esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### C40 Carotenoid biosynthetic Pathways in the Antarctic Ocean as Elucidated by Metagenomic Analysis
#### 3.4. C30 and C50 Carotenoid Biosynthetic Pathways of Antarctic Ocean Microbial Communities  
4.1 Assessment of the Prevalence of C30 Carotenoid Biosynthesis Genes in Antarctic marine Microorganisms  
C30 carotenoids, while less prevalent than C40 variants, are intriguing due to their unique synthesis in specific microbial strains. These strains produce diaponeurosporene/diapolycopene and derivatives like staphyloxanthin [34; 35]. The primary biosynthetic mechanism for these molecules involves the head-to-head condensation of two C15 farnesyl pyrophosphate (FPP) units, forming a 30-carbon skeleton. Alternatively, some pathways utilize the condensation of a C10 geranyl pyrophosphate (GPP) with a C20 geranylgeranyl pyrophosphate (GGPP) [36].  
Our metagenomic study revealed the absence of genes encoding key enzymes (CrtM and CrtN) in the diaponeurosporene/diapolycopene biosynthesis pathway (see Figure 7A). This indicates a scarce presence of C30 carotenoid-producing microorganisms in the Antarctic marine samples we analyzed [34]. However, genes encoding enzymes involved in modifying the C30 carotenoid backbone, such as 4,4-diaponeurosporene oxidase (CrtP), 4,4'-diapolycopenoate synthase (CrtNc), and 4,4'-diaponeurosporeanoate glycosyltransferase (CrtQ), were detected at both depths. The only exception was a gene encoding glycosyll-4,4'-diaponeurosporeanoate acyltransferase (CrtO), which was not found.  
4.2 Assessment of the Prevalence of C50 Carotenoid Biosynthesis Genes in Antarctic marine Ecosystems  
C50 carotenoids, though less prevalent than their C40 counterparts, are of significant interest due to their presence in specific microbial strains. These strains are known for producing unique compounds such as decaprenoxanthin [37; 38], bacterioruberin [39; 40], and C.p. 450 [41]. Characteristic of C50 carotenoids is their extended 50-carbon skeletons, distinguishing them from the standard 40-carbon carotenoid framework. Our metagenomic study revealed varied distribution patterns of genes involved in bacterioruberin synthesis (Figure 7B). Genes encoding enzymes such as 1-hydroxy-2-isopentenylcarotenoid 3,4-desaturase (CrtD) and bisanhydrobacterioruberin hydratase (CrtF) showed higher abundances at a depth of 25 m. Notably, we did not detect the lycopene elongase/hydratase (Lye)) gene in the deep-water samples.
# A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling
## 2 Multiphase Flow Model
### Governing Equations  
(1) Mass conservation equation of multiphase flow  
The mass conservation equation ensures that the total mass of the gas-liquid-solid mixture is conserved during transient flow through the annulus:  
\[\frac{\partial\rho_{\rm m}}{\partial t}+\frac{\partial(\rho_{\rm m}V_{\rm m}) }{\partial z}=\Gamma_{\rm m} \tag{1}\]  
where \(\rho_{\rm m}\) is the density of a gas-liquid-solid three-phase mixture in kg/m\({}^{3}\); \(t\) is the time in s; \(V_{\rm m}\) is the average flow rate of the gas-liquid-solid three-phase mixture in m/s; \(z\) is the depth in m; and \(\Gamma_{\rm m}\) is the mass exchange between a gas-liquid-solid three-phase mixture, \(\Gamma_{\rm m}=0\), in kg/\(\left(\text{m}^{3}\cdot\text{s}\right)\). The density and flow velocity of the mixture are the average density and flow velocity of the gas, liquid, and solid, so the change in mass with time and well depth can be expressed as follows:  
\[\frac{\partial\rho_{\rm m}}{\partial t}=\frac{\partial(\rho_{\rm G}\alpha_{\rm G })}{\partial t}+\frac{\partial(\rho_{\rm L}\alpha_{\rm L})}{\partial t}+\frac{ \partial(\rho_{\rm S}\alpha_{\rm S})}{\partial t} \tag{2}\]  
\[\frac{\partial(\rho_{\rm m}V_{\rm m})}{\partial z}=\frac{\partial(\rho_{\rm G} \alpha_{\rm G}V_{\rm G})}{\partial z}+\frac{\partial(\rho_{\rm L}\alpha_{\rm L }V_{\rm L})}{\partial z}+\frac{\partial(\rho_{\rm S}\alpha_{\rm S}V_{\rm S})}{ \partial z} \tag{3}\]  
where \(\alpha\) is the volume fraction, and subscripts G, L, and S indicate the gas, liquid, and solid phases. The mass exchange of the three phases of gas, liquid, and solid includes gas-liquid mass exchange, gas-solid mass exchange, and liquid-solid mass exchange. \[\Gamma_{\rm m}=\Gamma_{\rm GL}+\Gamma_{\rm LG}+\Gamma_{\rm GS}+\Gamma_{\rm SG} +\Gamma_{\rm SL}+\Gamma_{\rm LS} \tag{4}\]  
(2) Momentum conservation equation of multiphase flow  
The momentum conservation equation balances the pressure forces, gravitational effects, wall friction, and inter-phase drag forces. It reflects the dynamic behavior of multiphase flow and is crucial for accurately computing the annular pressure distribution that influences BHP. \[\frac{\partial(\rho_{\rm m}V_{\rm m})}{\partial t}+\frac{\partial(\rho_{\rm m }V_{\rm m}^{2})}{\partial z}+\frac{\partial(P)}{\partial z}+\rho_{\rm m}g \cos\theta+F_{\rm Wm}+F_{\rm Im}=0 \tag{5}\]  
where \(P\) is the annulus pressure in Pa; g is the gravitational acceleration in m/s\({}^{2}\); \(\theta\) is the well inclination angle in \({}^{\circ}\); \(F_{\rm Wm}\) is the wall friction force of each phase in kg/m\({}^{2}\)/s\({}^{2}\); \(F_{\rm Im}\) is the interaction force between phases in kg/m\({}^{2}\)/s\({}^{2}\); and the sum of the interaction force between phases is 0.
## 3 Estimation of Motion Parameters Based on the CCAE Method
### Performance Analysis
 \tag{30}\]Similarly, according to Equation (24), the velocity is calculated using the subtracted value of the two i.i.d variables \(f_{0}\) and \(a\). Thus, the RMSE of the velocity can be expressed as:  
\[RMSE_{v} =\frac{3c}{4\gamma T_{pr}}\sqrt{\frac{6(2 Bf_{s}+f_{s}^{2}/SNR_{ur})} {4\pi^{2}B^{2}SNR_{ur}\Delta^{2}N(N^{2}-1)}} \tag{31}\] \[\approx\frac{3c}{4 BT_{pr}}\sqrt{\frac{6(2 Bf_{s}+f_{s}^{2}/SNR_{ur} )}{4\pi^{2}B^{2}SNR_{ur}N}}.\]  
The energy loss is not considered in the above derived RMSEs. In Section 3.2, it is pointed out that the non-overlapping part of the conjugate multiplication result induces the loss of signal energy. The loss can be figured out to be small, which has little influence on the estimation performance. According to Equation (25), conjugate multiplication causes a decrease in SNR, consequent to which the performance of the proposed method also suffers. However, since the noise bandwidth of the stretched signal is reduced to be much smaller than that of the UR signal, the SNR of the stretched signal is higher than that of the UR signal. Therefore, better estimation performance can be achieved by applying the CCAE method to the stretched signals. In this paper, the cross-correlation operation is performed on the two adjacent echo signals. Actually, the cross-correlation can performed on any two echo signals. When the two echo signals are taken with an interval \(nT_{pr}\), the \"\(T_{pr}\)\" in Equation (29) should be modified as \"\(nT_{pr}\)\". As a result, the RMSE will decrease to \(RMSE_{v}/n\). In other words, the performance of the proposed method can be improved through increasing the interval of the echo signals. However, when the interval becomes too large, the correlation between the two echo signals decreases, as a consequence of which the accuracy of parameters estimation decreases. Therefore, for this study, the two adjacent signals are used for parameters estimation.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 3 Results and Discussion
### Impact of the Fiber Weave Effect on Impedance Fluctuations  
There are two main types of transmission lines, differential lines and single-ended lines. The influence mechanism of the fiber weave effect on different impedance types is different.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 3 Research Method  
### Research Framework and Assumption  
Placing products with a high degree of association close to each other raises the likelihood of cross-selling. This paper seeks to develop a method that allocates products to suitable shelves to maximize such associations and thus drive cross-selling. Figure 1 shows the proposed three-stage framework. The first stage constructs a Product Relationship Network (PRN) that represents the purchasing association among the product items. Two types of PRNs--transaction-based networks (TBN) and customer-based networks (CBNs)--are used, depending on how purchasing data are dealt with. The two PRNs will be further defined in Section 3.2. The second stage uses network analysis to derive the centrality value of each product item, which reveals the importance of the item in its network. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. Opportunity items are those to be reorganized/re-shelved. The third stage uses a product assignment algorithm to determine the optimal positions/locations for opportunity items to maximize cross-selling.  
The product-to-shelf assignment problem is strongly influenced by a large number of variables that are context specific. Therefore, the following assumptions are made in this study. First, although the type of stores discussed in this study is limited, it is assumed that product volumes are identical  
Figure 1: Proposed three-stage method.  
and can be smoothly exchanged across the shelves. Second, variables such as the bargaining power of the producers, and setup cost of moving product items are not discussed in this study.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 4 Simulation
### Simulation Steps  
In Simulink, we constructed a discrete system model based on the system block diagram shown in Figure 3. To analyze the transfer function characteristics of each individual noise source, we followed a simulation process that mirrors the practical use procedures with the instrument. The simulation process was as follows:  
1. Set input conditions. The input acceleration \(a_{l}(t)\) was set to zero to isolate the input noise effect.
2. Introduce noise. Only the specific noise source you investigated was introduced into the system.
3. Run the simulation. The simulation was executed for 100,000 s with a sampling rate of 1 kHz.
4. Collect output. The output signal at point O was collected and recorded as \(D_{out}(t)\).
5. Remove instrument response. The recorded \(D_{out}(t)\) signal was corrected by removing the instrument response, yielding the EIN in acceleration \(Ein(t)\).
6. Signal processing. The time-domain signal \(Ein(t)\) was processed to calculate its PSD \(Ein(f)\).  
The same procedure was applied to simulate the noise transfer functions of all components in the system.
## 4 Numerical Results and Discussion
### Dynamic Analysis
#### 4.1.1 Effect of BCS on Mean Values of Dynamic Response  
The mean values of the six components in ten minutes with different wind speeds in both cases and the fitted curves for No. 2 are shown in Figure 7. It can be seen that each mean value in No. 1 is larger than that in No. 2 with the same wind condition. It also can be seen that the six components in No. 1 have the same trend, the mean values increase with the increase of wind speed. The reason for this phenomenon is that in No. 1, the aerodynamic loads are large when the wind speed is high. However, in No. 2, the mean values increase first in the region of below-rated wind speed range and then decrease in the region of above-rated wind speed range. The reason is that the \(\beta\) begins to increase when wind speed exceeds the rated wind speed (Figure 6) so that appropriate aerodynamic loads are obtained to make sure there is a constant level of torque. Table 3 shows the mean values of the six components in cut-in, rated, and cut-out wind speed and the difference between the two cases. From the table we can know that there are mean values reductions of more than 68% in the cut-out wind speed when the BCS working compared to the No. 1. The three series sine function fits are carried out and are shown in Equations (17)-(22) to describe the relationship between the mean values and the wind speeds when the BCS is working. It was obtained that the BCS has a great effect on the mean values of modern wind turbine structural dynamic responses.  
\[BSX_{\text{mean}} =3.532\sin(0.1281V_{\text{mean}}+0.0614)\ +0.1689\sin(0.802V_{\text{mean}}-0.7839) \tag{17}\] \[\qquad\qquad\qquad\qquad\qquad+0.7025\sin(0.4435V_{\text{mean}}-3. 253)\] \[BFX_{\text{mean}} =205.7\sin(0.1007V_{\text{mean}}+0.0657)\ +32.76\sin(0.4366V_{\text{mean}}-3.217)\] (18) \[\qquad\qquad\qquad\qquad+7.927\sin(0.8067V_{\text{mean}}-0.9028)\] \[BMY_{\text{mean}} =6809\sin(0.1131V_{\text{mean}}+0.0607)\ +1243\sin(0.4366V_{\text{mean}}-3.217)\] (19) \[\qquad\qquad\qquad\qquad+293.7\sin(0.8037V_{\text{mean}}-0.8449)\]  
Figure 5: Rotor speed for different wind conditions with the BCS working.  
Figure 6: Blade-pitch angle for different wind conditions with the BCS working.  
\[TTSX_{\text{mean}} =0.2714\sin(0.1128V_{\text{mean}}-0.0716)\ \ +0.0545\sin(0.4313V_{\text{mean}}-3.136) \tag{20}\] \[+0.0124\sin(0.8087V_{\text{mean}}-0.9306)\] \[TBFX_{\text{mean}} =476.5\sin(0.1128V_{\text{mean}}-0.0197)\ +94.16\sin(0.4336V_{\text{mean}}-3.168)\] (21) \[+21.79\sin(0.8066V_{\text{mean}}-0.8975)\] \[TBMY_{\text{mean}} =44270\sin(0.1128V_{\text{mean}}-0.0528)\ \ +8838\sin(0.4321V_{\text{mean}}-3.147)\] (22) \[+2025\sin(0.8079V_{\text{mean}}-0.918)\]  
Figure 7: Comparison of the mean values with respect to different mean wind levels between the two cases and the fitted curves for No. 2: (**a**) BSX, (**b**) BFX, (**c**) BMY, (**d**) TTSX, (**e**) TBFX, and (**f**) TBMY.
## 7 Simulation Study  
### Methodology and Simulation Settings  
We implement both the ILP model and LNR-RCTD in MATLAB and evaluate their performance with simulations. In our simulation setup, clients (_i.e._, source, destination) nodes are fixed, total client source nodes are set to be 100, and each client source node generates multimedia traffic that has to be transmit to its corresponding destination node. Table 6 represents the QoS requirements of multimedia traffic flows used in our simulation. Table 6 is derived from 3 GPP TS 22.105 [54]. A random backbone mesh topology with different node density is generated within a 1000 \(\times\) 1000 area of two dimensional regions. The transmission range of each node is set to be 300 m, and if the Euclidean distance between two mesh backbone nodes or WMRs is less than the transmission range, an edge is being generated and channel is assigned based on channel interference model. We used IEEE802.11 a/b hybrid radios to utilize orthogonal channel of both IEEE 802.11 b and a. Therefore, number of radios available to each mesh node is directly proportional to the number of available orthogonal channels to that node. Capacity of each link is set to be 100 Mbps, and each link is marked as underutilized if its aggregated utilization is below 10% of the total assigned capacity. Our proposed solutions only operate on a connected topology. Therefore, before applying our solutions the overall connectivity of the mesh topology is assured. QoS-provisioned paths for each pair of source and destination nodes are computed using APAC.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Color (OC)
#### 2.5.1 Optical
IOPs outputs are mostly affected by uncertainties of \(R_{\mathrm{rs}}\) caused by sensor noises, deficiencies in atmospheric corrections, types of parameterization and approximations, as well as assumptions in the forward and the inverse solutions methods ([PERSON] et al., 2014). In the case of spectral inversion algorithms, the main weakness is related to finding the proper parameters of the IOP spectral shapes ([PERSON] et al., 2014). As discussed in [PERSON] et al. (2019), the limitations and challenges of diverse algorithms differ from one another and they highly depend on the intended OC applications. Additionally, there is no universally accepted approach for IOP retrievals in all ocean waters, such as coastal water, turbid water, and complex water. Thus, they usually suffer from region-specific parameterizations. Another issue is that alterations in SST and OSS can result in volatility of thermodynamic properties and changes in marine IOPs in some spectral ranges ([PERSON] et al., 2014). RS techniques for OC mapping have several limitations that are mainly related to data availability, sources of uncertainties in remotely sensed biomass and diffuse attenuation, sub-surface estimations, as well as seasonal and regional variations in phytoplankton photosynthetic parameters. These issues are more challenging in Arctic oceans due to frequent SI and cloud presence. Additionally, the near-surface fogs and clouds, which are typically caused by the melted SI in Polar regions, increase in summer. Therefore, the challenges increase in summer times mainly due to a lack of suitable RS observations ([PERSON] et al., 2014). ML algorithms have been reported to resolve some of the challenges discussed above. It was also reported that a combination of physical and DL models is a promising approach to reducing the limitations of the traditional RS models for OC mapping ([PERSON] et al., 2014). For example, [PERSON] et al. (2019)([PERSON] et al., 2014) developed a CNN architecture to parameterize the water column, including depth, bottom type, and IOPs, using 89 spectral bands of hyperspectral images. Moreover, combining the spatiotemporal autocorrelation and heterogeneity of oceanographic variables within a DL model or designing the spatiotemporally constrained DL models could be a practical solution for many challenges in future OC studies. Finally, it should be noted that despite the numerous advantages of DL techniques, their use for OC retrieval is limited due to the need for large in situ samples ([PERSON] et al., 2014).
# Poly(vinyl alcohol)/Gelatin Scaffolds Allow Regeneration of Nasal Tissues
## 3 Results  
### Chattacterization of PVA/G Spongy Scaffolds  
In this study, PVA/G sponges were produced to be tested as scaffolds for nasal tissue regeneration. In particular, starting from a PVA water solution and G, we fabricated PVA/G scaffolds at two weight compositions, i.e., 80/20 (\(w\)/w%) and 70/30 (\(w\)/w%), using the emulsion and freeze drying method followed by GTA vapor cross-linking. After production, the sponges underwent morphological and physico-mechanical characterization. The results of morphological evaluation are shown in Figure 1. Specifically, both lyophilized sponges showed a highly porous structure, with rounded pores, covering a wide range of diameters, that appeared to be interconnected, as displayed by SEM (Figure 1a,b). A representative surface analysis of these sponges performed using AFM is shown in Figure 1c. This technique highlighted the 3D spongious architecture, showing quite smooth polar surfaces. The results of microCT analysis confirmed the extremely porous character of the sponges (Figure 1d). Specifically, the volume porosity and the pore interconnectivity percentages were 84.43% and 97.44%, respectively, the latter calculated using a cut-off of 4 voxels (corresponding to 51.2 \(\upmu\)m pore-pore openings).  
PVA/G sponges were highly hydrophilic, as water absorption caused a fast swelling (Figure 2a); moreover, they can be squeezed and recover their initial shape (Supplementary video S1). The volume swelling ratio Q was 2.30 \(\pm\) 0.75 and 1.98 \(\pm\) 0.09 for PVA/G 80/20 and 70/30 (\(w\)/w%), respectively, with no statistically significant difference between the two (p = n.s.) (Figure 2b). The wet sponges were used for compressive mechanical tests, which revealed the soft nature of these materials. In fact, 0.69 \(\pm\) 0.04 kPa were necessary to produce 50% strain under compression in PVA/G 80/20 (\(w\)/w%) sponges (Figure 2c).
# An Algorithm for Making Regime-Changing Markov Decisions
## 2 Discrete-Time Stochastic Control
The policy value is obtained as a result of the recursive procedure  
\[v_{T}^{\pi}=r_{T},\quad v_{t}^{\pi}(x)=\mathcal{T}_{t}^{\pi(x)}v_{t+1}^{\pi}( x)\qquad x\in\mathcal{X},\ \ \text{for}\ t=T-1,\ldots,0 \tag{4}\]  
which is referred to as the _policy iteration_. The value of a \"best possible\" policy is addressed using  
\[v_{0}^{*}(x)=\sup_{\pi}v_{0}^{\pi}(x),\quad x\in\mathcal{X}, \tag{5}\]  
where \(\pi\) runs through the set of all policies, the function \(v_{0}^{*}\) is called the _value function_. The goal of the optimal control is to find a policy \(\pi^{*}=(\pi_{t}^{*})_{t=0}^{T-1}\) where the above maximization is attained:  
\[v_{0}^{\pi^{*}}(x)=v_{0}^{*}(x)\quad\text{ for each }x\in\mathcal{X}.\]  
Such policy optimization is well-defined (see [6], p. 199). Thereby, the calculation of an optimal policy \(\pi^{*}\) is performed in the following framework: For \(t=0,\ldots,T-1\), introduce the _Bellman operator_  
\[\mathcal{T}_{t}v(x)=\max_{a\in A}(r_{t}(x,a)+\mathcal{K}_{t}^{a}v(x)),\quad x \in\mathcal{X} \tag{6}\]  
which acts on each measurable function \(v:\mathcal{X}\to\mathbb{R}\) where \(\mathcal{K}_{t}^{a}v\) is well-defined for all \(a\in A\). Further, consider the _Bellman recursion_, (also called backward induction)  
\[v_{T}^{*}=r_{T},\quad v_{t}^{*}=\mathcal{T}_{t}v_{t+1}^{*}\qquad\text{for }t=T-1,\ldots,0. \tag{7}\]  
There solution \((v_{t}^{*})_{t=0}^{T}\) to the above Bellman recursion returns the _value function_\(v_{0}^{*}\) and determines an optimal policy \(\pi^{*}\) via  
\[\pi_{t}^{*}(x)=\operatorname*{argmax}_{a\in A}\big{(}r_{t}(x,a)+\mathcal{K}_{t }^{a}v_{t+1}^{*}(x)\big{)},\quad x\in\mathcal{X},\quad t=0,\ldots,T-1. \tag{8}\]  
**Remark 1**. : _In practice, discounted versions the above stochastic control are popular. These are obtained by replacing the stochastic kernel \(\mathcal{K}_{t}^{a}\) by \(\kappa\mathcal{K}_{t}^{a}\) in the backward induction where \(\kappa\in[0,1]\) is a discount factor. The advantage of this approach is that for long time horizons, the optimal policy becomes stationary, provided that all rewards and transition kernels are time-independent._
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 4 Simulation Experiment  
Plane fitting in the pose measurement process may exert a great impact on the accuracy of the method. Therefore, a simulation experiment is carried out on the plane fitting method. A plane is constructed in the space, several points in the plane are projected onto the corresponding plane of the binocular camera, noises of 0.25, 0.5, and 1 pixel are added to the image plane points, and the method is verified. In different noise environments, within the range of 10 to 32 m, the error is between the solved plane and the constructed plane equation. Figure 8a shows the result of the method obtained under a 0.25-pixel noise. The angle error is approximately \(1^{\circ}\) within 32 m. Figure 8b shows the result of the method obtained under a 0.5-pixel noise. The results show that the error basically increases as the distance increases. The error reaches 2.38\({}^{\circ}\). Figure 8c shows the result of the method obtained under a 1-pixel noise. The error reaches 2.44\({}^{\circ}\) at 25 m. At 32 m, the error is close to 4\({}^{\circ}\), which exerts a greater impact on the measurement accuracy.  
Corresponding to the experimental conditions, the pixel noise is used as a variable, and simulation experiments are performed at 10, 20, and 30 m, as shown in Figure 9. Figure 9c shows that at 30 m, after the error of more than 0.65 pixels, the angle error has exceeded 2\({}^{\circ}\); when the noise is close to 1 pixel, the angle error is close to 4\({}^{\circ}\), which echoes the previous experiment.  
In summary, the simulation experiment shows that the matching error must be reduced as much as possible to meet actual needs. This error should ideally be controlled to 0.5 pixels, that is, the angle error is controlled at about 2\({}^{\circ}\), which can better meet the demand.