esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The origin of the technique to obtain solar energy effectively using a concentrator is considered to be a combination of reflector and thermal collector, since the late 1970s [1, 2]. The technique has been further advanced, and concentrator photovoltaic (CPV) modules have been actively studied to reduce costs and improve performance [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. CPV modules are highly efficient power generators in high-direct normal irradiance (DNI) regions near the equator, but less effective in the global normal irradiance (GNI) mid-latitude regions, where these modules do not perform as well as flat photovoltaic (PV) modules. To overcome these limitations, several researchers have studied hybrid CPV modules that combine a CPV module with low-cost PV modules [13, 14, 15, 16], since these hybrid CPV architectures can collect sunlight in modest-DNI regions, which allows the same solar module design regardless of region. In addition, hybrid CPV modules can maximize power generation in the high-DNI regions without requiring as much volume, since they absorb scattered light more efficiently [11]. [PERSON] et al. experimentally analyzed two types of hybrid CPV modules with concentration ratios of 18X and 1000X and showed high power generation efficiency [12]. [PERSON] et al. combined the two-terminal triple-junction (TJ) solar cell and bifacial crystalline silicon solar cell for their hybrid CPV modules, and performed analyses based on the (GNI-DNI)/GNI ratio [13]. While many studies have been conducted on the secondary optical elements (SOEs) used in conventional CPV modules [17; 18; 19], we have not seen a study on the SOEs of hybrid CPV modules. In this paper, we analyzed the mirror-type SOE for the hybrid CPV modules and optimized it through simulation and experimental measurements under high-DNI conditions. Especially analyzed were the shading losses of the silicon solar cells by the SOEs on the hybrid CPV modules. In addition, the two-terminal TJ solar cell, mainly used in CPV modules, cannot convert light of wavelength above about 1.3 \(\upmu\)m, due to the current limit of the top cell [20; 21]. This longer wavelength light is also not absorbed by the silicon solar cells in the hybrid CPV modules around the TJ solar cell. For this reason, we considered the effective wavelength range for the optimization of the SOE. These hybrid CPV modules are promising as the next generation of the thermal collector system.
# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
###### Abstract  
The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time-frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time-frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.  
**Keywords:** stator inter-turn short circuit; fault detection; wavelet packet transform; PMSM; stator current signal; vibration signal +
Footnote †: journal: Applied sciences
## 2 Characterization and Measurement of Blade Twist
### Characterization of Blade Twist  
The blade is composed of a series of profiles which are twisted and stacked according to certain rules. The selection of the test profile and the extraction of the twist characteristic parameters are two key problems to be solved in the characterization of the blade twist. In order to evaluate the spatial torsion characteristics of blade efficiently and scientifically, two characteristic parameters of twist angular position \(\overline{\psi}\) of blade and twist angle \(\eta\) of blade are proposed in this study, which characterize and evaluate the twist level of the blade comprehensively from two aspects of position and form.  
The method of characterization and evaluation of the blade twist is described as follows:  
(1) As shown in Figure 2, two test profiles I and II are planned on the blade airfoil. Test profile I is set at 3 mm below the minimum radius of the blade tip and test profile II is set at 4 mm above the maximum radius of the blade root.  
(2) The twist angular positions \(\psi_{\rm I}\) and \(\psi_{\rm II}\) of two test profiles are measured respectively based on technologies of a priori planning and geometric analysis.  
(3) The characteristic parameters \(\overline{\psi}\) and \(\eta\) of the blade twist are extracted and calculated. In order to reveal the torsional characteristics of blade comprehensively and intuitively, two evaluation parameters are defined in the study: The twist angular position \(\overline{\psi}\) of blade characterizes the absolute torsion level of blade relative to the axial line of the engine, which can be determined by average twist angle position of the blade; the twist angle \(\eta\) of blade characterizes the relative torsion degree of the blade itself, which can be  
Figure 1: Definition diagram of the twist angular position of profile.  
determined by the difference value between maximum twist angle position and minimum twist angle position. The mathematical models of parameters \(\overline{\psi}\) and \(\eta\) are defined respectively as follows:  
\[\overline{\psi}=\frac{\psi_{\mathrm{I}}+\psi_{\mathrm{II}}}{2} \tag{1}\]  
\[\eta=\left|\psi_{\mathrm{I}}-\psi_{\mathrm{II}}\right| \tag{2}\]  
By optimizing the number and location of the test profiles, the measuring process of the blade twist is effectively simplified and the evaluation efficiency is significantly improved. Based on the parameter evaluations of the twist angular position \(\overline{\psi}\) and twist angle \(\eta\), the spatial torsion state of the blade is characterized intuitively and comprehensively. The method proposed in this study has the advantage of being a simple model that delivers a comprehensive evaluation, which authentically reveals the torsion rule of the blade.
## 3 Experiment and Result Analysis
### Performance Analysis of Traditional Medium-Long Baseline RTK
However, a confused phenomenon is shown in Figure 2. That is, the precision of the height component for the GPS/BDS-combined situation was lower than that for the single GPS system in the initial period (e.g., sessions 1 and 6). In fact, the precision of float RTK positions will also be influenced by the precision of float ambiguities, particularly for satellites with a low elevation angle. Figure 6 shows the bias of float ambiguities for satellites with low elevation angle when the aforementioned baseline was solved using GPS/BDS data during this period. The biases of float solutions were determined by subtracting integer ambiguities from float solutions. The initialization time of BDS satellites C01 and C05 was considerably longer than that of the other satellites. Notably, C01 and C05 are GEO satellites with positions that remain nearly static with respect to ground stations. GEO satellites lack variation in geometric structure, hence ambiguity convergence time is considerably longer than that of satellites with other orbit types when the elevation angle is relatively close. Although the GEO satellites have some disadvantages regarding parameter initialization, the positioning performance will be improved after their ambiguities convergence.
## 4 Proposed System for Patient-Robot Co-Navigation
### Shared Control in Crowded Environments Using Reinforcement Learning
#### 4.2.2 Shared Control Considering Dynamic and Static Obstacles (Model V2)  
In the previous section, the proposed model architecture in Figure 4 only takes into consideration dynamic obstacles, such as humans. However, in practical cases, there are static obstacles, such as walls, tables, etc., which the robot needs to avoid. Therefore, the proposed architecture is extended to consider both static and dynamic obstacles. Figure 5 shows the modified architecture where a block called \"Static map features\" is added. This block comprises the field of view (FOV) of the laser scanner data that shows all of the nearby obstacles (\(\bigcup_{i=1}^{n_{L}}L_{i}^{t}\)). These laser scanner data are used as input for the \(RNN_{s}\). The output of \(RNN_{s}\) is concatenated with the outputs of \(RNN_{p}\) and \(RNN_{c}\) (instead of just \(RNN_{p}\) and \(RNN_{c}\) in the case of dynamic obstacles only), and are provided as input to the attention mechanism. The rest of the architecture remains the same as the proposed architecture in Section 4.2.1. The modified architecture is shown in Figure 5. The total number of trainable parameters for this model is 766617. However, as the modified architecture needs to avoid static obstacles, the reward function in Equation (5) needs to be updated to include punishment for when the robot hits the static obstacles. Equation (6) shows the updated reward function, where the shortest distance between the robot and any obstacle is defined as \(d_{o}^{t}\).  
\[\mu_{k}=\left\{\begin{array}{ll}-20&d_{min}^{t}<0\\ -20&d_{p,r}^{t}>0.25\\ -20&d_{o}^{t}<0.3\\ 2.5(d_{min}^{t}-0.25)&0<d_{min}^{t}<0.25\\ 10&d_{goal}^{t}<\rho_{robot}\\ 2(d_{goal}^{t-1}-d_{goal}^{t})&otherwise\end{array}\right. \tag{6}\]  
The modified architecture can enable the robot and the patient to avoid both static and dynamic obstacles. The next section discusses the simulation environment used to train the model using PPO RL and the results between prior work and the proposed frameworks.
## 4 Computation Unit Design for the Decoder
### Vnu  
The VNU's schematic is depicted in Figure 8, where the numbers after the slashes represent their corresponding bit widths. Firstly, four \(R_{nm}\) values are generated by utilizing c2v and \(L_{n}^{(k-1)}\), which will later be converted to integers. The crucial step in VNU involves adding five integers within a two-stage pipeline adder, yielding a 7-bit result that will subsequently be transformed back into 2 bits as Equation (6). The first bit is stored directly as \(B_{s}\), while the remaining 6 bits are taken as absolute values based on the first bit. The absolute value is then compared to \(T_{L}\), obtaining the \(B_{m}\) that will be saved as v2c, along with \(B_{s}\). Through a Modified 2-bit MSA and substituting partial comparators with logic  
Figure 5: Schematic diagram of decoding system.  
Figure 6: Overlap timing relationship in decoding.  
circuits, the netlist is simplified, necessitating a mere 68 cells and 122 nets for each VNU in Vivado implementation.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 3 Modulation Techniques for VLC in E-Healthcare Systems
### Pulse Amplitude Modulation (PAM) and Quadrature Amplitude Modulation (QAM)
Such techniques, when adapted to healthcare, could enable long-range data transfer in large hospital facilities or remote monitoring scenarios. Further studies have demonstrated high-speed underwater VLC, achieving 1.45 Gbit/s over a 4.8-m channel using a blue-light laser diode (LD) [55]. Although this setup is designed for underwater communication, similar approaches could be adapted to healthcare for robust data transmission in challenging environments, such as those with high interference or when long-range communication is necessary.
## 3 Results
### SMCI Model Adaptations
#### 3.2.2 Feature Extraction  
When extracting features with the adapted model and the SMCI data, the Daubechies 4 level 8 wavelet was not compatible. The highest wavelet transform that could be run was a level 5 wavelet. As a result, the adapted model for SMCIs used a Daubechies 4 level 5 wavelet for the PPG and GSR signals. For the PPG and GSR signals, the number of features extracted with level 5 wavelets was 72 for each.  
Figure 2: Modifications to the DEAP model when adapting for participants with SMCIs (the colours match the process steps identified in Figure 1).
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 2 The Channel Model
### Model Limitations  
The proposed model is based on idealized assumptions to ensure analytical feasibility. It assumes a constant receiver gain, considers only the horizontal radiation pattern of the base station, and models signal propagation under line-of-sight (LoS) conditions, neglecting multipath fading, diffraction, and shadowing. While these simplifications limit accuracy in complex 5G scenarios with high environmental variability, the model remains applicable in most 4G and 3G deployments, where large-scale path loss is dominant. Additionally, the use of empirical RSSI measurements helps mitigate the limitations of these assumptions, as directional variations in the signal strength are extracted from real-world data. This enables the model to provide closed-form estimates that are practical for environments lacking detailed infrastructure information or computational resources. Field validation across diverse urban and suburban settings confirms the model's utility, achieving estimation errors below 7.6%.
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 4 Discussion
### Limitations of the Study
#### 4.5.3 Effects of Seasonal Vegetation and Precipitation Patterns  
This study dedicated nearly five years to collecting rockfall incidents in Taiwan. Despite this extensive period, the dataset comprises only 182 incidents, which is insufficient for conducting a detailed seasonal analysis of rockfalls and assessing the influence of vegetation changes and rainfall patterns. A more comprehensive temporal analysis of rockfall distribution may become feasible with the collection of additional incidents over a longer period.
## 3 Results and Discussion
### Post-Processing Approach
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**NVS (Garmin Antenna, GPS + GLONASS, Session Length = 10 min)**} \\ \hline  & **\(\mu\)E [m]** & **\(\mu\)N [m]** & **\(\mu\)h [m]** & **\(\sigma\)E [m]** & **\(\sigma\)N [m]** & **\(\sigma\)h [m]** & \\ \hline
5 km & 0.273 & 0.263 & \(-\)0.493 & 0.003 & 0.002 & 0.009 & FIX \\
10 km & 0.270 & 0.268 & \(-\)0.460 & 0.011 & 0.016 & 0.049 & FLT \\
25 km & 0.197 & 0.221 & \(-\)0.355 & 0.029 & 0.031 & 0.044 & FLT \\ \hline \multicolumn{6}{c}{**u-blox (Garmin Antenna, only GPS, Session Length = 10 min)**} \\ \hline  & **\(\mu\)E [m]** & **\(\mu\)N [m]** & **\(\mu\)h [m]** & **\(\sigma\)E [m]** & **\(\sigma\)N [m]** & **\(\sigma\)h [m]** & \\ \hline
5 km & 0.073 & \(-\)0.018 & 0.108 & 0.002 & 0.003 & 0.005 & FIX \\
10 km & \(-\)0.023 & 0.065 & 0.232 & 0.008 & 0.012 & 0.013 & FLT \\
25 km & \(-\)0.194 & \(-\)0.093 & 0.556 & 0.018 & 0.012 & 0.032 & FLT \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Difference between estimated and reference positioning of NVS (**Top**) and u-blox (**Bottom**) receivers using a VR at various distances for a session length of 10 min. obtain a fixed positioning, while if this distance increases, the solution is defined as a float. As shown in Table 9, no better results can be obtained with respect to a session length equal to 10 min. In general it is possible to affirm that the GLONASS constellation it is not useful for these types of receivers even if VR are considered from a post-processing approach. It must be stated that these results are obtained considering the same satellite constellation and the same observations; this is possible to do by splitting the signal that comes from the antenna into two receivers (Figure 2). Finally, Tables 11 (bottom) and 12 show the results obtained with 15 min of GPS measurements with the Leica LEIAR10 antenna. The results are better than those obtained with the Garmin antenna; however, they are very similar between the two receivers. Even the positions declared \"float\" have accuracies comparable to those declared \"fix\". It must be underlined again that we have summarized in the previous tables the results obtained considering 12 sessions in 5 days. We have decided to show mean values and the obtained accuracy considering the 95% of significance level, in order to have a complete analysis from a statistical point of view.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 3 Results
### Coordinate Repeatability  
Figure 7 illustrates a boxplot of 3D coordinate repeatability for all of the solutions in two periods independently and Table 6 contains mean coordinate repeatability values for all the stations during both test periods together. Once again GREC_L50 delivers significantly worse results in comparison with other solutions, with the maximum standard deviation of 3D coordinate repeatability reaching almost 90 mm for equatorial station NKLG in the first test period and 55 mm for the Canadian station UCAL in the second test period. We noticed that the coordinate repeatability in the majority of multi-GNSS solutions is degraded when compared to GPS only solution. The 3D coordinate repeatability is worse by 50%, 46%, and 110% for GREC_L10, GREC_GxN, and GREC_N&S, respectively. In GREC_GxN, we noticed a 4% improvement in the Up component, which is not the case in other abovementioned solutions.  
Figure 6: 3D formal errors of coordinates obtained in static PPP using different weighting schemes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **G\_L10** & **GREC\_L10** & **GREC\_L50** & **GREC\_GxN** & **GREC\_GxS** & **GREC\_N\&S** \\ \hline
**North [mm]** & 0.9 & 0.7 & 3.5 & 0.8 & 0.6 & 2.4 \\
**East [mm]** & 2.0 & 1.5 & 5.0 & 1.7 & 1.2 & 3.8 \\
**Up [mm]** & 3.8 & 3.0 & 14.7 & 3.3 & 2.3 & 9.9 \\
**3D [mm]** & 4.4 & 3.5 & 15.9 & 3.8 & 2.7 & 10.9 \\ \hline \end{tabular}
\end{table}
Table 5: Mean formal errors of coordinates for all stations during both test periods.  
Compared to the GPS only solution, the improvement in all of the components is visible only in the GREC_GxS solution. The mean 3D coordinate repeatability in GREC_GxS is 14.2 mm, which gives a 6% of improvement when compared to 15.2 mm obtained with G_L10. Only for station AREG in the first time period, the 3D repeatability of coordinates was degraded, by less than 2 mm.
# Determination of Effective Parameters for Hydropower Plants' Energy Generation: A Case Study
###### Abstract  
The current study aims to conduct a comprehensive analysis of the key parameters that affect the energy output of the Bagsli Hydropower Plant, located on the Zap River in the Dicle (Tigris) basin of Turkey. It considers the daily data for the time interval of 2016-2019 related to flow rate, precipitation, and temperature in relation to the generation of electricity. The relationship between energy output and flow rate is evident; however, the energy production is limited by the design flow rate. The largest flow rates were seen during the spring season after the occurrence of peak precipitation, which exhibited a shifting pattern. Similarly, energy generation also reaches its highest level during this time period. Another outcome of this study is that there is no apparent association between daily precipitation and daily energy generation. Moreover, a novel correlation with an R\({}^{2}\) value of 0.89 has been proposed, in the deviation band of \(\pm\)20%, to estimate energy generation when only flow rate data are available.  
hydropower plants; energy generation; seasonal effects +
Footnote †: journal: Applied Sciences
## 2 Role of Ports in the Decarbonisation of Maritime Transport
### Role of Ports in the Energy Transition and Technological Developments  
Ports play a crucial role in the transition towards sustainable maritime operations, acting as enablers of alternative fuel adoption and technological advancements aimed at reducing emissions. Alternative fuels, including liquefied natural gas (LNG), biofuels, methanol, hydrogen, and ammonia, are being tested and adopted to replace conventional heavy fuel oil (HFO). For example, major ports such as Rotterdam and Antwerp are developing bunkering facilities for LNG and hydrogen [16]. Additionally, hybrid and fully electric vessels are becoming increasingly common in short-sea shipping and port operations, reflecting the rapid electrification of the sector [17]. The trials carried out in the port of Valencia with a reach-stacker and a terminal tractor powered by fuel cells illustrate the potential of hydrogen in port applications.  
A key strategy for reducing emissions in ports is the implementation of shore-side electricity (SSE) systems, allowing ships to connect to the grid while berthed, eliminating the need for auxiliary engines running on fossil fuels. In Spain, the port of Barcelona is pioneering the construction of SSE installations to supply electrical power to container ships, cruise ships, and ro-ro/ro-pax vessels. Similarly, the ports of Valencia, Bilbao, Cadiz, Tenerife, and Las Palmas are developing their first SSE facilities to align with EU regulations [18].  
Beyond alternative fuels and SSE, technological advancements drive further decarbonisation in the maritime sector. The deployment of zero-emission vessels (ZEVs) powered by hydrogen, ammonia, and electric batteries is expected to dominate by 2050. According to [17], by mid-century, hydrogen and ammonia could account for over 30% of marine fuel consumption. Simultaneously, advanced digital technologies, including autonomous vessels and smart port systems, enhance energy efficiency by optimizing logistics and operations, reducing unnecessary fuel consumption, and supporting real-time decision-making.  
By integrating sustainable fuels, electrification measures, and cutting-edge digital solutions, ports are evolving into key hubs of maritime decarbonisation. This ensures compliance with international regulations while fostering innovation in the shipping industry.
## 2 Literature Review
### Value of Systems Engineering  
The value of systems engineering has been a matter of much debate, because numerous complex systems were developed before the systems engineering discipline was established. Over the years, many studies have attempted to identify the value or effectiveness of systems engineering. One of the earliest studies was completed by [PERSON] at NASA. The study examined 32 NASA projects and aimed to identify the relationship between the investment in systems engineering and cost overruns. Projects that invested more in systems engineering were found to have smaller cost overruns, whereas projects that invested less in systems engineering had larger cost overruns [19; 20]. In another study surveying many prominent engineering firms worldwide, including Boeing, NorthropGruman, Lockheed Martin, Airbus, and Ford Motor Company, most survey respondents indicated that project costs were reduced when systems engineering was applied [21].  
In the late 1990s, Boeing performed a study of three similar projects that they were conducting. The first project was to be completed without the use of systems engineering, whereas the other two used systems engineering. All three projects started simultaneously, and the two projects that used systems engineering were completed in half the time of the project that did not use systems engineering [20; 22]. A similar study on automobile prototypes by [PERSON] has shown that the development of prototypes is completed much faster when a systems engineering approach is used [23].  
[PERSON] has performed extensive studies on the value of systems engineering, showing that systems engineering improves development quality, and the quality of systems engineering affects development quality [20]. [PERSON] performed a second study on the return on investment on eight systems engineering activities: mission definition, requirements engineering, systems architecting, system implementation, technical analysis, technical management, scope management, and verification and validation. The results indicated that each activity significantly influenced project success [24].