esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison with Other Assay Methods  
Urinary metabolites are the most frequently used biomarkers for assessment of exposure to common insecticides. The aim of this study was to develop a rapid, sensitive, and cost-effective method for routine urinary analysis. We used LLE, instead of the commonly used SPE method, to retain as many metabolites in urine as possible; thus, the extraction efficiency could be preserved [22; 23]. In the meantime, conducting LLE was relatively time saving, compared to SPE cartridges dripping. Additionally, our method did not need large quantities of organic solvents, SPE cartridges, or expensive equipment, such as tandem mass spectrometry, indicating the cost effectiveness. This assay method is comparable to others, with even more metabolites being analyzed simultaneously than that of others (Table 6).  
There are two common methods applied to determine the urinary metabolites of pyrethroids. One is GCMS, the average LOD of which was found to be 0.3-0.5 \(\upmu\)g/L before the year 2000, and after, improved to 0.02-0.1 \(\upmu\)g/L [18; 24; 25]. The other is LC-tandem mass spectrometry (LC/MS/MS), the average LOD of which was about 0.015 \(\upmu\)g/L [26]. Due to the lower LOD and time consumption  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metabolites** & **DF (\%)** & **Mean \(\pm\) SD (\(\upmu\)g/L)** & **Median (\(\upmu\)g/L)** & **Maximum (\(\upmu\)g/L)** \\ \hline
3-PBA & 36.5 & \(11.34\pm 1.15\) & 8.52 & 23.61 \\ _cis_-DCCA & 61.3 & \(5.66\pm 2.41\) & 2.71 & 71.30 \\ _trans_-DCCA & 68.5 & \(17.33\pm 5.12\) & 10.19 & 34.60 \\ _cis_-DBCA & 72.6 & \(16.91\pm 4.12\) & 4.41 & 24.52 \\ _trans_-CDCA & 57.4 & \(14.74\pm 3.33\) & 7.24 & 21.69 \\ FPBA & 81.3 & \(5.12\pm 2.54\) & 1.25 & 27.87 \\ DMP & 14.5 & \(7.41\pm 5.51\) & 4.23 & 10.91 \\ DMTP & 13.2 & \(4.39\pm 1.16\) & 2.11 & 5.24 \\ DMDTP & 14.1 & \(5.33\pm 1.07\) & 1.36 & 6.94 \\ DEP & 6.6 & \(2.14\pm 3.45\) & 2.45 & 7.17 \\ DETP & 8.8 & \(6.26\pm 2.21\) & 4.47 & 8.23 \\ DETP & 3.3 & \(3.33\pm 1.77\) & 1.21 & 7.11 \\
7-phenol & 24.5 & \(7.65\pm 2.36\) & 3.39 & 34.49 \\
2-PBA & 100 & \(97.14\pm 1.26\) & 95.26 & 104.32 \\ \hline \hline \end{tabular} DF: detection frequency; SD: standard deviation.  
\end{table}
Table 5: Data of genuine urine samples (\(n=30\)).  
compared with using GCMS, LC/MS/MS has become a more popular analytical method, along with using SPE as a pre-analysis treatment. However, in view of the relative high costs for using LC/MS/MS, this method, using LLE and GCMS, is much more cost effective, albeit with slightly elevated LODs.  
We also conducted a comparison with previous studies regarding method precision. [PERSON] showed the within-day RSDs to be between 4.7% and 10% among six metabolites at 0.2 \(\mu\)g/L, and the between-day RSDs to be between 6.7% and 13.3% among five metabolites at 0.2 \(\mu\)g/L [25]. [PERSON] demonstrated the between-day RSDs ranging from 13.3% to 17.9% among five metabolites at 0.4 \(\mu\)g/L [24]. Our method resulted in relatively stable results for repetitive routine analyses within 24 hours and over days.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 3 Problem Formulation and Proposed System Architecture
\[T_{le}^{k}=T^{MIPS}\times T^{PR} \tag{4}\]  
Now, task priorities are calculated using Equation (5) below. They are defined as the ratio of the length of the task to the processing capacity of a VM to which the task is submitted. \[T^{pi}=\frac{T_{le}^{k}}{PR_{V}^{cap}} \tag{5}\]  
Now, VM priorities are calculated with respect to unit cost of electricity as our aim is to identify a VM with a low unit cost of electricity but with high computing power to execute tasks as the provider needs to minimize energy consumption in the datacenter. Therefore, they are calculated using Equation (6) below. \[V_{n}^{pi}=\frac{ele^{h}}{ele^{Dt}} \tag{6}\]  
After careful identification of priorities of both tasks and VMs based on unit cost of electricity, we carefully modeled the parameters for evaluation of parameters, i.e., makespan, rate of failure, and energy consumption at datacenters. Makespan impacts the task scheduling process directly as, if execution time on a VM is reduced, then the VM is available for the next task which needs to be executed and, in reverse, if execution time increases on a VM, this impacts the QoS of the scheduler and execution cost can be increased. This is the reason why we chose makespan as a primary objective in this work. It is calculated using Equation (7) below. \[ms_{k}=ava_{n}^{V}+ET_{k} \tag{7}\]where \(ms_{k}\) indicates makespan for \(k\) tasks, \(avu_{n}^{V}\) is availability to run tasks with \(n\) VMs, \(ET_{k}\) is execution time for \(k\) tasks. Minimizing energy consumption in datacenters is our next objective because, in a cloud computing paradigm, from the point of view of the cloud provider and from the point of view of the environment, to reduce carbon footprints to achieve green cloud computing it is important to minimize energy consumption at datacenters. It is calculated using Equation (8) below. \[ene_{cons}(V^{n})=\int_{0}^{n}ene_{cons}^{com}(V^{n},T)+\int_{0}^{n}ene_{cons}^ {idle}(V^{n},T)dt \tag{8}\]  
Total energy consumption at datacenters is calculated using Equation (9) below. Figure 1:0  
After Equations (8) and (9), we calculated rate of failure as our objective is to develop a fault-tolerant scheduler to minimize single points of failure in the cloud paradigm. It is calculated using Equation (10). Figure 1:1  
After designing a detailed mathematical model for the task scheduler, we integrated a machine reinforcement learning mechanism into our proposed \(DRFTSA\) scheduler.
# The Impact of Spoofing Attacks in Connected Autonomous Vehicles under Traffic Congestion Conditions
## 4 Evaluation  
To test the performance of the evaluated spoofing attack model as described in Section 3.2, we used OMNeT++ 5.1, INET 3.6, Veins 4.7, and SUMO 0.30.0 [19]. In particular, we simulate and compare two scenarios. We call the first the Normal Scenario, where vehicles move under normal conditions on city routes, without the presence of an attacker. For the the second scenario, called Spoofing Attack Scenario, a spoofing attack occurs with different random but reasonable discrete values (\(n=10,15,20,25\)) for the range of vehicles in an urban area. All simulation parameters applied for the conducted experiments are described in Table 1.
# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 4 Experiment
### Ablation Studies
The findings show that, when SPbottleneck blocks include the final bottleneck block in every layer and every bottleneck block in the final layer, the lowest mean angular error is \(10.21^{\circ}\), which produces a mean angular error reduction of \(0.12^{\circ}\) in comparison with the baseline method. Table 7 shows that network performance is improved when the multi-criss-cross attention module is added after every layer. In particular, network performance is improved most (with the lowest mean angular error of \(10.13^{\circ}\)) when the multi-criss-cross attention module is added after layer 1. All of the ablation experiments analyzed in Table 7 were added to the strip pooling module, and smooth L1 loss was adopted as the regression loss. In addition, the ablation experiments on the RT-Gene dataset were not very different, although they were both elevated, so we performed ablation experiments for the Gaze360 dataset only for the ablation experiments in Tables 6 and 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **SP Position** & **Gaze360** \\ \hline L2 CS-Net (\(\beta=1\)) (baseline) & - & \(10.47^{\circ}\) \\ L2 CS-Net (\(\beta=2\)) (baseline) & - & \(10.33^{\circ}\) \\ SPMCCA-Net (\(\beta=1\)) & A & \(10.23^{\circ}\) \\ SPMCCA-Net (\(\beta=2\)) & A & \(10.22^{\circ}\) \\ SPMCCA-Net (\(\beta=1\)) & B & \(10.24^{\circ}\) \\ SPMCCA-Net (\(\beta=2\)) & B & \(10.24^{\circ}\) \\ SMPCCA-Net (\(\beta=1\)) & C & \(10.26^{\circ}\) \\ SPMCCA-Net (\(\beta=2\)) & C & \(10.22^{\circ}\) \\ \hline
**SPMCCA-Net (\(\beta=1\))** & **A + B** & \(\mathbf{10.22^{\circ}}\) \\
**SPMCCA-Net (\(\beta=2\))** & **A + B** & \(\mathbf{10.21^{\circ}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation analysis of the position of the SP module. Figure 10: Comparison of feature map fusion visualizations on Gaze360. (**a**) face images; (**b**) visualization of fused feature maps of L2 CS-Net; (**c**) visualization of fused feature maps of SPMCCA-Net. Based on the above ablation experiments, we conclude that gaze estimation performance is optimized when adding the strip pooling module to the final bottleneck block in every layer and to the final layer of each block across the entire bottleneck, as well as adding the multi-criss-cross attention module after layer 1.
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 4 Monitoring Results and Discussion  
In this section, the long-term monitoring data using the TPAM system are presented and discussed. The location of the monitoring tree is 36.7650225 latitude and 127.2797814 longitude. The monitoring was conducted from spring (May), when the leaves on the trees were in full bloom, to fall (November), when all the leaves on the trees had fallen. After the wood tissue is damaged by inserting the probe into the tree, the impedance is expected to change as the area where the probe was inserted heals over time. To observe these changes, the impedance change over time after inserting the probe is observed. In the cross-sectional structure of a tree, a significant portion close to the center is dead tissue, and an active layer related to growth exists on the epidermis side. It can be expected that the impedance will vary depending on the depth of the insertion of the probe. Therefore, it is necessary to observe how the impedance changes depending on the insertion depth of the probe. It is clear that the tree growth is affected by the environment temperature and the light of the surrounding environment. It is necessary to examine whether the measured tree impedance is influenced solely by the tree's physiological activity or is also directly influenced by the environment temperature. This can be found by looking at changes in the tree impedance throughout the day when there are no leaves. If the tree impedance value changes during the day even though there are no leaves, it can be said that it is directly affected by temperature. To examine these details, the tree's impedance, environmental temperature, and light intensity were monitored in the long term for several months.  
Figure 9 shows the data on the tree impedance, ambient temperature, and solar intensity observed for about four months from July 25 to November 30. As the season changes from summer to fall, changes in the temperature and impedance can be seen. As shown in Figure 9a, the ambient temperature ranges from 20 to 30 degrees in summer in July and August, 10 to 20 degrees in October in early fall, and 0 to 20 degrees in November in late fall. In November, late fall, there is a large daily temperature range with a temperature fluctuation range of up to 20 degrees. Figure 9b shows the simultaneously measured light intensity, and seasonal variations at the macro scale are not clearly visible. As shown in Figure 9c, the impedance has a value of less than 1 k\(\Omega\) in July and August in the summer, increases to a value of about 1.5 k\(\Omega\) in October in early fall, and even increases to 2.5 k\(\Omega\) in November in late fall. During the summer, the impedance change was small, but as late fall approached, the impedance change can be seen to increase. When comparing Figure 9a,c, the change in the impedance depending on the season appears to be related to the change in temperature. The correlation between the temperature and impedance will be discussed in more detail later.
# Nickel-Based Structured Catalysts for Indirect Internal Reforming of Methane
## 3 Results and Discussion
### Raman Characterization  
The Raman spectra of NiCrAl foam (spectrum a) and calcined Ni-SmDC/NiCrAl structured catalyst (spectrum b) are shown in Figure 4. In spectrum (a), Raman peaks at 303 cm\({}^{-1}\) (A\({}_{1g}\)), 348 cm\({}^{-1}\) (E\({}_{g}\)), 552 cm\({}^{-1}\) (A\({}_{1g}\)), and 612 cm\({}^{-1}\) (E\({}_{g}\)) can be assigned to the Raman modes of Cr\({}_{2}\)O\({}_{3}\)[45; 46]; besides a weak and broad peak at 706 cm\({}^{-1}\) can be assigned to amorphous CrO\({}_{2}\)[46]. A very weak shoulder at 526 cm\({}^{-1}\) could be assigned to NiCr\({}_{2}\)O\({}_{4}\) spinel structure [47], whereas there is no evidence of NiO and Al\({}_{2}\)O\({}_{3}\). The Raman results suggest that the surface consisted mainly of chromium oxides, according to the EDX microanalysis previously reported. The Raman spectrum (b) shows a marked peak at 456 cm\({}^{-1}\) (F\({}_{2g}\)), assigned to the Raman mode of CeO\({}_{2}\). This peak is broadened and shifted toward lower frequency in comparison to pure ceria because of the lattice defects created by the introduction of Sm\({}^{3+}\) dopant ions. The sample shows another weak peak at 242 cm\({}^{-1}\) and two broad bands at 547 cm\({}^{-1}\) (D\({}_{1}\) band) and 591 cm\({}^{-1}\) (D\({}_{2}\) band), due to the oxygen defects generated by dopant ions in the lattice of ceria. Thus, Raman analysis confirmed that Sm\({}^{3+}\) ions formed solid solution with CeO\({}_{2}\)[29; 48; 49].  
Figure 3: SEM micrographs of structured catalysts coated with (**a**) single-layer, (**b**) double-layer (**c**) and triple-layer. (**d**) Cross-section SEM micrography of double-layer coated Ni-SmDC/NiCrAl after FIB milling, an enlargement is displayed in the dashed red circle.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Function and Precision Test
#### 5.2.2. Dormitory Building Area  
The second stage test was carried out in the campus dormitory building group area (actual area: 28,490 m\({}^{2}\)). The average building height in this area is 24 m, and there are many building corners. In terms of signal, due to shelters, such as buildings and trees, around the dormitory building, the satellite signal would be disturbed during the propagation process, resulting in signal attenuation and multipath effects, resulting in low and unstable signal strength received by the receiver.  
The test results are shown in Table 5, and the actual trajectory and test trajectory are shown in Figures 11 and 12, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Weather Condition** & **Measuring Area (m\({}^{2}\))** & **Actual Area (m\({}^{2}\))** & **Relative Error (\%)** \\ \hline
13 March & Cloudy & 7123 & & 0.23 \\
15 March & Light rain & 7188 & & 0.67 \\
17 March & Sunny & 7126 & 7140 & 0.19 \\
19 March & Cloudy & 7107 & & 0.46 \\
21 March & Sunny & 7159 & & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Football field area test results.  
Figure 10: Football field area test trajectory.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Weather Condition** & **Measuring Area (m\({}^{2}\))** & **Actual Area (m\({}^{2}\))** & **Relative Error (\%)** \\ \hline
13 March & Cloudy & 28,378 & & 0.39 \\
15 March & Light rain & 28,803 & & 1.09 \\
17 March & Sunny & 28,381 & 28,490 & 0.38 \\
19 March & Cloudy & 28,720 & & 0.81 \\
21 March & Sunny & 28,678 & & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dormitory building surrounding area test results.  
Figure 9: The actual treading track in the football field area. (**a**) Actual field scene (cloudy); (**b**) Schematic satellite view of the football field. Yellow line indicates the walking path during sampling.
## Nomenclature  
* _Item_ Unit
* _aR_ -
* \(B\) _rad_
* \(B\) _r_ad_
* \(B\) \(r\) _d  
\begin{tabular}{l l l} \(A_{R}\) & \(m^{2}\) & Rudder profile area \\ \(a_{H}\) & - & Increase factor of the rudder force \\ \(B\) & \(m\) & Breadth of ship \\ \(B_{R}\) & \(m\) & Average rudder chord length \\ \(C_{P}\) & - & Experimental constant due to wake \\ \(D_{P}\) & \(m\) & Diameter of propeller \\ \(d\) & \(m\) & Draft of ship \\ \(F_{N}\) & \(N\) & Normal force of the rudder \\ \(F_{X},F_{Y}\) & \(N\) & Surge and sway force acting on the ship \\ \(f_{a}\) & - & Lift gradient coefficients of the rudder \\ \(H_{R}\) & \(m\) & Span of rudder \\ \(I_{Z}\) & \(kgm^{2}\) & Moment of inertial of ship \\ \(I_{P}\) & - & Advanced ratio of propeller \\ \(K_{T}\) & - & Propeller thrust open water characteristic \\ \(k_{0},k_{1},k_{2}\) & - & Coefficients relative to \(K_{T}\) \\ \(L_{pp}\) & \(m\) & Ship length between two perpendicular \\ \(l_{R}\) & \(m\) & Effective longitudinal length of the rudder \\ \(M_{Z}\) & \(Nm\) & Yaw moment acting on the ship \\ \(m\) & \(kg\) & Ship mass \\ \(n_{P},n_{S}\) & \(rpm\) & Propeller revolutions per minute (rpm) \\ \(O-xyz\) & - & Earth-fixed coordinate system \\ \(o-x_{b}y_{b}z_{b}\) & - & Body-fixed coordinate system \\ \(R_{0}^{\prime}\) & - & Resistance of the ship in straight motion (-) \\ \(r\) & \(deg/s\) & Yaw rate \\ \(r\) & & Reward \\ \(T\) & \(N\) & Longitudinal propeller force \\ \(t\) & \(s\) & Time \\ \(t_{P}\) & - & Thrust deduction factor \\ \(t_{R}\) & - & Steering deduction factor \\ \(U\) & \(m/s\) & Resultant velocity \\ \(U_{0}\) & \(m/s\) & Initial resultant velocity \\ \(U_{R}\) & \(m/s\) & Resultant inflow velocity to the rudder \\ \(u,v\) & \(m/s\) & Longitudinal and lateral velocity of the \\  & & ship in the body-fixed coordinate system \\ \(u_{R},v_{R}\) & \(m/s\) & Longitudinal and lateral inflow velocity to \\  & & rudder position \\ \(w_{P}\) & - & Wake coefficient at the propeller in \\  & & maneuvering motion \\ \(w_{P0}\) & - & Wake coefficient at the propeller at straight \\ \(w_{R}\) & - & Motion \\ \(X\), \(Y\), \(N\) & \(N\), \(N\), \(Nm\) & Wake coefficient at the rudder position \\ \(X_{Y}\), \(Y\), \(N\) & \(N\), \(N\), \(Nm\) & Surge, sway force, and yaw moment \\  & & around the midship \\ \(X_{H}\), \(Y_{H}\), \(N_{H}\) & \(N\), \(N\), \(Nm\) & Surge, sway force, and yaw moment acting on ship's Hull \\ \(X_{P}\), \(Y_{P}\), \(N_{P}\) & \(N\), \(N\), \(Nm\) & Surge, sway force, and yaw moment due to the propeller \\ \(X_{R}\), \(Y_{R}\), \(N_{R}\) & \(N\), \(N\), \(Nm\) & Surge, sway force, and yaw moment due to the rudder \\ \(x_{G}\) & \(m\) & Longitudinal position of the center of \\  & & gravity \\ \(x_{H}\) & \(m\) & Longitudinal position of the acting point of \\  & & addition lateral force \\ \(x_{P}\) & \(m\) & Longitudinal position of the propeller \\ \(x_{R}\) & \(m\) & Longitudinal position of the rudder \\ \end{tabular}
## 2 Design and Performance of Ultra-Intense \"J-KAREN\" Laser System  
The J-KAREN laser is the flagship laser at Kansai Photon Science Institute (KPSI) of Japan Atomic Energy Agency (JAEA). The laser features a number of innovations which make it unique. In particular, the double CPA scheme, OPCPA preamplifier, and two saturable absorbers improving the contrast; cryogenically cooling of the power amplifier removing the thermal lensing; diffractive optics drastically improving the beam profile of pumping beams; index-matching liquid around the large Ti:Sapphire crystal removing parasiting lasing; automatic beam alignment systems ensuring the stability; extremely high power, beam quality, and contrast; possibility of daily operation during long experimental campaigns; and more. All these made possible several discoveries and pioneering experiments performed with the J-KAREN laser, as described in the Section 3.  
A schematic of the J-KAREN laser system is shown in Figure 1. The system consists of two successive CPA stages linked by a saturable absorber, which acts as a nonlinear temporal filter. The first CPA stage consists of an oscillator, a stretcher, a nine-pass amplifier and a compressor. The second CPA stage consists of a stretcher (using 1480 grooves/mm gratings), a three-stage OPCPA preamplifier, a four-pass Ti:sapphire preamplifier, a four-pass cryogenically cooled Ti:sapphire power amplifier, a three-pass Ti: sapphire final booster amplifier and a compressor that consists of four 1480 grooves/mm gratings.
## 2 Design and Modeling
### FEM Optimization Design  
For the bistable generator beam structure, the most critical design parameter is the height of the potential energy well. Optimal sizing is helpful in improving the energy capture efficiency of the energy harvester in low-frequency environments. To analyze and optimize the structure of the S-type generator beam of the energy harvester, we conducted finite element method (FEM) simulations using COMSOL Multiphysics 5.3. Using built-in solvers of the frequency domain, through the analysis of the motion state of the generator beam at the different heights (5 mm, 10 mm and 15 mm) of the potential energy well, the optimal height geometry of the potential energy well is determined.  
Using software, fixed constraints were applied at both ends of an S-shaped beam, and a mass block was added at the middle. A low-frequency vertical excitation (0-20 Hz) was then applied to the beam using COMSOL 5.3. The motion states of the power generation beam were analyzed in different potential well heights (5 mm, 10 mm, and 15 mm) to determine the optimal height and geometric shape of the potential well. To clearly demonstrate the motion states of the beam under different geometric sizes, the cloud diagram data hasbeen normalized, and the legend values represent the relative motion states of the power generation beam at different extreme positions.  
From Figure 2a,c, it can be found that when the height of the potential energy well is 5 mm or 15 mm, the S-type generator beam can only have small motion (vibration and torsion) in the well at the condition of low frequency (0-20 Hz) excitation, and the amplitude of vibration is very limited, which is not an ideal power generation state. When the height of the potential energy well is 10 mm, at the same external excitation condition, the generator beam can not only have a large vibration in the well, but also break through the potential energy barrier and generate inter-well movement (snap-through), as shown in Figure 2b. When the inter-well movement produces a large vibration, it also generates high-frequency vibration in the local part of the generator beam, so as to effectively improve the power generation efficiency.