esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mercury in BECCS System  
### BECCS System  
Biomass is a promising zero-carbon fuel [17]. Unlike other zero-carbon fuels, biomass molecules contain carbon atoms, and the combustion of biomass does produce CO\({}_{2}\). However, the carbon atoms in biomass are absorbed from the atmosphere during its growth and converted through photosynthesis. Therefore, from a lifecycle perspective, biomass can be considered as a zero-carbon fuel, and the CO\({}_{2}\) generated from its combustion should not be included in carbon emission calculations. Biomass and coal are both solid fuels with similar combustion processes. Co-firing biomass in traditional coal-fired boilers is feasible and can reduce carbon emissions from power plants.  
Oxy-fuel combustion is an important method for carbon capture. In an oxy-fuel combustion system, air is no longer introduced into the furnace. Instead, oxygen is mixed with recycled flue gas (primarily CO\({}_{2}\) and H\({}_{2}\)O) and fed into the furnace to react with the fuel [18]. As a result, N\({}_{2}\), which makes up the majority of air, is no longer present in the flue gas. The oxy-fuel combustion flue gas mainly consists of CO\({}_{2}\) and H\({}_{2}\)O. After removing H\({}_{2}\)O through condensation, a high concentration of CO\({}_{2}\) is left, with some other impurities. Therefore, compared to carbon capture under traditional air combustion conditions, the cost of separating and purifying CO\({}_{2}\) from flue gas in oxy-fuel combustion is significantly reduced [19]. Furthermore, the absence of N\({}_{2}\) in the flue gas of oxy-fuel combustion inhibits the formation of thermal NO\({}_{x}\)[20]. In summary, oxy-fuel combustion can drive the widespread application of carbon capture technology.  
Biomass co-firing and oxy-fuel combustion both contribute to reducing CO\({}_{2}\) emissions. Combining these two technologies can enhance carbon reduction dramatically. With a high proportion of biomass blending, negative carbon emissions can even be achieved. Additionally, retrofitting traditional coal-fired units with biomass co-firing and oxy-fuelcombustion has a relatively low difficulty and cost. Therefore, biomass co-firing under O\({}_{2}\)/CO\({}_{2}\) atmosphere plays a significant role in achieving global carbon reduction goals.
## 4 Results and Discussion
As shown in Figure 10, it is clearly shown that the phase fluctuation affects the SER performance seriously without any phase mismatch compensation. We can also see that a stable SER performance of 10 \(\times\) 10\({}^{-4}\) can be obtained after the ASOP in the range of 25\({}^{\circ}\). Instead, the GSOP and SYOP methods can maintain that SER performance only in the range of 5\({}^{\circ}\) and the GSOP performed more poorly than SYOP. Within the range of 5\({}^{\circ}\), their performance is about the same due to the M-th power carrier phase estimation scheme we applied [21]. Thus the ASOP can improve the system performance under weak turbulence conditions and when the phase mismatch degree is above 10\({}^{\circ}\), or the phase noise tolerance will be reduced for the phase estimation algorithm. In order to further verify the performance of our algorithm under different turbulence intensities and communication distances, we consider the different atmospheric structure constant and communication distance, which stands for weak, moderate, and strong atmospheric turbulence conditions. The parameters used in Table 3 are calculated using Equations (1) and (3). In order to conform to the practical application, the transmitted power in this simulation is 30 dBm. Therefore, the SER of the system will be improved in order of magnitude compared with the previous simulation results. It is clearly shown that with the increase of turbulence intensity, the SER gradually deteriorates. We can also see that the performance of the ASOP algorithm is better than the other two algorithms  
Figure 10: SER versus \(\phi_{\text{IQ}}\) with phase estimation under weak turbulence for \({C_{n}}^{2}\) = 1.5 \(\times\) 10\({}^{-15}\) m\({}^{-2/3}\). OP: orthogonalization procedure; PF: phase fluctuation; W: with; W/O: without. Figure 9: SER versus \(\phi_{\text{IQ}}\) with a carrier frequency offset estimation; W/O: without. under different turbulence conditions, which benefits from its flexible switching between the two algorithms.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
## 2 Materials and Methods
### Numerical Method  
To assess the impact of flow patterns on ignition and flame stability across various strut configurations, numerical simulations were conducted on the specified geometric model using the Reynolds-averaged Navier-Stokes (RANS) code based on the finite volume solver of commercial platform Fluent22.2. A standard k-\(\epsilon\) model was selected as a turbulence model, which has been verified to accurately simulate the flow characteristics in the combustor with the cavity-strut combustor without bypass flow [22]. A standard wall function method was utilized for processing the near wall. The coupling of pressure and velocity was accomplished through the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm.  
The simulation employed the gas transport equation, incorporating discrete kerosene-air mixture terms into the previously mentioned geometric model. Combustion was modeled using the Eddy Dissipation Concept (EDC) model, coupled with the previously mentioned simplified reaction mechanism, which comprises 13 species and 16 reactions. The ambient pressure of the combustion chamber is 101,325 Pa, and detailed boundary conditions are shown in Table 1.  
Figure 4: Test facility.  
To reduce the computational accuracy's reliance on mesh size, this study used ICEM 2022a software for mesh generation and refined the junction between the strut and pilot to a size of 1 mm. Numerical calculations were carried out for computational domains with total mesh counts of 2.36 million, 3.56 million, 5.90 million, and 8.55 million. Figure 5 shows the axial velocity at line 1, as shown in Figure 1, where it is influenced by the complex vortex generated between the cavity zone and strut zone. It was found that different mesh sizes have little effect on calculation results. Thus, balancing computational resources and the need for sufficient data, numerical simulations are conducted with 5.9 million elements.
# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 2 How Is a Radiomic Analysis Performed?  
The radiomic analysis process is carried out in different steps. The first step is image acquisition and segmentation. Then, the process continues with feature extraction and selection (these features are used to realize a model), followed by the final step which involves performance assessment of the algorithm.  
It could be applied to many imaging techniques such as X-ray, ultrasound scan (US), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). To begin, a field of interest (ROI) or volume of interest (VOI), as well as the dedicated area, must be outlined by humans or machines. To save time, as well as cost and subjectivity or error related to manual segmentation, clearly defined algorithms that are able to segment automatic or semi-automatic lesions have been developed [17]. Sometimes, the compound nature of the medical images (ill-defined borders of the lesion, changes in the form of tissues after chemotherapy or radiotherapy, recognizability of the lesion from adjacent anatomical structures) may limit the performance of these algorithms.  
In such situations, the segmentation and delineation of the ROI performed manually by an expert on one selected 2D slice or the entire 3D volume could provide the solution [18; 19].
## 4 Results and Discussion
### Sedimentation Rate  
Sedimentation rate is an important quantity both to understand the phenomenon and to plan the drainage project. According to the definition of Equation (5) the function \(u\) can be deduced from sedimentation profiles provided that one knows how calculating the derivative of the function \(H(t)\). Notwithstanding, experimental errors prevent a simple differential technique from being used to obtain the derivative. A method, already successfully used in the calculation of the partial molar volumes [15; 16; 17], the volume fraction distribution via self-diffusion measurements [18], the activation energy distribution function of complex reactions [19], and the degradation rate of indocyanine green in aqueous solution [20], is now proposed for computing, directly from experimental data, sedimentation rate.  
In order to get a good estimate of the derivative at a point, a function must be fitted to several data points on both sides of that point. Then, the resulting function can be differentiated analytically. To do this, it is convenient to start at the top of the table of data and move step-wise down, evaluating the derivative at each center point by _arc moving_ or _strip moving_. Furthermore, all experimental data require smoothing before differentiation is performed. Smoothing is performed herein by passing a least-squares polynomial through the data points to determine the polynomial coefficients and then replacing the experimental points with those calculated from the polynomial. The data points were smoothed, using five points in the movable strip and a third-degree polynomial. The smoothing was terminated at the stage where the random error was minimized. This technique is employed to calculate the sedimentation velocity and the solid concentration \(C_{s}\) by Equation (7).  
Figure 5(a) displays a semilog plots of \(u\) vs. \(C_{s}\) for dispersions polymer free, interestingly these samples exhibit a linear trend, indicating an exponential decreasing. Figure 5(b) shows sedimentation rates of dispersions treated with a polyacrylamide solution. It is evident that even these dispersions exhibit a linear trend, however, the straight lines, within the experimental error, are parallel.  
Figure 4: Master curve of clay material mixed with polyacrylamide. Samples from the Occhito reservoir.  
Figure 5: Procedure for extrapolating \(H_{\infty}\). The red dot represents the value of the normalized height measured after 4 days.  
This means that the lifetime [14] of suspended particles is independent of solids concentration of solids accordingly particles exhibit a same falling speed. Once again, the effect is to be attributed to the polymer-induced bridging-flocculation.
## 2 System Model and Assumptions
### Integer Delay Matrices and Traditional Network Interference Conflict Model  
In a linear acoustic communication network with nonzero propagation delay, the propagation delay between nodes \(i\) and \(j\) is defined as \(D^{\prime}_{ij}\), where \(\forall i\
eq j,1\leq i\leq N\). Assume that the channel is symmetric, i.e., \(D^{\prime}_{ij}=D^{\prime}_{ji}\). The geometric relationship between network nodes is represented by the delay matrix \(D^{\prime}\), which is defined by using a standard unit time slot length.  
\[D^{\prime}_{ij}=\frac{del\_Bellhop(w_{i},w_{j},env)}{\tau} \tag{1}\]  
where \(w_{i}\) represents the spatial location of node \(i\) in three-dimensional space; \(env\) represents the current network environment parameters, primarily including the sound velocity profile and submarine type; \(del\_Bellhop(\cdot,\cdot,\cdot)\) represents the propagation delay calculated by the Bellhop model under the current environment and network node location; and \(\tau\) represents the length of a time slot. It is important to note that the elements of the delay matrix are propagation delays between links in units of time slot length \(\tau\) and can be rational numbers, i.e., \(D\) can be a noninteger delay matrix. However, with an appropriate choice of time slot length \(\tau\), the given noninteger delay matrix can be approximated by an integer delay matrix \(D\) as:  
\[D_{ij}=\left\lfloor\frac{del\_Bellhop(w_{i},w_{j},env)}{\tau}\right\rfloor \tag{2}\]  
where \(\lfloor a\rfloor\) is the integer nearest to a and less than \(a\).  
The conventional model of network interference collision proposes that when the timing of two packets reaching the same receiver overlaps, the receiving node is unable to correctly receive any packet.  
The network transmission mentioned in this paper is unicast propagation, which means that a sending node corresponds to only one receiving node, and the length of the message \(\mu\) is equal to the slot length \(\tau\). Assuming normalization of the distance between adjacent nodes \(D_{j,j+1}=1\) as the standard, and normalization of node distance for communication to \(RC=1\) and node interference distance to \(RI=k\), this assumption implies that each node can only communicate with its neighbors and that nodes within a distance of \(k\) from the sending node will experience interference in reception.
## 2 Instrumentation and Methods
### Processing and Usage of Flux Data  
ChinaFLUX has used several mature methods to study the performance of the observational system and data quality [40; 72; 74]. The methods of raw data processing, data gap filling of CO\({}_{2}\) flux in the daytime and nighttime, and the threshold have been fully reported [40; 72; 75; 76; 77; 78; 79; 80] and were employed in this study.  
In this study, a multi-source-driven SIF product (named GOSIF) was used to evaluate the relationship between SIF and GPP [81], which is based on Orbiting Carbon Observatory-2 (OCO-2) SIF data, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data, and Modern-Era Retrospective analysis for Research and Application (MERRA-2) meteorological data. The SIF product provides a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days on a global scale (see GOSIF website at [[https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)]([https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)), accessed on 1 October, 2024). The long time series of the SIF product with a temporal extent from 2000 to 2022 makes it a powerful dataset for ecosystem change  
Figure 2: The eddy covariance (EC, **up left**) and solar radiation measurement (**up right**) systems in Qianyanzhou subtropical coniferous forest operated by the Qianyanzhou experimental station. Another set of solar radiation (global radiation, direct radiation, and PAR) measurement systems (**bottom left**) located at a top of the building, and a HOBO weather station (**bottom right**) located at a meteorological observation station operated by [PERSON]’s group.  
research. Here, we have selected the SIF product at 8-day temporal for the period from 2003 to 2017 within a 1\({}^{\circ}\) radius of the Qianyanzhou station to analyze the relationships between SIF and GPP under different atmospheric conditions.
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
###### Abstract  
In quantum chemistry, constructing the Fock matrix is essential to compute Coulomb interactions among atoms and electrons and, thus, to determine electron orbitals and densities. In the fundamental framework of quantum chemistry such as the Hartree-Fock method, the iterative computation of the Fock matrix is a dominant process, constituting a critical computational bottleneck. Although the Fock matrix computation has been accelerated by parallel processing using GPUs, the issue of performance degradation due to memory contention remains unresolved. This is due to frequent conflicts of atomic operations accessing the same memory addresses when multiple threads update the Fock matrix elements concurrently. To address this issue, we propose a parallel algorithm that efficiently and suitably distributes the atomic operations; and significantly reduces the memory contention by decomposing the Fock matrix into multiple replicas, allowing each GPU thread to contribute to different replicas. Experimental results using a relevant set/configuration of molecules on an NVIDIA A100 GPU show that our approach achieves up to a 3.75\(\times\) speedup in Fock matrix computation compared to conventional high-contention approaches. Furthermore, our proposed method can also be readily combined with existing implementations that reduce the number of atomic operations, leading to a 1.98\(\times\) improvement.  
GPU; Fock matrix; two-electron repulsion integrals; Hartree-Fock method; quantum chemistry; parallel algorithm; memory contention; atomic operations 2020
# DPS Clustering: New Results
## 2 Materials and Methods
### Discrete Perfect Sets  
DMA has a rigorous theory of discrete perfect sets (DPS-sets) in finite spaces, which is summarized in [5]. A complete rigorous justification can be found in [3].  
Let \(X\) be a finite set, and let \(A,B,\dots\) and \(x,y,\dots\) be subsets and points in it, respectively.  
Figure 1: Comparison of algorithms: (**a**)—initial array; (**b**)—the result of clustering by SDPS-algorithm; (**c**)—the result of clustering by the DBSCAN-algorithm.  
**Definition 1**.: _Let us call a density \(P\) on a set \(X\) a product of mapping \(2^{X}\times X\) to non-negative numbers \(\mathbb{R}^{+}\), increasing on the first argument and trivial on the empty:_  
\[\begin{array}{c}P(A,x)=P_{A}(x)\\ \forall x\in X,A\subset B\Rightarrow P_{A}(x)\leq P_{B}(x),\ P_{\varnothing}( x)=0\end{array} \tag{1}\]  
For a fixed \(x\), the function \(P_{A}(x)\) is a non-normalized fuzzy measure on \(X\), so the density \(P\) is a family of such measures parameterized by \(X\) itself.  
By fixing a density level \(\alpha\) and interpreting it as a limit level, one can define any topological notions in \(X\), and discrete perfection with level \(\alpha\) particularly.  
**Definition 2**.: _A set \(A\) consisting of exactly all \(\alpha\)-perfect points of the original space \(X\) is called an \(\alpha\)-discrete perfect (just perfect, DPS-) set in \(X\):_  
\[A\ -\text{DPS-set in }X\Leftrightarrow A=\{x\in X:P_{A}(x)\geq\alpha\} \tag{2}\]  
Numerous studies and examples below show that DPS-sets are clusters in \(X\) and are closely related to clustering in \(X\).  
In the works [2; 3; 5], a construction is given that allows a subset \(A(\alpha)\subset X\) to be constructed from a subset \(A\) in the space \(X\) and the level \(\alpha\) of the density \(P\). Under the condition of nontriviality, it will be an \(\alpha\)-DPS-set in \(X\). It does not have to lie in \(A\), but if \(A\) itself is an \(\alpha\)-DPS-set in \(X\), then the set \(A(\alpha)\) constructed for it will coincide with it. Thus, the construction \(A\Rightarrow A(\alpha)\) depends on the space \(X\), subset \(A\), density \(P\), and level \(\alpha\). To emphasize this fact, we introduce the notation  
\[A(\alpha)=A_{P}(\alpha|X). \tag{3}\]  
The properties of the construction \(A\Rightarrow A_{P}(\alpha|X)\) are formulated in Statement 1, which is proved in [3].  
**Statement 1**.: _The dependences on \(A\), \(P\) and \(X\) are increasing, and the dependence on \(\alpha\) is decreasing:_  
1. _If_ \(A\subseteq B\)_, then_ \(A(\alpha)\subseteq B(\alpha)\) _;_
2. _If_ \(P\)_,_ \(Q\) _are densities on_ \(X\) _and_ \(P_{A}(x)\leq Q_{A}(x)\ \forall x\in X\)_, then_ \(A_{P}(\alpha)\leq A_{Q}(\alpha)\)_;_
3. _If_ \(\alpha<\beta\)_, then_ \(A(\beta)\subseteq A(\alpha)\)_._
4. _If_ \(A\subseteq X\subset Y\) _and density_ \(P\) _is given on_ \(Y\)_, then_ \(A(\alpha|X)\subseteq A(\alpha|Y)\)_._
## 4 Experiment Results and Discussion
### Comparison and Qualitative Results  
The suggested tracking model was evaluated using VisDrone2019 [54] and OTB-100 [55] to compare it to recent state-of-the-art object tracking models based on deep reinforcement learning techniques. These datasets are available on the internet in a variety of image and video sets, including training, testing, and challenge sets.  
Figure 9 compares the performance of the proposed tracking methodology with that of recent state-of-the-art trackers, such as ADNet [52] and ASRL Track [56], which are both based on the DRL (Deep Reinforcement Learning) strategy. Comparison was carried out by testing video sets of the two public datasets VisDrone2019 [54] and OTB-100 [55] respectively. The graphic above depicts precision performance with regard to local error threshold outcomes in two public datasets. Table 3 (below) shows the precise results of the numerical comparison:  
In Table 3, the results of a comparison of recent RL-based object tracking approaches and the proposed model are emphasized. The table compares the accuracy, frames per second (FPS), and intersection over union (IoU) outcomes of two current approaches with our model. Among these approaches, our strategy outperformed the others when tested on the identical open-source datasets. For the testing process, video inputs were used to examine the tracking capabilities of the approaches. Moreover, we provide some qualitative results below in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{3}{c}{**VisDrone2019**} & \multicolumn{3}{c}{**OTB-100**} \\ \cline{2-7}  & **Precision** & **FPS** & **IOU** & **Precision** & **FPS** & **IOU** \\ \hline
**ADNet**[52] & 89.69\% & 4.45 & 0.599 & 80.52\% & 4.03 & 0.492 \\ \hline
**ASRL\_Track**[56] & 90.70\% & 6.94 & 0.602 & 81.62\% & 6.41 & 5.99 \\ \hline
**Ours** & 92.07\% & 9.11 & 0.785 & 82.35\% & 7.18 & 0.662 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of recent tracking algorithms with proposed method on VisDrone2019 and OTB-100 datasets.  
Figure 9: In this plot, comparison results of three different types of RL-based recent works are shown. The red (ADNet [52]), green (ASRL_Track [56]), and blue (ours) colors show the result of comparison in testing the (**a**) VisDrone2019 and (**b**) OTB-100 datasets respectively.  
Figure 10 shows the qualitative results of the tracking model with two types of targeted objects. As we can see in Figure 10, the percentages of predicted and targeted object classes indicate how well the object classes were predicted. The result in Figure 10 demonstrates a good performance in terms of predicted and tracked object classes. We tested our proposed algorithm only in this virtual environment, and it performed much better compared to the other models, which, in the majority, have been tested with real video sequences. We have tested our algorithm only with the default simulation environment case, which is the normal weather condition.
## 4 Results and Discussion
### Performance Evaluation  
Four video sequences, i.e., \"Coastguard\", \"Akiyo\", \"Foreman\" and \"Hall\", were used to compare the performance of the proposed codecs, against the DIS and Intra H.264 codecs. For each video sequence, the average PSNRs and coding rates are determined for different values of the quantization metric, \(\mathbf{Q_{m}}\). The average PSNR and coding rate of Intra H.264, DIS, DIVCOM, and PDIVCOM were computed for a frame rate of 15 Hz. Next, the performance of the Wyner-Ziv coder is analyzed by evaluating the average channel-decoding rate required to correct the errors of SI for successfully decoding the WZF. Finally, the rate distortion (RD) performance is compared between Intra H.264, DIS, DIVCOM, and PDIVCOM.