esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Total Fixation Duration According to Age, Reading Conditions, and Text Types  
A main effect of age (\(F\)(5, 545) = 4.086, \(p\) = 0.001), as well as an interaction effect between age and reading condition (\(F\)(5, 545) = 2.638, \(p\) = 0.023), on total fixation duration (TFD) were observed. A simple main effect test was administered, due to the significant interaction effect. The main effect of age was significant when reading fictional text under both R (\(F\)(5, 545) = 5.22, \(p\) < 0.001) and AR (\(F\)(5, 545) = 2.77, \(p\) = 0.046) conditions. Within the R condition, age-related differences were observed, showing a decline in TFD with an increase in age. Similarly, while reading fictional text, the AR condition exhibited these age-related differences, with the exception of the results from the first grade children, in which the shortest TFD was observed, deviating from the results for other age groups (see Figure 2). The [PERSON] post hoc test revealed significant differences in TFD between grade 1 and grade 4 (\(p\) = 0.001) and grade 1 and grade 5 (\(p\) = 0.003) in the R condition while reading fictional text. The interaction effect between age and reading condition was derived from the significant difference between R and AR conditions in the first grade, with the AR condition demonstrating lower TFD compared to the R condition (\(p\) = 0.006).  
The main effects of reading condition (\(F\)(1, 545) = \(-\)0.527, \(p\) = 0.468) and text type (\(F\)(1, 545) = 0.054, \(p\) = 0.816) were not significant. There were no significant two-way interactions between age and text type (\(F\)(5, 545) = 0.497, \(p\) = 0.779) and reading condition and text type (\(F\)(1, 545) = 0.174, \(p\) = 0.677), while a three-way interaction among age, reading condition, and text type (\(F\)(5, 545) = 0.853, \(p\) = 0.513) also failed to reach statistical significance.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 2 Traffic Modeling
To summarize, an important question meriting a detailed response from all previous works is whether it is feasible to model the traffic of a vast number of autonomous machines using source traffic modeling, which is, in general, more precise than aggregated traffic modeling (e.g., treating the accumulated data from all M2M devices as a single stream). Practically, we are not in need to know about the actions of a single system to evaluate multiple access and tremendous capability. Thus, the overall behavior of M2M traffic is matched by the aggregated traffic models represented by a simple Poisson process. Using a Poisson process, MMPP models can handle non-constant interval-based events. By employing one server, the capture of a single request at any given moment can constitute the event. This allows MMPP models to handle unstructured data. Furthermore, we spotted how MMPP models may be used for both structured and unstructured data, enabling the modeling of unstructured data utilizing several states like a multistate model. Additionally, we show that uncertainty can be represented as in a classical model. The terminology and mathematical notations required for the MMPP technique are first presented in Section 3. Next, the CHANAL model and its performance metrics are explained in Section 4. A use case and a numerical demonstration utilizing simulations are covered in the final part. Figure 1: The predicted arrival rate utilizing 3 GPP Models 1 and 2 over time for both synchronized and non-synchronized M2M traffic. Finally, because M2M traffic exhibits synchronized behavior, it is anticipated that the average arrival rate (\(\lambda\)) would change with time [22; 23]. Aggregated traffic models, such as heterogeneous models with time-varying arrival rates, are currently a hot topic that should be studied and compared with homogeneous traffic with a constant average arrival rate (\(\lambda\)), as explained in the following section.
## 2 Materials and Methods
### Case Study: Zhujiang New Town  
Guangzhou, identified as the most flood-prone city among 136 global coastal cities, experiences a subtropical monsoon climate with a substantial annual average rainfall of 1890 mm [25]. This study focuses on Zhujiang New Town, a prominent urban business district in Guangzhou, as the case study area. The catchment spans 102 hectares, characterized by relatively flat topography and a drainage network comprising 43 sub-catchments, 119 conduits, and 11 potential flow outlets (Figure 2). The Storm Water Management Model 5 (SWMM 5) was employed to simulate the rainfall-runoff processes within this catchment. Rainfall losses were modeled using the Horton infiltration method, while surface flow was routed using the dynamic wave approach [26]. Detailed descriptions of the sub-catchment characteristics and the corresponding hydrological and hydraulic parameters are provided in Table S1. The hydrological model was calibrated using observed rainfall-runoff data  
Figure 1: Illustration of the proposed optimization framework and workflow process. Key elements include the Storm Water Management Model (SWMM), Life Cycle Cost (LCC) analysis, and Recurrence Interval (RI) considerations.  
from 2013-2014, in accordance with the methodology of [PERSON] et al. [27]. Calibration focused on optimizing key parameters such as hydraulic conductivity, impervious area fraction, and infiltration rates using a trial-and-error approach supported by sensitivity analysis. Validation was conducted using 10 independent rainfall events not used in the calibration phase, covering a wide range of intensities and durations. Model performance was evaluated using the Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE), with results exceeding 0.75 and 0.70, respectively, indicating good to excellent model accuracy across diverse storm conditions [28; 29]. The validation results demonstrate high congruence between observed and simulated data, consistently achieving strong efficiency scores and maintaining relative errors well below 10% across various rainfall scenarios. These results affirm the model's accuracy and reliability for simulating stormwater dynamics in urban settings. Furthermore, this study builds upon insights and methodologies from prior research conducted by the same group of authors [30] on a comparable urban catchment. The integration of these prior experiences underscores the robustness of the methodological framework and enhances the credibility of the findings presented.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 6 Performance Evaluation  
### Performance of H2H Traffic in the Presence of the Overlay Network  
We consider the LTE cells with a mix of H2H and M2M traffic, using the PRACH and PM2M overlay parameters given in Table 2.  
We first evaluate the performance of H2H traffic in the presence of PM2M overlay. The H2H request arrival rate was varied between 20 and 220 requests per second. Figure 7(a) shows the H2H probability of success, which remains within 99% up to 220 H2H calls/s. The collision probability shown in Figure 7(b) is only 0.0045, which validates the probability of success. The presence of the overlay network is not overloading the cell with respect to H2H traffic, as shown in Figure 7(c), where we  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline codes per cell, \(N\) & 64 \\ codes for H2H traffic, \(N_{i}\) & 46 \\ codes for PM2M overlay traffic, \(N_{M}\) & 8 \\ LTE frame duration & 540 overlay backoff periods \\ LTE system bandwidth & 5 MHz \\ PRACH bandwidth & \(W=1.08\) MHz \\ preamble length & 839 elements \\ preamble duration & 1600 us \\ preamble elements & \(N_{b}=16\) \\ preamble format & 2 \\ RACH configuration index & \(c_{f}=5\) \\ preamble element rate & \(k_{p}rate=1.048\) M elements \\ traffic class & \(TC_{0}\) and \(TC_{1}\) \\ one backoff period & 18.51 μs \\ superframe beacon interval & 540 backoff periods \\ PM2M superframe duration & 410 backoff periods \\ PM2M MAC data packet size & 150 bytes with header \\ Maximum number of attempts to transmit the packet, \(R\) & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of PRACH and the PM2M overlay network.  
observe that, for the H2H arrival rate up to 220 calls/s, the overload probability for H2H traffic is only 0.00065, which is very low indeed. The mean access delay (Figure 8d) is essentially flat, with only a slight increase from 14.500 ms-14.556 ms in the observed range. Note that these performance limits would apply to M2M devices in case they use regular LTE access on PRACH: in other words, the random access procedure on PRACH is able to accommodate only about 220 calls per second, be they posted by H2H or M2M users.
## 4 Results and Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **FSV** & **\# of Samples** &
\begin{tabular}{c} **Execution Time** \\ **(ms)** \\ \end{tabular} \\ \hline \multirow{8}{*}{**Free Space**} & **Corner** & 0.18 & 37 & 96.07 \\ \cline{2-5}  & **Refficor** & 0.076 & 37 & 97.2 \\ \cline{2-5}  & **Refficor** & 0.121 & 37 & 97.2 \\ \cline{2-5}  & **Refficor** & 0.124 & 37 & 171.7 \\ \cline{2-5}  & **Refficor** & 0.119 & 37 & 99.1 \\ \cline{2-5}  & **Refficor** & 0.164 & 37 & 103.6 \\ \cline{2-5}  & **Refficor** & 0.171 & 37 & 103.3 \\ \cline{2-5}  & **Refficor** & 0.155 & 37 & 98.8 \\ \cline{2-5}  & **Refficor** & 0.111 & 37 & 107.2 \\ \cline{2-5}  & **Refficor** & 0.275 & 30 & 100.2 \\ \cline{2-5}  & **Refficor** & 0.262 & 30 & 98 \\ \cline{2-5}  & **Refficor** & 0.277 & 30 & 99.3 \\ \cline{2-5}  & **Refficor** & 0.381 & 30 & 99.8 \\ \cline{2-5}  & **Refficor** & 0.329 & 30 & 100.4 \\ \cline{2-5}  & **Refficor** & 0.204 & 30 & 104.2 \\ \cline{2-5}  & **Refficor** & 0.322 & 30 & 101.1 \\ \cline{2-5}  & **Refficor** & 0.126 & 30 & 106.1 \\ \cline{2-5}  & **Refficor** & 0.183 & 30 & 98.4 \\ \cline{2-5}  & **Refficor** & **0.199** & **34** & **105** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Free space and buried objects scenarios’ FSV results for VCM, with execution time and the number of samples used. Figure 13: FSV results for both the Cauchy and VCM methods, according to the comparison of the input signals and the corresponding reconstruction in the simulated and experimental setups. VCM algorithm was designed. An evaluation of the similarity between the input and the reconstructed signal (FSV) and increasing noise scenarios demonstrated the increased signal-to-noise tolerance of VCM and were used for a noise impact evaluation. Figure 14 summarizes these concepts. The FSV results data from all scenarios, starting with the simulations, then the free space, and then the buried samples, are considered to have a decreasing SNR, i.e., the backscattering response of interest hides in the response noise as the scenario becomes more complex. If we arrange the information in this manner, an understanding of how well this CNR extraction algorithm performs as noise increases is displayed in Figure 15.  
Figure 14: Summary of the signal-to-noise tolerance improvement and evaluation. Figure 15: FSV result for all backscattering scenarios arranged according to increasing signal noise. The green line corresponds to a moving average and is higher for buried objects scenarios. Here, the FSV result increasing alongside the noise and the green line corresponding to a moving average suggest that the signal recovery diminishes in precision as the SNR decreases. This agrees with [PERSON] in [8], which stated that only approximations to resonances in lossy mediums with frequency-dependent attenuation are ever available in the limits of vanishing noise.
# Recent Progress on Solution-Processed CdTe Nanocrystals Solar Cells
## 2 Fabrication of CdTe NCs  
In the case of CdTe NCs with dot-shaped, rod-shaped, or tetrapod-shaped, a high-temperature (up to 200 \({}^{\circ}\)C) hot injection based on organic solvent or a low-temperature (below 100 \({}^{\circ}\)C) method with water as solvent are widely used today [19; 20; 21]. In the case of the hot-injection method, Te precursors (e.g., trioctylphosphine tellurium (TOPTe)) are a classic method to introduce the Te source. Trioctylphosphine oxide (TOPO) is widely used as a coordinating solvent. In order to control the morphology of CdTe NCs, hexyl-, tetracdecyl-, and octadecyl-phosphine acids are used as binding ligands to stabilize the NC growth [22; 23; 24], which is also used for the fabrication of other CdE (E = Se or S) NCs [25]. In shape and phase control fabrication of CdTe with branched architectures, researchers have found that phase control and switching are very important roles to achieve a branched shape [26; 27]. During the formation of branched CdTe NCs, it should be nucleation in the zinc blende phase while growing in the wurtzite phase. Based on this concept, [PERSON] et al. [28] developed a seed-epitaxial route to fabricate CdSe\({}_{x}\)Te\({}_{1-x}\) NCs with branched shapes successfully. Additions of fatty acids (oleic acid or myristic acid) as weak binding ligands to the reaction system were also proposed by [PERSON] and co-workers [29] to fabricate CdTe NCs with rod or tetrapod-shaped. Using fatty acids as ligands will decrease the reaction temperature to around 240 \({}^{\circ}\)C, which can supply advantages of low cost and high quantity fabrication. To extract CdTe NCs with high purity from the reaction system, the CdTe NCs product is washed with solvent and separated by centrifugation. Finally, the collected CdTe NCs samples for devices fabrication are always refluxed in pyridine to finish the ligands exchange. In the case of water-based method, NaHTe is used as Te source, which is prepared by adding Te powder into NaBH\({}_{4}\) solution and refluxing. CdTe NCs is then fabricated by adding NaBH\({}_{4}\) into the Cd source (such as CdCl\({}_{2}\)) solution. The aqueous method has many advantages, such as environmental friendliness, low cost, and low temperature [30]. However, unlike the hot injection solvent method, it is still difficult to control the phase and morphology of CdTe NCs due to the large polarity of water.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 5 Improving Energy Efficiency in FL Using Emerging Technologies
### Simultaneous Wireless Information and Power Transfer  
SWIPT has emerged as a vital technology in the IoT era, enabling simultaneous data and energy transmission over a single wireless medium. This innovation benefits energy-constrained devices by eliminating the need for separate power sources [120]. SWIPT is becoming increasingly important in FL-enabled wireless systems, as it allows devices to harvest energy from RF signals while simultaneously receiving control information or updates [121]. The harvested energy supports local computations and facilitates uplink transmissions throughout the FL process. As a result, the integration of SWIPT into FL-enabled wireless systems has garnered significant attention in recent studies.  
In [122], the use of SWIPT in IoT with FL is explored, where devices simultaneously transmit model data and harvest energy. The study investigated the trade-off between communication rounds and time, ensuring that energy harvesting offsets energy expenditure. Additionally, SWIPT was shown to enable efficient model learning with minimal communication rounds while extending device battery life. The authors in [123] proposed the use of SWIPT in FL to address the energy limitations of edge devices. The central parameter server, co-located with the BS, uses SWIPT to transmit both the model and power to edge devices, which then train local models and send updates via NOMA. Similarly, Ref. [124] presented a SWIPT-assisted FL network and minimized long-term energy consumption while ensuring FL convergence through dynamic resource allocation and UE scheduling.  
SWIPT also provides significant benefits for FL-enabled UAV systems by addressing energy scarcity, particularly in micro-UAV swarm networks. Studies in [125; 126] tackle this issue using SWIPT for FL, where the BS broadcasts both the model and power to UAVs, which use the harvested energy to train and upload models. The research optimizes UAV scheduling and resource allocation, with results showing that the proposed suboptimal algorithm outperforms existing baselines. Furthermore, [127] introduced a UAV-based communication model for IoT in areas without ground BSs. This model uses SWIPT to transmit both information and energy to IoT devices, optimizing UAV trajectory, power splitting ratio, and communication scheduling to maximize energy efficiency through DRL.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
###### Abstract  
To enhance the control accuracy of lane-keeping assistance systems for trucks encountering crosswind-induced lateral deviations to improve the lateral stability of the vehicle, this study proposes a control strategy based on a linear quadratic regulator (LQR) using a path-tracking preview model. First, the lateral deviation is calculated using the path-tracking preview model. Then, an observer for the vehicle's sideslip angle is designed using a vehicle lateral tracking deviation model and a Kalman filter controller, and this is used to solve the deviation of the sideslip angle. Finally, a feedforward controller is designed based on the LQR controller and a linear two-degrees-of-freedom vehicle model to eliminate steady-state errors arising from LQR optimization, thereby obtaining the steering angle of the vehicle when subjected to crosswind conditions. Comparing the test results of the sideslip angle, yaw rate, and lateral acceleration demonstrates that this strategy effectively improves the control accuracy of lane-keeping under crosswind conditions. The proposed method is validated through hardware-in-the-loop experiments on a test bench, yielding results consistent with simulations.  
lane-keeping assistance; crosswind; linear quadratic regulator controller; hardware-in-the-loop experiments 2023
## 3 Principles of Speed Estimation for Inter-Symbol Processing
### Signal Demodulation
Since the QPSK signal can be superimposed by two-way BPSK in-phase and quadrature, in demodulation, the coherent demodulation of BPSK signals is carried out separately for the two-way signals, and the resulting data are finally converted by concatenation and serial conversion to obtain the transmitted baseband signal. For IQ demodulation, the received signal is multiplied by a carrier of the same frequency and phase. Ideally, for I demodulation, the received signal is multiplied by a \(\cos\omega_{0}t\), and for Q demodulation, it is multiplied by \(-\sin\omega_{0}t\):  
\[\frac{2}{T}\int\limits_{-\frac{T}{2}}^{\frac{T}{2}}r(t)\cos\omega_{0}t\mathrm{ dt}=a_{n} \tag{9}\]  
\[\frac{2}{T}\int\limits_{-\frac{T}{2}}^{\frac{T}{2}}r(t)(-\sin\omega_{0}t) \mathrm{dt}=b_{n} \tag{10}\]  
After integration, two baseband signals, in-phase and quadrature, are obtained, respectively, and the original QPSK baseband signal is obtained by summing the two signals.
# Mixed Order Fractional Observers for Minimal Realizations of Linear Time-Invariant Systems
## 3 Non-Adaptive Mixed Order Observer (NAMOO)
Since \(\left(s^{\alpha}I-F\right)^{-1}\) is a BIBO stable filter according to the choice of \(F\) and \(\Delta_{u,y}\) are bounded, we conclude that \(\Delta\) is a bounded function. It follows that \(\hat{x}\left(t\right)-x\left(t\right)=\xi\left(t;x_{0},F,\beta\right)-\xi\left( t;x_{0},F,\beta\right)+\Delta\left(t\right)\), and therefore \(\hat{y}-y\), are bounded. If the parameters are changed to \(p+\Delta_{p}\), then \(\hat{x}\left(t\right)-x\left(t\right)=\xi\left(t;\hat{y}_{0},F,\beta\right)- \xi\left(t;\hat{y}_{0},F,\beta\right)+\left[\eta_{1}\ldots\eta_{n}\right] \Delta_{p}\). Since \(u,y\) were assumed bounded, and since \(F\) was chosen such that system (8) is BIBO stable, we conclude that \(\hat{x}\left(t\right)-x\left(t\right)\) and therefore \(\hat{y}-y\), are bounded. **Remark 1**. :
1. _A benefit of the MO approach to design observers is that the transient behavior of the estimate_ \(\hat{x}\) _can be regulated by the choice of the FO_ \(\beta\)_. A NMFOO where_ \(\beta=\alpha\)_--like the Luenberger observer (_11_)--can only regulate the speed of convergence through the choice of_ \(F\)_._
2. _Unlike Luenberger observer (_11_), there is no need of an output error feedback term_ \(F\left(\hat{y}-y\right)\)_. Since this term is essential in the stability proof of the Luenberger observer, the introduction of this term in our designs can give some freedom to further improvements._
# Performance-Constraint Fault Tolerant Control to Aircraft in Presence of Actuator Deviation
## 6 Conclusions  
This paper proposed a barrier-Lyapunov function based practical FTC-FT control scheme for a class of strict-feedback multi-input and multi-output autonomous system with actuator faults, theoretical analysis indicates that the states of system are able to converge to the origin, and trajectories always keep within the given limitation, all signals satisfy the uniformly ultimate bound. Based on this scheme, we designed a FTC-FT controller with rate constraints to the yaw and vertical channels, The simulation results on the a helicopter prototype platform show its effectiveness and superiority, comparing with the traditional finite-time-convergence control and asymptotic convergence methods, it improves the yaw and vertical motion dynamics process and their rates are always keeping in the configured safe ranges. This fault-tolerant controller has the capability to resist pitch angle and speed fluctuation, and enhances the stability margin to an aircraft in presence of actuator deviation. Future work will focus on the extension test of other actuator failures and run on a real flight management computer.  
Figure 11: Vertical channel performance in case of both \(\delta_{col}\), \(\delta_{tr}\) and motor deviation. (**a**) Height; (**b**) Vertical velocity.  
Figure 10: Yaw channel performance in case of both \(\delta_{col}\), \(\delta_{tr}\) and motor deviation. (**a**) Yaw rate; (**b**) Yaw angle.  
**Author Contributions:** Conceptualization, P.T. and Y.D.; methodology, P.T.; software, C.Z.; validation, P.T., S.L. and Y.D.; formal analysis, P.T.; investigation, resources, Y.D.; writing--original draft preparation, P.T.; writing--review and editing, C.Z.; visualization, P.T.; supervision, Y.D.; project administration, Y.D.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partly funded by the Science and Technology Department of Sichuan Province, grant number: 2020 YFSY0004.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] were employed by the company AVIC Chengdu Aircraft Industrial (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.