esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Remote Sensing Surface Albedo Products
### Evaluated Dataset: C3S SA v1.0 Based on PROBA-V Data
The methodology, which is described in the Algorithm Theoretical Basis Document (ATBD) [35], follows the approach of separating the atmospheric correction, directional reflectance normalization, and albedo determination. First, the top-of-atmosphere (TOA) data are processed to obtain the cloud-free top-of-canopy (TOC) reflectances. Then, the spectral TOC reflectances acquired under different solar-viewing configurations during the synthesis period are normalized by inverting a linear kernel-driven model [42]. The synthesis period is 30 day and a semi-Gaussian weighting function with the maximum weight on the last observation of the period was selected for near real-time production. Then, the spectral albedos are computed using the angular integration of the kernel functions with the retrieved parameters for each pixel. Finally, the broadband albedo is defined as a linear combination of the spectral albedo values in the available spectral channels. The narrow to broadband conversion coefficients are applied both for the black-sky and for the white-sky albedos.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Link
#### 3.2.2 Uplink  
Various services are provided by hybrid beamforming in uplink.
* **Less Complexity with Interference Mitigation**  
For multi-user MIMO communication in uplink, the hybrid beamforming method is used to reduce inter-user interference in which the analog and digital beamforming step are implemented. The Gram-Schmidt technique with low complexity can handle the analog step matrix, and the small dimensional effective channel MMSE can be used to obtain the digital beamforming matrix. Figure 5 depicts the unit cell architecture in which the base station is connected with N quantity of antennas and NRF radio frequency chains such that the quantity of radio frequencies chains is less than number of transmitting antennas. When the number of radio frequency chains and phase shifters are equal, which is known as a fully connected architecture, the base station with a single antenna and user are served simultaneously and highly synchronized. Each phase shifter is connected with an adder and then connected with antenna through the amplifier which enhances the signal strength. This hybrid method has a large performance benefit over the existing ones and is robust enough to tolerate severe inter-user interference with low complexity, having a performance near to fully digital [45].  
* **Scattering Mitigation**  
The angle of arrival of different users varies in propagation pathways when they reach the base stations. The rough or diffuse scattering become significant when the wavelength is small or the frequency band is in mm-wave. Therefore, in multi-user MIMO, the scattering increases significantly. Different users with same angle of arrivals (AoAs) in the propagation pathways may cause user interference. Hybrid beamforming with the Gram-Schmidt method can be used to find the analog beamforming vectors, and in the digital domain the minimal mean square error (MMSE) method depending on the effective channel response is used to minimize the inter-user interference [45].  
* **Detection**  
A high-accuracy codebook is required for an exhaustive search technique, which is time consuming and requires more complexity. The opportunistic hybrid forming (OHBF) technique is used for detection in multi-user MIMO uplink communication. The base station randomly chooses some quantity of users for simultaneous transmission. This technique trains multiple slots and chooses the user group which has the best signal-to-interference-plus-noise ratio (SINR). To enhance the detection process more, a minimum mean square error (MMSE) filter is applied in the next step [46].  
Figure 5: Hybrid architecture with amplifiers [45].
# Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity
## 3 Contributions  
The disastrous aftermath of floods has reinforced the need for complementary access networks for critical recovery communications from the flood-stricken area to unaffected areas [21]. In the event where many conventional base stations spanning several cities are damaged, unlicensed, high-bandwidth wireless access technology is required for complementary post-flood recovery communications, to expedite communications to the flood-stricken area. Connecting first responders and healthcare workers to flood victims and hospitals is an integral part of recovery communications. Several UAV-based FSO strategies have previously been developed for recovery communications between base stations for recovery communications, focusing on downlink scheduling [31], the placement of FSO transceivers [32; 33], divergence angle [34], or hybrid RF/FSO links [35; 36]. An unveiled challenge to UAV-based FSO systems is to maintain a high SNR between the base station and UAV-based FSO relay. This is largely unattainable due to minute but rapid UAV propeller and body movements, which result in swift fluctuations in the beam alignment and water scattering from flood waters. Consequently, this would lead to frequent link failures. To address this issue, spatial mode diversity is proposed using linearly polarized modes for improving the resilience of the system from UAV movements.
## 2 Materials and Methods
### Assumptions  
Below is presented the set of additional assumptions and simplifications considered in this work, but not mentioned earlier.  
The adopted electricity tariffs were based on the 2017 electricity bills of the Endesa \"Tarifa Ahora\" type 3.0A (see Figure 5). The available consumption profiles were considered to be representative for the whole period of study. For the net present cost (_NPC_) calculations, we adopted an annual discount rate of 2% in order to compensate increments on the energy prices. The time horizon of the analysis (\(N\)) was 20 years (expected lifetime of the PV system). The simulation model included the hourly based climatic data of Granada (Spain) obtained from EnergyPlus Weather Data [19] and a data file covering a natural year of energy consumption and power demand (8760 h).  
According to a publication by the NREL (National Renewable Energy Laboratory) [31], the output power of polycrystalline silicon PV panels drops on average 0.5% per year. This means that in a  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Energy Feed-in Tariffs (\(\xi\)/kWh)** \\ \hline
**Exportation Approach** & **Average Energy Exporotation Tariff (\(\xi\)/kWh)** & **\% of Energy Retail Price** \\ \hline Net Metering & 0.096130 & 100\% \\ \hline  & 0.086517 & 90\% \\ \cline{2-3} Net Billing & 0.057678 & 60\% \\ \cline{2-3}  & 0.028839 & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average PV energy surplus exportation tariffs adopted in the simulations.  
timeframe of 20 years, the power output of a PV panel will decrease 9% and if we considered a rated efficiency of 14.7% [32], there is an efficiency drop of 1.3%. These numbers are based on previous work such as those performed by [PERSON] et al. [33]. However, the lack of historical data related to the most recently manufactured PV panels (with improved efficiency rates) and doubts about the non-linearity of the lifespan efficiency degradation rate, led the authors to avoid including this factor in the calculations and adopted an average PV/inverter efficiency value over the lifetime.  
The average efficiency of the PV panels for this case study was 11.7% with a module efficiency of 13.4% and inverter efficiency of 91.5%. Each single-panel occupies a surface of 1.64 m\({}^{2}\) and must be connected in parallel sets of seven panels each (the separate sets are connected in series). The complete list of the PV specifications and the main electrical specifications used to configure the PV component (type 190) on the TRNSYS model, according to the calculation method presented by [PERSON] et al. [34], can be found on the manufacturer's website [35].  
For the simulations and optimizations, the PV array slope and azimuth angles were initially considered as 0 degrees, which means that the PV panels were in a horizontal position and faced directly to the south. Due to the characteristics of the case study, no shading simulation effects were considered in this study.
## 5 Emerging Trends in FSO Communication
### Incorporation of Emerging Technologies
#### 5.1.2 Hybrid FSO/mmWave System  
mmWave frequencies (30-300 GHz) offer higher bandwidth compared to traditional RF frequencies, supporting gigabit per second (Gbps) data rates. Similar to FSO, mmWave communication utilizes highly directional beams, which can reduce interference and enhance security. So, combining FSO and mmWave technologies can significantly increase the available bandwidth, enabling ultra-high-speed communication. Also, both FSO and mmWave systems benefit from advanced beam steering and alignment techniques, often driven by AI and ML algorithms, to maintain optimal connectivity. Due to the high frequency, mmWave signals are best suited for short-range applications. Integrating them with FSO can extend the range and improve the reliability of these short-range links. Based on this, the scheme is crucial for the deployment of 5G networks and the development of 6G, providing the high data rates and low latency required for these advanced networks [12]. Also, the hybrid system can be used for high-speed data transfer within data centers and for backhaul connections between network nodes [2].
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
## 2 The Methodology
### Simplification Entropy of Key Points
#### 2.2.2 Profile-Keeping Simplification Entropy  
Profile-keeping simplification entropy is the value that can measure the profile information on the surface. It is generally known that the profile is important for the description of geometric features. The profile of the point cloud can be seen as the boundary around them. Thus, the boundary has to be treated as a special data type during point cloud simplification, and it should be protected and stored in the result set, \(S\). Dense point cloud has a similar vector angle and density values within a small enough area. In the small area, the points with minimum density are considered as the boundary of this area. To improve the algorithm's effect, the minimum angle of the neighbor normal vectors is introduced into the boundary feature extracting algorithm. Taking the \"bunny\" data set as an example, the boundary feature data are retrieved and are shown in Figure 5.  
We found that boundary points that are close to each other have a similar feature description capacity. Thus, the data entropy becomes smaller for close data. Then, the distances among boundary data points can be set as the parameters of profile-keeping simplification entropy. In order to verify this, we created some boundary clusters by applying the Octree boundary clusters, which are shown in Figure 6a. Additionally, the centers of clusters are shown in Figure 6b. These clusters of boundary data are created, and each cluster is color-coded in Figure 6. Each cluster is assigned the same simplification entropy value. Then, the total entropy of the boundary data, which is divided into one cluster, is  
Figure 5: The boundary extracted from the bunny data set: (**a**) the point cloud and the boundary; (**b**) the boundary points from another viewport.  
identical. Moreover, the boundary points in one cluster are different, and they are proportional to the distance from the cluster center.  
The key points which provide the rich boundary features, form the subset of R. Assuming that \(\alpha_{outlier}\) shows the percentages of the subset's size in the full set, \(\alpha_{outlier}\) is the probability of a subset including the profile-keeping feature and needs to be preserved in FPPS. Therefore, the probability of boundary data is in the interval [0, \(\alpha_{outlier}\)). Every cluster is considered to have the same capability for profile-keeping feature description, and the same simplification entropy value. The variable \(N\) is set as the number of clusters. All boundary data in one cluster have the same simplification entropy value, that is, \(1/N\times\alpha_{outlier}\). Then, based on the distance from the cluster center to the point data, we set the weight of profile-keeping simplification entropy for each point in the cluster. The profile-keeping simplification entropy \(H_{p}\) can be defined as follows:  
\[H_{p}=\log(\frac{1}{\alpha_{outlier}\times N}\times d_{i}). \tag{5}\]  
where \(d_{i}\) is the distance from the cluster center to \(p_{i}\).
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 2 Distributed Marine Monitoring System
### aMussel
#### 2.1.3 Sensors  
In line with its primary sensing role, the aMussel is equipped with a variety of sensors capable of monitoring different environmental variables. A sensor for measuring water turbidity and the amount of ambient light is mounted on the aMussel top cap. The top cap also contains a temperature sensor and a pressure sensor used for measuring depth. The aMussel is also equipped with an inertial measurement unit (IMU) located in the top section. The acrylic glass middle section of the aMussel contains a camera with LED lights capable of taking photographs while the aMussel is on the seabed. An additional feature of the aMussel is the electric sense (e-sense) which can serve as a proximity sensor for objects of various materials and shapes, as described in [12]. The swarm contains several units equipped with sensors for detecting dissolved oxygen. While on the surface, the aMussel can determine its position using its GPS system. This system is also used for clock synchronisation.
## 3 The Proposed Clustering-Based Bubble Meshing Method
### Procedures of the Proposed Bubble Method
#### 3.2.3 Procedure 3: Equation of Motion Control  
The motion of the bubbles is controlled by the center and radius in an analysis domain, wherein bubbles ultimately have a smooth dense and reach relatively stabilized position. Two kinds of forces act on the bubbles. One is an interaction force [17] that is non-linear and works within a specified distance of two adjacent bubbles. The other force is the viscous damping force [18] of the system to ensure the bubble system converges to a stable state. The following equation is considered in the bubble method:  
\[m_{i}\frac{d^{2}x_{i}(t)}{dt^{2}}+c_{i}\frac{dx_{i}(t)}{dt}=f_{i}(t)\quad(i=1,2,\cdots,n) \tag{5}\]  
where \(m_{i}\) is the mass, \(c_{i}\) is the viscosity coefficient, \(x_{i}\) is the position of bubble \(i\), and \(f_{i}\) is the resultant force, which depends on the center position and the distance between this bubble and neighboring bubbles. And \(n\) is the number of bubbles.  
For any two neighboring bubbles, the interaction force [32] can be set to:  
\[f(w)=\begin{cases}k(1.25w^{3}-2.375w^{2}+1.125),&0<w<1.5\\ 0,&1.5<w\end{cases} \tag{6}\]  
where \(w\) is denoted the ratio of the real distance and the desired distance of two bubbles \(i\) and \(j\). The desired distance is the sum of radius \(i\) and \(j\). And \(k\) is a constant.  
Euler predictor-correction method is used for solving Equation (5). The current position of the bubbles is the initial position, and the initial velocity is zero. In addition, the surface points are fixed at the process of simulation. Some internal nodes moved outside the domain must be pulled into the domain during simulation.  
The stability [33] for motion control means that the bubbles must eventually reach its equilibrium state. And the selection of physical parameters [34], the non-linear interaction forces [35] and the node constrain decide [17] the stability. The physical parameters [34] based on the original bubble meshing method can make the bubble motion obtain more stability and strike a quick response for bubble system. Viscous damping is introduced to consume motion energy of system and eventually become a static system. The interaction force is small where two bubbles are extremely close. This can prevent the force from increasing infinitely large and ensure the numerical stability of motion control. The force only works when two bubbles are within a specified distance and adjacent to each other. The force used for motion control is not linear so that the gradual distribution of bubbles can also be obtained by the magnitude and the range of the forces. Meanwhile, bubbles can not be allowed to move out the domain and the movement of bubbles must be corrected in each iteration so that all bubbles are exactly located inside the target geometries interior. The result shows the robustness and stability of motion control.
# The Influence of Pillar Displacements on Geodetic Measurements
## 1 Introduction  
We cannot afford to make gross errors in precise geodetic measurements or in defining the coordinates of reference points or points with a size of around 1 mm. Therefore, we try to eliminate or at least reduce systematic errors by comparing instruments, using appropriate measuring methods, taking into account the meteorological conditions in the environment, etc. [1]. However, there are certain sources of errors present that are hard to avoid. Systematic errors include the deflection of the measuring pillar, which can occur due to the variation of the temperature field inside the pillar; especially when the sun shines on one side and the other side is in the shadow. This results in temperature differences inside the pillar body, which are considered as a temperature load and lead to deformations of the pillar. If the pillar is improperly stabilized, i.e., if a small-diameter reference pillar and a dark drain tube with a high absorption factor are used for stabilization (Figure 1), the deformations are considerable and cannot be neglected. Based on calculations [2] and an experiment [3] we have established that a temperature difference of 16.2 \({}^{\circ}\)C between the two sides of a 1.5 m high pillar results in displacements of the screw for forced centering of the instrument of magnitudes around 1 mm, which is a considerable displacement for precise measurements. In the experiment, a ribbed tube was used made of plastic material with an outer diameter of 250 mm and an inner diameter of 217 mm. A welded reinforcement was rolled in the form of a cylinder, installed into the tube, and then the tube was filled with concrete C25/30. The Dewetron system for data capturing and the Agilent VEE Pro application were used to gather data from the thermocouple wire. The measurements are precise up to 0.3 K. For more details and the comparisons among other measurement error sources, the reader is referred to paper [3]. The photograph in Figure 2 shows a measurement we made on site, without waiting for extreme conditions. The photograph shows the temperatures measured on the sunny and shady side of the pillar. In this paper, we discuss how much the displacements of the pillar, which occur as a result of a temperature variation along the pillar body, could affect the control point on the object when the displacement occurs at the orientation point, survey point, or control point. The influence of the displacement is analyzed using a simple example of a measurement performed using the polar method (Figure 3), where we have measured the horizontal direction \(o_{B}\) from reference point \(A\) (survey point) to the second reference point \(B\) (orientation point) as well as the horizontal direction \(o_{C}\) and the distance  
Figure 1: An example of an inappropriately stabilized measurement pillar. Figure 2: Temperatures measured on the sunny and shady side of the measurement pillar. \(d_{AC}\) of the control point \(C\).
## 3 Results and Discussion  
### HE in the Base Metal  
A significant loss of percentage elongation (%EI) was found for the smooth BM samples tested in 100 bar H\({}_{2}\) when compared to 100 bar N\({}_{2}\), as can be seen in Figure 7a. A similar but more pronounced effect was observed in the notched BM samples, which is displayed in Figure 7b. The smooth samples show a reduction in %EI of 25.4 \(\pm\) 7.5% and an \(I\) of 27.9 \(\pm\) 4.6% in a 100 bar H\({}_{2}\) environment compared to 100 bar N\({}_{2}\). The notched BM specimens show a decrease in elongation of 40.4 \(\pm\) 13% and a reduction in %RA an \(I\) of 28 \(\pm\) 4.7%. The pronounced decrease in elongation is an effect of the strain localisation in the notch, while the similar \(I\) values for both types of sample mean the triaxial stress state in the notch does not seem to influence the HE behaviour. For both specimen geometries, the behaviour of the tensile curve is unchanged before the onset of necking. This emphasises the importance of plastic deformation on HE, which increases hydrogen solubility because of an increased density of dislocations and voids [40; 43; 44]. The X60 BM is susceptible to HE at large plastic strains even though it is a tough and ductile steel. The %RA and \(I\) values for all sample sets are shown in Table 3.  
Figure 7: (a) SSRT results from representative samples of smooth base metal samples. (b) Results from representative samples of notched base metal samples.