esa-sceva/satcom-chunk-collection huggingface dataset:
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 5 Eigenvector Perturbation
The explicit expressions of \(\left(\delta\mathbf{P}\right)_{kl}^{\left(a\right)}\), \(\left(\delta\mathbf{P}\right)_{kl}^{\left(b\right)}\) and \(\left(\delta\mathbf{P}\right)_{kl}^{\left(c\right)}\) are  
\[\left(\delta\left(\mathbf{P}\right)\right)_{kl}^{\left(a\right)} = \sum_{u=1}^{d}\left(\sum  
\[\left.\frac{dF\left(\theta\right)}{d\theta}\right|_{\theta=\theta_{n}^{ \left(0\right)}} = \sum_{l=1}^{M}\left(\sum_{k=1}^{M}\frac{d\left[\exp\left(j\frac{2 \pi}{\Lambda}\left(l-k\right)\Delta\sin\theta\right)\right]}{d\theta}\right|_{ \theta=\theta_{n}^{\left(0\right)}}\left(\mathbf{P}\right)_{kl}\right) \tag{29}\] \[= \sum_{l=1}^{M}\left(\sum_{k=1}^{M}f_{kl}\left(\theta\right)|_{ \theta=\theta_{n}^{\left(0\right)}}\left(\mathbf{P}\right)_{kl}\right)=0 \hskip 28.452756 ptn=1,\cdots,d.\]  
Substituting (4) in (17) yields  
\[\hat{F}\left(\theta\right)=\sum_{l=1}^{M}\left(\sum_{k=1}^{M}\exp\left(j\left( \psi_{l}\left(\theta\right)-\psi_{k}\left(\theta\right)\right)\right)\left( \hat{\mathbf{P}}^{\prime}\right)_{kl}\right). \tag{30}\]  
Using (21), (30) can be approximated as  
\[\hat{F}^{\left(g=1\right)}\left(\theta\right) = \sum_{l=1}^{M}\left(\sum_{k=1}^{M}\exp\left(j\left(\psi_{l}\left( \theta\right)-\psi_{k}\left(\theta\right)\right)\right)\left(\left(\mathbf{P} \right)_{kl}+\delta\left(\mathbf{P}\right)_{kl}\right)\right) \tag{31}\] \[= \sum_{l=1}^{M}\left(\sum_{k=1}^{M}\exp\left(j\frac{2\pi}{\Lambda} \left(l-k\right)\Delta\sin\theta\right)\left(\left(\mathbf{P}\right)_{kl}+ \delta\left(\mathbf{P}\right)_{kl}\right)\right),\]  
where the superscript \(\left(g=1\right)\) implies that the first order approximation of the eigenvector is adopted. Angles, at which \(\hat{F}^{\left(g=1\right)}\left(\theta\right)\) is maximized with respect to \(\theta\), are given by  
\[\left.\frac{d\hat{F}^{\left(g=1\right)}\left(\theta\right)}{d \theta}\right|_{\theta=\theta_{n}^{\left(0\right)}+\delta\theta_{n}} = \sum_{l=1}^{M}\left(\sum_{k=1}^{M}\exp\left(j\frac{2\pi}{\Lambda} \left(l-k\right)\Delta\sin\theta\right)j\frac{2\pi}{\Lambda}\left(l-k\right) \Delta\cos\theta\left(\left(\mathbf{P}\right)_{kl}+\delta\left(\mathbf{P} \right)_{kl}\right)\right|_{\theta=\theta_{n}^{\left(0\right)}+\delta\theta_{n}}\right) \tag{32}\] \[= \sum_{l=1}^{M}\left(\sum_{k=1}^{M}f_{kl}\left(\theta_{n}^{\left( 0\right)}+\delta\theta_{n}\right)\left(\left(\mathbf{P}\right)_{kl}+\delta \left(\mathbf{P}\right)_{kl}\right)\right)=0\hskip 14.226378 ptn=1,\cdots,d,\]  
where from (32), \(f_{kl}\left(\theta\right)\) is defined as  
\[\delta\mathbf{e}_{k}^{\left(g-1\right)} = \sum_{i=1,j\
eq k}^{M}t_{i,k}^{\left(g=1\right)}\mathbf{e}_{i} \tag{22}\]0  
Let \(\hat{F}^{\left(g=1,h=1\right)}\left(\theta\right)\) denote the first order [PERSON] expansion-based approximation of \(\hat{F}^{\left(g=1\right)}\left(\theta\right)\), where \(\left(h=1\right)\) implies that the first order Taylor expansion is used.
# Radiator Enablers for Wireless Communication Evolution
## 1 Introduction
### The 6G Use Cases  
These diversified networks, via 5 and 6G wireless communications, focus on specific use cases. Three are the stabilized (frozen), key ingredients of the otherwise still-floating Beyond 5G and 6G concepts (ref. [3]). Integration of sensors, integration of artificial intelligence, and further evolution of the already Shannon-approaching telecommunication networks. The whole endeavor is referred to as verticals, network slicing, edge computing, and machine learning and also mainly on the physical layer of the wireless cellular telecommunication system. The relative use cases, as enlisted by 3 GPP (ref. [4]), include evolutions of the previously stated 5G cases.  
* eMBB to immersive communications, where extended reality-like communications are becoming more and more real.
* Ultra-reliable and low-latency communications (URLLC) to hyper-reliable and low-latency communications (HRLLC), where network resiliency is a necessity, especially in minimizing critical (especially due to unexpected events) network outages.
* mMTC to massive communication, where massive connectivity, reduced power consumption, increased lifespan of sensors, predictive resource allocation, low-cost authentication and authorization, swarm networking, satellite communication for IoT, and other sensor-related functions are in focus as key components to evolve further.  
The use cases also include expansion to new \"dimensions\":  
* AI, where intelligent network automation and optimization is anticipated.  
* Sensor networks, where sensing and communication infrastructure for activity recognition, localization, and monitoring are expected.
* Ubiquitous connectivity, where ubiquitous and resilient coverage and enhancements including non-terrestrial, aerial, and maritime communications are targeted.
## 3 Results and Analysis
### Lane Changing Characteristics
#### 3.3.2 MLC Characteristics  
Expanding on the impact of CD on MLC, Figure 13 reveals that as CD decreases, drivers engage in quicker lane changes over shorter distances, leading to steeper trajectories. This observation is further detailed in Figure 14, which quantitatively analyzes the variations in LCD, LCA, and LCL, providing deeper insights into the specific ways CD influences MLC behavior in constrained environments.  
The results indicate that a shorter CD typically leads to a reduced LCD and an increased LCA in most cases. Specifically, at CDs below 300 m, vehicles face a limited time window for LC, with most LCDs centered around 2.55 s and LCAs exceeding 18\({}^{\circ}\). In contrast, for CDs greater than 700 m, LCAs tend to be within 14\({}^{\circ}\) and LCDs extend to about 3.59 s, closely resembling the GDS conditions and exhibiting a wider distribution. This trend points to a significant improvement in MLC stability as CD increases.  
The distribution of MLC largely mirrors that of MLCD, albeit with more distinct distribution variations. For large spacing sections, there's noticeable variability in the distance covered during the lane change, with LCL typically ranging between 50 and 180 m. As CD decreases, vehicles must complete the MLC within a restricted distance, leading to a more uniform LCL distribution. For example, at a CD of 100 m, the majority of samples maintain the LCL between 50 and 100 m.  
The results highlight that insufficient spacing between tunnels and interchanges limits drivers' LC maneuvers, compelling them to adopt a more aggressive strategy. To delve into these dynamics, Figure 15 presents the joint distribution of three indicators and shows clear relations: as expected, LCL decreases and drivers opt for larger steering angles within shorter LCD, illustrating the adjustments of drivers under various spacing constraints.  
Table 9 lists the intergroup differences in MLC features at a 95% significance level. It shows no significant variances in three indicators between large spacing and GDS samples. However, in small spacing sections, these differences become more pronounced. Combined with the results of Figure 15, for optimal LC stability, the CD should ideally not surpass 700 m, slightly above the critical spacing obtained by traffic conflicts [13].  
Figure 14: Distribution of MLC characteristics. The colors represent different sample groups.
# Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery
## 4 Experiments
### Visual Quality and Runtime Evaluation on Boat Image  
For better quality perception, experiments performed on the boat image presented similar PSNR results, shown in Figure 4. The CPU time vs. PSNR and the iteration number vs. PSNR of different algorithms carried out on the boat image with 20% sampling are presented in Figures 5 and 6, respectively. The corresponding recovered images are given in Figure 7. From Figure 7, we can clearly see that four nonlocal sparsity-based methods are still better than the others. Among them, BAIST enjoyed great advantages over ISTANR in producing a clearer image, e.g., on the area of the prow, due to the backtracking strategy. The reconstructed boat image of BAIST and D-AMP was close to some degree at 20% sample rates, but BAIST was still clearer than D-AMP with a zoomed-in version, also achieving higher PSNR results after 19 s as shown in Figure 5, and after 34 iterations as shown in Figure 6. Besides, for BM3D-CS with more iterations, its results are presented for every two iterations in Figure 6. The superiority of the proposed algorithm in visual quality and runtime could be demonstrated by these results.  
Figure 4: Average PSNR on boat image at different sample rates.  
Figure 5: CPU time vs. PSNR on boat image with 20% sampling.
## 3 Results
### Multi Modality  
The same group of experiments was also performed under the multi-modality approach, which consisted of using DualUNet as a segmentation architecture, in which data from the Cine and DE modalities are fused at an intermediate step.  
Table 2 presents the segmentation results for the test set, assessed through the mean Dice score and its corresponding standard deviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**UNet** & **UNet-50\% DA** & **UNet-100\% DA** & **UNet-200\% DA** & **UNet-ADA** \\ \hline
0.834 \(\pm\) 0.056 & 0.838 \(\pm\) 0.065 & 0.844 \(\pm\) 0.058 & **0.845 \(\pm\) 0.052** & 0.843 \(\pm\) 0.051 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean test Dice score for single-modality approach trained with fixed (DA) and adaptive (ADA) data augmentation for the myocardium segmentation. Best result in bold.  
Figure 7: Example of a higher (**top**) and lower (**bottom**) segmentation performance for the single-modality model after applying data augmentation (indicated by the black arrows). From left to right: ground truth, prediction of model without data augmentation, prediction of model with best data augmentation rate (200%). The left ventricle cavity and myocardium are presented in blue and red, respectively.  
It can be noted that, similarly to the single-modality approach, the segmentation model manages to consistently increase the Dice score and lower its standard deviation as more synthetic data become available for training. However, it reaches a plateau at 100%, after which the Dice score decreases and the standard deviation increases. For larger augmentation rates (>200% DA), the segmentation performance was either maintained or decreased.  
In the adaptive augmentation case, the obtained results are also good considering that the amount of synthetic data generated is much less when compared to any of the fixed augmentation scenarios. However, the best performance in the test set is obtained when the DualUNet is trained on a dataset with 100% data augmentation.  
Throughout a case-specific analysis, it was found that, in 89 out of 124 cases (71.77%), the Dice score was higher after applying the data augmentation algorithm. One example of this higher performance is presented in Figure 8. When focusing on the areas indicated by the arrows, it can be noted that the data augmentation algorithm indeed makes the model learn from different myocardial infarction shapes, thus making it capable of delineating the myocardium contour much more thoroughly, covering areas corresponding to myocardial infarction that were missed before applying the algorithm.  
In cases where the Dice was lower, the visual difference was generally difficult to spot. Figure 8 presents examples of such cases, and it can be observed that in the area selected by the arrow, there is a slightly worse segmentation of the myocardium as it becomes thicker (taking some area that should have been delimited as the left ventricle cavity).
## 2 Experimental Study
### Remote Real-Time Monitoring System
#### 2.2.3 Stability Analysis System  
As shown in Figure 9, a CWST01 remote optical fiber sensing stability system was developed. The system consists of a GPRS/CDMA 1x mobile data communication module, wireless transceiver module and mass data fast data processing module. CWST01 realizes the remote transmission of monitoring data in a rapid and reliable manner. The overall remote data processing platform is shown in Figure 10.  
Figure 8: The structure of remote transmission network.  
Figure 7: Network structure design of a remote data processing platform.
# Physical Activity Programmes in the Treatment of Addictions: A Systematic Review
## 3 Results
### Characteristics of the Studies: Results Synthesis
Regarding the study population in terms of sex, four (40%) studies analysed both men and women [21; 24; 25; 29], four (40%) analysed variables only in women [23; 26; 28; 30], and two (20%) only analysed men [22; 27]. With respect to the type of addiction, five (50%) studies explored the relationship between a physical activity programme and people addicted to smoking [25; 26; 28; 29; 30], four (40%) investigated such a relationship in drug addicts [22; 23; 24; 27], and one (10%) analysed it in alcohol addicts [21]. In relation to the type of physical activity programme applied in the different studies, five of them conducted programmes with aerobic exercise [21; 22; 24; 28; 29], one employed a cardiovascular programme [26], one used fitness activities [30], and three implemented body techniques such as yoga [25; 27] or Tai Chi [23; 27]. All ten articles included in this review carried out pre-test and post-test measurements of their study variables. Of the ten articles analysed, nine (90%) concluded that the physical activity programmes used in their studies provided benefits for people with addiction [21; 22; 23; 25; 26; 27; 28; 29; 30], and one (10%) did not find a positive correlation [24].
## 1 Introduction
It is also important to carefully choose the structuring method due to sensitivity of the BSW excitation and propagation to geometrical parameters of the BSW-supporting structures. The femtosecond laser polymerisation technique (or two-photon polymerisation technique 2 PP) is an advanced lithographic technique that is based on nonlinear absorption phenomena and provides sub-100 nm resolution [31; 32]. This method has found successful applications in microfluidics, tissue engineering, micro-optics (including X-ray optics [33]), micromechanics and photonics [34]. 2 PP provides precise control of geometrical shape and allows for fabricating several nanostructures with slightly deviating geometrical parameters on a single substrate in one exposition procedure [35]. In this paper, we proposed a femtosecond laser polymerisation technique for nanofabrication of novel BSW-supporting photonics devices. A compact excitation scheme provides full compatibility with integrated optics technology. The propagation of the BSW in the device was studied both experimentally and numerically. Obtained experimental results prove the applicability of 2 PP for the BSW-based integrated photonics as well as the applicability of diffraction grating for excitation of the BSW waveguide modes.
## 2 Materials and Methods
### Methods
#### 2.3.3 Accuracy Assessment  
To validate the observations, we complemented the EO observations with a field survey campaign, where ground truth points were collected and interviews of the locals were carried out to validate the results. By including the interviews, we could improve the reliability of the results obtained from satellite images acquired before 2005.  
The classification of the different crop cycles requires EO imagery, which is distributed evenly throughout the year and covers all crop cycles, so that the changes from one crop to another can be clearly detected. However, this was not the case for every input image set. In addition, the collected ground truth points included mostly \"3 \(\times\) irrigated rice crops\" and not enough \"2 \(\times\) irrigated and 2 \(\times\) rainfed rice crops\" to properly validate classification of various rice crop types. Thus, rice crop classes \"2 \(\times\) Irrigated rice crop\", \"2 \(\times\) Rainfed rice crop\" and \"3 \(\times\) Irrigated rice crop\" were merged into one rice class for validation. In addition, the \"Rainy season rice and shrimp culture\" class was merged with the \"Aquaculture\" class resulting in the \"Aquaculture2\" class for SPOT 2005 land-use map validation, because the 2005 SPOT image was captured during the dry season when the \"Rainy season rice and shrimp culture\" areas were used for shrimp farming.
## 4 Simulation Results and Analysis
However, the simulation results in Figure 5 also reveal that increasing the average signal-to-noise ratio and changing the detection method can effectively improve the outage probability performance of the system under the same system parameter conditions. Figure 4: The variation curve of outage probability of RF-UWOC hybrid double hop system with average signal-to-noise ratio under different ocean turbulence intensities. Figure 6 shows the theoretical outage probability and MC simulation values of a hybrid dual-hop RF-UWOC relay communication system with fixed channel parameters for the first hop RF link but varying transmission distance \(d=10/15/20\) m (Correspond to the pink, blue, and red curves in the figure respectively.) for the second hop UWOC link, as a function of the average signal-to-noise ratio. At this point, the horizontal and vertical distances between UAV and R in the first hop RF link are set to \((900\) m,\(2000\) m); the coefficients that follow generalized K-fading are set to \(m=2.0,k=2.6\), the strong pointing error \(\xi=1.0\) (receiver radius \(r_{a}=30\) cm, beam width \(w_{z}=30\) cm) of the second hop UWOC link, and the ocean turbulence scintillation index \(\sigma_{I}^{2}=0.1074\). From the simulation results in Figure 6, it can be seen that as the underwater transmission distance decreases, the outage probability performance of the corresponding hybrid double hop system improves. This is due to the significant attenuation of the Elamassie underwater path loss represented by Equation (10) after the transmission path is reduced. Similarly to the simulation results of other figures, under the same system parameter settings, the HD scheme belonging to coherent detection is significantly more effective than the IM/DD scheme belonging to non-correlated detection. Moreover, both the MC simulation values and asymptotes can perfectly match the theoretical values, indicating that the derivation of the outage probability theory formula and asymptote formula mentioned above is correct and reasonable. Figure 5: The variation curve of outage probability of RF-UWOC hybrid double hop system with average signal-to-noise ratio under different jitter deviations. Figure 6: The variation curve of outage probability of RF-UWOC hybrid double hop system with average SNR under different transmission distances of UWOC links.
## 3 Results  
### Coupling System Findings  
The results obtained from the coupling system showed that the simulated period was 1000 ms, and data were obtained every 1 ms. The piece of interest is C in the system. The assessed displacement values within the Cartesian mechanism box on the x-, y-, and z-axes during the analyzed interval are shown in Figure 6.  
It can be seen that C moves 63 mm on the \(y\)-axis and 60 mm on the \(x\)-axis. This distance is what the system needs to dissipate the energy caused by the impact so that it later begins to return to its original point. However, it will take more than 1000 ms to reach equilibrium. It should be noted that side impacts are less abrupt than frontal impacts. The 41 mm displacement in the \(z\)-axis is carried out in the negative direction since the system descends due to the exerted force added by gravity. The peak is smoother because the deceleration is not as sudden as in the \(x\)- and \(y\)-axes.  
Figure 5: Frontal impact scenario assembly.  
Figure 6: Displacement of C inside Cartesian mechanism box.  
The coupling response after 100 ms of deceleration is maintained at 0 G. Decelerations are shown during the first 120 ms, as seen in Figure 7.  
The deceleration of \(x\) and \(y\) are similar since the curve in Figure 4 was implemented for both axes. Both axes obtain a maximum deceleration of 23 G, and it remains from 21 G to 17 G for 30 ms. After 65 ms, it decreases from 17 G to 0 G. In the \(z\)-axis, it is observed that it begins at 1 G; the gravity acceleration is set on the axis -\(z\), and the system counteracts the gravitational acceleration in 31 ms, which becomes 0 G.  
The Cartesian mechanism box, spring interconnections, and dampers are the most critical areas in Figure 8.  
Figure 8: _Cont._  
Figure 7: Acceleration of C.  
The resulting acceleration reaches a maximum value of 33 G. The stresses (using [PERSON] failure theory) and strains of each mechanism's parts during the first 120 ms are obtained. The values are plotted every 20 ms of the Cartesian mechanism; this part records the mechanism's highest values of stress and strain (Figure 8).  
The C part has the highest stress with 94 MPa; the stress of the Cartesian mechanism box is 92 Mpa, that of the suspension box is 68 MPa, and the M\({}_{1}\) and M\({}_{2}\) parts have the lowest stress in the first 120 ms. The mechanical properties of the material, knowing that it has an elastic limit of 95 MPa, guarantee that the parts of the mechanism work in the elastic zone since they do not exceed the yield point (Figure 9).
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 3 The SS Method
### The SS Method for Broken Rotor Bars Detection  
Based on Equations (12)-(15), this paper presents a very simple method for BRB diagnosis by calculating the SS of the three-phase current signals, called the SS method. The process of the SS method is shown in Figure 1.  
First, three-phase currents need to be collected simultaneously by a data acquisition system.  
Then, the SS of three-phase current signals needs to be calculated.  
Finally, the FFT analysis of the SS signals is performed. Signature frequency components of BRBs can be sought out easily in the spectrum of the SS signal.
## 3 VLC for Underwater Communication
### Configurations of the Optical Link
#### 3.2.3 Retro-Reflector-Based LOS  
Retroreflector-based UVLC enables duplex communication for low-power, lightweight nodes by reflecting modulated light back to the transceiver, reducing power and size requirements [22]. However, it faces interference and double-channel attenuation. Limitations can be overcome using polarization discrimination and signal amplification [24].