esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Positioning Results  
### Code Decoding  
To ensure the accurate detection of the optical transmitters and to avoid possible errors due to temporal shadows, reflections, or inaccuracies, the same frame is transmitted twice at different times to correlate the obtained values. This approach involves collecting duplicate samples from the photodiode at distinct times and making two separate measurements of the channel, which results in a total of 400 samples, i.e., 4 samples per point in the plane. The data analysis indicated that the efficiency of the code was approximately 90%. Notice that, exploiting the correlation of the measured values, the efficiency can be increased up to 99%, as shown in Table 5.
# A Machine Learning Method for Modeling Wind Farm Fatigue Load
## 2 Main Framework and Theoretical Knowledge  
### Main Framework  
A typical machine learning process includes data collection, data preprocessing, input and output construction, model training, and testing. The load signal is affected by many aspects, such as wind speed (WS), wind direction (WD), turbulence intensity (TI), axial induction factor (de-rating), and yaw angles (\(\theta\)). The scope of this paper covers wind speed, wind direction, turbulence intensity, and yaw angles.  
The flow chart of the main work is shown in Figure 1. In the beginning, the simulations are executed to obtain the original load data corresponding to the inputs. Then, the function of fatigue modeling is to calculate outputs from the original load data. Further, the GP model is trained by inputs and outputs. Finally, the fatigue load can be predicted by the trained GP model.
## 2 Literature Review
The REQs sorted in the leftmost sub-tree are less important, while REQs on the rightmost sub-tree are more crucial; the root node is not moderate [23]. This method limits the number of comparisons in AHP by dividing the REQs into two sub-trees from every root. Value-oriented prioritization (VOP) technique follows the basic ordinal scale, and assesses requirements regarding their importance in the center production weight [24]. It also manages the weighting and identification of production reservations and the execution costs of each REQ. It builds a prioritization table by utilizing center production values,REQs, and reservations. A few dimensions are not considered, such as the resources (or effort) required for every REQ. This research proposes a specific framework based on integrating the AHP and Grouping models. The AHP and Grouping Models are selected due to the attributes these models possess [25]. AHP is preferred as it is a consistent, proficient technique [26; 27; 28]. Numerical Grouping is the most used practice endorsed in IEEE Standard 830-1998 and RFC [29; 30]. The primary objective of this research is to critically examine the existing methods of requirements prioritization to identify and address their deficiencies. Specifically, this study aims to enrich the existing body of knowledge for requirements prioritization by providing a detailed, comprehensive understanding of the fundamental terminologies and concepts prevalent in this domain. Our in-depth discussion covers a range of prioritization techniques and practices traditionally employed in software development, highlighting their respective dimensions and inherent challenges. A significant gap identified in the literature is the lack of a robust, quantitative approach in current prioritization methods, such as the commonly used grouping method, which merely categorizes requirements, without assigning quantifiable values. Moreover, while the Analytical Hierarchical Process (AHP) is a widely respected method for managing requirements, it struggles with scalability when dealing with large sets of requirements. These findings underscore the need for a more dynamic, scalable approach capable of handling complex, large-scale prioritization scenarios without compromising as to the accuracy or efficacy of the prioritization process. Our framework proposes solutions to these critical gaps, aiming to significantly enhance the precision and applicability of prioritization methods in contemporary software development environments, especially those involving complex systems like IoT applications.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Non-Isolated Topologies
#### 3.1.3 Buck-Boost-Derived Topologies  
Buck-boost-based converters have voltage step-up or step-down capabilities. The MPPT realization using a buck converter and a boost converter has the disadvantage that the efficiency decreases significantly as the difference between MPP voltage and bus voltage increases [44]. To overcome this, [44] proposed a buck-boost regulator (B2R) for application in regulated or unregulated bus systems for satellite EPSs. The topology demonstrated good performance and ensured that the bus voltage followed the maximum power-point PV voltage to maximize efficiency.  
Later, in [87], this topology was modified to the buck-buck-boost regulator (B3R) for regulated bus systems, especially for LEO satellites. The topology was obtained by merging the B2R topology and two-inductor buck topologies. It performs two functions: a step-up function from the PV to the storage unit and a step-down function from the storage unit to the regulated bus. By sharing some passive components for the solar array and bus power regulation, the converter benefits in terms of mass and cost. Moreover, it can be made modular to improve redundancy. In [88], a four-switch buck-boost converter based on GaN and employing an RCC-MPPT controller for deep-space missions was proposed. Although the simulation results of the converter showed high efficiency and power density, experimental validations were not provided. Finally, in [89], an improved bidirectional Weinberg topology was proposed for battery charge/discharge regulation. The topology combines the Weinberg topology [90] and the buck topology, leading to high power density, efficiency, and a non-complex structure. In addition, it also provides bidirectional power flow capabilities and works in buck or boost modes.  
Figure 11 shows the various buck-boost-derived topologies of DC-DC converters.  
Figure 11: Buck-boost-derived topologies: (**a**) B2R converter, (**b**) B3R converter, (**c**) improved Weinberg topology.
# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
## 2 Materials and Methods
### Remote Sensing Data  
This study utilizes remote sensing data from the Guanzhong Plain's wheat-growing regions during four growth stages in 2023. The data include the MODIS GPP product MOD17A2H, LAI product MCD15A3H, reflectance product MCD43A4, and the land cover product MCD12Q1, both of which are directly accessible via the Google Earth Engine (GEE) platform. The MODIS MOD17A2H product provides GPP data at an 8-day temporal resolution and a spatial resolution of 500 m. It estimates the amount of carbon fixed through photosynthesis by vegetation, based on remotely sensed vegetation indices combined with meteorological data. The MODIS MCD15A3H product provides LAI and FPAR at a spatial resolution of 500 m and a temporal resolution of 4 days. Derived from reflectance data using radiative transfer modeling, it delivers essential biophysical parameters that quantify vegetation canopy structure and function. The MODIS MCD43A4 product provides daily surface reflectance data at a spatial resolution of 500 m. It utilizes Bidirectional Reflectance Distribution Function (BRDF) modeling to correct reflectance data for viewing and solar angle effects, significantly enhancing the consistency and reliability of surface reflectance measurements over time. In this study, four remote sensing indices were acquired across the four main growth stages (green-up, jointing, heading-filling, and milk maturity stages) (Table 1). It is important to note that 8-day synthetic NDVI and EVI were calculated from MODIS MCD43A4 data products. LAI, NDVI, and EVI were selected for wheat carbon flux estimation because they comprehensively reflect the physiological characteristics of the crop in terms of key aspects such as canopy structure, growth status, etc. LAI quantifies the canopy structure of the vegetation and photosynthesis capacity, and is closely related to biomass accumulation; NDVI provides comprehensive information on the health of the vegetation and the growth status. By incorporating the blue band to correct for atmospheric scattering, and including coefficients that minimize soil background noise, EVI maintains  
Figure 1: Location of study area.  
sensitivity in high-biomass regions where NDVI typically saturates, thus providing more reliable estimates of canopy structure and function in dense vegetation areas. EVI serves as a valuable supplement to NDVI, offering improved accuracy in vegetation monitoring, especially under conditions of high canopy density or variable atmospheric influences.  
In this study, the MCD12Q1 product was used to extract the land cover type layer for the study area. By applying a cropland mask from the relevant band, remote sensing and climate data were extracted. The product contains 13 bands, and the LC_Type1 band, which corresponds to the International Geosphere-Biosphere Programme (IGBP) classification, was used. Specifically, land cover type 12 (croplands) was selected to mask agricultural areas, enabling the acquisition of remote sensing and climate data specific to farmland in the Guanzhong Plain.
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 6 Conclusions  
An effective firefly resource-optimized routing (FFROR) technique is introduced to control resource utilization and to improve the routing stability during the DP transmission in MANET. The firefly resource optimization (FFRO) algorithm in the FFROR technique generates the initial population of fireflies and calculates the light intensity of all fireflies  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No, Data Packets**} & \multicolumn{5}{c}{**Throughput (\%)**} \\ \cline{2-6}  & **Proposed FFROR** & **EESMR** & **EAER Protocol [1]** & **OEERA Scheme** & **MECOR Protocol** \\  & **Technique** & **Technique**[25] & **EAER Protocol [1]** & **[26]** & **[4]** \\ \hline
10 & 81.23 & 73.61 & 65.31 & 61.22 & 55.32 \\
20 & 83.96 & 75.26 & 66.32 & 62.34 & 57.34 \\
30 & 84.63 & 77.31 & 68.91 & 63.24 & 58.62 \\
40 & 86.01 & 80.11 & 70.23 & 65.87 & 59.11 \\
50 & 87.36 & 82.16 & 72.96 & 67.42 & 60.32 \\
60 & 89.63 & 83.61 & 74.12 & 70.05 & 64.32 \\
70 & 90.26 & 85.02 & 76.34 & 72.46 & 65.23 \\
80 & 91.85 & 86.07 & 78.65 & 73.55 & 66.74 \\
90 & 93.56 & 87.11 & 79.98 & 76.51 & 69.42 \\
100 & 95.78 & 88.63 & 81.36 & 78.16 & 70.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation of throughput.  
Figure 2: Measurement of routing stability.  
based on objective functions. The FFRO algorithm ranks fireflies consistent with light intensity and finds the best resource-optimized mobile nodes to send the DP to the destination node. Lastly, the route path is selected for transmitting the DP using the resource-optimized MN. Thus, the FFRO technique improves the routing stability and throughput. Experimental evaluation and parameter analysis are performed regarding energy consumption, bandwidth availability, routing stability, and throughput. Experimental analysis shows that the FFRO technique can minimize energy consumption by 29% and improves the routing stability by 10% compared to state-of-the-art techniques.  
Conceptualization, C.S.P. and R.P.; methodology, C.S.P. and R.P.; software, C.S.P., M.A.M. and S.K.S.; validation, S.K.S., M.A.M., and S.K.C.S.; formal analysis, writing--original draft preparation, R.P. and C.S.P.; writing--review and editing, S.K.S. and S.K.C.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement Data will be available from the author and will be shared on request. Conflicts of Interest: The authors declare no conflict of interest.
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 2 Methods
### Traditional Tomographic Equation
#### 2.2.2 Constraint Equations  
The tomographic result only utilizing the observation equation is typically undetermined in the inversion process. The source of this undetermined nature is related to SWV paths not penetrating every voxel and that the SWV observations have similar geometry (i.e., from a GPS satellite to a station). To overcome this inversion problem, the mostly widely-used method is to impose constraint information [29].  
In the research area, the water vapor density, \(x_{i}\), within a certain voxel can be represented by the weighted average of its neighbors because of the relatively stable horizontal distribution [30; 31]. The specific horizontal constraint equation is as follows:  
\[w_{1}^{i}x_{1}+w_{2}^{i}x_{2}+\cdots w_{i-1}^{i}x_{i-1}-x_{i}+w_{i+1}^{i}x_{i+ 1}+\cdots+w_{m}^{i}x_{m}=0 \tag{4}\]  
where \(m\) is the total number of voxels in the same layer and \(w\) denotes the horizontal weighting coefficient, which can be computed by the Gaussian weighting function as follows:  
\[w_{i-1}^{i}=\left.d_{i,i-1}\right/\sum_{j=1}^{m}d_{i,j} \tag{5}\]  
Figure 1: Geometric illustration of global positioning system (GPS) signals crossing the tomographic voxel.  
where \(d\) is the distance between the centers of the two corresponding voxels.  
The vertical constraint equation is established using the empirical formula of the water vapor density between two adjacent layers:  
\[x_{j}-p_{j+m}\cdot x_{j+m}=0 \tag{6}\]  
where \(x_{j}\) and \(x_{j+m}\) denote the water vapor density in the voxel, \(j\) and \(j+m\), respectively, and \(p_{j+m}\) is the vertical weighting coefficient, which can be obtained as follows:  
\[p_{j+m}=e^{(h_{j+m}-h_{j})/H} \tag{7}\]  
where \(e\) is the Euler's number and is approximately equal to 2.718, and H is the water vapor scale height, which is set as 1-2 km.
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 5 Engineering Implementation and Validation

Table 2: The experimental results of eccentricity signal detection. Figure 13: The results of practical data by FFT. The red circles indicate estimated frequencies. Then, the roll eccentricity model \(E\) was sent from the L2 computer to the L1 controller, and in the L1 controller, the eccentricity model was reversely compensated to the roll gap control loop. In the experiment, the eccentricity compensation control was put in the first pass of rolling. The thickness deviation of the exporting strip steels before and after the eccentricity compensation control is shown in Figure 15.  
After the eccentricity compensation control was performed, the thickness deviation of the exporting strip steels was reduced by 89.2%, indicating good compensation effects. The experimental results indicated that the proposed method can be used to establish a highly accurate eccentricity model and can meet the requirements of engineering applications.
## 3 Results and Discussion
### Gas Sensing Properties
An increase in the response time was observed for samples prepared with a higher O\({}_{2}\) content in the chamber during the deposition process, with all values ranging between 406 and 638 s for those examined at a 120 \({}^{\circ}\)C operating temperature and 563 to 668 s for samples measured at a temperature of 150 \({}^{\circ}\)C. In turn, the samples deposited with an Ar:O\({}_{2}\) gas ratio 7:1 have the lowest recovery time values for both operating temperatures. The sensor response oscillates around 102.0 to 103.8 for all prepared thin films. A higher sensor response was demonstrated for an operating temperature of 150 \({}^{\circ}\)C for all the prepared thin films. The largest obstacles in hydrogen sensing investigations are the slow response and recovery times, which require a high operating temperature, wh \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ArO\({}_{2}\) Ratio**} & \multicolumn{2}{c}{**Working Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Response Time (s)**} & \multirow{2}{*}{**Recovery Time (s)**} & \multicolumn{1}{c}{**Sensor Response S\({}_{R}\) (\%)**} \\ \hline \multirow{2}{*}{8:1} & 150 & 563 & 449 & 102.9–103.0 \\  & 120 & 406 & 308 & 102.3–102.6 \\ \hline \multirow{2}{*}{7:1} & 150 & 598 & 452 & 102.7–102.8 \\  & 120 & 638 & 217 & 102.0–102.4 \\ \hline \multirow{2}{*}{6:1} & 150 & 668 & 416 & 103.0–103.8 \\  & 120 & 581 & 440 & 102.2–102.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the gas sensing properties. the detection of a low concentration of hydrogen is weak [62]. In our work, a relatively low operating temperature was obtained, which can be considered an achievement.
## 4 Results & Discussion
### Test Instances  
Testing data was taken from a drop box dataset located around the Jakarta area [31], and reverse geolocation is carried out using the Google API to obtain its latitude and longitude. For customer locations, uniform random distribution using python programming is carried out around the drop box locations within a radius of 10 km. For small instances, the number of drop box facilities ranges from 5 to 30 and the number of customers ranges from 6 to 25. Meanwhile, for large agencies, the number of drop box facilities is 92, and the number of customers ranges from 70 to 150. After obtaining the drop box facilities and customers' location, the distance between the drop box facility and the customer is calculated using the haversine formula.  
Furthermore, as previously mentioned regarding common practice in reverse logistics, we also calculate the distance from the customer to the depot using the haversine formula. We assume that the \(R\) rate in the second echelon is also the same as in the first echelon, so to get the cost in common practice we multiply the customer's distance by the rate of Rp3000 per km.
## 3 Results  
### Algorithm Evaluation  
Comparing the results of the algorithms, the findings are summarized in Table 1. The technique that yielded the best results is gradient boosted trees, with XGBoost as the algorithm that performed the best for this problem, while deep learning also produced similar results. The algorithm with the best results was XGBoost, featuring hyperparameters of 500 trees, a learning rate (eta) of 0.05, and a maximum depth which allowed for each tree of 10. XGBoost outperformed other algorithms in the nUBEM framework due to its ability to manage complex, high-dimensional datasets and capture non-linear relationships critical for energy performance modeling. Additionally, XGBoost's efficiency with large datasets  
Figure 3: NRPEC ranges for the energy classification of multi-family buildings in peninsular climates according to Spanish legislation [52].  
and its interpretability through feature importance analysis make it ideal for understanding key drivers of energy performance.
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 4 Solid Rocket Propulsion (SRP) Systems  
### Operating Principle  
A Solid Rocket Propulsion System works on the principle of burning solid propellants and generating thrust by ejecting the gases formed during combustion. Similar to a LP bi-propellant system, an oxidizer is used in the SRP system. However, it differs from a LP system in a couple of ways: first, the solid propellants are stored within the combustion chamber itself; second, sloshing effects seen in LP systems are absent because both fuel and oxidizer are solids. Although SRP systems do not experience sloshing, the lack of control over propellant burn rate creates difficulty for thrust  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Company/Institution with Location** & **Engine** & **Thrust (mN)** & **Ly (s)** & **Propellant** & **Remarks** & **Ref.** \\ \hline AeroteRocketJorg, Sacramento, CA, USA & GPIM Propulsion System & 400-1100 & 235 & AF-M315E & - & [29] \\ \hline AeroteRocketJorg, Sacramento, CA, USA & MPS-120 CHAMPS & 260 & 215 & Hydrazine & - & [30; 34] \\ \hline AeroteRocketJorg, Sacramento, CA, USA & MPS-130 CHAMPS & 1.5 & 240 & AF-M315E & - & [30; 32] \\ \hline ECAPS, Solna, Sweden & HPGP & 1000 & 231-222 & ADN based LMP-100S & flown on PRISMA (180 kg) and SkySat.3 (10.5 kg tank) & [19; 25] \\ \hline Buse, Natick, MA, USA & BGT-X1 & 100 & 214 & AF-M315E & - & [9; 36] \\ \hline Buse, Natick, MA, USA & BGT-X5 & 500 & 220-225 & AF-M315E & - & [9; 37] \\ \hline Tethers Unlimited, Bottell, WA, USA & HYDROS & 250-400 & 256 & Liquid water & - & [9; 38] \\ \hline \end{tabular}
\end{table}
Table 2: Summary of Liquid Propulsion Systems.  
regulation [8]. The schematic of a SRP system is shown in Figure 3 and comprises of a combustion chamber that holds the solid propellant, an igniter that initiates the combustion process and a nozzle.  
Considering that thrust regulation is difficult in SRP systems, burn rate can be used in the initial phase of system design to understand the combustion process because it governs the mass flow rate of hot gases generated during combustion. The burn rate (\(r\)) (shown in Equation (8)) is dependent on the chamber pressure (\(P_{c}\)), temperature coefficient (\(a\)) and combustion index (\(n\)). Temperature coefficient is a non-dimensional empirical constant, while the combustion index describes the influence of chamber pressure on the burn rate. For a propulsion system equipped with a _de Laval_ (CD) nozzle, the characteristic velocity (\(C^{*}\)) (shown in Equation (9)) relates to the efficiency of the combustion process and is independent of nozzle characteristics [8]. The thrust, specific impulse and exit velocity of an SRP system can be calculated the same way as it is done for LP systems from Equations (1), (2) and (7) respectively. The mathematical relations summarizing burn rate and characteristic velocity are described below:  
\[r =aP_{c}^{m} \tag{8}\] \[C^{*} =\frac{P_{c}A_{t}}{m} \tag{9}\]
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3 GPP & Third-Generation Partnership Project \\
5G & Fifth-Generation mobile network \\ ECEF & Earth-Centered Earth-Fixed \\ EMC & Earth-moving cells \\ FRP3 & Three-color frequency reuse \\ FSPL & Free-space path loss \\ LEO & Low earth orbit \\ LOS & Line-of-sight \\ NLOS & Non-line-of-sight \\ NR & New Radio \\ NTN & Non-terrestrial networks \\ PUSCH & Physical uplink shared channel \\ QEFC & Quasi earth-fixed cells \\ SINR & Signal-to-interference-plus-noise ratio \\ UE & User equipment \\ VoNR & Voice-over New Radio \\ \end{tabular}