esa-sceva/satcom-chunk-collection huggingface dataset:
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 5 PUF Stability Evaluation and Automatic Detection of Unstable Bits
### Stability Against Temperature Variations
As the authors in [9] already stated, the changes in frequency (and therefore also phase difference) on FPGAs are non-linear over temperature; Ref. [36] showed that a third-order polynomial provides a better fit. Therefore, the linear approximation in [9] is not accurate for large temperature differences and not suitable for comparison to the course of the measured results in Figure 15.  
In summary, the unique features of the proposed EoA-PUF design allow very low error rates at reference temperatures, which is based on the calibration using the PDLs as well as the automatic detection of unstable bits. Even though the positions of unstable bits and the ideal calibration/tuning setting can partially change at other temperatures, the temperature dependency of the error rate is quite comparable with other SotA implementations. Still,  
Figure 15: Error rates of different FPGA-PUF implementations for varying environmental temperatures. For a better comparison, the difference from the reference temperature comprises the x-axis. The data from [35] were estimated from the supplied graph. Please note that Ref. [35] used 25 \({}^{\circ}\)C as reference room temperature, while we used 20 \({}^{\circ}\)C lab temperature as reference. the features of calibration and auto-detection of unstable bits can be further exploited to reduce the bit error rate over temperature, which will be shown next.
## 4 The Analysis for Embedding Capacities  
As mentioned previously, both [PERSON] et al. [16] and [PERSON] et al.'s [17] methods need a location map in the extraction process; thus, the location map should be transmitted through another channel or be embedded in the stego image. However, it is unreasonable to transmit the location map through another channel. Thus, in the following, we only consider the location map embedded in the stego image. Let an image size be \(N\times M\), block size be \(n\times m\), the embedding bits for each block be \(k\), then the total block number \(T=[N\times M/(n\times m)]\). Assume that in the location map, each block is represented by one bit, 1 stand for the corresponding block used for embedding; 0 for skipping. Let \(X\) be the size of the location map, assume that the location map is embedded in the first \([X/k]\) blocks, then the number of available blocks for secret data embedding is \(T-[X/k]\). Thus, \(X\) should satisfy the following equation:  
\[X=T-[X/k]. \tag{16}\]  
Note that the embedding capacity (bits) is \(kX\). Let an image size be \(256\times 256\) and block size be \(2\times 2\), then \(T=16384\). Let \(k=3\); through Equation (16), we can obtain \(X=12,288\), that is, the number of available blocks for secret data embedding is 12,288, and the block number needed for recording the location map is 4096. Table 2 shows embedding capacities for [PERSON] et al.'s, [PERSON] et al.'s, and the proposed methods. In the table, we can see that the proposed method is superior to [PERSON] et al. and [PERSON] et al.'s methods in embedding capacity.
## 3 Measuring F on the Leaf and Canopy Level
### Leaf Level
#### 3.1.2 Measurement Protocols
optimized and performed in real time for the incoming radiance arriving at the target (WR or leaf) during the measurements to obtain the highest dynamic range and SNR possible. The F signal, however, needs further processing. If the used band-pass filter does not cut off the light sharply at 650 nm (to be verified with a WR target measurement), some residual light may pass through the filter and add to the F\({}_{\text{up}}\) signal as reflected residual light, calculated as the residual light passing the filter times the calculated leaf reflectance. After removal of this reflected residual light, the F\({}_{\text{up}}\) is added to the F\({}_{\text{dw}}\) signal, giving the total F leaving the leaf's surface (Figure 3). Overall, it should be kept in mind that F is driven in the first order by E, and that further changes are driven by the functional state of the leaf, thus altering the fluorescence yield (FY) [67; 68]. Hence, because photoproduction mechanisms are activated, the relationship between FY and photosynthetic yield will differ between the morning and the afternoon (i.e., at similar irradiance conditions). This hysteresis effect should be kept in mind when designing protocols for measuring F in the field at a certain moment of the day. It further implies that additional ancillary data (cf. Section 4.3) are required for the correct interpretation and relation between F and photosynthesis.
## 5 Experiments and Results
### Experimental Results
#### 5.4.2 Comparison Results of Detection Accuracy
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{3}{c}{**Faster-RCNN**} & \multicolumn{3}{c}{**YOLO v5s**} & \multicolumn{3}{c}{**YOLO v7-Tiny**} & \multicolumn{3}{c}{**YOLO v8s**} \\ \cline{2-13}  & **FPS** & **F1** & **mAP** & **FPS** & **F1** & **mAP** & **FPS** & **F1** & **mAP** & **FPS** & **F1** & **mAP** \\  & **(f.s\({}^{-1}\))** & **(\%)** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** \\ \hline UAPD [2] & 9.14 & 47.9 & 48.8 & 59.7 & 52.7 & 57.7 & 74.51 & 56.7 & 52.8 & 65.4 & 57.4 & 58.6 \\ RDD2022 [31] & 11.36 & 69.5 & 68.8 & 63.21 & 65.2 & 60.9 & 65.47 & 63.1 & 65.6 & 53.71 & 66.5 & 67.7 \\ UMSC [19] & 11.72 & 73.4 & 68.8 & 97.87 & 68.7 & 74.3 & 76.81 & 63.8 & 70.1 & 89.78 & 72.8 & 70.4 \\ UAVRoadCrack [21] & 10.57 & 68.9 & 68.5 & 108.6 & 77.8 & 75.7 & 75.39 & 62.5 & 65.3 & 69.36 & 71.0 & 68.8 \\ CrackForest [32] & / & 57.4 & 59.1 & / & 57.8 & 58.8 & 67.45 & 61.2 & 63.5 & 61.21 & 60.9 & 65.2 \\ Our Datasets & 12.80 & 75.3 & 79.3 & 127.4 & 72.6 & 74.0 & 82.56 & 66.7 & 65.5 & 125.7 & 75.0 & 77.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of model valuation with various UAV cracks datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input Images**} & \multicolumn{2}{c}{**Faster-RCNN**} & \multicolumn{3}{c}{**YOLOv5s**} & \multicolumn{3}{c}{**YOLOv7-Tiny**} & \multicolumn{3}{c}{**YOLOv8s**} \\ \hline \hline \multirow{2}{*}{**Datasets**} & **FPS** & **F1** & **mAP** & **FPS** & **F1** & **mAP** & **FPS** & **F1** & **mAP** & **FPS** & **F1** & **mAP** \\  & **(f.s\({}^{-1}\))** & **(\%)** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** & **(\%)** & **(f.s\({}^{-1}\))** & **(\%)** & **(f.sIn summary, this experiment compared the accuracy and effectiveness of different models for crack recognition from UAV imagery. The Faster-RCNN model demonstrated the highest accuracy and effectiveness in recognizing fine cracks. On the other hand, the YOLO series model showed significant advantages in terms of training speed and low requirements for video memory. Among the YOLO models, YOLOv5s and YOLOv8s exhibited a comparable recognition accuracy, while YOLOv7-tiny performed the worst. The experiment primarily focused on evaluating the data acquisition quality of UAV imagery, which yielded optimal results in the testing phase.
## 3 Results and Discussion
### Effect of Different Sterilization Methods on Lipid Metabolites in Red Raspberry Juice
Noteworthily, changes in hexanal abundance positively correlated with its precursors (linoleic acid and linolenic acid), intermediate products (9-hydroxyperoxy octadecatrienoic acid), and key enzymes (LOX, ADH, HPL) involved in the fatty acid metabolism pathway. Conversely, there was a negative correlation with downstream aroma substances, such as hexanol and (Z)-3-hexenol. The synthesis of aroma compounds typically involves the oxidative cleavage of unsaturated and peroxid oxidized fatty acids, leading to the formation of C6/C9 aldehydes [40], which are generally inversely related to their precursor fatty acids--a pattern that diverges from our findings. Considering the negative correlation between hexanal and hexanol (Figure 3b), it is speculated that both the HHP and HTST sterilization treatments may have accelerated the conversion of hexanal, derived from the LOX pathway, to hexanol through ADH dehydrogenation [41]. Alternatively, hexanal also showed a negative correlation with \(\delta\)-decalactone, a compound typically derived from the \(\beta\)-oxidation pathway of peroxid oxidized fatty acids, suggesting that the competitive inhibition  
Figure 6: Correlation analysis between C6 aldehyde key aroma compounds and fatty acid metabolism in red raspberry juices after different sterilization treatments. of \(\beta\)-oxidation for LOX conversion could also contribute to the loss of hexanal in sterilized red raspberry juices. Similarly, changes in (Z)-3-hexenal abundance after sterilization were significantly correlated with acid precursors (linoleic acid and linolenic acid), intermediates (9-hydroxy octadecatrienoic acid and 9,10-epoxy octadecadienoic acid), and key enzymes (LOX, ADH, HPL), while significantly negatively correlated with downstream aroma substances, specifically (Z)-3-hexenol. These findings align with the studies reported by [PERSON] et al. (2019); [PERSON] et al. (2019), which also demonstrated that the C6 and C9 aldehydes identified in HHP-treated melon juices exhibited stronger correlations with fatty acids and aromatic compounds downstream of the fatty acid metabolic pathway. Within the LOX pathway, (Z)-3-hexenal can be converted to (E)-2-hexenal and hexenal via cracking by HPL or to (Z)-3-hexenol via dehydrogenation by ADH. Consequently, the loss of (Z)-3-hexenal is predominantly due to its conversion and decomposition within the LOX pathway, and the decomposition degree of (Z)-3-hexenal is greater than that of hexenal. In addition, the content of (Z)-3-hexenal in HTST-treated raspberry juice was significantly lower than in HHP-treated juice, indicating more substantial (Z)-3-hexenal loss during HTST processing. This discrepancy may stem from the further reduced fatty acid dehydrogenase (FAD) activity during HTST treatment, which hinders the enzymatic oxidation of saturated fatty acids ([PERSON], 2019), subsequently reducing the availability of unsaturated fatty acid precursors for LOX pathway conversion and resulting in a formation rate of (Z)-3-hexenal much lower than its decomposition rate.
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
## 3 Materials and Methods
### Input Layers  
At the European scale, the following three possible LULC input layers (which provide a sufficient classification of land cover types) were identified with differing spatial resolutions:  
* CORINE Land Cover (CLC) [41], published in 2018, presents a visual interpretation of high-resolution satellite imagery from 2017 and 2018. The raster layer has a spatial resolution of a minimum of 100 m, a data storage size of 206.2 MB, and contains 44 LULC classes.
* LUISA base map (2018) [42] builds on the CLC layer (2018), COPERNICUS high-resolution (10 m) layers from 2018, the COPERNICUS Urban Atlas (2018), the Global Human Settlement Layer (2015), the TomTom Multinet vector layer, and OpenStreetMap data (2020). The LUISA base map is a raster layer with a minimum spatial resolution of 50 m and a data storage size of 1.02 GB.
* OpenStreetMap (OSM) [43] is a crowdsourced vector layer produced and updated by volunteers digitising satellite imagery, and it is validated with aerial imagery, GPS devices, and low-tech field maps. The dataset has an accuracy of at best 2 m, and a data storage size of 26 GB for Europe in 2022.  
For the other identified suitability criteria in (Section 2), representative open-source datasets at the European scale were selected for the suitability mapping. The representative datasets, along with the related suitability criteria, their spatial resolution, temporal range, and source, are listed in Table 1.  
_Resampling and reprojection._ As all selected layers differ in their spatial resolution (see Table 1) and the suitability mapping works on cell level, the input layers were resampled to one common spatial resolution of 10 m. Discrete layers (soil texture and buildings) were resampled by applying the nearest neighbour method; whereas, layers with more continuous information (tree cover density, soil depth, and bulk density) were resampled using the cubic method. In addition, all layers were reprojected to the following com  
Figure 2: The GIS-based workflow of this test study.  
mon coordinate reference system: the ETRS89 Lambert Azimuthal Equal-Area projection coordinate reference system (EPSG:3035).
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
###### Abstract  
For earth observation, the optical systems in small satellites are crucial to obtain high-resolution images. However, the alignment between a primary and a secondary mirror in an optical system can be disturbed due to the harsh environments inside vehicles or space (i.e., vibrations, shock loading during launch, dramatic temperature changes, or high vacuum pressure in space). To compensate for such undesired deformations, a focusing mechanism should be embedded into the optical system. In this paper, we propose a novel Single Motor-Driven Focusing mechanism with Flexure Hinges (SMFH), allowing the Flexure Hinge (FlexHe) to displace in the longitudinal direction. The presented FlexHe incorporates radial zig-zag-patterned slits to achieve flexibility, and preloading of the hinge structures to reduce the resulting hysteresis. To investigate an optimal configuration of FlexHe, a numerical simulation is performed by means of ANSYS 19.2. The variation of Modulation Transfer Function (MTF), corresponding to an image resolution, is evaluated by using an optics simulation program (CODE-V). The experimental setups are built by exploiting the fabricated SMFH and five LVDT (Linear Variable Differential Transformer) sensors with a high resolution of 0.031 \(\upmu\)m. As a result, hysteresis can be reduced up to 6.52% with a pre-stretched length of 3 \(\upmu\)m. The proposed SMFH allows not only the De-space to displace up to 23.93 \(\upmu\)m, but also the De-center and the Tilt to achieve the desired displacements of 5.20 \(\upmu\)m and 88.45 \(\upmu\)rad, respectively. Conclusively, the SMFH shows promising characteristics to embed a feedback control, due to its high resolution (up to 0.1 \(\upmu\)m) for De-space with the MTF of 37%.  
small satellite; optical system; focusing mechanism; single actuator; flexure hinge; structure optimization
## 4 Results and Discussion
### Transient Analysis
#### 4.2.3 Energy Balance Study  
The energy balance study was performed for the NEDC, WLTC, US06 cycles, as well as real driving conditions cycles shown in the Methodology Section.  
Figure 27 reveals that all of the driving cycles tested provide a negative average power, which indicates energy harvesting. Energy is provided to the compressor during transient load and speed conditions. The motor-generator is almost inactive when running at low to medium load steady-state conditions. Finally, at extra-urban driving conditions, the generators can harvest a high amount of energy. The average amount of energy harvested is highly dependent on the driving behaviour. The combined driving conditions and the US06 cycle, which represents aggressive driving conditions, demonstrated the maximum amounts of energy gain (6.6 and 5.9 kWh, respectively). The NEDC and WLTC cycles that represent mild driving behaviour provided energy gains between 0.4 and 0.9 kW.  
The fuel consumption of the model in transient mode cannot be highly trusted, as it may be affected by the tuning of the different PID controllers used for the baseline and the electrically-assisted models, as shown in Figure 28.  
Figure 27: Average power provided/harvested by the motor-generator; negative values indicate energy harvesting.  
Figure 26: Fixed gear vehicle speed transient results; % values represent pre-turbine pressure increase compared to the baseline engine: **(a)** engine speed vs. time; **(b)** motor-generator power vs. engine speed; **(c)** BMEP vs. time; **(d)** Brake-Specific Fuel Consumption (BSFC) vs. engine speed.  
The obtained results for the transient cycles show that with the implementation of the e-turbocharger, the fuel economy of the engine is deteriorated by up to 1.8% (Table 7) due to the increased pre-turbine pressures. However, this fuel consumption increase is compensated by the generated power of the e-turbocharger. The combined driving cycle demonstrates the maximum net gain of 5.5 kWh, followed by the US06 cycle with 3.8 kWh. The WLTC is the only cycle that provides a marginally negative energy gain when considering the increased fuel consumption.
## 5 Discussion
### Relationship between Model Performance and Soil Texture  
The variation in the soil texture is a driving factor for the spatial and temporal changes in the SM. Soils with high clay or silt fraction are associated with high water-holding capacity, resulting in a generally higher SM value. On the other hand, such soils lose their moisture slower than the others. From an agricultural point of view, clay soils have the highest soil moisture content in general; however, silty soils are more favorable for plants.  
We provide a ternary plot in Figure 9 to show the MAE values of stations, which are scattered based on their soil texture contents. In the same figure, we also included each station's NDVI\({}_{mean}\) values in a color map. The combination of soil texture and NDVI\({}_{mean}\) allows us to observe the relationship between the amount of silt and clay in the soil and vegetation activity.  
The size of each circle, representing a station, is proportional to its MAE value. We observe that the smaller circles generally accumulate in areas where the sand fraction is high. Among all the stations, 61% have sandy soil with an average MAE of 0.03, and 38% of them are silty soils with 0.04 average MAE.  
As we focus on particular stations for an in-depth investigation, it was observed that the silt content of the stations, having cropland cover, given in Figure 8 are 52%, 61%, and 42% for stations 816, 827, and 1572, respectively. In the corresponding stations, we have similar findings that justify the performance of the model w.r.t. the change in the NDVI values.  
In addition to silt and clay-dominated soils, the soil types in which the sand proportion is higher generally have a lower trend in SM values because the sandy soil has low water-holding capacity. This property makes them less suitable for agricultural applications. In order to investigate the sand effect, we present the time series of SM predictions at stations 815, 1541, and 1569 in Figure 10. The typical features of these stations are the high percentage of sand fraction in soil content (81%, 52%, and 52% for stations 815, 1541, and 1569) and lower NDVI values along the time series. The mean NDVI value for these stations is 0.15, 0.19, and 0.11, respectively. Unlike the findings from Figure 8, we saw that in Figure 10a, the higher sand fraction leads to lower and less fluctuated SM values. Thus, the highest accuracy was obtained at stations with sandy soils having low NDVI values.  
Figure 9: Soil texture ternary plot w.r.t. MAE of each station. The circles are scaled based on their MAE value and are colored based on NDVI\({}_{mean}\).
## 3 An Inventory of the \"In Silico\" Experiments in 3D-Bioprinting of Cell-Laden Hydrogels
### Pre-Printing  
A crucial stage in biofabrication process is cell seeding. Using computational methodologies in conjunction with experimental activities, efficient cell seeding strategies are designed. For example, the relative impact of permeability and thickness, and coating on cell seeding efficiency and homogeneity was evaluated in [30] using a fully factorial experimental design, in conjunction with the Finite Elements Method (FEM) discretization of fluid flows equations, to predict the scaffold permeability effects on cell seeding effectiveness. For another example, the number of cells and the seeding time were optimized using the simulation of cellular interactions with biomaterials, cells, and dynamic medium in a situation where cell seeding takes place under perfusion conditions [31].  
Furthermore, the theoretical framework, based on tissue fluidity, considers that cells tend to aggregate like droplets of an exceedingly viscous and incompressible fluid. In the mathematical model, cell clusters are assumed to have a size lower than 0.5 mm. Otherwise, gas and nutrients exchange are prevented, enabling the formation of necrotic areas [5]. A good agreement was found between continuum models of the coalescence of highly viscous liquid droplets and tissue cell cluster fusion studies. But there is a crucial difference between very viscous inanimate fluids and cell populations: they do not follow the principle of volume conservation. Dechriste et al. designed a compressible viscoelastic fluid model describing the fusion of growing cell clusters, such as embryo cell aggregates or tumor cell spheroids [32]. The authors initially gave the whole partial differential system, which describes the fusing of expanding spheroid, based on the Stokes equation. The combined model included a viscous model representing the velocity field and a tumor growth model. It is interesting to note that both models produced similar results since the velocity field's divergence equals the rate at which volume changes. The authors reduced the complexity of the 3D model to a 0D model, which included an ordinary differential system, under the symmetry conditions. To show the consistency, the experiments were fitted to a numerical model. Similarly, Kuan et al. built a 2D continuum theory to model the aggregate formation in a system characterized by uniformly distributed cells, as well as the coalescence of aggregate doublets [51]. In particular, the authors explained the production of aggregates as an active-phase separation phenomenon, and the merging of aggregates as the coalescence of viscoelastic droplets, with the important timeframes associated with the active force's turnover. The theory offered a broad framework for investigating the nonequilibrium dynamics and rheology of dense cellular aggregates [52].  
Figure 2: Graph of the logical structure of the relationships between 3D bioprinting computational simulation and concerned technologies and tools.