esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
## 4 Performance Evaluation
### Greedy Forwarding and Void Handling Point-to-Point RPL in 3D Indoor Environments  
For routing simulations, 10 simulation runs are conducted for 125 nodes and 98 nodes with void, where each run has 235 different P2P traffic cases. All routing simulation parameter settings are listed in Table 3.  
Routing performance is evaluated for the following six different performance metrics:  
1. Route discovery success ratio is the average ratio of the number of successful P2P route discovery attempts to that of the total P2P route discovery attempts.
2. Number of DIO's sent is the average number of P2P-DIO messages sent by network nodes during the route discovery process.
3. Number of DIO's received is the average number of P2P-DIO messages received by network nodes during the route discovery process.  
Figure 13: Positioning error (cm).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Radio environment & UDGM with distance loss \\ Communication Range & 20 m \\ Transmission success ratio & 100\% \\ Reception success ratio (\%) & 90\% \\ Network size & 75 m \(\times\) 75 m \(\times\) 75 m \\ Number of nodes & 125, 98 with void \\ Node deployment & grid with (\(\pm\)5 m, \(\pm\)5 m, \(\pm\)5 m) randomness \\ P2P-RPL DAG lifetime & 16 s \\ P2P-DRO wait time & 1 s \\ P2P-RPL mode of operation & Non-storing mode \\ Objective function & MRHOF \\ Routing metric & ETX \\ \(I_{min}\) & 25 ms, 26 ms \\ \(I_{max}\) & 22 ms \\ Redundancy constant (\(k\)) & 1 (except when \(I=I_{min}\)) \\ Transmitter electronics (\(E_{TX-elec}\)) & 33.97 nJ/bit \\ Transmitter amplifier (\(\epsilon_{amp}\)) & 6 pJ/bit/m\({}^{2}\) \\ Receiver electronics (\(E_{RX-elec}\)) & 14.56 nJ/bit \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter setting for our routing simulation.  
Energy consumption is the average energy consumed during the whole route discovery interval, which is calculated according to the radio model parameters addressed in Table 2.
* Hop count is the average path length of discovered routes.
* The route discovery time is the average time passed from the route discovery initialization of the source node to the first receipt of any P2P-DIO message at the destination node.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 2 Background on LEO Satellite Power System and Methods
### Transformer Network
The decoder combines these representations with previously generated outputs to predict future values, thereby enabling tasks such as sequence-to-sequence learning and forecasting. Batteries degrade over time owing to various factors including charge-discharge cycles, temperature, voltage, and current variations. The transformer is particularly effective in learning the complex interactions among these factors, making it suitable for predicting the state of a battery, such as its voltage. In this study, a transformer was utilized to model the battery voltage as the output, using various features as inputs. The generated predictions were subsequently used as inputs for modeling battery aging via a UKF. A conceptual diagram of the transformer network used in this study is shown in Figure 2.
## 2 Materials and Methods
### Type of Training Sessions and Official Matches  
Apart from the official matches (OM, \(n=12\)), the types of sessions were differentiated based on the priority conditional objective that was developed: strength or intensity session (INT, \(n=10\)); endurance or extensity session (EXT, \(n=15\)); velocity session (VEL, \(n=7\)); and activation session (PREOM, \(n\)= 14). In total, 805 recordings were collected from official matches (\(n=49\)) and 756 training sessions, distributed as follows: VEL = 114, INT = 173, EXT = 239, and PREOM = 230. The number of records per player was 36.8 \(\pm\) 10.6.  
The order of the sessions in the training micro-cycle was conditioned to the number of days after (OM+) and before (OM\(-\)) of the OM, following the proposal of previous studies [10; 14].  
The VEL session was usually the first acquisition session, located 2 days after the OM (OM + 2) and 5 or 6 days before the next official match (OM \(-\) 6 or OM \(-\) 5), characterized by velocity work, locomotive-oriented and based on tasks of intermittent nature or in waves (with breaks of 1 or 2 min between drills). The tasks were mostly carried out with goalposts and goalkeepers and were made up of a large relative space per player (e.g., \(>\)250 m\({}^{2}\)).  
The INT session was the second acquisition session of the week, usually located 3 or 4 days after the OM (OM + 3 or OM + 4) and 3 or 4 days before the next competition (OM \(-\) 3 or OM \(-\) 4), characterized by neuromuscular and mechanically-oriented work, based on reduced and positional games, with a relative space per player of less than 100 m\({}^{2}\) and with a number of participants ranging from one to six per team.  
The EXT session was usually the third acquisition session of the micro-cycle, located 4 or 5 days after the OM (OM + 4 or OM + 5) and 2 or 3 days before the next competition (OM \(-\) 2 or OM \(-\) 3), characterized by cardiovascular work, based on large-format tasks, that is, with a moderate to high number of participants per team (\(>\)6), in a relative space equal to or greater than 250 m\({}^{2}\) per player, and with goalposts and goalkeepers.  
Pre-match day (PREOM) was the fine-tuning session. It was always conducted the day before match day (OM \(-\) 1). The contents that were developed in it were activation tasks based on activities with a large number of participants (\(>\)6) in a medium relative space (equal to or less than 250 m\({}^{2}\) per player) or small (equal to or less than 100 m\({}^{2}\) per player) with a polarized orientation and a tactical and strategic approach.  
The last day of the week was match day (OM). To calculate the load on the day of the competition, the previous warm-up carried out by the players was taken into account in addition to the two parts of the match, which had the following approximate load: duration was 20.1 \(\pm\) 2.1 min, and the physical demands were as follows: TD: 1280 \(\pm\) 266 m, HSR: 64.3 \(\pm\) 37.7 m, Sprint: 5 \(\pm\) 10.7 m, ACC2: 27.9 \(\pm\) 9.6 n, DEC2: 28.4 \(\pm\) 9.6 n, PL 20.6 \(\pm\) 4.3 au and TDmin 64 \(\pm\) 12.3 (m/min\({}^{1}\)).
## 6 Conclusions  
This paper proposes a low-complexity PSO-based algorithm to maximize the SEE in a cooperative relay CR-NOMA network with an EH user. The power allocation variables for each user are optimized to achieve SEE maximization under the following constraints: a minimum data rate for secondary users, a minimum level of harvested energy for the EH user, and the maximum permitted transmission power at both the secondary transmitter and the relay (based on a threshold for interference with the primary user). To validate the outcomes of the proposed solution, we compared the performance of our proposed PSO-based algorithm with the exhaustive search method. We confirmed that PSO achieves near-optimal performance compared to that obtained by the high computational-complexity exhaustive search method. Moreover, we studied SEE maximization in a cooperative relaying CR-OMA network with an EH user and compared it with our proposed scheme using the NOMA transmission strategy. The experimental results confirm the advantage of the proposed NOMA method compared to the OMA scheme, where an improvement in the SEE of around 40% to more than 100% is achieved by NOMA compared with OMA in the scenario varying the minimum energy harvested at user 3.  
[PERSON] conceived and proposed the research idea. All authors designed the experiments. [PERSON] performed the experiments. [PERSON] and I.K. analyzed the experimental results. [PERSON] wrote the paper under the supervision of I.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) through the Korean Government and the Ministry of Science and ICT (MSIT) under Grant NRF-2018R1A2B6001714.  
The authors declare no conflict of interest.
## 2 Materials and Methods
Class II: Partially cloudy with predominance of diffuse component: \(0.35<K_{T}\leq 0.55\) or \(0.3<\sigma\leq 0.6\). Class III: Partially cloudy with predominance of direct component: \(0.55<K_{T}\leq 0.65\) or \(0.6<\sigma\leq 0.85\). Class IV: Clear sky: \(K_{T}\) > 0.65 or \(\sigma\) > 0.85. The daily total global irradiation at the top of the atmosphere (\(G_{0d}\)) is estimated using the following equation [30]:  
\[G_{0d}=(24\times 3.6/\pi)\times G_{0n}\times\left[(\cos\phi\times\cos\delta \times\sin\omega_{s}+(\pi\times\omega_{s}/180)\times\sin\phi\times\sin\delta) \right]\,\left(\mathrm{kJ/m^{2}}\right) \tag{3}\]  
where \(G_{0n}\) is the irradiance falling on a plain at normal incidence at the top of the atmosphere and can be estimated as a function of the day number of the year (\(d_{n}\)) from the following equation:  
\[G_{0n}=G_{sc}\times\left[1+0.033\times\cos(360\times d_{n}/365)\right]\,\left( \mathrm{W/m^{2}}\right) \tag{4}\]  
where \(G_{sc}\) is the solar constant (1361 \(\mathrm{W/m^{2}}\)), and \(\omega_{s}\) is the sunset hour angle which is given by:  
\[\omega_{s}=\cos^{-1}(-\tan\phi\times\tan\delta) \tag{5}\]  
where \(\phi\) is the latitude of the location and \(\delta\) is the solar declination angle (deg). The daily values of the radiation components are estimated from the hourly values. Furthermore, the astronomical day length (\(S_{0d}\)), which is the computed time during which the center of the solar disk is above an altitude of zero degrees (without allowance for atmospheric refraction), is given by:  
\[S_{0d}=(1/7.5)\times\cos^{-1}(-\tan\phi\times\tan\delta)(\mathrm{h}) \tag{6}\]  
Both SD and global radiation data underwent a quality control process. Details about the quality criteria are given in [4]. All the stations showed high-quality data. Regarding the differences in daily sunshine duration records between the pairs of the four different methods of SD measurements we have adopted the classification scheme proposed by [PERSON] [10]: (1) insignificant grade (C1), within the margin of error [\(-0.1\) to \(0.1\) h]; (2) small grades which consist of a small negative grade level (\(-\)C2) [\(-0.5\) to \(-0.1\) h] and a small positive grade level (C2) (0.1 to 0.5 h); (3) medium grades in the negative region (\(-\)C3) within the margin of error [\(-1.5\) to \(-0.5\) h) and a medium positive region (C3) within the margin of error (0.5 to 1.5 h); and (4) high grades in the negative region (\(-\)C4) with a margin of error \(<\)\(-1.5\) h and a high positive region (C4) with a margin of error \(>\)1.5 h.  
The comparisons covered the daily and monthly sums and their statistical distributions. A linear fit was applied between the daily and monthly sunshine durations of the automatic and manual instruments. The correlations between the instruments are examined and the results are presented in the next section.
## 4 Discussion  
Figure 7 presents a graph demonstrating the combined predicted results of this study and existing data trends. A combination of Figures 2 and 6 effectively depicts the findings as well. In South Korea, the number of individuals between the ages of 25 and 34 years pursuing further education is expected to steadily decline after 2024. Conversely, the number of doctoral graduates will increase until 2028, probably due to the country's S&T labor policy. However, the number of PhD graduates will subsequently decline because of the decline in school-age population. Regardless, from 2036 onward, the proportional increase in S&T PhD scholars will surpass the population decline and show a moderate upward trend.  
Figure 6: Annual forecasting trends using predicted data.  
Figure 7: Annual trends for the science and technology workforce combining existing and forecasted results.  
Summarily, the workforce trends in this scenario are linked to the quality of PhD graduates and the quantity and quality of future labor positions. It is difficult to determine whether this scenario's outcome will lead to over- or understaffing in the future. However, earlier studies have expressed concern regarding the oversupply of PhD scholars, which can limit their utilization [20]. This suggests the necessity of implementing a complex combination of policies. To strengthen national S&T competitiveness, it is necessary to employ a PhD workforce in STEM fields [5]. Appropriate education policies should be implemented to maintain the highly qualified S&T PhD workforce in South Korea. They must be accompanied by industrial and employment policies relevant to their career paths as well [5; 11; 12; 14; 21]. These complex quantities and quality dynamics should be considered in future studies.  
This study is significant because it uses a time-series methodology to forecast the future size of the South Korean S&T PhD workforce. The projected size derived from this analysis can be used in detailed workforce planning by the integration of various methodologies. Additionally, it serves as a foundational basis for policy initiatives considering the demographic structure. Further, our forecasting model provides evidence to support the advancement of such policies. In particular, our forecast scenario is expected to serve as a starting point for future studies on the complexity of S&T labor policy.  
The current study did not include comparative analyses using models such as long short-term memory (LSTM) and ARIMA due to overfitting and model complexity issues with limited time-series data. This study's scope can be enhanced following the availability of additional time-series data. Further, as it involved leveraging a population-based predictive model, this study is limited by the constraint that current patterns continue to be maintained.
## 5 Applications of the Solubility Model  
### Prediction of \(So_{2}\) Solubility in Seawater  
The solubility model of SO\({}_{2}\) in multi-ion electrolyte solutions can be used to predict the solubility of SO\({}_{2}\) in seawater. The average absolute deviation of our model from experimental SO\({}_{2}\) solubility in seawater is about 7.48% (Table 5). It can be seen in Figure 5 that the calculated results are a bit lower than the experimental data. It may be that the experimental solubility data of SO\({}_{2}\) in aqueous MgCl\({}_{2}\), CaCl\({}_{2}\) and K\({}_{2}\)SO\({}_{4}\) solutions have not been found, so the second-order and third-order interaction parameters between SO\({}_{2}\) and Mg\({}^{2+}\) and Ca\({}^{2+}\) cannot be obtained, and they are only approximated as \(\lambda_{\text{CO}_{2}-\text{Ca}^{2+}}\), \(\xi_{\text{CO}_{2}-\text{Ca}^{2+}}\), \(\lambda_{\text{CO}_{2}-\text{Mg}^{2+}}\) and \(\xi_{\text{CO}_{2}-\text{Mg}^{2+}-\text{Cl}^{-}}\) from [PERSON] and [PERSON] [5].  
Figure 4: SO\({}_{2}\) solubilities in aqueous Na\({}_{2}\)SO\({}_{4}\) solutions (models vs. experimental data), [25,27,43]. (**a**) \(P=1\) bar; (**b**) partial pressure of SO\({}_{2}=1\) atm; (**c**) \(T=363.15\) K and molality of Na\({}_{2}\)SO\({}_{4}=0.546\) mol/kg; (**d**) \(T=393.15\) K and molality of Na\({}_{2}\)SO\({}_{4}=0.493\) mol/kg. \(m_{\text{SO}_{2}}\), \(T\) and \(P\) denote the solubility of SO\({}_{2}\), temperature and pressure, respectively. \(m_{\text{Na}_{2}\text{SO}_{4}}\) is the molality of Na\({}_{2}\)SO\({}_{4}\).
# European Geostationary Navigation Overlay Service Performance Prediction +
## 5 Machine Learning-Based Performance Prediction for EGNOS
### Machine Learning Labels  
To train our models, we have used data from the EGNOS Data Access Service (EDAS) offers free access to historical EGNOS data from 2013 up to the current day, including the ephemerides and range measurements collected by the RIMS, as well as the EGNOS messages that were broadcast. The latter contain the UDREI and GIVEI timeseries (labels) which the (supervised) machine learning can use to train models targeted at reproducing these labels.  
Investigations of the data showed that UDREI values of 11 and 12 were hardly ever broadcast. Hence, even though we had many years of historical broadcast data available, there were insufficient cases to train the machine learning effectively. We therefore decided to define a single class to represent UDREI values of 11, 12 and 14.
## 6 Future Development Problems and Prospects  
### Existing Problems  
The stability of furrow depth control is the main factor affecting seeding depth. To improve the furrowing depth control technology, scholars examined three key aspects: profiling adjustment devices, furrowing depth detection technology, and automatic control systems. Due to the complex field operating environment, the existing furrowing system cannot stabilize the furrowing depth efficiently with the uneven ground surface. The development of foreign furrow depth control technology is more mature, and intelligent planners with better operating results have been put into the market. China's research on an intelligent furrowing depth control system is still experimental. Compared with foreign countries, China's research on furrow depth control technology is still in the initial stage, and there is a big gap. According to the current furrowing system research, China's development of furrowing system research mainly exists in the following problem areas:  
1. The research on the furrowing system mainly focuses on flat land. The traditional furrowing depth control system can only cope with the simple ups and downs of sloping land. It is challenging to cope with sloping land's left and right ups and downs.
2. Downforce adjustment is a crucial factor affecting the stability of furrowing. Most of the current seeding machines rely on experience to adjust the downforce and do not consider different soil textures of different plots in real-time adjustment.
3. The profiling adjustment device is mainly micro-innovation or adds sensors to improve adjustment accuracy, but more research on vibration balance needs to be done. Due to the complex field operating conditions, the vibration of the planter unit would affect the stability of the furrow depth and reduce the machine's operating life.
4. The planter for furrow depth sensor detection is in the experimental stage, and there are fewer related operating machines equipped with furrow depth detection sensors. Most of the research is still in the single-sensor detection stage. Due to the limitations of the single-sensor, significant detection errors are frequent.
5. The research on the furrowing depth control system is in the experimental stage, mainly focusing on simple downforce control or sowing depth control with poor regulation accuracy. The research on the planter furrowing system should consider different agronomic requirements due to different soil textures in different regions to improve the system's universality.
## 3 Results
### Quantitative Analysis of Observation Space
#### 3.3.3 Sensitivity to Cluster Number K  
To ensure our findings were not an artifact of the chosen cluster number (K = 100), we repeated the analysis for K = 50 and K = 200. While the absolute values of the dispersion and TV distance metrics changed with K (as expected), the relative trends remained consistent. For instance, across all values of K, Freeway and Enduro agents consistently showed high, uniform dispersion and low pairwise TV distances. Conversely, Boxing and Qbert agents consistently exhibited significant differences in dispersion and high pairwise TV distances. This indicates that our core conclusion--that performance variance correlates with observation space divergence--is robust to the specific granularity of state space discretization. The complete tables for this sensitivity analysis, which confirm these trends, are provided in the Appendix A.
## 3 Development History of Navigation Signal Authentication Technology
### Design and Analysis of a Public Key Infrastructure for BDS Data Authentication Key Management
The key management for the BDS signal authentication service involves a series of technical challenges: one is to design a corresponding hierarchical key system for different authentication protocols, and the selection of a key hierarchical management structure is closely related to its application scenarios; the other is to research the key distribution scheme combining different methods such as over-the-air key update, receiver built-in, and network distribution to simplify the key distribution process under the premise of ensuring security; the third challenge is a key distribution strategy and optimization algorithm and the fourth challenge is the key revocation policy in the case of key leakage.
## 2 Materials and Methods  
### Data Gathering  
Street images were gathered using a web-scraping approach that automatically retrieved pictures matching a predefined set of keywords [29]. Google Images served as the primary search engine, employing queries such as \"construction sites\", \"work zones\", \"sidewalks\", \"pedestrians\", \"building materials\", \"temporary pedestrian walkways\", and \"pedestrian accidents\". These queries aimed to capture a wide range of sidewalk conditions adjacent to construction sites, resulting in the initial retrieval of approximately 300 images. Once retrieved, the images underwent a thorough screening process to ensure both relevance and consistent quality. Three researchers independently reviewed the collected images based on the following five key criteria. First, any images that did not clearly depict a sidewalk adjacent to a construction site were excluded from the dataset. Second, images containing pedestrians, apart from construction workers, were removed to minimize any potential effects attributable to personal or behavioral characteristics [30]. Third, to reduce the variability caused by differences in pavement materials and slopes, only sidewalks featuring brick-paved surfaces and flat gradients were retained. Fourth, images with resolutions below 320 \(\times\) 320 or above 1920 \(\times\) 1080 were excluded to ensure consistent visual clarity. Lastly, images containing large text overlays or visible watermarks were removed to avoid visual distractions. Ultimately, 47 images that met all five criteria were retained for analysis.  
Figure 1: Research framework that illustrates the overall process of the study.  
Because this study also aimed to compare obstructed sidewalks with unobstructed scenarios, an additional 14 images of normal sidewalks were collected to serve as a control dataset. Figure 2a presents an example of a typical construction site environment with barrier walls and building materials placed on the sidewalk, whereas Figure 2b depicts a representative normal urban sidewalk.
## 3 Ground Loss Effect Factors  
Ground loss \(g\) is the primary cause of stratum settlement and deformation. During shield tunnel construction, the causes of ground loss include curved propulsion, shield steering adjustment, soil thrusting into the shield tail gap, head-down propulsion, head-up propulsion, and tunnel lining deformation. The ground loss can be calculated by using the [PERSON]'s equation [36].  
\[g=G_{P}+U_{p}+\omega \tag{1}\]  
where \(G_{p}\) is physical gap, it is the gap between the outer wall of the shield shell and the outer wall of the segment. In the field, synchronous grouting is used to decrease the physical gap, but due to the hydration reaction characteristics and the water permeation loss of cement slurry, there is still a residual gap \(G_{p}\)' when the physical gap \(G_{p}\) is fully filled by grouting. Usually, \(G_{p}\)' = \(G_{p}\times\beta_{l}\), \(\beta_{l}\) is slurry strain rate; \(U_{p}\) is the plastic deformation of shield working face during tunnel excavation. Generally, \(U_{p}\) = 0 when the EPB shield is used; \(\omega\) is the additional ground loss induced by shield rectification, head-up propulsion, head-down propulsion and the soil consolidation in the tunneling disturbance zone. \(\omega\) can be assumed to be 0 because the working face thrust of shield is equal to the soil pressure during most of the time.