esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Strategic Planning for Renewable Energy Deployment
#### 4.6.2 Solar Energy  
Temperature and humidity were identified as the most influential factors for solar energy production, with the highest SHAP values observed in Kalamata, Kozani, and Argos. These regions, characterized by high temperatures and relatively low to moderate humidity levels, offer optimal conditions for solar energy generation.  
* Kalamata: Solar panels should be installed in areas with the highest temperatures and lowest humidity levels to maximize energy output. The region's climate is particularly conducive to solar energy production.  
* Kozani and Argos: In these regions, solar installations should focus on locations with consistent high temperatures and moderate humidity, ensuring optimal performance of solar panels. The strategic plan suggests prioritizing land availability and minimizing shading effects from surrounding structures or natural features when selecting specific sites for solar farms.
# Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans
## 3 Training Terrain Traversability
### Feature Computation  
Spatial features are extracted for each 3D point from its neighbourhood, which is computed with a fixed proximity radius of 0.3 m. Those Cartesian points with less than five neighbours are discarded from feature calculation to ensure a minimum of information. Nearest neighbour search for every levelled point cloud is accelerated by using a 3D-tree data structure, which is built with the Python function _KDTree_ from the _Scikit-learn_ library [24].  
The following combination of simple spatial features, which has been already used for reliable ground extraction [13], is employed for every 3D laser point.  
1. The minimum height coordinate among all the neighbours [11].
2. The vertical orientation, which is obtained from an eigenvector of the lowest eigenvalue of the principal components analysis (PCA) [17].
3. Scatterness, which is related with the value of the smallest eigenvalue from PCA [10].  
PCA is sped up for each point neighbourhood with the Python compiler _Numba_ ([[http://numba.pydata.org](http://numba.pydata.org)]([http://numba.pydata.org](http://numba.pydata.org))). Even so, the processing time of features still depends on the number of points of each 3D laser scan. To improve this time, by taking advantage of the four cores of the processor of Andabata (16 GB RAM, Intel Core i7 at 3.5 GHz), the Python library _multithreading_ ([[https://docs.python.org/3/library/multiprocessing.html](https://docs.python.org/3/library/multiprocessing.html)]([https://docs.python.org/3/library/multiprocessing.html](https://docs.python.org/3/library/multiprocessing.html)) has been tested, but with disappointing results, and it has been discarded. Thus, for an average synthetic scan of 76,000 points, where \(\sim\)3% of points do not have enough neighbours, data preprocessing is performed in 3.16 s.  
Figure 3: A view of the hills generated with Gazebo (**a**), a synthetic 3D scan taken from this zone (**b**), its gravity-levelled and intensity-tagged point cloud (**c**) and the traversability-labelled 3D data (**d**).
## 3 Image Processing Workflow
### Image Alignment  
Because of the different fields of view and angles during scanning, a given mineral particle can have different positions in QEMSCAN and MAPS images, so the two images cannot be directly used for comparison and correction. Thus, registering and alignment of QEMSCAN and MAPS images are required. The alignment methods are applied to the high-precision backscattering MAPS imagery during mineral quantification analysis of each mineral to carry out calibration and correction.  
For QEMSCAN and MAPS images, the differences between the two are mainly translational and rotational, namely, rigid body motion (Figure 2). Therefore, affine transformation can conduct image registration. Generally, parameters related to Equation (2) were selected for point characterization through affine transformation.  
\[\left\{\begin{array}{l}x^{\prime}=xcos\theta-ysin\theta+t_{x}\\ y^{\prime}=ycos\theta+xsin\theta+t_{y}\end{array}\right., \tag{2}\]  
where (\(x\), \(y\)) represents the original starting point coordinates, (\(x^{\prime}\), \(y^{\prime}\)) refers to the point coordinates following affine transformation, (\(t_{x}\), \(t_{y}\)) are translation vectors, and \(\theta\) is the rotation angle.
# Compact N-Band Tree-Shaped Multiplexer-Based Antenna Structures for 5G/IoT Mobile Devices
###### Abstract  
This paper presents a simple, compact and low-cost design method that allows one to obtain low-profile multi-band antennas for the overcrowded future generation networks, which are widely versatile and very heterogeneous in the K/Ka bands. The proposed antennas comprise \(n\) radiating monopoles, one for each of the desired operating frequencies, along with a frequency selective feeding network fed at a single point. This concept enables a single antenna to be shared with different radio-frequency (RF) frontends, potentially saving space. Typically, with \(n\)-_band_ structures the biggest challenge is to make them highly efficient and here this is assured by multiplexing the frequency, and thus isolating each of the monopoles, allowing the design of scalable structures which fit the 5G applications. Based on the vision proposed here, a dual-band and a tri-band structures were built and characterized by their main parameters. Both prototypes achieved peak efficiencies around 80%, with adequate bandwidths and gains, as well as great compactness.  
frequency multiplexed; IoT; millimeter-waves; multi-band; n-band antenna; antenna as a sensor +
Footnote †: journal: Sensors
## 4 Simulation Results
### Binary Phase Shift Keying (BPSK)
#### 4.1.2 Noncoherent BPSK  
Moving on, the performance of the noncoherent BPSK system is evaluated in both the AWGN and Rayleigh environment along with the AdaBoost algorithm being added to improve the system BER.  
* AWGN Channel  
Figure 6 displays the BER vs. SNR in the case of the AWGN, where the simulated noncoherent system shows a better result than that of the theoretical. The AdaBoost algorithm improves the performance, specially at low SNR values. However, at high SNRs, the improvement decreases but still it is better than that of the theoretical as the overall curve is close in results to that of the coherent BPSK performance.  
As mentioned, the main effect of the AdaBoost algorithm is to recover the transmitted data from the noisy received signal, and hence, allow for reconstruction of the constellation diagram of the modulation technique. As a result, the simulated BER is improved. The improvement appears in the form of better results of the noncoherent simulated BER in comparison to that of the theoretical. Also, it can be seen that the noncoherent simulated curve approaches that of the coherent system. This is caused by the fact that the noncoherent system is already worse in performance than the coherent one, so when the AdaBoost algorithm is added, it improves its BER. Therefore, the AdaBoost algorithm is able to achieve a coherent performance for the noncoherent system.  
* Rayleigh Fading Channel  
Figure 7 displays the BER vs. SNR in the case of the Rayleigh fading environment. The related simulation parameters are \(N=10^{5}\) (no. of bits), \(N_{s}=5\times 10^{5}\) (no. of symbols), and SNR = 0-20 dB. The Rayleigh fading channel parameters are given as follows: \(f_{c}=2\times 10^{9}\) Hz, \(c=3\times 10^{8}\) m/s, \(v=14\) m/s, \(f_{D}=93\) Hz, \(D_{s}=185\) Hz (Doppler spread), and \(T_{s}=1\times 10^{-4}\) s (sampling time).  
Figure 6: BER vs. SNR for noncoherent BPSK modulation over the AWGN channel.  
The noncoherent system appears to be better than that of the theoretical noncoherent one since the performance of the system in the Rayleigh environment is already worse than that of the system with the AWGN only. It can be recognized that at a low SNR, the BER of the noncoherent system in the Rayleigh environment appears to be even lower than that of the coherent theoretical system of Rayleigh channel. As the SNR increases, the simulated noncoherent curve appears to almost follow the curve of both the simulated and theoretical coherent systems.  
The same AdaBoost coherent performance achievement is also depicted in this figure. The AdaBoost algorithm manages to recover the transmitted data from the noisy signal received in the Rayleigh fading environment as well. Hence, it reconstructs the constellation diagram of the modulation technique. As a result, the simulated BER is improved. Again, when the AdaBoost algorithm is added, it improves its BER and achieves a coherent performance for the noncoherent system.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
###### Abstract  
This paper proposes a new approach to small-object change detection from high-resolution satellite images. We propose using feature points that can be quickly extracted from satellite images as a suitable unit of change for small objects and to reduce false alarms. We can perform feature-based change detection by extracting features from previous and recent images and by estimating change based on change magnitude of the features. We estimate the magnitude by calculating pixel-based change magnitude, and counting the ratio of changed pixels around the extracted features. We apply feature matching and determine matched features as unchanged ones. The remaining feature points are judged as changed or unchanged based on their change magnitude. We tested our approach with three Kompsat-3A image sets with a ground sampling distance of 50 cm. We showed that our approach outperformed the pixel-based approach by producing a higher precision of 88.7% and an accuracy of 86.1% at a fixed false alarm rate of 10%. Our approach is unique in the sense that the feature-based approach applying computer vision methods is newly proposed for change detection. We showed that our feature-based approach was less noisy than pixel-based approaches. We also showed that our approach could compensate for the disadvantages of supervised object-based approaches by successfully reducing the number of change candidates. Our approach, however, could not handle featureless objects, and may increase the number of undetected objects. Future studies will handle this issue by devising more intelligent schemes for merging pixel-based and feature-based change detection results.  
Keywords:change detection; feature-based; feature points; matching; vehicles; high-resolution satellite image +
Footnote †: journal:
## 5 Conclusions  
Our pre-clinical results open a new application of PDT for drug brain delivery. The study of reversible PDT-opening of the BBB using commercial PS such as 5-ALA and AlPcS as well as newly synthesized ZnPc and GalZnPc reveal a stronger PDT-GalZnPc increase in the BBB permeability to EBAC and FITCD vs. other tested PS. These data suggest that PDT-GalZnPc may prove to be a more promising tool for the BBB opening and for drug brain delivery. The limitation of this work is the study of PDT-opening of the BBB in healthy mice. We expect that PDT-opening of the BBB might be a useful tool in the theranostics of gliomas that we will show in our further research. However, these findings represent a first important informative platform that may help the development of novel strategies in the therapy of brain diseases, via optimization of PDT protocol in the light of new insights of PDT effects on the BBB permeability.  
**Author Contributions:** Preparatory work with animals in vivo and ex vivo tissue samples preparation, A.K., I.B. and I.A. ([PERSON]); phthalocyanines synthesis, V.M. and I.E.; novel phthalocyanines photophysical properties evaluation, E.B. and I.A. ([PERSON]); spectrofluorimetric analysis A.S., A.T. and M.K.; formal analysis and methodology, A.M., immunohistochemical assay, A.T., A.S., and N.L.; fluorescence spectral measurements and analysis, E.B.; image processing and analysis, E.B. and N.L.; analysis of general data, E.B., J.K. and O.S.-G.; project administration, O.S.-G.; supervision and funding acquisition, E.B. and O.S.-G.  
**Funding:** This research was supported by grant from the Russian Science Foundation, 17-15-01263 (The study of PDT-opening of the BBB), grant 19-15-00201 (The spectrofluorimetric assay of BBB permeability), and grant from the Ministry of High Education and Science, 17.1223.2017/PCh (Synthesis of Pts). Spectroscopic studies were supported by grant from the National Science Fund of Bulgaria, #KF06-N23/8/18.12.18: \"Innovative photodynamic methods for treatment of stem cells cultivated from glioblastoma tumours.\"  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Incoherent Digital Holography: A Review
## 4 Special Topics and Applications
### Single-Shot Imager for the Aim of Capturing High-Speed Incoherent 3D Objects  
Although FINCH has the ability of spatially high-resolution incoherent 3D imaging, its temporal resolution is not particularly high because of the employment of temporal phase-shifting interferometry. On the other hand, there are intensive requirements for the imaging of dynamics in 3D space in fluorescence and Raman scattering microscopy, machine vision, and other applications. Therefore, single-shot incoherent digital holographic techniques can extend the applicability of incoherent holography. Five types of single-shot incoherent digital holographic techniques have been proposed: off-axis setups with FINCH [75], Michelson interferometry [76], double-focal lens and gratings [77], and an in-line optical system with single-shot phase shifting [78; 79]. A Gabor-type configuration is the simplest setup for single-shot 3D imaging. Although zeroth-order diffraction and conjugate images superimpose on the object image, the technique is valid for the case in which objects measured are known or have a simple shape. When applied to arbitrary objects, a technique to separate unwanted images is needed. [PERSON] and [PERSON] have proposed a single-shot off-axis incoherent DH system based on FINCH [75]. A Fourier transform hologram has been obtained by passing a SLM twice, and an incoherent 3D image without unwanted images has been reconstructed from a single hologram. [PERSON] and [PERSON] have proposed another single-shot off-axis incoherent DH based on a Michelson interferometry and have demonstrated 3D imaging of multiple LEDs placed at different depths [76]. [PERSON] and co-workers have used a double-focal lens and a grating to generate an off-axis hologram [77]. Their proposal can enable common-path setup and phase modulation without modulating polarization. [PERSON] et al. have presented single-shot in-line phase-shifting DH and demonstrated the technique for transparent and reflective objects [79]. The technique is composed of the combination of a FINCH system and a single-shot phase-shifting interferometer, as shown in Figure 8. A FINCH system makes use of polarization to shift the phase, and it is easy to combine FINCH and single-shot phase-shifting utilizing polarization. Figure 9 shows experimental results in the case for which the light source is a white light LED and the object measured is a 1 mm aperture. From a recorded single image, multiple-phase-shifted incoherent holograms are obtained with a de-mosaicking process. The complex amplitude distribution of an incoherent object on the image sensor plane is derived with phase-shifting interferometry. After the calculation of diffraction integrals, the focused image of the object is retrieved. It is noted that single-shot in-line phase-shifting DH has an advantage in the space-bandwidth product available for recording an object wave in comparison to off-axis DH.  
Figure 8: _Cont._
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
## 3 The Proposed Approach (iCC)  
One of the main parameters in the CC framework is the number of subproblems at the decomposition stage. When solving BB optimization problems, the number is not known a priori. iCC gradually increases the number of variables in subproblems. In other words, iCC reduces the total number of subproblems during the optimization process. iCC uses an equal number of FEVs for optimizing each subproblem.  
Algorithm 2 shows a pseudo-code for iCCEA. We can use any EA (or another metaheuristic) as the optimizer for subproblems. In the study, iCC uses the following numbers of subproblems, \(S=\{10,\ 8,\ 4,\ 2,\ 1\}\). The number of FEVs for each stage is calculated as \(iCCFEVs=\frac{MAX\_FEVs}{|S|}\), where _MAX_FEVs_ is the budget of fitness evaluations in one run, \(|S|\) is the cardinality of \(S\) (the total number of stages of iCC). We have also investigated the strategy with decreasing the number of variables for each subproblem, but its performance is too low.  
```
Set \(EA\), \(pop\_size\), \(S=\{10,\ 8,\ 4,\ 2,\ 1\}\), \(iCCFEVs=\frac{MAX\_FEVs}{|S|}\), \(iCC\_index=1\), \(FEV\_counter=0\);
1:\(m=S_{iCC\_index}\);
2:for\(i=1\) to \(m\)
3: Generate the initial population;
4: Evaluate the \(i\)-th subcomponent using \(EA\) in a round-robin strategy;
5:\(FEV\_counter++\);
6:endfor
7:while (\(termination\)\(criteria\) is not satisfied) do
8:for\(i\)=1 to \(S_{iCC\_index}\)
9: Apply EA operators to the \(i\)-th subcomponent;
10: Evaluate the fitness function for each individual in the \(i\)-th subcomponent;
11:\(FEV\_counter++\);
12:endfor
13:if (\(FEV\_counter>iCCFEVs\)) then
14:\(iCC\_index++\);
15:\(FEV\_counter=0\);
16:endif
17:endwhile
18:return the best-found solution;
```  
**Algorithm 2** Pseudo-code of iCCEA
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **CDSS Intervention Group (n = 39)** & \multicolumn{4}{c}{**Dietary Advice Group (n = 21)**} \\  & **Baseline** & **Follow-Up** & **\(p\)-Value §** & **Baseline** & **Follow-Up** & **\(p\)-Value §** & \(p\)-Value \(\int\)** \\ \hline Age (y) & 48.9 \(\pm\) 13.4 & NA & NA & 67.0 \(\pm\) 16.4 & NA & NA & \(<\)0.001 \\ Sex (men), n (\%) & 28 (7.18\%) & NA & NA & 4 (16\%) & NA & NA & \(<\)0.001 \\ Body weight (kg) & 95.5 \(\pm\) 21.8 & 90.6 \(\pm\) 19.9 & \(<\)0.001 & 73.4 \(\pm\) 10.1 & 72.8 \(\pm\) 11.3 & 0.563 & \(<\)0.001 \\ BMI (kg/m\({}^{2}\)) & 35.2 (28.4-37.5) & 33.2 (27.4-35.4) & -0001 & 26.42 (24.8-28.5) & 24.9 (21.7-27.4) & 0.434 & \(<\)0.001 \\ Total body fat (\%) & 44.9 \(\pm\) 11.3 & 41.9 \(\pm\) 10.5 & \(<\)0.001 & 35.7 \(\pm\) 5.7 & 35.6 \(\pm\) 5.9 & 0.469 & 0.001 \\ Lean tissue (kg) & 25.4 (21.7-29.1) & 24.6 (21.8-27.9) & 0.050 & NA & NA & NA & NA \\ Lean mass index (kg/m\({}^{2}\)) & 9.3 \(\pm\) 1.29 & 8.98 \(\pm\) 1.31 & 0.048 & NA & NA & NA & NA \\ Glucose (mg/dL) & 89 (80-97) & 87 (85-97) & 0.478 & 91 \(\pm\) 12.4 & 91.2 \(\pm\) 15.6 & 0.612 & 0.733 \\ Glycerol hemoglobin, HBL4C (\%) & 5.2 \(\pm\) 0.55 & 4.97 \(\pm\) 0.41 & 0.017 & 5.10 \(\pm\) 0.84 & NA & NA & 0.573 \\ Total-cholesterol (mg/dL) & 186 \(\pm\) 45 & 164 \(\pm\) 37 & 0.628 & 193 \(\pm\) 38 & 197 \(\pm\) 40 & 0.582 & 0.554 \\ LDL-cholesterol (mg/dL) & 111 (91–120) & 108 (77–118) & 0.919 & 107 (100–143) & 109 (83–135) & 0.748 & 0.638 \\ HDL-cholesterol (mg/dL) & 55 (46–67) & 50 (40–65) & 0.569 & 49 (56–64) & 53 (65–74) & 0.125 & 0.552 \\ Triglycerides (mg/dL) & 137 (115–152) & 130 (108–160) & 0.005 & 95 (65–155) & 97 (70–121) & 0.808 & 0.554 \\ \hline \hline \end{tabular} Data are presented as mean \(\pm\) standard deviation or as median and interquartile range. \(\dagger\) Values logarithmized prior to statistical comparison to achieve normality. NA: Not available or not applicable. § Comparison between baseline and follow-up values in the CDSS intervention group. \(\ddagger\) Comparison between baseline and follow-up values in the dietary advice group. \(\int\) Comparison of baseline values between CDSS intervention and dietary advice group.  
\end{table} 
Table 1: Baseline and follow-up measurements of the patients in the CDSS intervention and dietary advice group. % body fat pattern were positively related to % weight loss in women. Baseline glucose levels were inversely related to % weight loss in women. In men, as well as in the total sample, baseline triglyceride levels were inversely related to % weight loss. Furthermore, multiple linear regression models were created to identify the factors that can independently \"predict\" weight loss (Table 5). As can be seen in the total sample, serum triglycerides were inversely associated with the magnitude of weight loss, irrespective of age, gender, physical activity at baseline and identified body weight patterns. In women, a pattern with high BMI and high lean tissue was consistently positively related to the % weight loss irrespectively of age, physical activity and triglyceride levels (Table 5).
# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
## 3 Methodology
### Improving VulRepair: ImpVulRepair
#### 3.3.7 Momentum  
Regarding optimizers, momentum refers to how the loss function accelerates convergence toward the minimum. We want a loss function to move horizontally, taking larger steps toward minimal loss while making smaller steps vertically. Momentum prevents gradient descent from oscillating as it moves toward the minimum. Betas are coefficients used in optimizers such as AdamW and LION. They are extensions of the momentum concept and improve efficiency and accuracy in training and testing transformer models [38]. There are two betas in both the LION and AdamW optimizers. They determine the update direction of the momentum. \(\beta\)-1 is the decay rate of the gradient, which is set to a default value of 0.9 in both the LION and AdamW optimizers. \(\beta\)-2 is the decay rate of the squared gradient, which is set to a default value of 0.99 in LION and 0.999 in AdamW [32].  
We trained the ImpVulRepair model with the LION optimizer and its default beta values, achieving a PP of 55.7%. To yield better accuracy, we tuned LION's \(\beta\)-1 to 0.09, 0.6, 0.8, 0.9 (default for LION and AdamW), and 0.99, with 0.8 yielding the lowest loss. We tuned LION's \(\beta\)-2 to 0.9, 0.96, 0.98, 0.99 (default for LION), and 0.999 (default for AdamW), with 0.98 yielding the lowest loss. Thus, we used the optimal values, specifically 0.8 for \(\beta\)-1 and 0.98 for \(\beta\)-2, to train our model. Although we were able to reduce the training from 9 epochs to 8 epochs, our training was unstable. In our first attempt, the training log saved only one out of ten epochs. We defined instability as instances where data were not saved, the GPU and files disconnected, and the screen froze. In our second attempt, we received an OS error at the end of testing indicating that a file did not connect. However, ImpVulRepair achieved a PP of 51.93%. This was not our best result, and the testing was unstable. Therefore, it is best to use the default beta values in LION, where \(\beta\)-1 is set to 0.9 and \(\beta\)-2 is set to 0.99, as they contribute to higher prediction percentages and stability.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 5 The Proposed VLSI Architecture and Implementation
### Input Table Module  
As shown in Figure 9, this module is the VLSI architecture of the input table module. This module is a simulation measurement values of \(x\), \(y\) locations and \(x\), \(y\) speeds at the time \(k\). Use counter to count, output each value in sequence, and enter to other modules for calculation.