esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Results and Discussions
### Laser Dynamic States under Double External Cavity
#### 4.2.1 Region of Short Double OFB (\(L_{\text{ex2}}\) = 3.5 \(\times\) 10\({}^{-2}\) m)
Figures show that the double external cavity leads to stabilizing the irregular states produced by the single external cavity and keeps the stable CW state, which reduces the RIN to lower levels near solitary laser noise level. That leads to improvements in the properties of the LD, especially in applications such as optical communication systems and others. The RIN corresponding to unstable operation states can be stabilized as rising double OFB strength \(K_{ex2}\) and optical phase \(\psi_{2}\), which helps in planning lasers at best dynamics and low noise level. Impacts of optical phase and double OFB strength \(K_{ex2}\) on the RIN with adapting laser design to adjust values of \(K_{ex2}\) and \(\psi_{2}\) are presented here and have not been investigated before. It may be helpful in increasing/decreasing the stability/RIN of LDs, respectively. The chaotic dynamic state under double OFB is important in many applications, such as secure communication systems. In this section, we have an interest in investigating the output photon number \(S(t)/S_{\rm av}\), time change, and the corresponding chaos dynamic RIN spectral characteristics under double OFB when \(K_{ex1}=0.04\) and \(K_{ex2}=0.005\) and at optical phase \(\psi_{1}=\omega\tau_{1}\), \(\psi_{2}=0.0\), and \(\omega(\tau_{1}+\tau_{2})\). Figure 7a plots the variations of \(S(t)/S_{\rm av}\), which are intensity-reduced and phase-shifted by increasing the optical phase \(\psi_{2}\) from zero to \(\omega(\tau_{1}+\tau_{2})\), which is similar to single OFB Figure 3(a3). The reduction of the intensity of \(S(t)/S_{\rm av}\) is translated into a decrease of RIN in Figure 7b. The RIN spectrum shows more enhanced peaks than the chaotic RIN with a single OFB shown in Figure 3(b3). The values of the peak frequency have no relationships and are random. The highest peak frequency is 3.4 GHz, which is equal to a single OFB peak frequency. Increasing the optical phase \(\psi_{2}\) from zero to \(\omega(\tau_{1}+\tau_{2})\) causes phase shift and suppression in the intensity of the RIN by about one order of magnitude.
# Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
###### Abstract  
Ocean wave parameters (OWPs), including wave propagation direction (WPD), significant wave height (SWH), and main wave wavelength (MWW) can be typically retrieved using an interferometric imaging radar altimeter (InIRA). However, the inversion accuracy of ocean waves in Ku (15.8 GHz) and Ka (35.8 GHz) bands has not yet been evaluated due to the lack of field observation data. In this paper, to assess the inversion accuracy of OWPs in Ku and Ka bands, an airborne observation experiment using simultaneous Ku and Ka bands was carried out for the first time in Rizhao, Shandong Province, China. A dual-band InIRA (DInIRA) was configured with small incidence angles (4\({}^{\circ}\)-18\({}^{\circ}\)) and a Global Navigation Satellite System (GNSS) buoy; a mobile weather station was placed at the intersection of the plane routes for validation. Afterward, the WPD, SWH, and MWW were retrieved based on the imaging of sea surface height. As compared with the field in situ data, the WPD inversion results of main wind wave were found to be consistent with the measurement environmental wind direction. The SWH inversion biases, retrieved by the Ku and Ka bands, were 0.38 m and 0.27 m; the MWW inversion biases for the swells were equal to 16.75 m and 3.67 m; and the MWW inversion biases about the wind waves were 2.32 m and 0.57 m. Ultimately, it was established that the OWPs could be effectively retrieved by the DInIRA, and the inversion accuracy of the SWH and the MWW in the Ka band outperformed that in the Ku band.  
Keywords:dual-band interferometric imaging radar altimeter; ocean wave inversion; wave propagation direction; significant wave height; main wave wavelength +
Footnote †: journal: remote sensing
# Method for Measuring Surface Charge on Insulating Materials Based on the Vibrating Capacitor Method
## 3 Results and Discussion  
### Theoretical Model of Parallel Plate Capacitor  
Based on the principle of the vibrating capacitor method, a theoretical model is established, as shown in Figure 3. \(\sigma_{in}\) represents the surface charge density of the dielectric material sample, caused by phenomena such as frictional charging, and remains unaffected by other variables during the measurement process. \(\sigma_{1}\) denotes the induced surface charge density on the lower surface of the upper metal plate (probe) after applying the input voltage, while \(\sigma_{2}\) represents the surface charge density induced on the lower metal plate (sample holder) and the lower surface of the insulating material sample.  
In addition, let \(z\) denote the distance between the lower surface of the probe and the upper surface of the sample, and \(h\) represent the thickness of the sample being measured. \(E_{1}\) represents the magnitude of the electric field between the probe plates, while \(E_{2}\) denotes the electric field within the sample. \(V\) represents the voltage output from the voltage source, and A denotes the inclusion of an ammeter in series within the circuit.  
Therefore, in electrostatic equilibrium, the applied voltage provided by the external circuit satisfies the following equation for potential measurement. In the equation, \(\varepsilon_{0}\) represents the vacuum permittivity and \(\varepsilon_{r}\) represents the relative permittivity of the dielectric material:  
\[V=E_{1}z-E_{2}h=\frac{\sigma_{1}}{\varepsilon_{0}}z+\frac{\sigma_{1}+\sigma_{ in}}{\varepsilon_{0}\varepsilon_{r}}h \tag{1}\]  
Figure 3: Parallel plate capacitor model with dielectric material.  
\[\sigma_{1}=\frac{\varepsilon_{0}\varepsilon_{r}V-\sigma_{in}h}{\varepsilon_{r}z+h} \tag{2}\]  
By adjusting the magnitude of the potential measurement to ensure that the current in the circuit is zero, we achieve:  
\[\frac{\partial\sigma_{1}}{\partial z}=0 \tag{3}\]  
At this point, the relationship between the surface charge density of the material and the potential measurement is:  
\[\sigma_{in}=\frac{\varepsilon_{0}\varepsilon_{r}V}{h} \tag{4}\]  
Formula (4) represents the relationship between the surface charge density of the sample and the measured voltage in the ideal state of the parallel plate capacitor model.
## 4 Simulation Results and Discussion
### Effects of the OIS's Speed on Various Aspects of Performance
#### 4.2.4 Manageable Load Percentage of the OIS  
Figure 21 shows the performance of the manageable load percentage of the OIS in relation to its speed in various channel environments. According to Figure 21a, in all three methods, the manageable load percentage of the OIS decreases in response to increasing train speed. This is because the chances of disconnection increase with increasing train speed. Nonetheless, the toss-and-catch algorithm performed better than the control groups at various train speeds, as its connection hold rate is higher than the control groups. Therefore, when a train is moving, there are more chances for the SBSs to continuously handle the OIS's load, and generally speaking, there are more OIS loads that can be handled by the SBSs. In addition, we can see that at low- and medium-speed intervals, the SBSs selected by the toss-and-catch algorithm are capable of handling over 90% of the OIS's load. This is mainly because the OIS has a longer retention time for the selected SBSs and there are fewer chances of signal disconnection. Moreover, since the toss-and-catch algorithm is designed to select SBSs effectively, it has a better load-handling capacity.  
When Nakagami-3 fading is employed, the toss-and-catch algorithm still produces a decent performance, while the performance of both control groups declined significantly. In the case of Nakagami-1 fading and Nakagami-1/2 fading, as shown in Figure 21c,d, respectively, the performance of Control Groups 1 and 2 are both below 50%. This effect is particularly pronounced when Nakagami-1/2 fading is employed, as the performance  
Figure 21: Comparing the performance of the manageable load percentage of the OIS in relation to its speed using the three methods in (**a**) nonfading environments; (**b**) Nakagami-3 fading environments; (**c**) Nakagami-1 fading environments; and (**d**) Nakagami-1/2 fading environments.  
of both control groups were only half of that of the toss-and-catch algorithm in most conditions. This can be explained by the better performance of the toss-and-catch algorithm in terms of SBS selection and MSBS mode adjustment, made possible with the assistance of the overload support mechanism in which the ASBSs may undertake the loads of MSBSs that are closer to the OIS.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
###### Abstract  
Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LSTM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond.  
5G; machine learning; Bi-LSTM; wireless communication; NOMA; CNN +
Footnote †: journal:  
17 August 2022  
Accepted: 13 September 2022  
Published: 15 September 2022  
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**Copyright**\(\copyright\) 2022 by the authors. Liconssee 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/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 1 Introduction
It has been reported that the presence of antibiotics and heavy metals in soil can jointly influence ARGs and metal resistance genes (MRGs) through various mechanisms, such as co-resistance, cross-resistance, and co-regulation [16; 17; 18; 19]. Consequently, when ARGs coexist with lead resistance genes (LRGs), the presence of Pb is closely associated with the proliferation of ARGs [20; 21]. [PERSON] et al. [22] reported a strong positive correlation between the dynamics of ARGs (e.g., _tet_W, _sul_1, and _sul_2) and LRGs such as _Pbr_T in the biofilms of the Cuyahoga River watershed. While several studies have examined the impact of heavy metals on MRGs and ARGs, there have been few reports on the distribution of LRGs in Pb-contaminated soils. Particularly, the effect of LRGs distribution on ARGs dynamics Pb-contaminated soil remains unknown. This study investigated the mechanisms driving ARGs variation in Pb-contaminated soils, with a focus on the influence of LRGs distribution during various remediation approaches. The methods include montmorillonite (Invite), the composite remediation agents of MgO and Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) (MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\)), and the composite remediation agents of montmorillonite, MgO, and Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) (Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\)), BES, and BES combined with Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) (BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\)). It examined ARGs, antibiotic resistance, and LRGs dynamics across these processes, as well as the effects of initial Pb concentration and pH on ARGs distribution following composite and BES-based remediation. The study further explored ARGs variation mechanisms by identifying potential ARGs and LRGs hosts through network and correlation analyses with microbial community abundance. The correlation between ARGs and LRGs, and their resistance functions, was analyzed under different remediation conditions. Redundancy analysis (RDA) evaluated the impact of Pb stress, pH, microbial communities, and LRGson ARGs. The findings offered theoretical insights and practical strategies for managing ARGs dissemination during heavy metal soil remediation.
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 2 Materials and Methods
### Drug Content  
To determine the drug content, a certain amount of PTX-loaded nanospheres was dissolved in ACN. The samples were assayed by high-performance liquid chromatography (HPLC) at \(\lambda\) = 229 nm, according to [PERSON] et al. [3]. The HPLC system was the Beckman Coulter system (Miami, FL, USA). Chromatographic separations were carried out using a Luna C18(2) column (25 cm, 5 um, 100 A, Phenomenex, Torrance, CA, USA). Chromatographic conditions were a mobile phase consisting of ACN/water/methanol (60/2/38, \(v/v/v\)) with TFA (0.1%), and a flow rate of 1 mL min\({}^{-1}\). The analyses were performed at 30 \({}^{\circ}\)C.  
Encapsulation efficiency (_EE_) was calculated according to the following equation (Equation (2)):  
\[EE(\%)=\frac{m_{e}}{m_{t}}\times 100\% \tag{2}\]  
where \(m_{e}\) is the amount of the drug entrapped in the nanoparticles, and \(m_{t}\) is the total amount of drug added.  
Drug loading capacity (\(DL\)) was calculated according to the following formula (Equation (3)):  
\[DL(\%)=\frac{m_{e}}{m_{n}}\times 100\% \tag{3}\]  
where \(m_{e}\) is the amount of the drug entrapped in the nanoparticles, and \(m_{n}\) is the total weight of nanoparticles.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 4 Process Flow of the Proposed Architecture
### Device Registration  
The physical layer contains many sensors and actuators, and all of these devices must be pre-registered with the private blockchain network. These devices are responsible for providing data that processed by the STM32 Microcontroller to the lightweight nodes, which process the registration request to the blockchain nodes as a suggested new transaction. The IoT devices are responsible for generating a unique device ID, the ID verification process based on a smart contract, if the device ID does not exist, then the PoAh algorithm is executed and the registration of the new device ID in the blockchain network is completed, then new certificate shared with all blockchain nodes. Otherwise, the transaction denied and a notification is generated for the administrator with an error message is returned. Algorithm 3 summarizes the device registration process in the blockchain network.  
```
1 start:
2 IoTs generate a new device unique ID
3 Device ID: Temperature-Inlet
4 Microcontroller STM32 sends the information to light nodes
5 Lightweight nodes process the registration request
6 if (Device ID not exist == true)
7 Execute PoAh algorithm
8 Register new devise ID in blockchain network
8 Notify nodes by share new device ID certificate
9 else
10 Deny transaction
11 Notify nodes
12 Return error ()
13 end: Go to step 1 for next process
```  
**Algorithm 2** User Registration.
## 3 Proposed Method
### Loss Function  
GAN ensures that the distribution of the generated image matches the distribution of the target domain image by adversarial loss. To achieve image-to-image translation, it is usually necessary to use the cycle consistency loss in CycleGAN to ensure a meaningful translation. However, CycleGAN assumes that there is a bi-directional mapping between the source and target domain images, which is a more stringent assumption for image-dehazing tasks, because the haze-free image domain has more information than the haze image domain and there is information inequality between the two image domains; thus, one-sided translation is more suitable for image dehazing. To achieve one-sided image translation, additional regular terms need to be introduced to ensure meaningful translation. This paper achieves one-sided image domain translation by introducing contrastive loss.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Input\(\rightarrow\)Output Shape** & **Layer Information** \\ \hline \multirow{4}{*}{Encoded Module} & \((h,\ w,\ 3)\)\(\rightarrow\)\(\left(\frac{h}{2},\ \frac{w}{2},\ 64\right)\) & Conv-(N64, K4, S2, P1), Leaky-ReLU \\  & \(\left(\frac{h}{2},\ \frac{w}{2},\ 64\right)\) & \(\left(\frac{h}{4},\ \frac{w}{2},\ 128\right)\) & Conv-(N128, K4, S2, P1), IN, Leaky-ReLU \\  & \(\left(\frac{h}{4},\ \frac{w}{4},\ 128\right)\)\(\rightarrow\)\(\left(\frac{h}{8},\ \frac{w}{2},\ 256\right)\) & Conv-(N256, K4, S2, P1), IN, Leaky-ReLU \\  & \(\left(\frac{h}{8},\ \frac{w}{8},\ 256\right)\)\(\rightarrow\)\(\left(\frac{h}{8}-1,\ \frac{w}{8}-1,\ 512\right)\) & Conv-(N512, K4, S1, P1), IN, Leaky-ReLU \\  & \(\left(\frac{h}{8}-1,\ \frac{w}{8}-1,\ 512\right)\)\(\rightarrow\)\(\left(\frac{h}{8}-2,\ \frac{w}{8}-2,\ 1\right)\) & Conv-(N1, K4, S1, P1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discriminator network architecture.  
Figure 6: Illustration of how to construct a feature map of the fourth decoder layer.
## 4 Results and Discussion
### ECG-ID Dataset Results
#### 4.4.2 Transformer Model Results  
These results for the transformer model demonstrate quite good efficiency in the task of non-uniform sampling optimization for the reconstruction of ECG signals. There is a clear improvement in the performance metrics as the number of samples increases from 18 to 120, hence depicting more accuracy and fidelity in the reconstructed signal. The mean squared error reduces drastically from 0.0031 to 0.0005, and the root mean squared error also declines significantly from 0.0554 to 0.0216, thus giving a far more accurate representation of the actual signal. Also, Pearson's correlation coefficient, r, climbs from  
\begin{table}
\begin{tabular}{l l l l} \hline  & \multicolumn{3}{c}{**For Higher Sampling Rates**} \\ \hline
**Samples per Second** & **MSE** & **RMSE** & **Pearson’s r** \\ \hline
46 & 0.0171 & 0.1307 & 0.7704 \\ \hline
56 & 0.0169 & 0.1299 & 0.7739 \\ \hline
66 & 0.0169 & 0.1299 & 0.7739 \\ \hline
76 & 0.0166 & 0.1290 & 0.7777 \\ \hline
86 & 0.0164 & 0.1281 & 0.7806 \\ \hline
96 & 0.0163 & 0.1277 & 0.7823 \\ \hline
106 & 0.0162 & 0.1273 & 0.7840 \\ \hline
120 & 0.0161 & 0.1267 & 0.7866 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 13: Scatter plot for TCN model showing actual outcome: (**a**) MSE vs. number of samples; (**b**) RMSE vs. number of samples; (**c**) Pearson’s r vs. number of samples.  
0.9355 to as high as 0.9904; thus, it is also shown that the linear relationship between the original and reconstructed signals is much stronger. These data show that the transformer model can still maintain high accuracy even with a smaller number of samples. In this case, all values for the Pearson r are always above 0.9, meaning that this model is likely to effectively give the essential features of the ECG signal, and hence, it could rebuild the signal with accuracy and reliability. The optimal performance of the model occurs at 120 samples. This is, therefore, a clear indication of the capacity of the model to efficiently make use of an increased number of samples in improving the quality of ECG signal representation. In Table 3, evaluations are performed for a transformer model for different lower to higher sampling rates. Figure 14 presents the scatter plot for transformer model results.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{4}{c}{**For Lower Sampling Rates**} \\ \hline
**Samples per Second** & **MSE** & **RMSE** & **Pearson’s r** \\ \hline
18 & 0.0031 & 0.0554 & 0.9355 \\ \hline
19 & 0.0040 & 0.0635 & 0.9129 \\ \hline
20 & 0.  
4.3 Comparative Analysis of TCN and Transformer Model Performance against State-of-the-Art (SOTA) Methods
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 2 Shielding Grounding Optimization Method  
Figure 1 shows the overall process of the shield grounding optimization method. The first step was to obtain relevant parameters, such as the number, lengths, and position relationships of cables according to the actual state of the cables, to establish the CST (Computer Simulation Technology simulation software) simulation model. The simulation results for all the shield grounding states were obtained by means of parameter extremization and parameter scanning simulation. The second step was to use MATLAB to encode the simulation results in binary form and generate the truth table. In the third step, the logic conversion module of Multisim was used to extract and simplify the logical expression of the truth table. The output logical expression indicated the corresponding relationship between the grounding state of the shield and the shielding effect. The fourth step was to analyze and summarize the logical expression used to guide the termination of the cable shield. The following is a detailed explanation of each step in the method.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 2 Multiwavelength Laser Attenuation Characteristics Test in Water-Filled Karst Caves
### Laser Attenuation Characteristics Test System  
#### 2.1.1 Multiwavelength Laser Emission Module  
In order to study the influence of laser wavelength on attenuation characteristics more comprehensively, the laser emission module selected five kinds of lasers with typical wavelengths, as shown in Table 1. They were a blue-purple laser emitter (405 nm), blue laser emitter (450 nm), green laser emitter (532 nm), near infrared laser emitter (650 nm), and far infrared laser emitter (808 nm), respectively. The emitters had a glass window laser diode, emitted continuous dot lasers, employed an optical coated glass lens, and maintained a constant output power. Since the far infrared laser was beyond the normal observation wavelength range, this test adopted an infrared camera (modified from Canon EOS M3 camera) with a Canon Image Stabilizer lens and an infrared filter with a 780 nm wavelength. To observe the 808 nm laser beam more clearly, we used an f/4.0 aperture and set the exposure time to 1/160 and the ISO (International Standardization Organization) to 12800. During the test, we also utilized a laser-fixed triangle support, which ensured the stability of the fixed support with a maximum vertical adjustment range of 2 m; and a three-dimensional servo rotating head, which controlled the emission direction, angle, and height of the laser emitter. The head was equipped with a horizontal level to ensure horizontal emission of the emitter and avoid refraction with the glass as much as possible.