esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
## 2 Methods
### Implementation Details  
We implemented all methods with NiftyNet, a TensorFlow based open-source deep learning framework specifically developed for medical imaging ([PERSON] et al., 2015), and code will be made available on publication. The multi-scale network consists of three independent instances of HighRes3 DNet, a high-resolution, compact convolutional network. Each network takes two \(80\times 80\times 80\) MR image patches (\(T_{1}\) and \(T_{2}\)) with different resolutions and fields of view as input. In order of high, medium, and low resolution, the MR patches are obtained by taking an initial high-resolution \(320\times 320\times 320\) patch and cropping the central \(80\times 80\times 80\) region (high), downsampling the initial patch by a factor of two and taking the central \(80\times 80\times 80\) patch (medium), and finally downsampling the initial patch by a factor of four to obtain a \(80\times 80\times 80\) patch (low). This series of cropping and downsampling operations keeps the input patch size constant for each network while increasing the field of view.  
The output feature maps of each network are combined in the same but reversed manner. Starting from the lowest resolution sub-net, the output feature map of size \(80\times 80\times 80\) is upsampled by a factor of two and centrally cropped. This patch is concatenated with the output feature map of the medium resolution sub-net. This concatenated patch of size \(80\times 80\times 80\times 80\times 2\) is then upsampled by a factor of two and centrally cropped, before being concatenated to the output feature map of the high-resolution sub-net. These upsampling and crop operations ensure that the final output feature maps contain the same field of view prior to the final set of four 3D convolutions of kernel size \(1\times 1\times 1\), which generates the pCT patch.  
An additional series of four \(1\times 1\times 1\) convolutional layers, that are identical to the convolutional layers for the synthesis branch, is used to model heteroscedastic uncertainty. For both training and testing, we set the channel dropout probability (i.e., the probability to discard any one channel) to \(0.5\) and \(N=50\) forward passes were carried out for each experiment. The batch size was set to one, the network was optimised using the ADAMoptimiser and all networks were trained until convergence. Here, convergence was defined as a sub 5% change of the loss over a period of 5000 iterations.
# Using Ground-Based Passive Reflectors for Improving UAV Landing
## 2 Navigation Systems for UAV Landing
### Microwave Landing System (MLS)  
The microwave landing system [41; 42] was created as a replacement for the ILS, but, as of today, its use in civil aviation is limited only to London's Heathrow Airport [43]. MLS [44] was developed with the aim of mitigating the interference from the airport-adjacent objects by switching to super-high-frequency (SHF, 3-30 GHz) waves, and by using narrow-width radiation patterns. Additionally, technical advances of the past years made it possible to switch to phased antenna arrays.  
At the core of MLS are two angle-measuring beacons [45] (see Figure 2), the first calculates the angle of approach relative to the runway, and the second calculates the angle of the landing aircraft. The operating frequency range for this system is around 5.05 GHz, which allows to use narrow radiation patterns, while keeping their dimensions relatively small. The azimuth transmitter one manages the aircraft course control and transmits information about the system status to the aircraft. The radiation pattern of such stations is, horizontally, a 2-degree fan beam (for a greater accuracy, the angle can be narrowed to 1 degree), while vertically, the fan beam is dozens of degrees wide. The azimuth transmitter two manages the aircraft that is taking off or has missed the approach. This installation is similar to the directional beacon and its functions may be changed when the approach angles are changed. The angle-measuring station 1 (AMS-1) emits the radiation pattern in the form of a vertical 1.5-degree fan beam. Vertically, its range is limited to the visibility range of the azimuth transmitter one. The glide slope angle is selected by the pilot and ranges from 0.9 to 15 degrees. The angle-measuring station 2 (AMS-2) pinpoints the flare moment and handles the aircraft until the touchdown [46].  
The disadvantages of that system include its high cost and a great amount of effort for its deployment, especially when there is no required infrastructure to support it.
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 2 Materials and Methods  
### Background  
Contactless, camera-based measurement of SpO\({}_{2}\) is a relatively new field of research. After pioneering work by [PERSON] [6] and [PERSON] [7] and [PERSON] [4], [PERSON] et al. [9] was the first to demonstrate the feasibility of calibratable SpO\({}_{2}\) estimation with a camera under normoxic and hypoxic conditions, which is non-trivial because of the fundamental differences in source-detector geometry. The conventional contact source-detector geometry collects light that has travelled through relatively deep vasculature (both in transmissive and reflective mode), whereas the contactless, wide-field illumination-detection geometry predominantly collects light that has travelled through much shallower tissue depths over much smaller distances. [PERSON] et al. used the commonly used \"ratio-of-ratios\" based approach and mentioned that low signal strength and subject motion present critical challenges that will have to be addressed to make camera-based pulse-oximetry practically feasible. As the name implies, with ratio-of-ratios, the ratio of two DC-normalized PPG waveforms is calculated and linked to an SpO\({}_{2}\) value via a calibration curve or look-up table, based on the assumption that the pulsatile, AC, component originates from variations in arterial blood only. This principle works fine for clean signals, but is rendered inaccurate when the signals are noisy or corrupted by motion artifacts. This motivated the development of the APBV method [8], inspired by the PBV method for camera-based pulse extraction by [PERSON] et al. [17] using the unique spectral signature of the blood volume pulse signal. A short description of the APBV method is provided in the next paragraph.
# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 4 Results and Discussion  
In this section, the experimental results will be discussed in detail, including the comparison between the different MODL algorithms and the comparison between MODL, MOS, MOML, and the original NWP results. The root-mean-square error (RMSE) and temperature prediction accuracy (Acc) are used to test the results of these algorithms in order to evaluate the effect of these methods on the forecast.  
The RMSE is a general evaluation metric for solving regression problems. The RMSE of temperature is denoted by the following \(T_{RMSE}\)  
\[T_{RMSE}(f)=\left\{\frac{1}{K}\sum\
olimits_{k}^{K}[f(x_{k})-y_{k}]^{2}\right\} ^{\frac{1}{2}}, \tag{8}\]  
where \(f\) is the deep learning regression model, \(K\) is the total number of samples of dataset, \(x_{k}\) is the input, and \(y_{k}\) is the label.  
Accuracy is an evaluation indicator for evaluating classification tasks. However, the regression results can be classified into two categories by setting a threshold (\(\Delta\)) for the temperature difference. Accordingly, the accuracy can be used as an indicator to evaluate the temperature regression problem which denoted as \(T_{acc}\). The positive samples are the samples with temperature difference \(\left|y_{k}-f(x_{k})\right|<\Delta\), and negative samples have the temperature difference \(\left|y_{k}-f(x_{k})\right|\geq\Delta\) on the contrary. According to [35], [PERSON] et al. choose 2 \({}^{\circ}\)C as the threshold of the temperature in Beijing area. Tianjin and Beijing have similar climate characteristics due to the geographical proximity. \(T_{acc}\) in this paper is defined as the percentage of absolute deviation of the temperature forecast not being greater than 2 \({}^{\circ}\)C, which is formularized as.  
\[T_{acc}=\frac{N_{p}}{N_{n}+N_{p}}\times 100\%, \tag{9}\]  
where \(N_{p}\) is the number of the samples in which the difference between the forecast temperature and the observational temperature does not exceed \(\pm 2\)\({}^{\circ}\)C and \(N_{n}\) is the negative samples. To prevent confusion, we specifically point out that the calculation of RMSE involves all data and it has no relationships with the threshold.
# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 4 Conclusions  
RAS was once referred to as an \"undruggable target\" because of the special structural characteristics of RAS, the complexity of signaling pathways, and the drug resistance of RAS mutant tumors. RAS proteins have high intrinsic affinity for their GDP and GTP substrates and lack distinct pockets in their catalytic domain for binding compounds. However, thanks to the relentless efforts of researchers and advances in CADD, inhibitors have consistently shown satisfactory effects in experiments. To date, only a few small molecules have been able to covalently bind to KRAS\({}^{\text{G12C}}\). Since the HVR mutations of RAS cause cancer, they can be used to develop small-molecule inhibitors targeting specific oncogenic RAS mutants. Recently, phosphorylation at multiple residues was identified to regulate the activation of RAS, which may be a new target for therapeutics against RAS diseases.  
In recent years, FDA-approved KRAS\({}^{\text{G12C}}\) mutation inhibitors (AMG 510, Sotorasib) have been introduced. The existing pan-RAS inhibitors, such as compound 3144, have unavoidable toxicity and off-target activity [144]. However, the discovery of RAS inhibitors is still a challenge. First, the discovery of specific inhibitors targeting other alleles is needed to provide a personalized medical approach, such as the common KRAS variants KRAS\({}^{\text{G12D}}\) and KRAS\({}^{\text{G12V}}\). Second, the effect of allele-specific inhibitors as monotherapy may be limited. Therefore, it is necessary to combine them with other inhibitors because cancers are often raised by multiple mutations [145]. Therefore, advanced knowledge of the pharmaceutical effects of other inhibitors on RAS alleles is necessary to develop strategic combination therapies. Combination therapy with solid therapeutic effects and relatively low toxicity is required. Third, further exploration of the mechanism of drug resistance by gene mutation and histological transformation is needed [146]. The rapid development of graphical computing, cloud computing, artificial intelligence technologies, and improvements in software and algorithms make this possible. Then, VS has become more diverse and high-throughput. MD simulation has even been extended to the millisecond level, accompanied by higher precision and lower consumption. QSAR and pharmacophore models have gained accuracy and efficiency as ML algorithms have improved. Overall,with increasing accuracy, CADD is becoming an indispensable component in RAS inhibitor discovery and other areas of drug discovery.  
Conceptualization, G.W., Y.B. and [PERSON]; writing--original draft preparation, [PERSON], [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON]; writing--review and editing, all authors; visualization, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 2 System Model and Problem Formulation
### Matrices for Spectrum Allocation  
**Matrices Definition** (See Table 1).  
**Explanation for Matrices**  
* _Available matrix_ \(L\). The matrix represents the availability of licensed bands for cognitive users. If \(l_{n,m}=1\), user \(n\) can access spectrum \(m\) without interference to primary users, otherwise \(l_{n,m}=0\). As shown in Figure 1, spectrum channel \(B\) is available for \(SU1\), then \(l_{1,2}=1\).
* _Benefit matrix_ \(B\). The matrix indicates the benefit that a cognitive user gets by successful access to a licensed spectrum band, where \(b_{n,m}>0\) only if \(l_{n,m}=1\).
* _Interference matrix_ \(C\). The three-axis matrix describes the interference relationship of any two vertices \(n\) and \(k\) when they access spectrum \(m\). As shown in Figure 1, \(SU3\) and \(SU4\) overlap in some area, then \(c_{3,4,1}=1\), \(c_{3,4,3}=1\), \(c_{3,4,4}=1\).
* _Allocation matrix_ \(A\). The matrix is a spectrum allocation result which is interference free. If \(a_{n,m}=1\), cognitive user \(n\) can access spectrum \(m\) and transmission data in this band. A conflict free allocation needs to satisfy the interference constraints: \(a_{n,m}\ +\ a_{k,m}\leq 1\), if \(c_{n,k,m}=1,\forall n,k<N,m<M\).
* _Degree matrix for cognitive users_ \(Z\). The matrix represents the available spectrum number for each cognitive users. In Figure 1, \(z_{1}=2,z_{2}=4,z_{3}=4,z_{4}=3\).
* _Degree ascending matrix_ \(K\). The matrix is another representation of the available matrix, which incrementally orders the rows according to the degree matrix \(Z\).
## 3 Results and Discussion
### Stiffening Effects
#### 3.5.3 Goodness-of-Fitting  
The stiffening potential differences of micromechanics models are further elaborated with the line of equality graph [39]. Figure 11 shows the good correlation of measured and predicted stiffness except for the [PERSON] model.  
Figure 11: Line of equality graph for (**a**) limestone (**b**) granite.  
In Table 5, the maximum packing fraction (\(\varphi_{\text{m}}\)) for limestone in the [PERSON] model is significantly higher than [PERSON], [PERSON], [PERSON], and [PERSON] models. \(\text{K}_{\text{E}}\) is significantly lower for granite in the same model as compared to [PERSON], [PERSON], [PERSON], and [PERSON] models. Moreover, [PERSON], [PERSON], [PERSON], and [PERSON] models seem to be in good agreement for limestone and granite. The increased value of \(\text{K}_{\text{E}}\) than 2.5 indicated is probably due to a number of factors such as particle shape, size, and surface roughness. The value 2.5 is corresponding to rigid, spherical, and smooth particles.  
In this research, the particle shape was assumed to be spherical. We recommend that \(\text{K}_{\text{E}}\) and \(\phi_{\text{m}}\) should be adjusted for real systems in future studies. These adjusted values will improve the prediction of the stiffening effect. For example, the agglomeration of roughly spherical particles will affect the packing and can be incorporated by adjusting \(\text{K}_{\text{E}}\) and \(\phi_{\text{m}}\).  
We recommend comparing the stiffening effects based on rheology, micromechanics, and approaches based on morphology and physical tests, etc. An example is the work of [PERSON] et al. (2016). The researchers studied the filler-bitumen interaction based on SEM, and simple tests of a specific surface, fractional voids and the bitumen number.
# Improved Duplication-Transfer-Loss Reconciliation with
## 2 Definitions and Preliminaries
### DTLx Reconciliation
Specifically, the reconciliation cost is given by \(P_{\Delta}\times|\Delta|+P_{\Theta}\times|\Theta|+P_{\mathbb{TL}}\times| \Theta_{L}|+P_{\mathbb{TX}}\times|\Theta_{X}|+P_{loss}\times\mathrm{Loss}_{ \mathrm{a}}\)._  
Given \(G\) and \(S\), along with specific event costs, the computational objective is to find a DTLx reconciliation of \(G\) and \(S\) with minimum reconciliation cost (i.e., a most parsimonious DTLx reconciliation of \(G\) and \(S\)). This yields the following problem statement. **Problem 1** (Optimal DTLx reconciliation (O-DTLx) problem). : _Given \(G\) and \(S\), along with \(P_{\Delta}\), \(P_{\Theta}\), \(P_{\mathbb{TL}}\), \(P_{\mathbb{TX}}\), and \(P_{loss}\), the O-DTLx reconciliation for \(G\) and \(S\) with minimum reconciliation cost._  
It is well-understood that there can exist a very large number of optimal DTL reconcilations for any given gene tree and species tree and that this number can grow exponentially in the sizes of the input trees [11,12]. One of the most efficient and widely used approaches for handling multiple optima and exploring the diversity of optimal reconciliations is to generate optimal reconciliations uniformly at random from the space of all optimal reconciliations [11,17]. Such random sampling for the efficient exploration of the space of multiple optima and makes it possible to distinguish well-supported and poorly-supported aspects of an optimal reconciliation. This motivates the following problem statement. **Problem 2** (Optimal DTLx reconciliation sampling (O-DTLx-Sampling) problem). : _Given \(G\) and \(S\), along with \(P_{\Delta}\), \(P_{\Theta}\), \(P_{\mathbb{TL}}\), \(P_{\mathbb{TX}}\), and \(P_{loss}\), let \(\mathcal{O}\) denote the set of all optimal DTLx reconciliations for \(G\) and \(S\) (i.e., with minimum reconciliation cost). The O-DTLx-Sampling problem is to compute an optimal DTLx reconciliation from \(\mathcal{O}\) in such a way that each DTLx reconciliation in \(\mathcal{O}\) has an equal probability of being computed._  
We provide efficient algorithms for both the above problems in the following section.
## Materials and Methods
### Experimental Characterization
#### 2.2.2 Airflow Resistivity  
The airflow resistivity \(\sigma\) was measured with testing equipment with ISO Standard 9053-2 [27] by progressively compressing 20 g of fibers placed in a cylindrical sample holder (range from 65 to 191 kg/m\({}^{3}\)). The apparent density was calculated as a function of the weight of the fibers (constant) and the occupied volume (variable in relation to the thickness of the sample).  
Preliminary tests showed that disassembling and reassembling the 20 g fiber sample from the sample holder and repeating the airflow resistivity measurements gives practically identical results. For this reason, average values were not considered in the calculations, nor is a statistical analysis of the results presented.  
Figure 6 shows the experimental values of airflow resistivity with respect to the apparent density of the fibers; to extend the experimental data to all bulk densities, an interpolation curve was calculated, using the relationship proposed in [31]:  
\[\sigma=\frac{\eta}{\left(2a\right)^{2}}\frac{\sqrt{1-\left(1-\phi\right)}}{0. 21\left(\frac{0.71}{1-\phi}-3\sqrt{\frac{0.71}{1-\phi}}+3-\sqrt{\frac{1-\phi }{0.71}}\right)} \tag{10}\]  
where \(a\) is the effective radius of the fibers and \(\eta\) the viscosity of air.  
Figure 5: Porosity and tortuosity values as a function of the apparent density of the fibers, calculated with (9) and (11).  
Figure 6: Comparison between the experimental airflow resistivity values and analytical model (10) with \(a=40.1\) μm.  
In relation to the experimental data, using Equation (10), the equivalent radius of the fibers \(a\) was minimized; this radius \(a\) was found to be equal to 40.1 \(\upmu\)m.  
The resulting average percentage difference between the experimental airflow resistivity and that calculated with Equation (10) was equal to 5%, with a maximum value of 8%.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
###### Abstract  
The fast estimation of synchronization parameters plays an extremely important role in the demodulation of burst signals. In order to solve the problem of high computational complexity in the implementation of traditional algorithms, a synchronization parameter (frequency offset, phase offset, and timing error) estimation algorithm based on Offset Quadrature Phase Shift Keying (OQPSK) burst signal detection is proposed in this article. We first use the Data-Aided (DA) method to detect where the burst signal begins by taking the segment correlation between the receiving signals and the known local Unique Word (UW). In the sequel, the above results are adopted directly to estimate the synchronization parameters, which is obviously different from the conventional algorithms. In this way, the complexity of the proposed algorithm is greatly reduced, and it is more convenient for hardware implementation. The simulation results show that the proposed algorithm has high accuracy and can track the Modified Cramer-Rao Bound (MCRB) closely.  
synchronization parameter estimation; burst signal detection; data-aided; OQPSK
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 2 Electrical Connection Faults  
First, consider the effects of three types of electrical connection faults in connectors, bonding wires, and solder balls on electrical characteristics; each type of fault is modeled and analyzed below. As illustrated in Figure 1, in engineering applications, the connection resistance is measured using a micro-ohmmeter to judge whether the connection is faulty.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 4 Information Fusion Method Based on the Factor Graph
### Fusion Algorithm with the Factor Graph  
For our application, the factor graph framework model is established for the MUAV navigation system. This graph-based optimization algorithm is particularly developed to solve the optimization problem for the current state prediction with asynchronous multi-sensor data. The main purpose is to calculate the best navigation solution by using the available information sources.  
The state variable of the MUAV system \(\mathbf{X}\) is described in Section 3.1. \(\mathbf{X}_{k}\in R^{n}\) is the state variable in the current time \(t_{k}\). Given the initial distribution \(p(\mathbf{X}_{0})\), \(\mathbf{X}_{k}\) can propagate with the probability density \(p(\mathbf{X}_{k}|\mathbf{X}_{k+1}\,)\). \(\mathbf{Z}_{k}\in R^{m}\) represents the independent measurement in the current time \(t_{k}\). The Bayesian estimation contains two parts, the prediction and the update. The prediction concerns the prior density of the state with the state model. The update process is to modify the prior density obtained by the previous step by using the newest measurement in order to obtain an a posteriori probability density \(p(\mathbf{X}_{0:k}|\mathbf{Z}_{1:k}\,)\). Our goal is to solve the a posteriori probability density in the navigation system [28].  
Given the observations up to time \(k\), the global conditional joint probability density function \(p(\mathbf{X}_{0:k}|\mathbf{Z}_{1:k}\,)\) for the state vector \(\mathbf{X}_{0:k}\) is the marginal function. According to the Bayes formula, \(p(\mathbf{X}_{0:k}|\mathbf{Z}_{1:k}\,)\) can be factorized as:  
Figure 2: An example of a factor graph.  
The estimation of the state \(\hat{\mathbf{X}}_{i/i-1}\) can be solved by state vectors propagating. When adding the new variable \(\mathbf{X}_{i+1}\) to the graph, the IMU measurement is used to calculate the state transition matrix to predict the values for \(\mathbf{X}_{i+1}\). In the factor graph. Let \(f()\) represent the local functions of the probability density function. Equation (12) can be written as  
\[\prod_{i=1}^{k}\frac{p(\mathbf{Z}_{i}|\mathbf{X}_{i})\,p(\mathbf{X}_{i}|\mathbf{X}_{i-1})}{p( \mathbf{Z}_{i}|\mathbf{Z}_{1:i-1})}\,p(\mathbf{X}_{0})\propto\!\!\prod_{i=1}^{k}\!f(\mathbf{Z}_ {i}|\mathbf{X}_{i})\,f(\mathbf{X}_{i}|\mathbf{X}_{i-1}) \tag{20}\]  
where \(f(\mathbf{Z}_{i}|\mathbf{X}_{i})\) and \(f(\mathbf{X}_{i}|\mathbf{X}_{i-1})\) are the local functions associated with the factor nodes \(P_{x,i}\) and \(Z_{x,i}\) in Figure 3, respectively.  
The factor graph model of the navigation system is dominated by the two local functions \(f(\mathbf{Z}_{i}|\mathbf{X}_{i})\) and \(f(\mathbf{X}_{i}|\mathbf{X}_{i-1})\). By connecting the variable nodes associated with each function to the factor node, the factor graph can be constructed. The factor graph method allows for a more general form of the conditional probability density.