esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Science Project Organisation--Industry 'Best Practices'  
As with any project, the question arises: where to begin? This is especially so with complex, data-driven projects, such as the 'bench-to-bedside' projects often seen in the medical and life science field. Fortunately, this problem has been seen before and some solutions, or rather, guidelines were devised. Industrial Micro Machines (IBM) researchers and automotive engineers (Daimler-Chrysler) did indeed face the same problem in the 1990s. The resultant guidelines were standardized in the CRISP-DM standard which stands for Cross Industry Standard Process for Data Mining [4]. The model, illustrated in Figure 1, consists of six steps [5].  
1. Business understanding. In the medical field, we would need to determine if the project is for research purposes only. Do we have obvious clinical implications (with rigorous safety, regulatory and legal requirements)? Do we eye business potential--which in turn may need strict documentation and reporting?
2. Data understanding. In the medical and life science field, we often have to gather data via experiments ourselves, and the quality of this data will be crucial. (In the era of big data, many projects may rely on published open data sources.) What data can we gather and in what amount? Can we collect enough in a realistic timeframe? Will the data be sufficient for the questions asked? Do we have good recipes so that the data is reproducible over large timespans, with changing personnel?
3. Data preparation. We must gather data from handwritten notes or other sources, collect them in machine-readable form, and normalize or otherwise standardize them. Watch out for reproducibility issues often encountered with difficult experiments. Can our data be quantified and compared across different investigators and with human subjects involved? Even standard laboratory tests may differ in testing methodology. With human subjects, legal issues also arise privacy rights need to be respected, and data anonymized.
4. Modelling. We need to use our data--somehow. Do we have continuous variables or categorical ones? Do we have an initial hypothesis to test, or do we need methods that are suitable for 'walking in the dark'?
5. Evaluation. We need to use metrics by which we quantify'successes. Do we have concise research questions so that an answer to them can be made? Are we descriptive or do we have new hypotheses as a result? Without clear questions, no metrics will be satisfactory, as metrics are often meaningless themselves. Can we compare our work to that of others via the metrics?
6. Deployment. Pure research may result in scientific outputs, such as presentations or scientific papers or openly published datasets. However, does our research yield tangible results such as new scientific hypotheses? Does our research have clinical implications?  
This is the model we will follow and around which we build our homemade software, which will be described in more detail in the next sections.  
Figure 1: The six elementary steps in a data science project as outlined in the CRISP-DM standard.
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 6 Conclusions  
Our in-orbit experiments capturing Earth surface images with an ultra-lightweight diffractive lens onboard the 3U CubeSat have successfully proven that this lens offers unique advantages for remote sensing. We showed that a combination of the diffractive optics and a convolutional neural network based image reconstruction results in acceptable image quality, despite using a camera with sub-optimal characteristics.  
Producing high-quality images of urban areas remains an important goal for us to show that diffractive optics can be used in all major remote sensing scenarios. Our work [55] on annular harmonic lenses shows that we can produce compact, high-aperture, long-focal-length optics useful for remote sensing. Finally, addressing artifacts as adversarial attacks will further improve the image quality for the current and future hardware lens designs.  
Conceptualization, V.S., N.K., R.S., A.N., Y.Y. and I.T.; methodology--optics, V.S., N.K. and R.S.; methodology--image reconstruction, A.N.; CubeSat hardware and engineering, D.A.; optical hardware and engineering, N.I., V.P., M.P. and S.G.; software, V.E. and M.P.; writing--original draft preparation, all authors; writing--review and editing, V.S., A.N., Y.Y. and R.S.; project administration, R.S., A.N. and D.A. All authors have read and agreed to the published version of the manuscript.  
The CubeSat design was funded as part of the government project of the Ministry of Education and Science of Russia (Project No. 0777-2020-0017), harmonic diffractive lens calculation and design were funded as part of the government project of FRSC \"Crystallography and Photonics\" of the RAS, deep learning-based image restoration algorithms were developed with the support of Russian Science Foundation grant #22-19-00364.  
The images captured in space and the models we used for their reconstruction are available online in our GitHub repository [[https://github.com/zenytsa/space_images](https://github.com/zenytsa/space_images)]([https://github.com/zenytsa/space_images](https://github.com/zenytsa/space_images)), accessed on 11 March 2022.  
The authors declare no conflict of interest.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 2 Motivation
### Why Handover for Interference Mitigation?
Handovers from macrocells to femtocells may not always be supported due to the different access modes of target femtocells:  
* _Open Subscriber Group_ (OSG) femtocells, where all users are allowed to connect;  
Figure 1: Achieved downlink (DL) signal-to-interference and noise ratio (SINR) of a macrocell user for different solutions in a three-tier hexagonal cellular network with cells of radius 500 m. Macro base stations (BSs) transmit with 10 W power while a femto BS at 300 m uses 0.1 W power. * _Closed Subscriber Group_ (CSG) femtocells, where only subscribed users are allowed to establish connections; and
* _Hybrid-access_ femtocells: Unsubscribed users are allowed to access if there are idle/spare resources. In general, a femtocell can support only a small number of concurrent sessions (e.g., eight or eight data sessions) due to its limited processing power. Thus, private femtocells are likely to operate in the CSG mode, although OSG mode may provide a higher capacity if interference is mitigated. Therefore, a hybrid of the two access modes may maximize capacity while preserving the privileged access of subscribed users. Thus, the use of hybrid access for all femtocells is assumed.
## 2 Methods
### Profile Retrieval
#### 2.5.5 Raman Lines  
The Raman lines are analyzed in the ranges where the layer calculation algorithm has found the ground layer and possible clouds. Above the region of full overlap, the aerosol extinction profile of a Raman line can be directly inverted [33]:  
\[\alpha_{\mathrm{aer}}(\lambda_{0},h_{i})=\frac{\frac{\mathrm{d}}{\mathrm{d}R_{ i}}\Big{[}\ln(n(h_{i}))-\mathcal{S}_{i}\Big{]}-\left(\alpha_{\mathrm{mol}}( \lambda_{0},h_{i})+\alpha_{\mathrm{mol}}(\lambda_{R},h_{i})\right)}{1+\left( \frac{\lambda_{0}}{\lambda_{R}}\right)^{k}}\, \tag{50}\]  
where \(n(h_{i})\) is the molecular density profile (see Section 2.5.1) evaluated at height \(h_{i}=(R_{i}\cos\theta+h_{\mathrm{LIDAR}})\) a.s.l, \(k\) is the Angstrom exponent of the extinction coefficients from the  
Figure 17: Layer Calculations for an elastic channel (355 nm), taken with reduced PMT gain during a clear night at La Palma. From a height of about 5 km to 7.5 km, a cloud is visible with a complicated sub-structure. The cloud layer analysis has detected it based on the analysis of deviations from the fit to the molecular signal expectation (Equation (46)), shown in red. The dark red parts mark those ranges, where \(\left(\chi_{i}^{2}/N_{\mathrm{dof}}\right)^{\mathrm{min}}\) exceeds the threshold of 3.5. The yellow line marks the fitted values of \(C_{i}\) (Equation (45)). On the right side, the extinction coefficients retrieved from an iterative Klett inversion (violet line) are shown, where a typical LIDAR Ratio of 20 sr has been obtained. The orange line shows the Klett inversion of the ground layer. elastic wavelength \(\lambda_{0}\) and its Raman counterpart \(\lambda_{R}\). However, it is well known that the derivative of the logarithm of a strongly falling function amplifies statistical fluctuations, and some smoothing or filtering of the signal is needed [68]. We used the Savitzy-Golay filter, (implemented in scipy's library savol_filter), both for filtering of the logarithm of RCS, as its derivative. Figure 18 shows the different algorithms used for the retrieval of the extinction profile and the corresponding VAODs, highlighting the effect of LORR ringing. One can observe that when the signals are small (here in the Raman line), the relative effects of ringing and their corrections become important: whereas the relatively large signals in the elastic lines have been retrieved correctly with or without ringing correction, only the Raman line with ringing correction yields a correct inversion product. In the case of the elastic UV line, both approaches yield insufficient (with correction) or even negative extinction coefficients. The accuracy requirement of \(\lesssim\)0.03 RMSD in the PBL VAOD retrieval is not yet met in this particular case. That inaccuracy can be cured with the use of an absolute LIDAR calibration [9], which needs quasi-continuous data and was not applied to this data set for that reason. To construct the backscatter profile \(\beta_{\text{aer}}(h)\) from a Raman and an elastic line, we follow the approach of Ansmann et al.
# Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning
## 2 Dynamic Model
### Trajectory Dynamics  
Let \(P_{i}^{t}\), \(V_{i}^{t}\), and \(a_{i}^{t}\) represent the location, velocity, and acceleration, respectively, of agent  
as indicated by Equations (6) and (7). Equations (6) and (7) can be iteratively expanded to be written explicitly as a function of the accelerations of the vehicle at the previous time steps. Therefore, the velocity of quadcopter \(i\) at time \(t\) is determined as follows:  
\[V_{i}^{t}=V_{i}^{0}+\Delta t\left[\sum_{k=1}^{t}a_{i}^{k}\right] \tag{8}\]  
From Equations (6) to (8), the position of quadcopter \(i\) at time \(t\) is represented by the following:  
\[P_{i}^{t}=P_{i}^{0}+\Delta t\ V_{i}^{0}+\frac{\Delta t^{2}}{2}\ \left[\sum_{k=1}^{t}a_{i}^{k}(2t-2k+1)\right] \tag{9}\]  
where \(t\) ranges from \(1\) to \(T\), and \(i\) ranges from \(1\) to \(N\). The \(V_{i}^{0}\) is equal to \(0\), so Equations (8) and (9) can be simplified as follows:  
\[V_{i}^{t}=\Delta t\left[\sum_{k=1}^{t}a_{i}^{k}\right] \tag{10}\]  
\[P_{i}^{t}=P_{i}^{0}+\frac{\Delta t^{2}}{2}\left[\sum_{k=1}^{t}a_{i}^{k}(2t-2k +1)\right] \tag{11}\]  
Equations (10) and (11) can be utilized to create an optimization problem involving the optimization parameter \(\mathbf{X}\). This parameter represents the acceleration of each agent at every time step, i.e., \(\mathbf{X}\in\mathbb{R}^{3 NT}\).
## 2 Control Strategy
### Identifying the Passive Seat Characteristics  
The integrated model shown in Figure 1 was modelled using Simulink and the MATLAB) (MATLAB 2017, MathWorks, Cambridge, CB4 0 HH, UK) GA optimisation tool box was utilised to solve the optimisation problem. To make the application more realistic, the characteristics of the passive seat suspension (\(m_{se}\), \(k_{se}\), and \(c_{se}\)), as well as the driver (\(k_{b}\) and \(c_{b}\)) were estimated based on experimental tests of the passive seat suspension with a dummy that represented a driver using the experimental test rig that is explained in more details in Section 4. These parameter values were determined by fitting the measured vertical acceleration transmissibility of the passive seat suspension with respect to the seat base (the MAST) to that obtained from the simulated model as illustrated in Figure 2. A swept sinusoidal displacement signal with amplitude of 10.0 mm and frequency range of 0.5-18 Hz was used to excite the MAST. The resulting seat pan and MAST accelerations were measured using a sampling frequency of 10 kHz and filtered using a low-pass filter with a cut-off frequency of 200 Hz.  
The simulation model is simplified to ease the optimization process but captures enough accuracy to yield a good overall result from the controller. It can clearly be seen that there is a good agreement between the measured and simulated seat acceleration transmissibility over the frequency range of interest (<12 Hz). A fundamental natural frequency of the passive seat suspension and the dummy was observed around 3.5 Hz together with additional passive seat suspension and dummy higher order dynamics above 12 Hz that were not predicted by the simplified model. While we recognise that the simplified model does not provide a good fit above frequencies of 12 Hz, it is later demonstrated that this mismatch does not unduly affect the controller performance, which is good evidence of robustness in the controller design.  
The QvM parameters used in this study and the estimated parameters of the passive seat suspension and the dummy are listed in Table 3 while the GA parameters are given in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Level of Comfort** & **Frequency-Weighted RMS Acceleration \(a_{v}\) (m/s\({}^{2}\)) Range** \\ \hline Extremely uncomfortable & \textgreater{}2.0 \\ Very uncomfortable & 1.25–2.5 \\ Uncomfortable & 0.8–1.6 \\ Fairly uncomfortable & 0.5–1.0 \\ A little uncomfortable & 0.315–0.63 \\ Not uncomfortable & \textless{}0.315 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vibration magnitude comfort levels (ISO 2631-1).
## 3 Materials and Methods
### Data Analysis--Logit Model  
Logistic or logit regression is used as a nonlinear model designed specifically for binary dependent variables [8]. This regression allows the estimation of the probability associated with the occurrence of a given event, considering a set of explanatory variables [71]; this type of analysis also aims to understand the differences between groups and to estimate the probability that an individual belongs to a particular category, based on a set of explanatory variables [72].  
Two logit models were estimated, one for fresh meat and another for cooked meat. The explanatory variables of each model, as well as the details of the logistic equations, are presented in Appendix A. The data related to the variables of the two models (fresh and cooked beef) were considered significant when \(p<0.05\). The analyzes of the models were based on the evaluation of the marginal effect provided by the coefficients of the explanatory variables on the dependent variable. Other models were estimated considering the presence of other variables, using a stepwise method and with the aid of the lowest value for the Akaike Information Criterion (AIC), and the most robust model (best statistical adjustment) was defined for each stage. The lower the AIC, the better the fit of the model [73; 74]. A way to evaluate the adjustment of logistic regression to data is to use Pseudo R-Squared. They play a similar role to R\({}^{2}\) in linear regression, by representing the proportion of variation that occurred in the dependent variable that is explained by the model [74]. These values can also be used to compare the performance of competing models, between two equally valid logistic equations. For this, the one with the highest Pseudo-R\({}^{2}\) should be chosen.
## 3 Modeling Techniques
### Gene Expression Programming (GEP)  
In spite of computer-based artificial intelligent models, based genetic calculations, such as genetic programming (GP) can construct formulas, which have high precision. It has been demonstrated that GP can be more accurate than traditional empirical models. Furthermore, models developed by genetic calculations and simulators and software systems are easy to use [49].  
Using correction and development of the GP method [50] led to a more relevant version, which was GEP [51]. Indeed, the latter removed the found weaknesses in GP, i.e., the big number of possible programs that can be built by the algorithm and the high sensitivity to small changes in fitness of the created solution. In general, the GEP [51] had 2 significant parts: The chromosome and the symbolic expression trees (ETs). The chromosome encoded the potential solutions, and after that, these solutions were converted to the particular applicant solution named ET [52; 53; 54]. Reliable ETs continuously were reproduced by unlimited genetic conversions because of the conversion method of the structural organization of the chromosome to the ET [52]. In fact, the unique genes' placements in the GEP method produced impressive development of the solutions leading to encryption of any downside [55]. Preceding researches recognized that the speed of convergence in the GEP scheme was much more than the GP scheme [53; 54].  
The chromosome included unchanging factors and variables as terminals and predetermined functions with equal length in one or more genes [52]. By the user, the constants were produced by the GEP in a specific range, however, the functions and variables were the input information. Each gene consisted of a tail, which comprised terminals and also a head that contained constants and variables in the role of terminals and functions [52]. The given value of the gene's head is taken into account as an input of the GEP method, and the value of gene's tail (t) is given by the following equation:  
\[t=h(r-1)+1 \tag{6}\]  
with \(t\) denotes the length of the tail, \(h\) is the length of the head, and \(r\) is the number of arguments of the function with the most arguments.  
The essential stages of the GEP method are illustrated in Figure 1. These steps can be elucidated briefly as follows: Firstly, a generation of chromosomes of an initial population was created randomly. In a second step, the chromosomes were expressed with ET, and their fitness was computed. Then, the individuals were selected to perform reproduction based on their fitness values. This reproduction created offspring with fresh signs. The same procedure was followed by individuals of the resulted generation, i.e., genomes expression, selection, and reproduction with adjustment. The stages expressed above were repeated until reaching the stopping criteria. More explanation of the GEP technique can be found in the literature [51; 55].  
In the current work, the mathematical expression used to predict the CO\({}_{2}\) solubility in ILs was generated in the following form: \(xCO_{2}\ =\ f(T,\ P,\ T_{c,\ IL},\ P_{c,\ IL}\) and \(w_{\ IL})\).
## 2 Materials and Methods
### Dynamic Asynchronous Level Decomposition  
In the asynchronous approach described in Algorithm 1, the component functions \(f^{\mathsf{j}}\) are statically assigned to workers--worker j always evaluates the same component function \(\mathsf{j}\). Likewise, the usual implementation of the synchronous LD strategy is to task workers with solving fixed sets of \(Q_{\mathsf{s}}\). We call these strategies static asynchronous LD and static synchronous LD. However, as previously mentioned, such task-allocation policies might result in significant idle times--even for the asynchronous method because we need the first-order information on all \(f^{\mathsf{j}}\) to compute valid bounds. To lessen the idle times, we implement dynamic-task-allocation strategies, in which component functions are dynamically assigned to workers as soon as they become available. Our dynamic allocation differs from Reference [15] because we do not use a list of iterates. To ease the understanding of the LD methods applied in this work--and to highlight their differences--we introduce a new figure: a coordinator process. The coordinator is responsible for tasking workers with functions to be evaluated. Note, however, that this additional figure is only strictly necessary in the dynamic asynchronous LD; in the other three methods, this responsibility can be taken by the master. Nonetheless, in all methods, the master has three roles: solving the MP, getting iterates, and requesting functions to be evaluated at the newly obtained iterates. By construction, in the synchronous methods, the master requests the coordinator to evaluate all functions \(f^{\mathsf{j}}\) at the same iterate, and it waits until the information of the all functions has been received to continue the process. On the other hand, in the asynchronous variants, the master computes a new iterate, requests the coordinator to evaluate it on possibly not all \(f^{\mathsf{j}}\), and receives information on outdate iterates from the coordinator. Given that the master has requested an iterate \(\mathbf{x}^{\prime}\) to be evaluated in some \(f^{\mathsf{j}}\), the main difference between the static and the dynamic asynchronous methods is that, in the static form, the coordinator always sends \(\mathbf{x}^{\prime}\) to the same worker who has been previously tasked with solving \(f^{\mathsf{j}}\), while in the dynamic one, the coordinator sends \(\mathbf{x}^{\prime}\) to any available worker.
# Porosity Elimination in Modified Direct Laser Joining of Ti6 Al4V and Thermoplastics Composites
## 3 Results and Discussion
### Modified Laser Joining Method  
In order to improve the bonding strength of Ti6 Al4V-PA66-GF30 joints, a modified laser joining process has been proposed, as schematically illustrated in Figure 1b, to eliminate the micro-pores generated in joints. In this process, the preset pads in Figure 1a are removed and a preset clamping force is applied on the overlapping area during laser joining process, so that the excess melted PA66-GF30 overflows from the micro-grooves and the formed micro-pores derived from the pyrolysis of PA66 are expelled out with the flow of PA66-GF30 melts. As the preset clamping force is always applied on the overlapping area during the laser joining process, the solidification shrinkage-induced pores near the boundary of melted zone (Figure 1a) can be reduced significantly, as shown in Figure S3. From Figure 5a, it can be observed that no large size pores are generated in the joint, and very few small pores with sizes of around 30 \(\upmu\)m are observed (see inset in Figure 5a). Figure 5b shows the fracture strength of the produced Ti6 Al4V-PA66-GF30 joints as a function of the structure density on Ti6 Al4V surface. The fracture strength of the produced joints monotonically increases with increasing structure density, and reaches up to around 41.5 MPa as the structure density increases to 75%. This fracture strength is much higher than the reported joints produced between cyclic olefin polymer (COP)/PA6.6-GF35 and steel (<22 MPa) [22; 23], PA66-GF30/Acrylonitrile butadiene styrene (ABS) and aluminum (<22 MPa) [9], PET and titanium (<20 MPa) [14; 24] by the laser joining process.  
All the produced Ti6 Al4V-PA66-GF30 joints by the modified laser joining process fractured at the bonding interface between Ti6 Al4V and PA66-GF30 during tensile shear tests. Figure 5c shows  
Figure 4: **(a)** Fracture strength of LAMP produced Ti6 Al4V-PA66-GF30 joints with various structure densities on Ti6 Al4V surfaces; **(b)–(d)** surface morphologies and cross-section morphologies of LAMP produced Ti6 Al4V-PA66-GF30 joints with structure density of 25% **(b)**, 42% **(c)**, and 58% **(d)**, respectively; **(e)** cross-sections of the Ti6 Al4V and PA66-GF30 parts of the failed Ti6 Al4V-PA66-GF30 joint in **(d)**.  
-fracture morphology of the joints after tensile shear test, which suggests that the fracture results in a smooth and neat fracture interface. Therefore, as the structure density on Ti6 Al4V increases, the fracture strength of the joints monotonically enhances, due to the increasing effective bearing area. The modified laser joining process has been demonstrated to be a feasible and reliable method to eliminate the pores generated in the joints. The elimination of large quantities of pores inside PA66-GF30 leads to significant enhancement of the joint strength, as shown in Figures 3-5.