esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Satellite-to-Ground Channel Loss Formulation  
We set the scene as terrestrial user equipment (UE) downloading from a satellite. In this scenario, the satellite can dynamically adjust the coding scheme according to the SNR to increase the throughput. The SNR in our scenario is defined as  
\[\text{SNR}=EIRP_{Sat}+\frac{G}{T}_{UE}-FSL-k-L_{r}-B_{n}-R_{b} \tag{1}\]  
In this equation, Boltzmann's constant \(k\) is fixed, transmitter power \(EIRP\), receiver power \(\frac{G}{T}\), and bandwidth \(B_{n}\) are set by the scenario in the beginning and usually remain unchanged. We assume that the transmission rate \(R_{b}\) is constant to observe the band utilization. Here, only the remaining \(FSL\) and rain loss \(L_{r}\) are changing.  
\[\text{FSL} = 20\log_{10}(\frac{4\pi df}{c}) \tag{2}\]  
FSL is only related to distance and frequency, and frequency changes only slightly, so we consider it fixed. Satellites become increasingly closer to us and then fade away, so FSL initially decreases and increases. According to the rain loss calculation method specified by ITU-R P.618-1, rain loss is mainly associated with average rainfall, the altitude, and latitude of UE, elevation angle, and communication frequency. As the communication frequency is fixed, the UE altitude does not easily change, and the elevation angle of each communication is also the same. Therefore, the latitude has the most significant impact. Generally speaking, low-latitude areas have thicker clouds and more significant annual rainfall, while high-latitude areas have lower annual precipitation. Therefore, the rain loss is usually more significant in low-latitude areas, while the rain loss in high-latitude areas is slight. The margin for conventional rain loss, fixed at 6 dB, thus wastes band resources. If we can accurately predict the SNR of the next moment and select a proper coding scheme, band utilization and the throughput can be significantly improved. Table 1 summarizes the terms and their abbreviations in this paper in a cross-reference table.  
Figure 1: Illustration of varying satellite-to-ground path loss in different areas caused by various weather conditions.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 2 The Planar Circular Photodiode Array (PCPA)
### Measurement Method  
The measurement setup is shown in Figure 2. The system contains beacons \(B^{(1)},B^{(2)},\ldots,\ B^{(K)}\), which are infrared transmitters, each of which having a unique blinking frequency, which allows the separation of the beacon signals in the measurements. The PCPA consists of \(N\) PDs arranged evenly along a cylinder, facing outwards, thus the angle between neighboring channels is \(\Delta\varphi=360/N\). The beacons and the PCPA are assumed to be approximately on the same plane.  
Notice that each PCPA channel measures the additive incoming light intensity, coming from all beacons. The beacons' signals are separated in the frequency domain by the digital signal processing unit [12] and the detected amplitude of each beacon is measured by each channel. Using the measured intensities the bearing \(\varphi^{(k)},\ k=1,2,\ldots,K\) of each beacon \(B^{(k)}\) is determined. A least squares (LS) and a fast frequency domain (FD) method will be introduced in Sections 2.3 and 2.4, respectively.
## 1 Introduction
### The Constants Identification Methods
#### 1.2.2 Numerical Methods
At the beginning, they performed a simple tensile test on a notched specimen and obtained displacement for some specified points in each loading increment via displacement filed optical measurement technique. For the next step, they simulated their experiment numerically and obtained the displacement of a specified point for different values of damage model constants. Then, by defining the objective function Equation (18) and minimizing it with respect to damage model constants \(p\), the optimized values of the constants were determined. \[\phi(p)=\frac{1}{2}\sum_{i=1}^{n_{l}}\sum_{j=1}^{n_{m}}\sum_{k=1}^{3}\Bigl{[} \bigl{(}u_{k}(p)\bigr{)}_{ij}-(u_{k})_{ij}\Bigr{]}^{2}, \tag{18}\]  
where, \(\phi(p)\) is the objective function, \(n_{l}\) is the number of loading steps, \(n_{m}\) is the number of points specified, \(u_{k}\)_(p)_ is the displacement calculated by simulation and \(u_{k}\) is the displacement obtained from experiment.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 2 UAV Tracking System Overview
### Microcontroller Unit  
This is the \"brainbox\" of the UAV tracking system as they fetch and execute instructions through programmable memory. Microcontrollers and embedded controllers come in various bit configuration, such as 4, 8, 32, 64, and 128 bits. The 32-bits microcontroller is primarily designed for automatic control of devices and was recently used in the design of a UAV tracking system [31]. There are different microcontroller types, namely, 8051, PIC, AVR, ARM, Renesas, MSP, Hitachi, and Motorola [32; 33]. The Renesas microcontroller offers attractive features, such as low power, high performance, modest packages, and the largest range of memory sizes combined together with character-rich peripherals [34; 35]. Apart from the fuzzy logic controller (FLC), the three most popular control algorithms are (i) proportional-integral (PI), (ii) linear quadratic gaussian (LQG), and (iii) Hoo UAV. A study on the performance, precision, tracking attributes, and shortcomings has been evaluated [36]. Reference [37] designed, discussed, and implemented a control system consisting of a BLDC motor and a PI controller. Expectedly, the PI is responsible for reducing position error and improving response towards the antennas. Although the PI is the simplest and most reliable algorithm, it suffers from a lack of stringent pointing accuracy and, hence, cannot be used to control large antennas. This limitation can be overcome by the use of a LQG algorithm. LQG is equipped with a shorter response time and turbulence rejection attributes. The LQG can be further enhanced by the use of Hoo. The Hoo algorithm is noted for acceleration limits because of the existing motor drives and gear system. This limitation can be addressed by the use of command pre-processor in software that modifies antenna commands. Reference [38] implemented an antenna control system using a step algorithm method around Hoo control algorithm. Results indicate that the algorithm was able to overcome significant torque disturbances from wind pressures and gusts on the antenna structures, as well as bearing and aerodynamic frictions. Reference [39] presented an overview on the development and application of antenna control systems, while [40] presented performance analysis of PID and LQG control algorithms for antenna position control system.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 2 Data and Methods
### Instruments  
For these experiments, a continuous wave Ka-band (37.5 GHz) Doppler radar was used [23]. It is equipped with a pair of identical conical horn antennas, one for transmission and one for receiving. The transmitted wave has mixed polarization while the receiver splits the incoming wave into vertical and horizontal polarization components. In this hybrid polarization mode, the received wave consists of like- and cross-polarized components. For regular sea surface backscattering, the latter component is much weaker in magnitude than the former component, and our radar measurements can be considered as dual-co-polarized measurements (see also Appendix B in [23]). For the present high wind measurements, with possibly larger volume backscattering from spray clouds, the effect of cross-polarization contamination will be discussed.  
Wind speed was measured by a cup anemometer installed on top of the platform mast. The observed 23 m height wind speed was converted to the standard 10 m height neutral wind speed using the COARE 3.0 bulk parameterization [38]. Sea currents were measured by a propeller current meter installed at 10 m depth. Wave measurements were performed by conductive wire wave gauges, mounted at the vertices of a triangle with 5.31 m, 11.63 m, and 15.48 m sides [39]. Two-dimensional (2-D) directional frequency spectra were estimated using a standard approach, with north-south and east-west wave slopes calculated from instantaneous triangle inclinations and wave elevations taken from one of the wires [40,41].  
Figure 1: Experiment overview: (**a**) Sentinel-1A Synthetic Aperture Radar (SAR) VV image taken at 03:57 UTC on 25 October 2018, (**b**) map of coastal line, water depth, and mountain elevation with schematic explanation of gap (katabatic) wind formation, (**c**) quasi-synchronous photo taken from the platform during Sentinel-1A overpass, (**d**) platform aerial view.
## 5 Discussion
### Affective Feedback Using Projected Avatars  
To limit the robot body to simple features, we combined it with projected avatars, which enhanced its communication capabilities without increasing the complexity of the hardware. For this reason, we evaluated the use of a small projector embedded in the robot body to convey the affective states related to two emotions. In this study, we explored the benefits of using a projector compared to abstract visual elements made by colored lights. The results related to the perception of colored lights patterns were in line with those of a previous study [36]. The participants perceived a happy state when the robot was blinking yellow lights and a sad state with blue lights (Table 2). These results also showed that avatars were more effective conveying affective expressions compared to the feedback made with colored lights. During the study, the robot was not moving or showing any behavior--only visual elements were being displayed. For a robot with limited features and an inexpressive face, there are few options to convey affective expressions visually. Colored lights are commonly used and explored, as they are a simple and effective solution, but the perception of these abstract representation of emotions can be different from one person to the other. Projected avatars with affective expressions have the potential of reducing the user's misinterpretation by providing a clear message.  
Regarding the robot embodiment, the results showed that most of the participants could perceive that the avatar was reflecting the expressions of the physical robot as only one entity. This exploratory study showed that, even though the avatar was displaying the affective expressions, since they were projected from the robot, it was possible for the participants to perceive them as part of the robot's behavior instead of an external agent. Based on these results, we are encouraged to keep exploring the possibility of using images projected from a robot to convey its expressions in a robot-like manner.
## 3 Methodology
### Post-Processing
#### 3.4.3 Grid Size Selection  
During the post-processing phase, handling spatial analysis across 13 stacks presented significant challenges. Each stack consists of datasets containing a variable number of PSCs with irregular spatial distribution, making it difficult to conduct an effective time-series analysis of vertical urban growth directly from these points.  
To address this challenge, we implemented a gridded approach by overlaying grids of different sizes on the study area and aggregating PS points within each grid cell. We systematically tested six different grid sizes: 200, 150, 100, 75, 50, and 20 m, evaluating each based on three criteria:  
* Spatial resolution adequacy: The grid cell's ability to capture distinct buildings and relevant urban features without excessive spatial averaging.
* Ensuring a sufficient number of PSCs fall within most grid cells across the different stacks to allow for robust height aggregation and minimize spatial gaps in the analysis.
* Validation accuracy: Comparing the aggregated height estimates derived from the PSInSAR data (for each tested grid size) against our independent, high-resolution drone-collected building height data within the Hanyang validation zone.  
The evaluation revealed clear trade-offs. Coarser grids (200 m and 150 m) performed poorly on the first criterion, obscuring important details of the urban landscape by merging multiple distinct buildings into single cells. Conversely, the finest grid size (20 m) often failed the second criterion, resulting in many cells with insufficient PSC density for reliable height estimation, creating a sparse and discontinuous representation of the urban fabric.  
Intermediate sizes (100 m, 75 m, 50 m) offered a better compromise between spatial detail and point density. The final selection of the 50 m grid was decisively supported by the validation accuracy assessment (criterion 3). Our comparative analysis showed that the aggregated height values calculated within the 50 m grids exhibited a stronger correlation and lower overall estimation error when contrasted with the high-resolution drone validation data, particularly demonstrating better performance than the 100 m and 75 m grid results.  
Thus, the 50 m grid size was chosen to provide the optimal balance across all three criteria. It maintained sufficient spatial detail, generally ensured statistical reliability with adequate PSCs per cell, and yielded the most accurate height estimations when compared against ground truth data. This 50 m grid resolution enabled the subsequent extraction of maximum building heights per cell and facilitated a more robust and detailed analysis of vertical urban growth dynamics over time in specific areas of the city.
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
## 5 Research Challenges and Future Trends
### Flow Distortion Optimization  
The BLI airflow inlet determines the smoothness of thrust. Excessive flow distortion may cause energy loss and compressor instability, which leads to propulsor malfunction. This problem can be solved by carefully designing the S-duct of intake flow ahead of the compressor. In appropriate circumstances, the use of a straight duct could avoid flow distortion problems more efficiently. It can also be addressed by active or passive flow control with boundary layer excitation to avoid the corresponding separation loss and flow distortion while reducing the adverse effects [72; 88; 89]. Flow control technology can change a fluid's flow. It does this by applying force, mass, heat, or electromagnetic energy to the moving fluid. This alters its force or motion. It can improve the flow distortion in BLI intake flow and shorten its length to reduce the surface area and skin friction drag, reducing the structural weight accordingly.  
Generally, there are four control methods to improve the airflow distortion of the engine inlet, namely, the vortex generator, pulsed jet, synthetic jet, and oscillating surface. These methods are distributed on the inner wall of the intake duct to change boundary layer flow and overall flow in the duct, thereby improving the airflow distortion of the flow before duct compression (refer to Figure 7) [72; 90; 91]. Currently, a control method combines these four physical quantities. It is the cross-flow fan. This method uses electric power and has not been practically studied as a BWB propulsion system. Its uninterrupted propulsion area possesses the potential to cover wake losses. However, the extent of its thrust and energy consumption still needs further improvement. Combinations of these methods are complex and varied.  
The current optimization method for the duct geometry is generally to optimize a duct after screening out the effective scheme for the designed aircraft, such as using an adjoint method to optimize and reduce the flow distortion of an S-duct geometry [92]. However, as mentioned in the previous sections, the results of composite local optimization do not meet the needs of the overall situation of an aircraft. A high fidelity (MACH) MDO framework for aircraft configuration is used in [PERSON] et al. works [49]. They adjusted and optimized the straight duct in an integral TWB (STARC-ABL). The result shows that the optimal distortion for a circumferential duct of STARC-ABL is approximately double that of the duct. These examples are all cases of local duct optimization or straight duct optimization. A framework for targeting the S-duct of an unconventional propulsor (elongated cross-flow fan) of BWB-DP as a large-dimensional MDO objective has not yet been revealed.
## 3 Results
### Through-Range Analysis  
Through-range analysis demonstrated that the jig was moved across velocities ranging from 1\({}^{\circ}\) s\({}^{-1}\) to 148\({}^{\circ}\) s\({}^{-1}\), where the typical lumbar flexion/extension movement velocity is reported as 25-43\({}^{\circ}\) s\({}^{-1}\) and \(\sim\)17-55\({}^{\circ}\) s\({}^{-1}\) ([28], estimated from Figure 3 [30]). Offset was plotted against RMSE for slow (average 49\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 11\({}^{\circ}\) s\({}^{-1}\)) and fast (average 83\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 24\({}^{\circ}\) s\({}^{-1}\)) velocity, demonstrating little difference in the error for the two velocities. Data were fit with a linear regression (R\({}^{2}\) = 0.988; y = 0.3725x + 0.7654 for slow, and R\({}^{2}\) = 0.987; y = 0.3189x + 0.5656 for fast), demonstrating a strong relationship between offset angle and RMSE during the movement phase (Figure 5).  
Therefore, for a sensor mounted with 25\({}^{\circ}\) of tilt and twist, moving at an angular velocity of around 49\({}^{\circ}\) s\({}^{-1}\), an error of 10.1\({}^{\circ}\) can be expected, demonstrating slightly greater predicted error during movement compared to a static predicted error of 7.1\({}^{\circ}\). Predictive modeling estimated a 3.7\({}^{\circ}\) increase in through-range sagittal plane error for every 10\({}^{\circ}\) increase in tilt and twist initial sensor orientation offset.  
To explore the overall effect of velocity on error across all the offsets, velocity was plotted against RMSE (Figure 6), with the mean RMSE values being presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sagittal Plane** & **Equation** & **R\({}^{2}\)** & **Frontal Plane Movement** & **Equation** & **R\({}^{2}\)** \\ \hline
**Tilt Offset** & Y = 0.0695x + 0.3081 & 0.975 &
\begin{tabular}{c} Pitch offset \\ (rot. about Y) \\ \end{tabular} & Y = 0.0250x + 1.1057 & 0.464 \\
**Twist Offset** & Y = 0.0558x + 0.2592 & 0.816 &
\begin{tabular}{c} Twist offset \\ (rot. about X) \\ \end{tabular} & Y = 0.0127x + 0.5408 & 0.362 \\ \hline
**Tilt/Twist Dual** & Y = 0.2514x + 0.7703 & 0.960 & Pitch/Twist offset & Y = 0.1115x + 1.1739 & 0.741 \\ \hline \end{tabular}  
Notes. Rot.: rotation; X: initial orientation angle; Y: error prediction in degrees.  
\end{table}
Table 1: Linear fit equations for both sagittal and frontal plane movements, for each of the offset variations.  
Figure 5: Average through movement RMSE for each time point where angular velocity is greater than 1\({}^{\circ}\) s\({}^{-1}\) across dual offsets of tilt and twist. (**A**) Slow movement trials: 0.5 Hz average 49\({}^{\circ}\) s \({}^{-1}\)\(\pm\) 11\({}^{\circ}\) s\({}^{-1}\). (**B**) Fast movement trials: 1 Hz average 83\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 24\({}^{\circ}\) s\({}^{-1}\).
## 2 Existence of a Stable Multicommodity Flow  
**Theorem 2.1**. : _In every multicommodity network with preferences there exists a stable multicommodity flow._  
Proof. : Let us consider a multicommodity network with preferences, as defined in Section 1.1. Let \(\mathcal{P}\) denote the set of all quasi-paths, and for an arc \(a\in A\) let \(\mathcal{P}_{a}\) be the set of quasi-paths that contain \(a\). Furthermore, for a node \(v\in V\), let \(\mathcal{P}_{v}^{\mathrm{out}}\) and \(\mathcal{P}_{v}^{\mathrm{in}}\) denote the set of quasi-paths that start and end at \(v\), respectively. Let us introduce the variables \(x_{P}^{j}\) (\(j\in[\ell],P\in\mathcal{P}\)) and \(y_{a}^{j}\) (\(j\in[\ell],a\in A\)). Given a set of quasi-paths \(\mathcal{P}^{\prime}\subseteq\mathcal{P}\), we use the notation \(x^{j}(\mathcal{P}^{\prime})\) for \(\sum_{P\in\mathcal{P}^{\prime}}x_{P}^{j}\). These variables do not have a clear meaning in terms of the problem that we want to solve; in order to help understanding their role, we describe the overall structure of the proof. We will define a full-dimensional polyhedron \(\Pi\) in the space of these variables that satisfies the conditions of Theorem 1.2, and has the property that we remain in \(\Pi\) if we decrease a coordinate \(x_{P}^{j}\) or if we increase it to 0, and we remain in \(\Pi\) if we increase a coordinate \(y_{a}^{j}\) or if we decrease it to \(c_{a}^{j}\). In place of [PERSON]'s law, we use the inequalities that at every node in \(V\setminus\{s^{j},t^{j}\}\) the incoming \(x^{j}\) is at most the outgoing \(y^{j}\), and the outgoing \(x^{j}\) is at most the incoming \(y^{j}\). The face  
\[F=\{(x,y)\in\Pi:x_{P}^{j}=0\text{ if $P$ is not a single arc, $x_{a}^{j}=y_{a}^{j}$ for every $a\in A$ and $j\in[\ell]$}\}\]  
corresponds to the set of multicommodity flows. We will prove the following:  
* We can define a suitable colouring of the facets of \(\Pi\) such that any multicoloured vertex of \(\Pi\) is on the face \(F\),
* We show that a multicoloured vertex corresponds to a stable multicommodity flow. This is where the variables \(x_{P}^{j}\) (where \(P\) has length at least 2) play a role: in some sense, they correspond to possibilities of changing a feasible solution along a blocking quasi-path. Let us turn to the details of the proof.
## 3 Current Applications
### Ocular Surface System
#### 3.1.1 Dry Eye Disease
Diabetic patients are known to be a population more prone to experience this complication. The pathogenetic mechanism is multifactorial, such as surgical technique, duration of the procedure, intensity of microscope light, type of lid speculum, and postoperative topical therapy, among others [22; 23]. In a recent randomized controlled double-masked clinical trial, [PERSON] and colleagues studied the effectiveness of LLLT in preventing iatrogenic DED in healthy patients undergoing senile cataract surgery. The study included 153 healthy patients, and 131 of them completed the study. The treatment study was performed using the Eye-light(r)device (Espansione Group S.p.A., Bologna, Italy) one week before and after surgery. The control group received a simulated LLLT session with reduced (<30%) output. Results showed a statistically significant improvement in ocular surface disease index (OSDI) score after LLLT treatment compared to the placebo group at all time points (1 week and 1 month after surgery). Additionally, there was a statistically significant improvement in NIBUT value 1 month postoperatively [24]. Despite the limitations related to the use of OSDI score for cataract patients with impaired vision and the relatively short follow-up period, this study is the first to highlight the potential role of LLLT for the prophylaxis of iatrogenic DED, opening up a new fascinating scenario that requires future research.