esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle
## 5 Experimental Results  
We conduct computational experiments to see how extended variables affect and improve the search efficiency. First, we see the performance (processing time) of solvers with/without one of the methods shown in Section 4.2 on three types of instances (no extension, full extension, and partial extension) in Section 4.1. Second, we analyze DRUP proofs generated from solvers on the three extensions and observe how many times extended variables are used to derive unsatisfiability. For both experiments, we use two popular SAT solvers: MiniSAT 2.2 [14] and Glucose 3 [15]. Both solvers are the most fundamental solvers used as a basis for today's subsequent solvers. We switch off the preprocessing, as described in Section 4.2, so not to remove the extended variables. In preliminary experiments, we found that the preprocessing for instances of no extension deteriorates the solvers' performance. Hence, we report results without the preprocessing even for (not extended) original PHP instances.  
We use the PHP instances with the number of pigeons (\(n\)) from 11 to 15. Note that instances with less than 10 pigeons are easy to solve within 10 seconds in our preliminary experience. We set the time limit for each instance as 10,000 s and the constant R in Algorithm 2 as 100 that showed good performance among the values 10, 100, 1000, and 10,000 in our preliminary experiments. Since the order of clauses and variables in a CNF can affect the solver's performance [41] (e.g., initial decision variables and initial watched literals [14]), we shuffle the order by random reordering and make four differently shuffled instances for each \(n\).  
We use a machine running Ubuntu 16.04 with two Xeon Gold 5218 CPUs and 384 GB RAM. Our codes, MiniSAT 2.2 and Glucose 3 with/without our proposed method, are compiled by GNU C++ Compiler version 5.4.0. Since both solvers run deterministically, we execute each solver on each instance once.
## 1 Introduction
It has also allowed monitoring of volcanos [37], bridge deflection [38], and buildings due to urban subsidence [39]. However, there are no available records found of case studies in the country that investigate specifically the exploitation of the new generation SAR system Sentinel-1 for tunneling-induced ground deformation and rainfall-induced landslides. Korean local authorities and city planners benefit from the results of these additional investigations through disaster mitigation and risk reduction and design effective plans ensuring the safety of buildings, private and public facilities and ultimately, the people. This paper describes the different types of InSAR techniques and presents the InSAR-derived results for various geotechnical engineering practices. Specifically, this work addresses the capability of processing freely available Sentinel-1 SAR data using the SeNtinel Application Platform (SNAP) and StaMPS free software packages. The InSAR techniques were implemented for DEM generation, and detection, mapping, and monitoring of ground deformation associated with earthquakes, underground tunneling, and rainfall-induced landslide events in the Korean Peninsula. The remainder of the paper describes InSAR analysis techniques, datasets, and tools used together with the description of the test sites (Section 2), presents the interferometry results (Section 3), discusses their main applications with advantages and limitations (Section 4), and concludes with some outlooks (Section 5).
# Performance Analysis of a Color-Code-Based Optical Camera Communication System
## 2 Proposed OCC System Architecture
### Transmitter Design  
In the transmitter part, Python-Tkinter was chosen as the most compatible application development tool. PyCharm was used as the compiler in the transmitter.  
With the application software written in the transmitter unit, it is aimed to send data such as audio, image, and video to the receiver. The colors used in the designed color-coded OCC system are red, green, blue, white, black, cyan, purple, and yellow.  
Figure 1: General view of the communication system.  
Figure 2: Block diagram of the communication system.  
In the transmitter part, 8 color values were assigned to an empty array with NumPy. R, G, B color values and 3-bit coded values are [255, 0, 0] = \"100\" for red, [0, 255, 0] = \"010\" for green, [0, 0, 255] = \"001\" for blue, [255, 255, 255] = \"111\" for white, [0, 0, 0] = \"000\" for black, [0, 255, 255] = \"011\" for cyan, [255, 0, 255] = \"101\" for purple, [255, 255, 0] = \"110\" for yellow.  
As shown in Figure 3, the 12-character sample text data selected as \"Li-Fi:#Test-\" was first converted into an 8-bit data sequence as ASCII code. Then, this 96-bit data sequence was divided into 32 parts of 3 bits and a color channel was defined for each 3-bit part. Thus, 32 color channels were obtained for the sample text data of 12 characters. These color channels were converted into 2 separate frames in 4 \(\times\) 4 matrix format. Thus, in the first stage, Frame-1 containing the text \"Li-Fi:\", and in the second stage, Frame-2 containing the text \"#Test-\", are color-coded and transferred from transmitter to receiver. If the text data are selected to consist of 14 characters instead of 12 characters, the size of the data packet will be 112 bits instead of 96 bits, so 3 separate packets of 48 + 48 + 16 bits must be transmitted. When dividing the 16 bits of data in the last packet into 3 bits, 1 bit will be left out. For an error-free transmission, the data packet is completed to 48 bits by adding 0 to the remaining part. Thus, it is possible to obtain the original data again at the receiving end.  
The coordinates of the color channels in Python software V3.9.7 are chosen as shown in Figure 4. Each channel is 40 \(\times\) 40 pixels in size. In the literature, there are several studies on the effects called inter-pixel interference (IPI) and inter-symbol interference (ISI) in OCC systems. Since the interference effects decrease when the distance between pixels increases, the channels are spaced 40 pixels apart horizontally and vertically [20, 21, 22]. Channel 1 covers the region between coordinates (600, 250) and (640, 290), with the upper left corner being the origin.  
The black background color of the monitor does not have a negative impact on information transfer. Since the image processing algorithm at the receiver takes into account the center coordinate of each color channel, the black color channel on a black background can be successfully detected.  
Figure 3: Color-coded channels on the monitor screen.
# Design and Implementation of Simulation System for Multi-UAV Mission
## 4 Path Planning Simulation Test for Two UAVs
### Path Planning for Two UAVs Based on Ant Colony Algorithm  
Ant colony algorithms are inspired by the foraging behavior of ant populations, in which each ant in the colony leaves pheromones on its movement path to communicate information to the colony. The shorter the movement path, the higher the pheromone concentration, and the more likely the path will be selected by the ants. This forms a positive feedback mechanism for the ants to find the shortest path. The basic principle of the ant colony algorithm is as follows [35; 36; 37; 38; 39].  
The ants select paths according to the pheromone concentration and the heuristic function, and the probability that ant k moves from grid \(i\) to grid \(j\) can be expressed as follows:  
\[P_{ij}^{k}=\left\{\begin{array}{l}\frac{\tau_{ij}^{\frac{\alpha}{\beta}(t) \eta_{ij}^{\beta}(t)}}{\sum\limits_{s\in A_{k}^{\alpha}}\tau_{ij}^{\alpha}(t) \eta_{ij}^{\beta}(t)},s\in A_{k}\\ 0,s\
otin A_{k}\end{array}\right. \tag{1}\]  
where \(\alpha\) is the pheromone heuristic factor and \(\beta\) is the distance expectation function factor, which affect the importance of pheromone and distance heuristic function respectively. Ak indicates the next destinations ants can reach. \(\tau_{ij}(t)\) is the pheromone concentration on the moving path at time \(t\); \(\eta_{ij}(t)\) represents the distance heuristic function, where  
\[\eta_{ij}(t)=\frac{1}{d_{ij}} \tag{2}\]  
\[d_{ij}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}} \tag{3}\]  
Each ant will leave a certain amount of pheromone when moving. Therefore, when the algorithm iterates continuously, the pheromone content in the path gradually accumulates and volatilizes at the same time. When all ants in the population complete each round of iteration, the pheromone content on the path will be updated according to the following rules:  
\[\tau_{ij}(t+1)=(1-\rho)\tau_{ij}(t)+\Delta\tau_{ij}(t) \tag{4}\]  
\[\Delta\tau_{ij}(t)=\sum\limits_{k=1}^{m}\Delta\tau_{ij}^{k}(t) \tag{5}\]  
Figure 6: Mission scenario.  
\[\Delta\tau^{k}_{ij}=\left\{\begin{array}{c}\frac{Q}{L_{k}}\\ 0\end{array}\right. \tag{6}\]  
where \(\rho\) is the pheromone volatilization coefficient; \(\Delta\tau_{ij}\) represents the sum of pheromones released on the path between the two nodes; \(\Delta\tau^{k}_{ij}\) denotes pheromone increment; \(L_{k}\) is the path length of ant \(k\), and \(Q\) is the pheromone enhancement coefficient.  
By smoothing the planned curves, the obtained UAV flight trajectory is shown in Figure 7. The algorithm can discriminate the intersection point, and the two UAVs will not reach the intersection at the same time. Therefore, the planned path is effective and feasible.
## 2 Materials and Methods  
### Design of CAD/CAM-Based Surgical Templates  
Ten dry skulls with an intact maxillary anterior surface were included in our study. 3D computed tomography (CT) scans (SOMATOM Sensation 10; Siemens, Germany) were obtained under 120 kVp and 80 mA with a slice thickness of 0.75 mm. CT data (T0) were saved in the Digital Imaging and Communications in Medicine (DICOM) format and imported into Mimics software (Materialise, Leuven, Belgium). To construct a 3D virtual skull model, the threshold of the CT value was set between 250 and 3000 Hounsfield units. After construction of the virtual skull model, Le Fort I osteotomy was performed on the virtual skull models along the conventional Le Fort I osteotomy line that was set as the line from the most inferior point in the curvature of zygomaticomaxillary buttress to the midpoint of the piriform aperture area. Then, surgical templates for Le Fort I osteotomy were designed and fabricated using 3D printing technology [1] (Figure 1a). The design of the templates is described in detail below (Figure 1b)--surgical templates included two distinct parts (right and left parts) separated at the midline. For exact positioning of the template in the planned position, the template was designed to cover the zygomaticomaxillary buttress, piriform aperture, and maxillary anterior wall including the canine eminence, which can provide \"3D lock\" [3; 30]. The upper boundary of the template was set at 8 mm above the Le Fort I osteotomy line and the lower boundary was set at 14 mm below the Le Fort I osteotomy line. The right and left parts of the template covered the anterior nasal spine and were joined at the maxillary midline with a wedge-form interlocking structure for precise adaptation. Between the upper and lower parts, Le Fort I osteotomy space was generated to provide information about the osteotomy line and bone interference. To evaluate the deviation of the surgical template, we added 14 conical reference structures with a bottom diameter of 2.0 mm and a height of 2.0 mm (seven reference structures on each of the right and left parts) onto the template. For all surgical templates, the thickness was set to 2.0 mm. The designed surgical template was fabricated using a 3D printer. The CAD/CAM-based surgical templates were fitted on the maxilla by an oral and maxillofacial surgeon, and fixed with adhesive tape (Figure 1c). Subsequently, CT scan data (T1) were obtained to compare the actual position of the template with the planned position.
## 3 Thermal Study of the Heater
### Direct Problem
#### 3.2.2 Preliminary Magnitude through Analytical Analysis  
To determine the first order of magnitude of the thickness, a simple analytical analysis was carried out. The attenuation and phase shift properties of a metal plate subjected to a variable temperature field as a boundary condition were to be estimated in a simple configuration. By considering a harmonic variation, one can analytically determine this behavior and identify the skin thickness at which the thermal information can no longer be identified.  
As the first approach, a harmonic time-dependent temperature was applied on the boundary of a semi-infinite wall (see Figure 8) with the following thermal Equations (6)-(9).  
\[\boxed{\rho C_{P}\frac{\partial\theta}{\partial t}=\lambda\frac{\partial^{2} \theta}{\partial x^{2}}} \tag{6}\]  
with \(\theta=T-T_{0}\), \(T\) the temperature, \(T_{0}\) the initial temperature.  
The boundary condition at \(x=0\), \(\theta_{wall}\), is defined as:  
\[\boxed{\theta_{wall}(x=0,t)=\theta_{0}cos(\omega t)} \tag{7}\]  
where \(\omega=2\pi f\) and \(f\) the frequency.  
The boundary condition for an infinite wall is defined as:  
\[\theta_{\infty}(x=\infty,t)=0 \tag{8}\]  
The initial condition \(\theta_{initial}\) is:  
\[\theta_{initial}(x,t=0)=0 \tag{9}\]  
Figure 8: Harmonic time-dependent variation imposed at the boundary of a semi-infinite wall.  
The solution of Equations (6)-(9) can be written as:  
\[\theta(x,t)=e^{i\omega t}f(x) \tag{10}\]  
Using the boundary and initial conditions, the solution \(\theta\) can be expressed as:  
\[\boxed{\theta(x,t)=\theta_{0}e^{\sqrt{\frac{\omega}{2\alpha}}x}\cos\left(\omega t -\sqrt{\frac{\omega}{2\alpha}}x\right)} \tag{11}\]  
where \(\alpha\) is the thermal diffusivity.  
With this simple expression, the temperature attenuation within the plate can be calculated. The signal frequency is deduced from time \(t_{g}\) and \(t_{w}\), characterizing the temperature variation under a nucleation site (see Figure 9).  
The plate will be made of INOX 316L, whose thermal diffusivity is \(\alpha=3.75\times 10^{-6}\) m\({}^{2}\cdot\)s\({}^{-1}\) (assumed to be constant). According to Unal [3], the following orders of magnitude for \(t_{g}\) and \(t_{w}\) can be considered:  
\[t_{g}\approx 5\times 10^{-3}\;\mathrm{s}\;\;\mathrm{and}\;\;t_{w}\approx 1 \times 10^{-2}\;\mathrm{s} \tag{12}\]  
Calculation details are presented in Appendix A. The frequency of the temperature variation is then equal to 60 Hz.  
Figure 10 shows the evolution of the bandwidth (blue curve) and time delay (red curve) within the plate as a function of the thickness. It indicates that after 100 \(\upmu\)m (green vertical limit) the information is not correctly detectable according to the blue curve: an attenuation by a factor of 2 is noted. This is the first limit that can be fixed for the thickness of the plate. Moreover, the time delay is almost 0.75 s for a 100 \(\upmu\)m thickness.
## 4 Improved Routing Protocol
### Route Maintenance  
Due to extreme weather, links on a route may fail. Route maintenance is the mechanism to deal with it. It is common knowledge that a node can distinguish whether a packet is correctly received by the downstream node through acknowledgment of listening to the forwarding. Once a link fails, a link status message is specifically sent to the precursor node, and the precursor node can find out a backup link to replace it from its forward/backward list. Furthermore, when a node finds the next node fails, link status will be marked as invalid. In traditional reactive routing protocols, a link status message is sent to the source and the source starts another route discovery, which has high consumption.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 4 Production Performance of Single Well Pair  
The numerical simulation is first carried out on a single well pair. The producer is produced under a constant BHP, and the injection pressure of the injector adopts a unit step signal to represent the pressure impulses. The SV-DC model is used to calculate the inter-well connectivity coefficient and time-lag coefficient, and the changes in these coefficients under different permeability channels are studied to reveal the influence of geological heterogeneity on reservoir production.
# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
###### Abstract  
The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure (COP) trajectory is sufficiently unique to identify a person with high certainty. Thirty-six adults walked for 30 min on a treadmill equipped with a force platform that continuously recorded the positions of the COP. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2250 segments with an overall accuracy of 99.9%. A second set of 4500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used to fine tune the pretrained CNNs. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures while walking and that CNNs can learn the distinctive features of these trajectories. By applying a pretrained CNN (transfer learning), a couple of strides seem enough to learn and identify new gaits. However, these promising results should be confirmed in a larger sample under realistic conditions.  
biometric recognition; footstep recognition; user verification; force platform; neural networks; machine learning Article
# FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection
## 2 InSAR Imaging Model
### TDBP Method  
The basic idea of TDBP is point-to-point coherent integration and time-delayed phase compensation. Firstly, the echo data of InSAR are range-compressed by the matched filter method, which usually achieves in frequency domain. Specifically, the InSAR echo is transformed into the range frequency domain and multiplied by the reference focusing function. The signal of InSAR after range compression can be described as follows:  
\[s(\mathbf{p},t,l)=\gamma(l(\mathbf{p},t))\exp(-j4\pi l(\mathbf{p},t)/\lambda) \tag{2}\]  
where \(l(\mathbf{p},t)=\left\|\mathbf{p}-\mathbf{p}_{m}(t)\right\|_{2}\), \(\mathbf{p}\) is the coordinates of the target point, \(\gamma(l(\mathbf{p},t))\) denotes the ambiguity function of range direction, and \(\lambda\) is the central wavelength of the radar system.  
Owing to TDBPA is not restricted by the imaging space and the echo can be projected to any plane, the suitable imaging space should be selected and divided into a grid first. For each grid point of imaging space, the range history will be calculated. According to the index value of the range cell, TDBPA projects range-compressed signal to the selected plane and simultaneously compensates the phase function from the range history. Finally, the formula of the focused signal of TDBPA can be expressed as follows:  
\[\begin{array}{rl}s(\mathbf{p},l)&=\int_{t}\gamma(l(\mathbf{p},t))\exp(-j4\pi l(\mathbf{p },t)/\lambda)\exp(j4\pi l(\mathbf{p},t)/\lambda)dt\\ &\approx\chi_{\Omega}(\mathbf{p},l)\exp(j4\pi l(\mathbf{p},t_{0})/\lambda)\end{array} \tag{3}\]  
where \(\chi_{\Omega}(\mathbf{p},l)=\int_{n}s(\mathbf{p},t,l)dt\), \(l(\mathbf{p},t)=\left\|\mathbf{p}\prime-\mathbf{p}_{m}(t)\right\|_{2}\) and \(\mathbf{p}\prime\) is the coordinate of the target \(p\) in the selected imaging plane.  
For the target point \(p\), the interferometric phase of InSAR can be written as follows:  
\[\varphi(\mathbf{p})=\sphericalangle\exp(j4\pi(l_{m}(\mathbf{p},t_{0})-l_{s}(\mathbf{p},t_{0}) )/\lambda) \tag{4}\]  
where \(l_{m}(\mathbf{p},t_{0})=\left\|\mathbf{p}-\mathbf{p}_{m}(t_{0})\right\|_{2}\) and \(l_{s}(\mathbf{p},t_{0})=\left\|\mathbf{p}-\mathbf{p}_{s}(t_{0})\right\|_{2}\); and \(t_{0}\) denotes the azimuthal time when the master and slave antennas are closest to the target scene.  
Obviously, \(l_{m}(\mathbf{p},t_{0})\) and \(l_{s}(\mathbf{p},t_{0})\) can be accurately calculated in the TDBP algorithm, hence the high property of phase preserving can be achieved according to (5).  
Figure 1: Geometric model of the interferometric synthetic aperture radar (InSAR) system.
## 3 Methods
### Resolution Study and Range Inversion Methods  
In this section, we study the peak inversion methods. For the limited resolution problem, we propose a correlation peak inversion method to determine the specific range information of targets based on one-dimensional detection data and the corresponding signal autocorrelation function. Moreover, the inversion result accuracy is related to the sampling frequency of the echoes. We propose two methods to find the approximate ranges of target positions in the time domain. In method 1, without target estimation information, we found the range as follows: (1) Acquiring matched filter results, we filtered it with a normalized amplitude > \(a_{0}\). (2) Next, we divided the results into small segments, between each pair of segments. There is a low amplitude period with a length of \(b_{0}\), where \(a_{0}\) and \(b_{0}\) can be utilized to adjust the target range for better adaptability of detecting targets. In method 2, we can obtain the rough target position in the echoes by rough detection. The inversion ranges can be expressed as \(\mathbf{Range}=[range_{1},range_{2},\ldots,range_{m},\ldots,range_{M}]\), and the corresponding sampling points can be expressed as \(\mathbf{N}=[n_{1},n_{2},\ldots,n_{m},\ldots,n_{M}]\). The inversion in \(range_{m}\) can be expressed as  
\[\sum_{q=1}^{n_{m}}{(s(n-n_{m}+q)-s(n+q))}Amp(q)=M(range_{m}) \tag{19}\]  
where \(Amp\) is the modulating amplitude of the received signals. When clutter, noise, and the correlation sidelobe have little influence, the specific radar cross-section (RCS) distribution can be expressed as  
\[RCS(range_{m})=Amp \tag{20}\]  
According to the literature [28], clutter and noise influence can be suppressed effectively during long-term accumulation. Because the RCS of the simulation ship is large, we accumulated 10 s to effectively suppress the clutter and noise in simulation 2 and corrected  
Figure 5: Sidelobe between two main lobe peak ratios of the B3I and chirp signals.  
the distance and velocity migration. Therefore, we can utilize the inversion method to acquire the specific range information of targets.