esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 3 Sources of SINR Variability
### Changes in Precoders of Interferers
With increasing \(N_{T}\), worst case precoders become increasingly unlikely -- typical interfering signals are almost orthogonal to the wanted signal channel. The observation that the distribution of the interference \(I\) becomes narrower with increasing \(N_{T}\) does not mean that it is easier to predict URC channel quality. Here, we have assumed that the channel quality estimated at the time of measurement is given by the interference power _averaged_ over possible precoders. It turns out that the average is reduced more than the interference at outage, when \(N_{T}\) grows. To see this, the CDF of the realized interference is plotted in units of \(I_{\text{ave}}\) in the left half of Figure 7. For clarity, the distributions are plotted only for the \(J=3\) case. For \(N_{T}=2\), the distribution extends to \(I/I_{\text{ave}}=2\). The average interference is half of the realized. This distribution becomes broader with increasing \(N_{T}\). The required backoff is plotted in the right half of the figure. The required backoff is \(b=\frac{I_{t}/N_{0}+1}{I_{0}/N_{0}+1}\), where \(I_{t}\) the interference at the URC target. The required backoff grows with \(N_{T}\). For \(N_{T}=2\), the backoff is virtually the same irrespectively of \(J\). As the 0.99999 point of the CDF is close to the maximum interference, \(I_{t}\approx 2I_{\text{ave}}\) in this case, and the backoff in an interference limited network when \(I_{0}/N_{0}\to\infty\) would be \(b\approx 3\) dB.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 1 Introduction
In the air-borne test, compared with the upward continuation of ground gravity data, the accuracies of repeated and cross lines were (\(-\)1.9 \(\pm\) 3.3) mGal and (\(-\)0.7 \(\pm\) 6.2) mGal, respectively. The accuracy was (\(-\)0.8 \(\pm\) 3.3) mGal when the laser misalignment area was removed from the cross-measurement line [36]. In the later air-borne test, the measurement accuracy was further improved to 0.6-1.3 mGal [37]. ZJUT team also carried out dynamic tests. In addition to low-speed traction in the laboratory, they also carried out a sea navigation test. Compared with the XGM2019 earth gravity model, the measurement accuracy of (2.6 \(\pm\) 13.6) mGal, (3.4 \(\pm\) 15.3) mGal, and (\(-\)0.3 \(\pm\) 8.1) mGal were obtained at average speeds of 22.6 km/h, 14.3 km/h and 2.1 km/h, respectively [38]. The author's team has gradually carried out research on the atom gravimeter, especially its dynamic measurement technology. We developed a dynamic measurement test system for an atom gravimeter, which has a measurement sensitivity of 447 \(\upmu\)Gal/Hz\({}^{1/2}\) and a long-term stability of 2.7 \(\upmu\)Gal under static conditions in the laboratory. We first used the self-developed test system to carry out experiments on the laboratory swing table, simulating the pitch and roll of the ship during navigation, and verified the feasibility of applying the equipment and method to the dynamic conditions of navigation. Then, we carried out an experiment in a certain lake area, as shown in Figure 1. Under the condition of the highest speed of 8.6 km/h, the external coincidence accuracies of the four voyages were 2.331 mGal, 1.837 mGal, 3.988 mGal, and 2.589 mGal, respectively. In addition, to meet the real-time requirements of dynamic measurement, the Kalman filter method was used for data processing, and the gravity value was obtained in real time. The measurement accuracy was (2.03 \(\pm\) 7.12) mGal [39; 40]. Compared with the current gravimeters, especially ONERA's [35], our system still has some room for optimization. In the follow-up, we will continue to improve the system structure, optimize the measurement algorithm, and carry out sea navigation measurement experiments with severe sea conditions to further improve the accuracy and verify the stability and environmental adaptability of the system. This paper focuses on the research progress of the atom gravimeter in dynamic measurement. Firstly, the principle, scheme, and device of atom gravimeter dynamic measurement are described. Secondly, the main error sources and suppression methods of atom gravimeter dynamic measurement are analyzed. Finally, the possible application direction of atom gravimeter dynamic measurement technology is discussed.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 3 Performance Analysis
### Outage Probability for RIS-DH Scheme
#### 3.1.2 \(K_{g}\) Distribution  
In this section, we present the analysis by using the \(K_{G}\) distribution as in [62]. Next, the PDF of \(Z\) is given as follows [62]:  
\[f_{Z}(x)=\frac{4\Psi^{k+m}x^{k+m-1}}{\Gamma(k)\Gamma(m)}K_{k-m}(2\Psi\tau). \tag{14}\]  
where \(k=\frac{-b+\sqrt{b^{2}-4 ac}}{2a}\) and \(m=\frac{-b-\sqrt{b^{2}-4 ac}}{2a}\) are the shaping parameters, \(\Gamma(.)\) is the gamma function [63], \(K_{X}(.)\) is the modified Bessel function of the second kind with zero order [60], \(\Psi=\sqrt{\frac{km}{\Gamma}}\), and \(\Omega=\mu_{Z}(2)\) is the mean power. Moreover, the parameters \(a\), \(b\), and \(c\) have been defined previously [63]. Their values are related to moment \(\mu_{Z}(.)\) of R, which is defined as follows:  
\[\begin{array}{l}\mu_{Z}(n)=\sum_{n_{1}=0}^{n}\sum_{n_{2}=0}^{n_{1}}\cdots \sum_{n_{N-1}=0}^{n_{N-2}}\binom{n}{n_{1}}\binom{n_{1}}{n_{2}}\cdots\binom{n_ {N-2}}{n_{N-1}}\\ \times\mu_{\chi^{i}}(n-n_{1})\mu_{\chi^{2}}(n_{1}-n_{2})\cdots\mu_{\chi^{N-1} }(n_{N-1}),\end{array} \tag{15}\]  
where \(\mu_{\chi^{i}}(n)=\Gamma^{2}\big{(}1+\frac{n}{2}\big{)}\) is the nth moment of \(\chi^{i}\). In addition, \(k\) and \(m\) are real numbers. When \(k\) and \(m\) are conjugate complex numbers, \(k\) and \(m\) are set to the estimated modulus values of the conjugate complex number.  
Based on (9), we can express the PDF of \(\eta\) as follows.  
\[f_{\eta}(x)=\frac{2\Psi^{k+m}x^{\frac{k+m}{2}-1}}{\Gamma(k)\Gamma(m)\bigg{(}\frac {\rho_{S}}{d_{1}^{2}d_{2}^{2}}\bigg{)}^{\frac{k+m}{2}}}K_{k-m}\Bigg{(}2\Psi \sqrt{\frac{d_{1}^{X}d_{2}^{X}x}{\rho_{S}}}\Bigg{)}. \tag{16}\]  
Then, the CDF can be obtained as follows.  
\[F_{\eta}(x)=\frac{1}{\Gamma(k)\Gamma(m)}G_{1,3}^{2,1}\Bigg{[}\frac{d_{1}^{X}d_ {2}^{X}\Psi^{2}}{\rho_{S}}x\bigg{|}\begin{array}{c}1\\ k,m,0\end{array}\Bigg{]}. \tag{17}\]  
For the \(K_{G}\) case, the closed-form of \(P_{out}\) is given as follows.  
\[P_{DH,out}=\frac{1}{\Gamma(k)\Gamma(m)}G_{1,3}^{2,1}\Bigg{[}\frac{d_{1}^{X}d_ {2}^{X}\gamma_{th}\omega_{3}\Psi^{2}}{\rho_{S}(\varpi_{1}-\gamma_{th}\varpi_{ 2})}\bigg{|}\begin{array}{c}1\\ k,m,0\end{array}\Bigg{]}. \tag{18}\]  
**Remark 1**.: _The outage performance of the two schemes mostly depends on transmit SNR at the access point \(\rho_{S}\), shown in (18). Furthermore, the impact of IQI can be observed in (9). The performance gaps of practical scenarios will be analyzed to provide guidelines for RIS deployment in suitable applications in IoT systems. Therefore, we expect that the improved performance at destination users could be adjusted by main parameters such as \(\rho_{S}\) and the configuration of RIS as well._
# An Overview of Emergency Communication Networks
## 1 Introduction
[40] reviewed various potential emerging communication technologies, such as Internet of Things (IoT), Device-to-Device (D2D) communication, Vehicle Network, Cloud and Fog Computing, Unmanned Aerial Vehicles (UAVs), and Wireless Sensor Networks (WSNs) in disaster situations. [PERSON] et al. [41] systematically summarized the disaster emergency management network. [PERSON] et al. [42] outlined various existing technologies and the latest progress in the field of emergency communication technology. By investigating mobile ad hoc networks, [PERSON] et al. [43] provided the classification of communication strategies for mobile ad hoc networks in disasters and emergencies. [PERSON] et al. [44] investigated the communication and network technology of UAVs. [PERSON] et al. [45] conducted a detailed investigation of Always-On Networks (AONs) and outlined AONs for emergency communication. Whenever natural or man-made disasters occur, the main communication infrastructure is usually overloaded and suffers partial or total failure. Therefore, it is crucial to create a reliable emergency network to ensure adequate coverage. At present, there are many proposals relating to emergency communication technology, and countries all over the world are also strengthening and improving the construction of emergency communication systems and their research into related technologies. Based on this situation, this paper summarizes the issues related to emergency communication networks and widely discusses the various technologies used in emergency communication networks. The key issues addressed are as follows:  
* What is the key technology of emergency communication networks? * What is the importance of emergency communication networks? * What are the main advantages of emergency communication networks compared with traditional networks? * What are the limitations of emergency communication networks? * What progress has been made in the research into emergency communication network? This paper presents extensive research into emergency communication technology, including satellite networks, ad hoc networks, cellular networks, and wireless private networks, and analyzes the networks currently used in emergency rescue and the future development direction of emergency communication networks.
## 3 Results and Discussions
### Design of the Conventional TCED Process
Figure 7: Effect of tray number (NT3) in SRC over solvent concentration in D3. However, the reflux ratio (RR3) has considerable effects on the separation in the SRC. The investigation range of RR3 was 0.4 to 1.5 in the EG regeneration column and in the range of 0.8 to 1.5 in the SRC for DMSO. As can be seen from Figure 8, the increase in RR3 values leads to a drastic decrease in solvent concentration in water D3 flow. It can be concluded that an RR3 value of 0.6 results in an EG concentration of 1.2 ppm and an IPA concentration of 5133 ppm. In the case of DMSO solvent, its concentration in D3 for an RR3 of 1.4 is 1.04 ppm, and the IPA concentration is 5132 ppm. After carrying out the iterative procedure, the most favorable operating parameters of the regeneration column SRC for the investigated solvents were identified:  
For EG solvent regeneration, the EG concentration in water flux D3 at 1.2 ppm and IPA concentration of 5133 ppm was depicted at an NT3 of 15, NF3 at 7, and RR3 at 0.6. For DMSO solvent regeneration, the DMSO concentration in water flux D3 at 1.04 ppm and IPA concentration of 5132 ppm was depicted at an NT3 of 16, NF3 at 7, and RR3 at 1.4. Table 2 provides details on the operating parameters of the regeneration columns of the two solvents investigated in this work compared to the PG solvent investigated in our previous work. For all SRCs investigated, it is observed that each solvent was very deeply regenerated (only 5 ppm water remains in the solvents). But in the water D3 distillate flow, the IPA loss is 5132 ppm concentration. Only the regeneration column of PG seems to offer an advantage in this regard because the IPA loss is only 1050 ppm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**SRC with PG**} & \multicolumn{2}{c}{**SRC with**} & \multicolumn{2}{c}{**SRC with**} \\  & & **Solvent * & **EG Solvent** & **DMSO Solvent** \\ \hline \multirow{2}{*}{D3 flowrate} & kmol/h & 58.51 & 58.42 & 58.44 \\  & kg/h & 1056.68 & 1065.00 & 1065.52 \\ \hline \multirow{2}{*}{D3 composition} & Water & 99.89 & 99.49 & 99.49 \\  & molar & solvent & 2.0 ppm & 1.2 ppm & 1.04 ppm \\  & IPA & 1050 ppm & 5133 ppm & 5132 ppm \\ \hline \multirow{2}{*}{B3 flowrate} & kmol/h & 569.81 & 209.00 & 133.77 \\  & kg/h & 43,359.60 & 12,972.31 & 10,451.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed design parameters of the SRCs. Figure 8: Effect of reflux ratio (RR3) in SRC over solvent concentration in D3.
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 3 Proposed Algorithm
### Background Noise Removal  
Background noise is a common problem for all the haze removal algorithms mentioned in Section 2. Figure 5 illustrates the input hazy image, which is from the Image and Vision Computing (IVC) image dataset [37], and the output dehazed images from DCP, filtering-based fast dehazing (FFD), CAP, and HPM. The enlarged regions show background noise after removing the haze owing to various reasons. Using DCP, it is found when observing image patches excluding the sky. Using FFD and HPM, the assumption that the atmospheric light can be set to (1, 1, 1) causes the noise. Since the contribution of the atmospheric light in the formation of hazy images is additive according to Equation (1), dehazing means this kind of unwanted contribution must be subtracted. As a result, using the maximum values of atmospheric light indicates that the subtraction is performed excessively and, thus, highly likely gives rise to background noise.  
Conversely, the cause of CAP background noise is related to the spike-like noise in the saturation channel. To delve deeply into this, a thorough examination is in order. Figure 6 shows another hazy image from the IVC dataset, its corresponding estimated depth map, and the recovered haze-free image. It is evident that the background noise in the marked region is carried over into the dehazed image, owing to the linearity of the atmospheric scattering model. To find the cause of the background noise, we looked at the saturation, brightness, and depth values of line 247, as depicted in Figure 7a. This shows that small or spike-like noise in saturation data creates the corresponding noise in the depth map of the CAP linear model. This noise eventually makes the background noise in the dehazed image and can be removed by using a low-pass filter. Figure 7b shows that the noise in the saturation data has been removed successfully, creating smooth depth data. Figure 8 shows the estimated depth maps before and after applying the low-pass filter. It is evident that the background noise marked by the red rectangle is filtered out efficiently. This feature clearly distinguishes the proposed ICAP from the original CAP. The coefficients and one-dimensional frequency response of the low-pass filter are illustrated in Figure 9.  
Figure 5: Results of different methods on a real hazy scene and their corresponding zoomed-up sky region: (**a**) input hazy image; (**b**) Dark Channel Prior (DCP); (**c**) Filtering-based Fast Dehazing (FFD); (**d**) CAP; (**e**) Hazy Particle Map (HPM).  
Figure 6: An example where background noise occurred after dehazing: (**a**) hazy image; (**b**) estimated depth map; (**c**) dehazed image.  
Figure 7: Plot of image line 247 data across the noisy region: (**a**) before applying the low-pass filter and (**b**) after applying the low-pass filter.
# Allocating Small Transporters to Large Jobs
## 3 Structural Results
### Saturating Transporter Sets  
Given a set of transporters \(\mathcal{R}\) and a job \(j\), recall that a set of transporters \(R\subset\mathcal{R}\) saturates job \(j\) if  
\[\sum_{r\in R}\kappa(r)\geq\delta(j)+\max_{r\in R}\kappa(j).\]  
Any (cardinality) smallest subset of \(\mathcal{R}\) that can saturate job \(j\) is called a _saturating transporter set_ for \(j\). The common cardinality of all saturating transporter sets for job \(j\) is called the _saturating transporter cardinality_, which is denoted by \(N_{\mathcal{R}}^{*}(j)\). If no saturating transporter set exists, that is, if even \(\mathcal{R}\) itself cannot saturate job \(j\), then we define \(N_{\mathcal{R}}^{*}(j)=\infty\). Note that, by definition, the \(N_{\mathcal{R}}^{*}(j)\) transporters in \(\mathcal{R}\) with biggest filling times are a saturating transporter set for job \(j\), if one exists.  
The saturating transporter cardinality of every job is a lower bound of the number of assigned transporters to this job in any saturating assignment. Our algorithms use this concept to compute assignments.  
Lemma 1: _The time complexity to compute the saturating transporter cardinality of all jobs is:_  
\[\mathcal{O}\big{(}(N_{R}+N_{J})\cdot\log[N_{R}]\big{)}.\]  
Proof: We sort the transporters by decreasing the filling time in \(\mathcal{O}(N_{R}\cdot\log[N_{R}])\). Then, we remove the biggest transporter, which will be the transporter whose return time has to be covered.  
For a single job, we can build a saturating transporter set by adding to the biggest transporter as many transporters as necessary from a sorted list such that their filling time exceeds the job's return time. Since this set contains the biggest transporters, there cannot be any smaller transporter set saturating the job.  
We can improve this process to compute the saturating transporter cardinality for every job. We first compute the cumulative filling times of the transporters in \(\mathcal{O}(N_{R})\). Then, for each, we find the minimum cumulative filling time bigger than the return time, using a binary search in \(\mathcal{O}(\log[N_{R}])\) time. Hence, for all jobs, it takes \(\mathcal{O}(N_{J}\cdot\log[N_{R}])\).  
Thus, the total complexity is \(\mathcal{O}\big{(}(N_{R}+N_{J})\cdot\log[N_{R}]\big{)}\).
## 2 Reliability Analysis Based on the AdaBoost Surrogate Model
### Adaptive AdaBoost Algorithm
#### 2.2.1 Adaptive Sampling  
The idea of adaptive sampling is to determine the optimal design point through the iteration process and pre-sample the sample center in each iteration to obtain an optimal training sample set that is as evenly distributed as possible on both sides of the failure curve and fully contains the information of the failure curve. To swiftly ascertain the optimal design point, this study applied the extended sample variance and adaptive iteration method. The detailed steps of the proposed adaptive sampling method are outlined as follows:  
1. It is presumed that the variables are mutually independent and follow a normal distribution, \(x_{i}\sim N(\mu_{i},\sigma_{i})(i=1,2,\ldots,n)\).
2. Define the expanded coefficient,\(f\), and let \(x_{i}\sim N(\mu_{i},f\sigma_{i})(i=1,2,\ldots,n)\).
3. Employ Latin hypercube sampling to derive 200 sample points, and then bring them into the system model for discrimination and calculate failure probability \(\eta_{j}(j=1,2,\ldots,m)\). Select the point \(\overline{x}^{(j)}\) with the largest joint probability density from among the failure points; this point is taken as the sampling center for subsequent sampling.
4. Define a sampling efficiency index, \(\epsilon_{j}=\left|0.5-\eta_{j}\right|\). The smaller the value of \(\epsilon_{j}\), the closer the sampling center is to the optimal design point, and the failure probability approaches 50%.
5. When \(\epsilon_{j}\leq 0.1\), the loop comes to an end. The sampling center at this time is take as the new sampling center, and resampling is conducted using \(x_{i}\sim N(\mu_{i},f\sigma_{i})\) (\(i=1,2,\ldots,n\)).  
The detailed flowchart illustrating the expanded sample variance and the adaptive iteration method is presented in Figure 1.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model  
In order to evaluate and compare our approach to some well-known existing ones, we use an analytic model. First, we present the framework. In this framework, we model the network, the vehicles' motion, and the dissemination of the message. Then, we use these models to give the analytic expression of the studied approaches. These approaches are compared using two factors: the network overhead generated by the ICN exchanges on the G5 channel and the data freshness, which is the percentage of vehicles in the network, which obtain the data before their expiration. In order to study the impact of each mechanism of our approach (GeoVCDN), we also evaluate two light versions of our proposed approach, namely, vehicular centralized data networking (VCDN) and vehicular centralized data networking over CAM (VCDNoCAM). VCDN works as NDN but instead of having one data per sensor, we have one versioned metadata that gathers the data of the same kind of sensors. With VCDNoCAM, the ICN exchanges are integrated into the CAMs. Additionally, the GeoVCDN extends VCDNoCAM by extending the vehicle's roles to act as a router. So, they can also disseminate the data in their CAMs. In this model, we made two assumptions:  
* To ease the representation, without distorting it, the used network topology is Manhattan.
* For our approach (GeoVCDN), we express the worst case for each performance indicator (i.e., for the overhead, we give the upper bound, and for the data freshness the lower bound).
* Vehicles are homogeneously spread in the network.
* Vehicles' speeds are constant.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Subset Matching
#### 2.6.1 Temporal Matching  
There are two reference image strategies for temporal matching: absolute and incremental. The absolute and incremental strategies define the reference image as either the first image of the image series or the previous image, relative to the current deformed image, respectively. The incremental strategy more reliably tracks more severe deformations between images but incurs accumulative errors when determining the displacement relative to the first image.  
Temporal matching uses the Phase Correlation Method (PCM) to determine initial estimates for the displacements between two subsets. PCM efficiently computes the correlation coefficients in the frequency domain for a range of integer displacements of up to half the subset size in each direction between the two subsets. The integer displacements with the best correlation coefficient are identified and used as initial estimates for the displacement SFPs while the deformation SFPs are set to zero. Consult the work of [PERSON] et al. [67] for a discussion on PCM. For the absolute strategy, PCM is only used for the first correlation run. Thereafter the SFPs of the previous correlation run, of the same image series, are used as an initial estimate for the SFPs of the current correlation run. The incremental strategy uses PCM for each correlation run.