esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Large-core polymer optical fiber (POF) links have limitations in capacity and reach due to the fibers' high modal dispersion and attenuation. Most of these links use red laser diodes, even though the attenuation spectrum of poly(methyl methacrylate) (PMMA), the basic polymer used to manufacture these fibers, has a lower minimum in the green region. Therefore, we set out to explore the potential use of green light in transmission systems, comparing the performances of three step-index polymer optical fibers (SI-POFs) with different numerical apertures. We obtained measurements of intensity distribution, frequency response and bit error rate (BER), as functions of fiber length. We have also compared the fibers' frequency responses with red and green light for a few selected lengths. Our results confirm that SI-POFs attenuate less in response to green light, which can increase their length. This advantage is partially counterbalanced by a slightly higher dispersion that limits the capacity of the high-aperture fibers, particularly at relatively short lengths. Our conclusions are critical to understanding SI-POF behavior and to designing thorough SI-POF models that can aid the design of OPF-based links for different scenarios.  
polymer optical fibers; step-index; SI-POF; green light; 520 nm; optical communications +
Footnote †: journal:
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 2 Technology Advancements  
GNSS-R was proposed in the late 1980s as a multi-static scatterometer [4], and a few years later, in 1993, the interferometric GNSS-R technique (iGNSS-R) [5] was proposed for use in sea altimetry. This technique was based on the cross correlation of the direct and reflected signals and makes use of the large-bandwidth military codes for a high-precision opportunistic altimeter. Three years after that, the most broadly used technique, known as conventional GNSS-R (cGNSS-R), was proposed by [PERSON] and [PERSON] [6]. It was not until 1998 that the GNSS-R concept was demonstrated [1] from an airborne platform. The technology used at that time consisted of a commercial GPS receiver from GEC Plessey semiconductors, which was modified to include an open-loop correlator at different delay lags from the peak point position. In this case, the antenna used was a single left-hand circularly polarized (LHCP) down-looking antenna. Besides iGNSS-R and cGNSS-R, which tend to be used for airborne and spaceborne platforms, a ground-based monitoring technique was also conceived of for remote sensing applications. This is called GNSS interferometric reflectometry (GNSS-IR) [7]. This technique is based on a geodetic GNSS receiver that receives a combination of both the direct and reflected signal, causing an interference pattern in the observed signal which can be used to estimate land-related properties. In this section, the latest technology advances in the last 10 years in the GNSS-R and GNSS-IR field will be covered, including the use of spaceborne and airborne receivers for GNSS-R cases and ground-based receivers for GNSS-IR cases.
## 4 Design Combination of SMC and NESO
### SMC Design  
This paper introduces a method that combines proportional rate reaching with power rate reaching to accelerate the attainment of the sliding surface [18]. Moreover, the NESO is designed to improve convergence performance against mismatched disturbances and reduce steady-state error. This allows for a reduction in the switching gain in SMC, focusing primarily on the speed of reaching the sliding surface, thereby decreasing chattering. The proposed SMC approach is expected to be more effective in increasing the speed of reaching and is detailed below:  
\[\begin{cases}s=-k|s|^{\alpha}\text{sign}(s)-ms\\ 0<\alpha<1,k>0,m>0\end{cases} \tag{36}\]  
where \(\alpha\), \(k\), and \(m\) are the design parameters that satisfy the conditions in the previous equation, while the sliding surface is denoted by \(s\).  
The proposed SMC diagram is shown in Figure 4. The sliding surfaces for each of the six controllers are described below:  
\[\begin{cases}s_{1}=\hat{i}_{dp}-i_{dp}^{*}\\ s_{2}=\hat{i}_{qp}-i_{qp}^{*}\\ s_{3}=\hat{i}_{ds}-i_{ds}^{*}\\ s_{4}=\hat{i}_{qs}-i_{qs}^{*}\\ s_{5}=\hat{\Omega}-\Omega^{*}\\ s_{6}=\hat{i}_{\beta s}-i_{\beta s}^{*}\end{cases} \tag{37}\]  
where \"*\" denotes the reference state, while \"*\" denotes the state observer estimated by the NESO.  
Based on (36) and (37), the laws of discontinuous control for each controller are given below:  
\[\begin{cases}u_{i}=-k_{i}|s_{i}|^{\alpha_{i}}\text{sign}(s_{i})-m_{i}s_{i}\\ i=1,\ldots,6\end{cases} \tag{38}\]  
where \(\alpha_{i}\), \(k_{i}\), and \(m_{i}\) (\(i=1,\ldots,6\)) are positive design parameters.  
With  
\[\begin{array}{l}\hat{f}_{dp}=c_{1}\hat{t}_{dp}+c_{2}\hat{\Omega}_{qp}^{i}, \quad\hat{f}_{qp}=c_{3}\hat{t}_{qp}-c_{4}\hat{\Omega}_{dp}^{i}-c_{5}\hat{\Omega }\\ \hat{f}_{ds}=c_{6}\hat{t}_{ds}+c_{7}\hat{\Omega}_{qs}^{i},\quad\hat{f}_{qs}=c_ {8}\hat{t}_{qs}-c_{9}\hat{\Omega}_{ds}^{i}-c_{10}\hat{\Omega}\\ \hat{f}_{\beta s}=c_{11}\hat{t}_{\beta s}-c_{12}\hat{\Omega}\cos(3\theta)\end{array}\]  
where \(\hat{d}_{i}(t)\) (\(i=1,\dots,6\)) represents the estimated disturbances of NESO.  
A Lyapunov function can be formulated in the following way:  
\[V=\dfrac{1}{2}(\sum_{i=1}^{6}s_{i}^{2}) \tag{40}\]  
Taking the time derivative of (40) yields  
\[\begin{split}\dot{V}&=\sum_{i=1}^{6}(-\dfrac{k_{i}}{ b_{i}}|s_{i}|^{a_{i}}\text{sign}(s_{i})-\dfrac{m_{i}}{b_{i}}s_{i})s_{i}\\ &=\left(\sum_{i=1}^{6}\left(-A_{i}|s_{i}|^{a_{i}}|s_{i}|-B_{i}s_{i}^ {2}\right)\right)\leq 0\end{split} \tag{41}\]  
where \(A_{i}=\frac{k_{i}}{b_{i}}\), \(B_{i}=\frac{m_{i}}{b_{i}}\). Achieving \(\dot{V}\leq 0\) stability requires satisfying the conditions \(0<a_{i}<1\), \(A_{i}>0\), and \(\dot{B}_{i}>0\).  
Figure 4: Diagram of the proposed SMC.
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 1 Introduction
However, these systems can be costly, with closed systems limiting users' possibilities to customize them. The configurations are already established by the manufacturer and may have limited options to be specialized for users' needs. Therefore, this study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to provide an open system that can be configured according to the user's needs. This research also includes the design and building of an alternative structure for the LiDAR support system, utilizing accessible materials and equipment. An interface between a LiDAR sensor and UAV was created through an integrated system to obtain a georeferenced point cloud, providing an alternative to collecting topographic information through a low-cost system compared to those currently in the market.
## 4 All Dielectric Unit Cell
### Phase Response Control with Air Inclusions  
The proposed cell is made of poly-lactic acid (PLA) (\(\epsilon_{r1}=2.85\) and tan\(\delta=0.0121\) at 40 GHz) [23], acting as a dielectric host of an air gap inclusion [24]. The size of the inclusion controls \(G\); therefore, it controls the \(\epsilon_{r,eff}\) of the cell. The geometry of the dielectric cell, both the host and air gap inclusion, is a square prism of dimensions \(a\times a\times t\) and variable dimensions \(W\times W\times L\), respectively, as Figure 5 depicts. The ratio between the air and dielectric material can be computed as:  
\[G=\frac{W^{2}L}{a^{2}t} \tag{6}\]  
The cell is studied with the electromagnetic software CST Microwave Studio, setting periodic boundary conditions in the \(x\)- and \(y\)-axes and open air in the propagation direction \(z\). The study is carried out for normal incidence using a plane wave with the electric field defined in the \(y\) direction. The air gap dimensions are swept between \(W\in(0,5)\) mm and \(L\in[0,20]\) mm, while the dielectric prism keeps its dimensions fixed at 5 mm \(\times\) 5 mm \(\times\) 20 mm. Applying (5) to this value, \(\epsilon_{r,eff}\) varies from 1.28 to 2.85, which is a full PLA cell.  
The study was carried out at a central frequency of 28 GHz. The amplitude and phase response of the transmission coefficients of the cell are shown in Figure 6 as a function of the air gap dimensions for normal incidence and 28 GHz. The phase response entirely covers a 360\({}^{\circ}\) range, and the transmission losses are lower than 1.5 dB in more than 85% of the cells.  
Figure 5: Sketch of a dielectric prism cell using air gap inclusions to change the filling of the cell and control the dielectric constant of the cell.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 2 Project-Oriented Numerical Algorithm for Impedance of Pile-Group Foundation
### Definition of the Impedance Functions
First, according to the actual situation of the bridge site, a refined finite element analysis model of the cap, pile group, and surrounding soil is established; second, based on the viscous-spring artificial boundary theory, a series of springs and dampers are set at the boundaries of the aforementioned model so as to simulate the influence of the infinite soil; third, a unit resonant force in each direction within the concerned frequency band is applied at the center of the cap, and the corresponding displacement response can then be obtained--namely, the dynamic flexibility matrix; finally, the impedance functions of the pile-group foundation can be obtained in virtue of the inverse properties of the dynamic flexibility matrix and dynamic stiffness matrix.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 1: Combat Search and Rescue
One of the great applications of these technologies in the CSAR scenario is the use of UAVs equipped with advanced hardware, such as IoT sensors and cameras, that are capable of locating and obtaining a first visualization of the real-time terrain to track and find potential survivors on the battlefield.
## 2 Materials and Methods
### Hardware Specifications  
All image processing and network training was performed on a personal computer (PC) running Microsoft Windows 10 (Microsoft Corporation, Redmond, WA, USA), AMD Ryzen 7 1700 central processing unit (CPU) (Advanced Micro Devices Inc., Santa Clara, CA, USA), 16 GB of 3333 MHz DDR4 SDRAM and a Zotac NVIDIA GeForce GTX 1070 AMP Edition graphics processing unit (GPU) with 8 GB of GDDR5 (Zotac International, Hong Kong) (NVIDIA Corporation, Santa Clara, CA, USA).
# Optimization of a Centrifugal Compressor Using the Design of Experiment Technique
## 7 Conclusions  
As described in this article, the geometry of meridional curves of the impeller, the angle distribution of the blade and the location of the main blade and splitter entrances were optimized. At the stage I, five parameters are considered for meridional geometry of impeller. Full Factorial analysis was performed and the most effective parameters were recognized which are the hub curve control points. The best points in this stage shows 2.5% and 7.24% improvement in efficiency and pressure ratio respectively.  
The best point in the previous analysis is used was Stage II as an initial geometry to investigate the effect of the \(\beta\) angle distribution of the blade at hub and shroud by varying four selected parameters. Full Factorial analysis shows that the slopes at starting and at the end points of the fitted Bezier curve at hub are the effective factors. The highest pressure ratio in this stage shows 3.0% improvement and the distribution with the highest efficiency shows 0.34% increase as compared to the baseline geometry.  
Considering the best performance in previous stages as an initial model in stage III, the function \(f\) (Equation (11)) is used to simultaneously consider the effect of efficiency and pressure ratio. Full factorial analysis is performed for four parameters which are the starting point locations of the main blades and splitters.  
Based on previous analysis, six parameters were selected for stage IV and for decreasing the number of runs the Box-Behnken method was used to construct the design space. The efficiency and the pressure ratio for all runs were obtained and second order polynomial functions were used to find the response surfaces. Response surface analysis introduces the best value of six variables to obtain maximum efficiency, maximum pressure ratio and a minimum for \(f\) function. The results were compared to the original impeller and by considering both factors simultaneously, the optimized model shows substantial improvement in both efficiency and pressure ratio at design point condition. A very encouraging result is at different operating conditions the performance has also improved in comparison with the original case rather than be sacrificed due to improved design point performance which is often the case.  
[PERSON] created the models, designed the optimization procedure; Both [PERSON] and [PERSON] analyzed the results and wrote the paper.  
**Funding:** The financial support for this research, from the Shahid Beheshti University G.C. (Grant No. 600/640) is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 20: Optimized and original cases meridional view and the pressure contours comparison at design point.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis
### Data
#### 3.1.5 Model Settings  
The paper compares three models including LSTM, Prophet-LSTM, and Prophet-EEMD-LSTM, validating the performance of the Prophet-CEEMDAN-ARBiLSTM. The parameters of these models are summarized as follows:  
1. Prophet-GRU [32]: The model first employs the Prophet approach to decompose the input series, yielding the trend components, periodic components, and residual terms. Then, GRU is used to learn the temporal information of the above components to obtain the final output. The unit size of GRU is 32. The dropout rate is set as 0.1, and the learning rate is 0.001 in this paper.
2. Prophet-LSTM [33]: Different from the Prophet-GRU, LSTM is used to capture the long- and short-term dependencies from input data. The unit size of LSTM is 32 in this paper. For dropout, the dropout rate is set as 0.1. The learning rate is 0.001.
3. Prophet-EEMD-LSTM [33]: Different from above method, the Prophet-EEMD-LSTM adopts the EEMD to decompose the residual data, producing the multiple Intrinsic Mode Functions (IMFs) and the final residue. Then, the above components are used as the input of the LSTM to produce the final output. The unit size of LSTM is 16. For dropout, the dropout rate is set as 0.1. The learning rate is 0.001.
4. Prophet-CEEMDAN-ARBiLSTM: Different from the Prophet-EEMD-LSTM, the CEEMDAN method promotes the performance of the model to recognize the complex signals, thereby improving the sensitivity to the intricate information. In addition, the designed ARBiLSTM can help the model learn the temporal features influencing the variation in the load sequence and produce more accurate forecasting results. The unit size of BiLSTM in the ARBiLSTM block is 16. For dropout, the dropout rate is set as 0.1. The learning rate is 0.001.
# Autostereoscopic 3D Display System for 3D Medical Images
###### Abstract
From the user's eye position by real-time pupil localization and tracking algorithms, the directional subpixel rendering method [17; 18; 19] not only optimizes pixel resources to reduce crosstalk but also enables real-time rendering based on eye position. Various consumer applications utilize eye tracking-based 3D autostereoscopic display techniques, such as game consoles [20], smartphones [21], tablets [17], HUDs [22], and personal computer monitors [17] including medical image diagnosis displays [23]. In previous studies, we published several attractive applications of eye tracking-based autostereoscopic 3D displays such as personal computer monitors, tablets, and HUDs [17; 24; 25; 26; 27; 28; 29; 30]. In contrast, processes for diagnosing medical images that utilize the 3D depth perception provided by stereoscopic 3D displays remain a rarity and require further research and development. To date, only a few studies have attempted to create 3D medical display systems [31; 32]. In this paper, we interviewed physicians across various medical departments of the Samsung Medical Center (SMC) to ascertain their requirements for autostereoscopic 3D display technology. By analyzing the collected opinion samples, we developed an autostereoscopic medical 3D display system based on eye tracking. We have previously described a preliminary study of glasses-free autostereoscopic 3D display systems in the medical research community and discuss its feasibility as a 3D display for clinical applications [31], validated on a few cases. In this work, we describe an improved autostereoscopic 3D display method based on eye tracking for medical images, which were validated on more various medical images. The proposed system seeks to deliver improved diagnostic accuracy and enable faster diagnoses for physicians handling cardiac CT data (Figure 1) as well as other medical image modalities and applications.
# VFLD: Voxelized Fractal Local Descriptor
## 4 Experimentation
### Robustness against Gaussian Noise  
We carried out an experiment to test the robustness against noise of the VFLD. We independently added Gaussian noise with a range of different standard deviations computed as a function of density \(D\) of the point cloud \(e=D\times F\). Namely, three random numbers were drawn from \(-F\) to \(F\), generating three different \(e\). Each \(e\) was added to the x, y, and z components of the point we intended to add noise to. In this case, \(F\) ranged from 0 to 3 (no noise to three times the density of the cloud). The effect of the Gaussian noise on the point clouds can be seen in Figure 15. The noise was added to the modified point cloud \(PC_{m}\)  
Figure 14: Accuracy (top) and precision–recall (bottom) curves for different densities of the sampling process for ModelNet (leftmost) and Simple Figures (rightmost) datasets.  
and the target point cloud \(PC_{t}\) was kept as it is. To carry out this experiment, the best set of parameters for the VFLD was used, namely, radii starting at 1, radius increment of 0.5, 18 radii, and \(n1 ters=5\). FPFH was tested in order to have a comparison for every noise level.  
The results of this experiment are shown in Figure 16. The performance of both methods--FPFH and VFLD--worsens as the noise level increases. Nonetheless, for the same noise level, FPFH provides lower accuracy than our proposal. Furthermore, the accuracy of VFLD with \(F\) up to 0.1 is even better than FPFH with no noise at all. In addition, it is worth noting that VFLD outperformed FPFH in every test for the same noise level. Given this result, it can be concluded that VFLD performance is affected by the presence of noise but is still more accurate than FPFH. The inclusion of noise alters the normals, negatively affecting the performance of the FPFH. Likewise, the addition of noise transforms areas with a low fractal dimension to a higher fractal dimension, thus affecting the descriptiveness of the VFLD.  
Figure 16: Accuracy (top) and precision–recall (bottom) curves for different noise levels added to ModelNet (leftmost) and Simple Figures (rightmost) datasets.  
Figure 15: Result of applying different Gaussian noise levels \(F=(0,0.5,1,3)\) to a random sample of the ModelNet10 dataset.
## 4 Spatial Variance Analysis and Correction for GTLR-BiSAR  
In this section, we focus our attention on the spatial variance characteristics of GTLR-BiSAR system. The bistatic range-Doppler geolocation functions are introduced first. On this basis, the azimuth variance characteristic in the range-Doppler domain is expounded in detail. Additionally, we propose a novel method to solve the azimuth spatial variance problem effectively. Finally, the range variance characteristic in the RD domain is described in detail. Furthermore, we derive the chirp scaling (CS) function, especially for GTLR-BiSAR system to correct the range variance problem.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 4 Vibration Evaluation Metrics  
### The Weighted RMS Acceleration Value in ISO 2631-1: 1997  
Various methods have been developed to evaluate the ride comfort of the passengers [34]. The most popular method is the ISO 2631-1: 1997 which is proposed by the international organization for standardization (ISO) [35]. This method is the most widely used to evaluate the WBV using the calculation of the weighted RMS acceleration as defined in Equation (1).  
\[a_{rms,weighted}=\left[\sum(W(\omega)\;a_{rms}\;(\omega))^{2}\right]^{0.5}, \tag{1}\]  
where \(a_{rms}\) presents the RMS acceleration at the frequency \(\omega\), and \(W(\omega)\) is the weighting factor which is plotted in a diagram stated in the ISO 2631:1997.  
The value of \(a_{rms,weighted}\) is compared with the level of comfort in the table which is specified in ISO 2631:1997. Table 1 presents the comfort level scales suggested by the ISO 2631 standard.  
On the other hand, ISO 8041-1:2017 is an international standard which is essentially used to assess the human response to vibration through the instrument vibration design. It principally specifies the performance specification for instrument designed to measure the vibration values. Vibration instruments specified in ISO 8041-1:2017 are intended to measure vibration for hand transmitted vibration, whole body vibration or low frequency whole body vibration [36]. The time averaged weighted acceleration value can be expressed mathematically as  
\[a_{w}=\left(\frac{1}{T}\int_{0}^{T}a_{w}^{2}(\delta)\;d\delta\right)^{\frac{1} {2}}\]  
where \(a_{w}(\delta)\) presents the translational or rotational frequency weighted vibration acceleration in a specified direction as a function of instantaneous time; \(a_{w}(\delta)\) is given in meters per second squared (m/s\({}^{2}\)) or radians per second squared (rad/s\({}^{2}\)).