esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 2 Related Works
### Higher Order Motion Prediction Models  
In all existing video encoding standards, translation-based prediction is supported. It can be defined mathematically by the following equation:  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix}=\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}v_{x}\\ v_{y}\end{bmatrix} \tag{1}\]  
where \(\begin{bmatrix}x^{\prime}&y^{\prime}\end{bmatrix}^{t}\) represent the coordinates of the points on the reference picture, \(\begin{bmatrix}x&y\end{bmatrix}^{t}\) the coordinates of the points on the current picture, and \(\begin{bmatrix}v_{x}&v_{y}\end{bmatrix}^{t}\) the motion vector.  
Higher order motion prediction models are models that use more than two parameters to represent motion. While it is possible to define motion models with an arbitrarily high amount of parameters, in practice two models have been used the most: the affine motion model, that uses six parameters, defined by Equation (2), and the zoom and rotation model, that uses four parameters, defined by Equation (3).  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix}=\begin{bmatrix}a&b\\ c&d\end{bmatrix}\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}v_{x}\\ v_{y}\end{bmatrix} \tag{2}\]  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix}=\begin{bmatrix}a&b\\ -b&a\end{bmatrix}\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}v_{x}\\ v_{y}\end{bmatrix} \tag{3}\]  
In these equations, \(a\) and \(b\) are the affine motion parameters, \(v_{x}\) and \(v_{y}\) are the translational motion parameters. By comparing with Equation (1), we can see that they are very similar, with an additional two or four parameters added.  
[PERSON] et al. [10] proposed using two 3-parameter models for affine motion compensation to replace the zoom and rotation model, that are defined as follows:\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix} =\begin{bmatrix}1+s&0\\ 0&1+s\end{bmatrix}\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}v_{x}\\ v_{y}\end{bmatrix} \tag{4}\] \[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix} =\begin{bmatrix}1&-r\\ r&1\end{bmatrix}\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}v_{x}\\ v_{y}\end{bmatrix} \tag{5}\]  
The two 3-parameter models are simplifications of the 4-parameter zoom and rotation model. The first model, described in Equation (4), sets \(b\) to \(0\) and \(a\) to \(1+s\), as a value of \(0\) for \(s\) represents a translation. The second model, described in Equation (5), sets \(b\) to \(r\) and \(a\) to \(1\), so a value of \(0\) for \(r\) represents a translation.  
Because it is common that the movement is either zooming or rotation rather than a combination of both, it is common that one of the two affine parameters is much smaller than the other. In this case, reducing the number of parameters will reduce the coding cost of the prediction without losing much accuracy.  
Using this dual model option allows for good efficiency, but it requires doing the parameter estimation process twice.
###### Abstract  
Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols. This protocol introduces two routing metrics: node energy based on node degree balancing and relative node mobility, to comprehensively account for both the balance of network node load and the stability of network links. The experimental results demonstrate that the AODV-EM protocol exhibits better performance compared to traditional AODV protocol in unmanned cluster networks with dense node distribution and high mobility, which not only improves the efficiency of data transmission, but also ensures the reliability and stability of data transmission.  
high dynamic scenarios; intelligent unmanned clusters; routing protocol 26 December 20247 January 2025  
7 January 2025
## 2 Experiment and Methodology
### \(C_{n}^{2}\) Estimation Model  
\(C_{n}^{2}\) estimation models are developed to convert standard meteorological data into the \(C_{n}^{2}\) vertical profiles based on the basic theory of turbulence. According to [PERSON]'s theory, the Tatarski \(C_{n}^{2}\) model [3] is  
\[C_{n}^{2}=aL_{0}{}^{4/3}M^{2} \tag{3}\]  
where \(a\) is a constant of 2.8 [17], \(L_{0}\) is the outer scale, which is the largest scale of inertial range turbulence, and \(M\) is the potential refractive index gradient defined as  
\[M=\frac{79\times 10^{-6}P}{T^{2}}\frac{\partial\theta}{\partial h} \tag{4}\]  
where \(M\) is related to atmospheric temperature \((T)\), atmospheric pressure \((P)\), and potential temperature \((\theta)\). \(h\) is the height above ground. From these Equations it can be noted that the vertical temperature gradient can be estimated from sounding data, and, thus, the selection of an appropriate \(L_{0}\) is the key factor for estimating \(C_{n}^{2}\).  
With the Tatarski model, several outer scale models have been proposed to estimate \(C_{n}^{2}\) profiles from different atmospheric parameters. Among them, the HMNSP99 model is the most widely used [21, 22, 23]. The HMNSP99 model was developed at the Air Force Research Laboratory(AFRL) Holloman Spring 1998 and Holloman Spring 1999 thermosonde campaigns (New Mexico), and the statistical relationships relating \(L_{0}\) parameterization to wind shear \((S)\) and temperature lapse rate \((dT/dh)\) [24]. The relationships are expressed as  
\[L_{0}{}^{4/3}=\left\{\begin{array}{ll}0.1^{4/3}\times 10^{0.362+16.7285-192. 347\frac{dT}{dt}},\text{Tropsphere}\\ 0.1^{4/3}\times 10^{0.757+13.8195-57.784\frac{dT}{dt}},\text{Stratosphere}\end{array}\right. \tag{5}\]  
where \(u\) and \(v\) are the north and east horizontal wind components.
## 3 Proposed Method
### Dynamic Scheduling
#### 3.2.1 Activation and Deactivation Based on Target Presence  
Before the system begins, the base station transmits target information, including a feature map of the target image, to the local database of each edge device. During the system initialization phase, edge devices are activated or deactivated based on whether a target is detected. Initially, the system activates all edge devices, to search for the target's initial position. Once the target is detected, its feature map is transmitted to the base station, where a target ID is assigned and subsequently distributed to all edge devices. After this step, only the edge device where the target was first detected remains active, while all others are deactivated to conserve computational resources. Even during the tracking process, edge devices are deactivated based on the presence or absence of the target. If an active edge device fails to detect the target for a predefined number of frames, the system assumes that the target has moved to another edge device and deactivates the current edge device. At this time, the system analyzes the target's previous trajectory data, to calculate its movement direction and transmits this information to the base station. This predefined number of frames is a tunable parameter set by the framework administrator, considering factors such as the occlusion rate and target speed.  
Figure 2: An overview of the TEDDY framework: (**a**) System architecture illustrating the base station’s role in scheduling and communicating with edge devices, which process image data locally and send target position updates. (**b**) An example of iterative edge device activation and deactivation, showcasing the dynamic scheduling mechanism across multiple iterations of the TEDDY framework. (**c**) The overall architecture of the proposed TEDDY framework.
## 1 Introduction
The entries that have been set by every input sequence or has \"votes\" from every input sequence are the motifs. As the number of possible strings of length \(l\) grows exponentially with \(l\), this approach becomes infeasible even for small values of \(l\). [PERSON] and [PERSON] [17] designed an algorithm to output motif stems _i.e._, a superset of motifs using regular expression. However, the number of possible motifs that can be generated from this superset might be very large. They also don't provide a mechanism to select the actual motifs. As a result, the stemming approach is difficult to assess. The PMS series of algorithms (PMS1-PMS6, PMSP, and PMSPrune) solve PMS instances relatively fast using a reasonable amount of storage for data structures. PMS1, PMS2 and PMS3 [18] first sorts the \(d\)-neighborhood of input \(l\)-mers using radix sort and then intersects them to find the motifs. PMS4 [19] proposes a very general technique to reduce the run time of any exact algorithm by examining only \(k\) input sequences out of total \(n\) input sequences. It relies on the fact that if there is a motif, it will be present in those \(k\) input sequences as well. PMSP [20] extends this idea further by only examining the \(d\)-neighborhood of the \(l\)-mers from the first input sequence. PMSPrune [20] improves upon PMSP by using dynamic programming branch-and-bound algorithm while exploring the \(d\)-neighborhoods. [PERSON] [21] uses wildcards to first determine the motif patterns and then does a exhaustive search within possible mappings of the pattern to find the motifs. PMS5 [22] improves other algorithms from PMS series by efficiently computing the intersection of the \(d\)-neighborhood of \(l\)-mers without generating the entire \(d\)-neighborhoods for all the \(l\)-mers. PMS6 [23], which is the fastest algorithm in the series, is almost two times as fast as PMS5. This algorithm gets its speedup over PMS5 by grouping \(l\)-mers whose \(d\)-neighborhood computation follows a similar process. Since exact algorithms for motif search are compute intensive, it is natural to attempt parallelization that reduce the observed run time. [PERSON], [PERSON] and [PERSON] [24] have proposed a multi-core motif search algorithm that is based on the voting approach. They followed this work with another parallel algorithm for Graphics Processing Units (GPUs) [25]. Their GPU algorithm is based on examining the branches of a suffix tree in parallel. In this paper, we develop a multi-core version of PMS6 by generating and processing many \(d\)-neighborhoods in parallel. In Section 2 we introduce some notations and definitions used throughout the paper and also describe the PMS6 algorithm in detail. The techniques used to develop PMS6 MC are described in Section 3. The performance of PMS6 MC is compared to that of other parallel motif search algorithms and PMS6 in Section 5.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 2 Preliminaries
### Tuning Parameter  
We now discuss the issue of the tuning parameter. Generally speaking, it is favorable to have less freedom for tuning parameters. The performance of an M-estimator should also be independent of the choice of tuning parameters. However, there is no universally accepted methodology to set these tuning parameters up. One of the standards is based on asymptotic relative efficiency as follows. This guarantees the M-estimator would perform well in a large sample regime. Let us assume that the true model is the normal distribution, and consider an M-estimator. Asymptotic relative efficiency \(\eta\) is defined by the ratio:\[\eta=\frac{\sigma_{\text{MLE}}^{2}}{\sigma_{\text{asymptotic}}^{2}}, \tag{6}\]  
where \(\sigma_{\text{MLE}}^{2}\) and \(\sigma_{\text{asymptotic}}^{2}\) denote asymptotic variances of the MLE and the M-estimator, respectively. Following the tradition, tuning parameters are determined by the condition \(\eta=0.95\)[8]. For bisquare and gamma M-estimators, explicit values for the tuning parameters are known as:  
\[c =4.68\,\vartheta\quad\text{bisquare},\] \[\gamma =0.2\quad\text{gamma},\]  
where \(\vartheta\) is an estimated standard deviation and is often set to the normalized version of the median absolute deviation, called the MADN (The MADN is defined by MAD/0.675 with MAD being the median absolute deviation.) Another tuning parameter of the gamma M-estimator is set to be the estimate of the standard deviation. A remark concerning gamma M-estimator is important. When outliers are not far away from the neighborhood of true data, the choice \(\gamma=0.5\) is observed to be best from many examples known in the literature. We also analyzed this peculiar trick for several gaussian models and reached the same conclusion. Therefore, we adopt the choice \(\gamma=0.5\) in the remaining part of the paper.
## 2 Principal component aggregation in wireless sensor networks
### Data aggregation
#### Communication costs  
This section details the communication costs caused by three types of network operation. The first is the _default_ data collection operation in which all measurement are routed to the sink without any aggregation. This is referred to as the D operation. The second is the _aggregation_ operation, referred to as A operation, which consists in tasking the network to retrieve an aggregate by means of the aggregation service. Finally, we denote by F the _feedback_ operation which consists in flooding the aggregate obtained at the sink back to the whole set of sensors.  
Let \(q\) be the size of a partial state record, \(C_{i}\) be the number of direct children of node \(i\) in the routing tree, \(RT_{i}\) be the size of the subtree whose root is the \(i\)-th node and \(i^{*}_{C}=\arg\max_{i}C_{i}\) the node whose number of children is the highest.  
The following analysis compares the orders of magnitude of the communication costs caused by the D, A and F operations, respectively. For this reason we consider the number of packets processed by each node in an ideal case where overhearing, collisions or retransmissions are ignored.  
Figure 3: Aggregation service at work for computing the norm of the vector measured.  
D operationWithout aggregation, all the measurements are routed to the base station by means of the routing tree. As mentioned before, the network load at the sensor nodes, i.e., the sum of received and transmitted packets, is ill-balanced. The load is the lowest at by leaf nodes, which only send one packet per epoch, while the load is the highest at the root node which processes \(2p-1\) packets (\(p-1\) receptions and \(p\) transmissions) per epoch. The load at a generic \(i\)-th sensor node depends on the routing tree, and amounts to \(2 RT_{i}-1\) packets per epoch.  
A operationDuring the aggregation, the \(i\)-th node sends \(q\) packets and receives a number of packets which depends on its number \(C_{i}\) of children. The total number of packets processed is therefore \(q(C_{i}+1)\) per epoch. The load is the lowest at leaf nodes, which only have \(q\) packets to send, while the load is the highest at the node whose number of children is the highest.  
F operationThe feedback operation consists in propagating the aggregated value back from the root down to the all leaves of the tree. This operation can be used, for instance, to get all sensor nodes acquainted with the overall norm of their measurements or with the approximation evaluated at the sink. The feedback of a packet containing the result of the evaluation generates a network load of two packets for all non-leaf nodes (one reception and one transmission for forwarding the packet to the children) and of one packet for the leaves (one reception only).
## 5 Applications of the Solubility Model
### Prediction of CO\({}_{2}\) Solubility of the CO\({}_{2}\)-SO\({}_{2}\) Mixture in Pure Water  
In this work, the fugacity-activity model can be used to predict the solubility of CO\({}_{2}\)-SO\({}_{2}\) mixed gases in multi-ionic electrolyte solutions. If a fugacity coefficient of CO\({}_{2}\) or SO\({}_{2}\) is calculated from the EOS of CO\({}_{2}\)-SO\({}_{2}\) of Li [48], combining the solubility model of CO\({}_{2}\) developed by [PERSON] and [PERSON] [5] and the solubility model of SO\({}_{2}\) here, we can calculate the solubility of CO\({}_{2}\) and SO\({}_{2}\) in multi-ion electrolyte solutions. However, only experimental CO\({}_{2}\)-SO\({}_{2}\) solubility data in pure water have been reported [54]. Figure 6 shows the calculated solubilities of CO\({}_{2}\) of CO\({}_{2}\)-SO\({}_{2}\) mixtures in pure water under different _T-P_ conditions. It can be seen in Figure 6 that the CO\({}_{2}\) solubilities of CO\({}_{2}\)-SO\({}_{2}\) mixtures in pure water increase with the increase of pressure at the same temperature and mole fraction of SO\({}_{2}\), but decrease with the increase of SO\({}_{2}\) at the same _T-P_ conditions. The calculated solubilities of CO\({}_{2}\) with a 0.1 mole fraction of SO\({}_{2}\) are in agreement with the experimental data of [PERSON] [54] (Figure 6a,c,d).  
Figure 5: SO\({}_{2}\) solubilities in seawater (models vs. experimental data) [41]. (**a**) seawater salinity = 10.065 g/kg; (**b**) seawater salinity = 20.034 g/kg; (**c**) seawater salinity = 30.075 g/kg; (**d**) seawater salinity = 40.005 g/kg. \(m_{\text{SO}_{2}}\) is the solubility of SO\({}_{2}\), \(T\) is the temperature and \(P_{\text{SO}_{2}}\) is the partial pressure of SO\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**System** & **Reference** & **_T_ (K)** & **_P_ (bar)** & \(\dfrac{m_{\text{S seawater}}}{\text{(g/kg)}}\) & **Nd** & **AAD (\%)** & **MAD (\%)** \\ \hline SO\({}_{2}\)-Seawater & Douabul and Riley [41] & 278.97–303.25 & 1\({}^{*}\) & 10.00–40.00 & 24 & 7.48 & 12.75 \\ \hline \hline \end{tabular}  
* Notes: _T, P_ and \(m_{\text{S seawater}}\) denote the temperature, pressure and salinity of seawater, respectively; Nd is the number of experimental data; AAD is the average absolute deviation calculated from this model; MAD is the maximal absolute deviation calculated from this model. 1\({}^{*}\) is the partial pressure of SO\({}_{2}\).  
\end{table}
Table 5: Calculated deviations of SO\({}_{2}\) solubilities in seawater.
###### Abstract  
Radiometric normalization is an essential preprocessing step that must be performed to detect changes in multi-temporal satellite images and, in general, relative radiometric normalization is utilized. However, most relative radiometric normalization methods assume a linear relationship and they cannot take into account nonlinear properties, such as the distribution of the earth's surface or phenological differences that are caused by the growth of vegetation. Thus, this paper proposes a novel method that assumes a nonlinear relationship and it uses a representative nonlinear regression model--multilayer perceptron (MLP). The proposed method performs radiometric resolution compression while considering both the complexity and time cost, and radiometric control set samples are extracted based on a no-change set method. Subsequently, the spectral index is selected for each band to compensate for the phenological properties, phenological normalization is performed based on MLP, and the global radiometric properties are adjusted through postprocessing. Finally, a performance evaluation is conducted by comparing the results herein with those from conventional relative radiometric normalization algorithms. The experimental results show that the proposed method outperforms conventional methods in terms of both visual inspection and quantitative evaluation. In other words, the applicability of the proposed method to the normalization of multi-temporal images with nonlinear properties is confirmed.  
Keywords:relative radiometric normalization; phenological normalization; nonlinear; multilayer perceptron; radiometric resolution compression; spectral index +
Footnote †: journal: Applied
## 6 Experimental Results
### Evaluation Metrics  
We used accuracy (Acc), F1-score (F1), precision (Pre), and recall (Rec) [43] to evaluate the performance of the model. All four evaluation metrics are based on a confusion matrix, which includes true positive (TP), false positive (FP), false negative (TN), and false negative (FN). The evaluation metrics can be calculated as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{16}\]  
\[\text{F1-score}=2\frac{\text{recall}\times\text{precision}}{\text{recall}+ \text{precision}} \tag{17}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{18}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{19}\]  
which are introduced in detail as follows:  
Accuracy: Accuracy reflects the classifier's ability to classify the whole sample, specifically, the overall evaluation metrics for determining positive samples as positive and negative samples as negative.  
F1-score: The F1-score is known as the reconciled mean. Precision and recall have a negative correlation; when recall is bigger, precision is smaller. Therefore, we used the F1-score to reconcile precision and recall.  
Precision: Precision is the proportion of true positive samples out of all samples predicted to be positive.  
Recall: Recall is the proportion of all positive samples that are predicted to be positive. Theoretically, the value of recall is closer to 1, which indicates that the model is more predictive of performance.
## 3 Proposed QCA Circuits
### Proposed 2-to-1 Multiplexer  
Figure 7 shows the QCA 2-to-1 multiplexers proposed in this study. The proposed multiplexers are circuits designed through interactions between electrons inside cells. They are designed to minimize the number of cells, space used, and delay time. Both consist of 13 cells, have an area of 0.01 \(\upmu\)m\({}^{2}\), and a latency of 0.25 clock cycles. In both circuits, the signal propagates through the inverter twice and the signal of one fixed cell is attenuated based on the value of SEL, the signal of the other fixed cell is strengthened, and the value is transmitted to the output cell. The proposed multiplexers are easy to connect with other circuits and have good scalability because the input and output cells are arranged on one side.  
Figure 7a is a connection-type multiplexer that has a symmetrical structure in which input and output cells are evenly distributed on both sides. Therefore, any expansion circuit can be used without a loop section. Figure 7b is a modified circuit that can be used when designing an extension circuit with a loop section. Since the output cell is placed above the center, it is convenient to connect the output cell OUT and the input cell A to create a loop section.