esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Assay Application into Metabolic Stability Study  
In the metabolic stability study, the concentration of 5-AIQ in post incubated samples (various time intervals) were quantified against prepared calibration curves between an area ratio of 5-AIQ/IS versus nominal concentration of 5-AIQ. The % remaining concentration of 5-AIQ at different time intervals in incubated reaction mixtures was measured. Figure 4A represents the % remaining of 5-AIQ versus the incubation time point interval. A biphasic pattern of curve indicates rapid metabolic degradation of 5-AIQ for the first 10 min followed by slow degradation up to 45 min. Around 40% of the compound 5-AIQ remained in the mixture after 45 min of the incubation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nominal Conc. (ng/mL)** & **Precision (RSD, \%)** & **Bias (\%)** \\ \hline \multicolumn{4}{c}{Intra-day (n = 5)} \\
1.0 & 5.6 & \(-\)8.6 \\
3.5 & 4.2 & 5.9 \\
50 & 7.3 & 2.3 \\
500 & 1.9 & \(-\)6.6 \\  & Inter-day (n = 15) & \\
1.0 & 12.7 & \(-\)4.3 \\
3.5 & 10.3 & \(-\)2.6 \\
50 & 8.1 & 1.8 \\
500 & 5.3 & \(-\)3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intra- and inter-day precision and bias of 5-AIQ in plasma at LLOQ, LQC, MQC and HQC concentration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** &
\begin{tabular}{c} **Nominal QC** \\ **(ng/mL)** \\ \end{tabular} & \multicolumn{2}{c}{**\% Recovery**} & \multicolumn{2}{c}{**\% Matrix Effects**} \\ \cline{3-6}  & **Mean** & **\%, RSD** & **Mean** & **\%, RSD** \\ \hline
5-AIQ & 3.5 & 76.9 & 7.2 & 92.5 & 5.5 \\  & 50 & 83.3 & 5.5 & 95.3 & 3.4 \\  & 500 & 77.0 & 9.5 & 89.4 & 3.9 \\ Overall & & 79.1 & 4.6 & 92.4 & 3.2 \\ IS & 20 & 81.9 & 10.7 & 91.7 & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The % recovery and % ME of 5-AIQ and IS in human plasma at LQC, MQC and HQC concentration (n = 5).  
Figure 4: The metabolic stability curve of 5\(-\)AIQ in HLMs (**A**) and the regression equation of the linear part of the curve (**B**).  
Figure 3: The ADME radar chart of 5-AIQ obtained from SwissADME software.  
From the constructed curve, the concentrations between the time duration between 0 to 10 min were selected to plot another curve of time versus logarithm (ln) % of remaining exhibited linearity (0-10 min) shown in Figure 4B. The slope of the line (0.0477) described the rate constant of elimination for 5-AIQ. The linear curve regression equation was \(y=-0.0477\times 4.55859\) with \(r^{2}=0.9926\), which was used for the determination of in vitro T1/2[26]. The slope was 0.0477, so the in vitro T1/2 was 14.5 min. The CLint was also calculated based on the in vitro T1/2 and was 47.6 \(\mu\)L/min/mg.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 3 Satellite Ground Verification System and Algorithm Hardware Acceleration Strategy  
### Algorithm Hardware Adaptation and Acceleration Strategy  
We used the Jetson TX1 as the on-board development board in order to realize on-board ship detection. The embedded vision computer system Jetson TX1, which was developed by NVIDIA integrates an ARM Cortex-A57 Central Processing Unit (CPU) and a 256-core NVIDIA Maxwell architecture GPU, can realize trillions of floating-point operations per second on Linux systems and is very suitable for artificial intelligence calculations. At the same time, its small size, low power consumption, and high integration make it suitable for real-time on-board satellite processing.  
In order to give full play to the computing power of the Jetson TX1 hardware, we fully analyzed the algorithm principles and assigned each module to the appropriate hardware to maximize the calculation efficiency. Specifically, due to the large number of logic operations required by the sea-land segmentation and CFAR algorithms, which belong to the serial logic flow algorithm, they are designated to the CPU, which has superior serial computing power. For the YOLOv4-tiny network, a large number of matrix convolution operations are involved with floating point type data. For these operations, the GPU can provide better throughput, albeit with the cost of a delay. Taking full advantage of the multi-core, the GPU is much better than the CPU in parallel computing and floating-point computing efficiency. Therefore, we assigned the YOLOv4-tiny network to the GPU.  
In addition, in order to prevent the time taken to transfer data between the CPU and GPU from affecting the overall performance of the program, we made the calculation workload of the GPU far greater than the workload of copying and transferring data so that it does not copy and transfer frequently. This can reduce the proportion of time that is spent copying and transmitting data relative to the total time consumed, which will reduce its effect on overall performance. At the same time, when multiple threads are ongoing on the CPU, one thread can be in the process of copying while the other thread is performing calculations. In this way, calculations and copying can be performed simultaneously to reduce the calculation time. Finally, using the stream mechanism of the CUDA GPU, in asynchronous mode, when one stream is copying the other is calculating.
## 3 Next-Generation Sequencing Efforts in _Haslea_
### First Insights into the Haslea strearia Genome and Transcriptome  
ONT sequencing and assembly of the HoB4 genome were carried out at IMBBC-HCMR [71]. ONT sequencing long reads were corrected with the Illumina short reads using Pilon version 1.23 [72]. Both ONT and Illumina data were used to generate several genome assemblies and to compare the performance of different tools and integration strategies, keeping, as a final assembly, the one that obtained the best balance between contiguity, completeness and quality (Figure 3).  
After removing six contigs corresponding to six complete bacterial genomes from contaminating organisms (see section about bacteria associated with _Haslea_ below), the assembly consisted of 3015 genomic scaffolds (N50 of 121 kb) covering 107 Mb; this is within the range of the estimated genome size of other pennate diatoms [73]. We found a GC content of 45%, which is close to what is generally found in diatoms [18]. BUSCO analysis, using the Eukaryota_odb9 database (100 species) and _Galdieria_ ssp. as a reference, showed 266 genes orthologous to genes of other eukaryotic organisms. This number constitutes ca. 88% of the total number of 303 genes found among eukaryotes in the database. A total of 230 of 266 were single-copy genes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Read Type** & **No. of Reads** & **No. of Bases** \\ \hline Illumina & 2 \(\times\) 25,693,372 & 7,708,011,600 \\ ONT & 2,598,470 & 22,685,729,920 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of HoB4 genomic sequencing reads produced in this study.  
A BLASTn (>95% identity) was executed to search for similarity with the already published _H. ostrearia_ strain NCC 396 mitochondrial genome, and the published chloroplast genome of _H. ostrearia_ strain NCC 320. The complete chloroplast and mitochondrial genomes were retrieved in this way, each into a single contig.  
The transcriptomic data were obtained using Illumina HiSeq sequencing (RNAseq) from HoB4 cultures grown under optimum growth conditions. After quality filtering and trimming, genome-guided de novo assembly of the reads using the Trinity software suite [74], with normalization parameters, showed 64,082,309 total assembled bases, 39,181 total genes and 48,308 total transcripts. The number of genes and transcripts are similar to those detected via de novo assembly of RNA-seq reads with Trinity in the strain NCC 153.8, i.e., 35,813 genes and 46,690 transcripts [75]. At the same time, the number of genes found in HoB4 is somewhat larger than the range of 11,448 to 34,642 genes reported in nine other diatoms [73].
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 6 Risk Analysis
### STRIDE Application to System Components
#### 6.3.3 Warning Devices  
**Warning devices deliver critical safety alerts to the driver and are susceptible to threats that could undermine their effectiveness. Spoofing attacks could lead to false, excessive, or suppressed alerts, distracting the driver or hiding real dangers. Tampering with devices or connections could silence critical alerts or generate false warnings. Repudiation risks involve attackers denying responsibility for malicious activations. While less critical, Information Disclosure through intercepted commands could reveal system behaviors. DoS attacks could render devices unresponsive, preventing critical alerts.**  
**Mitigations include access controls (AAA frameworks) [32] to prevent unauthorized activation (Spoofing). Redundant device configurations enhance resilience against Tampering, supplemented by health monitoring. Digital signatures on commands ensure accountability, addressing Repudiation, potentially logged immutably using blockchain in future designs. Lightweight encryption protects command data against Information Disclosure. Redundancy and anomaly detection (possibly AI-driven for subtle attacks) also help maintain availability during DoS attacks.**  
**Challenges and residual risks include careful configuration of access controls (Spoofing) to avoid delays or scalability issues. Redundancy (Tampering, DoS) adds cost and complexity and does not preclude common-mode failures. Digital signature security (**Repudiation**) relies on secure key management and faces challenges in shared vehicle contexts; blockchain logging would introduce its own overheads. Lightweight encryption (**Information Disclosure**) needs evaluation against emerging attacks. **DoS** anomaly detection can be bypassed. Balancing cost, complexity, and security remains an unresolved issue, requiring innovative approaches to simplify implementation without compromising effectiveness.
## 5 Demonstration Experiment and Position Identification in Actual Headrace Tunnel  
### North Chiba Headrace  
The target of this study was the North Chiba Headrace (Figure 4), which is being inspected and repaired as part of a life extension plan. At this headrace, we examined the validity and applicability of the proposed method. The North Chiba Headrace is approximately 28.5 km long and plays a role in removing inland water during floods, supplying city water to Tokyo and purifying nearby rivers and swamps such as the Saka River and Tega Swamp. It has been over 20 years since the start of its operations in 2000, and the deterioration of the water pipes has become apparent, necessitating periodic inspection and repair. Therefore, we formulated a maintenance plan for the headrace and started inspection and repair in each section. The first round of inspection and repair was conducted from 2013 to 2017, and the second round of inspection started in 2018. However, owing to the role played by headrace tunnels, a long water supply interruption period is impossible, and this applies to the North Chiba Headrace. Therefore, when deformities, which need to be removed, are found when removing mussels clinging to the wall (Figure 7), they are circled with chalk. The chalk color depends on the type of deterioration; white chalk is used in the case of corrosion, and red chalk is used in cases of blistering (Figure 3, right).
## 3 Results  
Summary statistics are presented for the CV in Table 1, ApEn in Table 2, and DFA \(\alpha\) in Table 3. All data were normally distributed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CV (\%)**} & \multicolumn{3}{c}{**CV (\%)**} \\ \hline  & R & L & R-L & D & ND & D-ND \\  & & & Absolute & & Absolute \\  & & Diff. & & & Diff. \\ \hline
**Min** & 2.01 & 2.28 & 0.02 & 2.22 & 2.01 & 0.02 \\
**Max** & 3.51 & 6.20 & 3.64 & 3.51 & 6.20 & 3.64 \\
**95\% CI** & 253, 2.96 & 2.67, 3.82 & 0.20, 1.22 & 2.54, 2.93 & 2.68, 3.83 & 0.20, 1.22 \\
**Mean** & 2.75 & 3.24 & 0.71 & 2.74 & 3.25 & 0.71 \\
**SD** & 0.40 & 1.07 & 0.96 & 0.37 & 1.08 & 0.96 \\
**ES** & 0.45 & & & 0.47 & & \\ \hline \hline \end{tabular} CV = coefficient of variation; R = right; L = left; R-L Absolute Diff. = right \(-\) left (\(+/-\) sign removed); D = dominant; ND = nondominant; D-ND Absolute Diff. = dominant \(-\) nondominant (\(+/-\) sign removed); Min = minimum; Max = maximum; 95% CI = 95% confidence interval (lower bound, upper bound); SD = standard deviation; ES = effect size.  
\end{table}
Table 1: Summary statistics and effect sizes for the coefficient of variation (n = 16).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ApEn**} & \multicolumn{3}{c}{**ApEn**} \\ \hline  & R & L & R-L & D & ND & D-ND \\  & & Absolute & & Absolute \\  & & Diff. & & Diff. \\ \hline
**Min** & 0.25 & 0.14 & 0.01 & 0.25 & 0.14 & 0.01 \\
**Max** & 0.65 & 0.54 & 0.29 & 0.65 & 0.62 & 0.29 \\
**95\% CI** & 0.37, 0.50 & 0.31, 0.42 & 0.06, 0.14 & 0.34, 0.46 & 0.33, 0.46 & 0.06, 0.14 \\
**Mean** & 0.43 & 0.36 & 0.10 & 0.40 & 0.39 & 0.10 \\
**SD** & 0.12 & 0.10 & 0.07 & 0.11 & 0.13 & 0.07 \\
**ES** & 0.73 & & & 0.08 & & \\ \hline \hline \end{tabular} ApEn = approximate entropy; R = right; L = left; R-L Absolute Diff. = right \(-\) left (\(+/-\) sign removed); D = dominant; ND = nondominant; D-ND Absolute Diff. = dominant \(-\) nondominant (\(+/-\) sign removed); Min = minimum; Max = maximum; 95% CI = 95% confidence interval (lower bound, upper bound); SD = standard deviation; ES = effect size.  
\end{table}
Table 2: Summary statistics and effect sizes for approximate entropy (n = 16).
## 4 Discussion  
The experimental results both on the single model and ensemble model showed the ability to improve the system's recall using an additional entity discovery task. According to the results from previous CNER models [7; 12; 19; 24; 41; 42], those models with a single task always had a relatively lower recall compared to their precision; however, it could be used to discover more entities in practice. In our model, the multitask mechanism was used to balance discovering more entities and correctly identifying entities, and the use of the additional task can be seen as a process to add extra weights to discovering clinical entities. For the models with a single task, the model optimizes parameters only by the loss of the ground-truth tags and estimated tags by cross-entropy. Compared to the models with a single task, the multitask model tends to optimize parameters based primarily on if a token is an entity, and correspondingly reduces the reliability on a token being assigned the right tag. In the backpropagation process, gradients are independent of each other in the softmax and CRF parts. Then, by backpropagating to the encoder parts, the gradients from the two parts are merged; thus, the multitask mechanism mainly changes the way of encoding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity Type** & **F1** & **Precision** & **Recall** \\ \hline Medical Test & 88.37 & 88.61 & 88.13 \\ Problem & 89.03 & 89.40 & 88.66 \\ Treatment & 88.96 & 89.34 & 88.58 \\ SD & 0.21 & 0.25 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of each type of entity.
# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 3 Dynamic Resource Management Architecture and Task Scheduling Method
### Dynamic Resource Management Architecture
Accordingly, the results of the monitoring layer can help the resource layer better monitor and manage the satellites' various resources. Based on the results of the monitoring layer, the resource layer can also do proper planning with the help of the missions assigned to the satellites. The monitoring layer performs registration, capability modeling, semantic acquisition, and semantic analysis on the resources acquired by the resource. The primary role of the virtual layer is to support the virtualization of resources, which can realize the unified management and share among satellite resources, thus improving the efficiency of satellite resource utilization. According to the results of the monitoring layer, the virtual layer virtualizes the resources and generates virtual resources. The coating contains the generation of virtual resources of satellites, the modeling of some responsible mission requirements, etc. The virtual layer also constructs the semantic model through the results of the monitoring layer. The virtual layer can improve the efficiency of satellite resources by virtualizing them and providing the results to the application layer for analysis. The primary role of the application layer is to provide services to the relevant users. The application layer can realize the interaction between users and the satellite system, so this layer is an interface between the satellite system and the users. The application layer forwards the service requests received from the users to the corresponding parts for processing and use. The application layer can also use satellite-related resources through the virtual layer and monitor various satellite operational status and other data through  
Figure 4: The difference between the satellite number of the task at a certain moment and the satellite number of the task at the initial time, if it is 0, it means that the task has not been scheduled to other satellites, if it is not 0, it means that the task has been scheduled to other satellites. the monitoring layer. The application layer's prominent role is to manage the virtual resources and query whether the satellite resources are updated to process the data. The application layer can also monitor the status of the satellites through the monitoring layer to ensure the proper operation of the satellites. The application layer needs to meet the various needs of the users in the process of use. It should also consider the efficiency of utilizing the satellite resources and the priority of the tasks assigned to the satellite.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 4 Discussion
These values are determined by means of mean transit times of ultrasound pulses, similar to, e.g., the mean speed of driving a car along a road section from A to B. Such values change smoothly, not in a stepwise fashion, so jumps in value are caused by detection errors and signal fades. Thus, considering the analysis of the sinogram in Figure 16, the optimal water temperature should be chosen \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{
\begin{tabular}{c} **Water** \\ **Temperature** \\ \end{tabular} } & \multicolumn{2}{c}{**Skin @ \(c_{Fat}\) = 1470 m/s**} & \multicolumn{2}{c}{**Fat @ \(c_{Skin}\) = 1624 m/s**} & \multicolumn{2}{c}{**Breast @ \(c_{Skin}\) = 1624 m/s**} \\  & \multicolumn{2}{c}{\(c_{Breast}\) = 1515 m/s**} & \multicolumn{2}{c}{\(c_{Breast}\) = 1515 m/s**} & \multicolumn{2}{c}{\(c_{Fat}\) = 1470 m/s**} \\ \cline{2-7}  & \(c_{\frac{\text{Min}}{}}\) [m/s] & \(\Delta_{opt}\) [\({}^{\circ}\)] & \(c_{\frac{\text{Min}}{}}\) [m/s] & \(\Delta_{opt}\) [\({}^{\circ}\)] & \(c_{\frac{\text{Min}}{}}\) [m/s] & \(\Delta_{opt}\) [\({}^{\circ}\)] \\ \hline \multirow{3}{*}{20} & 1537 & 3.75 & 1412 & 3.98 & 1430 & \(-\)6.57 \\  & 1720 & 1.95 & 1490 & 2.74 & 1564 & 9.30 \\  & 1537 & 2.33 & 1412 & 2.56 & 1430 & \(-\)7.65 \\  & 1720 & 0.69 & 1490 & 1.39 & 1564 & 7.71 \\  & 1537 & 1.15 & 1412 & 1.38 & 1430 & \(-\)8.58 \\  & 1720 & \(-\)0.38 & 1490 & 0.26 & 1564 & 6.38 \\  & 1537 & 0.16 & 1412 & 0.38 & 1430 & \(-\)9.36 \\  & 1720 & \(-\)1.28 & 1490 & \(-\)0.69 & 1564 & 5.28 \\ \hline \end{tabular}
\end{table} 
Table 1: Values of the \(\Delta_{opt}\) parameter calculated for selected minimum and maximum ultrasound velocity in skin, fat, and breast glandular tissue. in the range of \(25\,\,^{\circ}\mathrm{C}\leq T<30\,\,^{\circ}\mathrm{C}\). The structure of the breast tissue in the images of Figure 17 is most pronounced for measurements at a water temperature of \(30\,\,^{\circ}\mathrm{C}\). On the other hand, analyzing the \"distortion ring\", it can be seen that the area with significantly underestimated ultrasound velocity values localized inside the ring (which obscures the glandular breast tissue area) is wider for the case of water temperature of \(30\,\,^{\circ}\mathrm{C}\) and the pixel values are smallest there. This is due to the large difference in ultrasound velocity values in water and breast tissue, which is unfavorable as analyzed earlier.
## 2 Modeling and Experimental Validation
### Numerical Method
#### 2.1.4 Mesh Independence Examination  
Due to the intricacy of the physical model, a tetrahedral grid was utilized to discretize the computational domain. Grids of 1.6 million, 3.38 million, 5 million, 6.67 million, and 8 million points were divided before the simulation to examine the independence of the grids. Figure 3(a) shows the average velocity at the entrances and exits of A, C, and D with a SW wind of 2 m/s. The wind speeds at the three entrances remained practically unchanged as the number of grid cells increased from five to eight million. Therefore, the model was divided into five million grids for subsequent calculations.  
Figure 3: Wind direction rose chart in Tianjin.  
In Figure 4b, the maximum grid sizes of the outside flow field and the station hall were 2 m and 0.3 m, respectively. The grid was refined in regions with a high velocity gradient or concentration gradient, such as the entrances and exits of the station hall, exhaust outlets, and the fire source, with maximum sizes of 0.2 m, 0.04 m, and 0.05 m, respectively.
# Fine-Grained Boundary Conditions in Field-Based Routing
## 5 Advanced Fine-Grained Boundary Conditions
### Advanced Fine-Grained Boundary Conditions
#### 5.2.2 Inner Boundary Conditions  
Inner boundary conditions are the boundary conditions applied to gateways as inner boundary nodes. As discussed in the previous section, the limitation of the existing inner boundary conditions is that they do not reflect neighboring congestion levels and assign the lowest potential value identically to all gateways. To overcome this limitation, one possible approach could involve continuously adjusting the potential values of gateways based on surrounding congestion levels, treating them similarly to general mesh nodes.  
However, as mentioned in the previous section, this approach can pose challenges to field convergence since gateways play a crucial role as drainage points for the potential field. In this context, we aim to find an alternative approach as a compromise to address the limitations of existing inner boundary conditions while avoiding dynamic potential value changes for gateways during network operation.  
We propose advanced Dirichlet-based boundary conditions as new inner boundary conditions. Instead of assigning the lowest potential value to all gateways, we suggest assigning different fixed potential values to each gateway. For load balancing, it is recommended to assign potential values inversely proportional to the expected congestion levels for each gateway. This rule would encourage the forwarding of a portion of packets from a congested sub-domain to another sub-domain in cases where multiple gateways are present. In this way, the proposed inner boundary conditions can lead to an improved load balancing effect while maintaining stable convergence, even with a relatively straightforward approach.
# Subwavelength-Cavity High-Gain Circularly Polarized Antenna with Planar Metamaterials
## 2 Simulations and Experimental Measurements
\[b_{1}=e^{-j\varphi}:y-pol \tag{2}\]1 Experimental measurements of the proposed LHCP metamaterial antenna: (**a**) reflection coefficient (\(S_{11}\)); (**b**) measured axial ratio (\(AR\)). Comparison of LHCP and RHCP patterns of the LHCP metamaterial antenna at 2.80 GHz on two mutually orthogonal planes: (**c**) \(\phi=0^{\circ}\); (**d**) \(\phi=90^{\circ}\).