esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microdsimetric Measurements Using TEPC
#### 2.2.2 Calibration of Lineal Energy  
In microdosimetry, lineal energy (denoted as \(y\)) is defined as the ratio of the energy imparted (\(\varepsilon\)) by individual ionizing particles to the mean chord length (\(\bar{l}\)) of the SV. Lineal energy distributions are often used to describe radiation quality. Higher lineal energy values indicate a greater probability of localized energy deposition, which can lead to more severe biological effects. The unit of lineal energy is usually expressed in keV/\(\upmu\)m.  
\[y=\frac{\varepsilon}{\bar{l}} \tag{1}\]  
Microdosimetry deals with two probability density functions, namely the frequency distribution, \(f(y)\), and the dose distributions, \(d(y)\), which are related by the following equation:  
\[d(y)=\frac{yf(y)}{\int_{0}^{\infty}yf(y)dy} \tag{2}\]  
Figure 2: A 3D rendering of the TEPC with coaxial helix for avalanche confinement and interchangeable cathode walls.  
More details on microdosimetric variables and distributions can be found in [22]. In this study, we calibrated the lineal energy distributions using the electron-edge technique. We assigned the value of 10.8 keV/\(\upmu\)m to the flex position of the fitted Fermi function, following the methodology outlined in the work by [PERSON] et al. [23].  
To account for environmental noise conditions, the detection threshold was set at approximately 0.3 keV/\(\upmu\)m. Consequently, we linearly extrapolated the frequency distributions down to 0.01 keV/\(\upmu\)m, as detailed in [24]. For a more comprehensive understanding of the methodology, please refer to [23; 24].
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 2 Materials and Methods
### Mathematical Model of the DPC-WR System
#### 2.2.2 Mathematical Model of the Second Stage of the DPC-WR System  
The balance equation for sensible heat related to the air stream in the primary channels (primary air) in the 2 nd stage is given with Equation (10). The governing equation describing sensible heat related to the primary air in the 2 nd stage is described with Equation (11). The balance equation for mass transfer related to the primary air in the 2 nd stage is given with Equation (12). The governing equation for mass transfer related to the primary air in the 2 nd stage is described with Equation (13).  
\[\left(\frac{dY}{L_{Y}}\right)G_{4}c_{p4}\frac{dt_{4}}{dX}dX=-d\dot{Q}_{4}^{S} \tag{10}\]  
\[\frac{dt_{4}}{dX}=\mathrm{NTU^{\prime\prime}}_{4}\big{(}t_{p4}-t_{4}\big{)} \tag{11}\]  
\[\left(\frac{dY}{L_{Y}}\right)G_{4}\frac{dx_{4}}{dX}dX=dM_{4} \tag{12}\]  
\[\frac{dx_{4}}{dX}=\mathrm{NTU^{\prime\prime}}_{4}\bigg{(}\frac{1}{\mathrm{Le} _{4}}\bigg{)}\left(x^{\prime},_{p4}-x_{4}\right) \tag{13}\]  
The balance equation for sensible heat related to the air stream in the secondary channels (secondary air) in the 2 nd stage is given with Equation (14). The governing equation describing sensible heat related to the secondary air in the 2 nd stage is described with Equation (15). The balance equation for mass transfer related to the secondary air in the 2 nd stage is given with Equation (16). The governing equation for mass transfer related to the secondary air in the 2 nd stage is described with Equation (17).  
\[-\left(\frac{dY}{L_{Y}}\right)G_{3}c_{p3}\frac{dt_{3}}{dX}dX=-d\dot{Q}_{3}^{S} \tag{14}\]  
\[\frac{dt_{3}}{dX}=-\mathrm{NTU^{\prime\prime}}_{3}\big{(}t^{\prime},_{w}-t_{3} \big{)} \tag{15}\]  
\[-\left(\frac{dY}{L_{Y}}\right)G_{3}\frac{dx_{3}}{dX}dX=dM_{3} \tag{16}\]  
\[\frac{dx_{3}}{dX}=-\mathrm{NTU^{\prime\prime}}_{3}\bigg{(}\frac{1}{\mathrm{Le }_{3}}\bigg{)}\left(x^{\prime},_{w}-x_{3}\right) \tag{17}\]  
The balance equation for mass transfer related to the water stream in the secondary channels in the 2 nd stage is given with Equation (18) and the governing equation is described with Equation (19).  
\[\left(\frac{dY}{L_{Y}}\right)\frac{dG_{w}}{dX}dX=-dM_{w} \tag{18}\]  
\[\frac{dG_{w}}{d\overline{X}}=G_{3}\mathrm{NTU^{\prime\prime}}_{3}\bigg{(} \frac{1}{\mathrm{Le}_{3}}\bigg{)}\left(x^{\prime},_{w}-x_{3}\right) \tag{19}\]  
The balance equation for total heat transfer (sensible and latent) related to the 2 nd stage is given with Equation (20).  
\[\frac{dt_{w}}{d\overline{X}}=W_{4}/W_{w}\mathrm{NTU^{\prime\prime}}_{4}\Bigg{[} \left(t_{p4}-t_{4}\right)+\left(\frac{r}{c_{p4}}\right)\left(x^{\prime},_{p4} -x_{4}\right)\Bigg{]}+W_{3}/W_{w}\mathrm{NTU^{\prime\prime}}_{3}\bigg{[}\left( t_{w}-t_{3}\right)+\left(\frac{r}{c_{p3}}\right)\left(x^{\prime},_{w}-x_{3} \right)\bigg{]} \tag{20}\]  
\[\text{where: }W_{4}/W_{w}=\left(G_{4}c_{p4}\right)/\left(G_{w}c_{w}\right);W_{3}/W_{ w}=\left(G_{3}c_{p3}\right)/\left(G_{w}c_{w}\right).\]
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 2 Methodology  
### General Design Objectives of CubeSat  
The design objectives were identified for the whole system to specify the hinge mechanism constraints and assumptions, leading to the investigation and optimization of the critical parameters of the DLTS hinge for reduced \(\sigma_{\text{v}}\) and steady-state moment. Two optimized/surrogate models were employed, Taguchi (L9 array) and RSM, for the verification between them and the obtainment of the optimal R, \(t\), and \(\theta\) parameters of the DLTS hinge. More explicitly, the Taguchi method utilizes a set of predefined orthogonal arrays to designate the parameters affecting the analysis performance and level of variation [19]. On the contrary, the RSM is considered a data approach which aims to optimize the input simulated variables and assesses the relation between acquired response surfaces and controllable input variables [20].  
The design specifications for the mechanical elements of CubeSat are classified into three categories: general standards, specified motorized requirements, and the mission's objectives and demands. The subsystem of CubeSat accommodates the payload board, ADCs (attitude determination and control system), OBC (on-board computer; communication system), and EPS (electrical power system), which are the electrical elements required to navigate and process planet surface image interpretation. Furthermore, it accommodates the motors required to actuate the coupling mechanisms of the solar arrays from the compact to the deployed state. The specifications for each of these systems influence the exterior dimensions of the subsystem, the center of gravity, the overall mass, the environment for the minimization of space debris, polytechnic devices, and optimization of the performance of the CubeSat. The general standards of 1U CubeSat are designated to be 100 \(\pm\) 0.1 mm for the x and y dimensions and 114.3 \(\pm\) 0.1 mm for the z dimension and a mass of <1.33 kg. The 1U skeleton structure conventionally weighs around 0.346 kg (Figure 1). The system requires simplified complexity and versatility, and the utilization of pyrotechnic devices is prohibited as it can lead to mechanical, structural, and electrical failures as well as the creation of space debris in the deployed state. The motorized requirements are focused on the design of the coupling mechanisms (hinges) for the retainment of the array from the moment of deployment until release. Hinge mechanisms operate through actuators and deploy the solar arrays with a rotation from 90\({}^{\circ}\) to 135\({}^{\circ}\) from the stowed state, generating power of 5 W to this subsystem. Within a high transition temperature environment, the design requires appropriate coupling mechanisms to minimize failures. Coupling mechanisms attribute to 10% of overall failures and thus optimization must be undertaken to maximize the durability and minimize the failure rate [21].  
Figure 1: Evolution of the subsystem’s skeleton of the CubeSat from initial (**a**) to final (**c**) CAD model.
# Algorithm Based on Morphological Operators for Shortness Path Planning
## 3 Experimental Analysis and Results
### Experimental Process  
The experimental process consists of the following stages.  
1. _Testing the location of the best trajectory from a grid topology._ This experiment uses the city map encoded as a single binary image that represents a grid space. The starting and final path trajectories are selected according to the most representative topologies. The graph belongs to a part of Queretaro City. This graph is the result of automatic graph generation from a satellite map via avenue segmentation. The scale and resolution refer to the real dimensions of maps expressed in terms of pixels.
2. _The location of the best trajectory from a graph space._ In this stage, the city is encoded as a graph after computing the avenue intersections as nodes. The tested scenario becomes the same as that used in the last point. The weighting considers the distance in a metric space to weigh the graph. The coding process uses the vertex detection since the detection of the maximum values of the distance transformation is given by the segmentation of the road into a connected map. All maxima detected represent the nodes, and the connection arcs result from the connected avenues. Each vertex represents an avenue intersection.
3. _A comparison with other well-accepted approaches._ This process evaluates another search method as a reference. The comparison is considered in terms of their computational complexity.
## 2 Materials and Methods
### Preparation of Cu-CHA  
For the Cu-CHA catalysts, the as-synthesized CHA (K-CHA) was used as the parent material. Before loading Cu onto the framework of CHA zeolites, some of the potassium form of CHA zeolite (K-CHA) was transformed to an ammonium form of CHA zeolite (NH\({}_{4}\)-CHA). This was obtained by ion exchange of K-CHA with 1 M of ammonium nitrate (NH\({}_{4}\)NO\({}_{3}\), \(\geq\)98% Sigma-Aldrich). For each gram of K-CHA, 30 mL of 1 M NH\({}_{4}\)NO\({}_{3}\) was used and heated at 80 \({}^{\circ}\)C for two h. The process was repeated with a fresh solution of ammonium nitrate. The aqueous solution ion exchanging technique introduced copper ions over K-CHA and NH\({}_{4}\)-CHA zeolites. As the Cu source, Cu(NO\({}_{3}\))\({}_{2}\)\(\bullet\)3H\({}_{2}\)O (\(>\)99%, Wako) was employed. In a typical synthesis procedure, 1 g of each K-form and NH\({}_{4}\)-form of CHA were ion-exchanged with nominal Cu concentrations of 0.05 and 0.1 M. In the procedure, the nominal Cu concentrations were dissolved in deionized water, followed by adding 1 g of each form of as-synthesized CHA powder under continuous stirring at room temperature for 24 h and 80 \({}^{\circ}\)C for three h, separately. Then, the solution was centrifuged to separate the products. The obtained solid products were washed, dried overnight at 110 \({}^{\circ}\)C, and then calcined at 500 \({}^{\circ}\)C (heating rate 10 \({}^{\circ}\)C/min) for five h using a muffle furnace. The products were labeled according to the CHA-form and its respective Cu concentration, as well as the temperature at which the product material was synthesized. Thus, that describes how the Cu ion exchange was carried out over the K-form and NH\({}_{4}\)-form of the as-synthesized CHA zeolite catalyst. The ion exchange of CHA catalyst with different nominal Cu concentrations is summarized in Table 1.
## 5 Conclusions  
This paper has presented an operative ship monitoring approach that combines the information obtained from AIS polls with SAR image processing. A state-of-the-art web interface supports output presentation with the aim of increasing user interaction and added-value capabilities. Key functionalities are alarm generation, user profile definition and interactive ship information displaying.  
The overall system has been described and some tests have been presented. Proper land masking, accurate ship detection and reliable ship classification has been reached in an algorithm that meets NRT requirements. In addition, the system is fully operational, reliable and unsupervised as no operator is required to control the processing chain. The operator is only required for interpreting the results; a step that becomes straightforward thanks to the large number of information layers provided by the system. Among them, coastline detection, ship category and the confidence assigned to the detected spots are the most important ones. They are key advantages with respect to other monitoring services.  
End-users have found the system very advanced for supporting decision making in the field of immigration and ship activities management. With medium resolution single-pol images, encouraging results have been found (high detection ratio plus medium classification ratio). They are expected to be improved with the exploitation of the new generation of SAR sensors, as TerraSAR-X, Cosmo-Skymed or Radarsat-2. Their features permit the detection of small and less reflective ships and tackling automatic ship classification following the seed of recent works [9, 10].  
With this experience, future work trying to fix the following upgrades is envisaged:  
* **To improve coastline detection** with a state-of-the-art wavelet-based methodology [11].
* **To increase the detection capability of small targets**.
* **To improve ship classification**. Key topics regard the revision of ship categories and the associated fuzzy'sets\". This implies to evaluate if other membership functions and grade distribution (low, medium, high) provide higher classification ratios. The inclusion of additional parameters within the fuzzy processing chain will also be a matter of concern.
* **To include auto-learning capabilities** for generating a database of detection and classification collections. This will be used to train an auto-learning algorithm based on neural networks and/or genetic algorithms. The output of the algorithm will update, if required, the membership functions and the associated grades of the different reference categories.
* **To include new added-value capabilities**, such as ship tracking and route propagation. The former will be tackled via multi-temporal processing algorithms dealing with stacks of SAR images while the latter by using statistical approaches.
* **To include new data formats**, with special concern on TerraSAR-X and Cosmo-Skymed. The improved resolutions and polarimetric capabilities of these data may make advisable to lightly modify the processing approach, especially for ship detection and fuzzy sets.
# Superior Mechanical Behavior and Fretting Wear Resistance of 3D-Printed Inconel 625 Superalloy
## 4 Conclusions  
This study investigates the micro-hardness, tensile strength, yield strength, friction and wear behaviors of AM-processed Inconel 625 alloy to evaluate the effect of AM processing on the mechanical and tribological performance of nickel-based superalloys. AM-processed Inconel 625 shows the significant improvement in tensile and yield strengths, as well as the wear-resistance compared to traditional nickel-based superalloys under the current test conditions. The results concluded from the experimental investigation are as follows:  
* Mechanical test results reveal that the AM-processed Inconel 625 have high hardness value (335 HV), superior tensile strength (952 \(\pm\) 18 MPa) and yield strength (793 MPa), as well as excellent wear-resistance.  
Figure 9: High-resolution XPS spectra obtained on the wear tracks of AM-Inconel 625 after fretting wear.  
* Contributions of precipitates and GB strengthening can be quantitatively calculated for the AM-Inconel 625 alloy. The precipitates and GB contribute 27.9 and 66.7 MPa, respectively, to the overall strength.
* The wear depth of traditional nickel-based materials usually varies 40-140 \(\upmu\)m per 200 cycles depending on the operating conditions. However, the AM-processed Inconel 625 alloy demonstrates the good wear-resistance, which controls wear depth of the fretting surface within 2.4 \(\upmu\)m after 2 \(\times\) 10\({}^{4}\) cycles.
* The wear characteristics of AM-processed Inconel 625 depend significantly on the continuous tribo-layers. XPS analysis shows that the observed stable and coherent tribo-layer is characterized by a mixture of Fe\({}_{2}\)O\({}_{3}\), Fe\({}_{3}\)O\({}_{4}\), Cr\({}_{2}\)O\({}_{3}\) and Mn\({}_{2}\)O\({}_{3}\).  
[PERSON] and [PERSON] developed the methodology. [PERSON] leads the project. [PERSON] prepared samples. [PERSON] and [PERSON] carried out the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the manuscript. [PERSON] and [PERSON] revised the manuscript.  
This research was funded by [the National Key R&D Program of China] grant number [2017 YFB0103000].  
The authors declare no conflict of interest.
## 3 Testing Conditions
### Structure Models  
In order to conduct the experimental investigation, a single-storey and two-storey structure models were prepared. The welded steel frames were constructed using the rectangular hollow section elements (RHS 15 \(\times\) 15 \(\times\) 1.5 mm). The columns were arranged in a rectangular pattern with spacing of 0.465 m in the longitudinal direction and 0.556 m in the transverse one. Diagonal bracing was used in the sidewall planes to counteract transverse and torsional vibrations. Moreover, concrete plates (50 \(\times\) 50 \(\times\) 7 cm) were employed to simulate the weight of the floor and foundation slabs. The welded steel frames were clamped together using M10 bolts. The single-storey structure model consisting of one steel frame and two concrete plates is 1.20 m high and weighs 95.12 kg, whereas the two-storey structure model consisting of two steel frames and three concrete plates is 2.30 m high and weighs 148.04 kg. The dynamic behaviour of these two experimental models, both fixed-base and base-isolated, was extensively studied during shaking table tests. The structure models with and without the PBs are shown in Figures 7 and 8.  
Both single-storey and two-storey structure models have been specifically designed in order to accurately reflect the dynamic behaviour of real buildings. Therefore, the single-storey structure model, characterized by the fundamental frequency of 3.34 Hz, is representative for low-rise buildings, whereas the two-storey structure model, characterized by the fundamental frequency of 1.94 Hz, reflects the dynamic behaviour of medium-rise buildings.  
Figure 6: Technical sketch of the experimental setup (1: middle-sized shaking table platform, 2: linear actuator, 3: actuator controller).
## 2 SCMA System  
We consider a downlink SCMA system with \(J\) independent users and \(K\) orthogonal REs (overloading factor \(\lambda=J/K>1\)). The quantities of orthogonal REs occupied by a user and neighbors on an orthogonal RE are \(N_{r}\) and \(N_{u}\), respectively. The definitions of the parameters in the downlink SCMA system mentioned in this paper are presented in Table 1.  
The transmitter encodes the \(\log_{2}(M)\) data bits of user \(j\) and maps them to a \(K\)-dimensional sparse codeword, \(\mathbf{x}_{j}\). The \(K\)-dimensional non-sparse overlaid codeword is then broadcast over \(K\) REs after the \(K\)-dimensional sparse codewords of \(J\) users are multiplexed.  
At the transmitter, the transmitted signal \(\mathbf{r}\) can be represented as follows:  
\[\mathbf{r}=\sum_{j=1}^{J}\mathbf{x}_{j}, \tag{1}\]  
where \(\mathbf{x}_{j}=\left(x_{j1},x_{j2},\ldots,x_{jK}\right)^{T}\) is the codeword in the codebook \(\mathbb{C}_{j}\) with size \(M\) of user \(j\), and \(\mathbb{C}_{j}\) is derived from the total codebook \(\mathbb{C}=\left\{\mathbb{C}_{j},1\leq j\leq J\right\}\) constructed based on a multi-dimensional constellation.  
At the receiver, the signal received by the user \(j\) can be expressed as follows:  
\[\mathbf{y}_{j}=\text{diag}\big{(}\mathbf{h}_{j}\big{)}\mathbf{r}+\mathbf{n}_{ j}, \tag{2}\]  
where \(\mathbf{h}_{j}=\left(h_{j1},h_{j2},\ldots,h_{jK}\right)^{T}\) is the channel vector between the transmitter and the user \(j\), and \(\mathbf{n}_{j}=\left(h_{j1},h_{j2},\ldots,h_{jK}\right)^{T}\) is the additive white Gaussian noise (AWGN) with zero  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Definition** \\ \hline \(J\) & Number of independent users \\ \(K\) & Number of orthogonal resource elements \\ \(M\) & Size of codebook \\ \(\lambda\) & Overloading factor \\ \(N_{r}\) & Number of orthogonal resource elements occupied by a user \\ \(N_{u}\) & Number of neighbors on an orthogonal resource element \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters in the downlink SCMA system.  
mean and variance \(\sigma^{2}\). For the downlink Rayleigh fading channel, \(h_{jk}\) experienced by the transmitted signal on the \(k\)-th RE is the radial component of the sum of two Gaussian distributed random variables, whereas for the AWGN channel, \(h_{jk}\) is constant. A basic downlink SCMA system model is presented in Figure 1, where \(J=6\), \(K=4\), and \(M=4\).
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 4 Multi-User Equalizer Design
### Complexity Analysis  
The steps presented in Algorithm 1 describe the procedure to obtain the analog and digital parts of the proposed equalizer. In the following, the computational complexity of Algorithm 1 is analyzed. Matrix \(\mathbf{P}^{(i)}\) at line 4 may be computed with \(\mathcal{O}(R^{2})\) complexity, with \(R=N_{\pi x}/N_{RF}\), since matrix \(\mathbf{U}^{(i-1)}\) is block diagonal and each column has only \(R\) non-zero elements. To obtain vector \(\mathbf{r}_{i,k}\) at line 7, the vector \(\mathbf{A}_{\pi x,\mu_{i}}^{H}\mathbf{D}_{\mu_{i}}\mathbf{P}^{(i-1)^{H}} \mathbf{h}_{\alpha\mu,i\mu,k}\) must be calculated. As both \(\mathbf{D}_{u_{i}}\) and \(\mathbf{P}^{(i)}\) are block diagonal with \(N_{RF}\) blocks of size \(R=N_{\pi x}/N_{RF}\) and \(\mathbf{D}_{u_{i}}\) is a block diagonal matrix where all blocks are zero except \(u_{i}\), which is equal to the identity matrix, then the product \(\mathbf{D}_{u_{i}}\mathbf{P}^{(i-1)^{H}}\mathbf{h}_{\alpha\mu,i\mu,k}\) requires \(\mathcal{O}(R^{2})\) complexity. As the resulting vector has only \(R\) non-zero elements and \(\mathbf{A}_{\pi x,\mu_{i}}\) is a matrix with dimension \(N_{\pi x}\times N_{cl}N_{\pi\eta y}\), \(\mathbf{A}_{\pi x,\mu_{i}}^{H}\mathbf{D}_{\mu_{i}}\mathbf{P}^{(i-1)^{H}} \mathbf{h}_{\alpha\mu,i\mu,k}\) may be computed with \(\mathcal{O}(R^{2}+N_{cl}N_{\pi\eta y}R)\) complexity. As the computation done in line 7 is repeated for all \(N_{c}\) subcarriers and all RF chains, the overall complexity of lines 6-9 is \(\mathcal{O}(N_{c}(R^{2}+N_{cl}N_{\eta y}))\). Line 10 requires the computation of the minimum of vector \(\mathbf{f}^{(i)}\in\mathbb{R}^{N_{cl}N_{\eta y}}\), whose complexity is \(\mathcal{O}(N_{cl}N_{\eta y})\). In line 11 we compute vector \(\mathbf{w}_{a}^{(i)}\) with complexity \(\mathcal{O}(R^{2})\). The complexity of line 12 is identical to line 11. As the previous must be repeated for all RF chains, the overall complexity is \(N_{RF}\) times the individual complexities previously described. The computation of the digital equalizer must be performed for all subcarriers and requires the inversion of a matrix with size \(N_{RF}\times N_{RF}\), resulting in a complexity \(\mathcal{O}(N_{c}N_{RF}^{3})\). Therefore, the computational complexity of the proposed algorithm is linear in the number of subcarriers, quadratic with the number of antennas per RF chain, and cubic with the number RF chains. The full connected architecture would require a complexity scaling quadratically with the number of antennas and for the full-digital architecture the complexity is a cubic function of the number of antennas.