esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This research has successfully demonstrated a long-distance wireless communication system using photonics-aided technology, allowing for a stable transmission of 50 Gbps (25 Gbaud) QPSK signals over 4600 m at a carrier frequency of 128.75 GHz. At an optical power of 8 dBm, the BER reached 5.3 \(\times\) 10\({}^{-3}\), which is lower than the threshold of 1.56 \(\times\) 10\({}^{-2}\) for SD-FEC with a 15% overhead. A method using a 2D-CNN which allows for the simultaneous correction of phase noise and nonlinear distortion was proposed to equalize nonlinear impairments and phase noise interference in the RoF system. Compared to traditional Volterra series and DNN methods, it has shown significant improvements, providing a new and effective way to fix signal problems in high-speed wireless communication systems. It offers essential technological assistance for the development of future 6G and higher-speed, high-capacity wireless communication networks.  
Conceptualization, Y.J.; methodology, S.X. and Q.W.; software, Y.J.; validation, Y.J. and S.X.; formal analysis, Y.J. and J.Z.; investigation, Y.J. and J.G.; resources, J.L. and Z.O.; data curation, Y.M. and S.W.; writing--original draft preparation, Y.J.; writing--review and editing, S.X.; visualization, Y.J.; supervision, S.X.; project administration, S.X. and W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
National Key R&D Program of China (2023 YFB2905600), National Natural Science Foundation of China (No. 62127802, 62331004, 62305067, U24B20142, U24B20168, and 62427815), and Key Project of Jiangsu Province of China (No. BE2023001-4).  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.  
The authors declare no conflicts of interest.  
Figure 10: The impact of various optimizers on the BER of 2D-CNN.
###### Abstract
The first layer is called the convolutional layer. This study conducted 13 trials, each utilising different CNN models with filters ranging from 16 to 64. Additionally, the feature map from the convolutional layer underwent max pooling. Five iterations of the convolutional and max pooling layers were performed. The last feature map was flattened to obtain the input values entering the Fully Connected layer (FC). Additionally, dropout (a regularisation technique) was used in the FC layer of the NN to tackle the overfitting problem. The dropout rate used was 0.45. The Adam optimiser and a learning rate of \(10^{-5}\) were employed in all trials. Finally, the output layer of the CNN yielded predictions in the form of SNR values. In this study, our newly designed CNN instrument is equivalent to a computational processor for the optical receiver electronic circuitry decision unit. The training and validation loss exhibit minimal disparity, and the congruity in the performance metric suggests that the proposed model is more precise and comprehensive. Additionally, if the neural network's size (the number of parameters) increases, the model's performance also increases. This study demonstrates that CNNs can make decisions using cost-effective functions with limited trainable parameters (ranging from 516,881 to 2,267,201). These decisions apply to various types of waters, even in the presence of ISI noise and fluctuations in the water environment. This study is organised as follows: Section 2 briefly reviews related studies. Section 3 reviews the basics of UWOC systems. Section 4 discusses the foundations of CNN modelling with basic theory. Section 5 presents the CNN algorithm design and implementation. Section 6 provides a comprehensive overview of the results of the SNR and BER predictions, the performance metrics, and the statistical summary of the obtained results. Sections 8 and 9 of the document discuss the conclusion and future studies, respectively.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 2 EMG Signals
### Feature extraction from an EMG signal
#### 2.2.3 Time-Frequency Domain (TFD)  
TFD features are more sophisticated computationally than time-domain features. However, there are fast algorithms with which the characteristics can be implemented in TFD in order that real-time requirements necessary for MES classification are still met [9; 11; 50; 57].
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 2 Literature Review
### Multiple Temporal Aggregation  
In order to avoid selecting a single temporal aggregation level, researchers have been combining the forecasts produced at multiple levels. The multiple temporal aggregation approach has been empirically tested in many forecasting applications [12; 36; 37], report ing significant improvements over both simple temporal aggregation and conventional forecasting (i.e., producing forecasts at the original frequency of the series).  
Even though multiple temporal aggregation can mitigate the uncertainty of selecting a single temporal aggregation level, defining the weights to be used for combining the base forecasts remains an open issue. Many approaches have been proposed to address this task [38; 39; 40]. Equal weights, despite their simplicity, constitute a competitive choice in many cases and there has been no proof that a more sophisticated method can consistently provide superior results [3]. However, there has been evidence that unequal weighting schemes may be more suitable for forecasting seasonal series since they can avoid unreasonable seasonal shrinkage. As a result, [PERSON] et al. [12], [PERSON] et al. [16], and [PERSON] et al. [20] proposed some alternative weighting methods for preserving the seasonal patterns of the series, while similar results can be achieved when forecasting with temporal hierarchies [13].
## 1 Introduction
Thus far, TiO\({}_{2}\) has been frequently utilized as an ETL material in organic and inorganic PSCs [33; 34]. However, TiO\({}_{2}\) has specific limitations in being a reliable and effective ETL for PSCs [35; 36; 37; 38]. TiO\({}_{2}\) has a slightly higher conduction band minimum (CBM) than perovskite materials, causing it to extract electrons from the ETL in a more difficult manner [39; 40; 41; 42; 43]. In addition, TiO\({}_{2}\) decomposes over a long period when exposed to ultraviolet (UV) light, making it unsuitable for PSC commercialization. The manufacturing of complex devices is hampered by the high-temperature annealing applied during the processing of TiO\({}_{2}\). Device performance is affected by defect trap states, such as oxygen vacancies in TiO\({}_{2}\), thus  
Figure 1: Perovskite crystal structure with the general form of ABX\({}_{3}\), in which A\({}^{+}\) is confined within a cage determined by the octahedral coordination of B\({}^{2+}\) with X\({}^{-}\) anions. Reprinted with permission from [PERSON] et al. [28] Copyright 2015. The Materials Research Society. increasing the loss of non-radiative states. Numerous efforts have been made to propose new ETL materials, such as SnO\({}_{2}\)[44], La-doped BaSnO\({}_{3}\)[45], and ZnO [27], to circumvent the drawbacks of TiO\({}_{2}\). Compared with dissimilar ETLs, SnO\({}_{2}\) has achieved improved band alignment, high UV resistance, strong charge extraction, and low photocatalytic activity among numerous contenders [46; 47; 48; 49; 50; 51; 52; 53]. SnO\({}_{2}\) has attained good CBM adherence to lead halide perovskite, enabling a minor open-circuit voltage to be detrimental. Furthermore, SnO\({}_{2}\) demonstrates stronger electron extraction than TiO\({}_{2}\), as indicated by femtosecond transient absorption and UV spectroscopy measurements. Visible light is mostly permitted through SnO\({}_{2}\), owing to the wide band gap (approximately 3.5 eV vs. 3.0 eV of TiO\({}_{2}\)). SnO\({}_{2}\) shields against UV light exposure by preventing UV light absorption, owing to its large Eg. Additionally, compared with TiO\({}_{2}\), SnO\({}_{2}\) possesses a two-order-of-magnitude higher bulk electron mobility. The low-temperature processing of SnO\({}_{2}\) is simple and appropriate for widespread commercialization. To better understand the advantages and disadvantages of commonly used ETLs in PSCs, Table 1 is presented.
## 3 Numerical Validation Examples  
To evaluate the proposed Bayesian approach's performance, FE models were constructed in a MATLAB environment for two numerical examples with different degrees of complexity. In the present work, it is convenient to use a ratio between actual and theoretical parameters as an updating index: \(\theta=K_{a}/K_{t}\), \(\beta=M_{a}/M_{t}\), where \(K_{a}\) and \(K_{t}\) are actual and theoretical stiffness; \(M_{a}\) and \(M_{t}\) are actual and theoretical mass. In the case of a healthy condition, \(\theta\) and \(\beta\) are unity. Additionally, a comparative investigation was implemented for various damage cases to demonstrate that the proposed BMUA outperforms the traditional one. Note that, before system updating, the measured mode shapes have to be normalized or scaled by the mass-normalized FE model method [58] or scaling methods [47; 48] to ensure the tested and analytical mode shapes are comparative. In addition, due to limited sensors installed in practice, measured mode shapes are usually incomplete and only available with a few DOFs related to the sensors' location. Therefore, it is desirable to expand reduced measured mode shapes onto complete mode shapes with full DOFs in these two examples by mode shape expansion techniques [59; 60].
## 3 Calibration of ATMD with Viscous Damping-Stiffness Control Force
### Equations of Motion and Dynamic Amplification
Substituting this into (20) allows us to represent the absolute acceleration of the primary structure as a function of both relative displacement and relative velocity:  
\[\ddot{x}_{1}=\left(\frac{c_{Nis}}{m_{2}+g_{Nis}m_{1}}\right)\dot{x}_{d}+ \left(\frac{k_{2 dh}-\omega^{2}m_{2}}{m_{2}+g_{Nis}m_{1}}\right)x_{d}. \tag{21}\]  
Thus, for harmonic excitation, the control force (18) can be alternatively calculated using only relative displacement and relative velocity signals. After substituting (21) into (18), we could directly determine the parameters \(c_{a}\) and \(k_{a}\) for the control force (12), but only if the ATMD configurations in this study and in [17] are identical. At this point, it is important to note that the ATMD configuration presented in [17] differs from the one in Figure 1a. Specifically, [PERSON] et al. determine the optimal stiffness of the passive spring, denoted below as \(k_{2 Ni-opt}\), in contrast to the \(k_{2 dh}\) selected in this work. Additionally, they introduce a passive viscous damper that is connected in parallel with both the passive spring and the active actuator. Considering these differences in ATMD configurations, the parameters we are seeking for the control force (12) can be formulated to ensure full compatibility with the optimal ATMD proposed by [PERSON] et al. :  
\[c_{a}=\frac{c_{Nis}m_{2}}{m_{2}+g_{Nis}m_{1}}, \tag{22}\]  
\[k_{a}=-g_{Nis}m_{1}\left(\frac{k_{2 Nis-opt}-\omega^{2}m_{2}}{m_{2}+g_{Nis}m_{1} }\right)+\left(k_{2 Nis-opt}-k_{2 dh}\right), \tag{23}\]  
where \(\left(k_{2 Nis-opt}-k_{2 dh}\right)\) is added to eliminate a small discrepancy between the spring stiffness \(k_{2 dh}\) and the optimal value \(k_{2 Nis-opt}\), and the gain \(g_{Nis}\) determined in [17] is:  
\[g_{Nis}=\frac{2+\mu-\mu\cdot DAF_{max}^{2}}{DAF_{max}^{2}+1}, \tag{24}\]  
with \(DAF_{max}\) which expresses the resulting maximum dynamic amplification factor that can be achieved with the ATMD.
# A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands
## 4 Discussion  
The proposed antenna shows a way to design a shared aperture antenna by etching microstrip patches in different layers of dielectric materials and, thus, reuse the aperture for the size reduction in the antenna. The lower and higher operating bands of the proposed shared aperture antenna had a frequency ratio of four. Having a high frequency ratio means that the antenna can be used for two separate communication systems that require two antennas. As CubeSats have a very small form factor and different design restrictions, the proposed antenna can be an excellent option for CubeSat missions that have difficulties accommodating multiple antennas. Table 2 compares the performance of the proposed antenna with recent published works on shared aperture antennas for CubeSat application [26; 27; 28].  
From the Table, it is evident that the proposed antenna achieved similar or better performances while keeping the antenna size compact. Both S/X-band antennas in [25; 26] had an array of patch antennas for high gain in the X-band, which required a feed network. The dual-band antenna in [28] consisted of a 1 \(\times\) 3 linear array antenna for the X-band operation, which had 6 ports in total for dual-polarization. Our proposed antenna has only two ports and provides a high gain due to the dielectric loading. Moreover, the proposed antenna uses coaxial probe feeding, which is desirable for CubeSat developers due to the low complexity of cable placement from the radio to the antenna in a concise space.
## 4 Results
### On-Body Access Point Network Architecture
#### 4.1.3 Gain in Network Lifetime and Evaluation of Energy per Bit  
Figures 8 and 9 present results from a more comprehensive real-time run taken over 37 minutes. The accumulated energy data was processed to evaluate ratios of EpB and NL for \(M=100\)_vs._\(M=0\). The recorded data was partitioned into groups of 100 consecutive routing cycles (each group spans 22 seconds). For finding the EpB ratio, the total energy spent across all nodes was evaluated and divided by the number of routing rounds. This provides the EpB normalized per routing round for \(M=0\) and \(M=100\). The result for \(M=100\) was divided with the result for \(M=0\), providing the EpB ratio. For finding the NL ratio per group, the battery energy was defined as the EpB result times 40. This is equivalent to assuming that each ED would be able to sustain an average of 40 routing rounds. The NL was then extracted by finding the number of routing rounds it took for the first ED to accumulate energy equal to the defined battery energy. This provides the NL normalized to the routing cycle time. The result for \(M=100\) was divided with the result for \(M=0\), providing the NL ratio.  
Figure 8 presents the NL ratio results. It is evident that NL was improved for all groups of 112 groups of 100 routing rounds except three. The average improvement ratio is 1.4 meaning that on average NL was increased by 40%.  
Figure 8: Improvement in Network Lifetime for \(M=100\)_vs._\(M=0\). Note that the proposed system provides an average gain of approximately 40%.  
Figure 9 presents the EpB ratio for the same data. As expected EpB is higher for \(M=100\) since more energy is used to increase NL. On average, 0.4 dB more energy was used. It is interesting to note that the EpB was lower for \(M=100\) for the three instances where the NL wasn't improved. This implies that the network links were such that no relaying was available to balance the energy consumption across the network.
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques  
This section discusses the details of the rain attenuation scaling techniques as per the developed taxonomy in the earlier section.
# Throughput of Buffer with Dependent Service Times
## 6 Numeric Illustrations
### Dependence on Buffer Size  
So far, only a buffer of size 20 packets has been used. Now, we will increase the buffer size and check how it influences the throughput of the buffer.  
In Figure 16, the dependence of the stationary throughput on the buffer size is depicted. Five different correlations of transmission times are used, ranging from no correlation, \(\rho_{1}=0\), up to \(\rho_{1}=0.4\). As was easy to predict, in every case, the throughput grows monotonically with the buffer capacity. It is more interesting to see how the correlation strength influences the throughput as the buffer capacity grows. For no correlation or a weak correlation \(\rho_{1}=0.1\), an almost maximal throughput of 100 pkts/s is achieved for a buffer sizes of about 400 packets. For a mild correlation of \(\rho_{1}=0.2\), it equals 96.71 pkts/s when \(K=500\). For \(\rho_{1}=0.3\) and \(\rho_{1}=0.4\), however, the throughput is far from the maximum, even for \(K=500\). In fact, if \(\rho_{1}=0.4\), the throughput is only 83.38 pkts/s and grows very slowly with the buffer capacity.  
All of these observations illustrate again the strong effect that the correlation of transmission times has on throughput.  
Figure 16: Stationary throughput \(T\) versus buffer size \(K\) for five different values of \(\rho_{1}\). \(C=4\).  
Figure 15: Stationary loss probability versus \(C\) and \(\rho_{1}\).
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### Attitude and Navigation System (ANS)
#### 5.5.3 Real-Time 3D (RT3D)  
The TCN system, as described in the previous subsection, requires on-board optical navigation algorithms. We have developed a proof of concept for the real-time 3D (RT3D) modelling environment in the 3D game engine Unreal Engine 5 (UE5), as shown in Figure 14. The motivation to use UE5 for ESTCube-LuNa is the engine's novel ability to render near-photorealistic images in real time. The TCN solution will be used for attitude manoeuvres; hence, the modelling environment should respond in real time. In later phases, the ESTCube-LuNa mission can be simulated with the RT3D feed working with TCN cameras in a loop, either via a digital interface or by filming screens (multiple screens are necessary to cover the whole sky). In a way, ESTCube-LuNa ANS will play RT3D as a video game, with TCN providing the visual input.  
The current RT3D concept includes 3D models of the Sun, the Moon and the Earth. The celestial object textures are extracted from the Solar System Scope [61]. The celestial bodies can follow pre-programmed orbits, which should be replaced with a position input from an orbit propagator. For example, DOCKS includes an open-source propagator [62]. The rotation of celestial bodies can also be extracted from DOCKS. We would need to develop spacecraft attitude dynamics ourselves while taking advantage of the DOCKS's trajectories to model attitude and orbit disturbances.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 2 Applications of Personalized Video Summarization
### Personalized Egocentric Video Summarization  
Plenty of egocentric cameras have led to the daily production of thousands of egocentric videos. A large proportion of these self-centered videos are many hours long, so it is necessary to produce a personalized video summarization so that each viewer can watch the snapshots that interest them. Watching egocentric videos is difficult from start to finish due to extreme camera shakes and their unnecessary nature. Summary tools are effectively required by these videos for consumption. But traditional summary techniques developed for static surveillance films or videos and sports videos cannot be adapted to egocentric videos. However, it is a limitation to focus on important people and objects using specialized summary techniques developed for egocentric videos [25]. To summarize egocentric videos that are a day long, [PERSON] et al. [25] proposed an unsupervised learning framework to generate personalized summaries in terms of both content and length.
## 2 Materials and Methods
### Test Program
#### 2.3.2 Loading Procedure  
A fatigue test was then conducted at room temperature. Nevertheless, the applied fatigue load had been increased to take the structural response at low temperatures into account. The maximum cable force range of the B17 anchor box under the most unfavorable load combination is calculated from structural analysis as 1905 kN, which considers vehicle load, pedestrian load, overall temperature change, the temperature gradient in the girder, and the temperature difference between the cable, girder, and pylon. Based on the principle of similarity and scale ratio, the specimen load range was determined as 135 kN. The test was conducted in a PMW-500 closed-loop servo-controlled universal testing machine, and a minimum fatigue load of 50 kN was maintained during the fatigue test to ensure the stabilization of oil pressure.  
A static load test was carried out before fatigue testing to ensure that test equipment and instrumentations were working properly, and the specimen was loaded without eccentricity. The test specimen was loaded monotonically up to design maximum fatigue load and then unloaded to its initial status. The loading and unloading process was repeated several times until a stable structural response was achieved.  
Fatigue testing was divided into two stages: confirmatory test and destructive test. The fatigue verification test aims to confirm the safety of the cable-girder anchorage structure under the design cable force range. Should the specimen not suffer apparent damage after 2 million cycles of constant amplitude loading, a destructive fatigue test would then continue to explore the safety margin of the anchor box in terms of fatigue performance. At the fatigue failure test phase, the fatigue load range was gradually increased until the test specimen was damaged or the testing was deemed unsafe. For collecting strain data and checking possible fatigue cracking, the testing machine was shut down temporarily at pre-set intervals. Details of the specimen loading procedure are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Phase** & **Number of Cycles (10\({}^{4}\))** & **Shutdown Interval (10\({}^{4}\))** & **Loading Frequency (Hz)** & **\(P_{min}\) (kN)** & **\(P_{max}\)** & **\(\Delta P\)** \\ \hline Static load test & — & — & — & 0 & 180 & — \\ \hline Fatigue verification test & 0–200 & 20 & 3.8 & 50 & 185 & 135 \\  & 200–210 & 10 & 3.8 & 50 & 195 & 145 \\  & 210–240 & 10 & 3.8 & 50 & 245 & 195 \\ Fatigue failure test & 240–270 & 10 & 3.8 & 50 & 270 & 220 \\  & 270–280 & 10 & 3.8 & 50 & 285 & 235 \\  & 280–290 & 10 & 3.8 & 50 & 300 & 250 \\  & 290–320 & 10 & 3.8 & 50 & 320 & 270 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Loading procedure.  
Figure 5: Fatigue test setup.