load, and interference. Such adaptability enables IoT devices to operate with minimal energy consumption under various conditions. For example, reducing the frequency of data transmission when a network signal is weak can reduce the power consumption. This example signifies that a dynamic network configuration not only lowers the energy consumption but also mitigates network congestion.
## 3 Results and Discussion
### Analysis of pH and Redox Potential
In the case of control treatment C, a statistically significant increase of redox potential was observed by 18.1 mV after four weeks of storage and after eight weeks of storage it went down by 6.8 mV. The redox potential value in the C treatment (246.5 mV) after eight weeks of storage was significantly higher from the value after production (235.2 mV). The redox potential in the M treatment increased significantly during eight weeks of cold storage from 238.3 to 263.0 mV. The most stable redox potential was observed in the SL treatment, in which no significant redox value changes were observed during eight weeks of storage (Table 1). Lower redox potential value was reported in the C and M treatments after production, which could be the result of lower residual nitrite that is strongly anti-oxidative [28]. Along with a lower NaNO2 level in the product, higher redox potential was observed (Table 1, Figure 1a). Figure 1: Means (bars) and SD (line segments) of residual nitrites (**a**) and nitrates (**b**) in model meat products during storage. a–c: means with different letters differ significantly among treatments (\(p<0.05\)); A–C: means with different letters differ significantly among timepoints (\(p<0.05\)). C—control treatment with NaNO2 in an amount of 100 mg/kg; M—treatment with NaNO2 in an amount of 50 mg/kg; SL—treatment with NaNO2 in an amount of 50 mg/kg and \(L\)_._fermentum_ S8 at about 10\({}^{7}\) cfu/g, and _S.__anrossus_ ATCC 51365 at about 10\({}^{7}\) cfu/g. LAB are relatively anaerobic bacteria. In unfavourable conditions, LAB may use oxygen as a terminal electron acceptor. One of the products occurring due to oxygen reduction by bacteria within the respiratory chain strongly oxidises hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) [29; 30]. H\({}_{2}\)O\({}_{2}\) is toxic also for bacteria itself and is usually quickly secreted into the environment [31]. H\({}_{2}\)O\({}_{2}\) produced by bacteria in raw meat batter could have an effect on higher redox potential value in SL treatment after cooking compared to other variants. Pyruvic acid is an intermediate product of each of the carbohydrates' metabolism processes of _Lactobacillus_ bacteria. This chemical compound plays the role of an electron acceptor, but it also enables keeping oxidation and reduction balance in the cell [32]. This acid could occur in the product after cooking and have an effect on redox potential stability in the SL variant during storage.
## 5 Conclusions  
This work proposes an improved single-probe method for online, in-circuit impedance measurement with an extended frequency range down to 10 kHz. With respect to the approaches available in the literature, the proposed method eliminates the complex composition of the signal amplification and makes use of a simpler test setup. This is achieved by performing a sensitivity analysis, which indicates that the number of turns of the wire (twisted around the BCI probe) should match the same number of windings inside the BCI probe. The proposed setup is verified by discrete RLC components. The analysis shows that the general single-probe setup has a high sensitivity for measuring high impedances. In contrast, low impedance measurement accuracy suffers from low sensitivity, which can be improved by selecting a bulk current injection probe with only one-turn winding or increasing the turn numbers of the wire twisted on the probe.  
The enhanced single-probe technique is then utilized to extract the equivalent CM/DM circuits for a motor drive system by measuring its online impedances. Finally, using the network theory and the knowledge of equivalent modal circuits, in-circuit modal ILs are predicted under different operating conditions of the motor drive system.  
From the analysis, two main conclusions can be drawn. The DM impedance is significantly different if the system is powered ON and OFF. Once the system is powered ON, even if the variable-frequency device is not turned ON, the measured DM impedance can be used for in-circuit DM IL prediction because no significant differences can be observed for different operating conditions. Conversely, it has been proven that the CM impedance does not depend on the ON and OFF status of the system, conclusion that is in line and confirmed by other works in the literature.  
Consequently, the predicted DM IL based on the DM impedance measured offline cannot reflect the actual DM IL of the filter, even if the in-circuit DM IL does not depend on the switching frequency of the system. Conversely, the CM impedance measured offline can be used to predict the in-circuit CM IL. However, in both CM and DM cases, the modal IL evaluated in the test setup foreseen by the standard cannot provide a reliable estimate of the actual IL of the filter installed in the motor drive system under analysis.  
Conceptualization, L.W., S.N., G.S., F.G. and S.A.P.; methodology, S.N. and G.S.; software, L.W.; validation, S.N., L.W. and G.S.; formal analysis, S.N. and G.S.; investigation, L.W., S.N. and G.S.; resources, F.G.; data curation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, S.N., G.S., F.G. and S.A.P.; visualization, L.W.; supervision, S.A.P. and F.G.; project administration, S.A.P. and F.G.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 812753.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### ATR Experimental and Numerical Data
Interestingly, the calculated reflected power decay as defined by the \(k_{2}\) and correspondingly \(\alpha_{abs}=8\) cm\({}^{-1}\) is \(l_{abs}=1/\alpha_{abs}=1.25\) mm, was up to three times smaller. This was judged from the initial slope of the calculated FDTD data and experimental results (Figure 5) for the thicknesses larger than \(d>0.15\) mm. At the position of slope change, the evanescent field (Equation (1)) ceases to be important. This is consistent with the measurement methodology where top Au-mirror reflected the otherwise transmitted light back into the detection direction. Hence the FDTD monitor captures the optical near-field and far-field contributions. An additional factor is that the angle of incidence in ATR is usually \(\theta\approx 45^{\circ}\), however, parabolic focusing mirrors are used and this changes the s-pol. to p-pol. ratio at the sample-prism interface. Further efforts have to be made to achieve theory-experiment matching for non-normalised data presentation. To achieve this, the phase of the incident and reflected signal has to be determined (see Appendix A for phase changes upon reflection) as well as the change of reflection coefficients \(R_{s,p}\) (Figure 3) which are both dependent on the sample properties \((n_{2}+ik_{2})\). Polarisation sensitive measurements could allow determination of orientation dependent \(n\) and \(k\), and hence the birefringence \(\Delta n\) and dichroism \(\Delta k\) as we demonstrated for the transmission [30; 31; 32] at the IR spectral range.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 4 Discussion and Conclusions
### Challenges Due to Waviness  
The previous section showed the capability of SRAS measurements on a range of surface finishes. It is clear that for many real parts, the fact that these surfaces represent small-scale roughness is not an impediment to microstructural imaging.  
The current versions of the instrument suffer mostly from challenges from the form and waviness of the samples. This is because, for large changes in the sample surface position (as the form changes) the measurement surface goes out of focus, or the light reflects and leaves the optical system due to the change in surface gradient. Some examples of this are shown in Figure 12.  
The influence of changes in focus could be reduced by incorporating an auto-focus system, either via image analysis of the surface and the generation fringe pattern, or by sensing the stand-off distance variation from the sample via other means. Adding this to  
Figure 12: (**a**) Titanium diboride (TiB2) made by WLAN, where the build has large changes in surface height across the surface. (**b**) Ti 64 made by WAAM, where the build strategy incorporates single-well beads producing deep ridges between layers. (**c**) Ti 64 sample made by L-PBF, with both large changes in height between features and fine-scale roughness from the powder.  
the instrument will allow the measurement surface to be kept at the optimal distance and so greatly improve signal integrity.  
Maintaining the measurement plane normal to the surface to combat changes in form and waviness is more challenging. For parts where there is a CAD model, registering the part in the measurement volume and using a robotic manipulator to present the surface normal to the laser head would allow the impact of changes in surface gradient to be reduced. Combining this with auto-focus would produce a very robust measurement system.  
There are still challenges from wire-based AM samples where the side wall of the sample is wavy and the exact form of this is unknown and therefore hard to follow. In Figure 12a, the waviness of the WLAN sample shows clear drop outs, but the data from the upper flat regions are good. Similarly, for the WAAM sample in Figure 12b, the variation in layer thickness and bead curvature would be difficult to follow without a dedicated surface following system built into the instrument. Again, where the curvature is small and the sample is in focus, the data obtained are still good as the roughness is well within the operating range of the instrument.
###### Abstract  
Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing-Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster-Shafer (D-S) evidence theory. A joint daytime-nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions.  
Keywords:geothermal anomalies; thermal infrared remote sensing; daytime and nighttime LST analysis; D-S evidence theory; multi-source data fusion; magma chamber; Yangbajing-Yangyi Basin
# Biomedical Comparison of Salvage Pedicle Screw Augmentations Using Different Biomaterials
## 3 Results  
### Radiological Images after the Insertion of Screws with Various Materials  
Prior to biomechanical pullout testing, X-ray images were obtained after pedicle screw insertion in the test blocks with various filling biomaterials. The radiopacities of these materials were different. CS and PMMA demonstrated greater radiopacity in comparison to ABP and HA/\(\beta\)-TCP (Figure 5). This finding could be interpreted as suggesting that paste-like materials, such as CS and PMMA, are distributed more homogeneously around the screw than particle-like materials, such as ABP and HA/\(\beta\)-TCP.  
Mean Maximal Pullout Strength of Different Materials with the Different Pilot-Hole Sizes and Bone Densities  
In samples in the 3.2 mm pilot-hole group, in which primary surgery was simulated, the control group, ABP, HA/\(\beta\)-TCP, CS, and PMMA in samples in the 7.5 pcf group showed a mean maximal pullout strength of 35.75 \(\pm\) 6.35, 38.26 \(\pm\) 4.99, 54.02 \(\pm\) 10.14, 67.08 \(\pm\) 5.91, and 180.60 \(\pm\) 6.09 N, respectively, compared with 62.97 \(\pm\) 8.26, 93.30 \(\pm\) 22.31, 95.25 \(\pm\) 16.55, 214.59 \(\pm\) 19.92, and 745.49 \(\pm\) 94.20 N, respectively, in samples in the 15 pcf group, and 342.70 \(\pm\) 40.94, 507.04 \(\pm\) 117.92, 520.58 \(\pm\) 171.90, 947.20 \(\pm\) 191.97, 1417.47 \(\pm\) 158.22 N, respectively, in samples in the 30 pcf group.  
In samples in the 7.0 mm pilot-hole group, in which revision surgery was simulated, the mean maximal pullout strength in samples in the 7.5 pcf group for the control group, ABP, HA/\(\beta\)-TCP, CS, and PMMA was 35.75 \(\pm\) 6.35, 23.80 \(\pm\) 7.97, 44.14 \(\pm\) 1.74, 162.94 \(\pm\) 29.90, and 549.16 \(\pm\) 183.42 N, respectively, compared with 62.97 \(\pm\) 8.26, 53.89 \(\pm\) 20.68, 75.86 \(\pm\) 0.33, 253.20 \(\pm\) 21.30, and 885.76 \(\pm\) 315.39 N, respectively, in samples in the 15 pcf group, and 342.70 \(\pm\) 40.94, 414.69 \(\pm\) 168.87, 442.14 \(\pm\) 156.84, 521.41 \(\pm\) 85.54, and 1232.65 \(\pm\) 204.27 N, respectively, in samples in the 30 pcf group.  
From the above results, the maximal pullout strength of these five conditions in descending order was PMMA, CS, HA/\(\beta\)-TCP, ABP and the control (Figure 7).