esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Pre-Radiation and Photo-Bleaching on the Yb-Doped Fiber Laser Radiated with Gamma-ray
## 1 Introduction
[PERSON] found that extrinsic color centers of pure-silica-core optical fibers were eventually eradicated after a synthetic treatment of ultra-high dose gamma radiation and visible light fluence, and the hardening ability against radiation of this fiber was improved [30]. [PERSON] demonstrated a method to enhance the radiation hardness of the fibers with respect to induced optical loss by a combined treatment of pre-radiation exposure and thermal annealing, which are applied in a low dose gamma radiation environment, such as in satellite communication systems [31]. The influence of pre-radiation on the radiation resistance of high-power fiber lasers also needs to be further researched. On the other hand, the cumulated radiation-induced attenuation caused by pre-radiation and radiation would contribute to some negative effects at high output power, such as additional thermal load and the mode instability (MI) effect [35; 36; 37]. The crucial factor of the photo-bleaching technique is the choosing of bleaching lasers' wavelength, which should be effective enough to bleach the radiation-induced defects, while should not cause additional defects of silica fiber [32; 33; 34]. Researching the influence of pre-radiation and photo-bleaching on the radiation resistance against gamma-ray of high output power Yb-doped fiber lasers is urgent and meaningful work. In this work, we mainly radiated the Yb-doped fiber amplifiers and kept the seed's state steady. With the same gamma radiation dose, the power reduction of pre-radiated fiber laser is approximately 9.80%, less than the 11.10% in the non-radiated fiber laser. Whereas, the cumulated radiation-induced attenuation indeed caused the MI effect, which was observed from the time domain and frequency signal of the output laser. Through the subsequent photo-bleaching process, the output power of pre-radiated fiber laser was further recovered by 80 W, and the MI effect also disappeared. With the radiated Yb-doped fibers bleached to their saturation state, the radiation-induced absorbing intensity at 640 nm approximately decreased by 45.2%. This research is of great significance in the design and fabrication of related fiber lasers or amplifiers against gamma radiation.
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 3 System Parametrization
One can note the good amplitude linearity of the USRP TX channel. Even if the TX gain (dB) does not directly correspond to the measured SA power in dBm, good linear agreement between the two variables was discovered. For calibrating the USRP RX channel, a continuous wave signal was generated with a signal generator and measured alternatively using the USRP RX interface and the SA. Figure 9a presents the experimental setup used for USRP RX channel parametrization. The multiplication constant in Figure 5 was modified to achieve agreement between the SA and USRP measured power values, as presented in Figure 9b. Figure 7: Phase alignment measurement for 3.53 GHz TX frequency.
# Real-Time Circuit Breaker Availability Assessment in the Transmission Network
## 4 Input Variables of Decision Making and Conclusion Process Based on Fuzzy Regulator
### Determining the Availability of a Breaker Status in Network Events Using Fuzzy Logic  
The fuzzy system conclusion rule base was determined based on expert knowledge, and it was determined and stored in the form of rules to determine the availability of a state and a possible fault condition of a switch. Defining the rules and reaching conclusions minimizes the possibility of errors that may occur when operating the power system. Based on the mentioned expert rules for determining the possible faults of a circuit breaker of a network element, a database of rules for the conclusion based on a fuzzy system for determining the fault of a switch was derived, as it is symbolic and shown in Table 1. The database has a total of 36 rules (\(2\times 2\times 3\times 3\) states).  
Where PROT represents the protection start or trip (D start or trip, N no start or trip), CB represents the circuit breaker state change (D change, N no change), SEN represents the current sensitivity change (NH negative sensitivity, L low sensitivity, PH positive sensitivity), A represents the alarm (NA no alarm, IP intermediate position, PA permanent alarm), and Xli represents the fault (NF no fault, IF fault).  
The creation of a list of breaker states from the SCADA systems and protection devices with network elements is done automatically based on the already established priority and order, as shown in Figure 6.  
The method itself is used within the application, where data from SCADA and protection devices are displayed in the EXCEL table, transferred from the EXCEL table, and used in FIS within MATLAB, as shown in the schematic in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nr** & **PROT** & **CB** & **SEN** & **A** & \(\mathbf{X_{li}^{r}}\) \\ \hline
1 & D & D & PH & PA & IF \\
2 & N & D & PH & PA & IF \\
2 & D & D & PH & NA & NF \\
3 & N & N & NH & NA & NF \\
4 & N & N & NH & NA & NF \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
36 & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & NF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rules base for concluding the breaker’s availability state based on fuzzy logic.  
Figure 6: Schematic representation of information processing and breaker fault detection.  
Figure 7: Schematic representation of the used applications.  
From Table 1, it can be noted that the output variable \(X^{r}{}_{li}\) can have two states: IF (there is a fault) and NF (there is no fault or no fault). The numerical values of the output quantities corresponding to the language variables states are one and zero (Table 2).  
Each diagnosed switch or network element can receive only one state or one value and have binary states 1 or 0, respectively, a fault state or a fault-free state. Finally, from Table 2, a list, to which a numeric value of one is assigned, is created. This is a list of elements with faulty breaker states.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Radiation Patterns  
The radiation pattern shows how the antenna radiates energy depending on the direction. It represents the normalized distribution of the electric field or the relative distribution of the surface power density. The radiation patterns are determined in two planes, horizontal and vertical, and can also be presented in a three-dimensional form. The designed antenna should have an omnidirectional radiation pattern. Figures 13 and 14 show the three-dimensional appearance of the radiation pattern of the proposed antenna for the assumed center frequencies of the 5G system and the LTE system, 3.6 GHz and 5.5 GHz, respectively. Figure 15 shows the normalized radiation patterns of the proposed antenna for the frequency of 3.6 GHz (black line simulation, red line measurement) in the polar coordinate system for vertical and horizontal polarization planes. Figure 16 shows the normalized radiation patterns of the proposed antenna for the 5.5 GHz frequency (black line simulation, red line measurement) in the polar coordinate system for vertical and horizontal polarization planes.  
Figure 12: Surface current distribution for the proposed 5G and LTE-U antenna at 3.6 GHz and 5.5 GHz frequency.  
Figure 13: The 3D view of the radiation pattern for the proposed antenna model at 3.60 GHz frequency.
## 3 Number of Dominant Rays: Simulation Results
### Omni-Directional Transmission  
Using omni-directional antennas, the rays are assumed to have the same intensity transmitted at each beam angle. The rays are attenuated by two factors: the free space loss, determined by the distance each ray travels, and the total accumulative reflectivity from the walls. Figure 4 is an example showing the minor contribution of additional rays in the link budget calculation for a millimeter wave 28 GHz link operating inside a narrow underground pedestrian tunnel made of concrete, with \(2a=1\) m and \(2b=2\) m.  
In order to identify how many dominant rays play a role in the link along a section of length \(L\) in the tunnel, an error analysis of the modification was made. The root mean squared error (RMSE) for the \(n^{\text{th}}\) additional ray is calculated as follows:  
\[Error(n)=\sqrt{\frac{1}{L}\int\limits_{0}^{L}\left|\frac{E_{n}(z)}{E_{LOS}(z)} -\frac{E_{n-1}(z)}{E_{LOS}(z)}dz\right|^{2}} \tag{13}\]  
The results are summarized in Figure 5 for different tunnel section lengths inside the underground pedestrian tunnel. A longer tunnel requires the consideration of more rays in the summation.  
Figure 3: (a) \(A_{m}\) and \(B_{n}\) dependence for the n and m orders. (b) \(\tau(m,n)/\tau_{0}\) dependence for n and m values, where \(a=0.5\) m, \(b=1\) m, and \(z=300\) m.
## 5 The Challenges Related to Industrial Scalability and Regulatory Considerations  
The transition from laboratory-scale techniques to industrial-scale production poses several challenges, particularly in terms of scalability and regulatory compliance. Among the methods discussed, microfluidics and ethanol injection are identified as semi-continuous processes that offer promising potential for industrial scalability. Microfluidics allows for precise control over the size and uniformity of liposomes, which is critical for maintaining product consistency at a larger scale. Its ability to produce monodisperse liposomes with high encapsulation efficiency makes it suitable for commercial applications with relatively minor adjustments. Similarly, the ethanol injection method, known for its simplicity and rapid processing time, is another semi-continuous technique that is well-suited for scaling up. This method can produce liposomes with consistent characteristics, which is advantageous for industrial production. The straightforward nature of the process also facilitates easier integration into existing production lines, enhancing its viability for large-scale manufacturing. In contrast, methods such as Supercritical Reverse Phase Evaporation (SCRPE) and Supercritical Anti-Solvent (SAS) are batch processes, which present inherent challenges for scalability. The batch nature of these techniques limits their throughput, making them less suitable for industrial-scale production without significant modifications. To address these challenges, further development and optimization of these methods are required to enhance their scalability and efficiency in large-scale operations. The Depressurization of an Expanded Solution into Aqueous Media (DESAM) method, on the other hand, is a continuous process that offers greater potential for industrial scale-up. Its ability to produce unilamellar liposomes with consistent size distribution makes it a strong candidate for commercial implementation. The continuous nature of DESAM allows for higher throughput, making it more practical for large-scale production.  
In addition to scalability, regulatory considerations are critical for the commercial implementation of these techniques. Ensuring that the processes are consistent, reproducible, and compliant with safety standards is essential. This includes adhering to guidelines set by regulatory bodies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) [84]. Rigorous validation and quality control measures must be implemented to ensure that the final products meet the necessary safety and efficacy standards required for food, pharmaceutical, and cosmetic applications.  
By addressing these scalability and regulatory challenges, the reviewed techniques can be more effectively translated into commercial products, thereby maximizing their potential impact in various industries.
# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
## 3 Methodology  
There are two ways to utilize wind energy to charge EVs as a source. The first one is via the electricity grids, where energy storage is required for both wind and the grid. The second could potentially be an off-grid solution to avoid expensive energy storage equipment, power conversion equipment, and conversion stages, because a direct current (DC) bus system can be used in the second case.  
Figure 2 shows the general procedure of an isolated wind energy, direct EV fast-charging system. The system objective is to perform power analyses of different wind turbines to investigate their ability to provide a near-constant power supply over specific time intervals. As shown in Figure 2, the wind speed data were first extrapolated to match the hub height of the selected wind turbine. The output power of the wind turbine was then calculated over overlapping time intervals. The time interval corresponds to the charging time of the EV battery (e.g., from 20% to 80% state-of-charge (SoC)) with a fast charging capability. The interval-based output power converted from the entire wind speed data was then analyzed to satisfy the near-constant power criteria. The power intervals were then used to calculate the daily energy output of the wind turbine. Using the output energy and the EV charging point characteristics, the number of EVs that could be directly fast-charged was calculated.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 4 k-NN, SVM, and U-Net CNN Algorithms Evaluation
### SVM Classifier Results  
This subsection presents the results of the SVM classifier with the polynomial kernel and C = 1, applied for five and ten days. In addition, the results contain the confusion matrix, Precision, Recall, F1-score, macro average, and weighted average. Definitions of those evaluators are given in Appendix B.  
As shown in the preprocessing phase, classifiers would provide a highly accurate result for swarming detection five days before the event. The confusion matrix of this experiment is shown in Table 4, using the calibrated hyperparameters  
Figure 5: Amplitude response in mV RMS, 5 days before the swarming event over the frequency band of 300–600 Hz (using 10/25 Hz frequency bins).  
Figure 6: Amplitude response in mV RMS ten days before the swarming event over the frequency band of 300–600 Hz (using 10/25 Hz frequency bins).  
As shown in Table 5, SVM provides a high (97%) accuracy, while the precision of swarming reaches 98%. The results arose from 366 samples of swarming and 1024 samples of non-swarming raw sound data recordings.  
The confusion matrix in Table 6 is the result of the SVM ten days before the swarming event with the calibrated hyperparameters.  
As expected, the results did not provide as high accuracy as the experiment applied in 5 days. Although, with 90% accuracy, it can be considered an accurate classifier to detect swarming events early. The results of this experiment are shown in Table 7 and are taken from 350 samples of swarming and 966 non-swarming raw sound data recordings.
## 6 Conclusions  
As the EMI has been a major threat to the reliability of railway communication systems, this paper proposed a novel methodology to supervise the real-time performance of RSTT under disturbances in the HSR environment. Based on the joint statistical characteristics of pulsed interference, a dynamic MI model is proposed to predict the degradation of RSTT using FEC codes. This model transforms joint time-domain statistical characteristics, i.e., APD and PDD of disturbances, to an MI metric through which the average \(BLER\) is obtained. According to the simulation results, the method based on joint statistical characteristics is valid under different interferences, even if they have the same distribution of amplitude, and the maximum estimated SINR errors at 1% \(BLER\) are no greater than 1.2 dB. That is to say, this method is effective in more complex situations where transmitted signals with various MCSs are affected with different disturbances, as shown in Table 1. Moreover, since this method does not require prior statistical information of the disturbance, it has the potential to be applied to the performance evaluation of disturbances in more practical applications, such as real-time supervising, risk analysis and early warning in the future.  
**Author Contributions:** Conceptualization, [PERSON], [PERSON] and X.G.; methodology, J.Z. and X.G.; validation, J.Z. and X.G.; investigation, J.Z. and Y.W.; writing--original draft preparation, X.G.; writing--review and editing, J.Z., Y.W., and D.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. U1734203) and Beijing Municipal Science & Technology Commission (No. Z181100001018032).  
**Acknowledgments:** The authors wish to thank the reviewers for their valuable comments and suggestions concerning this manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Design of the Wideband GGW Bandpass Filter
### Stepped Impedance Analysis  
Figure 3 represents the structure and the equivalent circuit of the \(i\)th-section (\(i=1,\dots,6\)) of the fifth-order stepped impedance filter shown in Figure 1. Each section of the filter has a length \(L_{i}\), which includes a metallic pin \(t_{xi}\times t_{yi}\ \times\ t_{zi}\) centered on the width \(W\) of the GGW. The metallic pin works as an impedance inverter \(K_{i}\), while the length \(L_{i}\) realizes a phase shift of \(-\theta_{e}-90^{\circ}\). Figure 4 depicts the simulated \(|S_{21}|\)-parameter of the \(i\)th-section of the filter structure as a function of the three dimensions (\(t_{xi}\), \(t_{yi}\), \(t_{zi}\)) of the metallic pin. The dimensions of the base GGW were the same as in Table 2. The centered metallic pins were considered square like those of the PMC sidewalls. The initial size of the centered metallic pin was fixed to that of the square metallic pin of the PMC sidewalls, except the height, which was selected at half: \(t_{xi}=t_{zi}=a=2\) mm and \(t_{yi}=d/2=4\) mm. As can be seen in Figure 4, the variation between 0.5 mm and 8 mm of the pin width (\(t_{xi}\), \(t_{zi}\)) according to the \(x\) or \(z\) axis has little effect on the \(|S_{21}|\)-parameter, since its magnitude can only vary between 0.88 and 0.7. However, the behavior is different when the pin height \(t_{yi}\) increases from 0.5 mm to 8 mm since the magnitude of \(|S_{21}|\) decreases from 0.99 to 0.01. Therefore, to achieve the design specifications of the filter (Table 1), the height \(t_{yi}\) of the metallic pin and the length \(L_{i}\) of the six sections will be adjusted, while the widths \(t_{xi}\) and \(t_{zi}\) of all inverters will be fixed to 2 mm for practical considerations.  
Figure 3: Structure (without the upper plate) and equivalent circuit of the \(i\)th-section of the GGW filter shown in Figure 1.  
Figure 2: Dispersion diagram for the GGW dimensions defined in Table 2.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 10 Performance of the Multimode Tree Coder  
### The Bitrate of the Multimode Tree Coder  
The bitrate of the Multimode Tree coder is controlled by the mode decision output. The mode decision output, the frame header, is coded with 2 bits. The two header bits, 00--S, 01--UV or H, 10--V, and 11--ON, are used to specify the mode of each frame. Since the frame length for the narrowband Multimode Tree coder is 5 msec, the bitrate of the header is 0.4 kbps.  
Since we use the quantizer of G.727 in the code generator, the bitrate of UV, V, ON, and H can be 16, 24, 32, or 40 kbps. In addition, G.727 is an embedded ADPCM coder. Therefore, the number of core bits and enhancement bits can be adjusted as well. For simplicity, we do not use enhancement bits in our experiments. In the tree coder, \(M\) is 4 and \(L\) is 10 for the \(M\)-\(L\) Tree Search algorithm.  
To lower the average bitrate, the Comfort Noise Generator (CNG) motivated by the CNG of AMR-NB [38] is used for Silence (S) mode. In the CNG, the pole-zero predictor coefficients from the short-term predictor are averaged between each transmission frame and encoded every 15 th frame. The absolute magnitude of each frame is averaged and transmitted every 8 th and 15 th frames. The averaged absolute magnitude is transmitted with 5 bits, the averaged pole coefficients are transmitted with 7 bits, and the averaged zero coefficients are transmitted with 5 bits. Since there are 2 pole coefficients, 6 zero coefficients and 2 averaged absolute magnitude transmitted for every 15 frames, the total transmitted bits is 54 bits. Therefore, the bitrate for Silence (S) mode is 0.72 kbps.
## 3 Results and Discussion  
### Tensile Tests  
The stress-strain curves of composite samples are shown in Figure 3, and the parameters obtained from these data are summarized in Table 1. Note that the tensile strength \(\sigma_{b}\) and stiffness \(E_{b}\) of pure PS samples obtained in this work were higher by 30% and 15%, respectively, than the values specified by the manufacturer for original PS (38 MPa and 2.2 GPa [45]). As can be seen from Table 1 introduction of small amounts (0.1 and 0.5 wt%) of spherical (fullerenes) and lamellar (graphene) carbon particles led to an increase in strain at the break of composite samples. The addition of the same amounts of anisometric particles (nanotubes), on the contrary, caused a noticeable decrease in this parameter. The introduction of the higher amount (5 wt%) of fullerenes provided a decrease in both strength and strain at break, while the sample rigidity remained at the level of pure PS. The same amount of carbon nanotubes also did not cause any noticeable increase in the sample rigidity, the elastic modulus rose only slightly and remained within the measurement error bars. Both the composite strength and strain at break of this sample demonstrated a more profound decrease than that of the sample with the same amount of fullerenes and comprised about 30% for \(\sigma_{b}\) and over 50% for \(\epsilon_{b}\). On the contrary introduction of 5 wt% of graphene provided a significant increase in composite rigidity, the tensile modulus \(E_{b}\) rose by 30% over that of pure PS. At the same time both the strength and strain at the break of the composite filled with 5 wt% of graphene decreased only slightly as compared to that of pure PS. However, \(\epsilon_{b}\) decreased by more than 40% as compared to that in the sample filled with 0.1 wt% of graphene.  
Figure 3: Stress–strain curves of composite samples with carbon nanofillers: PS + fullerenes (**a**), PS + CNT (**b**) and PS + graphene (**c**). Curves (1)–(4) correspond to filler concentrations of 0, 0.1, 0.5, and 5 wt%, respectively.  
Thus, the tensile tests revealed the dependence of the mechanical properties of PS-based composites on the shape and concentration of filler particles. Spherical and lamellar carbon particles at low concentrations led to an increase in the tensile elasticity of the composites. The increase in the content of anisometric particles up to 5 wt% caused the decrease in composite strength. The introduction of a layered filler at the concentration of 5 wt% provided an increase in the composite rigidity and its embrittlement, with its strength remaining almost unchanged.