esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
To show the feasibility of the proposed scheme in joint channel estimation and symbol detection, several simulations of the proposed scheme over the IM/DD channel are conducted under different training conditions to investigate the corresponding convergence and BER performance. For simplicity, the DCO-OFDM symbol containing total 128 subcarriers are employed, where the QPSK with \(\mathcal{Q}=4\) are used as the modulation method. The CP with the ratio of \(1/64\) is adopted for mitigating ISI. In addition, we choose \(DC=0.4\) and \(f_{0}=20\) MHz for a LED model in (4). For an empty room scenario, the indoor VLC channel following the model in [39] is used for training.  
As can be seen from the above analysis, the \(\mathcal{S}1\) network contains one hidden layer and the dimension of the input tensor is \(\mathcal{M}\), which is the combination of the real and imaginary parts for one DCO-OFDM symbol. As for the initialization, the input of \(\mathcal{S}1\) can be set as the results of the traditional LS solution with the help of the arranged pilots, which improve the training speed to some extent. The \(\mathcal{S}2\) network involves four layers with the neuron numbers of \(\mathcal{M}\), 256, 256, and \(\mathcal{P}\). At the end, the refine layer with the \(\mathcal{P}\)_Signum_ function is connected to further refine the data. It should be noted that the number of neurons in the input and output layers is determined by the subcarriers \(N\) and that of the output layer also depends on the modulation level \(\mathcal{Q}\). However, the number of neurons in the hidden layers is chosen based on an empirical trial. In the early training stages, the learning rate is fixed to a big value of 0.01 and then gradually decreased in the later training stage. The training specification and parameters of \(\mathcal{S}1\) and \(\mathcal{S}2\) are shown in Table 1.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 4 Experiments
### Quantitative Results on Synthetic Images
There are also issues with brightness enhancement and contrast in the results generated via FFA-Net [36], GCA-Net [52], GUNet [54], and AOD-Net [25]. IFE-Net handles details better and maintains color consistency with the ground truth. From the results, it can be observed that the results of IFE-Net are significantly better than other networks in terms of fidelity of image details and color. Table 2 shows the average quantitative results of the quality evaluation indicators in Figure 5, and the PSNR and SSIM values of IFE-Net are superior to the other methods. Tables 3 and 4 show the PSNR and SSIM results of our images after dehazing, respectively. Meanwhile, Table 5 shows the average time it takes for different networks to process each image with a size of 548 \(\times\) 412. The results in Tables 2-5 indicate that IFE-Net is effective and efficient. Figure 5: Quantitative comparison of IFE-Net with other methods on SOTS. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Evaluation** & **DCP** & **Dehaze-Net** & **AOD** & **FFA** & **GCA** & **DWGAN** & **GUNet** & **IFE** \\ \hline PSNR & 20.37 & 20.66 & 22.51 & 20.87 & 19.52 & 14.27 & 21.97 & **24.38** \\ \hline SSIM & 0.913 & 0.886 & 0.928 & 0.909 & 0.902 & 0.815 & 0.921 & **0.942** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average quantitative results of the quality evaluation indicators in Figure 5.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Key Technology
#### 2.4.2 ISAC Beamforming
Given that the beamforming technique can implement a novel approach to energy allocation, we can improve the system performance, including power loss, accuracy, anti-interference, etc., which is conducive to the development and application of ISAC in future communication systems. Figure 1: Symbol-level precoding principle: schematic diagram. Figure 2: Antenna beamforming without modulus constraint (four users). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 2 Access to Android Raw GNSS Measurements and GNSS Observation Generation
### Carrier-Phase Observation Generation  
The carrier-phase observation in cycle can be obtained as:  
\[\varphi=AccumulatedDeltaRangeMeters/\lambda, \tag{3}\]  
where \(AccumulatedDeltaRangeMeters\) is the accumulated delta range (ADR) since the last channel reset which is one of the variables from GNSSMeasurement class within the Android API package \"location\". \(\lambda\) also denotes the signal's wavelength. It should also be noted that it is better to use only valid measurements for the carrier-phase observation calculation. Validity of the carrier measurements can be checked using the \(AccumulatedDeltaRangeState\) variable.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
###### Abstract  
The safety of unmanned ship berthing is of paramount importance. In order to explore the influence of wind and wave coupling, a berthing computational fluid dynamics (CFD) model was established, _and_ the characteristics of speed field, pressure field, and vortex have been obtained under different speed, wind direction, and the quay wall distances. The results show that the total resistance of the hull against the current can be about 1.60 times higher compared to the downstream resistance, water flow resistance is the dominant factor, accounting for more than 80% of the total resistance. When changing the distance between ship and shore at fixed speed, the results found that the torque is small, but the growth rate is very large when driving below 2 m/s, and the torque growth rate is stable above 2 m/s. Based on the established coupling model, a multi-factor berthing safety study is carried out on an actual unmanned ship. The results show that when the speed increases from 4 m/s to 12 m/s, the curve slope is small, the resistance increases from 3666 N to 18,056 N, and the rear slope increases. The pressure increases with the speed, and when the speed is 24 m/s, the maximum pressure is up to 238,869 Pa. When the wind speed is fixed, the vertical force of the unmanned ship increases first and then decreases to zero and then reverses the same law change, and the maximum resistance is about 425 N at the wind angle of about 45 degrees: At 90 degrees, the maximum lateral force on an unmanned boat is about 638 N. The above results can provide control strategy for unmanned ship berthing safety, and provide theoretical basis for unmanned ship route planning and obstacle avoidance, safety design, etc.  
unmanned ship; CFD coupling model; berthing safety; autonomous berthing algorithm 2021  
11720202128 July 2021
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 1 Introduction
### Optimization Process for Linearized Image Reconstruction  
This section describes the optimization process for image reconstruction using a linearized forward process. As a good approximation, the \(\overline{\mu}\) of the reconstructed image \(\mu\) is obtained, and the measurements are approximated using the Taylor expansion as  
\[\mathbf{M}(\mu)=\mathbf{F}(\overline{\mu})+J(\overline{\mu})\delta\mu+\cdots, \tag{6}\]  
where \(\mathbf{F}(\overline{\mu})\) is the predicted measurement with \(\overline{\mu}\) (or the measurement equivalent to the predicted measurement) and \(\delta\mu=\mu-\overline{\mu}\). When the series expansion is terminated in the first order, the forward process can be linearized, and the linear equation  
\[(\mathbf{M}-\mathbf{F})=J\delta\mu, \tag{7}\]  
is obtained. The linearizations of the forward process for the absolute and logarithmic values of the measured light intensities are referred to as the Born and Rytov approximations, respectively. Then, the optimization process can be formulated in a manner similar to that in Equation (1) with regularization, as follows:  
\[\min_{\delta\mu}\Bigl{\{}\bigl{(}\delta\mathbf{M}-J\delta\mu\bigr{)}^{T}W( \delta\mathbf{M}-J\delta\mu)+\lambda R(\delta\mu)\Bigr{\}}, \tag{8}\]  
where \(\delta\mathbf{M}=\mathbf{M}-\mathbf{F}\). When employing classical Tikhonov regularization with a weighting matrix \(L\) formulated as \(R(\delta\mu)=\|L\delta\mu\|^{2}\), \(\delta\mu\) is obtained by setting the derivative of the objective function of Equation (7) to zero,  
\[\delta\mu=\Bigl{(}J^{T}WJ+\gamma\cdot L^{T}L\Bigr{)}^{-1}J^{T}W\delta\mathbf{ M}, \tag{9}\]  
and the reconstructed image is expressed as \(\mu=\overline{\mu}+\delta\mu\). Depending on the type of regularization term, a nonlinear optimization process is needed to solve Equation (7). The limitations of linearized image reconstruction have been previously discussed [30]. The linearization approach is appropriate when changes in the optical properties are sufficiently small and exist in small regions.
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 4 Polymer-Based Nanomaterials  
The above mentioned conventional techniques and more sophisticated ones developed typically involve expensive chemicals, difficult processing techniques and specialized chemistry. However, treatment with ionizing radiation of monomers or polymers with the same aim has been shown to be a simple, rapid and so-called green technique operated at ambient conditions [3].  
Polymer-based nanomaterials can be prepared by irradiating either polymers or monomers with ionizing radiation to comply with the two basic approaches of nanotechnology, namely top-down and bottom-up. The polymers predominantly degrading with radiation are suitable for lithographic applications and developing track-etched membranes, demonstrating typical top-down applications already matured to industrial level. However, there is more room in the preparation of polymer-based nanomaterials by the bottom-up approach. In the following examples, bottom-up synthesis of polymer-based nanomaterials by ionizing radiation is discussed.  
Radiation-induced grafting has been successfully used to functionalize track-etched membranes, improve properties of fuel cell membranes, make the surfaces of cell culture dishes thermoresponsive and develop specialty adsorbents and molecularly imprinted polymer matrices, just to name the most established applications. Irradiation of aqueous solutions of polymers leads to the formation of nanoparticles, nanogels, polymer-clay nanocomposites, and polymer-metal nanocomposites.
## 3 Design and Implementation
### Implementation of the Model
#### 3.3.2 LSTM Model with a Dropout Layer  
To avoid overfitting, training was performed after adding a dropout layer [14] to LSTM. As shown in Figure 6, after three epochs, the F1-score and loss of the training set gradually improve, but the validation F1-score does not change, and the validation loss gradually increases, which has been overfitted. After 10 epochs of training, the validation F1-score is 90.76% and the validation loss is 0.4069, and the performance does not improve compared with the basic LSTM model. The model training results are shown in Table 2.  
Figure 5: F1-score and loss for each epoch of each model.  
Figure 6: Training results of the LSTM model with a dropout layer.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 6 Conclusions  
The rapid development of the economy derives more and more applications which are transmitted by satellites. However, a large amount of energy is required to maintain the flow of satellite traffic. Aiming to save energy consumption in a software-defined STIN, in this paper, we researched the EECP problem in the countries along the Belt and Road. The energy consumption model was developed to describe the energy consumption of ISL (switch-to-controller) and UDL (gateway-to-satellite). Based on the model, we considered traffic demands in the different geographical locations, which were then formulated as an optimization problem. To solve the EECP problem, we proposed the MSAP algorithm to compute the optimal locations of controllers. The simulation was evaluated by real-world parameters with an LEO constellation and Internet usage traces. The results indicated that the considered SDN-enabled architecture achieved superior performance to the controllers deployed on the satellite gateway. In the performance evaluation, the satellite controller placement provided by the MSAP algorithm outperformed other algorithms in network energy consumption and latency.  
Conceptualization, L.W. and C.C.; methodology, L.W. and C.C.; software, C.C.; validation, L.W. and C.C; formal analysis, L.W. and C.C.; investigation, L.W. and C.C; resources, L.W., C.C., and Y.L.; data curation, C.C.; writing--original draft preparation, C.C.; writing--review and editing, L.W., C.C., and Y.L.; visualization, L.W.; supervision, Y.L. and Y.W.; project administration, Y.L. and Y.W.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (No.2019 YFB1803103) and in part by the BUPT Excellent Ph.D. Students Foundation (No.CX2021113).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the anonymous reviewers for their valuable comments and helpful suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Number of Controllers**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Random & 1 & 0.62 & 0.49 & 0.42 & 0.32 & 0.22 & 0.15 \\ K-means & 1 & 0.70 & 0.60 & 0.39 & 0.32 & 0.25 & 0.15 \\ GI & 1 & 0.61 & 0.46 & 0.38 & 0.26 & 0.20 & 0.16 \\ SAP & 1 & 0.64 & 0
## 6 System Development
### Microservices Interface
#### 6.2.5 RTC  
As previously discussed, the document editing sessions will be handled over a Web-Socket connection based on the need for a direct asynchronous connection. The number of live established connections may be considered a sensitive point in the architecture of the system, as any server has a physical limit on live connections. Thus, due to scalability concerns, a designated microservice for handling live connections was created.  
The RTC (real-time communications) microservice is responsible for handling the connections with the clients and propagating changes between the RTC instances.  
Having a horizontal scaling approach on live-maintained connections introduces the problem of unpredictable connection location. This scaling issue is described in its own section due to the required messaging service modifications.  
If the client-server connection fails, the server cannot re-initiate it. The task of reconnecting and synchronizing must be initiated by the client.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 3 System Model  
The system model introduced in this paper aims to address the security issue in UAV-based IoMT networks. The model consists of three key sub-sections. In the first subsection, we explore the characteristics of black hole UAVs, which maliciously drop data packets. In the second subsection, we propose a Stackelberg game theory-based scheme to mitigate the impact of attackers in the network, including black hole UAVs. Finally, in the third subsection, we present a pseudo-code algorithm for data protection that can be implemented in the network to safeguard against potential attacks. These sub-sections provide a comprehensive solution for securing UAV-based IoMT networks against attacks, including those from black hole UAVs.
## 3 Experiments and Results
### Performance of the Proposed Method against Adjacent and Occlusion Conditions
However, it could be seen that the developed approach was sensitive to the color information and the training samples had important influence on the final detection results. Figure 8: Detection process of the grape clusters under the different occlusion degrees. (**a**) a success detection with partially occlusion (PRA > 75%); and (**b**) a failure detection caused by large region occlusion (PRA < 75%). Figure 9: Detection process of one of the multiple overlapping grape clusters (**a**) Original image; (**b**) Initial segmentation image by the developed classifier; (**c**) The contour of the detected grape clusters; and (**d**) Detection result.
## 6 Salivary Proteomics: Techniques and Applications
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Biomarker** & **Molecular Category** & **Biological Pathway** & \begin{tabular}{c} **Function and** \\ **Significance** \\ \end{tabular} & **Reference** \\ \hline Matrix & Enzyme & \begin{tabular}{c} It is released by \\ neutrophils and \\ fibroblasts \\ \end{tabular} & Breaking down collagen & [95] \\  & & \begin{tabular}{c} Enhancing recognition and \\ clearance of periodontal \\ pathogens by phagocytes, \\ recruiting neutrophils and \\ other immune cells to the \\ chronic inflammation \\ \end{tabular} & [96; 97] \\  & & \
\begin{tabular}{c} Rho GTPase signaling \(\rightarrow\) \\ Profiling-1 \(\rightarrow\) Actin \\ polymerization \(\rightarrow\) Cell \\ movement and adhesion. \\ \end{tabular} &
\begin{tabular}{c} Regulating actin \\ polymerization and \\ cytoskeletonal growth \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The characteristics of biomarkers found in proteomic untargeted analysis. Some recent studies applied mass-spectrometry-based untargeted proteomics to salivary samples of individuals affected by periodontitis [18; 87; 96; 103; 107; 109; 110; 111; 10; 112; 113; 114; 115] (Table 5). Among the cited studies, the most recurrent proteins which were found as promising biomarkers after proteomic untargeted analysis are S100A8, found in three studies [18; 100; 103] and Cystatin SN, found in two studies [107; 106]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Cystain-type endopeptidase \\ inhibitor activity, protease \\ binding \\ \end{tabular} & [106; 107; 108] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 5: The characteristics of periodontal proteins. Proteins involved in periodontitis differ in sources, function, and periodontitis diagnostic potential. The figure summarizes such characteristics of the principal groups of periodontitis proteins. These analyses identified several proteins consistently overexpressed in periodontitis patients, including complement C3, profilin-1, S100A8, and fibrinogen. Conversely, proteins, such as cystatin-SN and leukocyte elastase inhibitors, were found at elevated concentrations in healthy individuals. These findings suggest that specific salivary proteins could serve as potential biomarkers for distinguishing periodontal health from periodontitis [11]. The progress and efficacy of the untargeted proteomic methods in periodontal diagnosis have been studied to assess their clinical utility. For instance, Blanco-Pintos et al. [100] studied the diagnostic accuracy of salivary protein biomarkers for periodontitis, considering the impact of age and smoking. They collected salivary samples from healthy and periodontitis patients and analyzed them by sequential window acquisition of all theoretical mass spectra (SWATH-MS), and proteins were identified by employing the UniProt database. They discovered that eight single salivary proteins
## 3 Numerical Study
### Effect of Void Size  
Figure 15 illustrates the time histories of the four void size conditions. For condition S0, the acceleration is relatively smaller with a peak value of 6.49 m/s\({}^{2}\). For condition S1, the acceleration is larger with a peak value of 7.02 m/s\({}^{2}\). This is because, part of the vibration energy is absorbed by the soil beside the void, and part of the energy reflects at the tunnel-void interface. For conditions S2 and S3, with the increase of the void size, the peak value is higher, which means more energy reflects at the interface rather than transmitting into the soil. In short, as the void is larger, the peak value of the waveform is higher, which means more energy is reflected back.  
Figure 16 shows normalised Fourier spectra of the four void size conditions. For condition S0, the vibration amplitude distributes in a wide frequency range, and the dominant frequency is not obvious. The normalised peak amplitude is approximately 0.50. With the increase of void size, the amplitude increases, and the dominant frequency can be clearly observed. In general, as the void is bigger, the frequency distributes a narrower range, and the dominant frequency at 90 Hz is more obvious.  
Figure 16: Normalised Fourier spectra of void conditions.  
Figure 15: Time histories of void conditions.  
Figure 17 illustrates that the dynamic stiffness varies with frequency for the four conditions. With the increase of void size, the dynamic stiffness decreases in almost all the frequencies.  
Figure 18 shows the colour contours of the wavelet spectra for the four void size conditions. With the increase of the void size, the spectrum becomes larger, and an increasing energy concentration can be obviously found. More energy tends to concentrate over 80-120 Hz, which in turn becomes the dominant frequency band. The vibration duration is longer especially in the range of 60-120 Hz. While the frequency is lower, the vibration energy lasts longer.  
Figure 17: Dynamic stiffness analysis.  
Figure 18: Wavelet spectra for different void size conditions.  
When the impulse force is applied to the tunnel, the P wave propagates into the structure as a semi-spherical wave. Figure 19 shows a sectional draw of the reflection of the stress wave with different void sizes. According to the wave impendence introduced in Section 2.1, there is a strong reflection (red part) in the tunnel-void interface and a weak reflection (blue part) in the tunnel-soil interface. While the void becomes larger, the angle of the red part is larger, which means more energy reflects back and the attenuation lasts longer. This may cause the spectrum becomes larger in proportion to the void size.