esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods: Computational Schemes  
All our calculations are based on density functional theory (DFT) within the local density approximation (LDA). In some cases a comparison with the generalized gradient approximation (GGA) of DFT has been performed. The computations are carried out using the plane-wave code VASP [30; 31], implemented with the projector augmented-wave (PAW) method to account for the valence electron-ion core interaction.  
In our VASP calculations, we have employed supercells of different sizes--namely 64, 108, 128, 144 and 192 atoms per supercell are used to check the influence of finite size effects on the relaxation of the atoms and the derived total energies. In all calculations the first Brillouin zone of the superlattice is sampled using a Monkhorst-Pack (MP) **k**-point mesh [32]. Employing the 108-atom supercell for face-centered cubic (fcc) Al we compare the results obtained with \(4\times 4\times 4\) and \(6\times 6\times 6\)**k**-pointmeshes to check the convergence of the total energy. Differences in the total energy of the systems are less than 5 meV per atom. All the calculations have thereafter been performed with the finer MP-mesh. A plane-wave cutoff of 300 eV is used in the calculation of the pseudo valence wave functions.  
In the defect calculations, atomic positions are relaxed and the total energy is minimized until the forces acting on atoms are less than 0.04 eV/A. The volume relaxation is not performed systematically, because we found that the use of a larger (108 and more atoms) supercells gives well-converged results without volume relaxation. However, results of test calculations employing the smallest (64 atom) supercell are given below. In the plane wave calculations we have used lattice constants optimized for each set of computational parameters, i.e., the cut-off energy and MP-mesh, used. For error cancellation, the total energy differences and relative ionic relaxations between defect and perfect bulk systems are calculated from results for supercells of the same size and obtained with the same computational parameters.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 4 Computational Experiments
### Preliminary Experiments
#### 4.1.2 Multi-Threading Computation  
CP solvers notoriously benefit greatly from the use of multi-threading computation, typically substantially more than MILP solvers, due to the different nature of the solving routines. On the other hand, nowadays, home-computers and laptops offer multi-core architectures, making parallel computation affordable. In this section, we assess the effect of multi-threading on the performance of solvers while running on the PDISP.  
For the test reported, we selected the instance _eil101\(0\)0_1\(2\)1_ first proposed in [7] since it reduces to a classic traveling salesman problem, with no drone-eligible customers. This also enables previously proposed MILP-based solvers for the PDSTSP to close the instance. A maximum running time of 180 s was set. We solve the CP model described in Section 3 with the CP-SAT solver and the MILP model described in [10] attacked by the Gurobi ([[https://www.gurobi.com](https://www.gurobi.com)]([https://www.gurobi.com](https://www.gurobi.com)), accessed on 5 January 2023) 9.1.1 solver. For both cases, we allow the use of a number of parallel threads between 1 and 8; in Figure 3, we report on the time required by the solvers (180 is reported when the time limit is reached).  
The results reported in Figure 3 indicate that the CP approach is able to benefit substantially from the use of multi-threading computation. This result was expected, given the general characteristics of routines solving constraint programming and their scalability properties. More surprising is the drastic change in performance between four and five threads. This behaviour was observed in several other insta  
Figure 3: Time required by the MILP solver and by the CP solver with different numbers of threads (180 s maximum time)—instance _eil101\(0\)0_1\(2\)1_ from [7].  
not always between four and five) and is likely a consequence of activation of crucial sub-solvers on newly available parallel cores. In general, the chart also testifies to the importance of the heuristic routines used to distribute the computation economically and efficiently and to select the alternative solvers that are eventually run in parallel. On the other hand, the MILP solver does not seem to benefit from multi-threading in these short tests, possibly due to the overhead of distributing tasks and collecting results and to the reduced variety of the methods run in parallel. It should be noted that the situation might be less extreme when more challenging instances are considered, but this is difficult to observe, since, even with longer computation times (hours) allowed and eight threads, generic instances were not closed.  
In conclusion, multi-threading appears to be the basis of the success of modern CP solvers; therefore, all the experiments reported in Section 4.2 were run on eight cores.
###### Abstract  
Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405-465 nm), we performed retrievals of NO2. Some first retrieval results of NO2 from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO2 show similar spatial distributions to OMI and TROPOMI (r \(>\) 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO2 patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 \(\times\) 10\({}^{15}\) molec cm\({}^{-2}\)) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO2 from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO2 VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO2 monitoring.  
GaoFen 5; Environmental trace gases Monitoring Instrument; DOAS; stratosphere and troposphere NO2 +
Footnote †: journal:  
Article NO2 Retrieval from the Environmental Trace Gases Monitoring Instrument (EMI): Preliminary Results and Intercomparison with OMI and Tropomi
## 3 Results
### Evaluation of the Results  
The expert was asked to label the new data records according to the obtained clusters to evaluate the results from the records clustering. For this purpose, traffic and signaling data documents were provided to the expert separately. Having the labels for the obtained clusters in the previous step, it was now possible to test the accuracy of the clustering results by running different classification algorithms and using the test data labeled by the expert.  
Therefore, different classification algorithms were applied to the data in both traffic and signaling data parts. The evaluation results of the other models on the test data are given in Table 3. It should be noted that, due to the lack of balance in the training data in the records related to each class, Clementine software was used to balance the records of each type. This software was also used to apply different classification methods to the data. The number of training set records was 8006, and the number of traffic and signaling data test set records was 53.  
By obtaining the different classification methods' results, accuracies of 90.38%, 86.54%, and 78.85% for traffic data were obtained from some forms of summarizing opinions, such as voting, weighted voting concerning model confidence, and the highest confidence method. The standard voting method worked better on our data. The results of the used methods for the data of the signaling section are shown in the following table. The voting method is the best way to collect opinions, with an accuracy of 92.31. Other practices, including weighted voting, had an accuracy of 88.46 and 80.77 in relation to model confidence and the highest confidence (Table 4).
## Appendix A Derivation of \(ne^{+}(\tau)\) and \(ne^{-}(\tau)\) for the 1-D Vehicular Scenario
Indeed, those are the only nodes that can meet node \(n\) if the speed difference between them and \(n\) is enough to cover the required distance. In a fluid model we can assume that the number of nodes within an interval of infinitesimal length \(ds\) is equal to \(\rho ds\). Then, the probability of a contact between the nodes in the region \([s,s+ds]\) is simply given by \(\Gamma(\tau,s-l_{tx})\) for \(s\geq l_{tx}\) and \(\Gamma(\tau,s+l_{tx})\)for \(s\leq-l_{lx}\). Finally, we leverage the symmetry of the function \(\Gamma(\tau,s)\) with respect to the variable \(s\), to derive the following expression:  
\[ne^{+}(\tau)=2\int\limits_{0}^{d_{m}(\tau)}\Gamma(\tau,s)\rho ds. \tag{10}\]  
By substituting (11) into (10), and solving the integral, we obtain (12). To derive \(ne^{-}(\tau)\) we could follow a similar line of reasoning as above, considering two nodes \(ne_{i}^{-}\) and \(ne_{j}^{-}\) moving in opposite directions. However, there is no need to derive the exact distribution of relative speed between two nodes \(ne_{i}^{-}\) and \(ne_{j}^{-}\), but it is intuitive to see that their average relative speed is simply given by \(2v_{m}\). Then, it holds that  
\[ne^{-}(\tau)=2\rho v_{m}\tau\, \tag{11}\]  
where \(v_{m}=(v_{b}+v_{a})/2\) is the average node speed.
## 2 Model and Method
### Model and Method for the Rail Mobility/_Receptance_
#### 2.1.3 Spectral Element Method
The one-node Timoshenko beam right-throw-off element which prevents the reflecting waves from travelling to the left is shown in Figure 5.  
\(U_{\mathrm{R}}\), \(\Theta_{\mathrm{R}}\), \(F_{\mathrm{R}}\) and \(M_{\mathrm{R}}\) are the spectral vertical displacement, spectral bending rotation, spectral vertical force and spectral bending moment at the node, respectively. Similarly, the relation between the spectral nodal displacements and the spectral nodal forces in a Timoshenko beam right-throw-off element can be expressed as:  
\[\mathbf{F_{R}}=\mathbf{S_{R}}(\omega)\mathbf{U_{R}} \tag{9}\]  
where \(\mathbf{F_{R}}\) = (\(F_{\mathrm{R}}\), \(M_{\mathrm{R}}\))T is the spectral nodal force vector, \(\mathbf{U_{R}}\) = (\(U_{\mathrm{R}}\), \(\Theta_{\mathrm{R}}\))T is the spectral nodal displacement vector, and \(\mathbf{S_{R}}(\omega)\) is the frequency-dependent spectral element stiffness matrix. The expression of \(\mathbf{S_{R}}(\omega)\) can be given by:  
\[\mathbf{S_{R}}=\left( \begin{array}{cc}EI\frac{k_{2}^{2}-k_{2}^{2}}{R_{1}-R_ {2}^{2}}&EI\frac{R_{2}k_{2}^{2}-R_{1}k_{2}^{2}}{R_{1}-R_{2}}+\omega^{2}\rho I \\ EI\frac{\mathrm{i}(k_{1}-k_{2})}{R_{1}-R_{2}}&EI\frac{\mathrm{i}(R_{1}k_{2}-R_ {2}k_{1})}{R_{1}-R_{2}}\end{array} \right) \tag{10}\]  
The derivation of \(\mathbf{S_{R}}\) (\(\omega\)) is omitted as in the case of Equation (2). Because the derivations of the spectral element stiffness matrixes of the spectral spring element and the Timoshenko beam left-throw-off element follow the same pattern, these spectral elements are not represented for the purpose of conciseness. Using the coordinate transformation approach, the spectral element stiffness matrix in the local coordinate system can be transformed for the global coordinate system. The spectral stiffness matrix of the whole model can be obtained by assembling the spectral element stiffness matrixes and processing the constraint conditions, which is same as the conventional finite element method. The relation between the spectral nodal displacements and the spectral nodal forces for the whole model can be expressed as:  
\[\mathbf{F}=\mathbf{S}(\omega)\mathbf{U} \tag{11}\]  
Figure 5: One-node Timoshenko beam right-throw-off element. where \(\mathbf{F}\) is spectral nodal force vector of the whole model, \(\mathbf{U}\) is the spectral nodal displacement vector, and \(\mathbf{S}(\omega)\) is the spectral stiffness matrix. By solving Equation (11), we can obtain the frequency-domain dynamic characteristics of rail vibrations in the Timoshenko-beam track model. For a static and linear system, the amplitude of the mobility is \(2\pi f\) times that of the receptance at the frequency \(f\), while the amplitude of the accelerance is \(2\pi f\) times that of the mobility. Based on this knowledge, the rail mobility/accelarence can be obtained by the simple post processing.
# Characteristics of Vibration Velocity Signal Using Liquid Carbon Dioxide Rock-Breaking Technology
## 2 Field Test
### Test Parameters  
The experiment was conducted in an open pit mine in Panzhihua City, Sichuan Province. In order to understand the basic mechanical properties of rock mass in the rock-breaking area, we conducted core sampling on-site, processed the core into standard test pieces with a diameter of 50 mm and a length of 100 mm in accordance with relevant specifications [24], and conducted uniaxial compression tests using the RMT-150C testing machine (Figure 2).  
Figure 1: L-CDRB system: (**a**) fracturing tube; (**b**) tube’s construction; (**c**) liquid carbon dioxide storage equipment; (**d**) filling machine.  
The stress-strain curve of the uniaxial compression test is shown in Figure 3, and the test results are summarized in Table 2. Rock mechanics tests showed that the average uniaxial compressive strength of the rocks in this area was 114.43 MPa, the tangent modulus of elasticity was 52.14 GPa, and the Poisson's ratio was 0.31.  
Figure 3: Stress-strain curve of uniaxial compression test.  
Figure 2: Uniaxial compression test: (**a**) field coring; (**b**) test piece; (**c**) RMT-150C; (**d**) damaged test piece(Test piece No.: U-1\(\sim\)U-4).  
The L-CDRB tests were conducted three times. The first test was designed as a single-row hole, and the second and third were double-row holes with plum-shaped layouts with a hole spacing of 1 m and a row spacing of 0.7 m, See Table 3 for specific parameters. In addition, blast vibration monitoring was conducted three consecutive times for explosives at the same test site, the design parameters for which are shown in Table 4.
## 3 Results and Discussion
For comparison, the cooling performances of the tubes with conventional radial rectangular fins are presented for various thicknesses and numbers of fins in Figure 12. The optimal cooling performance of the tube with conventional radial rectangular fins is 0.513 W/K. Finally, Figure 13 shows the comparison of the optimal cooling performances of tubes with tilted rectangular fins and conventional radial rectangular fins. As shown in the figure, the optimal cooling performance of the tilted finned tube with a tilt angle of 60\({}^{\circ}\) is 6% greater than that of the radial finned tube. Therefore, the tube with tilted rectangular fins can have better cooling performance compared to the tube with radial rectangular fins, if properly designed. In addition, the cooling performance of the tube without fins is also shown in Figure 13. The optimal cooling performance of the tilted finned tube with a tilt angle of 60\({}^{\circ}\) is 9.2 times greater than that of the tube without fins. Figure 10: Contour plot of the effective surface area. Figure 9: Contour plot of cooling performance. Figure 11: Contour plot of the heat transfer coefficient. Figure 12: Contour plot of cooling performance of the tube with conventional radial rectangular fins. Figure 13: Comparison of the optimal cooling performances. Last of all, the optimal dimensions and optimal cooling performances of the tube with tilted fins in the horizontal and vertical configuration are listed in Figure 7:0 The table shows that the optimal cooling performance varies significantly, depending on the orientation. Therefore, orientation dependence should be considered carefully if the orientation of the tube with tilted fins is not always fixed, and may need to be adjusted for the cooling of electronic devices, such as retrofit LED bulbs [5].
# Satellite Image Categorization Using Scalable Deep Learning
###### Abstract  
Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset.  
m aritime monitoring; satellite imagery; deep learning; remote sensing; image classification 2023  
Satellite Image Categorization Using Scalable Deep Learning  
1
## 2 Materials and Methods
### Concepts for Discriminating the Oil-Slick Category
#### 2.3.1 Concept 1: SAR Signature  
To cope with the seep-spill discrimination, the initial exploratory analysis used straightforward LDA-based algorithms applied to measurements of two forms of SAR signatures:  
* **SAR backscatter coefficients:**\(\sigma^{\circ}\), \(\beta^{\circ}\), and \(\gamma^{\circ}\)[55; 56; 57]; and
* **SAR calibrated products:** back-scattered radar beam measurements given in amplitude (_amp_) or in decibel (_dB_), both with or without the application of a despeckle filter [58].  
Even though the relationship among \(\sigma^{\circ}\), \(\beta^{\circ}\), and \(\gamma^{\circ}\) are geometrically related at the pixel level assuming the ocean to be a horizontally flat surface, the sea surface undergoes changes in height and inclination relative to the incident radar beam due to long-period waves [59]. Given that differences in sea surface heights are measured by satellite microwave altimeters (e.g., significant wave height [60; 61]), we believe that such variations may influence the SAR backscatter coefficient within the oil slicks' surface, thus affecting our ability to discriminate the slick category.  
The calibrated products differ one from another mathematically, representing possible ways to influence the discrimination of seeps from spills. In fact, _dB_ values are derived by applying a logarithm function (log\({}_{10}\)) to the amplitude of the back-scattered radar beam (denoted _amp_) and multiplying it by a constant value (in this case: 20)--this _dB_ transformation occurs in the pixel level. Moreover, despeckle filtering strategies also alter the value of each pixel (e.g., the Frost filter [62]).  
At the outset of the initial analysis [29; 30], the full SAR signature set was investigated together as one entity, then, all calibrated products (four) were analyzed together for each backscatter coefficient (three). On the other hand, only \(\sigma^{\circ}\)_amp_ with no despeckle filter (_SIG.amp_) was explored in the refined study [31; 32], thus providing a firmer basis and more control in the understanding of the seep-spill categorization process.
## 2 Overview of DME and JTIDS  
### First Principles and Baseband Signal of DME  
DME is an aeronautical radio navigation system and consists of transponder installed on the ground and interrogator fitted in the aircraft. DME can provide the slant range \(d\) between aircraft and ground beacon continuously. Interrogator transmits interrogations to transponder. After receiving interrogation, transponder delays it for quite a period named as dead time \(t_{d}\), transponder refuses to receive other interrogations in the dead time. After the delay of a dead time, interrogations are renamed as reply and transmitted back to interrogator, it takes a period \(\tau_{r}\) for interrogator to transmit interrogations and receive reply [1]. The slant range \(d\) can be calculated as follows:  
\[d=\frac{(\tau_{r}-t_{d})\cdot c}{2} \tag{1}\]  
where \(c\) is velocity of light.  
DME signal is a pair of pulses illustrated in Figure 1, the waveform is depicted to be rectangular for simplicity. DME pulse pair characteristics are shown in Table 1. Pulse duration \(w_{m}\) is 3.5 \(\upmu\)s to all signals, while pulse interval \(s_{m}\) is determined by DME mode (X or Y) and signal type (interrogation or reply), which equals to 12 \(\upmu\)s or 30 \(\upmu\)s or 36 \(\upmu\)s. Signal duration \(\tau_{d}\) equals to \(w_{m}\) plus \(s_{m}\). The average PRF of interrogations is (10-30) Hz or (40-150) Hz when interrogator is operating on tracking or searching condition, respectively. The transponder operates at a transmission rate, including randomly distributed pulse pairs and identification signal and distance reply pulse pairs, of no less than 700 pulse pairs per second (ppps) and no more than 2700 ppps.