esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Pd/CB Embedded in Carbon Nanofibers with Different Carbon Black Contents
Referring to the pore distributions shown in Figure 3 and Figure S4, and the nitrogen adsorption and desorption isotherms shown in Figure S5, the mesopore volume of the Pd/KECNFs increased compared to the Pd/CNF irrespective of the weight ratio of CB/PAN, although the volumes of the mesopores and micropores were smaller than those of Pd/C\({}_{\text{k}}\)-all. Considering the fact that the mesopore volume of the Pd/KECNF [0.50] was high, it is assumed that a large part of the surface of CNF was covered by the Ketjen, similar to the case of Pd/VECNF [0.50]. In the case of Ketjen, the influence of the weight ratio of the Ketjen/PAN on the BET surface area and the crystallite size of Pd was insignificant; however, it is suggested that the ratio of the Ketjen at the CNF surface would affect the FAOR activity. On the basis of these results and the FESEM observations, it was suggested that the surface of Pd/VECNF [0.50] was almost covered with the Vulcan, which may restrict the utilization of the Pd catalyst due to agglomeration of the fiber structure. Few pores with sizes above 20 nm were observed in the Pd/VECNF [0.125], similar to the case of Pd/CNF. This result indicated that a large amount of CB existed inside the fibers, in agreement with the FESEM observations. \begin{table}
\begin{tabular}{c c c c} \hline
**Catalyst** & **Surface Area (m\({}^{2}\) g\({}^{-1}\))** & **Pore Volume (cm\({}^{3}\) g\({}^{-1}\))** & **Pd Crystalline Size (nm)** \\ \hline Pd/CNF & 544 & 0.28 & 17.0 \\ Pd/VECNF [0.125] & 321 & 0.20 & 15.8 \\ Pd/VECNF [0.25] & 391 & 0.44 & 15.8 \\ Pd/VECNF [0.50] & 250 & 0.43 & 13.4 \\ Pd/KECNF [0.125] & 405 & 0.25 & 13.7 \\ Pd/KECNF [0.25] & 573 & 0.43 & 14.1 \\ Pd/KECNF [0.50] & 558 & 0.49 & 15.9 \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of the BET surface areas, pore volumes, and Pd crystallite sizes for VECNF and KECNF catalysts with different CB/ polyacrylonitrile (PAN) ratios.
# Phytoplankton as CO\({}_{2}\) Sinks: Redirecting the Carbon Cycle
## 3 Phytoplankton Culture
### Key Parameters for the Growth
#### 3.3.4 Culture Mixing  
Although some species of phytoplankton can actively move using flagella [59] or gas vesicles [60], most of them need an external source of movement to stay in the water column. Natural water bodies are usually large enough to generate an upper well-mixed layer using different forces such as surface contact with the wind (waves), convective currents, or even the tide cycles [61]. Nevertheless, PBRs are usually small and insulated water blocks without an artificial source of turbulence, resulting in fast water column stratification. This stratification impedes the system from reaching its maximum efficiency and finally ends with the collapse of the culture [62; 63]. Briefly, the absence of turbulence produces a progressive mass gradient where the culture and the insoluble material are progressively secluded in a small volume (bottom, surfaces, etc.) within PBR. The negative consequences of maintaining this status for a long time can be multiple. If the culture sediments toward an abiotic area, the cells will not receive enough light and gradually lose their energy reserves.  
Conversely, if the culture sediments are on an over-lit surface, they may suffer photoinhibition and photodamage. Alternatively, the confinement of the culture entails overpopulation and competence by the progressively reduced access to the main nutrients. If this status is prolonged, an anoxic and nutrient-deprived zone is developed in the PBR, ending with gradual cell death. Meanwhile, most of the volume remained idle. Finally, the lack of movement can create a thermocline, causing severe overheating in some zones of PBR that stay overexposed to direct radiation or near the temperature control systems. Therefore, a proper mixing system is essential to disrupt the PBR stratification and to improve heat and mass transfer. In this sense, turbulence in PBRs is usually implemented in three main ways:  
1. Through mechanical devices such as paddle wheels [64; 65] or hydraulic pumps [66], the origin currents and flows raise the biomass;
2. Through pneumatic devices that allow bubbling with air or CO\({}_{2}\)-enriched air, generating a current toward the atmosphere and airlift circulation [67];
3. Through mixed systems that use both strategies [32].  
Note that the mixing forces must be finely adjusted for each species, considering its morphology and resistance. Excess turbulence can severely damage the cells with shear  
Figure 7: Main differences between one-stage and two-stage growth systems and their effect on the growth rates (\(\upmu\)) of the culture.  
forces, friction, and mechanical shocks [68]. Moreover, mixing systems significantly impact the production model's expenditures [69] since, in one way or another, they represent electrical consumption (air compressors, air blowers, centrifugal pumps, etc.).
## 2 Materials and Methods
### Exposure Assessment  
In order to evaluate the interactions between the EMF emitted by the simulated wearable antenna and the human tissues, the SAR was calculated; it can be defined as the electromagnetic energy absorbed by a human tissue. With more details, the SAR is defined as the time derivative of the incremental energy consumption by heat involved in an incremental mass in a volume element, characterized with its density \(\rho\) and its conductivity \(\sigma\)[14]. More specifically, the distribution of SAR value averaged on a cubical mass of 10 g (indicated from now on as SAR\({}_{\mathrm{10g}}\)) was estimated with the input power of the antenna set at 1 W. The values of the SAR\({}_{\mathrm{10g}}\) distribution have been extracted considering a cubical box with sizes of 250 \(\times\) 250 \(\times\) 250 mm in all the simulations except the scenario in which the antenna was placed on the head of the human model; in that case the data has been extracted from the entire domain, however limited to the head area. The box was always centered on the antenna. The size of the cubical box was chosen to be sufficient to include all significant SAR\({}_{\mathrm{10g}}\) values, including the first slices of the human tissues in which the calculated SAR\({}_{\mathrm{10g}}\) was null. Furthermore, to better quantify the SAR\({}_{\mathrm{10g}}\) distributions, the descriptive statistics (minimum, 25 th, 50 th, 75 th percentile, and maximum) of the  
Figure 2: Overview of the six simulated exposure configurations on Duke for the antenna tuned at f = 2.45 GHz: (**a**) antenna on the ankle, (**b**) on the arm, (**c**) on the leg, (**d**) on the shoulder, (**e**) On the torso at the height of the heart and (**f**) on the head, placed on a steel helmet.  
SAR\({}_{10g}\) distributions in the tissues included in the cubical box were calculated. Finally, the peak SAR\({}_{10g}\) have been compared with the ICNIRP Guidelines [14] limits for the occupational exposure in the frequency range of 100 kHz to 6 GHz.
## 2 Materials and Methods
### Volatile Component Analysis  
Complying with the approach reported by [31], volatile flavor components of CFB were analyzed using HS-SPME-GC/MS technology (6890-5973, Agilent Technologies, Inc., Beijing, China). In brief, 14 mL samples of centrifuged fermentation broth were blended with 4 g of NaCl and 200 \(\upmu\)L of an internal standard (2-octanol, 1 mg/L, Al-addin Industrial Corporation, Shanghai, China) and then sterilized. A 50/30 \(\upmu\)m divinylbenzene/carboxon/polydimethylsiloxane (DVB/CAR/PDMS)-coated fiber (Supelco, Inc., Bellefonte, PA, USA) was introduced for headspace extraction at 50 \({}^{\circ}\)C for 10 min. Volatile fragrance compounds were dissected using an Agilent 8890-5977B GC-MS (Agilent Technologies, Inc., Beijing, China) with a DB-WAX UI capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies, Inc., Beijing, China). The oven temperature program was initiated at 40 \({}^{\circ}\)C for 6 min, increased to 100 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and kept there for 6 min, and ultimately ramped to 150 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min for 8 min. The flow rate of the carrier gas (helium) was set at 1 mL/min. The temperatures for the MS interface, MS source, and MS quadrupole reached 250 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively. The MS ran in an electron impact pattern with an electron energy of 70 eV and a scanning range of 47-550 \(m/z\). Volatiles were characterized and analyzed using the National Institute of Standards and Technology (NIST) spectral library. The retention index (RI) of the substances was calculated based on the retention times of n-alkanes C\({}_{7}\)-C\({}_{40}\) (49452-U, Sigma-Aldrich, Shanghai,China) detected under the same chromatographic conditions [32] and the formula for RI was as follows:  
\[RI=100*(Z+\frac{T_{x}-T_{z}}{T_{Z+1}-T_{Z}})\]  
where Z is the carbon number of the n-alkane adjacent to (before) the target peaks. \(T_{x}\), \(T_{z}\), and \(T_{Z+1}\) are the target compound retention time, retention time of n-alkanes adjacent to (before) the target peak, and retention time of n-alkanes adjacent to (after) the target peak, respectively.  
The concentration of each volatile compound was calculated using the internal standard method with the following formula:  
\[Concentration\ of\ flavor\ compound=\frac{C*F_{1}}{F_{2}}\]  
where C is the ratio of the peak of the flavor compound to the peak area of the 2-octanol. F\({}_{1}\) and F\({}_{2}\) are the quality of the 2-octanol and the sample, respectively.  
To determine the effects of various flavor compounds on the overall sensory profile of CFB, the primary flavor components during CFB fermentation were studied. Consequently, further analysis of the odor activity value (OAV) of various flavor compounds during CFB fermentation was required. The OAV is the ratio of the concentration of a flavor compound to its threshold.
## 3 Results and Discussion
### Physicochemical Property and Subcellular Localization Analysis of GpOSC Family  
The basic physicochemical parameters of the GpOSC proteins were calculated. The lengths of the GpOSC proteins ranged from 356 amino acids (GpOSC6) to 767 amino acids (GpOSC11), with molecular weights varying between 40,113.93 Da (GpOSC6) and 88,595.63 Da (GpOSC11). The pIs of these proteins span from 5.90 to 8.08, while the instability index ranges from 40.11 to 50.30 (Table 1). The subcellular localization analysis predicted that the proteins encoded by one gene reside within the peroxisome, four genes were located in the cytoplasm, two genes were associated with the plasma membrane, and four genes were found in lysosomes. These findings are similar to previously reported _OSC_ genes in _Astragalus membranacus_[44], but differ from those observed in _Chenopodium quinoa_[45]. In C. _quinoa_, among the fifteen _CqOSC_ genes identified, all except for _CqOSC2_, which is located both in the cytoplasm and chloroplast, are exclusively situated within the chloroplast [45].
## 7 Conclusions and Future Work  
### Conclusions  
Three sensing coverage has been assumed in the literature [52, 53] as a sufficient condition for 2-dimensional acoustic target tracking using WSNs. Using geometric representation for modeling, we theoretically proved that three sensing coverage generates outliers in target tracking results and that these outliers occur irrespective of the target tracking method that is used; they occur in target tracking using Kalman filtering, particle filtering and many other methods that assume three sensing coverage is sufficient for 2-dimensional acoustic target tracking.  
To reduce the computational overhead of finding the spatio-temporal information of a target object by way of solving quadratic equations, we proposed an alternative method based on geometric algebra that uses linear equations instead of quadratic equations. Our proposed method solves target tracking equations with comparably lower computational overhead and it is applicable to WSNs whose sensor nodes have low processing power. Our simulation results for 2-dimensional target tracking with three sensing coverage in ideal cases, where time synchronization and localization errors were ignored, showed that the target tracking results contain outliers most of the times. Simulation results also  
Figure 16: Target tracking error using RAF method.  
showed that in more than half of the cases, three sensing coverage generated outliers in acoustic target tracking. We theoretically proved that four sensing coverage, rather than three sensing coverage, is the necessary condition for accurate 2-dimensional target tracking.  
We proposed two other extended methods based on the basic proposed method to improve it, in order to accurately compute the spatio-temporal information of a target object in 2-dimensional space. Both of these methods were based on having four sensing coverage for accurate target tracking. We proved that the AFCT method, which was based on algebraic geometry, accurately computes the spatio-temporal information of a target object with 100% confidence under defined conditions. We also showed through simulation that target tracking errors can be eliminated. The RAF method used a customized fusion method and assumed that each set of simultaneous equations uses the sensing information of only three sensing nodes. But because of four sensing coverage we had at least two different sets of simultaneous equations that allowed us to exploit the capabilities of the second proposed fusion method. Simulation results showed that target tracking was carried out perfectly without any error.  
The main contribution of our paper was in formally correcting the belief that _three sensing coverage_ is the sufficient condition for accurate acoustic target tracking, by proving that _four sensing coverage_ is the necessary condition for accurate acoustic target tracking.
## 2 Materials and Methods
### Methods
#### 2.2.3 Performance Evaluation Metrics  
Using six evaluation metrics (accuracy, precision, specificity, recall, NPV, and f1-score), we evaluated the performance of the classification model. The application of these various evaluation metrics could provide a more detailed explanation of model performance [34].  
In the present study, accuracy refers to the ratio of the number of correctly classified cases among all the classification problems. In the confusion matrix table (Table 3), accuracy can be calculated by dividing the sum of all cases in the confusion matrix by the sum of TP and TN. Precision, which is also known as the PPV, is an evaluation metric for measuring positive predictive performance. It can be measured by dividing the TP value by the sum of TP and FP. Recall represents cases classified as true and is the ratio of TP cases to the sum of TP and FP. Specificity is the ratio of TN cases to the sum of FP and TN cases. NPV is the ratio of TN cases to the sum of FN and TN cases and shows the model's performance in classifying TN cases in predicted negative cases. The f1-score returns one metric value by combining precision and recall. By calculating the harmonic average of precision and recall, the f1-score is an evaluation metric that considers both precision and recall.
## 2 Proposed Test Setup
### Alternative Test Setup for the Identification of Bond Properties
In order to identify the influence of such unbonding layer on the measured capacity, a control test was performed on an anchor entirely wrapped with paper tape and Teflon tape (see Figure 6d). In the second configuration (see Figure 6b), the forcing of the failure to start at the inner interface is solved by unbonding the threaded bar from the concrete with a 30 mm deep notch cored across the outer interface with a coring bit. This configuration is referred to as Cored. The third configuration (see Figure 6c) was not unbonded and was considered as the Reference case and used to compare the results of the proposed configurations with the results of the guideline configuration. Figure 5: Test setup used for the experiments. Precise bonded length was ensured by milling the thread of the excessive section of the bar. As previously mentioned, milling the threaded bar surface also allows for the coupling of the anchor to the LVDT holder. The bond between the milled section of the bar and the adhesive mortar is avoided by a sleeve made of paper and duct tape that surrounds the milled section. In order to identify the influence of the sleeve on the measured capacity, another control test was performed on an anchor entirely milled and covered by such a sleeve (see Figure 6e). The supporting ring used for the Cored and Teflon configurations pull-out had an inner diameter of 36 mm, while for the other configurations, the support inner diameter was 24 mm, which is within the range prescribed by the guideline. Bonded lengths of 30 and 40 mm were tested for the three configurations. Note that both bonded lengths are not in accordance with the guideline since they are below 40 mm and four times the anchor diameter. However, in this comparative study, due to the capacity of the anchor testing machine limitations, these two dimensions were chosen. Nevertheless, 30-40 mm is much larger than the characteristic length of the studied problem, i.e., thread spacing, annular gap, or size of the filler inside the adhesive. Note that the concrete coarse aggregates and cone break-out are not contributing to the studied interface failure. Four repetitions were performed to estimate mean value and standard deviation of the obtained bond strength and to determine the variability of the load displacement curves for each one of the three configurations.
# Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images
## 3 Method
### Transform from sRGB to XYZ  
As described above, if the XYZ values of each pixel in an image and the XYZ values of the white point under the scene illumination are known, the XYZ values of this image under a known canonical illuminant could be obtained. For different RGB color spaces, such as sRGB, Adobe RGB and Prophoto RGB, the transforms from the RGB space to the XYZ space are different due to different reference primaries and illuminations [42]. Given an RGB image, it is difficult to judge the RGB color space in which the image is encoded since the color space information recorded in the exchangeable image file format header can be changed or lost after post-processing by using image editing software [43]. A learning-based method can be used to identify the color space for an arbitrary RGB image in the subsequent research. Here, we simply assume that the images are encoded in sRGB color space since sRGB is representative of the majority of devices on which color is and will be viewed [15]. For an image encoded in the sRGB color space, the CIE 1931 XYZ values can be computed from the RGB values in three steps [44]. First, the digital code values [\(R_{n\text{bit}}\ G_{n\text{bit}}\ B_{n\text{bit}}\)] are converted to the nominal  
Figure 2: Color correction flow.  
range [0, 1] following Equation (5). Since the same operation will be performed on the three channels, the notation \(V\) is used to represent any of the three channels.  
\[V\prime_{sRGB}=\frac{V_{\text{{\emph{r}}ubit}}}{2^{n}-1} \tag{5}\]  
where \(n\) represents the number of bits per channel, for example, \(n\) = 8 for 24-bit RGB images. Second, \(V^{\prime}\)\({}_{sRGB}\) is linearized, known as de-gamma correction, as follows:  
\[\left\{\begin{array}{cc}V_{sRGB}=\frac{V\prime_{sRGB}}{12.92}&\text{if }V \prime_{sRGB}\leq 0.03928\\ V_{sRGB}=\left(\frac{V\prime_{sRGB}+0.055}{1.055}\right)^{2.4}&\text{else} \end{array}\right. \tag{6}\]  
Third, the linear RGB values [\(R_{sRGB}\)\(G_{sRGB}\)\(B_{sRGB}\)] are converted to XYZ values using the following derived relationship:  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=\left[\begin{array}{ccc}0.4127&0.3586&0.1808\\ 0.2132&0.7172&0.0724\\ 0.0195&0.1197&0.9517\end{array}\right]\left[\begin{array}{c}R_{sRGB}\\ G_{sRGB}\\ B_{sRGB}\end{array}\right] \tag{7}\]
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 3 Application of Attack-Defense Tree Model in UAV Cyber Range
### Solving Bayesian Equilibrium of Attack and Defense Game Model of UAV Cyber Range
#### 4.3.2 Mixed Strategy Bayesian-Nash Equilibrium  
Given the \(UAV-ADM=((N_{A},N_{D}),(T_{A},T_{D}),(B_{A},B_{D}),(P_{A},P_{D}),(U_{A},U_{D}))\) mixed strategy Bayesian-Nash equilibrium, \(F_{A}(t_{A})=\left\{f_{1}^{A}(t_{A}),f_{2}^{A}(t_{A}),\cdots,f_{n1}^{A}(t_{A})\right\}\) represents the probability distribution of mixed strategies for attackers and the probability distribution of mixed strategies for defenders \(F_{D}(t_{D})=\left\{f_{1}^{D}(t_{D}),f_{2}^{D}(t_{D}),\cdots,f_{m2}^{D}(t_{D})\right\}\). If the following conditions (3) and (4) are met at the same time, the mixed strategy \(\left(F_{A}^{*}(t_{A}),F_{D}^{*}(t_{D})\right)\) is called a Bayesian-Nash equilibrium.  
\[\sum_{t_{D}\in T_{D}}P_{A}(t_{D}|t_{A})U(F_{A}^{*}(t_{A}),F_{D}^{*}(t_{D}),t_{ A})\geq\sum_{t_{D}\in T_{D}}P_{A}(t_{D}|t_{A})U(F_{A}(t_{A}),F_{D}^{*}(t_{D}),t_{ A}) \tag{3}\]  
\[\sum_{t_{A}\in T_{A}}P_{D}(t_{A}|t_{D})U(F_{A}^{*}(t_{A}),F_{D}^{*}(t_{D}),t_{ D})\geq\sum_{t_{A}\in T_{A}}P_{D}(t_{A}|t_{D})U(F_{A}(t_{A}),F_{D}^{*}(t_{D}),t_{ D}) \tag{4}\]  
Based on cording to the rational assumption, both sides will not change their strategy when the benefits are maximum. Therefore, both attack and defense will adopt the strategy of the best return situation so as to achieve the game equilibrium. In this case, the probability distribution of the attack behavior \(F_{A}^{*}(t_{A})=\left\{f_{1}^{A^{*}}(t_{A}),f_{2}^{A^{*}}(t_{A}),\cdots,f_{n1}^{ A^{*}}(t_{A})\right\}\) is represented by the defender's prediction of the attacker's strategy, which is used as the basis for risk evaluation.