esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of SFE-CO\({}_{2}\) of Hops at 24-45 MPa Pressure
#### 3.2.2 Analysis of the Response Surface Plots  
Two-dimensional and three-dimensional response surface plots visualize the effects of the independent variables on the extract yield (Supplementary Materials, Figure S1) and TEACORAC (Supplementary Materials, Figure S2). For example, the plots illustrating the effect of T and P at fixed \(\tau\) of 60 min indicated that the amount of the extract and TEACORAC did not exceed 20 g/100 g and 300 mg TE/g, respectively, at the minimal P (-1 level) of 25 MPa. Nevertheless, extraction at 25 MPa and 40 \({}^{\circ}\)C already after 30 min was more efficient than 180 min extraction at 10 MPa, amounting to 166 and 216% of the final 10 MPa yield and TEACORAC, respectively; the results for both responses after 90 min were higher by 5-43% than those measured for 12.5 and 15 MPa after 300 min (Tables 1 and 2). This may be explained by the substantially higher CO2 density and solvating power towards lipophilic constituents at 25 MPa and 40 \({}^{\circ}\)C (880 kg/m\({}^{3}\)) in comparison to 10, 12.5 and 15 MPa under the same extraction temperature (629, 705 and 780 kg/m\({}^{3}\), respectively).  
The analysis of 2D and 3D response surface plots in Figure S1 (Supplementary Materials) also outlined that combinations of 37-42 MPa and 40-45 \({}^{\circ}\)C augmented the yields to the maximum values (>26 g/100 g) within the selected region of operability. Maximum yield values were also reached due to the prolonged \(\tau\) (>75 min), as depicted in Figure S1a,b (Supplementary Materials). Although the CO2 diffusivity and solute vapor pressure are greater at higher temperatures [22], the yield reduction was observed >45 \({}^{\circ}\)C at all tested P levels. This can be explained by the decreasing solvent density due to the T increase from 40 to 60 \({}^{\circ}\)C: by 11% at 25 MPa (from 880 to 787 kg/m\({}^{3}\)), 8% at 35 MPa (from 935 to 863 kg/m\({}^{3}\)) and 6% at 45 MPa (from 975 to 913 kg/m\({}^{3}\)). Thus, the effect of density governs the retrograde behavior of T in the _Ella_ hop extraction model. Moreover, even high-end experimental P (45 MPa) remains lower than the so-called crossover (inversion) P value, when the higher T would favor the extraction since increasing solute vapor pressure would outweigh the impact of decreasing CO2 density [32].  
For TEACORAC, the plots with temperature and pressure effects at the fixed extraction time acquired a slight saddle shape, as presented in Figure S2a (Supplementary Materials). The highest values were reached at the 35-40 MPa and 40-43 \({}^{\circ}\)C combinations, which overlapped favorably with the optimal T and low-to-middle range of the desired P for the maximum yield. Based on TEACORAC, even higher amounts of radical scavengers were recovered by continuing extraction (\(\tau\) > 80 min), especially at T < 45 \({}^{\circ}\)C, which is in agreement with the observations for the yield.
# Accurate Prediction and Key Feature Recognition of Immunoglobulin
## 2 Materials and Methods
### Feature Extraction  
#### 2.2.1 Profile-Based Cross Covariance (CC-PSSM)  
The CC-PSSM feature representation method is based on the position-specific scoring matrix (PSSM) [36; 37] as a feature. Each immunoglobulin sequence runs PSI-BLAST [38] through NCBI's NR database for local information comparison to obtain PSSM matrix information. The element \(S_{ji}\) in the PSSM matrix represents the substitution score of the amino acid \(i\) at the sequence position \(j\). The twenty kinds of natural amino acids are composed of a set \(\{A,R,N,D,C,Q,E,G,H,I,L,K,M,F,P,S,T,W,Y,V\}\).  
Each protein sequence containing \(D\) residues can be represented as  
\[R=R_{1}R_{2}R_{3}R_{4}\ldots R_{D} \tag{2}\]  
where \(R_{j}(j=1,2,3\ldots D)\) represents the position \(j\) of the residue in the protein sequence sample \(R\).  
CC-PSSM [39] transforms PSSM matrices of different sizes into vectors of the same length. CC [40] was used to calculate the different properties of the two residues separated along with the sequence lag. The formula was calculated as follows  
\[CC(i_{1},i_{2},LG)=\sum_{j=1}^{D-LG}{(S_{j,i_{1}}-\overline{S}_{i_{1}})(S_{j+ LG,i_{2}}-\overline{S}_{i_{2}})}/D-LG \tag{3}\]  
where \(i_{1}\)\(i_{2}\) represents two different amino acids, \(\overline{S}_{i_{1}}\), \(\overline{S}_{i_{2}}\) represents the mean of substitution scores for amino acids \(i_{1}\) and \(i_{2}\) along the sequence, and D represents the length of the protein sequence. Calculated in this way, the PSSM matrix resulting from each protein sequence alignment will be transformed into a vector of length \(380*lag\), and lag is the maximum value of LG (LG = 1, 2..., lag). In this study, we set the maximum lag number to 2. When LG = 1, the extracted features such as \"CC(A,R,1)\", \"CC(A,N,1)\", \"CC(A,D,1)\", etc., are transformed into a vector of length 380. When LG = 2, the extracted features such as \"CC(A,R,2)\", \"CC(A,N,2)\", \"CC(A,D,2)\", etc., are transformed into a 380-length vector. Therefore, each protein sample was finally computationally transformed into a vector of length 760. The demonstration process is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence Length** **(Amino Acids)**} & \multicolumn{3}{c}{**Immunoglobulin Dataset**} & \multicolumn{3}{c}{**Non Immunoglobulin Dataset**} \\ \cline{2-7}  & **Human** & **Mouse** & **Rat** & **Human** & **Mouse** & **Rat** \\ \hline \textless{}400 & 26 & 11 & 3 & 16 & 31 & 3 \\
400–700 & 22 & 12 & 2 & 8 & 24 & 2 \\ \textgreater{}700 & 20 & 10 & 3 & 5 & 28 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of sample sequence lengths in immunoglobulin and non immunoglobulin datasets.
## 2 Materials and Methods
### Micrometeorology Measurements
#### 2.5.2 Flux Partitioning  
Eddy covariance NEE measurements were partitioned into gross ecosystem productivity (GPP) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) and respiration (R\({}_{\rm eco}\)) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)). R\({}_{\rm eco}\) was calculated using the [PERSON] et al. [64] partitioning algorithm. Periods of poor mixing, identified by low frictional velocity (\(u^{\star}\) < 0.21 m s\({}^{-1}\)) and representing possible unreliable NEE measurements, were removed. A light-response curve model [65; 66; 67; 68] was used as an independent method for GPP derivation and calculations of apparent quantum yield. This method uses rectangular hyperbolic functions to express the response of photosynthesis to radiation, using the general expression:  
\[\mathrm{NEE}=-\left(\frac{A_{max}\alpha\cdot\mathrm{PPFD}}{A_{max}+\alpha\cdot \mathrm{PPFD}}\right)+R_{10}Q_{10}^{\mathrm{(T_{air}-10)}/10} \tag{11}\]  
where \(A_{max}\) is the maximum carbon assimilation, or GPP, (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) at maximum photosynthetic photon flux density (PPFD) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(\alpha\) is the apparent quantum yield calculated from the initial slope of the light-response curve (mol CO\({}_{2}\) mol\({}^{-1}\) PPFD); \(R_{10}\) is the ecosystem respiration rate at 10 \({}^{\circ}\)C (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(Q_{10}\) is the respiration temperature response coefficient during temperature changes of 10 \({}^{\circ}\)C; and T\({}_{\rm air}\) is the air temperature (\({}^{\circ}\)C). \(A_{max}\), \(\alpha\), \(R_{10}\) and \(Q_{10}\) variables are resulting outputs of the non-linear least square, Gauss-Newton, regressions applied (using the statistical package Systat10, Systat Software, Inc., San Jose, CA, USA, 2000), of the input diurnal meteorological (PPFD and T\({}_{\rm air}\)) and NEE flux data. Before light-response curves were derived, NEE data was binned over two days to derive representative diurnal patterns.
## 3 Application
### Channel Candidate Models of Different Widths  
In this study, candidate models of different channel widths were established by combining drilling and seismic data based on target simulation and human-computer interaction according to the sedimentary background of the block and modern sedimentary research, as shown in Figure 10.  
In establishing the model, the background and modern sedimentary research achievements in the study area and the geological understanding were taken into account, through the well and seismic combination of regional sedimentary background, previous research results, and coring wells in an integrated analysis of sedimentary facies marks. Full use was made of well logging, coring, and other data, combined with the superimposed relationship of sand bodies to further study the sedimentary microfacies and sand body distribution. According to the lateral comparison of the logging data from the dense well network, the direction of the river channel could be basically determined. From the statistical results and the planar microphase distribution map, a table of geometric parameters for modelling the underwater diversion channel using the target-based approach was determined (Table 5). The width of the channel sand body follows the triangular distribution in Table 6. Combined with the results of well seismic prediction, the underwater diversion channel models were developed using target-based simulation and human-computer interaction for channel widths of 100, 130, 160, 190, 220, and 250 m, respectively. The results can reflect the qualitative distribution pattern of underwater distributary channels, and can also provide a model basis for multi-point geostatistical stochastic simulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Amplitude (m)** & **Wavelength (m)** & **Thickness (m)** & **Width (m)** \\ \hline
400\(-\)1000 & 1500\(-\)3000 & 1\(-\)5 & 100\(-\)250 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Geometric parameters of underwater diversion channels in the study area.  
Figure 10: Candidate models for different channel width conditions.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## Materials and Methods  
The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression (GTEx) Data Sets  
_FOXM1_ somatic copy-number alterations were compared in provisional datasets for TCGA primary tumors obtained from cBioPortal [65]. TOIL GTEx (cell lines removed) and TCGA (normal and primary tumor only) total gene and transcript RNA-seq datasets were obtained from UCSC Xena ([[https://xena.ucsc.edu/](https://xena.ucsc.edu/)]([https://xena.ucsc.edu/](https://xena.ucsc.edu/))) and were used for pan-normal and cancer analysis of _FOXM1_ mRNA expression and _FOXM1_ mRNA isoform expression (Ensembl Genome Reference Consortium release GRCh38: FOXM1a, Ensembl transcript ENST00000342628.6; FOXM1c, Ensembl transcript ENST00000359843.7; FOXM1b, Ensembl transcript ENST00000361953.7). TOIL reprocesses raw GTEx and TCGA RNA-sequencing data to correct for batch effects and to allow for the merging of samples across GTEx and TCGA datasets for pan-analyses [27]. The level 4 TCGA RPPA dataset was obtained from The Cancer Proteome Atlas [66; 67] and was used for pan-cancer analysis of FOXM1 protein expression. The RPPA level 4 dataset was a reprocessing of raw TCGA RPPA data using a novel approach, called'replicates-based normalization' (RBN) to correct for batch effects and ultimately allow for the merging of samples across 19 different cancer types for pan-cancer analyses [68]. TCGA aneuploidy scores and arm call files were obtained from the Genomic Data Commons Pan-Cancer Atlas [38; 39]. Aneuploidy Cluster Membership was obtained from the published supplemental section [38]. The CCLE transcript RNA-seq dataset was used for comparison of _FOXM1_ isoform mRNA expression in human cancer cell lines [69].
## 4 Data Collection and Preparation
### Generating Non-Crash Cases for the Crash Likelihood Modeling  
In the crash likelihood model the matched case-control methodology was used to introduce the non-crash cases to match the crash cases. The methodology is implemented by generating four non-crash cases for each crash at the same location, day of the week, and time-of-day: one case in the week before, one case two weeks before, one case in the week after, and one case two weeks after the corresponding crash occurrence. The 1:4 ratio of crash cases to non-crash cases were selected based on [PERSON] and [PERSON] [27] who found this ratio to provide slightly better results when compared to other ratios of crash-to-non-crash cases. It should be noted that the matched case-control method employed in this study accounted for the confounding effects of the location (with the corresponding roadway characteristics) and time on crash occurrence. The other factors, such as vehicle, driver, weather and road condition parameters at the time of crash were not considered
## 2 Measurement Method and Experimental Setup Design  
M-DLS employs off-axis integrated cavity output spectroscopy (OA ICOS) technique [11]. Two DFB lasers are used in the M-DLS for spectroscopic measurements of the Martian air probe in the vicinity 2.656 \(\upmu\)m and 2.808 \(\upmu\)m. The frequency of each laser is tuned continuously in the range of about 1 cm\({}^{-1}\) by current modulation. As there is a frequency sweep in the signal, the M-DLS measures fully resolved absorption line profiles of CO\({}_{2}\), H\({}_{2}\)O and theirs isotopologues with a spectral resolution of \(\sim\)0.002 cm\({}^{-1}\). The ICOS cell is exploited in the M-DLS for several reasons. Firstly, an ICOS cell is a compact construction, which allows reaching a long effective optical path needed to provide a high sensitivity of the M-DLS for detection of narrowly-spread isotopologues in the Martian atmosphere; secondly, the ICOS system has a construction resistant to the external impacts (strike, vibration), as compared with multipass optical systems.  
It is essential to carry out the proper accumulation of the measured signal to reach a high SNR and measurement sensitivity of the M-DLS. An integration time of the majority diode laser spectrometers does not exceed the value of several seconds [12]. In our case, it is possible to accumulate a measured signal during periods of up to tens of minutes, since we exploit the ICOS cell as a reference channel, where absorption line peaks registered by the ICOS cell are used for synchronization of laser frequency tuning cycles. Such a method provides the level of the frequency stabilization less than 10\({}^{-3}\) cm\({}^{-1}\) and will be described further in a separate subsection.  
The instrument functionality could be divided into three main systems, which will be described in details below:  
* Martian air sample preparation system;
* Optical measurements system;
* Control electronics system.  
The natural form of the M-DLS is presented in Figure 2a,b. The M-DLS operates in tandem with another instrument of the landing platform--MGAP (Martian gas analytical package), in particular, they have a joint air sample preparation system. This system contains two external interfaces--one for taking Martian air probe from the atmosphere into the spectrometer and one for sharing this probe with the MGAP. After air sample preparation procedure has been finished, the M-DLS makes spectroscopic measurements in the optical cell and transmits results to a landing platform computer BIP (a Russian acronym for the block of interfaces and memory).  
The M-DLS has radiation-resistant unibody aluminum case with an oxide-fluoride coating. The whole weight of the device is about 3 kg with dimensions of 350 \(\times\) 188 \(\times\) 113.5 mm (not including air intake device contribution to the length of the M-DLS). It is expected that the M-DLS is going to take measurements in the temperature range of \(-\)20 \({}^{\circ}\)C to 40 \({}^{\circ}\)C, which are provided by the landing platform.
## 3 Proposed Opportunistic Routing Algorithm
### Remaining Energy Distribution  
In MSR-WSNs, an important metric for determining the priority of candidate nodes is the node's remaining energy. However, collecting the remaining energy data of the marine node in real-time will generate a large communication overhead. For the sending node \(i\), the energy probability distribution \(\widetilde{\xi}_{i}=\Big{(}\widetilde{\xi}_{i_{1}},\widetilde{\xi}_{i_{2}}, \ldots,\widetilde{\xi}_{i_{j}}\Big{)}\) is defined based on the energy regularized random variable \(\overline{\xi}_{i}=\Big{(}\overline{\xi}_{i_{1}},\overline{\xi}_{i_{2}}, \ldots,\overline{\xi}_{i_{j}}\Big{)}\), and the expressions of \(\overline{\xi}_{i_{j}}\) and \(\widetilde{\xi}_{i_{j}}\) are as follows [31],  
\[\overline{\xi}_{i_{j}}=1+\bigg{(}\frac{\overline{\xi}_{j}}{\epsilon_{0}} \bigg{)},\forall j\in F_{i_{j}} \tag{13}\]  
\[\widetilde{\xi}_{i_{j}}=e^{\big{(}\widetilde{\xi}_{i_{j}}\big{)}^{\gamma_{ \Phi}}}/\sum e^{\big{(}\widetilde{\xi}_{i_{j}}\big{)}^{\gamma_{\Phi}}},j\in F _{i_{j}}\text{ and }\gamma_{\Phi}\geq 0 \tag{14}\]where: \(\gamma_{\Phi}\) is energy distribution control parameter, \(\tilde{\xi}_{j}\) is the remaining energy of the candidate node \(j\), \(e_{0}\) is the initial energy of the node, \(F_{i_{j}}\) is the set of candidate nodes. To increase the lifetime of MSR-WSNs, routing metrics should be dynamically updated in cycles \(T=t_{\text{min}}\) based on the node's remaining energy. Otherwise, marine nodes with high priority will prematurely exhaust their energy, resulting in routing holes and link failures. We utilize the energy probability distribution (Equation (14)) and combine the state of the marine environment to update the routing metrics. Whenever an marine node loses 5% of its energy, it broadcasts the current energy state to its neighbor nodes. The neighbor nodes then update their energy probability distribution and communication link quality. Among them, the bandwidth resources and energy consumption required to update the communication link quality are very low. The pseudo-code for updating the energy distribution of neighbor nodes is shown in Algorithm 1.  
```
1. Each forwarding the sending node \(i\) loses 5% of its energy do
2. Inform the \(F_{i_{j}}\) about the current remaining energy value.
3. For each candidate node \(j\in F_{i_{j}}\)do
4. Recalculate \(\tilde{\xi}_{i}=\left(\tilde{\xi}_{i_{j}},\tilde{\xi}_{i_{2}},\ldots,\tilde{ \xi}_{i_{j}}\right)\) by Equation (14)
5. Update \(P_{i_{j}}(t)\) by Equation (15)
6. End for
7. For each candidate node \(j\in F_{i_{j}}\)
8. For each \(P_{i_{j}}(t)\)do
9. If \(P_{i_{j}}(t)\geq\frac{1}{1+\tilde{q}_{i_{j}}}\)do // \(q_{i_{j}}\) is the number of neighbor nodes.
10. Add neighbor node \(j\) to \(F_{i_{j}}\)
11. End if
12. End for
13. End for
```  
**Algorithm 1** Updating the Energy Distribution of Neighbor Nodes
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 1 Introduction
Noncoherent channel combining is studied for GNSS signal tracking with an adaptive antenna array to improve signal tracking precision and measurement accuracy after interference suppression. 3. A thorough comparison and analysis of the proposed method with several representative blind anti-jamming techniques is performed to show its superiority over conventional methods. The remainder of this paper is organized as follows. Section 2 briefly introduces the signal model. Section 3 presents the proposed method with detailed analysis. Section 4 carries out simulations and analyzes the results. Finally, Section 5 concludes this paper.
## 5 Mathematical Analysis of the Proposed Mechanism
### End-to-End Delay  
The end-to-end delay for data transmission depends on the number of hops, transmission delay \(t_{d}(t)\), data processing time \(pr_{d}(t)\), propagation delay \(p_{d}(t)\), queuing delay \(q_{d}(t)\), and the number of nodes involved between the source and destination.  
\(l_{d}\) is defined as the link delay of data packet between the nodes, as given in Equation (12)  
\[l_{d}=q_{d}(t)+t_{d}(t)+p_{d}(t)+pr_{d}(t) \tag{12}\]  
M/M/1 queuing model is utilized in the proposed work to find the delay experienced in the queue and it is calculated as  
\[q_{d}(t)=\frac{\frac{\beta k}{b}}{1-\frac{\beta k}{b}}\times\frac{k}{b} \tag{13}\]  
where \(k\) denotes the length of the data packets, \(b\) is the transmission bit rate, \(\beta\) is the average arrival data rate.  
The propagation and transmission delay is determined using Equations (14) and (15)  
\[t_{d}(t)=\frac{k}{b} \tag{14}\]  
\[p_{d}(t)=\frac{t_{r}}{v} \tag{15}\]  
where \(t_{r}\) is the transmission range, \(v\) is the propagation speed.  
End-to-end delay \((e_{d})\) between the source and destination is calculated as  
\[e_{d}=n_{hop}\times e_{d} \tag{16}\]  
where \(n_{hop}\) is the number of hops.  
The time complexity for the proposed model is \(O(n)\) which depends on the number of intermediate nodes (both routers and MNs).
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 5 Analysis and Discussion
### Limitations  
The decomposition module of our method was adopted from [24], where we trained a transfer and guided module as an independent CNN layer and our model was trained based on the synthetic dataset. However, the proposed method may be not be able to generate clear images, while the dehazing model is not suitable for hazy images. According to our experiments, our model does not work well for hazy night images or for especially hazy images, probably because our decomposition module cannot effectively extract the high-frequency components of background information in hazy night images and particularly hazy images, as shown in Figure 8.  
The following images were collected from the China Weather Net. Our method was shown not to work on hazy night images, possibly because our training dataset did not contain hazy night images, so the GAN module could not learn mapping from a hazy image to a clear image. Future work should involve collecting hazy night images and add them to our existing dataset.  
Our method draws a clear distinction between the close-range contours and the foreground profile in the particularly hazy image, but cannot recover objects covered by severe haze.  
Another limitation of the network is its processing time, which is also a problem with many existing deep learning methods. The processing time of each image using our method is 0.91 s, which does not reach the level of real-time processing, and tehrefore cannot be applied to software that requires real-time processing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination** & **PSNR** & **SSIM** \\ \hline cGAN & 20.35 & 0.855 \\ cGAN + DM & 22.03 & 0.869 \\ cGAN + DM + pipeline & 22.95 & 0.867 \\ cGAN + DM + pipeline + PL(ours) & **23.06** & **0.878** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation of the effects of different components in the outdoors.