esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mechanisms for Triggering \(\sigma\) Factor Release from Anti-\(\sigma\) Factors  
The mechanism for releasing cytoplasm-located \(\sigma\) factors in response to signals that often stem from the external environment is understood in only a small number of cases. They can be broadly divided into partner-switching, direct sensing and regulated proteolysis mechanisms (Figure 4). In the case of partner-switching and regulated proteolysis, an emerging theme is the integration of distinct  
Figure 3: The inhibition of \(\sigma\) activity by anti-\(\sigma\) factors. A comparison between complexes of \(\sigma^{\text{E}}\)-RseA (PDB: 1 OR7-2) and a \(\sigma^{\text{CnrH}}\)/CnrY/ (PDB: 4 CXF). The \(\sigma_{2}\) and \(\sigma_{4}\) domains are colored green and red, respectively. The anti-sigma binding domains of RseA and \(\text{CnrY}\) are colored blue.  
signals involving separate input pathways that enable \(\sigma\) activation in response to varied environmental and physiological cues.  
Figure 4: Mechanisms for \(\sigma\) factor release from anti-\(\sigma\) factors. (**a**) Activation of \(\sigma^{\mathrm{E}}\) in _E. coli_. The membrane-spanning anti-\(\sigma\) RseA binds \(\sigma^{\mathrm{E}}\) through its cytoplasmic ASD. The C-termini of OMPs that accumulate in the periplasm activate DegS protease through binding to its PDZ domain, resulting in site-1 cleavage of RseA. DegS–dependent cleavage of RseA is inhibited by RseB, and this can be relieved by the accumulation of lipopolysaccharide (LPS) in the periplasm that binds directly to RseB. Site-1 cleavage is sensed by RseP, which subsequently catalyzes site-2 cleavage of RseA on the cytoplasmic side of the membrane, releasing a soluble \(\sigma^{\mathrm{E}}/\mathrm{RseA}\) complex. \(\sigma^{\mathrm{E}}\) is released when the RseA ASD is finally degraded by ATP-dependent proteases such as ClpXP. (**b**) Activation of \(\sigma^{\mathrm{R}}\) in _S. coelicolor_. RsrA binds to and inactivates \(\sigma^{\mathrm{R}}\) via its ZASD domain. The RsrA zinc ion is coordinated by three cysteine (C) residues and one histidine (H). In response to oxidative stress, RsrA forms at least one disulphide bond, which concomitantly displaces the zinc and causes a structural change that prevents \(\sigma^{\mathrm{R}}\) binding. The system can be reset by the reduction of RsrA by cellular thiol-disulphide oxidoreductase such as thioredoxin that are activated by \(\sigma^{\mathrm{R}}\). (**c**) Activation of \(\sigma^{\mathrm{B}}\) in _B. subtilis_. RsbW is an anti-\(\sigma\) factor/kinase that binds to \(\sigma^{\mathrm{B}}\), and additionally inactivates its alternative binding partner RsbV by phosphorylating it to RsbV-P. Activation of \(\sigma^{\mathrm{B}}\) occurs by partner-switching when RsbV-P is dephosphorylated by alternative phosphatases (RsbTU or RsbQP) in response to environmental or energy stresses, which allows RsbV to bind and sequester RsbW.
###### Abstract  
Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports 0.9367 \(\pm\) 0.0138 (mean \(\pm\) SD) and 95% confidence interval (0.9221, 0.9512); the experimental Mean Absolute Error (MAE) reports 73.9149 \(\pm\) 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) reports 0.9943 \(\pm\) 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue reports 0.7051 \(\pm\) 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounfield Units (m = 0.87; adjusted R\({}^{2}\) = 0.91; \(p\) < 0.001). The Bland-Altman plot shows that the average of the differences is low (\(-\)38.6471 \(\pm\) 199.6100; 95% CI (\(-\)429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.  
deep learning; image synthesis; PET/MR; pseudo-CT +
Footnote †: journal:  
Acticle
## 4 Simulation
### Antenna Configuration  
A total of ten parabolic reflectors antennas were configured for comparative analysis, and the detailed configurations are shown in Table 2.  
Figure 8: Analysis results: (**a**) errors for each criterion; (**b**) overall error.  
Figure 9 shows the structure of the parabolic reflector antennas corresponding to each antenna configuration set in Table 1. Additionally, it presents a partial enlargement based on the feeder configuration (position and beam steering direction) and the normalized patterns based on the feeder analysis model (cosine-q model). Note that according to the cosine-q model with q values of 1, 2.2, and 4.5, the directivities are 7.7 dBi, 10.3 dBi, and 13.0 dBi, and the half-power beam widths (HPBWs) are 89.8\({}^{\circ}\), 62.5\({}^{\circ}\), and 44.3\({}^{\circ}\), respectively. Furthermore, the 10 dB beamwidths are 143.1\({}^{\circ}\), 107.3\({}^{\circ}\), and 78.5\({}^{\circ}\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Parabolic**} & \multicolumn{2}{c}{**Feeder**} \\  & **Diameter [\(\lambda\)]** & **FD Ratio** & \(q\)**-Factor** & \
\begin{tabular}{c} **Position** \\ **(x, y, z) [\(\lambda\)]** \\ \end{tabular} &
\begin{tabular}{c} **Beam Steering** \\ **(\(\theta,\phi\)) [\({}^{\circ}\)]** \\ \end{tabular} \\ \hline Case \#1 & 30 & 0.35 & \(1\) & (0, 0, 0) & (180, 0) \\ Case \#2 & 60 & 0.35 & \(1\) & (0, 0, 0) & (180, 0) \\ Case \#3 & 30 & 0.35 & 4.5 & (0, 0, 0) & (180, 0) \\ Case \#4 & 40 & 0.5 & 2.2 & (0, 0, 0) & (180, 0) \\ Case \#5 & 30 & 0.35 & \(1\) & (1, 0, 0) & (180, 0) \\ Case \#6 & 30 & 0.35 & \(1\) & (0, 1.5, 0) & (180, 0) \\ Case \#7 & 40 & 0.5 & 2.2 & (0, 0, 2) & (180, 0) \\ Case \#8 & 40 & 0.5 & 2.2 & (0, 0, -1) & (180, 0) \\ Case \#9 & 30 & 0.35 & \(1\) & (0, 0, 0) & (160, 0) \\ Case \#10 & 30 & 0.35 & 4.5 & (\(-1, 0, 0)\) & (160, 0) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of parabolic antennas for simulation.  
Figure 9: _Cont._
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 3 Test Analysis  
### Choosing of Constellations and Ground Monitoring Stations  
#### 3.1.1 Design of Constellation  
Considering that the main purpose of this paper is to design an LEO constellation that can enhance the existing service and monitoring areas of BDS-3 MEO satellites and have similar regression characteristics to BDS-3 MEO satellite, a Walker 60/6/1 LEO constellation was designed, with typical LEO constellations used as a reference, such as \"Yunhai\" and \"Jason-3\". The orbital altitude of the LEO satellite was 1000 km and inclination is 55\({}^{\circ}\). Then, a hybrid constellation with 24 BDS-3 MEO satellites and 60 LEO satellites was constructed, which is shown in Figure 4.  
In addition to the construction of the simulation constellation, a hybrid constellation under the conditions of real observation data was also constructed, which consisted of BDS satellites and five LEO satellites. The LEO satellites were equipped with BDS monitoring receivers, whose orbits are shown in Figure 5.  
Figure 4: Designed constellation with BDS-3 MEO satellites and 60 LEO satellites. (The satellites in blue, yellow and pink lines are BDS-3 MEO satellites and the satellites in low orbit lines are LEO satellites).  
Figure 5: LEO constellation with 5 LEO satellites.
## 4 Discussion  
According to the test results in Part 3, solar harvesting modules use supercapacitors (0.47 F - 5.5 V) to store energy when the light is turned off and the ECG device only runs for a short time. The supercapacitor has a large discharge time, so it does not affect the age of the device. To increase usage time, we need to increase the capacity of the supercapacitor or replace the supercapacitor with rechargeable batteries with a larger capacity but that also requires solar cells with higher power and performance. We also find that high-capacity batteries such as the battery NCR18650B direct charger solution or the wireless charger are more feasible than solar cell solutions.  
In this design, the main components supply only 1.9 V, reducing power consumption significantly. At voltage 1.9 V, the consumed current is only about 1/4 of the voltage 3.3 V, which extends the use time and is suitable for a low power energy source as a solar cell. However, this also reduces the data transmission distance of the Bluetooth module.  
The ECG device presented in this article can operate with the sampling frequency from 100 Hz to 2133 Hz, which creates the flexibility to select the device to receive the signal (with some smartphones with a low processing level). With low-speed equipment, we can choose a low sampling frequency. Meanwhile, the PC data processing speed is high, and many other supporting software can choose higher sampling frequencies for a higher accuracy of the ECG signal. However, this leads to the need for designing filters with many different coefficients to be suitable for many specific sampling frequencies. In addition, the design uses 8-bit MCU with relatively low data processing and transmission speeds (UART module speed), so it is impossible to transmit large data. Thus, the highest sampling frequency is 2133 Hz and the ADS1293 chip supports sampling frequencies up to 6400 Hz. That can be solved by changing the new MCU with a higher processing speed to be able to process real-time data with a high sampling frequency, which, after being processed, is transmitted to a high-performance smartphone. PC is a device that displays results.  
The ECG signal improved after using the FIR filter shown in Section 3. Low pass filters filter noise from the power frequency noise, partially reducing ECG signals. However, the shape and characteristics of ECG signals were still guaranteed. The high pass filter eliminated the high-frequency signal and eliminates baseline deviation. The application of both filters led to the elimination of power line noise (60 Hz) and low-frequency noise while ensuring the accuracy of the signal type. However, compared to the original signal, the amplitude of the signal was still partially degraded, so other methods should be considered to achieve a better signal quality.
## 4 Results and Discussion
### Effect of Frequency on Glass Transition Temperature  
For the calculation of activation energy, \(T_{g}\) values at different frequencies are necessary. In general, as the frequency increases, \(T_{g}\) increases. This trend is signified by the right-biased shift of the plots in Figure 4a,b. The shifts in \(T_{g}\) range from 16-28 \({}^{\circ}\)C when the frequency is varied between 1 Hz to100 Hz, as seen in Figure 4a. The value of peak \(tan\delta\) also slightly increases at higher frequencies. Similarly, \(T_{g}\) differences ranging from 10-20 \({}^{\circ}\)C are noted in loss modulus curves shown in Figure 4b. The peak values of loss modulus also slightly increase with an increase in frequency.  
Figure 4: The viscoelastic properties of the various composites’ configurations under frequency sweep at 30 \({}^{\circ}\)C. (**a**) Tan delta, (**b**) Loss modulus, and (**c**) Storage modulus.  
As seen in Figure 4c, the storage modulus values tend to increase with frequency. At 100 Hz, the values are much higher than those at lower frequencies. Also, significant noise was observed in the data after passing the corresponding glass transition temperature at this frequency. However, these trends hold for this configuration, where just CNTs are embedded as nanofillers. For other configurations, though the \(T_{g}\) increases with frequencies, fluctuations were noticed in the peaks of tan delta and loss modulus curves. The storage modulus values after the rubbery plateau, which is around 100 \({}^{\circ}\)C, are largely independent of temperature and frequency changes. The slight shift of the storage modulus curves to the left as the frequency increases indicates that the glass transition depends on the frequency, as reported before [53]. Glassy to rubbery transitions shift to higher temperatures at higher frequencies. The shift to higher temperatures is a direct consequence of time-temperature equivalence. As the frequency is increased, the time allowed for molecular motion is decreased, increasing the possibility of shorter timescale motions. Hence, the polymer responds more as if it were at a lower temperature than a sample run at a lower frequency but the same temperature. Consequently, higher temperatures are required for a sample to achieve an equivalent mechanical state at higher frequencies, and the transitions shift to higher temperatures.
## 4 Stability Analysis of the High Fill Slope
### Numerical Simulation Analysis of High Fill Slope Stability with Different Slope Rates
#### 4.4.2 Parameter Setting of the Numerical Model  
Prior to conducting numerical simulation calculations, it is essential to establish the material properties of each soil layer, encompassing the specific weight, cohesion, and internal friction angle of each soil layer, as well as other fundamental physical and mechanical parameters. Furthermore, consideration should be given to the unsaturated characteristics of the soil, necessitating determination of the soil-water characteristic curve and permeability coefficient for each soil layer.  
Based on the geotechnical investigation report of the high embankment slope project, the soil mechanical parameters were determined through in-house tests [14], combined with practical experience in geotechnical engineering. Simplified calculations were conducted by assuming a permeability coefficient of 2 \(\times\) 10\({}^{-6}\) m/s for medium-weathered rock and 5 \(\times\) 10\({}^{-5}\) m/s for the remaining soil layers.  
The soil-water characteristic curve, also known as the soil-moisture characteristic curve, reflects the relationship between soil saturation and matrix suction. It is an essential  
Figure 17: The slope calculation model with a slope rate of 30.  
requirement for simulating the stability of unsaturated soil slopes. Based on the results of the survey report and relevant research findings [16], the numerical simulation analysis of slope stability in this paper adopts the soil-water characteristic curve shown in Figure 18.  
Using [PERSON]'s empirical model and [PERSON]'s permeability coefficient model, the permeability coefficient curve of the slope soil was obtained, as shown in Figure 19.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 4 Experiments
### Experimental Results
#### 4.2.1 Results on the Synthetic Dataset  
The synthetic dataset is primarily utilized to evaluate the performance of the local-global dual-path feature fusion network in object detection. To comprehensively assess the algorithm's effectiveness, the objects for detection include not only TTs but also other objects commonly encountered during power line inspections or those that pose significant risks to the safety of power lines, such as trees and large vehicles involved in construction breaches. The AP of these ground objects is also included as an evaluation metric. The final detection results for the proposed method and the YOLOv7 algorithm across the first six scenarios are presented in Table 1.  
From the results presented in Table 1, it can be observed that, regardless of whether the data originates from complex or simple scenarios, the proposed algorithm employing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario No.** & **YOLOv7** & **Ours** \\ \hline
1 & 0.84 & 0.90 \\
2 & 0.75 & 0.81 \\
3 & 0.74 & 0.78 \\
4 & 0.95 & 0.99 \\
5 & 0.70 & 0.76 \\
6 & 0.79 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of mAP\({}_{50}\) under six scenarios.  
Figure 7: Example of airborne LiDAR data.  
local-global dual-dual feature fusion demonstrates a significant improvement in mAP\({}_{50}\) compared to the original YOLOv7, with an average enhancement of over 4%. This indicates that the use of local amplification can augment the network's capability to extract spatial and semantic feature information of targets.  
To validate the effectiveness of the network modules proposed in this paper for detecting TTs, an ablation study was conducted on Scenario 7. The results of the experiment are detailed in Table 2. Scheme 1 corresponds to the original YOLOv7 network; Scheme 2 introduces a local path module for feature extraction upon the original network; Scheme 3, building upon Scheme 2, merges features derived from the local path with those obtained from the primary pathway at every stage of feature extraction. All schemes employ the same pre-trained network weights. The detection results for Scenario 7 in Table 2 indicate that the addition of a local path to the backbone network led to a roughly 3% increase in mAP\({}_{50}\), demonstrating that the local path module can effectively enhance the feature information of small-sized targets. Furthermore, when the local path was incorporated into the backbone network and feature fusion was performed, the detection accuracy could be further improved to 95%. This indicates that dual-path feature fusion can better preserve object feature information, effectively enhancing detection capabilities.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 5 Simulation
### Symbol Error Rate Performance  
We study the SER performance of the communication source embedding scheme using Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), 16-Phase Shift Keying (16-PSK) and 256-Phase Shift Keying (256-PSK) constellations and compare the original waveform with the encoded waveform. The original waveform corresponds to data rate of \(R=32\); 64; 128 and 256 Mbps, respectively. To obtain encoded waveform, a convolutional encoder of rate 2/3 is employed in the original waveform. We use a Viterbi decoder to decode the received encoded waveform at the communication receiver. The communication channel coefficient is taken as \(|\alpha_{c}|=1\), and the phase of \(\alpha_{c}\) is uniformly distributed within the interval \([0,2\pi]\). To test the SER performance, we generate a number of \(10\times 10^{17}\) random BPSK, QPSK, 16-PSK and 256-PSK symbols. The SERs versus SNR for all constellation sizes is illustrated in Figure 7.  
The results demonstrate that the smaller the constellation size is, the better the SER performance will be. As the constellation size increases, it is more difficult to detect the symbols. It can be explained that the defective cross-correlation between the non-orthogonal transmit sequences influences the detection performance. Obviously, the encoded waveform achieves greater SER performance as compared to the original waveform. Therefore, intersymbol interference is a significant source of detection error resulting in performance degradation. It is expected that the SER performance gets worse if longer FH waveforms are used. It is worth noting that, for all techniques tested, the SER performance offered by the encoded waveform outperforms the performance provided by the original waveform. Therefore, the SER superiority comes at the price of lower data rate.  
Figure 6: Ambiguity Function (AF) contours showing delay-Doppler resolution at (**a**) iteration \(1\) and (**b**) iteration \(20\), which demonstrates smaller focal area in (**b**) as compared with (**a**).  
Figure 8 illustrates the throughput result provided by the proposed optimization waveform versus distance for BPSK, QPSK, 16-PSK, and 256-PSK constellation. 256-PSK waveform provides a data rate of approximately 8 Mbps at a distance of 10 m, which is better than that generated by BPSK, QPSK, 16-PSK constellations. The highest data rate is acquired by the 256-PSK waveform within a distance of 60 m, as the distance between the system nodes increases the data rate decreases.
## 5 DRRF Extensions (for Non Real-time Traffic)
### Traffic Priority Extension  
In IEEE 802.16e Mobile WiMAX Networks, traffic priority is one of the QoS parameters which differentiate the relative importance of flows within the same QoS class in which all other QoS parameters are identical. The standard does not specify how to differentiate between traffic priorities; however, it recommends that the scheduler should give lower delay and/or higher buffering preference for flows with high priority [1,3].  
In a priority based system, flows of higher priority are serviced before those of lower priority. Fairness implies that multiple flows of the same priority should get similar service, that is, if the system is overloaded, all flows of the lowest priority should be penalized equally. It is also a good practice to police the higher priority flows so that they do not overload the system, and starve lower priority flows.