esa-sceva/satcom-chunk-collection huggingface dataset:
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 6 Conclusions  
In this paper, we studied the uplink outage probability for LEO NOMA and cooperative OMA, where both GEO and LEO satellites are considered. Realizing that there exists much uplink beam gain difference between beam-center and beam-edge users, we can allocate these two users in one resource block with the help of NOMA. To deeply analyze the performance of outage probability, we provided analytical expressions for beam-edge and beam-center users under the circumstances of LEO NOMA and GEO-LEO coopera  
Figure 6: Outage probability with channel ordering in FHS when \(\alpha=0.2\).  
Figure 7: Outage probability with channel ordering in FHS when \(\alpha=0.4\).  
tive OMA. Considering the channel fading difference in the beam-center and beam-edge users, the outage probability is further discussed under the condition of channel ordering. Moreover, the asymptotic outage probability is analyzed and the diversity order is also derived. The simulation results show that NOMA outperforms cooperative OMA in the low-SNR region. Since NOMA will provide interference in the process of demodulation, the diversity order would be zero, which may cause the outage probability to attain an error floor in the high-SNR region. When the uplink channel is assumed to be ordered, the interference would be relieved, which can be judged from Remarks 1 and 2. In this case, NOMA is able to provide a promising outage probability compared with cooperative OMA. Note that high reliable transmission methods for a multi-user scenario would be discussed in our future work. One possible solution for multi-user uplink NOMA is that we separate users as beam-center and beam-edge sets. Then, we select one user from each set as a user pair. The two users in one pair are allocated in one resource block, while different pairs take separate timeslots or carrier resource blocks. This can be viewed as TDMA-NOMA or FDMA-NOMA. Following this, user paring would be one of the key technologies that should be solved.  
Conceptualization, G.L. and T.L.; Methodology, T.L. and G.L.; Validation, T.L. and X.Y.; Writing--Original Draft Preparation, T.L., X.Y., T.H., and B.D.; Writing--Review and Editing, T.L., X.Y., T.H., and B.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant Nos. 62201533 and 62201283.  
Not applicable.  
The authors declare no conflict of interest.
# The State of the Art of the Control Strategies for Single-Phase Electric Springs
## 4 Control Strategies of ES-3
### Control Strategy with Battery Management System  
The DC link in ES-2 has an energy storage unit, such as a battery. Due to the limited battery capacity, it is necessary to consider changes in the state-of-charge (SOC) so that the battery will not be over-charged or over-discharged. It has been revealed by [PERSON] et al. [25] that when the load power on the demand side increases at a certain moment, the ES must provide a certain active power during the transient to regulate the frequency at the reference value. However, after the transient, the ES still continued to provide active power. In other words, if there is no mechanism for managing the ES to provide an active power, the battery will be fully discharged for a long time. In response to this problem, a battery management system (BMS) is added for the auxiliary control in [25], as shown in Figure 7.  
In Figure 7, an instant SOC signal \(SOC_{f}\), which is normalized from 0 to 1, is introduced. In this case, \(SOC_{ref}\) is set to 0.5, which is to maintain the battery at 50% state of charge so that it can easily absorb or deliver active power for grid regulation. There are two factors that determine the active power reference value of ES-3, namely \(P_{f}\) and \(P_{SOC}\), where \(P_{f}\) is a signal that controls the active power provided or absorbed, in order to adjust the frequency, and \(P_{SOC}\) is a signal that controls the SOC of the battery storage system. The actual active power reference signal \(P_{ref}\) is the weighted sum of these two signals, as given by Equation (9).  
\[P_{ref}=aP_{SOC}+(1-a)P_{f} \tag{9}\]  
It is obviously seen that when the weight of \(P_{SOC}\) is 0, the control strategy is exactly the same as the original control strategy. The ultimate goal is to achieve a slow charge or discharge rate when the instantaneous SOC is close to 0.5; otherwise, the charge and discharge rate will be fast. With the  
Figure 6: Power decoupling control scheme of ES-3 [25].  
automatic charging and discharging strategy integrated in the active power control loop, ES-3 is expected to improve the quality of the grid in the long run and has a higher stability. This concept can also be applied to other versions of the electric springs.
# Curved Path Following Control for a Small Fixed-Wing UAV with Parameters Adaptation
## 3 Controller Design
### Figures, Tables and Schemes  
For a given initial state of the nonlinear system (9), The behaviors of \(e_{\chi}\) and \(e_{d}\) are affected by the two control parameters \(k_{1}\) and \(k_{2}\). In order to study how the parameters will affect the dynamics of the nonlinear system (9), the straight-line and circular paths are chosen for case analysis. The function of a given straight-line path can be expressed as \(ax+by+c=0\), where \(a,b,c\in R\) and \(\sqrt{a^{2}+b^{2}}=1\). The function of a given circular path can be described as \(\sqrt{\left(x-C_{x}\right)^{2}+\left(y-C_{y}\right)^{2}}-C_{R}=0\), where \(\left(C_{x},C_{y}\right)\) and \(C_{R}\) represent the center and radius of the circle, respectively. With the functions of the straight-line and circular paths described above, the values of the corresponding gradient modulus are all \(\|\
abla f\|=1\).  
Figure 2(a) shows the different trajectories of the nonlinear system (9) with \(\|\
abla f\|=1\), \(V\)\(=25\) m/s, and \(k_{2}=0.0003\), but under five different values of parameter \(k_{1}\). The trajectories of \(e_{d}\) and \(e_{\chi}\) are shown in Figure 2(b),\(c\), respectively. The parameter \(k_{1}\) directly affects the dynamic characteristics of the two states \(e_{d}\) and \(e_{\chi}\). With larger value of \(k_{1}\), the rise times of the two states \(e_{d}\) and \(e_{\chi}\) are all shorter, but the corresponding overshoots and oscillation amplitudes become larger. The smaller or larger value of the parameter \(k_{1}\), the faster the convergence speed of the two states.  
Figure 3(a) shows the different trajectories of the nonlinear system (9), with \(\|\
abla f\|=1\), \(V\)\(=25\) m/s, and \(k_{1}=0.00025\), but under five different values of \(k_{2}\). The trajectories of \(e_{d}\) and \(e_{\chi}\) are shown in Figure 3(b),\(c\), respectively. It can be seen that the parameter \(k_{2}\) significantly influences the dynamics of \(e_{d}\) and \(e_{\chi}\). With a larger value of \(k_{2}\), the overshoots of \(e_{d}\) and \(e_{\chi}\) will both be smaller. The smaller value of \(k_{2}\) will lead to a larger oscillation amplitude and more oscillation times of \(e_{d}\) and \(e_{\chi}\). With the increase of \(k_{2}\), the rise times of \(e_{d}\) and \(e_{\chi}\) are both longer. Equation (9) shows that the dynamics of \(e_{\chi}\) is a nonlinear combination of the variable \(e_{d}\) and its differential, which is similar to a PD controller. The term \(-k_{2}V_{g}\dot{e}_{d}\) can predict the error trend and correct the error in advance. Thus, the parameter \(k_{2}\) cannot blindly pursue a large value, but should take an appropriate value.
# Diagnostic Accuracy and Agreement Between AI and Clinicians in Orthodontic 3D Model Analysis
## 2 Materials and Method  
### Study Design  
This retrospective diagnostic accuracy study was conducted in accordance with the STARD 2015 (Standards for Reporting Diagnostic Accuracy Studies) guidelines [31]. An a priori sample size calculation was performed using G*Power 3.1 (Heinrich-Hein-Universitat Dusseldorf, Dusseldorf, Germany) to determine the minimum number of participants required to detect a statistically significant difference between artificial-intelligence-assisted and manual measurements. The analysis was based on a two-tailed paired t-test, assuming a medium effect size ([PERSON]'s dz = 0.45), a significance level (\(\alpha\)) of 0.05, and a statistical power (1-\(\beta\)) of 0.85. Based on these parameters, the minimum required sample size was calculated to be 47 patients.  
Patient records from the Inonu University Faculty of Dentistry Hospital dated 2017-2022, including male and female individuals aged 13-25 years, were reviewed. An initial pool of 735 dental models meeting the study's predefined inclusion criteria was identified from the records of 1050 patients. From this pool, 50 patient models were randomly selected using an online random number generator ([[https://www.calculatorsoup.com](https://www.calculatorsoup.com)]([https://www.calculatorsoup.com](https://www.calculatorsoup.com)), accessed on 8 July 2025). Prior to any analysis, all patient data were anonymized by a resident in the orthodontic department before being uploaded to the respective digital platforms. A total of 50 participants were initially included in the study. However, due to irreparable issues with the intraoral scan mesh files of two patients, the final analysis was conducted using data from 48 patients.
## 2 Study Area and Data Preparation
### Data and Pre-Processing  
Three HJ CCD datasets, six GF-1 wide field of view camera (WFV) datasets and six MODIS surface reflectance datasets were used in this study (Table 1). All of these data were acquired during clear sky conditions and are of good quality.
## 2 Phenomenological Model
### Collection Efficiency  
The actual water mass flowing on the wall surface will be the combined result of the first impact, possible rebound, secondary impingement and, potentially, the interaction between rebounding and incoming droplets. Several CFD tools may be used at this step: here, we combine the SU2 open-source solver with an in-house Lagrangian droplet tracking code. Eulerian modeling is in general more efficient, but it is trickier to take into account the rebound and the secondary impacts on the walls and between droplets.  
A population of incoming droplets is generated according to the LWD and airspeed, and their individual behavior is followed. The threshold values from the previous paragraph are used to determine whether the impinging droplet sticks on the surface or re-bounds: in the latter case, the residual kinetic energy determines the speed at which the bouncing droplet leaves the surface and, thus, the subsequent droplet trajectory and its maximum height. The droplet re-emerging from the surface may be driven away from the wall, or experience a secondary impact a bit downstream. Since at any impact we have some dissipation, most droplets will then stick at a location a bit downstream of the first one. In such a case, the final position is noted and collection efficiency is evaluated by integrating over a reasonable time interval \(\Delta t\). If the incoming droplet volume is \(V_{d}\), and \(n_{i}\) is the number of droplets that came to halt on a wall element of the CFD mesh of surface \(\Delta S\), during \(\Delta t\), the collection efficiency \(\beta\) is computed as:  
\[\beta=\frac{u_{0}LWC\cdot\Delta t\cdot\Delta S}{n_{i}\cdot\rho V_{d}} \tag{9}\]  
Figure 3: Rebound threshold (lower branch) and splashing threshold (upper branch) as a function of \(We\), \(Re\) and wettability. Solid lines: correlations Equations (7) and (8). Symbols: computed values. For any given \(Re\) and contact angle rebound occurs if \(We\) falls between the two thresholds.  
The correlations defined in Equations (7) and (8) are extended to the case of droplets impinging at a non-zero incidence by using the normal component of the incoming velocity, as suggested in [23].
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 7 Challenges of 6G
### THz Band
In addition, from the perspective of hardware development, providing high-speed communications, like terabits per second, is highly challenging, because in this frequency range, the electronic circuitry needs to be moved to more optical circuitry, and a large amount of bandwidth will be required. There are also health and safety concerns in the utilization of the high-frequency range, in which, for the safety of users, the power constraint should be considered and applied to the devices.
## 3 Experiment
### Performance Analysis of TDCP-Aided GNSS with Method B
Although the degradation of state estimation still emerged during time 1963-1967 s, where the vehicle was performing a turning motion, and the auxiliary constraint and FDE in (19)-(23) were deactivated due to the continuous heading change, the remaining aiding methods still provided better performance compared with others, as shown in the trajectory (Figure 12). Based on this proposed solution, the overall analysis in Table 6 presents an RMSE of 2.51 m and 3.63 m in the horizontal and vertical directions with improvements of 35.1%, and 49.6%, respectively. The indicators of the percentage of epochs, including errors of less than 3 m (narrow-road accuracy) and 5 m (wide-road accuracy), were 86.1% and 97.3% in the horizontal direction and 63.9% and 84.4% in the vertical direction for these two levels, respectively. The corresponding analysis of the empirical cumulative distribution function (CDF) is shown in Figure 13. An analysis of vehicular tracking in Table 4 shows that the proposed combination scheme reduced the forward error and lateral error with an RMSE of 1.63 m and 1.95 m, corresponding to improvements of 45.3% and 21.1%. Statistically, this finding shows that, by using a low-cost automotive-grade receiver, the proposed GNSS/odometer scheme and the combined methods without any GNSS correction services can satisfy the accuracy requirement for locating a vehicle's position on a designated road in GNSS-challenging environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \multirow{2}{*}{**Unit: m**} & \multirow{2}{*}{**Conventional**} & \multirow{2}{*}{**TDCP-Aided**} & **TDCP-Aided** & **TDCP-Aided** & **TDCP-Aided** \\  & & & **GNSS +** & **GNSS +** & **GNSS +** & **GNSS + Method** \\  & & & & **Method A** & **Method B** & **A Method B** \\ \hline Mean & 2.83/4.78 & 2.47/3.41 & 2.50/3.17 & 2.57/3.05 & 2.28/2.88 \\ Max & 27.66/34.18 & 15.20/14.95 & 13.15/17.47 & 16.82/14.97 & 14.61/12.67 \\ RMSE & 3.87/7.20 & 3.01/4.55 & 2.79/4.16 & 2.98/4.11 & 2.51/3.63 \\ RMSE & - & 22.3\%/36.8\% & 28.0\%/42.0\% & 23.0\%/42.9\% & 35.1\%/49.6\% \\ Improvement & - & 22.3\%/36.8\% & 28.0\%/42.0\% & 23.0\%/42.9\% & 35.1\%/49.6\% \\ \hline Which Road 1 (**3 m)**2 & 72.0\%/42.4\% & 75.4\%/49.3\% & 81.8\%/60.4\% & 76.7\%/58.1\% & 86.1\%/63.9\% \\ Which Road 1 (**5 m)**2 & 89.5\%/65.8\% & 93.5\%/75.4\% & 96.3\%/82.9\% & 94.4\%/79.5\% & 97.3\%/84.4\% \\ \hline \hline \end{tabular}  
* The accuracy category refers to [47; 48; 49; 50]. 2 Percentage of epochs that had errors of less than 3 m or 5 m.  
\end{table} 
Table 6: Performance analysis in position domain.
###### Abstract  
Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (T\({}_{B}\)) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat--designed to provide T\({}_{B}\) data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 T\({}_{B}\) and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models--covering thin ice up to 0.6 m and full-range thickness--were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.  
predictive regression neural networks; sea ice thickness; microwave radiometry; CubeSats 2021, 1366. [[https://doi.org/10.3390/rs13071366](https://doi.org/10.3390/rs13071366)]([https://doi.org/10.3390/rs13071366](https://doi.org/10.3390/rs13071366))  
1
## 1 Introduction
### Rationale and Research Questions  
The key insights from the pandemic can be summarised as the need to meaningfully integrate technologies in both online and in-person learning while embracing an ongoing digital transformation of higher education. From the perspective of this broader context of digital transformations of higher education, this paper describes a novel approach to enhancing and scaling up in-person social learning experiences through Robot-Supported Collaborative Learning (RSCL). Specifically, this paper describes the design, implementation, and evaluation of a collaborative game-based learning activity facilitated by the social robot NAO. The collaborative activity \"Planning poker with NAO\" was designed to provide a scalable in-person learning activity in small groups of students. In the case study, the activity was facilitated by the robot NAO, which was applied as an embodied technology to provide an engaging social learning experience for university students in the course on agile project management. As outlined in the OECD 2021 report, social robots can be assigned different educational roles, such as tutor, teacher, or peer, to support social interaction and learning in an efficient and nonjudgemental way [9].  
The broader rationale for the case study \"Planning poker with NAO\" is threefold and encompasses the need (a) to provide scalable collaborative learning experiences in small groups for a larger number of groups; (b) to prepare university students for robotic interactions in future workplaces, which will be on the rise due to technological innovations in humanoid robotics (HR) and artificial intelligence (AI); and (c) to explore how emerging embodied technologies such as social robots can be used on the campus to enhance in-person, social learning. The case study presented in this paper explores the potential of applying a social humanoid robot as a facilitator of a collaborative learning activity by providing a structured and scalable template for a learning activity, which can be offered repeatedly for a larger number of small groups of students, while at the same time enhancing the potential of collaborative learning in small groups, such as increased engagement, teamwork, and communication. The focus of this study was on exploring the quality of the social learning experience of students playing planning poker in small groups while interacting with the robotic facilitator. Moreover, the study aimed to explore whether previous experience in interacting with the robot NAO had any influence on the quality of the learning experience. Hence, the key research questions of the study were: (1) What was the quality of the social learning experience in a group of students playing planning poker? (2) What was the quality of the interaction with the social robot NAO during the game of planning poker? (3) To what extent did the previous experience with the robot influence the quality of the learning experience?
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 5 Application Layer
### Probabilistic Model  
This model relies on replicated dynamic probabilistic models to minimize transmissions between WSN nodes and the base station [48]. In this model, the WSN maintains a pair of dynamic probabilistic models. One copy is distributed over the WSN nodes, and the other is saved at the base station. Each time, the base station computes the expected values sampled by the WSN nodes according to the model that does not require WSN nodes' data. When WSN nodes sense data that the model cannot predict, they forward the data towards the base station, that is, information is communicated to the base station only when the base station's predictions are not valid.
## 5 Probability of Data Error in IEEE 802.15.4
### Packet Reception Rate  
In 802.15.4 2.4 GHz PHY, four data bits are mapped to a data symbol [2]. When a packet of \(b\)-bytes is transmitted, the probability of successfully receiving the packet, \(P_{r}\), is calculated as:  
\[P_{r}=(1-P_{d})^{2b} \tag{40}\]  
It is worth noting that the upper bounds on \(P_{d}\) in Equation (38) and Equation (39) will become lower bounds in Equation (40). \(P_{r}\) as a function of SIR is shown in Figure 7 for the coherent and non-coherent demodulation schemes considered in Sections 3 and 4. Comparison between analysis and simulations is also shown. Results for the case of no thermal noise (cross-points for coherent and filled circles for non-coherent) have been obtained for a PHY service data unit (PSDU) of 14 bytes. The figure shows that the agreement between analysis and simulations is very good.  
Experimental results shown in Figure 7 of [19] suggest \(7\) dB for the threshold of SNR, where the performance of the system, in terms of PRR, completely switches from zero to one. In this sense, the interval from \(SNR=7\) dB to \(SNR=\infty\) should be adequate for representing a great variety of different SNR operation conditions, where the noise is not dominant, but the performance of the system is mostly determined by the interferer. This interval results in a considerably narrow region of PRR (the shaded area in Figure 7), and the experiment results in [16; 18; 19; 20] fall into this region; therefore, it would not be wrong to conclude that the mathematical analysis presented here captures the essential features of the real-world performance, where the dominant factor of performance degradation is interference. Two  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(h_{i}\) & 13 & 14 & 15 & 16 & 17 & 18 & 31 \\
**reoccurrence (\(r_{i}\))** & 2 & 2 & 3 & 3 & 2 & 2 & 1 \\ \hline \end{tabular}
\end{table}
Table 5: Recurrences of unique Hamming distance values.  
different values of PSDU are considered in Figure 7: 14 bytes (experiments in [20]) and 127 bytes (the maximum allowable, which is used in experiments in [16; 18; 19]). Finally, It is also worth noting that \(0\) dB is the absolute minimum SIR necessary to successfully detect the useful packets in non-coherent demodulation.