esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison of Different Decomposition Methods
### Decomposition with Givens Rotations  
In [16] a method of decomposition is described that uses the Givens rotation matrices to perform the QR factorization of a unitary matrix. Each Givens rotation nullifies the element on the \(i\)th column and \(j\)th row of a \(U(2^{n})\) matrix, as  
\[{}^{1}G_{n,n-1}U=\begin{bmatrix}u_{1,1}&u_{1,2}&\cdots&u_{1,n}\\ \vdots&\vdots&\ddots&\vdots\\ u_{n-2,1}&u_{n-2,2}&\cdots&u_{n-2,n}\\ \bar{u}_{n-1,1}&\bar{u}_{n-1,2}&\cdots&\bar{u}_{n-1,n}\\ 0&\tilde{u}_{n,2}&\cdots&\tilde{u}_{n,n}\end{bmatrix} \tag{11}\]  
The modified elements of \(U\) are indicated with a tilde, and the element on the lower left \(u_{n,1}\) is nullified by the Givens rotation. Each Givens rotation matrix is equal to the identity matrix except for \(c=cos(\theta)\) and \(s=sin(\theta)\) for the elements at positions \((i,i)\), \((i,j)\), \((j,i)\) and \((j,j)\), with \(\theta\) the angle of the Givens rotation. These are to nullify elements until all elements below the diagonal are zero, at which point the following equality holds [16]:  
\[\left(\prod_{i=1}^{2^{n}-1}\prod_{j=i+1}^{2^{n}}\ {}^{i}G_{j,j-1}\right)U=I \tag{12}\]  
By reordering the base vectors according to Gray code (see Section 3.6), the cosine and sine elements will all be adjacent. This is convenient for quantum computation, because that means that each Givens rotation matrix can be implemented by a controlled one-qubit gate, \(C_{i}^{k}\), with \(k\) control bits. For one specific combined state of the control qubits, the \(\Gamma\) gate is applied to qubit \(i\), while for all other states, the target qubit is left unaffected. This means that  
\[\prod_{i=1}^{2^{n}-1}\prod_{j=i+1}^{2^{n}}C_{\gamma(i)}^{n-1}(\Gamma_{(j,k)}^{ \dagger})=SU(2^{n}) \tag{13}\]  
Figure 7: The circuit \(v\) used to construct a universal two-qubit gate [15].  
where \(\gamma(i)\) denotes the \(i\)th number of the Gray code, and the gates \({}^{i}\Gamma_{j,k}\) are formed from the matrix for the Givens rotations. This results in the circuit shown in Figure 8 for the decomposition of a twp-qubit gate.  
The numbers of elementary gates and CNOTs were calculated using [17], which are the numbers included in the table. Generally, this decomposition requires approximately \(8.4\times 4^{n}\) controlled gates, which follows from a recursive relation of \(g_{n}(k)=g_{n}^{0}(k)+g_{n-1}(k)+g_{n-1}(k-1)\)[16].
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 3 Basic Algorithms
### Aca
#### 3.1.1 Path Selection Probability  
In the traditional ACA, ants need to select movement paths. Relying on factors such as the 'pheromone concentration (PC)' on the path and the distance between nodes, they calculate the probability of transferring from the current node to other nodes through the state transition probability Formulas (2) and (3). In this way, when searching, one can choose an appropriate path, thereby better exploring and utilizing the space of solutions.  
\[P_{ij}^{k}(t)=\left\{\begin{array}{l}\frac{[\tau_{ij}(t)]^{2} \cdot[\eta_{ij}(t)]^{\beta}}{\sum_{s\in allowed_{k}}[\tau_{ij}(t)]^{\beta}},j \in allowed_{k}\\ 0,j\
otin allowed_{k}\\ \eta_{ij}(t)=\frac{1}{d_{ij}(t)}\end{array}\right. \tag{2}\]  
\[\eta_{ij}(t)=\frac{1}{d_{ij}(t)} \tag{3}\]  
In Equations (2) and (3), \(P_{ij}^{k}(t)\) means the probability that the \(k\)th ant moves from node \(i\) to node \(j\) at time \(t\); \(\tau_{ij}(t)\) represents the PC on the path between nodes \(i\) and \(j\) at time \(t\); \(\eta_{ij}(t)\) represents the distance HF; \(d_{ij}(t)\) represents the Euclidean distance between nodes \(i\) and \(j\); \(allowed_{k}\) is the set of nodes that the \(k\)th (\(k=1\), \(2\), \(\ldots\), \(m\)) ant has not visited yet, that is, the nodes that can be chosen next; \(s\) is a certain city in the set of unvisited nodes (\(allowed_{k}\)); \(\alpha\) is the importance factor of PC; and \(\beta\) is the importance factor of the distance HF.
# Allocating Small Transporters to Large Jobs
## 5 The Saturated Transporter Allocation Problem
### General MIP Formulation  
The following mixed integer program \((\mathrm{SMIP})\) describes the saturated problem \((\mathrm{STAP})\), depending on the assignment \(\overline{X}_{r,j}\), the transporter filling times \(\kappa(r)\), and the return times \(\delta(j)\):  
\[(\mathrm{SMIP}):\] \[\min_{\overline{X}}\sum_{r\in\mathcal{J}}\sum_{j\in\mathcal{J}} \overline{X}_{r,j} \tag{6}\] \[\mathrm{s.t.}\sum_{j\in\mathcal{J}}\overline{X}_{r,j}\leq 1, \forall r\in\mathcal{R}\] (7) \[\sum_{r^{\prime}\in\mathcal{R}}\left[\overline{X}_{r,j}\cdot \kappa(r^{\prime})\right]-\overline{X}_{r,j}\cdot\kappa(r)\geq\delta(j), \forall r\in\mathcal{R},\forall j\in\mathcal{J}\] (8) \[\overline{X}_{r,j}\in\{0,1\}, \forall r\in\mathcal{R},\forall j\in\mathcal{J}. \tag{9}\]  
Equation (8) represents the saturation constraints: the return time of each assigned transporter is covered by the total filling time of the remaining assigned transporters, hence preventing any idle time. Recall that, actually, it is sufficient to test this for the transporter with maximum capacity, leading to  
\[\sum_{r\in\mathcal{R}}\left[\overline{X}_{r,j}\cdot\kappa(r)\right]-\max_{t \in\mathcal{R}}\overline{X}_{r,j}\cdot\kappa(r)\geq\delta(j), \forall j\in\mathcal{J}.\]  
However, since this is not a valid constraint for a MIP, Equation (8) is used instead.
## 5 Performance Evaluation and Results Analysis
### Percentage of Packets with Low Latency  
The metric of the percentage of packets with low latency exposes the percentage of data packets delivered with a latency that is considered low and acceptable for low-power devices in IoT applications. Several aspects can affect the metric results; among these, it is possible to cite: the length of the path constructed during the route discovery process; the quality of links among the nodes; the radio duty cycle frequency; the medium access control protocol; the density of nodes, etc. The PLL metric is computed according to Equation (5):  
\[PLL=\frac{\sum_{i=1}^{N}lat(Pr_{i})<L_{th}}{\sum_{i=1}^{N}Pr_{i}} \tag{5}\]  
where \(N\) is the number of nodes in the network, \(Pr_{i}\) is the number of packets received by each node \(i\), \(lat()\) is a function that returns the latency of each packet received \(Pr\), and \(L_{th}\) is the latency threshold of a data packet be considered delivered with low latency. In this work, the \(L_{th}\) was defined in 500 milliseconds.  
Figure 9 shows the results obtained for the metric of PLL. According to the results, the proposed LOADng-IoT was able to overcome the other compared solutions in the majority of studied network configurations. In the grid sparse network, except for the network with 16 nodes, the studied approaches were able to maintain almost constant results. Thus, LOADng-IoT delivered approximately 65% of the data packets with a latency lower than 0.5 s. This result outperformed the default LOADng by \(\sim\)8% and LOADng-SmartRREQ by \(\sim 14\%\). With the use of LOADng-IoT, the nodes do not need to use a predefined gateway to forward Internet messages. Thus, the process of Internet route discovery was able to find closer nodes, constructing shorter paths and reducing the number of hops required to send an Internet message. In the dense random scenarios, the proposed LOADng-IoT was also superior to the other studied proposals. In the mobility scenario, the performance of all studied approaches was almost the same, except for the network with 25 nodes, where LOADng-IoT presented slight better results. However, it was perceived that the performance of all approaches decreased with the increase in network density. This behavior is already expected, considering that the latency tends to be increased when several nodes try to use the same frequency spectrum in a common region. The MAC protocols tend to spend more time sending the messages due to the high number of devices accessing the wireless medium [30].  
Figure 8: Control message overhead per delivered data message in function of number of nodes.
## 2 Proposed Method
### Spatiotemporal Correlation Analysis  
Air pollutants are distributed at each site. Due to the wind influence, the air pollutants of one site will be affected by the neighboring sites air pollutants. Therefore, PM\({}_{2.5}\) concentration should be predicted according to the air pollutants at the central site and the air pollutants at neighboring sites.  
In this study, the Pearson correlation coefficient [24] is used to measure the spatial correlation of PM\({}_{2.5}\) concentrations at all sites. Then, the autocorrelation function [25] is used to measure the temporal correlation between PM\({}_{2.5}\) concentration series at each site. The calculation formulas are as follows:  
\[r(s_{i},s_{j})=\frac{Cov(s_{i},s_{j})}{\sigma(s_{i})\sigma(s_{j})} \tag{1}\]  
\[\rho_{k}=\frac{Cov(y(t),y(t+k))}{\sigma_{y(t)}\sigma_{y(t+k)}}, \tag{2}\]  
where \(r(\cdot)\) represents Pearson correlation coefficient between sites; \(\rho_{k}\) represents autocorrelation coefficient at \(t\) and \(t+k\) time within the same site; \(y(t)\) and \(y(t+k)\) respectively represent the air pollutants concentration at time \(t\) and \(t+k\); \(Cov(\cdot)\) is the covariance; and \(\sigma(\cdot)\) is the standard deviation.
## 2 Materials and Methods
### Methods
#### 2.4.3 SDA Indexes with Dynamic Threshold  
Higher Pc values provide higher sensitivity to apnea/hypopnea events. However, the imperfect nature of human respiration signals creates Pc values with high false alarm rates in healthy patients and those with mild apnea-hypopnea. Hence, our proposed SDA indexes are designed to balance Pc values to provide a more robust fusion system for distinguishing apnea-hypopnea events. Normal breath cycles are stable because inhalation and exhalation patterns are regular. The SD of amplitudes is applied to quantize the stability. Unlike Pc values, the SDA indexes are computed using the entire 130 s window.  
Without the identification of cycle peaks, the proposed algorithm is applied on the whole airflow signal \(x_{i}\) in the window (Wp plus Wm), where \(i=1\ldots\) N, and N is equal to sampling frequency times 130 s. The SDA indexes are obtained as follows:  
\[SDA(n)=\sqrt{\sum_{i=1}^{N}\left|x_{i}-\overline{x}\right|^{2}/\text{N}} \tag{5}\]  
where \(\overline{x}\) is the average value of sample points of the x vector. The SDA index is updated every 5 s, described as \(\text{W}(t-5n)\), \(n\in Z\). Due to variant breath patterns in patients, a dynamic threshold is necessary as a baseline to classify the SDA risk level:  
\[\text{Threshold}(n)=\text{SDA}(n)*0.75 \tag{6}\]  
\[\text{Threshold}(n+2)=\text{Threshold}(n+1)*0.75+\text{Threshold}(n)*0.25 \tag{7}\]  
where n represents the current window time, and Th changes dynamically when W(t) updates. If SDA indexes are higher than the dynamic threshold, the risk of apnea is lower; if they are lower than the dynamic threshold, the risk is higher. Hence, when a patient breathes normally, the SDA index stays in a certain range, and when breathing stops the deviation should be close to zero.
# On the Communication Performance of Airborne Distributed Coherent Radar
## 2 Materials and Methods
### System Model
#### 2.1.4 Statistical Model of \(F_{1}^{i}\) and \(F_{2}\)  
\(F_{1}^{i}\) and \(F_{2}\) correspond to the fading on communication link 1 and communication link 2, respectively. When radar UAV detects near-ground targets, it is reasonable to consider the fading of communication system in ADCR. That is because the communication signal will be reflected by the complex obstacles on the ground. Since communication link 1 is generally far away from the ground, there is a main component of the communication signal at the communication UAV. We apply Rician distribution to model it. Communication link 2 is an A2G link and the terrain near the ground is much more complex than that of link 1. To represent this fading more accurately, we apply Nakagami distribution. The PDF of \(F_{1}^{i}\) is:  
\[f_{F_{1}^{i}}(x)=\frac{x}{\left(\sigma^{i}\right)^{2}}\exp\left(\frac{-\left( x^{2}+\left(\dot{v^{i}}\right)^{2}\right)}{2\left(\sigma^{i}\right)^{2}} \right)I_{0}\left(\frac{xv^{i}}{\left(\sigma^{i}\right)^{2}}\right), \tag{9}\]  
where \(K_{1}^{i}=\frac{\left(v^{i}\right)^{2}}{2\left(\sigma^{i}\right)^{2}}\) is the shape parameter and \(K_{2}^{i}=\left(v^{i}\right)^{2}+2\left(\sigma^{i}\right)^{2}\) is the scale parameter.  
The PDF of \(F_{2}\) is:  
\[f_{F_{2}}(x)=\frac{2}{\Gamma(m)}\Big{(}\frac{m}{\Gamma}\Big{)}^{m}x^{2m-1}\exp \left(-\frac{m}{\Gamma}x^{2}\right) \tag{10}\]  
In fact, since (9) contains a Bessel function, which is not conducive to subsequent analytical derivation such as (5), we use the mixed gamma distribution [32] to approximate rice distribution. Thus, the PDF of \(F_{1}^{i}\) can be written as:  
\[f_{F_{1}^{i}}(x)=\sum\limits_{g=1}^{G}2a_{g}^{i}x^{2b_{g}^{i}-1}e^{-\dot{c}^{ i}x^{2}}, \tag{11}\]  
where \(G\) is the order of approximation, and other parameters in (11) are:  
\[a_{g}^{i}=\frac{d_{g}^{i}}{\sum\limits_{g=1}^{G}d_{g}^{i}\Gamma(b_{g}^{i})c^{ -b_{g}^{i}}} \tag{12}\]  
\[b_{g}^{i}=g \tag{13}\]  
\[c^{i}=1+K_{1}^{i} \tag{14}\]  
\[d_{g}^{i}=\frac{\left(K_{1}^{i}\right)^{g-1}\left(1+K_{1}^{i}\right)^{g}}{e^ {K_{1}^{i}}\left[\left(g-1\right)!\right]^{2}} \tag{15}\]  
Figure 2: Geometric relationship between communication UAV and radar UAV with position error.
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
###### Abstract  
In the study, a new correction method was applied to reduce error during Raman spectral detection on mixed pesticide residue in apples. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetaminophen residues in apples could be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which affected the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method was proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 100 mg-kg\({}^{-1}\) to 10\({}^{-3}\) mg-kg\({}^{-1}\) and Lagrangian interpolation were applied in the correction method. And a smooth surface was applied to describe the correction coefficient of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction coefficient would be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide would be acquired by the correction method. Correlation coefficient of model validation exceeded 0.88 generally and Root Mean Square Error also decreased obviously after correction, which proved the reliability of the method.  
Raman spectra; mixed pesticides; apple; correction method; rapid; real-time 2019 9 1699 1.3390/app9081699
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 4 Results
### Constraint Construction Validation Tests  
To verify the effectiveness of DTSTC for constructing the spatiotemporal domain constraints, the temporal difference cumulative motion heat map method [6], U-Net road extraction algorithm [28], and D-LinkNet road extraction algorithm [29] were compared with DTSTC. The method in this study used the threshold settings of \(T_{A}\) = 5 and \(T_{R}\) = 0.3. The results of the comparison tests are presented in Figure 7. As can be seen from the results, the motion heat map accumulated using the temporal difference is less effective, with no superior road masks obtained in the six trial sets and large non-road areas accumulated in both Areas 2 and 3. This is because there were fewer vehicles in the video frame, and it was not possible to effectively accumulate sufficient thermal areas of motion in a few frames to form a road mask. In addition, noise accumulated in the video, creating pseudo-motion heatmap areas. Therefore, this method is only suitable for city centers and areas with high traffic volumes. The U-Net road extraction algorithm extracted only relatively complete road masks in Areas 4 and 5, with incorrect extraction in all other areas. This is because the U-Net road extraction network has a small feature perception field and does not fully consider the complex structural characteristics of roads; therefore, there are obvious omissions and breakdowns. D-LinkNet largely ensures the integrity of the extraction results. However, road breaks still exist, such as in the upper-right trail in Area 3 and the lower-middle bypass in Area 6. DTSTC fully utilizes the time domain information to complement the road mask extracted by D-LinkNet and achieves better results. Most of the road area was extracted in all six trial sets, and there was improvement in breakage and missed detections that existed with D-LinkNet. Breaks and misses in the red areas in the Figure 7 have been effectively addressed. Therefore, the proposed algorithm can build constraints more robustly and provide a good basis for the subsequent detection of moving vehicles.  
To verify the effect of stepwise optimization of the mask generation method in this study, a set of video data was selected for experimentation. The D-LinkNet mask was supplemented every 15 frames using the motion heat map generated by the frame difference method, as illustrated in Figure 8, which shows the effect of mask fusion at each stage. As shown in the diagram, the missing roads are gradually supplemented and do not cause excessive noise. The feasibility and superiority of DTSTC in constructing constraints based on the spatiotemporal domain are demonstrated by the progressive addition of road network masks.
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### The Signal Propagation Model with Flat Mirrors in the IRS  
For both IRS scenarios with flat and convex mirrors, we consider an LED located at a position \(S\) with coordinates \((x_{s},y_{s},z_{s})\). The IRS is composed of an array of \(M\times N\) flat mirrors (or convex mirrors if applicable), each with coordinates \((x_{ij},y_{ij},z_{ij})\), where \(i=1,\ldots,M\) and \(j=1,\ldots,N\).  
Each mirror in the IRS can be controlled to reflect light toward a receiver, such as a photodiode placed at a position \(R\) with coordinates \((x_{r},y_{r},z_{r})\).  
In this first case, the direction of reflected light for the flat mirror \((i,j)\) is calculated using the law of reflection. For a flat surface, the specular reflection follows:  
\[\theta_{ij}=\theta_{r}, \tag{5}\]  
where \(\theta_{ij}\) is the angle of incidence with respect to the normal of the mirror surface \((i,j)\).  
The total distance traveled by light from the source \(S\) to the receiver \(R\) through the mirrors \((i,j)\) is:  
\[d_{ij}=d_{S,ij}+d_{ij,R}, \tag{6}\]  
where \(d_{S,ij}\) is the distance from the source to the mirror, and \(d_{ij,R}\) is the distance from the mirror to the receiver.  
To provide a more detailed and accurate model of the received power, it is essential to derive the analytical expressions of the reflection factor \(R(\theta_{ij},\theta_{r})\) and the spectral transmission factor \(T_{ij}(\lambda)\). These elements are essential for modeling how light interacts with mirrors in a VLC system that features components with NLoS channels.
## 4 Experimental Evaluation
### Antenna SI Cancellation  
The SI cancellation performance, which resulted from antenna isolation, is evaluated by the spectrum analyzer and the results are shown in Figure 10. From the values shown in Figure 10, it can be observed that the noise floor is around \(-110\) dBm. Additionally,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline USRP Operating Frequency & 2.15 GHz \\ RF Operating Frequency & 4.7 GHz \\ Signal Bandwidth & 16 MHz \\ Antenna Spacing & 15 cm \\ Modulation & QPSK \\ OFDM FFT Size & 256 \\ Operating SNR & 10–25 dB, with 2.5 dB interval \\ AF FIR Taps \(N_{\text{taps}}\) & 20 \\ Step-size \(\mu\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters for experiments.  
Figure 9: Experiment settings.  
the marker 3 in Figure 10 marks the SI power before the antenna, while marker 2 marks the SI signal power after antenna isolation. The value measured by marker 3 is 3.0 dBm, while the value marked by marker 2 is \(-43.9\) dBm. From the results shown in Figure 10, it can be observed that the SI signal power after the antenna is attenuated by 46.9 dB, which significantly validates the SI cancellation performance of antenna isolation.
## 4 Energy Consumption
### Average Energy Consumed by RN2 in the Data Period  
For RN2, the average energy it consumes along the _data_ period of a cycle can be expressed as,  
\[E_{d,2}=\Big{(}E_{tx,s}^{d,2}+E_{tx,f}^{d,2}+E_{oh}^{d,2}\Big{)}R_{1,0}. \tag{7}\]  
where \(E_{tx,s}^{d,2}\), \(E_{tx,f}^{d,2}\) and \(E_{oh}^{d,2}\) have the same terms as those shown in (3)-(5), but appropriately adapted for the specific features of C2 SNs.
# A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy
## 2 Materials and Methods
### Dosimeter Distribution of CIRS ATOM Phantom  
In this study, 40 OSLDs were utilized to measure doses in the left breast of the adult female phantom and to compare them with the calculated doses of the 3D-CRT, IMRT, and VMAT plans. In addition, the skin around the target area was covered by lead shields to protect critical organs from radiation. Two types of lead sheets thickness of 0.125 mm and 0.25 mm were used. The density of the lead shields was 11.34 g.cm\({}^{-3}\), as shown in Figure 4.  
During the radiotherapy planning stage, the left breast of the phantom was contoured as the planning target volume (PTV), and the organs at risk (OARs) included bilateral lungs, heart, and thyroids that were further away. Subsequently, eight organ dose measurement points were selected across different sections of the adult female phantom, including the thyroids on Section 11 (No. 28 and 29), the heart on Section 17 (No. 95), and the heart (No. 108) and the lungs (No. 97, 100, 102 and 106) on Section 18. Two heart locations were chosen for dose measurement to represent the anatomical structure of the atrium and the ventricle, respectively. As the largest organ in the body, multiple measurement points were set in the lungs of the phantom, which divided the anatomy on Section 18 into the left upper lung (No. 97), the right upper lung (No. 102), the left lower lung (No. 100), and the right lower lung (No. 106).  
Figure 4: The CIRS ATOM phantom with the locations of nanoDot OSL dosimeters labeled and associated with organs. (**a**) The adult female phantom with the skin around the left breast covered by lead shields. (**b**) Thyroid measurement points No. 28 and 29. (**c**) Heart measurement point No. 28; (**d**) Heart measurement point No. 108 and lung measurement points 97, 100, 102, and 106.