esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Efficient Modal Modification Analysis Method
### Neumann Series Expansion- and Reduced Basis-Based Preconditioner Method
#### 3.1.1 Procedure of the Proposed Preconditioner Method  
The procedure of the proposed method shown in Figure 3 can be summarized as follows.  
**Step 1**: Obtain the change in the combined matrix by \(\Delta\mathbf{A}=\mathbf{A}_{m}-\mathbf{A}\);  
**Step 2**: Compute the first \(r\) terms of the basis vectors by using Equation (20). Since the lumped matrices \(\mathbf{M}\) and \(\mathbf{H}\) are diagonal, the inverse of the combined matrix \(\mathbf{B}_{m}\) is trivial. Thus, the basis vector can be efficiently calculated by only sparse matrix multiplications. The algorithm for obtaining the basis vectors is given in Algorithm 1;  
**Step 3**: Normalize the basis vector using the Gram-Schmidt orthonormalization procedure in Algorithm 2. Note that the basis vectors will become linearly dependent  
Figure 3: Flowchart of the modal repeated analysis method.  
if a large value of the number \(r\) is chosen. With the Gram-Schmidt orthonormalization algorithm, the proposed method is robust;  
**Step 4**: Calculate the reduced system matrices by Equation (24) and solve the reduced eigenproblem in Equation (25) for the first-order mode. The eigenvalue of the first-order mode is the modified eigenvalue \(\lambda_{mi}\);  
**Step 5**: Validate the convergence of the Neumann series expansion with the spectral radius of the expanded term \(\mathbf{Y}=\xi_{i}\mathbf{B}_{m}^{-1}\Delta\mathbf{A}\) in Equation (16).
## 3 Datasets and Evaluation Metrics  
### Datasets  
The choice of dataset is an important part of the research and development of HDR image reconstruction algorithms. The success of deep learning models and image processing techniques depends largely on the availability and quality of the data used for training and validation [75]. However, obtaining HDR images is a costly and complex process, which requires captures with multiple exposures and the use of specialized equipment. In addition, the varied lighting and movement conditions in dynamic scenes further increase the difficulty of generating large volumes of data with consistent quality [26].  
This section presents the main datasets used in HDR image reconstruction research that can be found available for research use and accessible for download; they are classified in a taxonomy that considers their characteristics and capture types. These datasets range from image sequences captured with multiple exposures in real environments to synthetic images generated by simulation or captured on mobile devices. Also discussed are data augmentation methods applied to recent and relevant datasets in the field of HDR imaging research, which seek to increase data variability and mitigate the limitation of small datasets.  
Table 1 summarizes the datasets analyzed, with their specifications, main characteristics, and recommended application areas.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Reference** & **Quantity** & **Resolution** & **Type** & **Key Features** & **Applications** \\ \hline [PERSON] et al. [15] & 89 sequences & 1500 \(\times\) 1000 & Real & \begin{tabular}{l} Dynamic Scenes, \\ Multi-Exposure, Deghosting \\ \end{tabular} & \begin{tabular}{l} HDR Reconstruction, \\ Machine Learning \\ \end{tabular} \\ \hline [PERSON] et al. [65] & 8 sequences & 1350 \(\times\) 900 & Real & \
\begin{tabular}{l} Extracted from HDR Videos, \\ Continuous Motion, Deghosting \\ \end{tabular} & HDR Video Reconstruction \\ \hline IISc VAL [48] & 582 sequences & 1000 \(\times\) 1500 & Real &
\begin{tabular}{l} Variety of Scenarios, Moving \\ Objects, Deghosting \\ \end{tabular} & Multi-Frame Fusion, Deghosting \\ \hline \end{tabular}
\end{table}
Table 1: Summary of HDR image reconstruction datasets.  
Figure 4: Examples of two common structural artifacts: (**a**) checkerboard patterns arising from improper deconvolutional layers [74]; (**b**) simplified contours caused by high-frequency interpolation failures.
###### Abstract  
In this paper, we analyze the performance of a full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) relaying system with an amplify-and-forward (AF) protocol in the presence of loopback interference in FD transceivers. Particularly, by considering channel estimation errors and quantization noise in low-resolution analog-to-digital converters (ADCs), the accurate approximation expression for the ergodic rate and closed-form solution for the outage probability are derived, respectively. The validity of the theoretical results is verified by Monte Carlo simulations, which show that both channel estimation errors and quantization noise have deleterious effects on ergodic rate and outage performance for moderate and high signal-to-noise ratios (SNR). In the second phase of the C-NOMA system, both the outage performance and ergodic sum rate decrease at high SNRs due to the effects of loop interference. When the ADC dynamic range reaches a certain level, the system performance is more affected by loopback interference and channel estimation errors compared to the quantization noise of the ADCs.  
full duplex; cooperative communication; non-orthogonal multiple access; channel estimation error; low-resolution ADC
# Numerical Investigation of Methodologies for Cavitation Suppression Inside Globe Valves
###### Abstract  
Cavitation inside globe valves, which is a common phenomenon if there is a high-pressure drop, is numerically investigated in this study. Firstly, the cavitation phenomenon in globe valves with a different number of cages is compared. When there is no valve cage, cavitation mainly appears at the valve seat, the bottom of the valve core, and the downstream pipelines. By installing a valve cage, cavitation bubbles can be restricted around the valve cage protecting the valve body from being damaged. Secondly, the effects of the outlet pressure, the working temperature, and the installation angle of two valve cages in a two-cage globe valve are studied to find out the best method to suppress cavitation, and cavitation number is utilized to evaluate cavitation intensity. Results show that cavitation intensity inside globe valves can be reduced by increasing the valve outlet pressure, decreasing the working temperature, or increasing the installation angle. Results suggest that increasing the outlet pressure is the most efficient way to suppress cavitation intensity in a globe valve, and the working temperature has a minimal effect on cavitation intensity.  
c 2020 July 2020; Accepted: 7 August 2020; Published: 11 August 2020
## 3 Error Model and Cross-Identification Algorithm
### Cross-Identification Method Based on LM Algorithm
#### 3.2.1 LM Algorithm  
Equation (23) establishes the linear transformation relationship between the pose error of the TCF in the MCF and the geometric parameter error. Obviously, as long as there are enough sets of tool end pose data, an overdetermined linear equation system can be constructed to represent the tool end pose error and geometric parameter error, and then the calibration of robot parameters can be achieved. Assuming that there are \(h\) sets of tool end poses, according to Formula (23), we have the following:  
\[\begin{bmatrix}n^{t}d^{1}\\ m^{t}d^{2}\\ m^{t}d^{2}\\ \vdots\\ m^{t}d^{h}\\ m^{t}d^{h}\\ m^{t}d^{h}\\ \end{bmatrix}=\left[\begin{array}{cccccccc}M_{\theta}{}^{1}&M_{d}{}^{1}&M_{ a}{}^{1}&M_{\alpha}{}^{1}&M_{\beta}{}^{1}&E_{u}{}^{1}&F_{v}{}^{1}\\ M_{\theta}{}^{1}&M_{d}{}^{1}&M_{a}{}^{\prime}{}^{1}&M_{a}{}^{\prime}{}^{1}&M_{ \beta}{}^{1}&E_{u}{}^{1}&F_{v}{}^{1}\\ M_{\theta}{}^{2}&M_{d}{}^{2}&M_{a}{}^{2}&M_{a}{}^{2}&M_{\beta}{}^{2}&E_{u}{}^{2 }&F_{v}{}^{2}\\ M_{\theta}{}^{2}&M_{d}{}^{2}&M_{\eta}{}^{2}&M_{\eta}{}^{2}&M_{\theta}{}^{2}&E_{ u}{}^{2}&F_{v}{}^{2}\\ &&\vdots\\ M_{\theta}{}^{h}&M_{d}{}^{h}&M_{a}{}^{h}&M_{a}{}^{h}&M_{\beta}{}^{h}&E_{u}{}^{h }&F_{v}{}^{h}\\ M_{\theta}{}^{h}&M_{d}{}^{h}&M_{a}{}^{\prime}{}^{h}&M_{a}{}^{\prime}{}^{h}&M_{ \beta}{}^{h}&E_{u}{}^{h}&F_{v}{}^{h}\\ \end{array}\right]\begin{bmatrix}\Delta\theta\\ \Delta d\\ \Delta\alpha\\ \Delta\beta\\ \Delta u\\ \Delta\
u\\ \end{bmatrix} \tag{25}\]  
Equation (25) can be simplified as follows:  
\[\Delta P=J\cdot\Delta\rho \tag{26}\]  
Thus, the industrial robot geometric parameter identification problem is transformed into a linear equation system solving the problem. The LM algorithm can solve the above problem, and its iteration steps at the \(k\)-th iteration are as follows:  
Step 1: Calculate the Jacobian matrix \(J(\rho_{k})\) based on the industrial robot structural parameters \([\theta_{i},\ d_{i},\ a_{i},\ a_{i},\ \beta_{i}]\) and the transformation parameters \([u,\ v]\).  
Step 2: Calculate the geometric parameter error vector \(\Delta\rho_{k}\) according to the following equation:  
\[\Delta\rho_{k}=-\left[J(\rho_{k})^{T}J(\rho_{k})+\mu_{k}I\right]^{-1}J(\rho_{k} )^{T}\Delta P(\rho_{k}) \tag{27}\]where \(\mu_{k}\) is the damping coefficient, which can be calculated as follows:  
\[\left\{\begin{array}{l}\mu_{0}=0.001\\ \mu_{k+1}=\left\{\begin{array}{l}0.001\lambda,\ \parallel\Delta P(\rho_{k+1}) \parallel\geq\ \parallel\Delta P(\rho_{k})\parallel\\ \frac{0.001}{\lambda},\ \ \ \parallel\Delta P(\rho_{k+1})\parallel\leq\ \parallel\Delta P(\rho_{k}) \parallel\\ 2.5\leq\lambda\leq 10\end{array}\right.\end{array}\right. \tag{28}\]  
Step 3: Update the geometric parameters based on \(\rho_{k+1}=\rho_{k}+\Delta\rho_{k}\).
## 5 Delay Calculation Error Analysis
### Calculation Errors of Single Conversion Standard and By-Vehicle Type  
Based on the average delay simulation results of 178 sets of examples, the delay calculation errors of the single conversion standard and by-vehicle type in the multi-conversion standard were calculated. The average errors of the average delay of cars are shown in Figure 4, which are 7.32 s and 5.69 s, respectively. The average errors of the average delay of buses are shown in Figure 5, which are 21.75 s and 3.52 s, respectively. It can be seen that the multi-conversion standard delay calculation method was more accurate, and the errors of the average delay of cars and the average delay of buses were reduced by 1.63 s and 18.23 s, respectively, with a reduction rate of 22.54% and 83.82%.  
Figure 4: Average error in average delay of cars.  
Figure 5: Average error in average delay of buses.  
The following are the average delay of cars and buses for the 178 sets of examples, including the simulation results, the calculation results of delays of by-vehicle type in single, and multi-conversion standard.  
(1) Average delay of cars  
As can be seen from Figure 6, the number ratio of buses to cars varies between 10% and 20%, compared with the single conversion standard, most of the calculation results of the multi-conversion standard are closer to the simulation results, with 99% of the average delay for cars being calculated with less error.  
(2) Average delay of buses  
As can be seen from Figure 7, the number ratio of buses to cars varies between 10% and 20%, compared with the single conversion standard, most of the calculation results of the multi-conversion standard are closer to the simulation results, with 98% of the average delay for buses being calculated with less error and a greater reduction in error.  
The calculation result of bus delay was improved because the single conversion standard method calculated the bus delay with the small car as the standard, and the speed was calculated as the average value of the small car and the bus, while the multi-conversion standard method proposed in this paper was calculated with the bus as the standard, and  
Figure 6: Average delay of cars.  
Figure 7: Average delay of buses.  
the speed was calculated as the bus speed, which was more consistent with the actual situation.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 5 An Algorithmic Metatheorem for Constant-Depth Circuits and Its Applications
### Bounded Tree Depth
Then, the language_ \(\{\mathcal{G}\mid\mathcal{G}\models\phi\text{ and }\mathcal{G}\text{ has tree depth at most }d\}\) _can be decided by an AC_\({}^{0}\)_-circuit family._Let \(\phi(X)\) be a formula in monadic second-order logic with a free monadic second-order variable \(X\). Then, the language \(\{(\mathcal{G},s)\mid\mathcal{G}\models\phi(C)\) for some \(C\subseteq V\) with \(|C|=s\), and \(\mathcal{G}\) has tree depth at most \(d\}\) can be decided by a TC\({}^{0}\)-circuit family. In the theorem, I used the classes AC\({}^{0}\) and TC\({}^{0}\) to formalize \"constant parallel time\". Both are defined in a similar way as the circuit class NC\({}^{1}\), only the depth of the circuits must now be constant (which means that we get results out of them in constant time), but the and- and or-gates may now have any number of inputs (otherwise, the output of such a circuit could depend on only a constant number of input bits). Furthermore, TC\({}^{0}\)-circuits may contain \"threshold gates\" that output 1 when the number of 1s in their inputs exceeds a threshold. Intuitively, TC\({}^{0}\)-circuits are good at counting things, while AC\({}^{0}\)-circuits are not (and this is precisely the reason why we need TC\({}^{0}\)-circuits in the above theorem in the part where we count how many elements there are in \(C\)). One can even prove that TC\({}^{0}\)-circuits are more powerful than AC\({}^{0}\)-circuits: the latter cannot compute the parity of a bitstring. When we apply the above algorithmic metatheorem to unary-subset-sum, we see that we need the second part of it (since we are interested in sets \(C\) of a certain size \(s\)) and get the following result:  
Theorem 4.1: unary-subset-sum\(\in TC^{0}\)_._  
Since it is not hard to prove that unary-subset-sum is also hard for TC\({}^{0}\), we have finally answered [PERSON]'s question of how difficult unary-subset-sum really is: it is complete for TC\({}^{0}\).
## 3 Virtual Space-Time Diversity Turbo Equalization  
The proposed virtual space-time diversity SDA-TEQ (VSTD-TEQ) for SISO UWA communication systems is demonstrated in Figure 1. To exert plenitathous performance of single carrier system for the data accuracy demand of oceanic PNT, we integrated Soft FS-SE with an anti-Doppler module, time-reversal bidirectional processing and turbo equalization technology systematically into the proposed scheme. The parameters are adaptively updated based on PRLS-type algorithms. The red line in Figure 1 represents the input and output of the adaptive updating scheme, the black line represents the signal flow, and the blue line represents the feedback signal. The meanings of the parameters in Figure 1 have been stated in related sections. Symbols \(\bigotimes\) and \(\bigoplus\) represent the multipliers and adders, respectively.
# Grasp Planning Based on Metrics for Collaborative Tasks Using Optimization
## 4 Experiments and Results
### Grasp-Planner-Metrics Analysis  
Area and centroid metrics aims to balance disturbances and stability problems that can happen in a grasp. Being a geometric element, the grasp polygon should cover the largest area on the object to avoid as much as possible any external force that affects human-robot grasp. If the area metric is analyzed individually for a specific human grasp on a desk, we observe, as shown in Figure 8, that the grasp polygon has the largest possible area, but it is not enough because it does not take into account the stability that the centroid metric would give. Yellow triangles represent human grasping points and the red ones represent the solution set of possible grasp points for the robot as a result of the grasp planner.  
Similarly, the polygon centroid property allows to control how stable a grasp can be if it gets as close as possible to the grasped object's center of mass. Again, if we only evaluate the centroid metric independently, we can see, for the same grasp on the desk, as Figure 9 shows, the planner optimization will converge on a zone where the centroid can be set much closer to the object's center of mass, but the result grasp points are different than the results of the area metric; that is why we integrate both in an MO optimization problem to have the best of each one.  
Now, if we combine these two metrics as a bi-objective problem, the planner will try to cover zones of points where the two metrics analyzed above are optimized as much as possible, as seen in Figure 10a. These grasping point sets are not necessarily the best for  
Figure 7: Pareto Front for ZTD6 Function. (a) MOPSO; (b) MOGWO; (c) NSGA-II.  
only area or only centroid metrics. However, in a scenario of imminent grasp at any of these points, there is a possibility of obtaining large inertial forces when the object begins to move in any task, because of areas furthest away from the desk like lower parts of the legs or the top of oneself.  
To neutralize this problem, a heuristic inertial metric has been created that considers more grasping point zones and, thus, obtains a more reliable set of points with reference to the properties we are considering for our grasp planner. In Figure 10b, this improvement is observed because the grasping point zone increases, emphasizing the importance of the created metaheuristic metric and giving us more possible grasp positions for the robot.  
Figure 8: Grasp planner solution for a desk considering only area metric.  
Figure 10: Grasp planner solution for a desk. (**a**) Without and (**b**) with heuristic metric. Dimensions in centimeters.  
Figure 9: Grasp planner solution for a desk considering only centroid metric. Dimensions in centimeters.
## 5 Energy Management
### Active Demand-Side Response  
Four different DR requests can be managed by the VPP architecture: Price-Based DRs, Reduction Bids, Incentive-Based DRs, and Event-Based DRs. The first three DR requests, usually applied during the normal operating mode of the grid, are managed by the EMS, while Event-Based DRs--which are usually sent during critical events or emergencies--are managed by the DER Automated Controller as discussed in Section 6.  
Price-Based DR requests can be sent by the DSO or by DR aggregators. DR requests are collected by communication agents of the VPP and sent to the Optimal Scheduling Unit, which uses the price information for the computation of the optimal working point of the VPP. At the same time, Reduction Bids can be generated by the OSU and sent to the DSO or to DR aggregators. Finally, Incentive-Based DRs are usually sent by the DSO or the IO by requiring active power limitations (i.e., upper and lower bounds) at the Point of Delivery of the prosumer. Incentive-Based DRs are sent to the OSU in terms of power constraints over the time period specified by the DR request along with the economic value of the proposed incentive. If an optimal solution is determined, the DR request is accepted by the EMS, and the related optimal schedules are sent to the programmable DERs and loads. In addition, the active power limitations defined by the Incentive-Based DR are sent to the DER-AC, which performs the active power control of the supervised DERs in order to ensure that, in case of unexpected variations in the forecasted DER profiles, the required constraints set by the DR request can be accomplished.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 3 Materials and Methods
### The TFSW Algorithm for LST Retrieval
With these constants, it is able to retrieve LST from the FY-3D MERSI-II TIR bands under the conditions where thetwo required parameters AT and LSE are estimated. This algorithm is just for the cloud-free pixels' LST estimation. Thus, the cloud detection product was used as the cloud mask layer to filter clear-sky pixels. The LST retrieval algorithm (Equation (2) requires three major parts: (1) radiation calibration for the computation of brightness temperature, (2) water vapor content, and (3) atmospheric transmittance based on water vapor. A more detailed description of these parameters and its calculation methodology required for the improved TFSW algorithm can be found in [7; 25].
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Resolution System  
At the initial stage of multicast routing, it is crucial to identify a suitable tree node for subsequent path construction from the multicast tree to the receivers. Both source-initiated and receiver-initiated routing methods entail the selection or search process for tree nodes. Aimless search processes may result in significant message overhead. The ICN NRS manages the mapping between multicast service IDs and the network addresses of tree nodes. Direct interaction with the resolution system facilitates convenient access to information about multicast nodes. By designing a registration and resolution mechanism to simplify the tree node search and selection process, it is possible to reduce the signaling cost associated with searching for suitable tree nodes.  
We focus on the ICN network employing a name-based lookup mechanism. A flat naming approach is utilized to identify data entities, with the Multicast Service Identifier (MID) generated based on the content hash. Therefore, a resolution service is needed to map the MID to the NAs. Our proposed resolution system consists of a Global Name Resolution System (GNRS) and multiple Enhanced Name Resolution Systems (ENRS).  
The GNRS allows all nodes to register information and respond to resolution query requests. Multiple ENRS are divided based on node latency. Within each ENRS, independent instances are deployed, allowing only nodes within the domain that satisfy latency constraints to register and respond to queries within those constraints. The structure of the resolution system is illustrated in Figure 4.  
The root node (RN) of the multicast tree registers its information to the GNRS, while tree nodes (TN) within an enhanced resolution domain register their information to their respective Enhanced Name Resolution System (ENRS). As depicted in Figure 5, the left side illustrates a multicast tree with MID 3, comprising five tree nodes, including S and N\({}_{1}\)-N\({}_{4}\), where S serves as the root node of the multicast tree. The table on the right offers a concise description of the entries within the resolution system. Root node S is registered in the GNRS, whereas the information of tree nodes N\({}_{3}\) and N\({}_{4}\) is recorded in Enhanced Name Resolution System 1 (ENRS-1). Each MID is associated with an NA List, which stores the network addresses NA\({}_{i}\) of node N\({}_{i}\).  
Figure 4: The process of interacting with the NRS. The node will initially interact with the ENRS within the yellow region, and subsequently, based on the outcome, engage with the GNRS within the blue region.
## 3 Results and Discussion
### Communication Overhead
#### 3.2.1. Public Key  
Figure 9 shows the comparison of public key sizes using different access mechanisms. In LIBAC and MCP-ABE-BG, the public key size maintains a constant value of 0.256 kB and 0.896 kB, respectively, for all attributes. However, in the proposed MCP-ABE-BG, the key size, at both lower and higher attributes, is only 0.198 kB, which is minimal compared to the other two approaches.  
Figure 8: Decryption time.  
Figure 7: Encryption time comparison.