esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Shapes  
Table 1 shows the updated classification of natural snow crystals and other hydrometeors. Original particle shapes are from the Magono-Lee classification. Added shapes from [PERSON] [23] are marked as [_Li_] and from [PERSON] et al. [21] are marked as [_Ki_]. Gray background and [_KRN_] mark the new particle shapes found in Kiruna. Figure A1. Updated shape classification (part 1): The 34 new shapes found in Kiruna are highlighted by thick gray frames. A list with the complete names of these shapes is shown in Table A1. Same scaling for all images: 1.0-mm scale bar is shown. +++ ==ERROR: No output for this page==
**Figure A2. ** Updated shape classification with 135 different shapes (part 2): For caption, see Figure A1. **Table A1. ** Updated classification. ++  
**Table A1. **_Cont._ \begin{tabular}{l c} \hline
**Level (N, C,...), Sublevel (1, 2,...), Name** & **Label** \\ \hline a. Pyramid & C1a \\ b. Cup & C1b \\ c. Solid bullet & C1c \\ d. Hollow bullet & C1d \\ e. Solid column & C1e \\ f. Hollow column & C1f \\ g. Solid thick plate & C1g \\ h. Thick plate of skeleton form & C1h \\ i. Scroll & C1i \\ j. Twin columns & C1j [Li] \\
2. Combination of columns & \\ a. Combination of bullets (bullet rosettes) & C2a \\ b. Combination of columns (column rosettes) & C2b \\ \hline
**P = Plane crystal** & \\
1. Regular crystal developed in one plane & \\ a. Hexagonal plate & P1a \\ b. Crystal with sector-like branches & P1b \\ c. Crystal with broad branches & P1c \\ d. Stellar crystal & P1d \\ e. Ordinary dendritic crystal & P1e \\ f. Fernlike crystal & P1f \\ g. Triangular form/plate & P1g [Li] \\ h. Rectangular plate & P1h [KRN] \\ i. Hollow plate & P1i [Li] \\ j. Plate with a central hole & P1j [KKN] \\ k. Split plate & P1k [Li] \\ l. Split stellar crystal & P1l [Li] \\ m. Irregular split plate & P1m [KKN] \\ n. Irregular split stellar crystal & P1n [KRN] \\ o. Double plate & P1o [Li] \\ p. Double stellar crystal & P1p [KRN] \\ q. Stellar over plate & P1q [KRN] \\ \hline
2. Plane crystal with extensions of different form & \\ a. Stellar crystal with plates at ends & P2a \\ b. Stellar crystal with sector-like ends & P2b \\ c. Dendritic crystal with plates at ends & P2c \\ d. Dendritic crystal with sector-like ends & P2d \\ e. Plate with simple extensions & P2e \\ f. Plate with sector-like extensions & P2f \\ g. Plate with dendritic extensions & P2g \\ h. Triangular form with plates at ends & P2h [KRN] \\ i. Triangular form with dendrites at ends & P2i [KRN] \\ j. Concentric plates with sector-like extensions & P2j [KRN] \\ k. Concentric plates with stellar or dendritic extensions & P2k [KRN] \\ \hline
3. Crystals with irregular number of branches & \\ a. Two-branched crystal & P3a \\ \hline \end{tabular}   
**Table A1. **_Cont._
## 1 Introduction
The paper [5] also asserts that for a GPS constellation with 24 satellites, there are a total of 8-16 links--they can be forward and backward links in the same orbit, but as well as lateral links between adjacent orbits. Moreover, the distance of GPS inter-satellite cross-links can reach 49,465 km. The establishment of inter-satellite links (ISL) and measurement communications is of primary importance for the relative ranging and relative velocity determination between the satellites from one constellation or from different constellations such as the GPS system, the Russian GLONASS, the European Galileo and the China Beidou second-generation system; all of them are considered to be inter-operable with each other. The theory of inter-satellite communications (ISC) is developed in the series of papers by S. Turyshev, V. Toth, M. Sazhin [6; 7] and S. Turyshev, N. Yu and V. Toth [8]. The theory in these three paper concerns the space missions GRAIL (Gravity Recovery and Interior Laboratory) [6], GRACE- FOLLOW-ON (GRACE-FO--Gravity Recovery and Climate Experiment--Follow On) mission [7] and the Atomic Clock Ensemble in Space (ACES) experiment [8; 9; 10] on the International Space Station (ISS).
## 3 Thermography Data Collection and Preparation
### Real Thermographic Image Dataset Generation
Through this process, the square matrices from \(T^{t}\) to \(T^{t+k-1}\) are transformed into images. \begin{table}
\begin{tabular}{c c c c} \hline
**Specimen ID** & **CFRP Thickness** & **GFRP Thickness** & **Defect Depth** \\ \hline RN-1, RN-2, RN-3 & 2.4 mm & 6.3 mm & - \\ RN-4, RN-5, RN-6 & 2.9 mm & 3.6 mm & - \\ RD29-10,20,30,35,40,50 & 2.9 mm & 3.6 mm & 2.9 mm \\ RD42-10,20,30,35,40,50 & 2.4 mm & 6.3 mm & 4.2 mm \\ RD60-10,20,30,35,40,50 & 2.4 mm & 6.3 mm & 6.0 mm \\ RD78-10,20,30,35,40,50 & 2.4 mm & 6.3 mm & 7.8 mm \\ \hline \end{tabular}
\end{table} 
Table 2: Specification of Real Specimens: ID, Thickness of CFRP and GFRP, and Defect Depth and Size. Figure 2: Photographs of Real Specimens and Visualization of Defects. The minimum temperature observed in a sequence of \(k\) consecutive thermographic data is generally represented by \(T^{t}_{min}\) at time \(t\), while the maximum temperature is typically represented by \(T^{t+k-1}_{max}\) at time \(t+k-1\). Through this process, an image that visually maximizes the temperature difference between the defect and non-defect regions in the thermographic data \(T^{t}\) acquired at time \(t\) can be obtained. Setting \(t\) to 1 introduces significant noise in a single thermographic image, necessitating the determination of an optimal \(t\) value. In this study, \(t\) was set to 9 to minimize noise and produce an image that effectively maximizes the contrast between the defect and non-defect regions. The preprocessing method was applied to the real thermographic dataset obtained from the actual specimens, and the processed thermographic images were assigned to the real thermographic image dataset. This generated dataset was then utilized for performance evaluation as validation data for defect detection in the deep learning model. Table 3 presents the number of data samples in each class that constitute the real thermographic image dataset. Among these, approximately 9% (600 images) of the non-defect class, consisting of two image sequences, were used as the training dataset for the non-defect class of the deep learning model, while approximately 91% (6038 images) were employed for performance evaluation. All 7755 thermographic images from the defect class were used exclusively for performance evaluation.
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
## 4 Investigating Reasons for False Targets
### A Special Case for Looking into False Targets
Moreover, using (4) and (5), we can validate  
\[[\mathbf{g}]_{0}\approx\underbrace{[\overline{\mathbf{v}_{0}};\mathbf{u}_{0}; \overline{\mathbf{v}_{0}};\overline{\mathbf{v}_{1}};\mathbf{u}_{1};\mathbf{v} _{1}]}_{\langle\mathbf{e}\rangle_{0}^{\mathrm{H}}}[\mathbf{u}_{1};\overline{ \mathbf{v}_{1}};\underline{\mathbf{v}_{0}};\mathbf{u}_{0};\overline{\mathbf{v} _{0}};\underline{\mathbf{v}_{1}}]}_{\langle\mathbf{e}\rangle_{0}^{\mathrm{H} }}\approx\mathbf{v}_{0}^{\mathrm{H}}\mathbf{v}_{0}+\mathbf{v}_{1}^{\mathrm{H} }\mathbf{v}_{1}=2Q, \tag{7}\]  
where other vector multiplications are suppressed due to the independence between multipliers. The overlines and underlines are only indicators introduced to differentiate the same sub-vectors in different locations. Moreover, we note that \(2Q\) is based on the assumption that signals carried by sub-carries have the unit amplitude. Similarly, we can obtain  
\[[\mathbf{g}]_{2N}\approx\underbrace{\left[\underbrace{\cdots}_{N-Q}\overline{ \mathbf{v}_{1}};\mathbf{u}_{1};\mathbf{v}_{1};\overline{\mathbf{v}_{0}}; \underbrace{\cdots}_{Q}\right]}_{\langle\mathbf{e}\rangle_{2N}^{\mathrm{H}}} ^{\mathrm{H}}\left[\mathbf{u}_{1};\overline{\mathbf{v}_{1}};\underline{ \mathbf{v}_{0}};\mathbf{u}_{0};\overline{\mathbf{v}_{0}};\underline{\mathbf{v }_{1}}\right]}_{\langle\mathbf{e}\rangle_{2N}^{\mathrm{H}}}\approx\mathbf{v}_{ 0}^{\mathrm{H}}\mathbf{v}_{0}+\mathbf{v}_{1}^{\mathrm{H}}\mathbf{v}_{1}=2Q, \tag{8}\]  
where the dots are entry fillers with their numbers given underneath. Using (4) and (5), we can further confirm that \(2Q\) is the second maximum value that \([\mathbf{g}]_{i}\) can take. These two CCC results are actually the false targets, as one can easily check that they are the strongest maximum CCC results after the peak \([\mathbf{g}]_{N}\). From (7), we see that the nontrivial value of \([\mathbf{g}]_{0}\) is due to the \(Q\) samples at the tail of each essential OFDM symbol. These samples are copied to the beginning of an OFDM symbol, acting as CP. From (8), we see that the nontrivial value of \([\mathbf{g}]_{2N}\) is caused by actual CPs. These observations, though based on a special case, can be generalised.
## 4 Outage Probability Analysis  
The outage will occur if the instantaneous SINR for all links, that is from the source-to-relay cluster and from the relay cluster to the destination, falls below a certain target SINR corresponding to a target data rate. The probability of a successful transmission from the source-to-relay in general is given by:  
\[p_{SI}^{S\to R}=\frac{1}{K_{SI}^{S\to R}}\left(1-\left(1-e^{-\left(\frac{ \mathcal{I}_{1}-1}{\gamma_{S\to R}}\right)}\right)^{K_{SI}^{S\to R}} \right), \tag{18}\]  
and from the relay-to-destination is given by:  
\[p_{SI}^{R\to D}=\frac{1}{K_{SI}^{R\to D}}\left(1-\left(1-e^{-\left(\frac{ \mathcal{I}_{1}-1}{\gamma_{R\to D}}\right)}\right)^{K_{SI}^{R\to D}} \right), \tag{19}\]  
where \(K_{SI}^{S\to R}\) is the number of available \(S\to R\) links, which are those for which the buffers of the corresponding relay nodes are not full; otherwise, no relay will be available for receiving the data. Similarly, \(K_{SI}^{R\to D}\) is the number of \(R\to D\) links for those relays whose buffers are not empty.
# A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots
## 3 Neural Network with Error Minimization
### Numerical Error Minimization  
Despite NN's robustness in fitting any non-linear function without boundary on the minimum error ideally, some limitations pose substantial challenges in achieving the optimal performance, including the problem complexity, limited training time, scarce training data, and restricted computational resources. From previous works, for IKP, it is challenging to make a prediction with the precision of micrometer level on a 6-DOF decoupled manipulators [21; 25; 27; 35], let alone our more complex system. Thus, apart from establishing improved NN architectures, finding a suitable method to refine the primary solution from trained neural networks would be an ideal choice. In order to avoid increasing the overall computational time, we choose a straightforward, efficient root seeking approach, the Newton-Raphson method, whose error minimization process is shown below.  
With the minimum representation of the end-effector pose defined in Equation (3), we can write the FK equation as  
\[\mathbf{p}=[x,y,z,\phi,\theta,\psi]^{\mathrm{T}}=f(\mathbf{q}) \tag{8}\]  
where \(f(\cdot)\) is the forward kinematic function, while \(\mathbf{q}\) is the vector of joint variables.  
Then, denoting the goal pose as \(\mathbf{p_{g}}\), we construct a new function \(g(\mathbf{q})\) as  
\[g(\mathbf{q})=f(\mathbf{q})-\mathbf{p_{g}} \tag{9}\]  
Figure 2: NN architecture for IKP. (**a**) NN for the first five joints; (**b**) NN for the last joint.  
Let \(\mathbf{q_{0}}\) be the initial solution of the NN. Expanding \(g(\mathbf{q})\) with Taylor series at \(\mathbf{q_{0}}\) yields:  
\[g(\mathbf{q})=f(\mathbf{q_{0}})-\mathbf{p_{g}}+\mathbf{J_{a}}(\mathbf{q_{0}})( \mathbf{q}-\mathbf{q_{0}})+h.o.t. \tag{10}\]  
where \(\mathbf{J_{a}}\) is the analytical Jacobian matrix.  
Note that seeking the root of equation \(\mathbf{p_{g}}=f(\mathbf{q})\) is equivalent to finding the root of \(g(\mathbf{q})=0\). Substituting \(g(\mathbf{q})=0\) into Equation (10) and ignoring the higher order terms leads to:  
\[\mathbf{q}\approx\mathbf{q_{0}}+\mathbf{J_{a}^{-1}}(\mathbf{q_{0}})[\mathbf{ p_{g}}-f(\mathbf{q_{0}})] \tag{11}\]  
Hence, we can use the following iterative equation to minimize the error of the initial solution:  
\[\mathbf{q_{k+1}}=\mathbf{q_{k}}+\alpha\mathbf{J_{a}^{-1}}(\mathbf{q_{k}})[ \mathbf{p_{g}}-f(\mathbf{q_{k}})] \tag{12}\]  
where \(\alpha\) is the step size. After each iteration, \(\mathbf{q_{k}}\) makes a step towards the expected solution and the process will stop after the error \(|f(\mathbf{q_{k}})-\mathbf{p_{g}}|\) meets the condition of convergence. In our analysis, the convergence error is set 0.001 mm for position, and 0.001 rad for orientation, which are high-level criteria for practical tasks [21].
## 4 Discussion  
In this paper, GPP measurements from eight EC flux towers (Flux_Tower GPP) were used to verify four groups of GPP values calculated from the MODIS GPP algorithms (_i.e._, MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP and Tower_GLASS GPP). The results show that the four groups of GPP values deviated from the tower-based GPP values to varying degrees. The extent of the deviations varied depending on the time interval, the site and the biome type used in the calculation. The deviations can be attributed to many potential factors. Clearly, the accuracy of each parameter input in the MODIS GPP formula will influence the accuracy of the GPP estimates. That is, the meteorology, LAI/FPAR, land cover and \(\varepsilon_{\text{max}}\) data are all error sources [48]. Furthermore, the accuracies of the tower-based GPP measurements and the reasonableness of the MODIS GPP algorithm itself should also be taken into account [49].
## 3 Other Applications
### Environmental Monitoring Other Than Rainfall Estimation
#### 3.1.2 DSD Estimation  
Assuming that the DSD follows a gamma distribution, [PERSON] et al. [21] and [PERSON] et al. [114] estimated the average DSD over the path using dual-frequency and dual-polarized microwave links, respectively, which was in good agreement with the reference measurements. However, the above studies simplify the three-parameter model of DSD by setting a fixed shape parameter in the former and defining the slope parameter as a function of the shape parameter in the latter. This may lead to inherent errors in the algorithm. Therefore, a three-parameter estimation model for DSD based on dual-frequency, dual-polarized CMLs has been proposed in the literature [115]. [PERSON] et al. [116] compared the three-parameter and two-parameter models for DSD estimation and found that the three-parameter model has limited superiority and slower retrieval.
## Nomenclature  
\begin{tabular}{l l l}
**Symbol** & **Explanation** & **Unit** \\ \(h_{i}\) & Enthalpy of stream \(i\) & kJ/kg \\ \(\dot{n}_{i}\) & Mass flow of component/element \(i\) & kg/h \\ \(M_{i}\) & Molar mass of component/element \(i\) & g/mole \\ \(\dot{n}_{i}\) & Mole flow of component/element \(i\) & kmole/h \\ \(p\) & Power & kW \\ \(p\) & Pressure & bar \\ \(R_{\mathrm{OC}}\) & Oxygen transport capacity of oxygen carrier & - \\ \(m_{air\_AR}\) & Mass flow of air entering the AR & kg/h \\ \(T\) & Temperature & \({}^{\circ}\)C \\ \(x_{i}\) & Mass/mole fraction in gas phase & - \\ \(X_{i}\) & Conversion of component \(i\) & - \\ \(Y_{i,j}\) & Mass yield of component/element \(i\) from substance \(j\) & - \\ \(z_{i}\) & Mass/mole fraction in solid phase & - \\ \(\eta_{\mathrm{CC}}\) & Carbon capture efficiency & - \\ \(\eta_{\mathrm{CGE}}\) & Cold gas efficiency & - \\ \(\lambda\) & Air-to-fuel equivalence ratio & - \\ \(\phi\) & Oxygen carrier-to-fuel equivalence ratio & - \\
**Subscript** & **Explanation** \\ AR & Air reactor \\ devol. & Devolatilization. \\ FR & Fuel reactor \\ init & Initial \\ net & net \\ O & Oxygen \\ OC & Oxygen Carrier \\ ox & Oxidation \\ red & Reduction \\ s & Solid \\ stoich & Stoichiometric \\ th & Thermal \\
**Abbreviation** & **Explanation** \\ AR & Air Reactor \\ ASU & Air Separation Unit \\ CGE & Cold Gas Efficiency \\ CLC & Chemical Looping Combustion \\ CLG & Chemical Looping Gasification \\ FR & Fuel Reactor \\ GHGE & Greenhouse Gas Emissions \\ LHV & Lower Heating Value \\ OC & Oxygen Carrier \\ RED II & European Union Renewable Energy Directive \\ WGS & Water-Gas-Shift \\ \end{tabular}
## 2 Materials and Methods
### Printability Assessment  
Cellink INKREDIBLE+ (Cellink AB, Gothenburg, Sweden) was used to print the SA-G biobank. Such a 3D bioprinter is based on pneumatic extrusion technology and is equipped with dual printheads featuring a heating system and built-in photocuring modules. Moreover, it includes patented clean-chamber technology that efficiently filters air through an H13 HEPA filter, thus providing a clean printing environment. All structures introduced in this section were designed with Solidworks 2020 (Dassault Systems Solid WorksCorp, Waltham, MA, USA), sliced with PrusaSlicer 2.3.3. (Prusa Research, Prague, Czech Republic), and printed at 25 \({}^{\circ}\)C. Two conical nozzles with inner diameters of 22G (0.41 mm) and 25G (0.25 mm) were used. Prior to printing, the hydrogel precursor solution stored at 4 \({}^{\circ}\)C was incubated at 37 \({}^{\circ}\)C for 15 min, thus allowing the solution to be transferred to a printing cartridge. The printing cartridge was left at 25 \({}^{\circ}\)C for 40 min before printing to enable partial gelatin crosslinking. After printing, all structures were physically crosslinked at 4 \({}^{\circ}\)C for 5 min followed by chemical crosslinking in a 2% \(wt./v\) CaCl\({}_{2}\) bath for 10 min.  
Printability was assessed by evaluating the filament formation and printing accuracy in terms of filament merging and shape fidelity in multi-layered structures [16]. For the first test mentioned, four lines were printed at different increasing velocities, i.e., 5, 10, 15, and 20 mm/s, by applying a pressure of 25 kPa. The test was repeated using a printing pressure of 30 kPa. The line widths were measured using ImageJ software (NIH, Stapleton, NY, USA, ver. 1.54i) after imaging the lines with an optical microscope (Eclipse Ti2, Nikon, Tokyo, Japan). Based on the results of this test, optimized printing parameters were chosen for further analyses. The printing accuracy on the x-y plane was determined through the filament fusion test [29]. For this test, a precise pattern was designed that involved parallel lines spaced at different distances (from 0.5 mm to 3 mm), with a progressive 0.5 mm increase for each subsequent line. Ultimately, circular discs with a diameter of 30 mm and a thickness of 0.4 mm were designed and printed with four layers to evaluate the shape fidelity of multilayered constructs. Specifically, the dimensions of the printed structures were measured using ImageJ software and compared with the original dimensions set in the digital model of the structure. Moreover, optical images taken by the Eclipse Ti2 microscope were analyzed to investigate the pore geometry of the printed discs. Subsequently, the printability index (Pr) was computed using Equation (6) [16]:  
\[\text{Pr}=\frac{\text{L}^{2}}{16\times\text{A}} \tag{6}\]  
where L and A are the pore perimeter and area, respectively.
## 2 Literature Review
### The US-Mexico Border  
[PERSON] and [PERSON] [26] found that the devaluation of the Mexican peso affects Texas retailers, showing their reliance on Mexican customers (economic agents). Moreover, the influence of currency rate fluctuations differs by city, distance from the border, retail sector, and domestic market size. Mexican consumption is determined by purchasing power after considering currency conversion, inflation, interest rates, and available disposable income.  
[PERSON] [27] investigated the influence of Mexican tourists on retail sales in eight U.S. border counties by employing the simple compound growth model of retail sales. According to the research, currency fluctuations impact retail sales, with nondurable goods, fashion merchants, and general merchandize outlets being more vulnerable. [PERSON] [14] examined the relationship between the exchange rate and the cross-border flows in three international bridges in El Paso. He used monthly data for same-day personal vehicle, passenger, and pedestrian trips; the exchange rate; and the consumer price index. The data revealed each bridge's border-crossing behavior. The findings demonstrate that crossing flows are not random, and peso devaluation has a varying effect on traffic on each bridge. The Mexican peso's depreciation negatively impacts the bridge in that more personal vehicles cross, although it has a positive effect on the bridge where pedestrians prevail.  
[PERSON] et al. [28] studied how toll tariffs and currency exchange rates influence border crossings using 1990-2006 monthly pedestrian, personal vehicle, and cargo crossing data. The authors found a negative relationship between the toll and flow of border crossings using ARIMA transfer functions. They also found a negative relationship in land ports where pedestrians and personal vehicles predominate and a positive relationship in ports where cargo crossings dominate.  
[PERSON] et al. [13] analyzed the relationship between exchange rate and personal vehicle passenger crossings in the 9 most active PoEs using monthly data from 1997 to 2018. Using panel data fixed effects and augmented mean group models and controlling for the difference in economic growth rates between Mexico and the U.S., they found a negativerelationship between the exchange rate and border crossings and an increase in crossings when the Mexican economy improves faster than the U.S. economy.
## 5 Simulation Results and Discussion
### Simulation Analysis  
After obtaining the optimal ESS configuration through the optimization algorithm, it is incorporated into the simulation model. The energy interaction simulation results are depicted in Figures 7 and 8. PV generation significantly impacts the high-speed railway hybrid power system, as PV output is influenced by time and seasonal variations. Figures 7 and 8 illustrate the energy interactions within the hybrid power system during a single round trip in summer and winter, respectively. As observed from these figures, compared to winter, summer PV generation is more robust and stable. By examining the power output curves of the high-speed railway grid during both seasons, it is evident that the electricity output of a grid is lower in summer than in winter. Consequently, the proposed high-speed railway hybrid power system exhibits higher energy efficiency in summer, leading to reduced electricity costs.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Chaotic Mapping-Driven** & **Trust-Region** & **Elite-Guided** & **Fitness** \\ \hline \(\checkmark\) & & & 236,102 \\ \hline \(\checkmark\) & \(\checkmark\) & & 235,949 \\ \hline \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 235,760 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Component ablation effects on EDMOA performance.  
Figure 6: The comparative graph of algorithm iteration results.  
From an economic perspective, the calculated REC trading revenue amounts to USD 216,137.2 in summer and USD 120,565.4 in winter. The analysis reveals a significant positive correlation between seasonal climate fluctuations and the economic benefits of REC trading. Specifically, solar irradiance intensity, as the key driving factor, enhances REC revenue by 79.3% in summer compared to winter. Furthermore, at sites with abundant solar resources, the REC trading mechanism demonstrates superior economic benefits.