esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
## 4 Results  
In order to demonstrate that fast path generation is possible using the RC-Map proposed in this paper, a simulation experiment was performed. Figure 3(a) is a floor plan of an actual residential environment, and mapping was performed using a robot in that space. Figure 3(b) is a virtual residential space created for simulation, and it was used to check the robot's movement path.  
Figure 5 shows the experimental environment of Figure 3(a) expressed in RC-Map. About 80 V nodes and 204 A nodes were used, and 8768 bytes of memory were used. This corresponds to about 3% of the (300 \(\times\) 300)-sized grid map.  
Figure 6 shows the path generation results using RC-Map and the path generation results using Distance Transform path generation in the experimental environment of Figure 4a. The total moving distance was 7.19 m when RC-Map was used, and the total moving distance was 6.18 m when the Distance Transform method was used. In terms of total moving distance, RC-Map moved a longer distance, but the number of turns during the movement was 6 for RC-Map and 40 for Distance Transform. This shows that the robot using Distance Transform moved along a narrow path and made many turns, as in the middle part of Figure 6b. This caused the robot to use more battery. In contrast, RC-Map moved to the destination by moving nodes in the empty area where the robot could move freely, as in Figure 6a. In order to generate the path in Figure 6, RC-Map took a total of 10 ms and Distance Transform took 740 ms. This can be an important issue for mobile robots using embedded boards.  
Figure 4: Experimental environment. (**a**) Typical residential floor plan in Korea (100 m\({}^{2}\)). (**b**) Simplified floor plan for the experiment.  
Figure 5: Visual representation using internal data of RC-MAP.  
Figure 7 shows the path generation results using RC-Map and Distance Transform path generation in the experimental environment of Figure 4b. When RC-Map was used, the total moving distance was 18.87 m, the total number of turns was 10, and the path generation time was 15 ms. When Distance Transform was used, the total moving distance was 9.64 m, the total number of turns was 19, and the path generation time was 1238 ms. The experimental results show the same results as in Figure 6 above. However, depending on the complexity of the grid map, the path generation calculation time of Distance Transform showed a large difference compared to RC-Map. When using a grid map like this, you can see that the Distance Trans path generation time also increased as the map resolution and complexity increased.  
Figure 6: Path generation results in a real residential environment. (S: start position, G: Goal position) (**a**) Path generation using RC-MAP. (**b**) Path generation using Distance Transform.  
Figure 7: Path generation results in a real residential environment. (S: start position, G: Goal position). (**a**) Path generation using RC-MAP. (**b**) Path generation using Distance Transform.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 2 Theory and Method
### Using the CycleGAN Model for Data Augmentation
#### 2.1.2 Generators and Discriminators of the CycleGAN Model  
In the proposed CycleGAN model, each generator consists of three parts, namely, down-sampling, feature extraction, and up-sampling, as shown in Figure 3. Down-sampling consists of three convolutional layers, which can reduce the image size and enlarge the receptive field. Feature extraction consists of nine stacked residual convolution blocks. Each block plays a role in learning more complex features, which helps improve the quality of generated images. Up-sampling consists of three convolutional layers. Up-sampling restores the feature map to an image of the same size as the input.  
The discriminator used in our CycleGAN model is a PatchGAN [14; 15], which is a fully convolutional network, as shown in Figure 4. The receptive field of PatchGAN is set as 70 \(\times\) 70 pixels, which corresponds to a small region in the input image so that the training model can focus more on image details.  
Figure 2: Network structure of the CycleGAN model. \(Gc(C)\) and \(Gs(S)\) refer to pseudo-sonar and optical images, respectively. T/F means True/False.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 2 Literature Review
### Task-Technology Fit  
Among the factors driving the adoption of technology is the ability of technology to provide efficiency and effectiveness of tasks performed by humans. One of the most popular theories to represent this situation is the Task Technology Fit (TTF) [43]. In this TTF theory, there are three main drivers that drive technology adoption, namely, technology characteristics, task characteristics, and task-technology fit. Research in information systems has advanced technology-to-performance as a model for connecting information technology and individuals, encouraging individual perspective of users to make the use of information systems more effective [43]. In other words, the proposed model is a technology attribute that refers to the technology consumed by individuals to perform their tasks and the characteristics of the tasks that refer to the activities taken by individuals in influencing user adoption.  
The TTF model acts in an important role in the area of use of information technology and has been commonly applied in previous studies [44; 45; 46]. Conditions where technological features can help someone in carrying out the task and in accordance with the requirements of the task are called suitability of task technology. The function of the three components, namely, individual properties, technological attributes, and task characteristics is the task-technology fit proposed in the TTF model. In turn, task-technology fit directly impacts user adoption, and through TTF, technology indicator variables and task characteristics are proposed to indirectly influence adoption.  
The research has been carried out for the intention to use information systems or the sustainable use of information systems based on the TTF model including knowledge management [47], adoption of e-books [48], e-learning [45; 49; 50], adoption of wireless technology [46], mobile banking services [51], and data mining tools [52]. We deliberate them in our research model to comprehend the problem driven by the task behind the user's adoption intention in e-health laboratories.
# Genomic Characterization of a Halovirus Representing a Novel Siphoviral Cluster
## 3 Results
### Genomic Property of Phage YPHTV-1  
Ninety-nine bp direct repeats were identified at both ends of the genome, indicating that YPHTV-1 was linear double-stranded DNA. The sequence was found to be 37,980 bp in length with a GC content of 36.2%. GeneMark predicted 57 ORFs in the YPHTV-1 genome, 51 in the positive strand, and six in the negative strand (Figure 5). Among them, 30 were assigned a putative function based on significant sequence similarity to genes of known functionality in the NR database. Furthermore, 27 ORFs showed no similarity to genes in the NR database, and their products were classified as hypothetical proteins with unknown functions (Table S1). Potential tRNAs were not detected in the phage genome.  
The YPHTV-1 genome exhibited an overall modular organization (Figure 5). The functional gene modules annotated in the YPHTV-1 genome and their proportions of the total number of genes were as follows: lytic/lysogenic proteins (14%), structural protein (14%), DNA replication/regulation and nucleotide metabolism proteins (19%), packaging proteins and transcription-related proteins (12%), and other functional genes (10%).
## 5 Performance Analysis and Discussion
### Practical Issues  
The PAC is assumed for MRC, EGC, two-stage CV and the proposed LRT-BER fusion rule. When each sensor simultaneously transmits a signal to the FC via such a PAC, which can be realized through frequency division multiple access, the bandwidth requirement for the sensor network is large, especially for the large-scale sensor network. When each sensor transmits a signal by TDMA, a large detection delay is required. The bandwidth requirement or detection delay is equal for identical scale networks.  
Under the PAC assumption, the type-based distributed detection strategies are proposed in [36; 37], where the power consumption for signal transmission is established. The same power consumption model can be employed in the LRT-BER fusion rule, which is given as:  
\[P_{\text{E}}=\sum_{i=1}^{N}E[E_{i}\cdot(u_{i})^{2}] \tag{46}\]  
where \(E[\cdot]\) denotes the expectation and \(E_{i}\) denotes data symbol transmit power. Note that all of these fusion rules are based on the PAC assumption. Thus, the power consumption for signal transmission are identical for all of these fusion rules. Further, the proposed fusion rule LRT-BER at the FC requires more power consumption due to the information decoding, which is not required for MRC, EGC, two-stage CV and LRT-CS.
# A Feasibility Study of Utilizing Nuclear Energy for an Existing MED-TVC Desalination Plant
## Abbreviations  
DEEP  
Desalination Economic Evaluation Program  
GHG  
Greenhouse Gas  
GOR  
Gain-Output-Ratio  
Gt  
Giga tons  
IAEA  
International Atomic Energy Agency  
IDC  
Interest during Construction  
IHX  
Intermediate Heat Exchanger  
IL  
Intermediate Loop  
kW (th)  
Kilowatt Thermal  
LCOE  
Levelized Cost of Electricity  
MED-TVC  
Multi-Effect Distillation-Thermal Vapor Compression  
MP  
Medium Pressure  
MSF  
Multi-Stage Flash  
MW (th)  
Megawatt Thermal  
NHR  
Nuclear Heating Reactor  
PR  
Performance Ratio  
RCS  
Reactor Coolant System  
RO  
Reverse Osmosis  
RPV  
Reactor Pressure Vessel  
SG  
Steam Generator  
SMRs  
Small Modular Reactors  
TBT  
Top Brine Temperature  
**Nomenclature**  
B  
Brine Mass Flow Rate [ton/h]  
C\({}_{\text{pf}}\)  
Specific Heat Capacity of seawater [k]/kg \({}^{\circ}\)C]  
F  
Feedwater Mass Flow Rate [ton/h]  
\(h\)  
Enthalpy [k]/kg]  
Lv  
Latent Heat of Vaporization [k]/kg]  
M\({}_{\text{f}}\)  
Mass Flow Rate of The Seawater Intake [ton/h]  
\(\dot{m}\)  
Mass Flow Rate [ton/h]  
n  
Total number of effects  
P  
Pressure [bar]  
T  
Temperature [\({}^{\circ}\)C]  
TDS  
Total Dissolved Solids [ppm]
## 2 Materials and Methods
### AR-Based Surgical Navigation
#### 2.2.1 Registration
\(CS_{\text{P}}\), \(CS_{\text{R}}\) and \(CS_{\text{F}}\) are the local coordinate systems of the pointing device, pelvic marker and fragment marker, respectively. \(CS_{\text{H}}\) is the local coordinate system of the HoloLens, defined by its built-in marker-less inside-out tracking [64]. [PERSON]'s absolute orientation method to those point pairs until the root-mean-square error between the point pairs was below a heuristically determined threshold of 0.001 mm, leading to the final registration \(T_{\text{IntPre}}\) (Figure 6). Once the registration was verified (Figure 5, step 4), it was possible to express the preoperative planning including the pelvic bone (\(Plv\)) and the current and desired acetabular fragment positions (\(Frg\) and \(Frg_{post}\), respectively) in the intraoperative coordinate system \(CS_{Int}\) (Figure 6). To formally describe the tracking of said anatomy, we introduce the following two definitions. **Definition 1**. : _Let \(M_{PlvReg}\) and \(M_{PlvCur}\) be the marker poses of \(CS_{R}\) in \(CS_{Int}\) (Figure 6) at the time of registration and at the current time step, respectively._  
**Definition 2**. : _Let \(M_{FrgReg}\) and \(M_{FrgCur}\) be the marker poses of \(CS_{F}\) expressed relative to \(CS_{R}\) (Figure 6) at the time of registration and at the current time step, respectively._  
The marker positions, which are obtained in real-time, were then used to update the positions of the models as follows:  
\[Plv =M_{PlvCur}(M_{PlvReg})^{-1}T_{\text{IntPre}}CS_{Plv} \tag{1}\] \[FrgPre =M_{FrgCur}(M_{FrgReg})^{-1}T_{\text{IntPre}}CS_{FrgPre}\] (2) \[FrgPost =M_{FrgCur}(M_{FrgReg})^{-1}T_{\text{IntPre}}CS_{FrgPost} \tag{3}\]  
The intervention requires to work with surgical tools such as chisels or surgical saws which induces motion of the anatomy. Therefore, it may happen that the overlay between preoperative plan and intraoperative anatomy is lost and a motion compensation strategy becomes necessary. Our approach implicitly allows motion compensation by querying the pelvis marker pose and updating Equations (1)-(3) when needed. For providing real-time feedback during fragment reorientation, the fragment pose defined in Equation (2) is updated in real-time with respect to the fragment marker.
# Adaptive Bit-Labeling Design for Probabilistic Shaping Based on Residual Source Redundancy
## 4 Experimental Results  
The system settings for different cases are presented in Table 1. In particular, since the source coding rates are \(1/2\) and \(3/8\), the resulting system rates are derived from (7) are 6 bits and 8 bits per symbol, respectively. In all experiments, the length of the source sequence is set as 3600 bits. For cases with different source coding rates, the source and channel codes are designed from case to case with respect to the source and channel coding rates, respectively. The optimal labelings obtained by Algorithm 1 for different cases are shown in Figure 5 and 6 above the constellation points.  
Figure 4: Achievable system rates for a 16 QAM constellation using different labelings.  
The BER curves of the system with the optimized labelings (solid lines) and the labeling \(L\) (dashed lines) for different cases are depicted in Figures 7-10. For instance, in Figure 7, for the source code \(\mathbf{B}^{s,1}\), the optimized labeling \(L_{opt1}\) achieves approximately 0.5 dB gain over labeling \(L\) at a BER of \(10^{-6}\). Additionally, for the source code \(\mathbf{B}^{R4 JA}\), our designed \(L_{opt2}\) outperforms \(L\) by approximately 1.3 dB at a BER of \(10^{-5}\). Similar observations can be found in Figures 8-10. Particularly, the source code \(\mathbf{B}^{s,2}\) in Figure 10, which has extremely poor BER performance with labeling \(L\), achieves a performance gain of at least 3 dB after labeling design. Specifically, the performance gain varies depending on the matching degree of the utilized labeling to the source code and the source probability. For example, using a bit labeling that is highly mismatched to the source probability and the source code  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Probability** & **Source Code** & **Target Rate (Bits/Symbol)** & **Optimized Labeling** \\ \hline \(p=0.05\) & \(\mathbf{B}^{\prime,1}\) & 6 & \(L_{opt1}\) \\ \hline \(p=0.05\) & \(\mathbf{B}^{R4J}A\) & 6 & \(L_{opt2}\) \\ \hline \(p=0.03\) & \(\mathbf{B}^{\prime,2}\) & 8 & \(L_{opt3}\) \\ \hline \(p=0.03\) & \(\mathbf{B}^{\prime,3}\) & 8 & \(L_{opt2}\) \\ \hline \(p=0.94\) & \(\mathbf{B}^{\prime,1}\) & 6 & \(L_{opt4}\) \\ \hline \(p=0.94\) & \(\mathbf{B}^{R4J}A\) & 6 & \(L_{opt2}\) \\ \hline \(p=0.98\) & \(\mathbf{B}^{\prime,2}\) & 8 & \(L_{opt5}\) \\ \hline \(p=0.98\) & \(\mathbf{B}^{\prime,3}\) & 8 & \(L_{opt6}\) \\ \hline \end{tabular}
\end{table}
Table 1: System settings for performance comparisons.  
Figure 7: BER comparison of different labelings with \(p=0.05\) and \(R=6\).  
Figure 8: BER comparison of different labelings with \(p=0.03\) and \(R=8\).  
Figure 9: BER comparison of different labelings with \(p=0.94\) and \(R=6\).
## 2 Materials and Methods
### Identification and Sequence Analysis of Candidate Geranoid Synthase Genes  
To identify the putative geranoid synthase cDNAs (_RgGES_) from _R. glutinosa_, the published geranoid synthase from _C. roseus_ (CrGES, GenBank accession No. JN882024.1) was used as the query sequence to perform a TBLASTN search against the _R. glutinosa_ transcriptome. Multiple sequence alignment was conducted by the CLC Sequence Viewer 6.8 program and the phylogenetic tree was constructed using the Neighbor-Joining method in the MEGA 7.0 software.
# SCCDNet: A Pixel-Level Crack Segmentation Network
## 3 Results and Experimental Evaluations
### Ablation Study and Discussion
#### 3.5.2 Ablation Analysis of the Decoder Module  
In Section 2.3, we specifically introduce the structure and characteristics of the Decoder module. In order to verify the effectiveness of our Decoder module, we design a comparative experiment as shown in Table 4. Between them, the Normal Decoder uses the most common Decoder module of U-Net [27], and the Dense Decoder uses the densely connected Decoder module as shown in Figure 3. Furthermore, we unsure the other parts of the models are the same. In order to prove the generalization of the model, we also use 5-fold cross-validation to compare the influence of the Decoder module on the results.  
It can be seen from the table that the Dense Decoder module we designed greatly improves the accuracy of crack segmentation compared to the Normal Decoder module, and greatly reduces the complexity of the model. Specifically, m-Precision, m-Recall, m-score, m-Dice, and m-IoU increase by 5.3%, 1.8%, 3.7%, 4.3%, and 6.0%, respectively; FLOPs and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **m-Precision** & **m-Recall** & **m-F-score** & **m-Dice** & **m-IoU** & **FLOPs/G** & **Parameters/M** \\ \hline Normal Decoder & 0.6929 & 0.8150 & 0.7489 & 0.7232 & 0.6040 & 111.639 & 45.245 \\
**Dense Decoder** & **0.7294** & **0.8296** & **0.7763** & **0.7541** & **0.6402** & **65.004** & **31.705** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different decoder modules. The best average scores are in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **m-Precision** & **m-Recall** & **m-F-Score** & **m-Dice** & **m-IoU** & **FLOPs/G** & **Parameters/M** \\ \hline None-SSE & 0.7153 & 0.8024 & 0.7563 & 0.7364 & 0.6181 & **64.997** & **30.875** \\ SSE1 & 0.7192 & 0.8084 & 0.7612 & 0.7409 & 0.6230 & **64.997** & 31.269 \\ SSE2 & 0.7139 & 0.8046 & 0.7566 & 0.7379 & 0.6187 & **64.997** & 31.203 \\ SSE3 & 0.7126 & 0.8168 & 0.7612 & 0.7387 & 0.6211 & 64.998 & 30.957 \\ SSE4 & **0.7326** & 0.8017 & 0.7656 & 0.7489 & 0.6316 & 64.998 & 30.895 \\ SSE5 & 0.7313 & 0.8046 & 0.7662 & 0.7465 & 0.6297 & 65.000 & 30.880 \\
**Fully-SSE** & 0.7294 & **0.8296** & **0.7763** & **0.7541** & **0.6402** & 65.004 & 31.705 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different SSE modules. The best average scores are in bold.  
Parameters decrease by 41.8% and 29.9%, respectively. The dense connection of the Decoder module enhances the gradient correlation of the model and improves the performance of the model by utilizing feature maps from early layers. This allows us to reduce the number of convolutions of each layer and reduce the amount of model parameters.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 5 Discussion
### Role of Chemical Components Identified by GC-MS Method
Shifts in terpene blends and concentrations are strongly linked to tree stress and interactions with bark beetles and fungal symbionts. In healthy trees, oxygenated monoterpenes are represented only in trace amounts. However, after bark beetle attack and fungal inoculation, the concentration of oxygenated monoterpenes gradually increases via detoxification of monoterpene hydrocarbons in the beetle gut and by symbiotic fungi [74; 75]. Thirty days after inoculation of Norway spruce (_Picea abies_) with the blue-stain fungus _Ceratocystis polonica_, the absolute amount and relative proportion of (+)-3-carene, sabinene, and terpinolene increased and (+)-\(\alpha\)-pinene decreased [76]. Similarly, inoculation of Norway spruce with a blue-stain fungus, _Endoconidiophora polonica_, significantly increased monoterpenes, six sequiterpenes, and five diterpenes [77].
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model  
The propagation of photons underwater is modeled by the RTE, which is based on the law of conservation of energy of an underwater beam of light and takes into account all losses and gains of the initial beam. Its formalism is based on the definitions of the water properties and the radiance of the beam. In its standard form, this model yields [11]  
\[\cos(\theta)\cdot\frac{dL(\theta,\phi)}{cdz}=-L(\theta,\phi)+\omega_{0}\int_{4 \pi}\tilde{\beta}(\theta^{\prime},\phi^{\prime}\rightarrow\theta,\phi)\cdot L \big{(}\theta^{\prime},\phi^{\prime}\big{)}d\Omega\big{(}\theta^{\prime},\phi ^{\prime}\big{)}+S(\theta,\phi), \tag{1}\]  
where \(c\) is the beam attenuation coefficient (from the Beer-Lambert law), \(\omega_{0}\) is the single scattering albedo, \(\tilde{\beta}\) is the scattering phase function, \(S\) represents the combined path function for inelastic scattering and spontaneous emission and the radiance of the beam, \(L\), is given in units of \(\mathrm{W}\,\mathrm{m}^{-2}\,\mathrm{sr}^{-1}\,\mathrm{nm}^{-1}\) by  
\[L(\theta,\phi)=\frac{P(\theta,\phi)}{\Delta A\cdot\Delta\Omega\cdot\Delta \lambda}, \tag{2}\]  
where \(P\) is the spectral radiant power, \(\Delta A\) represents the cross-sectional area of the detector, \(\Delta\Omega\) is the solid angle in the direction of \((\theta,\phi)\) and \(z\) is the distance along the beam propagation direction. Note that \(L\), \(c\) and \(\tilde{\beta}\) also depend on \(z\) and \(\lambda\), and these dependencies are not represented here for the sake of simplicity.  
The simulation of UOWC systems using the Monte Carlo algorithm is the simplest method for solving the photon propagation phenomena since, due to the complexity of the RTE equation and the problem configuration, analytical solutions only exist for very simple cases [15]. This statistical method based on the Monte Carlo algorithm mimics the physical behavior of individual photons underwater and is far more accurate than the Beer-Lambert law, which underestimates the received optical power due to assuming that scattered photons are completely lost. In this algorithm, photons are viewed as particles that move through the underwater medium with a given path length between events. These events are absorption of or collision with suspended particles in the water that scatters the photon. These are the two major inherent optical properties (IOPs) identified by [PERSON] for light propagation [10].