esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Study Area and Dataset  
The study encompasses the Bode region in Norway (Figure 1). Norway's climate is predominantly maritime, strongly influenced by its latitude and extensive coastline, with the Gulf Stream bringing milder air to its shores. Bode, with its proximity to the sea, experiences a more temperate oceanic climate, which can lead to varied precipitation patterns and influence drought frequency and severity. The Bode region in Norway is characterized by its unique geographical, social, and cultural attributes, which collectively shape the experiences of its inhabitants. Located in Nordland County, Bode serves as a central hub for various services and interactions among diverse populations, including both native Norwegian and immigrants. The region's infrastructure, comprising significant harbors and an airport, facilitates connectivity and access to essential services, thereby influencing the utilization of healthcare and social services in the area [31]. The complex topography of the Bode region further influences local wind patterns. Studies have shown that the interaction between large-scale winds and local terrain can create significant variability in wind speeds and directions. This variability is particularly pronounced in regions with complex topography, such as fjords and mountains, which can channel and amplify wind flows [32,33]. Additionally, the presence of snow and other natural disturbances can modify the wind's impact on the local environment, affecting both vegetation and forest dynamics [34]. Future projections suggest that climate change will continue to alter wind patterns in the Bode region. Recent studies indicate a potential decrease in extreme wind speeds in northern Norway, while southern regions may experience an increase [35]. This shift could have profound implications for local ecosystems, infrastructure, and energy production, particularly as Norway seeks to expand its offshore wind capabilities. Maximum wind speed patterns can be seen in Figure 2. Data used for the study areas are given in Table 1.  
A full-scale meteorological observatory is operated by the Norwegian meteorological office in Bode (station ID: SN82290; variables: temperature, precipitation, snow, and wind). Additional information about the station, based on their station ID, can be obtained from the Norwegian Climate Service Center \"[[https://seklima.met.no/](https://seklima.met.no/)]([https://seklima.met.no/](https://seklima.met.no/)) (accessed on 28 January 2025)\".  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & **Number of Data** & **Initial Data** & **End of Data** & **Mean** & **Min.** & **Max.** & **Standard** & **Skewness** \\ \hline Maximum Monthly & 492 & January & December & 2023 & 17.66 & 9.3 & 29.3 & 3.25 & 1.15 \\ Wind Speed & & 1983 & 2023 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of dataset.
# Remote Sensing Applications in Agricultural, Earth and Environmental Sciences
## 3 Conclusions  
The research presented in this Special Issue accentuates the growing significance of remote sensing in agricultural, earth and environmental sciences. Advances in artificial intelligence, satellite technology, and data fusion have substantially improved the accuracy and efficiency of environmental assessments, thus facilitating informed decision-making in land use planning, resource management, and climate adaptation.  
Future remote sensing research should further explore the integration of machine learning, high-resolution imagery, and cloud-based analytics to address pressing agricultural, earth, and environmental challenges. By utilising these technological innovations, researchers can contribute to sustainable development and resilience against global changes in these fields. As the field progresses, interdisciplinary collaborations will be essential in maximising the potential of remote sensing for scientific discovery and policy implementation.  
R.L., K.P. and S.A.: writing--original draft preparation; R.L., K.P. and S.A.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 7 Orbit Determination
Thus, 12 sigma points are considered, if the state vector is not accounted for. After propagation, the sigma points are merged to get the propagated covariance matrix. The covariance matrix is achieved by the calculation of the deviations of propagated sigma points in comparison to its mean value. Within the measurement update, state vector and covariance matrix are updated using the Kalman gain [43; 48]. A further extension of the KF is the EnKF that was originally proposed for the data filtering within ocean models [51], and then applied to other applications such as geological sciences [52] or weather forecasting [53]. Within the research at the Institute, the EnKF is tested within orbit determination. Similar to the UKF, the EnKF uses sampling points, called ensembles, that approximate the covariance matrix. Certainly, the ensembles are randomly distributed. The ensembles are propagated likewise to achieve the propagated ensemble, whereof the covariance matrix is calculated. Another difference is that the uncertainty of the detection (the measurement noise) is approximated  
Figure 11: Sampled sigma points around the state vector that map the uncertainty (covariance matrix). by sampling points [51; 54], which are used within the measurement update. Finally, the ensemble is merged to achieve the desired state vector and covariance matrix.
# Energy Management Scheme for an EV Smart
## 1 Introduction
The proposed scheme can also incorporate the power references for EV charging/discharging long-term strategies (LTSs) such as peak shaving and load leveling by communicating with the DSO. The modeling and the simulations are conducted using EMTP/ATPDraw [25]. Various simulation scenarios are designed to test the functionality of the EMS, and the proposed power allocation scheme is compared with a battery capacity-based power allocation method [24]. The main contributions of this work can be summarized as follows:  
* An aggregation-based power reference allocation approach for individual EVs has been established by considering their battery size and SoC. * The proposed scheme is studied with different types of EVs. * The EMS calculates instantaneous power references for the EV smart charger and the EV side converters, based on dynamic grid conditions and communication with EVs to prevent off-limit operations. * Equivalent circuit modeling of power electronics converters and the EMS implementation in the electro-magnetic transient program (EMTP-ATPDraw), utilizing a short time step of 1.667 \(\upmu\)s and a 6000-Hz converter switching frequency; show the instantaneous behavior of the scheme. * The EMS can include long-term schemes under the command from the system operator. The rest of the paper is organized as follows: Section 2 discusses modern distribution grids, and Section 3 states the modeling and control of the various components of the modern grid: integrated renewables and EVs. The proposed EMS and their functionalities are explained in Section 4. Section 5 outlines the simulation conditions and the parameters. A demonstration of the results and their analysis are presented in Section 6. Finally, conclusions are drawn in Section 7.
# Improved Solar Operation Control for a Solar Cooling System of an IT Center
## 2 Materials and Methods
### CE Method and CE Controller
With the modified slope parameters \(\rm K^{*}_{4}=K_{Eo}\cdot K_{4}\) and \(\rm K^{*}_{5}=(K_{Eo}\cdot(K_{5}-K_{6})+K_{6})\), the characteristic equations conform to:  
\[\rm Q_{E}=K^{*}_{4}\cdot\rm\Delta\Delta t^{*}, \tag{7}\]  
\[\rm Q_{D}=K^{*}_{5}\cdot\rm\Delta\Delta t^{*}+K_{6}\cdot\rm\Delta\Delta t^{*} _{min}. \tag{8}\]  
In addition, for serial cooling water flow from the absorber to the condenser, the two characteristic temperature differences \(\rm\Delta\Delta t^{*}\) and \(\rm\Delta\Delta t^{*}_{min}\) are linked by the external thrust \(\rm\Delta t_{Ti}=t_{Di}-t_{Ai}\)[12]:  
\[\rm\Delta\Delta t^{*}_{min}=(t_{Di}-t_{Ai})-\rm\Delta\Delta t^{*}. \tag{9}\]  
Thus, with \(\rm K^{**}_{5}=K_{Eo}\cdot(K_{5}-K_{6})\), the characteristic equation for the driving heat can be rewritten as:  
\[\rm Q_{D}=K^{**}_{5}\cdot\rm\Delta\Delta t^{*}+K_{6}\cdot\rm\Delta t_{Ti}. \tag{10}\]  
A comparison of the measured and calculated values for the cooling capacity (i.e., \(\rm Q_{E}\) and \(\rm Q_{E}^{C}\)) and driving heat (i.e., \(\rm Q_{D}\) and \(\rm Q_{D}^{C}\)) is depicted in Figure 4a for the operating period discussed in Section 2.2. The same values are plotted as a function of \(\rm\Delta\Delta t^{*}\) in Figure 4b. Due to the variations in the hot and cooling water flow rate (cf. \(\rm V_{D}\) and \(\rm V_{H}\) in Figure 2b), the slope parameters \(\rm K^{*}_{4}\) and \(\rm K^{**}_{5}\) are not constant. Thus, neither the calculated values nor the measured values of the cooling capacity are displayed as a straight line. For the driving heat, a straight line was not to be expected at all (even at constant flow rates), because the temperature thrust \(\rm\Delta t_{Ti}=t_{Di}-t_{Ai}\) will cause a scatter around the straight line given by \(\rm K^{**}_{5}\) and \(\rm\Delta\Delta t^{*}\). An additional reason for the differences and scatter results from the dynamic operating conditions of the measurements in contrast to the calculated values, which assume steady state conditions by definition in the method. Nevertheless, the agreement of the measured and calculated values seems to be satisfying. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Effective Coefficients (Dimensionless)** & \multicolumn{3}{c}{**Slope Parameters (in kW/K)**} & **Conversion Factor** \\ \hline \(\rm K_{1}\) & \(\rm K_{2}\) & \(\rm K_{3}\) & \(\rm K_{4}\) & \(\rm K_{5}\) & \(\rm K_{6}\) & \(\rm K_{Eo}\) \\ \hline \(\rm 0.07\) & \(\rm-1.02\) & \(\rm-0.09\) & \(\rm 1.20\) & \(\rm 1.45\) & \(\rm 0.15\) & \(\rm 1.23\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristic coefficients of the absorption chiller at the Federal Environment Agency (UBA) in Dessau, Germany.
# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 2 Fire Detection Using Computer Vision on UAVs
### Related Work  
In the recent years, there are significant research projects for fire detection algorithms on a UAV platform, using Computer Vision methods. Firstly, fire detection is a standalone research field. There are a lot of proposed applications and algorithms, to obtain the best accuracy of the early detection of a fire. Images and videos are used for data acquisition. It is proven that when the data are forwarded through a Machine Learning Algorithm, prediction is more accurate than using a bare sensor [9]. These results are getting better using more advanced Artificial Intelligence algorithms. When the proposed fire detection models went deeper, using Convolutional Neural Networks, then the accuracy increased and showed a lot of potential [10]. There are great Computer Vision models, such as Visual Geometry Group (VGG) or GoogleNet with tremendous accuracy in image classification [11; 12]. The various models are continuously improving and obtain better scores on various benchmarks such as the ImageNet Large Scale Visual Recognition Challenge [13]. Moreover, there are literature reviews, focusing on UAV and Computer Vision in different fields, foremost in navigation [14].
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 1 Introduction  
The increasing demand for applications such as virtual and augmented reality, expansive cloud services, and high-definition video streaming has led to a significant need for enhanced capacity in optical and wireless networks [1]. As these technologies evolve, they require higher bandwidth and lower latency, driving continuous improvements in network infrastructure. This poses a substantial challenge for 5G networks, as the existing backhaul infrastructure from previous generations is insufficient to meet the stringent requirements for very low latency and efficient dense traffic management [2]. Currently, optical systems are nearing their theoretical capacity limits due to advancements like probabilistic constellation shaping and forward error correction, which have maximized data transmission efficiency and pushed towards the Shannon limit. Future progress must focus on optimizing the physical layer and enhancing the control layer with more efficient resource allocation and management [3; 4; 5]. Emerging technologies such as Software-Defined Networking (SDN), Elastic Optical Networks (EONs), and self-driving optical networks are pushing performance further. SDN separates network control from hardware, enabling dynamic resource management and reconfiguration. EON allows flexible spectrum allocation based on traffic demands, improving bandwidth efficiency. This approach enables the automation and optimization of the network operations, enhancing overall efficiency and reliability [6].  
Modeling Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical networks, as these components determine transmission bandwidth and significantly impact the Optical Signal-to-Noise Ratio (OSNR) [7]. Although EDFAs are well-established, parameters such as gain, Amplified Spontaneous Emission (ASE), and noise figure can vary significantly with signal attributes like input power and channel frequency, and these variations do not follow simple analytical formulas [8]. Therefore, developing accurate predictive models for EDFA parameters is a promising challenge. Several models for EDFA parameters exist in the literature [7; 9; 10], primarily focusing on gain estimation [11]. Explicit models are cost-effective as they rely on a priori knowledge of the devices but often lack high accuracy. Data-driven models, based on neural networks, offer greater accuracy [12; 13; 14] but are challenging to implement in real-world applications due to the large datasets required for training, which are often unavailable or expensive to obtain.  
This paper proposes a polynomial model for the noise figure of EDFAs, emphasizing high-speed implementation to integrate seamlessly with comprehensive machine-learning EDFA models without significant computational delays. The model is designed for flexibility and portability, allowing users to adjust the training dataset size to balance accuracy and measurement costs.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach  
Our approach is denoted GeoVCDN for geographical vehicular central data networking. Geographical for the geographical routing aspect, and central data networking because of the processing applied to the data and the way to deliver them, as it is explained in this section.
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 3 Protocol and Results
### Test Scenarios and Results
#### 3.3.2 In-Vehicle Experiments with Smartphones  
We have tested several scenarios inside the vehicle, all using the frequency range between 16.7 kHz and 20 kHz but requiring different placements of the smartphones: (i) both smartphones placed on the dashboard, (ii) one smartphone placed on the driver seat and the other one on the passenger seat and (iii) both smartphones inside the door pocket as is shown in the right side of Figure 11. In Table 6 we show the percentage of the correctly received keys and in Table 7 we show the BER for each scenario. When the smartphones were placed in the door pocket we obtained a lower BER, between 0% and 4.83%. We consider that this is due to the good acoustics and short distance between the smartphones, approximately 5 cm. On dashboard the BER is between 0% and 5.92% and on the front seats the BER is between 1.1% and 6.96%.  
In Figure 13 we depict the BER (up) and the percentage of correct keys (down) for in-vehicle experiments with smartphones for each smartphone pair except the Samsung A3-Samsung S7 pair (for which in some cases the exchange does not work). For each pair of smartphones, we have three bars marked with blue when the devices were placed in the door pockets, red for dashboard and yellow when the devices were placed on the front seats. We depict on the abscissa the pair of smartphones as we already specified above.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Door Pockets**} & \multicolumn{3}{c}{**Dashboard**} & \multicolumn{3}{c}{**Front Left-Right**} \\ \hline
**Snd.**Rev.** & **E6** & **S7** & **A3** & **Nexus** & **E6** & **S7** & **A3** & **Nexus** & **E6** & **S7** & **A3** & **Nexus** \\ \hline
**E6** & X & 100\% & 90\% & 100\% & X & 80\% & 70\% & 100\% & X & 60\% & 60\% & 60\% \\ \hline
**S7** & 80\% & X & 70\% & 100\% & 60\% & X & 60\% & 90\% & 50\% & X & NW & 70\% \\ \hline
**A3** & 90\% & 100\% & X & 60\% & 70\% & 80\% & X & 60\% & 50\% & NW & X & 50\% \\ \hline
**Nexus** & 100\% & 100\% & 100\% & X & 70\% & 100\% & 80\% & X & 90\% & 80\% & 80\% & X \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pairing success rate for in-vehicle experiments (8.7–12 KHz range).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Door Pockets**} & \multicolumn{3}{c}{**Dashboard**} & \multicolumn{3}{c}{**Front Left-Right**} \\ \hline
**Snd.**Rev.** & **E6** & **S7** & **A3** & **Nexus** & **E6** & **S7** & **A3** & **Nexus** & **E6** & **S7** & **A3** & **Nexus** \\ \hline
**E6** & X & 0\% & 0.93\% & 0\% & X & 4.29\% & 5.57\% & 0\% & X & 6.47\% & 5.57\% & 5.12\% \\ \hline
**S7** & 1.85\% & X & 4.83\% & 0\% & 5.84\% & X & 5.23\% & 0.53\% & 6.96\% & X & NW & 3.85\% \\ \hline
**A3** & 0.37\% & 0\% & X & 3.83\% & 5.92\% & 1.87\% & X & 4.46\% & 7.43\% & NW & X & 6.72\% \\ \hline
**Nexus** & 0\% & 0\% & 0\% & X & 3.33\% & 0\% & 2.59\% & X & 1.1\% & 1.74\% & 2.31\% & X \\ \hline \hline \end{tabular}
\end{table}
Table 7: BER for in-vehicle experiments (16.7–20 KHz range).
## 2 Characteristics of Scintillation  
_Relation of scintillation to solar progression_  
Sunspot activity serves as an indicator of the Sun's overall magnetic activity, and it follows an approximately 11-year cycle. As shown in Figure 1, the last sunspot number peaked around 2014, and the next peak is expected in 2025.  
_Scintillation severity around the geomagnetic equator and solar equinox_  
Scintillation is known to be more severe \(\pm 20^{\circ}\) around the geomagnetic equator (also known as the equatorial anomaly region) because the Sun's intense rays cause increased ionization in the ionosphere, creating areas with high electron density. This, combined with the Earth's magnetic field and specific electric currents, contributes to the formation of irregularities in the ionospheric plasma [3]. Figure 2 displays a map illustrating the variation in scintillation activity across different geographic locations.  
_Scintillation changes quickly and is mostly visible at sunset_  
Scintillation is a dynamic phenomenon that can change rapidly due to the complex and ever-changing nature of the Earth's ionosphere. The occurrence of scintillation is influenced by various factors including solar activity and geomagnetic conditions. These factors can vary significantly from one day to the next and even within short timeframes. This variability results in rapid and unpredictable changes in scintillation patterns, posing challenges for navigation systems that rely on stable radio signals.  
The period around sunset, characterized by the transition from daylight to darkness, creates optimal conditions for the development of small-scale ionospheric irregularities [4]. Figure 3 shows a 3D graph of S4 values as a function of UTC time over a 30-day period  
Figure 1: Solar cycle sunspot number progression (www.swpc.noaa.gov).  
Figure 2: Variation in scintillation activity across different geographic locations.  
(August 2023). The S4 values show significant scintillation and amplitude fading (above 0.5) from around 20:00 to 1:00 (sunset and post-sunset) local time.  
Figure 4 shows a polar plot of GPS, Galileo, QZSS and GLONASS satellites and their associated S4 values. It illustrates the variability in scintillation intensity and locations between consecutive days. As seen in this figure, on 6 February, there is no scintillation, 7 February shows a slight increase in scintillation, and 8 February experiences a considerable uptick. This figure also highlights that scintillation can shift its location in the sky even from one day to the next.
# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
## 3 Validation of Mobility Models
### Density Wave Phenomenon  
This mobility metric represents the average number of mobile neighbors of a definite mobile node at a specific time instant during the experiment. Density wave phenomenonrepresents the average number of neighbors that belong to each node and that fluctuate during the simulation, where nodes located in specific zones of the simulation area can have many more neighbors than others, i.e., their density changes. [PERSON], [PERSON], and [PERSON] (2016) noticed that, in random waypoint models, the density recurrently fluctuates with time, as depicted in Figure 3a, with a moderate fluctuation between 0 and 11 nodes. However, this issue at most affects the performance of other models ([PERSON] et al., 2016). This feature shows the independent mobility of mobile nodes. According to Figure 3, it is worth remarking that.  
* Entity models show, in Figure 3a,b, the worst density wave behavior when they are limited between 0 and 11 and between 0 and 9 for the RWPMM and the MGMM, respectively. Due to their independent selection of destination positions, nodes move independently without taking into consideration other nodes' mobility decisions, with regard to directions and destinations.
* In Figure 3e, the SLAW oscillates moderately between 0 and 14 with certain moments in which the node has no neighbor due to the probabilistic distribution of nodes.
* However, the correlated RPGMM and NCMM show the best outcomes when considering only the effect of this issue. The output of these models fluctuate in a satisfactory range with fewer time instants without any neighbor, as shown in Figure 3c,d. These sub-figuresrefer to group-based movement where a mobile node always belongs to a specific group with the attached nodes.
* SMOOTH outperforms the other models when considering this property in the range of 8 to 35, as shown in Figure 3f. We can remark that the mobile node had at least eight neighbors during the simulation, which shows the best packet delivery ratio.  
We conclude that most of the validated mobility models suffer from the density wave phenomenon except for SMOOTH and the correlated models. The _NCMM shows the best robustness_ against this problem given that it offers a wide neighbor range of [0 to 49]. This is due to its method of operation of  
Figure 3: The density wave phenomenon. (**a**) The RWPMM; (**b**) the MGMM; (**c**) the RPGMM; (**d**) the NCMM; (**e**) SLAW; (**f**) SMOOTH.  
assigning one leader to all mobile nodes. The packet delivery rate of a given experimental simulation is noticeably affected in the presence of this issue [35]. To avoid this mobility flaw, mobile nodes must adopt a mobility strategy that allows them to move in a group with several nodes or they must be uniformly distributed within the simulation region. In addition, in order to ensure having at least one neighbor, they must move at the same speed when following rotational movements. This condition is not met by all mobile nodes.