esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Irradiance Synthesizer for Optical GEO Satellite Downlinks in 5G Networks
## 5 Conclusions  
Optical communication satellite systems are the next step to a global network of the upcoming 5G cellular systems as they offer a variety of advantages but they also suffer from harsh atmospheric phenomena. In the case of an optical GEO downlink, air turbulence causes the scintillation of the optical signal, resulting in received irradiance fluctuations. The scintillation effects are more intense for short aperture ground receivers which are the most practical and affordable to employ and therefore the need emerges for scintillation profile modeling. In this contribution, a time series synthesizer for the accurate prediction of downlink irradiance scintillation is proposed. For modeling, the Rytov weak disturbance theory was considered and the Kolmogorov spatial spectrum was applied along with the integration of stochastic differential equations (SDEs). First and second order long-term irradiance statistics from the ARTEMIS optical link program were presented and used to successfully validate the proposed methodology while numerical results of received power statistics demonstrated the impact of atmospheric turbulence on the optical link.  
All authors contributed extensively to the work presented in this paper. [PERSON] and [PERSON] had the original idea on which we have based our current work and had the overall coordination in the writing of the article. [PERSON] and [PERSON] were responsible for the overall orchestration of the performance evaluation work. All the authors have contributed to the writing of the article.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{V_{g}}\) (\(\mathbf{m}\)/s) & \(\mathbf{A_{0}}\) & \(\mathbf{r_{0}}\) (\(\mathbf{m}\)) & \(\mathbf{SI_{point}}\) \\ \hline
0 & \(10^{-15}\) & 0.32 & 0.126 \\ \hline
8 & \(1.7\times 10^{-14}\) & 0.21 & 0.215 \\ \hline
12 & \(10^{-13}\) & 0.11 & 0.302 \\ \hline
12 & \(8\times 10^{-13}\) & 0.04 & 0.459 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fried parameter and scintillation index values for different \(C_{n}^{2}(h)\).  
Figure 10: Downlink received power for various weather conditions.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The work presented in this paper was carried out under the ONSET-CCN ESA project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Procedures  
All procedures were similar to those used in [PERSON] et al. [34]. The status of both ears of each subject was assessed using otoscopy (visual inspection of the ear, particularly the tympanic membrane) together with pure tone audiometry, tympanometry, and DPOAEs (Figure 1). Otoscopic examinations did not reveal any abnormalities.  
Pure tone HTs were assessed using a Madsen Astera clinical audiometer (GN Otometrics, Taastrup, Denmark). Air conduction HTs were determined for frequencies from 0.125 to 16 kHz using Sennheiser HDA-200 headphones (tested frequencies: 0.125, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, 8, 10, 12.5, 14, and 16 kHz). For normal hearing, a criterion of better than or equal to 25 dB Hearing Level (HL) was used.  
Tympanometry measurements were made in both ears using a Madsen Zodiac 901 impedance bridge (GN Otometrics). A standard test tone of 226 Hz was used (tympanometric peak pressure between \(-\)100 and +100 daPa and peak compensated static acoustic admittance of 0.3-1.3 mmhos).  
DPOAE tests were conducted using the HearID system (Mimosa Acoustics Inc., Champaign, IL, USA) with an ER-10C probe (Etymotic Research, Elk Grove Village, IL, USA). DPOAEs were evoked by two tones at frequencies of F\({}_{1}\) and F\({}_{2}\), and responses were measured at a frequency of 2F\({}_{1}\)-F\({}_{2}\). DPOAEs were measured at 9 selected frequencies for F\({}_{2}\) of 1, 1.5, 2, 4, 6, 8, 10, 12, and 16 kHz; the F\({}_{2}\)/F\({}_{1}\) ratio was 1.2, and the stimulus levels were 65 and 55 dB sound pressure level (SPL). The equipment settings used were the default of the HearID system, with the only change being the extension of frequencies up to 16 kHz. Only ears which gave a signal-to-noise ratio (SNR) greater than 6 dB and a response level greater than \(-\)20 dB SPL at 3 or more of the 7 frequencies from the range of 1, 1.5, 2, 3, 4, 6 and 8 kHz were analysed.  
Synchronized SOAEs (SSOAEs) were acquired using the in-built routine (SOAE50) provided by the HearID system. This routine applies linear click stimuli of 50 dB SPL and records OAEs in a 40 ms window. The first 20 ms of each averaged response (containing largely the evoked part) was discarded. The spectra of responses from the last 20 ms were analysed in a search for SOAE peaks. SOAEs were considered to be present when at least one peak was found in the 1-6 kHz range that exceeded the noise floor by at least 6 dB.
## 5 Applications and Challenges
### Challenges
#### 5.2.4 Optical Performance Challenges  
Tuneable lenses may introduce a variety of optical aberrations, such as spherical aberration, coma, etc., during the deformation process, which can significantly affect the performance of the imaging system and lead to a decrease in the clarity and accuracy of the images. The existence of such aberrations greatly limits the application of DETL in fields with high imaging quality requirements, such as high-precision microscopic imaging and astronomical observation. In the current design structure of most tuneable lenses, a single electrode region is usually used for open-loop control, which cannot adjust the morphology of the lens in real time or dynamically according to the detected aberration, and it is difficult to achieve adequate compensation and optimisation of the aberration. As a result, the non-ideal morphology produced by the lens during the deformation process cannot be corrected in time, which directly affects the imaging quality [18]. The active aberration adjustment can be accomplished by designing a multi-electrode region [49]. Real-time compensation for aberration based on this scheme can further enhance the imaging quality of the lens.  
Additionally, existing tuneable lenses based on dielectric elastomer actuation still face limitations regarding zoom range. Currently, the zoom ratio of dielectric elastomer-driven liquid lenses is typically only a few times, which makes it challenging to meet the demand for high-magnification zoom in certain specialised fields such as telephoto photography, astronomical observation, and telephoto imaging. Consequently, there is an urgent need to explore new driving principles and design methods to achieve adjustable focus technologies with higher resolution and greater adjustment capability range.
## 3 Results and Discussion
### Mass Transfer Coefficient
#### 3.2.2 Influence of the Gas and Liquid Composition on the Mass Transfer Coefficient
Five points in the upper part of Figure 6a highlight the rest of the experimental cases. These values correspond to the experiments where the initial NaOH concentration was 0.5 and 1 mol L\({}^{-1}\) in the column. A high initial concentration avoids the rapid exhaustion of the hydroxides, reaching best mass transfer and capturing more CO\({}_{2}\). These coefficients were obtained only when the first reaction between the contaminant and the hydroxides was involved. A barrier rounding the 50% NaOH/Na\({}_{2}\)CO\({}_{3}\) limits the possibility to reach mass transfer coefficients one order of magnitude higher than the majority collected. Thus, as soon as the carbonate ions were more and more present, the coefficient became lower. This performance is more pronounced in the column but it can be also appreciated when the plot is zoomed, as it is addressed in Figure 6b. Here, when the NaOH has already disappeared, the smallest removal of CO\({}_{2}\) is reached because now the carbonates are the only species with the capacity to absorb; therefore, bicarbonate appear. From an industrial point of view, a low coefficient means poorer yield of the equipment; i.e., the column or the contactor. Nevertheless, the efficiency of the whole set up did not follow the same logic; i.e., the worst efficiency of the equipment was not always the worst performance of the complete system. This performance is seen on the membrane contactor if both the coefficients and the removal are contrasted in opposition with the apparently more coherent trend in the column, where high mass transfer and CO\({}_{2}\) elimination perfectly fit each other. The comparison between the packed column and the membrane module must take into account the dimensions of each technology. Although the flue gas and the absorbent flow rates in the column where lightly higher than the one used though the membrane contactor, the intensification is clearly demonstrated since a membrane contactor whose volume is 0.13 m\({}^{3}\) is able to obtain similar results than a packed column of 0.7 m\({}^{3}\). Thus, the intensification factor; i.e., the ratio between the packed column volume and the membrane contactor volume--is 5.38. Studies about packed columns and membrane contacts [35; 36] collected intensification factors between 1.5 and 11 using monoethanolamine, diethanolamine, triethanolamine or piperazine as absorbents. None of the previous research using NaOH as a sorbent has been found that uses the intensification factor parameter.
## 4 Performance and Simulation Results
### The Original BA Protocol  
As explained before, the proposed modifications of BA anti-collision protocol can handle the capture effect phenomenon and ensure that the full read rate of the tags existed in reader interrogation zone. Moreover, the distinction between collision and interference signals for the period of staying tags can be obtained. Before showing our modifications improvements, a simulation of the original BA protocol was conducted with varying capture effect probability \(\alpha\) from zero (no tag has stronger signal than other tag) to one (one tag has the strongest signal), as shown in Figure 4. The results indicate that a number of tags will be killed (not identified) increasingly as C.E probability increases. Also, as interference probability increases, the consumed total number of cycles will increase significantly, even when the number of staying tags is large (for 1 m/frame velocity), as shown in Figure 5. Consequently, MBA attempts to solve these two drawbacks in the following subsections.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Simulation area & 3.5 \(\times\) 10 m\({}^{2}\) \\ Identification range & 3.5 \(\times\) 3.5 m\({}^{2}\) \\ Total number of tags (\(n\)) & 300 or (100 to 500) \\ Stationary probability (\(ps\)) & 0 (moving constantly) \\ Tags velocity (\(v\)) & 3.5 m/frame (MBA) non-blocking velocity, \\ Weight factor (\(z\)) (Reader estimation factor) & 0.5 \\ Capture effect probability (\(\alpha\)) & 0–1 \\ Interference probability (\(pi\)) & 0–0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3: The simulated area.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Evaluation Results
#### 5.4.1 Benefits of Collaborative Sensing
Figure 16 shows a comparison of the maximum, minimum and average energy consumption when: (i) they only have the prototype application (CoSP) running and (ii) the devices are only sensing GPS; (iii) they have the application running and the AllJoyn service active, but they are not sharing any data. Thus, we intend to evaluate the cost of maintaining an AllJoyn session (16 mWh in average), in comparison with the cost of sensing. These results also show that the cost of sensing GPS is considerably higher than the cost of using AllJoyn. The second set of bars in Figure 16 shows the average cost of sensing and transmitting GPS data (corresponding to a Producer role) using AllJoyn, versus the cost of receiving it (Consumer). From the figure it is clear that the cost of sensing and transmitting is slightly higher than the cost of sensing GPS, whereas the cost of receiving information is smaller (15.06 mWh in average). This result indicates that there is an obvious benefit for the receiving device (GPS Consumer), while the cost in the transmitting device (GPS Producer) is relatively low, which clearly points to the advantages of collaborative sensing in terms of social welfare because there is some benefit for the overall group of users (3.41 mWh in average). It seems reasonable to think that, if number of devices increases, these benefits could increase too. Thus, we could compensate the communication cost introduced by maintaining the AllJoyn session. Nevertheless, further tests are needed to confirm this claim. Figure 15: Comparison of the overall collaborative sensing costs of AllJø
# Research Developments of Aerostatic Thrust Bearings: A Review
## 2 Theoretical Models and Experiment Methods
### Experimental Methods
#### 2.2.3 Measurement of Dynamic Characteristics  
The measurement of dynamic characteristics is achieved by measuring the dynamic force generated in the bearing film. The dynamic forces are the results of harmonic variation, either (i) in the film thickness, at constant supply pressure, or (ii) in the supply pressure, at a constant film thickness. The measurement of the dynamic characteristics is also a challenge, owing to the small gap sizes involved. In general, the first type of measurement is more convenient and suitable considering the measurement procedure and error estimation [41].
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 5 Conclusions
### Nomenclature  
\(A_{i,t}\)Random variable representing availability of generator \(i\) at time \(t\) (1 if available, 0 otherwise)  
\(c\)Element of \(C_{t-\Delta t}\)  
\(C_{t-\Delta t}\)Set of combinations of \(A_{i,t-\Delta t}\) when \(O_{i,t-\Delta t}\) is nonzero for all \(i\)  
\(e\)Element of \(E_{t}\)  
\(E_{t}\)Set of \(NLFE_{t}\)  
\(FL_{t}\)Forecast load at time \(t\)  
\(FNL_{t}\)Forecast net load at time \(t\)  
\(FVG_{t}\)Forecast variable generation at time \(t\)  
\(i\)Index of generator  
\(I\)Set of generators  
\(LTE_{t}\)Random variable representing load forecast error at time \(t\)  
\(NLFE_{t}\)Random variable representing net load forecast error at time \(t\)  
\(O_{i,t}\)Value representing whether generator \(i\) is online at time \(t\) or not  
\(P_{i,t}\)Output of generator \(i\) at time \(t\)  
\(P_{\max,i}\)Maximum output level of generator \(i\)  
\(Prob(\cdot)\)Probability in parentheses  
\(Prob_{i}[\cdot]\)Probability of \(c\) if condition \([\cdot]\) is satisfied, 0 otherwise.  
\(RCR_{t}\)Ramping capability requirement at time \(t\)  
\(rr_{i}\)Ramp rate of generator \(i\)  
\(RSP_{t}\)Ramping capability shortage probability at time \(t\)  
\(SRC_{t}\)System ramping capability at time \(t\)  
\(t\)Index of time  
\(\Delta t\)Minimum interval between operating points  
\(VGFE_{t}\)Random variable representing variable generation forecast error at time \(t\)
## 3 Methods
### Convolutional Neural Network  
The operation of CNNs is based on convolutional filters. When a signal passes through these filters, it is transformed into a vast array of features that are then classified by a fully connected layer. In the search for the optimal CNN architecture, the impact of varying the number of convolutional layers (ranging from 2 to 5) was examined. Additionally, the effect of the number of filters with values of 2, 4, 8, 16, 32, 64, 128, and 256 was evaluated. Subsequently, the influence of different filter sizes--2, 4, 8, 16, 32, and 64--was investigated. The selection of the network structure was derived from an automated search for the optimal combinations of layer count, filter count, and filter size. During our research, we did not consider the impact of the dropout layer on the CNN training process. During the selection of the best parameters, different optimizer algorithms (ADAM, SGD) and a range of values for InitialLearnRate (0.0001, 0.001, 0.01) and L2 Regularization (0.01, 0.001, 0.0001) were evaluated. The search for the optimal combination of network structure and learning parameters spanned several days. The best network structure and parameters were determined based on the classification accuracy obtained for the validation data. The accuracies for the validation set for the considered structures ranged on average for all users from 0.65 to 0.72. The best results were achieved for the CNN network structure, which consisted of four convolutional layers, applying a ReLU activation function after each. The final convolutional layer, along with the subsequent ReLU layer, consists of 128 filters, resulting in a considerable number of features fed into the SoftMax classifier. The ADAM optimizer [37] was employed to train the CNN network, with an InitialLearnRate set at 0.001. Training was conducted over a maximum of 50 epochs, with a MiniBatchSize of 128 and an L2 Regularization factor of 0.0001. The architecture of the CNN used in this study is detailed in Table 1. During the training of the network, the learning curve and error for the validation data were observed. No signs of overfitting in the CNN were noticed.  
To train a CNN, a large number of training examples are needed. During training, we utilized a dataset obtained through the proposed augmentation method. However, for testing the performance of the CNN, we employed EEG signals recorded during the test session. The schematic for CNN application is presented in Figure 5. In the Supplementary Materials, there is the source code of our developed method for EEG data augmentation, as well as the code for the implementation of a CNN network that enables the classification of SSVEP.  
Figure 4: Spectrum of the real EEG signal (blue) and the generated signal (red).
## 3 Numerical Analysis  
Numerical analysis provides a powerful tool for understanding the performance of solar air heaters and optimizing their design for increased efficiency. Therefore, a numerical analysis of a solar air collector was carried out to simulate the performance of the system. The goal was to predict the temperature and velocity distribution of the passing air and the wall temperature on the absorber surface with stainless-steel scouvers.  
All of the simulations in this study were run using the commercial CFD code software ANSYS-CFX-R18.2. The geometric modeling of the experimental setup served as the first step in the solution process. The collector in the experimental setup was modeled one-to-one in 3D. Mesh independence was also carried out in order to attain the proper level of precision and to utilize the available computer resources as efficiently as possible. The equations of continuity, momentum (Navier-Stokes) and energy were solved for fully developed, turbulent, steady-state, incompressible flow. As the discretization method, the finite element method with pressure velocity coupling was used to tackle the current issue. Every plan was second-order upwind.
## 4 Results and Discussion
### Comparative Experiments and Baselines  
In order to evaluate the performance of the proposed method, several baseline models were considered for comparison. These included XGBoost [61], DQN [62], BERT [63], transformer-based prediction market models [64], and LSTM-based prediction market models [65]. Each of these baselines represents a different approach to pricing optimization, enabling a comprehensive comparison across various state-of-the-art techniques. XGBoost (Extreme Gradient Boosting) is a highly effective implementation of gradient-boosted decision trees and is widely used for supervised learning tasks. It has been particularly successful in many machine learning competitions due to its scalability, efficiency, and ability to handle various types of data. The deep Q-network (DQN) is another baseline model used for reinforcement learning tasks, particularly in the context of dynamic pricing. The DQNmodel approximates the \(Q\)-value function using a deep neural network, allowing the model to learn optimal pricing strategies by interacting with the environment. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model that has achieved significant success in natural language processing tasks. BERT models are able to understand contextual relationships between words in a text by training on large corpora with both left-to-right and right-to-left contexts. The transformer-based prediction market model employs self-attention mechanisms to capture dependencies across various market conditions and consumer behavior. By using multi-head attention layers, the transformer model learns the relationships between market inputs and predictions, allowing it to make more accurate forecasts over long sequences. The LSTM-based prediction market model is another baseline used to model sequential dependencies, especially in time-series data such as sales and pricing history. LSTM is well known for handling long-range dependencies by using gating mechanisms that control the flow of information.
## 3 Proposed Frame Aggregation with Block Acknowledgment Mechanism
### Revisiting MP-EDCA
#### 3.1.2 Limitations of MP-EDCA  
A critical limitation of the MP-EDCA protocol is its inability to enforce deterministic latency guarantees for life-critical emergency traffic under high network loads. Specifically, MP-EDCA relies on probabilistic contention window adjustments and traffic prioritisation, which fail to ensure bounded packet delays when the density of emergency nodes exceeds 40. In such scenarios, network performance degrades sharply, suffering higher collision rates and unstable channel access, and cannot meet the latency thresholds required for mission-critical applications (e.g., emergency medical services or disaster response systems). The proposed FASBA mechanism introduces a hybrid MAC layer framework that combines dynamic time slot reservation with adaptive contention parameters to address this. FASBA enforces strict priority preemption for emergency traffic and optimises resource allocation in ultra-dense deployments, thereby achieving compliance with industrial QoS standards for life-saving applications.
## 4 Feature Extraction and Control Methods  
### Monitoring and Control Program  
For the active balancing system, a control unit was necessary to control the balance actuator, and the control loop is shown in Figure 7. A monitoring and control program was constructed to monitor the vibration status of the rotor system during normal operation. When the unbalance vibration amplitude exceeds its set value, the program makes a calculation of the compensation mass by adopting of the known influence coefficient, and then commands are sent to the solenoid valves. The relevant solenoid valves are opened and compressed air flows into the target chamber via the related air pipeline, and then the balance liquid is transferred. Figure 7 shows a block diagram of the control loop.  
Figure 6: Rotor dynamics analysis of the rotor system: (**a**) initial rotor system mode; (**b**) rotor system model with actuator; (**c**) analysis result of initial rotor system; and (**d**) analysis result of final rotor system.  
Figure 7: Block diagram of the control loop.