esa-sceva/satcom-chunk-collection huggingface dataset:
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 2 System Model
### Satellite Ground Channel Model  
Basic path loss \(PL_{b}\) includes signal free space path loss (\(FSPL\)), clutter loss (CL), and shadow fading (SF). The unit of free space path loss is dB, which satisfies:  
\[FSPL(d,f_{c})=32.45+20\log_{10}(f_{c})+20\log_{10}(d), \tag{8}\]  
where, the tilt distance between the satellite and the ground terminal is \(d\), \(f_{c}\) is the carrier frequency (in GHz) and \(d\) is related to the satellite's orbital altitude, \(\mu\), geocentric angle, and \(\alpha\),communication elevation. The specific relationship is as follows:  
\[d=(R+h)\sin(\mu)/\cos(\alpha), \tag{9}\]  
By solving the equation simultaneously, it can be seen that the relationship between \(d\) and \(\alpha\) is as follows  
\[d=\sqrt{(R\sin\alpha)^{2}+h^{2}+2 Rh}-R\sin(\alpha), \tag{10}\]  
Clutter loss \(CL(\alpha,f_{c})\) simulates signal power attenuation caused by surrounding buildings and ground objects. This depends on the elevation angle, carrier frequency, and specific environment. When the UE is in LOS state, the clutter loss can be ignored and set to 0 dB in the basic path loss model. Shadow fading \(SF\) is a zero mean normal distribution, measured in dB, with a standard deviation of \(\sigma_{SF}^{2}\). The following table provides the sum values of partial elevation angles in different scenarios. The remaining elevation angles can refer to the values corresponding to the nearest reference angles in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(L_{s}\) (dB) & \(a\) & \(b\) & \(\alpha\) \\ \hline \(-20\) & \(2.58\) & \(6.32\) & \(2\) \\ \(-25\) & \(2.88\) & \(6.32\) & \(2\) \\ \(-30\) & \(3.16\) & \(6.32\) & \(2\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Valuable of \(a\), \(b\) and \(\alpha\).
# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 4 Discussion
### Integration with Intelligent Systems  
Our work demonstrates strong potential for implementation in intelligent transportation networks and adaptive traffic signal systems. By integrating ITTC-based risk levels into real-time traffic management platforms, traffic authorities could prioritize signal phases at high-risk intersections or dynamically reroute traffic to mitigate risks. For instance, real-time risk thresholds could trigger warnings in connected vehicle networks, enabling proactive speed adjustments and effectively reducing collision risks. Additionally, the improved IDM and ITTC-based risk assessment framework could be incorporated into adaptive traffic signal control systems, allowing for dynamic adjustments to signal timing based on real-time conflict risk levels. This would enhance traffic flow efficiency. Building on this, the enhanced IDM could also be integrated into autonomous vehicle decision-making processes. By incorporating real-time conflict risk assessments, autonomous vehicles could adjust their driving strategies--such as speed and following distance--to minimize collision risks. This application would require further refinement to account for the unique decision-making logic of autonomous systems, ensuring seamless interaction with other vehicles and infrastructure. Furthermore, the model could serve as a core component of intelligent transportation networks. For example, integrating ITTC predictions with signal optimization algorithms could minimize conflict points during peak hours. Additionally, coupling the model with edge computing infrastructure would enable localized risk assessments, reducing latency and enhancing real-time responsiveness. This integration wouldnot only improve traffic flow but also support the development of adaptive traffic signal systems that dynamically adjust to changing traffic conditions.
## 2 Model Description
### Model Equations
\begin{array}{ccc}\left(\frac{\partial\overline{\mathcal{C}}_{i}^{I}}{ \partial t}\right)&=&-\left(\frac{\partial\overline{w^{\prime}}\overline{ \mathcal{C}}_{i}^{I}}{\partial z}\right)&+&\overline{\mathcal{S}}_{i}\\ \left(\frac{\partial\overline{\mathcal{C}}_{i}}{\partial t}\right)&=&-\left( \frac{\partial\overline{w^{\prime}}\overline{\mathcal{C}}_{i}^{I}}{\partial z }\right)&+&\left(\overline{\mathcal{S}}_{i}+\overline{\mathcal{R}}_{ec}\right) \\ \left(\frac{\partial\overline{\mathcal{S}}_{i}^{I}}{\partial t}\right)&=&- \left(\frac{\partial\overline{w^{\prime}}\overline{\mathcal{C}}_{i}^{I}}{ \partial z}\right)&+&\left(\overline{\mathcal{S}}_{i}+\overline{\mathcal{R}}_{ ec}\right)\\ \mathrm{I}&&\mathrm{II}&&\mathrm{III}\end{array} \]  
where, term I is the local rate of change of the variables, term II is the vertical turbulent flux transport, term III is the source and sink term, and \(i\) is the species \(A\) and \(B\). The source term in term III is \(\overline{\mathcal{S}}_{i}=E/\Delta z\), where \(E(\mathrm{ppb\cdot m\cdot s^{-1}})\) is the emission flux of species \(A\) at the surface level \(\Delta z\), or it is the entrained flux of species \(B\) at the top level \(\Delta z\). The effective reaction rate, \(\overline{R_{e}}_{c}\left(\mathrm{ppb\cdot s^{-1}}\right)\) for the sink term in term III is calculated for the mean phenomenal extents of reaction based on the CSM (see Section 2.3 for details). In this study, the mean concentration of reactive species is calculated using the inert surrogate mean concentration \(\overline{\mathcal{C}}_{i}^{I}\) and the phenomenal extent of reaction \(\overline{\xi}\) instead of using Equation (3): \(\overline{\mathcal{C}}_{i}=\overline{\mathcal{C}}_{i}^{I}-\overline{\xi}\) for reactants and \(\overline{\mathcal{C}}_{i}=\overline{\mathcal{C}}_{i}^{I}+\overline{\xi}\) for products. The governing equations of variables in the TKM are solved by forward finite difference methods (FFDM). For the numerical integration of \(\mu_{i}\) and \(\mu_{j}\), \(\partial\mu_{i}/\partial t=\left(\left.\mu_{i}\right|_{m+1}-\left.\mu_{i} \right|_{m}\right)/\Delta t\) and \(\partial\mu_{j}/\partial t=\left(\left.\mu_{j}\right|_{m+1}-\left.\mu_{j} \right|_{m}\right)/\Delta t\), with the current time step \(m\) and the next time step \(m+1\) and the time difference \(\Delta t\) between \(m\) and \(m+1\).
## 3 Results
### In-Water Irradiance
#### 3.2.1 Spectral Irradiance  
Here, we consider the spectral dependence of planar and scalar irradiances and DNA action-weighted irradiance. The latter is defined as a product of the spectral irradiance and the DNA damage action spectrum [48] (see also [[https://www.temis.nl/uvradiation/product/action.html](https://www.temis.nl/uvradiation/product/action.html)]([https://www.temis.nl/uvradiation/product/action.html](https://www.temis.nl/uvradiation/product/action.html)), accessed 30 March 2022). Figure 3 shows an example of absolute values of the hyper-spectral planar and scalar irradiances at different depths and the corresponding normalized irradiances along with the DNA action spectrum.  
A comparison of the normalized irradiance spectra (Figure 3 right) near the ocean surface and at the depth of 20 m shows how the spectra changed with depth. The most significant reduction of the irradiance occured at shorter wavelengths. As the depth increased, the ocean water mostly cut down the short-wave part of the solar spectrum due to higher absorption at shorter wavelengths.  
Figure 2: (**a**) A comparison of the smoothed \(E_{s}\) measurements from MOBY (solid line) and OMI (symbols) using the Solar Ultraviolet Spectral Irradiance Monitor (plus signs) and TSIS (asterisks) extraterrestrial solar irradiance spectra for a selected OMI pixel. (**b**) The mean difference and standard deviation between the OMI-derived surface irradiance and the MOBY-measured irradiance.  
The DNA-weighted irradiances are shown in Figure 4. It is interesting to note that the spectral maximum of the DNA-weighted irradiances slightly shifted from 303 nm to about 305 nm with depth increasing from 2 m to 20 m. This is explained by the larger absorption of ocean water at shorter wavelengths resulting in the reduction of contribution of shorter wavelengths to the DNA-weighted irradiance. An estimate of the diffuse attenuation coefficient at 305 nm from in situ measurements [49] or satellite observations [16] can provide information on the DNA damage penetration depth.
## 3 Methodology
### MDP Formulation  
MDP is a mathematical model for the sequential decision problem. It is often used to model the stochastic policies and rewards achievable by an agent in an environment that has Markovian properties. HPO can also be formulated as a MDP. Assume that we have \(n\) hyperparameters that need to be optimized, since the agent selects hyperparameters sequentially, each episode has \(n\) steps. According to our task, since the probability of transition is unknown, we define HPO as a MDP with a four-tuple \(<S,A,\gamma,R>\), which is represented as follows:  
* \(S\) is the state space. As mentioned above, the agent selects hyperparameters sequentially, and the state at time \(t\) is the output of the agent at time \(t-1\), i.e, \(s_{t}=\mathcal{N}(s_{t-1})\).
* \(A\) is the action space. This task treats the hyperparameter \(\lambda_{t}\) selected at time \(t\) as an action \(a_{t}\). The hyperparameter \(\lambda_{t}\) is sampled from the distribution \(\mathcal{N}(s_{t})\), which is the output of the agent.
* \(R\) is the reward function. We need to obtain the metrics result as the reward signal on the validation set after selecting all hyperparameters. The reward is only available when the last hyperparameter is selected, and the rest of the time steps use the prediction error as intrinsic reward \(r_{i}\). The agent is updated once at the end of each episode.
* \(\gamma\) is the discount factor.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions
### Spaceborne LiDARs for Terrestrial Applications
#### 5.1.5 ATLAS--Advanced Topographic Laser Altimeter System (ICESat-2)  
ICESat-2, an altimeter instrument, was launched in September 2018 as a high-priority follow-up to the successful ICESat-1 mission. ICESat-2 aims to measure polar ice sheet massbalance, sea ice thickness, and vegetation canopy height to understand climate change and its impacts. Its ATLAS system also captures atmospheric data, improves climate models, and extends existing data records [46; 61; 124]. The system itself can operate in two models: (1) backscattering mode to measure the volume properties of the targets such as clouds and aerosols and (2) sounder mode, where the laser pulse TOF is used to measure surface topography, vegetation mass, sea ice thickness, etc. [46; 61; 124].  
Earth observation accuracy and reliability with ICESat-2 were improved compared to ICESat-1, which had a 70 m footprint and a 170 m spacing. In its first two years, ICESat-2 collected global surface data with a density of 70 cm apart in the along-track direction and a maximum spacing of less than 2 km in the vertical track direction at the equator. As mentioned above, a multibeam approach is used to obtain denser spatial sampling [26].
## 1 Introduction
The latter company, Medin a.s. [8], developed a calcaneal nail for angular stabilization under the commercial name C-NAIL (Figure 5). This calcaneal intraosseous nail is used for the mini-invasive fixation of intraarticular and extraarticular fractures of the heel bone, stabilizing the fragments of the heel bone by the nail in conjunction with seven locking screws; this leads to the formation of angularly stable fixation. To achieve maximum stability, the sustentacular fragment (i.e., part or whole broken sustentacular tail) is fixed into the nail with two locking screws guided by a targeting device [8]. Mini-invasiveness is one of the major advantages of using C-NAIL. A small lateral approach of about 3 cm from  
Figure 4: Lateral incision and plate application during heel bone osteosynthesis (alternative method of treatment). Figure 3: Internal fixator–plate for treatment of heel bone fracture (alternative method of treatment). the apex of the outer ankle towards the base of the 5 th metatarsal is sufficient for fragment repositioning; further, just a few mini-incisions are needed for introducing Kirschner guide wires, the actual nail, and the individual screws. This mini-invasiveness significantly reduces the risk of possible infection. The stability of the implant and, thus, the firm fixation of the fragments constitute additional advantages. The lower age limit for using this treatment is given by the closure of the growth cleft of the child's heel bone, which closes at the age of 16-17 years. The upper age limit is not specified, it depends on the biological condition of the patient. From doctoral experiences: the youngest patient was 17 years old, and the oldest patient was 75 years old. Presently, two types of materials are compatible with human tissue (titanium alloy Ti6 Al4V and surgical stainless steel AISI 316L) are most widely used for osteosynthetic plates and nails [9], and locking screws are also from the same materials. The diameter of the screws is 3.5 mm, but the length varies from 22 mm to 70 mm [8]. Here, the finite element (FE) analysis of a calcaneal nail made of titanium alloy will be performed. A homogeneous isotropic linear elastic material model will be used for the actual analysis. The material constants were determined by tensile testing [10]. The modulus of elasticity E was determined to be 105 666 MPa, and the Poisson's number, which indicates the ratio between longitudinal and transverse elongation, for this titanium is alloy \(\mu_{\text{titanium}}=0.342\), see [11].
# Intersatellite and Downlink Agile Attitude Maneuvers
## 6 Attitude Maneuvering for Intersatellite Connection  
This section considers two satellites placed in two repeating ground-track orbits. When they are mutually visible, the first spacecraft, labeled with S, must point toward the second target satellite, labeled with T. Correct pointing allows establishing an interlink connection for data sharing. Attitude maneuvering is split again in two phases:  
* attitude tracking, to point S toward T when the two spacecraft are visible to each other, and
* attitude reorientation, aimed at gaining the correct pointing direction while waiting for the succeeding trajectory arc where T and S are mutually visible.  
During attitude tracking, a time-varying attitude and angular velocity are pursued. Instead, attitude reorientation represents a slewing maneuver, with zero angular rate and specified attitude, identified by the constant matrix \(R_{c}\). This corresponds to aligning a specified body axis of S with the direction from S to T, as soon as T becomes visible from S. It is straightforward to recognize that the commanded attitude and the angular velocity are discontinuous across the two trajectory arcs.  
Figure 14: Average pointing error \(\bar{\Phi}_{i}[^{\circ}]\) for all simulations of the Monte Carlo campaign.  
Figure 15: Average magnitude of the angular velocity error \(\bar{\omega}_{i}[^{\circ}/\text{s}]\) for all simulations of the Monte Carlo campaign.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\bar{\omega}_{i}[^{\circ}/\text{s}]\) & \(\sigma_{\bar{\omega}_{i}}[^{\circ}/\text{s}]\) & \(\bar{\Phi}_{i}[^{\circ}]\) & \(\sigma_{\bar{\Phi}_{i}}[^{\circ}]\) \\ \hline
1.18\(\cdot\)10\({}^{-3}\) & 8.45\(\cdot\)10\({}^{-4}\) & 1.93\(\cdot\)10\({}^{-1}\) & 1.38\(\cdot\)10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Downlink: numerical results from the Monte Carlo campaign.  
In this context, visibility is related to the line of sight between S and T. In particular, let \(\theta\) be the angle between the two position vectors; \(\theta_{S}\) and \(\theta_{T}\) are the angles at vertex O (cf. Figures 16 and 17). The following relations hold:  
\[\theta=\cos^{-1}\biggl{(}\frac{\vec{r}_{T}\cdot\vec{r}_{S}}{||\vec{r}_{T}||\,|| \vec{r}_{S}||}\biggr{)} \tag{65}\]  
\[\theta_{S}=\cos^{-1}\biggl{(}\frac{r_{\oplus}}{||\vec{r}_{S}||}\biggr{)} \tag{66}\]  
\[\theta_{T}=\cos^{-1}\biggl{(}\frac{r_{\oplus}}{||\vec{r}_{T}||}\biggr{)} \tag{67}\]  
From inspection of Figures 16 and 17 it is apparent that visibility and non-visibility arcs can be distinguished based on these three angles, i.e.,  
\[\theta>\theta_{S}+\theta_{T}\rightarrow\text{non-visibility arc} \tag{68}\]  
\[\theta<\theta_{S}+\theta_{T}\rightarrow\text{visibility arc} \tag{69}\]
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 2 Background
### Communications Systems Enabled by UAVs  
UAV-assisted wireless communication systems can be classified into two groups. The first group is represented by high-altitude, powerful aerial platforms symbolized by satellites in orbits. They can remain for a long time in space thanks to the advantages of orbital resources and solar energy. The second group represents the low-altitude aerial platforms which are symbolized by tethered aerostats/UAVs and relay communication UAVs and are widely used in fourth-generation (4G) and fifth-generation (5G) communications [15].  
Many base stations are required to achieve dense communication coverage in 5G communications, so this would be quite expensive if terrestrial base stations were implemented. Using UAVs, low-altitude air base stations can be implemented, with significant advantages such as reduced costs, increased efficiency, effective coverage improvement, and flexible deployment [3]. Figure 2 shows the different UAV-enabled communication systems.  
However, the challenge to develop communications assisted by low-altitude UAVs has difficulties such as power consumption, interference avoidance, information security, etc. The energy consumption problem is given because batteries of finite capacity power the payload of a UAV and the flight mechanisms. Despite this, the air communication of the base station is expected to be sustainable and allow comprehensive coverage with battery replacement efforts or entire UAVs [17]. As a solution to this problem, trajectory op  
Figure 1: Classification of UAVs [1].  
timization (i.e., the path planning) uses less power and achieves higher mission profitability, usually when power usage cannot be avoided.  
With the evolution of technology and an exponential increase in users in the field of communication, UAVs are being widely used to meet this extraordinary demand required in the area of wireless communications, either for data collection from nodes Internet of Things (IoT) or to increase the range of communication and provide wireless connectivity to users. UAVs operate as base stations in disaster areas where communication systems are damaged [18]. UAVs are also used as a base station in locations and times when traffic flow increases. In the same way, they are widely used as links from UAVs to base stations to record and transmit the readings of an instrument as audio and video (known as radio communication). Another widespread use is the satellite-to-UAV link to carry weather and GPS information (satellite communication). Lastly, the UAV-to-UAV link, where data are shared with other UAVs through wireless communication [15].
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV-DT Based on Developmental Stage
#### 3.1.3 PV Module Power Estimation, Optimization, and Management  
This is perhaps one of the most significant usages of PV DTs as it enables power management at all levels (standalone, hybrid, residential, commercial, and utility-scale installations). There are several industrial applications of this type of system based on fundamental academic research. The authors of [43] implemented a residential management system based on reinforcement learning of energy demand for a hybrid (PV/battery) system, while [39; 44; 45] applied DT concepts, multilayer perceptron neural networks (MLPNNs), and long short-term memory (LSTM) algorithms for power estimation in large scale PV installations. The authors of [46] implemented a DT for energy management in smart islanded solar-based microgrids, while [47] designed a framework using a DT and improved pigeon-inspired optimization algorithm for urban scale solar energy and battery storage system management. The work aimed to ensure effective power production and management. A dynamic digital model of bifacial solar panels was developed in [48]. This model aimed to develop a DT for optimal power extraction from the bifacial module using measured electrical parameters that change continuously based on environmental conditions. The authors of [49] applied DTs to gain insights into the real-time thermal characteristics of vertical PV systems to provide more information on the high-yield gains of vertically installed solar panels, which is gaining traction as a design model for Agri-PV systems. In this work, the appreciable increase in yield was related to an observed increase in the heat transfer coefficient. In [50], a DT model was developed for power estimation and control for a large-scale solar thermal PV system. The work aimed to enable rapid testing (within microseconds) for a large number of reflectors in the design space to estimate the required positioning for accurate power control. Ref. [51] applied numerical methods for developing a DS for overall system modeling and power estimation of a PV pumping system in South Romania.