esa-sceva/satcom-chunk-collection huggingface dataset:
# Beam Control in an Intracavity Frequency-Doubling Semiconductor Disk Laser
## 2 Semiconductor Disk Laser (SDL) Design and Bonding Process  
Figure 1 shows the SDL chip structure in our experiment. The wafer was grown by metal-organic vapor phase epitaxy (MOVPE) in a reverse order on a GaAs substrate. It consists of three sections: a highly reflective distributed Bragg reflector (DBR) mirror, a gain region and a cap layer. The DBR mirror is made up of 20 pairs of alternating AlAs/GaAs layers, and each layer has an optical thickness equivalent to a quarter of the designed lasing wavelength. The active region includes 10 layers of 8 nm InGaAs compressively strained quantum wells (QWs) with GaAs barrier and a tensile strained GaAsP layer for strain compensation. Those QWs are distributed periodically at the anti-nodes of the optical standing wave in the micro-cavity formed between the DBR and the cap layer [17]. A large energy bandgap InGaP cap layer with an optical thickness of a half wavelength is utilized to confine the excited carriers in the gain region in order to prevent the surface recombination [18]. Furthermore, the InGaP cap is transparent for both of the pump light and the emission laser. In this paper, the SDL is designed to emit at around 1021-1045 nm, and the active region is optimized for efficient optically pumping in the wavelength range of 790-820 nm.  
Efficient thermal management is crucial for high power operation of SDLs. In order to enhance the heat dissipation in the SDL, we use a pre-metallization method of the diamond-semiconductor chip bonding process [19]. A \(5\times 5\times 1.2\) mm diamond deposited by chemical vapor deposition (CVD) is used as the heat-spreader. The SDL size is 3 mm \(\times\) 3 mm. Figure 2 shows the detail of the SDL bonding structure. The pre-metallization of diamond is performed by depositing Cu-Sn alloy (Sn component: 5-7%) on both the top and bottom surfaces of the diamond. The thickness of the pre-metalized layer on the diamond surface is 100 nm. Then Ti/Pt/Au layers are also deposited on both sides of the pre-metalized diamond and the DBR side of the SDL chip. After that, the chip is bonded on the diamond using indium. In order to dissipate the heat efficiently, the diamond is bonded on a large copper heatsink using indium. Finally, the GaAs substrate is removed by selective wet etching. A thermoelectric cooler (TEC) is mounted underneath the copper heatsink to control the temperature of SDLs.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 4 Performance Evaluation
### Application and Discussion
#### 4.4.2 Contribution to Automotive IDS  
The CAN message translation method proposed in this paper can assist with research on automotive intrusion detection systems (IDS). The CAN intrusion detection system was proposed to detect anomalies by analyzing CAN traffic [37; 38; 39; 40; 41]. However, studies on this system were based on message transmission characteristics, such as periodicity, entropy [38], and remote frames [37], which are irrelevant to the behavior and state of the vehicle. These methods are almost ineffective when an attacker sends attack messages with transmission characteristics that are consistent with those of regular messages. The method proposed in this paper can help researchers quickly obtain the specific meaning contained in CAN messages so that they can use the message contents to design more direct and effective intrusion detection rules, e.g., no control commands for stalling or braking should appear while the vehicle is driving normally and no doors should appear open when the vehicle is accelerating. Therefore, IDS researchers and designers can use the results obtained with the method described in this paper to design content-based intrusion detection systems that can identify attacks with greater efficiency than existing methods. In addition, the content-based approach is faster and more applicable than IDSs, which utilize machine learning algorithms.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Step** & **Time Cost (s)** \\ \hline Data Collection 1  & 1380 \\ Labeling and Filtering 2  & 97.85 \\ Fuzzing 3  & 2460 \\ Packet Analysis 4  & 150 \\ AND Bit-by-Bit 5  & 0.00001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time consumption of the reverse method.
## 3. Freestanding Position-Based Routing Protocol
### Next-Hop Selection Algorithm
#### 3.3.2 Selection Algorithm
Note that \(\beta_{c}<1\) in order to avoid selecting vehicles near the border which may lead to significant packet losses. Although \(R_{1}\) and \(R_{2}\) are within the adjusted \(\beta_{c}*R_{n}\) radio range, \(R_{2}\) is the vehicle most forward (see Figure 1(c)) and therefore it is chosen as a relay node (\(FN_{n+1}\))
## 5 Numerical Simulation Results
### Training Performance Analysis  
The accuracy rate is an index to judge the ability of the neural network to replicate human behavior. Therefore, the accuracy rate in \(\Lambda_{train}\) and \(\Lambda_{test}\) was calculated by (16) and (17) at the end of each iteration, as shown in Figure 6. The accuracy increased with the number of iterations and tended to be stable after 900 epochs, which proves that the network parameters were continuously updated and finally converged. The accuracy rate in \(\Lambda_{train}\) exceeded 90% while it reached 85% in \(\Lambda_{test}\), which confirmed that the trained model was theoretically capable of making a decision similar to a human being based on the input information. Other alternative design parameter specifications were also tested and the final estimation accuracy rates were either lower than or similar to the above results.  
To determine why the accuracy rate could not further increase, the dimensionality reduction method called t-Distributed Stochastic Neighbor Embedding (t-SNE) was applied to convert the high dimensional elements \(\lambda_{i}\) in the database \(\Lambda\) into a 2D plane for a straightforward point of view. The results are shown in Figure 7, and it is clear that the data points in some particular regions had more than one label. Therefore, the data structure was not sparse enough to allow the trained model to reach a high estimation accuracy, and it was difficult fo the model to identify the data type in those regions. This is due to the fact that there inevitably exist some occasions in which the human test candidates cannot make  
Figure 5: Different layers, neurons, and learning rates affect the accuracy of the neural network replicating human decision-making process. (**a**) neural network; (**b**) learning rate.  
Figure 6: The estimation accuracy rates of Algorithm 1 with respect to \(\Lambda_{train}\) and \(\Lambda_{test}\) along the training epochs.  
deterministic decisions. Therefore, the obtained performance of Algorithm 1 is acceptable in the autonomous mobile robot navigation scenario.
## 4 Sparse Cost Functions
### Circle Structure
\begin{cases}\mathcal{K}^{(K,1)}&\text {if }k=K,\\ \mathcal{K}^{(k,k+1)}\left(\left(\phi^{k+1}\right)^{(0)}\odot\left(\beta_{k+1 }\right)^{(0)}\right)&\text{otherwise}\end{cases} \)
5:endfor
6:do
7:\(\left(\phi^{1}\right)^{(r+1)}\coloneqq\mu^{1}\oslash\left\langle\mathcal{K}^{ (1,2)},\left(\left(\phi^{2}\right)^{(r)}\odot\left(\beta_{2}\right)^{(r)} \right)^{\mathsf{T}}\right\rangle\)
8:for\(k=2,\cdots,K\)do
9:\((\gamma_{k})^{(r)}\coloneqq \begin{cases}\mathcal{K}^{(1,2)}&\text{if }k=2,\\ (\gamma_{k-1})^{(r)}\left(\left(\phi^{k-1}\right)^{(r+1)}\odot\mathcal{K}^{( k-1,k)}\right)&\text{otherwise}\end{cases} \)
10: Compute dual vector \(\left(\phi^{k}\right)^{(r+1)}\coloneqq\mu^{k}\oslash\left\langle\left(\beta_{k }\right)^{(r)},\left(\left(\phi^{1}\right)^{(r+1)}\odot\left(\gamma_{k} \right)^{(r)}\right)^{\mathsf{T}}\right\rangle\)
11:endfor
12:for\(k=K,\cdots,2\)do
13: Compute \(\left(\beta_{k}\right)^{(r+1)}\) according to step 4
14:endfor
15: Compute \(\mathcal{S}^{(r)}\coloneqq\eta\left(\sum_{k=1}^{K}\left(\mu^{k}\right)^{ \mathsf{T}}\log\left(\phi^{k}\right)^{(r)}-\left(\mu^{1}\oslash\left(\phi^{1} \right)^{(r+1)}\right)^{\mathsf{T}}\left(\phi^{1}\right)^{(r)}\right)\)
16: Increment \(r\gets r+1\)
17:while\(|S^{(r)}-S^{(r-1)}|\geq\delta\)
18:return Optimal plan \(\hat{\mathbf{\Pi}}=\mathcal{K}\odot\hat{\mathbf{\Phi}}\), where \(\hat{\mathbf{\Phi}}=\bigotimes_{k\in[K]}\left(\phi^{k}\right)^{(r+1)}\)
```  
**Algorithm 3** Sinkhorn algorithm for circle structure  
The tree-structured and circle-structured \(\text{MOT}_{\eta}\) problems both have a sparse cost function that considerably improves the computational complexity of the Sinkhorn algorithm. In each iteration step, Algorithm 2 requires only \(2(K-1)\) matrix-vector products, which have a complexity of \(\mathcal{O}(N^{2})\), where \(N\coloneqq\left\|\mathbf{n}\right\|_{\infty}\), and Algorithm 3 requires \(2(K-1)\) matrix-matrix products, which have a complexity of \(\mathcal{O}(N^{3})\). This can be considerably improved by employing fast Fourier techniques, as we will see in the next section.
# A New Coding Paradigm for the Primitive Relay Channel +
## 2 Existing Upper and Lower Bounds
### Compress-and-Forward Lower Bound  
In compress-and-forward, the relay does not attempt to decode the received sequence, but it sends a (possibly compressed) description of it, denoted by \(\hat{Y}_{\text{\tiny SR}}\), to the destination. Since this description is correlated with the sequence received by the destination from the source, Wyner-Ziv coding is used to reduce the rate needed to communicate it to the destination.  
For the _general relay channel_, compress-and-forward allows for achieving the following rate ([17] Theorem 16.4):  
\[R_{\text{CF}  
Clearly, \(I(X_{\rm S};\hat{Y}_{\rm ss},Y_{\rm S0})\) is maximized by setting \(p_{X_{\rm S}}\) to the uniform distribution. Furthermore,  
\[\begin{split} H(\hat{Y}_{\rm ss}|Y_{\rm ss},Y_{\rm S0})& =H(\hat{Y}_{\rm ss}|Y_{\rm ss})=(1-\varepsilon_{\rm ss})h_{2}( \hat{\varepsilon}_{\rm s}),\\ H(\hat{Y}_{\rm ss}|Y_{\rm S0})&=h_{2}(\varepsilon _{\rm ss}\circ\hat{\varepsilon}_{\rm s})+\varepsilon_{\rm S0}(1-\varepsilon _{\rm ss}\circ\hat{\varepsilon}_{\rm s}).\end{split} \tag{26}\]  
As a result, the rate (23) can be rewritten as  
\[R_{\rm CF}=\max_{0\leq\varepsilon_{\rm R}\leq 1}\{1-(\hat{\varepsilon}_{\rm R }\circ\varepsilon_{\rm ss})\cdot\varepsilon_{\rm S0}:h_{2}(\varepsilon_{\rm ss }\circ\hat{\varepsilon}_{\rm R})+\varepsilon_{\rm S0}(1-\varepsilon_{\rm ss }\circ\hat{\varepsilon}_{\rm s})-(1-\varepsilon_{\rm ss})h_{2}(\hat{ \varepsilon}_{\rm R})\leq C_{\rm BD}\}. \tag{27}\]
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
## 3 The Proposed BPSF Algorithm
### Analysis of Critical Set  
According to Section 2.3, the size of the critical set is determined by the Rate-1 nodes. For \(N=256\), the size of CS is \(T=39\). Similarly for \(N=512\) and \(N=1024\), \(T\) equals \(60\) and \(116\) respectively. There are many elements in CS that could correct the error frames by flipping them and one flipping means one attempt. The BPF-1 and GBPF-1 decoders are analyzed in Figure 5 when \(N=256\) and \(N=1024\). It illustrates the number of successful flipping attempts in the CS-T that could correct the error frames by flipping them at 2 dB.  
As shown in Figure 5, there are nine attempts of BPF-1 decoding and three attempts of GBPF-1 decoding that could correct the error. Frame 10 is marked with a blue rectangle, but the other decoding attempts are failed to correct this error frame. Those successful decoding attempts can be expressed as \(r_{\Omega}^{M}=\{r_{\Omega}^{1},r_{\Omega}^{2},\cdots,r_{\Omega}^{M}\}\), where \(\Omega\) is the maximum  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **One-Bit-Flipping Digits** & **Two-Bit-Flipping Digits** \\ \hline BPF [15] & \(2\times T\) & \(2^{2}\times C_{T}^{2}\) \\ GBPF [17; 18] & \(\Gamma\) & \(C_{T}^{2}\) \\ OA-BP [18] & \(\tau\) & \(C_{\tau}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics for the flipping bits.  
Figure 4: The BLER performance and the average number of iterations for the BP [10] ([PERSON]; [PERSON]; [PERSON] 2009), BPF-1, BPF-2 [15] ([PERSON]; [PERSON]; [PERSON]; [PERSON] 2019), GBPF-1 [17] ([PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] 2020), GBPF-2, OA-BP-1 [18] ([PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] 2020) and CA-SCL [9] ([PERSON]; [PERSON] 2012) algorithm. (**a**) The BLER comparison of existing algorithms with \(N=256\). (**b**) The average number of iterations for the existing algorithms with \(N=256\).  
bit-flipping order and \(M\) is the number of successful BP-flipping decoding attempts. When \(\Omega=1\), \(M\) is less or equal to the number of BP-flipping decoding attempts (\(C_{T}^{1}=39\)). When \(\Omega=2\), \(M\) is less or equal to the number of BP-flipping decoding attempts (\(C_{T}^{1}+C_{T}^{2}=780\)). The decoding attempts are similar when \(N=1024\), as shown in Figure 5b.  
The distribution of \(M\) is listed in Table 2. In the BPF-1 and GBPF-1 decoders, 87.9% and 79.3% of the error frames can be corrected with \(M\leq 9\). It means that the number of successful decoding attempts is not larger than 9 for most error frames. Likewise, 79.1% and 78.3% of the error frames can be corrected with \(M\leq 99\) in the BPF-2 and GBPF-2 decoders. It can be observed that only a few BP-flipping decoding attempts can correct the error frames while the others are not helpful. In addition, the BP-flipping decoding attempts are different for each error frame. Thus, we propose a method to detect ineffective decoding attempts and step them to reduce the average number of decoding attempts.
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 5 Conclusions  
In this research, we addressed the application of underwater wireless optical communication at different water quality conditions. A simplified model of the optical communication channel was developed, allowing us to analyze the relationship between water quality and communication range. The validity of the model was verified through the design and implementation of precise measurement experiments, and the causes of model errors were analyzed. Furthermore, the concept of \"effective communication space\" was introduced for UWOC in our study. This concept aims to address the limitation of using communication distance as the sole metric for assessing the spatial coverage of communication systems. By incorporating this concept, we provide a more comprehensive and multi-dimensional approach to evaluating the extent of communication reach, transcending the traditional reliance on linear distance measurements. Building upon the range model, we developed a model to analyze the effective communication space and conducted tests to determine the effective communication space of optical wireless communication devices under different water quality conditions. Additionally, we designed experiments for wireless optical communication on an AUV underwater mobile platform and conducted tests in a lake. The results demonstrated the applicability and value of the effective communication space model, providing a basis for future engineering applications. In conclusion, our future  
Figure 8: Placement of optical communication equipment. (**a**) Transmitter and (**b**) receiver.  
Figure 9: Effective communication space for the AUV optical communication experiment.  
research endeavors will focus on expanding the current knowledge on UWOC. These efforts will involve enhancing the accuracy of the communication channel model, exploring different modulation techniques, analyzing the impact of environmental factors on communication performance, and investigating new applications for underwater equipment. In particular, conducting experiments in marine environments will be the next focal point of our research. This step is crucial to validating and refining the adaptability of our theoretical models to more challenging ocean conditions. Through these endeavors, our goal is to advance our comprehension and capabilities in the field of underwater optical wireless communication, with the ultimate aim of contributing to the development of practical engineering solutions in this domain.  
Conceptualization, K.S.; methodology, K.S. and Y.L.; validation, Z.H.; formal analysis, Y.L.; investigation, all authors; writing--original draft preparation, Y.L.; writing--review and editing, all authors; visualization, K.S.; supervision, K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Key Laboratory of Robotics foundation (no. 2017-Z05).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
## 2 Background Knowledge
### Asset and Disaster Risk Management
International organizations, such as the United Nations, may also help countries with limited resources implement their policy agendas by providing financial assistance or technical expertise. Both asset and risk management approaches offer critical processes for controlling and minimizing hazards in urban systems, and for improving the safety, reliability, and efficiency of assets and asset systems [31; 32]. Resilient systems are built and utilized for recovery and adaptation rather than just resistance to the initial disturbance. Resilience thinking supports asset and disaster risk management by accelerating system recovery, especially when common risk management measures struggle to mitigate a disruption [33]. The importance of resilience as applied to urban infrastructure and buildings, and its role in achieving sustainable development goals, will be discussed in the next section.
## 5 Conclusions  
This study revealed that compared to growth at NT, MHT treatment results in a significant increase in solanesol content, dry weight, and net photosynthetic rate in tobacco leaves. In MHT-exposed tobacco leaves, 492 and 1440 genes were significantly up- and downregulated, respectively, as compared to plants exposed to NT. Functional enrichment analysis revealed that most of the DEGs were related to secondary metabolite biosynthesis, metabolic pathway, carbohydrate metabolism, lipid metabolism, hydrolase activity, catalytic activity, and oxidation-reduction process. Moreover, 122 TFs of the DEGs were divided into 22 families. The qRT-PCR results confirmed that MHT treatment led to a significant upregulation of the transcription levels of _NUHMGR_, _NtDXR_, _NUGGPS_, and _NtSPS_in tobacco leaves, and a significant downregulation of _NtDXS_ and _NtFPS_ transcription. This study indicates that solanesol accumulation in tobacco leaves can be manipulated through regulation of the environmental temperature and revealed the effect of MHT on the transcription of genes essential to solanesol biosynthesis, thus establishing a basis for further elucidation of the molecular mechanism of temperature regulation of solanesol accumulation. As far as we know, there are few studies on the TFs related to solanesol biosynthesis. Thus, future studies should gradually clarify the role of TFs in MHT induced solanesol biosynthesis.  
The following are available online at [[http://www.mdpi.com/2218-273X/8/4/165/s1](http://www.mdpi.com/2218-273X/8/4/165/s1)]([http://www.mdpi.com/2218-273X/8/4/165/s1](http://www.mdpi.com/2218-273X/8/4/165/s1)), Table S1: Summary of sequencing results of transcriptome of tobacco leaves grown at normal temperature (NT) and moderately high temperature (MHT), Table S2: Sequence alignment results of reads generated from tobacco leaves grown at normal temperature (NT) and moderately high temperature (MHT) mapped to the reference genome, Table S3: The FPKM values of 122 TFs of DEGs between NT and MHT exposed tobacco leaves.  
[PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments and drafted the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data.  
This research was funded by the National Natural Science Foundation of China (no. 31801279), the Fundamental Research Funds for Central Non-Profit Scientific Institution (no. 1610232018003), and the Agricultural Science and Technology Innovation Program (no. ASTIP-TRIC05).  
The authors declare no conflict of interest.
## 4 Conclusions  
In this paper we have proposed 3D Dense-U-Net: a new upgraded U-Net architecture with densely connected layers optimized for high resolution 3D medical image data analysis. We evaluated the performance of the network on two independent datasets. The first dataset is the MRI T1 brain dataset and this network achieved pixel accuracy on testing data \(99.72\pm 0.02\) percent which exceeded human expert performance done as in standard medical praxis (\(99.51\pm 0.03\) percent). On the second spine dataset the network achieved \(99.80\pm 0.01\) percent accuracy, which surpassed its results on brain dataset. The network can segment high resolution 3D data in less than one minute using standard PC equipped with Nvidia GTX 1080 ti. Using data preparation technique described in Section 2.1.5, we were able to analyse data with a deep neural network for 3D data segmentation using GPU with 11 GB RAM in its original resolution. Many other related segmentation algorithms are designed for data in smaller resolution (because of time or memory demands) and therefore the results are often not usable in practice.  
Our approach can easily be applied to any segmentation method already using U-Net architecture. Resulting source-code was released as an open-source and its link is provided in Section 2.2.2. In Appendix A. we provide a manual how to use the source code for other data.  
In future we plan to further upgrade data preparation technique so we will be able to train more densely interconnected architectures, plan to evaluate our method on other publicly available datasets and to design a more universal data preparation technique. Also we plan to learn the network using denoising training in a semi-supervised manner to detect novelty in image dataset.  
Data curation, K.R.; Formal analysis, M.K.D.; Methodology, M.K., R.B., V.U. and M.K.D.; Software, M.K. and V.U.; Supervision, R.B.; Visualization, K.R.; Writing--original draft, M.K.  
This research was funded the Ministry of the Interior of the Czech Republic by the grant VI20172019086, Ministry of Health of the Czech Republic by the grant NV18-08-00459 and the National Sustainability Program under grant LO1401.  
Research described in this paper was financed by the Ministry of the Interior of the Czech Republic by the grant VI20172019086 and the National Sustainability Program under grant LO1401. For the research, infrastructure of the SIX Center was used. This work was also supported by Ministry of Health of the Czech Republic, grant nr. NV18-08-00459.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GPU & graphic processing unit \\ MRI & magnetic resonance imaging \\ CT & computed tomography \\ NRRD & nearly raw raster data medical image format \\ FSL & FMRIB's Automated Segmentation Tool \\ \end{tabular}