esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### CqSL Model Overview
#### 2.1.1 Disentanglement  
Referring to Figure 2(a), the disentangler block factorizes the image features into spatial (skeleton/physique) features, as well as non-spatial (sentiency) features that carry residual information. The skeleton block is a modified U-Net type architecture--EvoNorm-Projection-UNet++ (EPU-Net++) as shown in Figure 4. We attach eight different decoders at the common bottleneck layer of EPU-Net++. Each decoder captures bottleneck features from 2D cropped images and transforms them into different feature maps consisting of a number of binary channels which are then combined together to form eight most effective channels: \(x_{ST}\xrightarrow[]{(0,1)_{(k\times m\times c)}}\{\sum_{i=1}^{i=8}f_{SK_{i}}\}\). These feature maps are responsible for capturing the domain-invariant features and contain cardiac structures (myocardium, the left and the right ventricle), effective for segmentation and some surrounding structures, effective for reconstruction (Figure 5).  
We use a separate neural network for capturing the sentiency information i.e., domain-specific information. We combine the crop image and the domain-invariant features to penalize the deviation of latent features from the prior distribution employing _Kullback-Leibler divergence_ by applying a VAE architecture (Figure 3b) with the following objective function:  
\[L_{vae}=\sum\left|\left(p(z_{i})\log\frac{p(z_{i})}{p(z_{i}|x_{i}^{ul},f_{sk_{i} })}\right)\right| \tag{1}\]  
Figure 4: Illustration of EPU-Net++ Block: skip connections are replaced with a long projection block.  
Figure 5: Representative examples showing the 5 (out of 8) most semantic disentangled multi-channel binary maps of the spatial information generated from the skeleton decoder from the base to apex (top to bottom rows). Some channels indicate anatomical portions that are well-defined, such as the myocardium, left ventricle or the right ventricle, while others represent the remaining anatomy needed to characterize the input image.  
A VAE learns a low dimensional latent space such that the acquired latent representations fit a prior distribution that is predetermined to be an isotropic multivariate Gaussian \(p(z)=\mathcal{N}(0,1)\). An encoder and a decoder make up a VAE. Given an input, the encoder guesses the Gaussian distribution's parameters. In order to enable learning through back propagation, this distribution is then sampled using the reparameterization technique, and the resulting sample is sent through the decoder to reconstruct the input.  
We use disentangled features as the prior distribution in a VAE (Equation (1)) to remove class-irrelevant features (e.g., background pixels) and ensure that domain-invariant features are well-disentangled from class-specific features, because the image-only a priori aligns the latent features to a normal distribution.
# Protein Dynamics in F-like Bacterial Conjugation
## 5 The Structures of Conjugative DNA Transfer Proteins
### TraI  
The relaxase protein, TraI is a large 1756 aa cytoplasmic protein responsible for nicking F DNA at _oriT_. This protein has proven to be bifunctional with the NTD from Met1-Pro306 presenting relaxase activity while two independent regions of Thr303\(-\)Ala844 and His830\(-\)Ser1437 corresponding to helicase folds, with the conserved motifs of DNA helicase family found in the latter fold [129; 130; 131]. The role played by the CTD residues Arg1476-Asp1756 of TraI is uncertain. Indirect evidence has shown that the region may interact with TraM; however, it has also been hypothesized that the domain may bind TraD to couple the relaxosome to the T4 SS (Figure 5D) [132]. The N-terminal relaxase domaincontains two pairs of adjacent tyrosines, which are thought to catalyze the cleavage reaction performed by TraI, however Tyr16 can act as a lone catalytic tyrosine to ensure DNA transfer occurs and has been shown to be essential to conjugation [1; 130; 133]. The high-resolution structure of the relaxase domain of TraI has been resolved by X-ray crystallography (Figure 5C) [134]. The relaxase domain of TraI binds to ssDNA in a bent conformation in which mainly \(\alpha\)-helical domain borders the \(\beta\)-sheet [135; 131]. In order for cleavage to occur a divalent metal ion like Mn\({}^{2+}\), Zn\({}^{2+}\), Ni\({}^{2+}\), Cu\({}^{2+}\), Ca\({}^{2+}\), etc., must be present [136]. TraI binds specifically to the site and strand, catalyzing a transesterification reaction at _oriT_, and 'nicking' the DNA at the 5' end. TraI helicase then unwinds the DNA in 5' \(\to\) 3' direction to allow for ssDNA to be transferred, as mediated by the TraM signal protein [37; 129]. The structure of TraI is conserved within the F-like plasmid family and is currently considered as a useful target for novel antibiotics due to its ubiquitous nature in the conjugation process [1].
## 3 Numerical Results  
In this section, the DGTDPO method is used to calculate the RCS and scattering fields of three kinds of composite PEC scatterer, which include a sphere-plate model, a multi-scale object-plate model and a multi-scale, multi-target-rough sea surface model. The calculation time, memory consumption and accuracy of the DGTDPO method are analyzed and compared. In all composite scattering scenarios, the incident wave is a plane wave excited by a Gaussian pulse modulation:  
\[\overset{\rightarrow}{E}_{\text{inc}}(r,t)=\hat{\rho}E_{0}\mathbf{G}(t-k \cdot r/c_{0}) \tag{18}\]  
\[\mathbf{G}(t)=\exp(-(t-t_{0})^{2}/\tau_{m}^{2})\cos(2\pi f_{m}(t-t_{0})) \tag{19}\]  
where \(\hat{\rho}\) is the polarization; \(k\) is the direction of propagation; \(c_{0}\) is the speed of light. \(E_{0}=1V/m\) is the amplitude; and \(\mathbf{G}(t)\) is a Gaussian pulse with modulation frequency \(f_{m}\) delay \(t_{0}\), and duration \(\tau_{m}\).
# Photonic Crystal Cavity with a Thin Low-Index Layer for Silicon-Compatible Nanolight Source
## 5 Conclusions  
In conclusion, we propose a high-\(Q\) photonic crystal cavity with a thin low-index SiO\({}_{2}\) layer sandwiched at the center of the silicon cylinders. Owing to the optical property of the TM waveguide slot mode, the electric field is strongly confined at the 10-nm thick low-index layer with a confinement factor of 0.23, where rare-earth dopants with telecommunication emission wavelengths can be embedded. By constructing photonic crystals with square lattices of silicon cylinders with a height of 1000 nm, lattice constant of 500 nm, cylinder radius of 125 nm, and thin low-index layer thickness of 10 nm, the photonic bandgap, including the telecommunication wavelength, can be built. By decreasing the radius of the center cylinder to 90 nm and shifting the four nearest cylinders into the center over a distance of 60 nm, we can optimize the cavity mode with a resonant wavelength \(\lambda_{c}\) of 1537 nm, high \(Q\) factor of 15,000, and mode volume of 0.01 um\({}^{3}\) = 0.0089 \(\left(\frac{\lambda_{c}}{n_{\text{SiO}_{2}}}\right)^{3}\)um\({}^{3}\). If an emitter is embedded at the low-index layer, a high Purcell factor is estimated as 127,000, owing to strong electric field localization in the low-index layer. We believe that the design of the proposed cavity based on the silicon cylinder and SiO\({}_{2}\) layer will be useful in the development of a silicon-based nanolight source, which is urgently required for the CMOS compatible photonic integrated circuit.  
Due to spatial spreading, the ion implants of rare-earth dopants have difficulties applying to the thin SiO\({}_{2}\) layer of the proposed rod-type photonic crystal structure. The proposed structure can be fabricated by radio frequency confocal magnetron sputtering, and the rare-earth doped thin SiO\({}_{2}\) layer also can be prepared by the co-sputtering of three targets: Si, SiO\({}_{2}\), and the oxides of rare-earth materials [23; 24].  
[PERSON] performed simulations and wrote the manuscript; [PERSON], [PERSON] and [PERSON] designed structures and performed simulations; [PERSON] supervised all simulations and revised the manuscript.  
This research was funded by National Research Foundation of Korea (NRF) grant funded by the Korean government under Grants NRF-2016R1C1B2007007, and NRF-2013M3C1A3065051.  
The authors declare no conflict of interest.
## 7 Conclusions
The result of training algorithms on a discrete set of actions--Stage I. \begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\(\mathbf{Type}\backslash N\times\mathbf{No}\) **Board Size**} & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline PPO (DA) & END & 90 & 92 & 75 & 68 & 69 & 72 & 68 & 65 & 66 & 64 \\ \hline A2C (DA) & END & 86 & 65 & 48 & 52 & 49 & 47 & 43 & 40 & 41 & 37 \\ \hline DQN & END & 93 & 53 & 48 & 50 & 46 & 44 & 38 & 25 & 23 & 21 \\ \hline \hline \end{tabular}   
**Table A1. ** The result of training algorithms on a discrete set of actions--Stage I. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Type\(\backslash\)N \(\times\) N Board Size**} & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline PPO (DA) & MN & 20 & 40 & 60 & 85 & 103 & 121 & 140 & 162 & 180 & 200 \\ \hline A2C (DA) & MN & 20 & 40 & 60 & 80 & 103 & 120 & 144 & 160 & 180 & 207 \\ \hline DQN & MIN & 20 & 41 & 60 & 82 & 104 & 120 & 140 & 168 & 184 & 204 \\ \hline PPO (DA) & MAX & 20 & 54 & 71 & 157 & 127 & 206 & 167 & 174 & 234 & 217 \\ \hline A2C (DA) & MAX & 20 & 46 & 60 & 83 & 158 & 130 & 156 & 184 & 189 & 215 \\ \hline DQN & MAX & 28 & 105 & 203 & 468 & 289 & 536 & 179 & 235 & 304 & 403 \\ \hline PPO (DA) & TMS & 1.1 & 1.1 & 1.1 & 1.3 & 1.5 & 1.5 & 1.6 & 1.6 & 1.7 & 2 \\ \hline A2C (DA) & TMS & 1.1 & 1.1 & 1.4 & 1.6 & 2 & 2.1 & 2.2 & 2.3 \\ \hline DQN & TMS & 1.2 & 1.2 & 1.2 & 1.3 & 1.3 & 3.7 & 3.9 & 4.1 & 4.1 & 4.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of training algorithms on a continuous set of actions—Stage I. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Type\(\backslash\)N \(\times\) N Board Size** & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline PPO (CA) & END & 78 & 72 & 68 & 61 & 64 & 59 & 55 & 54 & 50 & 47 \\ \hline A2C (CA) & END & 80 & 77 & 78 & 75 & 73 & 70 & 68 & 60 & 54 & 46 \\ \hline SAC & END & 88 & 78 & 74 & 76 & 70 & 58 & 65 & 57 & 56 & 52 \\ \hline DDPG & END & 90 & 87 & 85 & 78 & 74 & 69 & 67 & 59 & 51 & 49 \\ \hline TDS & END & 87 & 86 & 81 & 78 & 73 & 62 & 67 & 54 & 50 & 45 \\ \hline PPO (CA) & MIN & 7 & 15 & 25 & 57 & 52 & 48 & 82 & 88 & 98 & 100 \\ \hline A2C (CA) & MIN & 7 & 19 & 31 & 43 & 48 & 50 & 48 & 89 & 98 & 101 \\ \hline SAC & MIN & 8 & 14 & 22 & 39 & 49 & 48 & 49 & 89 & 98 & 101 \\ \hline DDPG & MIN & 13 & 22 & 28 & 38 & 49 & 51 & 49 & 90 & 101 & 100 \\ \hline TDS & MIN & 8 & 19 & 34 & 38 & 52 & 53 & 53 & 89 & 99 & 102 \\ \hline PPO (CA) & MAX & 18 & 47 & 65 & 83 & 115 & 132 & 140 & 190 & 213 & 230 \\ \hline AAC (CA) & MAX & 25 & 67 & 59 & 70 & 114 & 111 & 123 & 165 & 190 & 178 \\ \hline SAC & MAX & 11 & 78 & 58 & 64 & 123 & 113 & 133 & 112 & 165 & 128 \\ \hline DDPG & MAX & 28 & 35 & 94 & 63 & 80 & 129 & 92 & 200 & 189 & 234 \\ \hline TDS & MAX & 47 & 71 & 89 & 111 & 131 & 145 & 148 & 176 & 117 & 130 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of training algorithms on a continuous set of actions—Stage III.
### Our Contributions  
To address the challenges in the communication of emergency cases, we propose an iterative scheduling algorithm for trajectory and resource (ISATR) for path planning and limited resource allocation in a UAV-assisted emergency communication network. In addition to commonly discussed variables, we consider the dynamics of ground users and present a comprehensive approach using a quasi-convex method to optimize UAV path, power, and bandwidth allocation across different time slots. This approach spansdimensions of time, space, spectrum, and energy; therefore, it can provide a rather accurate and comprehensive plan. The main contributions of this paper are summarized as follows:  
1. UAV-assisted communication model with the dynamic environment. For emergency cases, few researchers test their algorithm with dynamic users. We have established an A2G communication model with moving ground users, where the energy consumption and assistance communication quality are jointly optimized.
2. Dynamic bandwidth allocation. For resource allocation in UAV-assisted communication, few researchers focus on bandwidth, due to its high complexity. Our work tackles dynamic bandwidth allocation, providing an algorithm for real UAV planning.
3. Designed iterative algorithm. For the NP-hard optimization problem of UAV planning in emergency communication, we leverage the idea of subtask iterative algorithm and work out an effective iterative scheduling algorithm of trajectory and resource.
4. Simulation analysis. Experiments are implemented to evaluate the effectiveness of the proposed optimization algorithm, which achieves obvious performance compared with non-optimized and several other methods and can maintain the performance in different environments.  
Benefiting from its high accuracy, ISATR can serve as a necessary baseline in case of emergency communication situations, applied for a pre-planning scheme derived before emergency strikes.  
This paper is organized as follows:  
* The A2G communication model, user moving strategy, and mathematical optimization model are established in Section 2.
* The iterative scheduling algorithm for trajectory and resource (ISATR) is elaborated on in Section 3.
* In Section 4, the results and performance are discussed, with comparisons in different environments.
* Section 5 concludes the paper.
## 3 Results and Analysis
### Damage Status and Failure Mode of Different Coal Impact Types  
The RA-AF distribution of the AE can reflect the damage status and failure mode of the coal rock [40; 41]. The tensile failure AE signal duration and rise time are short, AF value is high, RA value is low, while the shear crack AE signal duration and rise time are long. Shear failure, on the contrary, has a long duration and rise time of the AE signal, high RA value, and low AF value [42; 43; 44]. A typical coal rock AE waveform information and RA-AF crack classification are shown in Figure 5. Among them, AF is the ratio of AE counts to duration (Equation (1)), and RA is the ratio of AE rise time to amplitude (Equation (2)).  
\[\text{AF}= \frac{\text{Counts}}{\text{Duration time}} \tag{1}\] \[\text{RA}= \frac{\text{Rise time}}{\text{Amplitude}} \tag{2}\]Figure 5: Distribution characteristics of AF-RA about different coal impact types: (**a**) AE waveform information; (**b**) RA-AF crack classification.  
Figure 6: _Cont._  
From Figure 6 and Table 3, it can be observed that during the coal body failure process, there are more AE signals associated with tensile failure and fewer signals related to shear failure. For the same coal impact type, as the degree of damage increases, the proportion of the tensile failure AE signals gradually decreases, while the proportion of the shear failure AE signals gradually increases. For example, the tensile cracks in coal with no, weak, and strong impact decreased from 76.4%, 88.9%, and 90.6% in stage I to 60.1%, 49.1%, and 46.3% in stage IV, respectively. For the different coal impact types, as the impact tendency increases, the proportion of the tensile failure AE signals gradually increases in stages I, II, and III of loading, but the magnitude of the increase gradually decreases. In failure stage IV, as the impact tendency increases, the AE signal of the tensile failure gradually decreases, and the proportion of the AE signal of the shear failure gradually increases. For example, the shear cracks in the failure stage IV coal with no, weak, and strong impact are 39.9%, 50.9%, and 53.7%, respectively. As the impact tendency increases, the coal structure becomes dense, and the tensile failure gradually changes to shear failure. When shear failure occurs, the coal body experiences severe fracture and friction, and the IRT and AE signals rapidly increase. The shear failure location corresponds to the dense area of the AE positioning points and the high-temperature concentration area of the infrared thermal imaging. The stronger the impact tendency of coal, the denser the AE positioning points and the larger the range and intensity of the infrared high-temperature area.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
###### Abstract  
In this paper, we provide a light-weighted Machine Learning (ML) approach to channel estimation for New-Radio (NR) systems. Specifically, based on the equivalence between the Channel Impulse Response (CIR) in the time domain and its corresponding Channel Frequency Response (CFR) in the frequency domain, the light-weighted ML model for the channel estimation is shown to be established in comparison to the existing ML-based channel estimator. Furthermore, for practical use, the quantized weights for the light-weighted ML-based estimator are shown to be feasible without significant performance degradation in the sense of mean square error (MSE), which shows the effectiveness of the proposed approach from the perspective of memory overhead. Consequently, we show that there exists Signal to Noise Ratio (SNR) gain in comparison with the existing ML-based estimator, which is validated by numerical results considering the Sounding Reference Signal (SRS) for NR in the 3 rd Generation Partnership Project (3 GPP).  
channel estimation; Channel Impulse Response (CIR); Channel Frequency Response (CFR); Machine Learning (ML); New-Radio (NR); quantization; Sounding Reference Signal (SRS); 3 rd Generation Partnership Project (3 GPP) +
Footnote †: journal: _Electronics Letters_  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
## 3 Testing and Experimentation
(**a**) Pulse channel timing diagram. (**b**) Channel 1 gate driver output pulse. (**c**) Channel 1 GaN MOSFET drain voltage pulse. Figure 13: Pulse width and jitter testing. (**a**) Channel 1. (**b**) Channel 2. (**c**) Channel 3. (**d**) Channel 4. In Table 1, we measured the drain voltage, negative pulse width, and peak voltage of the GaN MOSFETs across all channels. All pulse widths are less than 5 ns, and the negative pulse peak voltages are all less than 0 V. The test results indicate that we have achieved a pulse laser array drive with a single channel operating frequency of 125 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time of 1 \(\upmu\)s. The measured oscillation decay time of the drain voltage of the MOSFETs across eight channels is between 100 ns and 110 ns. The maximum noise voltage fluctuation range of the MOSFETs across eight channels is less than 1 V, while the change in drain voltage when the MOSFET is turned on is greater than 18 V, which is much larger than the noise voltage, meeting the design requirements. In addition, we measured the output optical characteristics of the LD, as shown in Table 2. Firstly, the diameters of the eight spots displayed in Figure 12 range from 7.5 mm to 8.6 mm. The diameter of each spot is between 0.96 and 1.10 times the average diameter, with a deviation of less than 10%, indicating good consistency. Additionally, we measured the average power of the eight beams using an optical power meter, ranging from 36 mW to 46 mW. The peak power calculation method is shown below:  
\[P_{\text{Peak}}=P_{\text{Average}}\cdot\frac{T}{t} \tag{10}\]  
where \(P_{Peak}\) represents the peak power, \(P_{Average}\) denotes the average power, \(T\) signifies the pulse laser period, and \(t\) stands for the pulse width. The peak powers of the eight LDs were calculated using Equation (10) and are presented in Table 2. The peak powers are all above 75 W, demonstrating that the output power of the circuit meets the design requirements.
## 2 Materials and Methods
### Experimental Field and Laboratory Trials with Herbicide Treatments
#### 2.3.5 Analysis of Interactions between the Herbicidal Treatments and the Substrate  
The mineralogy of the samples before and after the interventions on the wall was determined by X-ray diffraction (XRD) analysis, with the same device and conditions described in Section 2.2.  
In order to analyse the presence of soluble salts due to herbicidal treatments, the ion content (Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{-2}\), HCO\({}_{3}\)\({}^{-}\) and PO\({}_{4}\)\({}^{-3}\)) and cation content (NH\({}_{4}\)\({}^{+}\), only in the first trial) were measured in samples of the wall taken before and after the application of the herbicide. The soluble salts were extracted during 24 h from 1 g of sample ground to a particle size of less than 4 mm with 10 mL of ultrapure water. The extracts were filtered (0.45 um) and the ions present were analysed. The chloride (mg Cl\({}^{-}\)/g), nitrate (mg NO\({}_{3}\)\({}^{-}\)/g), sulphate (mg SO\({}_{4}\)\({}^{-2}\)/g) and phosphate (mg PO\({}_{4}\)\({}^{-3}\)/g) contents were determined in a 930 Compact IC Flex (Metrohm, Switzerland) ion chromatography system. The ammonium ion content (mg NH\({}_{4}\)\({}^{+}\)/g) was determined with a selective ORION 720 electrode. The bicarbonate content (mg HCO\({}_{3}\)\({}^{-}\)/g) was analysed by potentiometric titration (Compact titrometer, Crison, Barcelona, Spain).  
The colour change was recorded immediately before and three months after herbicide application, with a Konica Minolta tristimulus colourimeter equipped with a CR-300 measuring head (1998 and 1999) or with a portable spectrophotometer (Konica Minolta CM-700d) equipped with CMS100w (SpectraMagicTM NX) software (2016 and 2018). The measurement conditions in both devices were illuminant D65, observer 2\({}^{\circ}\) and target area, 8-mm diameter. A number of readings (in proportion to the size of sample) were made at different randomly selected zones on each sample [29]. The measurements were made in the CIELAB colour space [30] and expressed as L\({}^{*}\), a\({}^{*}\), and b\({}^{*}\). Partial colour differences (\(\Delta\)L\({}^{*}\), \(\Delta\)a\({}^{*}\), and \(\Delta\)b\({}^{*}\)), i.e., before and after treatment, represent the change in lightness (\(\Delta\)L\({}^{*}\)), from black (negative value) to white (positive value), the change in the green-red component (\(\Delta\)a\({}^{*}\)), from greenness (negative value) to redness (positive value), and the change in the blue-yellow component (\(\Delta\)b\({}^{*}\)), from blueness (negative value) to yellowness (positive value). The total or global colour difference (\(\Delta\)E\({}^{*}\)\({}_{\mathrm{ab}}\)) was determined according to UNE-EN 15886 [31] by using the following equation:  
\[\Delta\mathrm{E}^{*}\mathrm{_{ab}}=(\Delta\mathrm{L}^{*2}+\Delta\mathrm{a}^{*2 }+\Delta\mathrm{b}^{*2})^{1/2}, \tag{1}\]  
To determine any perceptible colour changes in these three colour attributes and in the global colour, a threshold of 3 CIELAB units was assumed to indicate a notable change in colour [32; 33; 34].