esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 3 Mobile Channel Simulations by IMT-2020 Channel Model
### Simulation Generation Procedures
According to many measurements [23; 24], cluster characteristics evolve smoothly. Considering this, parts of steps of the original procedures must be updated. In the IMT-2020 channel model, there are two optional methods, SC-I and SC-II. In this paper, SC-I was selected. The initial states of cluster delay, power, and angles were generated based on the steps of the primary module. Then, at moment \(t_{k}\), the delay of the \(n\)th cluster is given as  
\[\tilde{\tau}_{n}(t_{k})=\tilde{\tau}_{n}(t_{k-1})-\frac{\hat{r}_{rx,n}(t_{k-1 })^{T}\tilde{v}(t_{k-1})}{\tilde{c}}\Delta t \tag{17}\]\[\tau_{n}(t_{k})=\tilde{\tau}_{n}(t_{k})-min\{\tilde{\tau}_{n}(t_{k})\} \tag{18}\]  
where \(\hat{\tau}_{rx,n}(t_{k-1})\) is the spherical unit vector that consists of \(\theta_{n,EoA}(t_{k-1})\), and \(\phi_{n,AoA}(t_{k-1})\)  
\[\hat{\tau}_{RX,n}(t_{k-1})= \begin{bmatrix}sin(\theta_{n,EoA}(t_{k-1}))cos(\phi_ {n,AoA}(t_{k-1}))\\ sin(\theta_{n,EoA}(t_{k-1}))sin(\phi_{n,AoA}(t_{k-1}))\\ cos(\theta_{n,EoA}(t_{k-1}))\end{bmatrix}  \tag{19}\]  
\(\tilde{v}(t_{k-1})\) is the velocity of Rx in 3D, which is given as  
\[\tilde{v}(t_{k-1})=[V_{X}(t_{k-1})\quad V_{Y}(t_{k-1})\quad V_{Z}(t_{k-1})]^{T} \tag{20}\]  
There are no more updates to cluster-power generation. This changes by updating delays. With regard to angle generation, the updated AoD and EoD formulations are given as  
\[\phi_{n,AoD}(t_{k})=\phi_{n,AoD}(t_{k-1})+\frac{\tilde{v}^{\prime}(t_{k-1}^{T} )\cdot\alpha}{r_{n,3D}^{\prime}(t_{k-1})}\frac{180}{\pi}\Delta t \tag{21}\]  
\[\theta_{n,EoD}(t_{k})=\theta_{n,EoD}(t_{k-1})+\frac{\tilde{v}^{\prime}(t_{k-1 }^{T})\cdot\beta}{r_{n,3D}(t_{k-1})}\frac{180}{\pi}\Delta t \tag{22}\]  
where \(\alpha\), \(\beta\), \(\tilde{v}^{\prime}(t_{k-1})\), and \(r_{n,3D}^{\prime}(t_{k-1})\) are  
\[\alpha= \begin{bmatrix}-sin(\phi_{n,AoD}(t_{k-1}))\\ cos(\phi_{n,AoD}(t_{k-1}))\\ 0\end{bmatrix}  \tag{23}\]  
\[\beta= \begin{bmatrix}cos(\phi_{n,AoD}(t_{k-1}))cos(\theta_{n,EoD}(t_{k-1}))\\ cos(\theta_{n,EoD}(t_{k-1}))sin(\phi_{n,AoD}(t_{k-1}))\\ -sin(\theta_{n,EoD}(t_{k-1}))\end{bmatrix}  \tag{24}\]  
\[\tilde{v}^{\prime}(t_{k-1})=[V_{X}^{\prime}(t_{k-1})\quad V_{Y}^{\prime}(t_{k -1})\quad V_{Z}^{\prime}(t_{k-1})]^{T} \tag{25}\]  
\[r_{n,3D}^{\prime}(t_{k-1})=r_{n,3D}(t_{k-1})sin(\theta_{n,EoD}(t_{k-1})) \tag{26}\]  
More details of other angles' generation can be found in Reference [3]. After updating all this cluster information, the channel coefficient can be formed with Equation (16).
###### Abstract  
A Light-Emitting Diode (LED) has a nonlinear characteristic, and it contains fundamental limitations for the performance of Visible Light Communication (VLC) systems in indoor environments when using intensity modulation with Orthogonal Frequency Division Multiplexing (OFDM). In this paper, we investigate this nonlinear characteristic with analysis and proposal. At first, we identified the LED nonlinear characteristics in terms of bit-error performances. After analysis, we propose initial predistortion schemes to mitigate the nonlinearity matters. In the predistortion schemes, the nonlinear distortion compensation model contains predistortion features with the LED inverse characteristics. Considering a Direct-Current-biased Optical OFDM (DCO-OFDM) system, we compared the Bit-Error Rate (BER) performances with and without compensation via simulations. The performance on the LED with the compensation showed LED nonlinearity could significantly improve the bit-error performance. In addition, with consideration that the predistortion model is insufficient to represent LED distortion, we investigated possible opportunities of distortion correction using Bidirectional Long Short-Term Memory (BLSTM), one of the leading deep learning approaches. Its result showed promising improvement of the distortion compensation as well.  
n  
onlinearity; VLC; LED; predistortion; coefficient approximation; BLSTM
# A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind
## 1 Introduction  
The number of visually impaired and blind (VIB) people has been increasing at an alarming rate with the World Health Organization (WHO) highlighting this concern. For the next three decades it is projected that individuals with moderate to severe visual impairment will rise to more than 550 million people up from approximately 200 million individuals in 2020 [1, 2]. There are numerous causes for the increase in visual impairment and blindness, which can be attributed from different sources or instances such as genetics, accidents, diseases, or the trending aging population from both developing and developed countries. The level of severity for visual impairment and blindness varies among genders, and an individual's and their country's economic income contributes to how they respond to such a crisis, whether it be through access to the latest research in vision correction or medication that could prevent or reduce the worst case scenarios of this disability beforehand [3, 4].  
Visually impaired and blind people are met with difficulties in their daily lives as they lack the ability to perceive visual information. This limits their capability to process their surroundings and interact with society, hindering their day-to-day activities, which decreases their quality of life (QoL) [5]; thereby, VIB people are in most need of assistance for their mobility constraints, which requires the adaptation of assistive devices and accessible infrastructure [6]. The evolution of assistive technologies for VIB people has gradually increased over the years, and electronic travel aids for the blind have been developed by researchers globally in a response to address their complications. However, using thecurrent assistive technologies comes with challenges, as the flexibility and the portability aspect remains as an issue due to hardware and usability limitations [7]. Traditional assistive devices such as eyeglasses, tactile symbols, magnifiers, walking canes, etc., are used by the visually impaired and blind as it allows them to get by with basic daily tasks. However, these devices can be further expanded by exploring and applying smart sensing technologies [8].  
In this paper, the researchers developed an application that caters to both cognitive and spatial awareness using an embedded 3D depth camera on a smartphone, which analyses depth images to process and calculate distances for obstacle and object detection. An inclusive user interface is developed for VIB people to navigate with ease by using both gesture and a voice command feature.
# On Hyperbolic Complex Numbers
## Appendix A Proof of Remark 1  
For completeness, we give here an elementary proof of the well known result stated in Remark 1. It follows from (24) and the definitions of the hyperbolic functions that \(x^{2}-y^{2}=r^{2}\). This proves the first statement in (8). The matrix  
\[O(\varphi)=\left(\begin{array}{cc}\cos\varphi&-\sin\varphi\\ \sin\varphi&\cos\varphi\end{array}\right)\]  
describes an orthogonal counterclockwise rotation by angle \(\varphi\) around the origin of \(\mathbb{R}^{2}\). Let  
\[P^{\prime}=\left(\begin{array}{c}x^{\prime}\\ y^{\prime}\end{array}\right)=O(\frac{\pi}{4})\left(\begin{array}{c}x\\ y\end{array}\right)=\frac{1}{\sqrt{2}}\left(\begin{array}{c}x-y\\ x+y\end{array}\right),\]  
\[A^{\prime}=O(\frac{\pi}{4})\left(\begin{array}{c}r\\ 0\end{array}\right)=\frac{r}{\sqrt{2}}\left(\begin{array}{c}1\\ 1\end{array}\right)\]  
and  
\[O(\frac{\pi}{4})C_{h}(r)=\{\frac{1}{\sqrt{2}}\left(\begin{array}{c}x-y\\ x+y\end{array}\right):(x-y)(x+y)=r^{2}\}=\{\left(\begin{array}{c}x^{\prime} \\ y^{\prime}\end{array}\right):\,2x^{\prime}y^{\prime}=r^{2}\},\]  
see Figure 11.  
We denote the projection points of \(P^{\prime}\) and \(A^{\prime}\) on the \(x^{\prime}\)-axis with \(Q\) and \(E\) and accordingly those on the \(y^{\prime}\)-axis with \(K\) and \(F\). The distances from these points to the point \(\circ=(0,0)^{T}\) are denoted \(|Q|,|E|,|K|\) and \(|F|\), respectively. Let \((\xi,\eta)^{T}=\frac{1}{r}(x,y)^{T}\). Then,  
\[|Q|=\frac{r}{\sqrt{2}}(\xi-\eta),\,|E|=\frac{r}{\sqrt{2}}=|F|,\,|K|=\frac{r}{ \sqrt{2}}(\xi+\eta).\]  
The area contents of the triangles \(\circ KP^{\prime}\) and \(\circ FA^{\prime}\) are  
\[|\circ KP^{\prime}|=\frac{r^{2}}{4}=|\circ FA^{\prime}|. \tag{11}\]  
The hyperbolic trapezoids \(\circ KP^{\prime}A^{\prime}\) and \(\circ P^{\prime}A^{\prime}E\) allow the disjoint decompositions  
\[\circ KP^{\prime}A^{\prime}\doteq\circ KP^{\prime}\cup\circ P^{\prime}A^{ \prime}\doteq\circ FA^{\prime}\cup FKP^{\prime}A^{\prime} \tag{12}\]such that  
\[\omega P^{\prime}A^{\prime}E\doteq\omega P^{\prime}A^{\prime}\cup\omega A^{ \prime}E\doteq\omega P^{\prime}Q\cup QP^{\prime}A^{\prime}E.\] (A3)  
It follows from (A1) and (A2) that the area contents of the hyperbolic sector \(\omega P^{\prime}A^{\prime}\) and the hyperbolic trapeze \(FKP^{\prime}A^{\prime}\) satisfy  
\[|\omega P^{\prime}A^{\prime}|=|FKP^{\prime}A^{\prime}|.\] (A4)  
Notice that  
\[|FKP^{\prime}A^{\prime}|=\int\limits_{|F|}^{|K|}x^{\prime}(y^{\prime})dy^{ \prime}=\int\limits_{|F|}^{|K|}\frac{r^{2}}{2y^{\prime}}dy^{\prime}=\frac{r^{2 }}{2}\ln(\xi+\eta).\] (A5)  
It follows from (A4) and (A5) that  
\[2|\omega P^{\prime}A^{\prime}|=\kappa\cdot r^{2}\] (A6)  
with  
\[\kappa=\ln(\xi+\eta).\] (A7)  
One can show in an analogous way that  
\[\kappa=\ln\frac{1}{\xi-\eta}\]  
also holds. It follows from the latter equation and (A7) that  
\[\kappa=\ln\sqrt{\frac{x+y}{x-y}}\]  
and  
\[x=r\cosh\kappa,y=r\sinh\kappa.\]  
This proves the second equation in (8), thus Remark 1 is true with \(\psi=\kappa\).
## 4 Biomedical Applications
Magnetite@silica nanoparticles proved to be biocompatible and excellent contrast agents in imaging studies through cellular magnetic resonance, and magnetite coated with silica showed great applicability for HER2 in targeting breast tumours. Magnetite coated with silica can be used in radiofrequency-induced heating techniques, as well as for the treatment of breast cancer [5; 68]. Figure 4: Graphical representation of biomedical applications. Another interesting study that researched [PERSON]'s disease investigated a new technique involving enzymes immobilized in magnetic microspheres through electrospray mass spectrometry (ESI-MS). To explain the model for immobilization of the acetylcholinesterasecholinesterase (AChE) enzyme on the modified 3-glycospropyltrimethoxysilane (GLYMO) silica, the role of acetylcholinesteraseinesterase (AChE) in the nervous system is demonstrated. The role of the enzyme was to transfer impulses to cholinergic synapses through an active neurotransmitter (acetylcholine) to inactive choline. It has been demonstrated that a reduction in AChE inhibition activity improves Alzheimer's treatment, leading to a slower progression of the disease [144]. Because of the properties of magnetic nanoparticles and their biocompatible character, they were used intensively in biomedical applications. By exposing them only to a magnetic field, magnetic nanoparticles are guided directly towards the tumor tissue, allowing nanomaterials to accumulate in the tumor. Because of the superparamagnetic and ferromagnetic properties, magnetic nanoparticles are used in MRI and hyperthermia techniques [145]. [PERSON] et al. [146] have synthesized, through a simple, traditional, and inverted co-precipitation method, highly suitable and promising magnetic nanosystems for diagnosis and therapeutic applications. One study revealed that the obtained nanomagnetic particles with supermagnetic properties, having a saturation magnetization of 88.1 emu/g and squareness ratio of 0.14, can be used as a contrasting agent in magnetic resonance imaging (MRI) diagnosis [147]. For using magnetite nanoparticles for drug delivery, certain aspects have to be taken into consideration such as saturation magnetization, size, shape, surface charge, drug loading capacity, release rate, profile and factor, biocompatibility, and toxicity. Therefore, when selecting magnetite nanoparticles for drug delivery applications, they should be chosen carefully based on the mechanism of binding with other conjugates or else they can produce toxic effects that could harm the body system, which is not desirable [148].
## 5 Conclusions  
In this study, we have introduced deep learning to extract phase-shift images from single-shot interferograms. A large amount of interferograms and their corresponding phase-shift images were prepared by simulation, and deep neural networks with them as input and output, respectively, were trained by supervised learning. The learned network showed higher performance in phase extraction from unknown interferograms than the Fourier-transform method which is conventional phase extraction method. In addition, even when the interferogram includes closed ring-shaped fringes, the deep learning can extract the phase-shift without decreasing accuracy; in contrast, the Fourier-transform method includes much error. From these results, we can conclude that deep learning is useful for phase extraction from interferograms than the Fourier-transform method. However, we cannot determine the accuracy for arbitrary interferograms, since the performance is evaluated for the cases where the true solutions are known. If the actual experimental data is far from the data used in the supervised learning, the accuracy might be worse. In such cases, we should tune the algorithm to generate the output of supervised learning. Therefore, we leave to be considered about the interferogram generation model that more closely reflects the measurement conditions such as distortion due to the aberration of the interferometer lens, or the phase-shift generation model using some distribution other than Gaussian distribution; however, we cannot determine whether the experimental data is far from the supervised learning data set or not. The next task with high priority will be development of scheme to evaluate the accuracy of the output from the trained network for arbitrary input.  
Conceptualization, D.K.; Methodology, D.K.; Software, D.K., S.T. and N.M.; Validation, D.K and R.U.; Formal analysis, D.K.; Investigation, D.K. and R.U.; Resources, S.T.; Data curation, D.K., S.T. and N.M.; Writing-original draft preparation, D.K. and S.T.; Writing-review and editing, D.K. and S.T.; Visualization, D.K. and S.T.; Supervision, S.T.; Project administration, S.T. Funding acquisition, S.T. This research was funded by Japan Society for the Promotion of Science KAKENHI grant number 18K04158.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Microstructural Characterization
#### 3.1.2 Microstructural Features in the As-Cast Condition  
The as-cast microstructures and representative intermetallic compounds in Al-Si-(Cu)-Mg alloys (Alloy A and Alloy B) are shown in Figure 2 and Table 3. Both alloys exhibited typical eutectic architectures, characterized by a dendritic \(\alpha\)-Al matrix interlaced with a three-dimensional network of eutectic Si along interendritic regions. Several intermetallic phases were also identified coexisting within the matrix.  
In Alloy A, the \(\alpha\)-Al(FeMn)Si phase was uniformly distributed, appearing in globular or irregular morphologies. The EDS analysis at point No. 1 (Fe:Mn = 4.13:6.87 wt.%) confirmed the effective substitution of Mn within the \(\alpha\)-phase. This phase is thermodynamically more stable than \(\beta\)-AlFeSi and plays a beneficial role in maintaining microstructural stability at elevated temperatures by enhancing the load transfer and serving as a crack-bridging structure. The presence of Mn regulates the Fe precipitation behavior, suppresses the formation of brittle \(\beta\)-AlFeSi, and thereby improves the mechanical strength and wear resistance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{6}{c}{**Compositions (wt.\%)**} & \multirow{2}{*}{**Intermetallic**} \\ \cline{2-2} \cline{4-7}  & **Al** & **Si** & **Fe** & **Mg** & **Cu** & **Mn** & **Phase** \\ \hline
1 & 66.84 & 22.16 & 4.13 & & & 6.87 & \(\alpha\)-AlFeMnSi \\ \hline
2 & 54.31 & & 19.21 & & 26.48 & & AlCuFe \\ \hline
3 & 62.88 & 16.38 & 20.74 & & & \(\beta\)-Al\({}_{5}\)FeSi \\ \hline
4 & 4.21 & 36.27 & & 59.52 & & Mg\({}_{2}\)Si \\ \hline
5 & 67.03 & & & 32.97 & & Al\({}_{2}\)Cu \\ \hline \hline \end{tabular}
\end{table}
Table 3: EDS point analysis results of representative intermetallic phases identified in the as-cast microstructures.  
Figure 2: SEM micrographs and EDS elemental maps of (**a**) Alloy A and (**b**) Alloy B in the as-cast condition.  
In contrast, Alloy B exhibited a widespread presence of lamellar and clustered Mg\({}_{2}\)Si precipitates, resulting from the supersaturation of Mg beyond its solubility limit. The EDS analysis at point No. 6 (Mg: 32.97 wt.%, Si: 36.27 wt.%) confirmed a stoichiometric composition representative of Mg\({}_{2}\)Si. These precipitates formed sharp interfaces with the surrounding matrix, likely serving as preferential sites for stress concentration. Additionally, intermetallic compounds containing both Fe and Mg were detected in localized regions, suggesting the formation of Fe-Mg complex phases or the partial incorporation of Mg into the \(\beta\)-AlFeSi structure. These microstructural features indicate that Alloy B possesses a higher degree of heterogeneity and localized softening potential compared to Alloy A, particularly under high-temperature conditions.
# Multifunction RF Systems for Naval Platforms
## 2 Requirements for Naval RF Functions
### EA Function Requirements
With coherent jamming, an EA technique will generally be successful to some extent if the jammer signal power arriving at the threat radar antenna is higher than that of the radar returns from the ship target. It is seen in Table 1 that the EIRP requirement for the EA function is significantly less than the corresponding radar subfunction values, where these values can be considered to be representative of threat radar systems as well. However, the EA function enjoys a relative signal power advantage that is proportional to \(r^{2}\), where \(r\) is range to the threat radar, because of the fact that the jamming signal only suffers one-way geometric propagation losses proportional to \(r^{2}\) along the path from the ship to the threat radar, as opposed to the two-way geometric propagation losses proportional to \(r^{4}\) that are experienced by the threat radar returns from the ship. Consequently the jamming signal arriving at the threat radar antenna can be significantly stronger than the ship's signature even though the EIRP value for the EA function may be less than that of the threat radar. There is no independent beamwidth requirement for the EA function. The beamwidth is simply determined by the AESA size that provides the transmit gain needed to meet the EIRP requirement. When the EA function is active, the duty cycle of its transmission may be up to 100%, as would be the case for example when AGPO is being attempted against a threat radar that is illuminating the ship with a CW waveform for the purposes of providing terminal guidance to a semi-active missile. The EA function cannot tolerate any interruptions while active. The EA function is required to have the capability to transmit on any polarization. This is due to the fact that threat radar or communication systems may operate with linear, vertical or circular polarization, as mentioned in the ES function discussion of Section 2.2. The EA function may obtain the necessary information on the polarization state of the threat system from the list of threat signal attributes determined by the ES function. Alternatively, operation at the correct polarization is ensured if digital samples of the threat signal that have been captured on orthogonal polarizations by the ES function and stored in DRFMs are re-transmitted by the EA function.
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 2 Related Work
In [16], the authors proposed RE-ATTEMPT an extension of M-ATTEMPT protocol to overcome its shortcomings. The M\({}^{2}\)E\({}^{2}\) protocol proposed in [17] is focused on low energy consumption and improved network lifetime. The protocol works by selecting parent nodes that are sensors with comparatively high data rate placed closer to the sink. The first level and second level child nodes are identified, depending on the hierarchy in which they are linked or transmits data. The protocol works in two modes. When at home, a node on the human body is connected to the Home nodes that perform the required routing using single-hop communication. In case of non-availability of Home-Signal, the routing tables are maintained by nodes that were implanted on the human body and use multi-hop for the transmission of packets. To solve thermal problem of WBAN, [PERSON] and [PERSON] proposed a thermal aware routing algorithm [18]. In the proposed algorithm, the packets are transmitted on the basis of assigned priority. The objectives of this work are to maintain the temperature of the sensor nodes, reduce the packet delay, and increase packet arrival ratio. An Adaptive Thermal-Aware Routing (ATAR) protocol is proposed in [19] on the basis of adaptive route diversion, avoiding the heated nodes eliminating the chances of the hotspot node. The multi ring routing approach is applied in this protocol that finds an alternate optimum route. In [20], a secondary base station is selected that helps to reduce the temperature of the neighbor nodes of master base station, as these neighbor nodes will not be a part of the new data routes from the sensing odes to the base station. In this time, the sensor node will only transmit the priority packets to the secondary base stations. Accordingly, the network load reduces by this way, and at this time the nodes have the opportunity to cool down. However, this protocol only works in the network of low and intermediate traffic and it increases the delay during high traffic. Table 1 presents the summary of commendable work that was conducted in previous years when considering thermal and energy aware routing for WBANs.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 4 Results
### Impact of Deformable Mesh and Loss Functions on Image Rectification
Figure 7: Trends of evaluation indexes under different combinations of loss functions and mesh shapes. The chart displays from left to right, with the vertical axis indicating SSIM, PSNR, and FID values, respectively, while the horizontal axis represents different combinations of mesh shapes and loss functions. Higher SSIM and PSNR values indicate better image rectification effects, and a lower FID value suggests superior image rectification performance. An upward arrow in the table indicates that the larger its value, the higher the image quality, and vice versa. sentation efficiency [60]. In contrast, rectangular meshes may require more mesh points to represent the same image area, resulting in larger input dimensions [61]. Although triangular meshes, compared to rectangular meshes, can better adapt to irregular shapes and exhibit greater flexibility in handling complex image structures [62; 63], the special interpolation between triangular meshes and the relationships between neighboring triangles may result in the need for more mesh points to represent the same image area, thus increasing redundancy [64]. Additionally, when dealing with image boundaries, triangular meshes may encounter issues of discontinuity or lack of smoothness at the borders.