esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 4 Experimental Setup
### Receiver and Algorithm Implementation  
The GOOSE(r) platform, developed by Fraunhofer IIS and marketed through TeleOrbit GmbH, is a GNSS receiver with an open software interface [29; 43]. A picture of the receiver is shown in Figure 11. The receiver contains a customized tri-band radio-frequency front-end (RFFE), a Xilinx Kintex7 field-programmable gate array (FPGA), and a dual-core ARM processor. The RFFE amplifies, filters, downconverts, discretizes the GNSS signals, and sends the digital samples to the FPGA. The analog-to-digital converter (ADC) discretizes each frequency band at a sample rate of 81 MHz and a resolution of 8 bits. The FPGA includes one acquisition module and sixty tracking channels, which the processor can control. The processor performs the acquisition of the incoming digital samples using the acquisition module of the FPGA. The tracking starts once the acquisition achieves a rough estimate of the frequency Doppler \(f_{\mathrm{d}}\) and code phase \(\tau\). The tracking stage of this GNSS receiver is partially implemented in the FPGA (e.g., correlators and NCO) and software (e.g., discriminators and loop filters). This stage consists of three steps. First, the FLL and the DLL refine the acquired \(f_{\mathrm{d}}\) and \(\tau\) estimates. Second, the PLL starts and synchronizes with the carrier phase. Finally, the FLL stops and the PLL works unaided when the latter successfully achieves a good lock with the carrier phase. The receiver synchronizes with the navigation data at this stage, and the integration time increases to the symbol period. In the case of Global Positioning System (GPS) L1 C/A, the integration time is increased to 20 ms.  
The \(C/N_{0}\)-based DSKF, the LBCA-based DSKF, and the LBCA-based LUT-DSKF are implemented in the third-order Costas PLL of the GOOSE receiver in software.  
Figure 10: Non-linear model of LBCA-based LUT-DSKF.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Dm** & **N** & **P** & **K** & **Ca** & **Mg** & **Na** & **S** \\ \hline \hline \multirow{3}{*}{clay} & CAN + S & 19.2 \(\pm\) 0.3 & 157 \(\pm\) 23 & 27 \(\pm\) 3 & 161 \(\pm\) 14 & 40 \(\pm\) 1 & 23 \(\pm\) 2 & 1.0 \(\pm\) 0.1 & 15 \(\pm\) 1 \\ \cline{2-7}  & AS & 18.3 \(\pm\) 0.8 & 178 \(\pm\) 9 & 28 \(\pm\) 3 & 154 \(\pm\) 12 & 38 \(\pm\) 2 & 22 \(\pm\) 1 & 0.9 \(\pm\) 0.1 & 15 \(\pm\) 0.5 \\ \hline \multirow{3}{*}{sand} & CAN + S & 22.1 \(\pm\) 1 & 235 \(\pm\) 14 & 33 \(\pm\) 2 & 288 \(\pm\) 12 & 38 \(\pm\) 2 & 23 \(\pm\) 1 & 1.0 \(\pm\) 0.1 & 18 \(\pm\) 1 \\ \cline{2-7}  & AS & 23 \(\pm\) 1.1 & 252 \(\pm\) 13 & 38 \(\pm\) 2 & 256 \(\pm\) 12 & 37 \(\pm\) 2 & 24 \(\pm\) 1 & 1.0 \(\pm\) 0.1 & 19 \(\pm\) 1 \\ \hline \multicolumn{7}{c}{Grass 2021} \\ \cline{2-7}  & CAN + S & 6.6 \(\pm\) 0.3 & 118 \(\pm\) 5 & 21 \(\pm\) 0.3 & 178 \(\pm\) 14 & 26 \(\pm\) 1 & 8 \(\pm\) 0.3 & 9 \(\pm\) 2 & 19 \(\pm\) 1 \\ \cline{2-7}  & AS & 6.7 \(\pm\) 0.6 & 131 \(\pm\) 5 & 21 \(\pm\) 1 & 178 \(\pm\) 10 & 28 \(\pm\) 3 & 9 \(\pm\) 1 & 10 \(\pm\) 3 & 21 \(\pm\) 2 \\ \hline \multirow{3}{*}{sand} & CAN + S & 7.7 \(\pm\) 0.8 & 142 \(\pm\) 15 & 24 \(\pm\) 2 & 145 \(\pm\) 27 & 29 \(\pm\) 5 & 13 \(\pm\) 2 & 17 \(\pm\) 1 & 22 \(\pm\) 2 \\ \cline{2-7}  & AS & 6.8 \(\pm\) 0.6 & 132 \(\pm\) 13 & 22 \(\pm\) 2 & 141 \(\pm\) 16 & 23 \(\pm\) 4 & 11 \(\pm\) 1 & 18 \(\pm\) 1 & 21 \(\pm\) 2 \\ \hline \multicolumn{7}{c}{Grass 2022} \\ \cline{2-7}  & Blank & 2.9 & 46.1 & 8.3 & 55.3 & 12.5 & 3.8 & 1.7 & 5.1 \\ \cline{2-7}  & CAN + S & 6.3 \(\pm\) 0.4 & 181 \(\pm\) 11 & 19 \(\pm\) 1 & 156 \(\pm\) 11 & 33 \(\pm\) 3 & 11 \(\pm\) 1 & 13 \(\pm\) 1 & 23 \(\pm\) 2 \\ \cline{2-7}  & AS & 5.9 \(\pm\) 0.6 & 171 \(\pm\) 16 & 18 \(\pm\) 2 & 146 \(\pm\) 13 & 30 \(\pm\) 3 & 10 \(\pm\) 1 & 11 \(\pm\) 3 & 28 \(\pm\) 3 \\ \hline \multirow{3}{*}{sand} & Blank & 3.2 & 41 & 7.3 & 48 & 15.7 & 4.8 & 4 & 6.2 \\ \cline{2-7}  & CAN + S & 7.0 \(\pm\) 0.1 & 182 \(\pm\) 12 & 23 \(\pm\) 1 & 96 \(\pm\) 9 & 44 \(\pm\) 2 & 16 \(\pm\) 1 & 31 \(\pm\) 7 & 27 \(\pm\) 2 \\ \cline{2-7}  & AS & 6.9 \(\pm\) 0.2 & 191 \(\pm\) 18 & 24.2 \(\pm\) 0.3 & 96 \(\pm\) 14 & 37 \(\pm\) 1 & 15 \(\pm\) 1 & 31 \(\pm\) 6 & 30 \(\pm\) 2 \\ \hline \end{tabular}
\end{table} 
Table 8: Average N\({}_{2}\)O emissions (% N of applied N) determined in the greenhouse experiment (see Table 5 for applied N) on the basis of 10 measurements in a period of 31 days. Figure 11: Cumulative ammonia emissions (expressed as mg of N pot\({}^{-1}\) day\({}^{-1}\)) between day of fertilization (day 0) and day 10 (cumulative) for ammonium sulfate (AS) and calcium ammonium nitrate (CAN) in a greenhouse experiment for (**a**) maize, and (**b**) grass (mean values are shown and their standard deviation of six repetitions and both soil types, clay and sand).
## 3 Results
### Results on Cerebral Palsy Quality of Life (CP-QOL)  
#### 3.4.1 Adolescents' Quality of Life  
The CP-QOL self-report for adolescents aged 14-18 was administered to three participants. The quality-of-life scores across different domains were analyzed using a two-way mixed ANOVA, comparing the intervention and control groups. The analysis showed no significant differences in the overall quality of life or in any of the specific subscales measured. Table 4 presents the results from the adolescent quality-of-life questionnaire. The data across the three time points (baseline, middle, and end) revealed no statistically significant variations in the quality of life between the groups.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Subscales of the Adolescent Quality-of-Life Questionnaire** & **Assessment Time Points (df = 2)** & **Study Groups (df = 1)** & **Interaction: Time Points \(\times\) Groups** \\ \hline Quality of Life in Adolescents & \(p=0.88\) & \(p=0.44\) & \(p=0.88\) \\ \hline General Well-being and Adolescent Participation & \(p=0.83\) & \(p=0.46\) & \(p=0.85\) \\ \hline Communication and Physical Health & \(p=0.89\) & \(p=0.40\) & \(p=0.93\) \\ \hline School Well-being in Adolescents & \(p=0.84\) & \(p=0.36\) & \(p=0.84\) \\ \hline Social Well-being in Adolescents & \(p=0.76\) & \(p=0.44\) & \(p=0.76\) \\ \hline Perceptions of Functionality & \(p=0.37\) & \(p=0.67\) & \(p=0.37\) \\ \hline \end{tabular}
\end{table}
Table 4: Summary of \(p\) values from the comparative analysis of the adolescent quality-of-life questionnaire subscales, comparing intervention and control groups across three time points (baseline, 4 th intervention week, and 8 th intervention week).  
Figure 2: Graph showing the mean ABILHAND scores (with standard deviations) for two diagnoses, CP-hemiplegia and CP-quadriplegia, in the control group (physiotherapy only) and intervention group (physiotherapy plus Upper Extremity Vibration Therapy using a Whole-Body Vibration Platform) at three assessment time points: baseline, 4 th intervention week, and 8 th intervention week.
## 3 Results and Discussion
### Effect of Pyrolysis Temperature on Biochar Yield and Physicochemical Characteristics
#### 3.1.4 Surface Morphology of Biochar Samples  
The specific surface area usually has a direct relationship with the pyrolysis temperature and the adsorption capacity with different contaminants, but this is not always the case, e.g., [17]. An increase in the surface area with increasing pyrolysis temperature is expected; however, in some cases, high temperatures result in the collapse of the biochar structure due to the porosity generated by the volatilization of various compounds [16, 33]. In the present study, the specific surface area increased from 2 to 4 m\({}^{2}\) g\({}^{-1}\) for BC300 and BC450, respectively; however, an increase in the pyrolysis temperature from 450 to 600 \({}^{\circ}\)C did not improve the surface area, which remained constant (Table 2). Similarly, the main increase in the pore volume was observed between 300 \({}^{\circ}\)C and 450 \({}^{\circ}\)C. As was previously mentioned, above 350 \({}^{\circ}\)C, the process of decomposition of organic and inorganic matter occurs more slowly, which could explain the minimal difference in the structural properties of BC450 and BC600.  
Overall, these values are lower compared to other seaweed biochar [17], which could be explained by the high ash content obtained here, as this is indirectly correlated with the surface area [11, 33]. Finally, the SEM analysis of the surface of the biochar samples (Figure 1) showed a very porous structure for the BC450 sample, with a smaller pore size (12.4 nm), compared to the larger pores registered for the BC300 and BC600 samples (18.1 and 19.9 nm, respectively) (Table 2). Considering the pore size value and the isotherm type IV with an H3 hysteresis loop, all BCs are related to slit-like mesoporous materials (Figures S1-S3) [34].  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Unit** & **BC300** & **BC450** & **BC600** \\ \hline BET surface area & m\({}^{2}\) g\({}^{-1}\) & 2.0 & 4.0 & 4.0 \\ Pore volume & cm\({}^{3}\) g\({}^{-1}\) & 0.009211 & 0.012871 & 0.018898 \\ Pore diameter & nm & 18.1 & 12.4 & 19.9 \\ \hline \end{tabular}
\end{table}
Table 2: Structural characterization of biochar samples from _Macrocystis pyrifera_ produced at different pyrolysis temperatures (300, 450, and 600 \({}^{\circ}\)C).
###### Abstract  
Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change.
## 2 Related Theoretical Foundation and Modeling Framework
### Belief Reliability Modeling Framework  
Firstly, the degradation mechanism of AEMR is analyzed, and the release time is extracted as the critical performance degradation characterization parameter for the stress relaxation of needs. Second, the storage degradation model of AEMR is established based on the degradation eternity principle. Then, based on the margin reliability principle, the margin model is established by combining the performance degradation threshold and combining the margin model with the AEMR's model of storage degradation to establish the margin degradation model. Finally, the uncertainty analysis is carried out from the perspectives of inherent conditions and the cognitive influence of the margin model. The uncertainty is quantified based on uncertainty theory to establish a belief reliability model describing the storage degradation of AEMR. The whole framework is shown in Figure 1.
## 3 Results and Discussions
### Particle Concentrations  
The air cleaner caused different air velocities depending on its position, with different air movement patterns occurring in the room, as shown in Figures 4 and 5. The effect of device location on air movement also directly affected the particle concentration in the environment. Figures 6 and 7 show the variation in particle concentrations formed in the plane in the middle of the room over time. By increasing the blowing speed for both positions of the ACD, the particle concentration decreased faster in the environment. At the end of a 45 min period, for a blowing flow rate of 253.4 \(\rm{m^{3}/h}\), the concentration of particles in the room became almost zero. On the other hand, at a low flow rate (126.7 \(\rm{m^{3}/h}\)), there were regions where the particle concentration was about 10% of the initial concentration in the room after 45 min.  
Figure 6: Particle concentration change over time while the ACD was located near the wall.  
While the ACD was in position 1, the most critical regions in terms of particle concentrations were found to be 1.8 m above the floor of the room. When the ACD was in position 2, the most critical area was 1.5 m in front of the device. Particulate concentrations were found to be effective in this region from the floor to the ceiling of the room. Therefore, when these two different ACD locations need to be evaluated in terms of health, the inhabitants are exposed to these pollutants. From this point of view, position 2 should be preferred over position 1, which would expose occupants to high concentrations from floor to ceiling. This is because the high concentrations formed in position 2 would occur at a higher position than the respiratory organs, even for a standing person.
## 3 Results
### Electrode Fabrication and Structure
Analysis of both the Ru 3p region and the Ru 3d region showed the presence of Ru in the Ru-containing electrode and the absence of Ru in the Mn-Mo electrode (Figure S2b-e). Peak fitting of the Ru 3d\({}_{5/2}\) peak showed the majority of the Ru present as Ru\({}^{6+}\) (Figure 4a). Other Ru oxidation states are possible but are challenging to resolve close to the C1s peak. The assessment accounted for the majority of Ru 3d\({}_{5/2}\) at 282.4 eV as Ru\({}^{6+}\) based on published reference binding energy for RuO3 [32]. Ru\({}^{8+}\) can be difficult to detect because of peak overlap with the C 1s line. In order to verify that we accounted for the majority of detected Ru, we used the Thermo Fisher Nexsa spectrometer elemental sensitivity factors and Ru peak area ratios. The sensitivity factor (SF) for Ru 3d\({}_{5/2}\) and Ru 3p\({}_{3/2}\) was 8.74 and 8.78, respectively, for a SF ratio of 0.996. The calculated peak area after peak fitting the Ru 3d\({}_{5/2}\) was 29,323 counts, whereas the peak area for Ru 3d\({}_{3/2}\) was 29,671 counts for a (Ru 3d\({}_{5/2}\))/(Ru 3d\({}_{3/2}\)) area ratio of 0.988. Since the sensitivity factor and peak area ratios were so similar, the majority of the Ru measured was accounted for at 282.4 eV. Analysis of the Mo 3d region showed Mo\({}^{6+}\) (Figure 4b), and the Mn 3d region showed primarily Mn\({}^{4+}\) (Figure 4c). Further work is needed to quantify the ratio Ru species both before and after testing. Ru stability is dictated by its oxidation state in the catalyst and when oxidized (Ru\({}^{8+}\)) will be far less stable leading to a decrease in catalytic activity [33]. A similar trend would be expected for other transition metal dopants. Figure 3: XRD patterns of the Mn-Mo-0.006 M Ru and Mn-Mo-0.009 M Ru electrodes and associated peak fits. CPS = counts per second.
## 5 Future Work  
Several areas of improvement remain to further address the circuit integration challenges in such a miniaturized injectable implant. Firstly, the two circuit blocks implemented in the two ASICs can be combined monolithically in a future version of the ASIC. Secondly, as an intermediate step to test the performance of the system and for experimental flexibility, the presented system used a COTS microcontroller to implement the control logic and data conversion. We have already implemented an on-chip ADC in one of the ASICs using a successive-approximation register (SAR) topology for lower power consumption. It samples the output voltage of the conditioning block and creates a parallel stream of 8 bits of data. It was difficult to utilize this ADC in the current system, because miniaturized COTS microcontrollers do not offer sufficient number of input/output (IO) ports required for accessing the 8-bit ADC output. However, we plan to leverage this on-chip ADC design in the next iteration of the ASIC where we will also integrate the logic for the control signals in order to completely remove the external COTS microcontroller.  
Moreover, we observed a very low SNR with the existing design in the _in vivo_ experiments. One of the main differences between our custom ASIC and the commercial solution is the differential-to-single-ended versus the fully-differential implementation, which removes most of the systematic noise. The next iteration of our ASIC is expected to reduce this noise and eliminate the need for post-processing. Furthermore, we have only evaluated non-rechargeable batteries in this work due to the unavailability of rechargeable batteries in this form factor. A reasonable next-step is to integrate the wireless charging capability for sustainable use of the implant in animal monitoring. Finally, the current topology is designed for PPG readings where only one wavelength of the LEDs is utilized. However, it could be adapted to perform pulse oximetry by including another LED driver for the second wavelength, changing the control logic accordingly to multiplex the same AFE, and allocating the data from both LEDs in the output stream.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Decision Making
Overall research implies that operators' decision-making is a process that is affected by both human-human and human-machine challenges during remote ship operations.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 2 Construction and Preprocessing of Insulator Image Dataset
### Transmission Line Insulator Image Dataset  
The transmission line insulator image dataset used in this paper was partly derived from the China power line insulator dataset (CPLID) [14], which was already published on the Internet, including 600 normal insulator images and 248 defective insulator images. Another 216 images were collected from the UAV inspection results of high voltage transmission lines. Other images were generated by image augmentation method based on the GraphCut segmentation. The above samples together constitute the transmission line insulator image dataset, with a total of 2403 pictures.  
The transmission line insulator image dataset needs to be annotated before training the object detection model. In this paper, the MRLabeler labeling tool was used to annotate the location and category of the insulator and its defect by a bounding box. Regardless of normal or defective insulators, their location and category were labeled as 'insulator'. While, for defective insulators, the location and category of the defect were labeled as 'defect'. This labeling method makes it possible to learn the spatial relationships between defects and insulators during model training. The annotation format is Pascal VOC [34], a common format in the field of computer vision, and the target location information can be obtained from the upper left vertex coordinates (\(x_{\text{min}}\), \(y_{\text{max}}\)) and lower right vertex coordinates (\(x_{\text{max}}\), \(y_{\text{min}}\)) of the bounding boxes.  
Figure 2: Comparison of images before and after Laplacian sharpening: (**a1**) Original image, (**a2**) Original image, (**b1**) Sharpened image, (**b2**) Sharpened image.
## 2 Materials and Methods
### Leaf Angle Calculation  
Definition and Calculation  
The leaf angle is defined as the clockwise angle from the zenith (\(z\) axis) (shown in Figure 5), which is the angle between the normal vector (\(\overrightarrow{N}\)) and the vector in the \(z\) direction.  
The leaf angle \(\theta_{L}\) is calculated with respect to the normal vector from the fitted plane (\(n_{x}x+n_{y}y+n_{z}z=\text{d}\)) of segmented clusters. The following equation is used to calculate the leaf angle. Multiplication by pi/180 is essential to convert units into degrees:  
\[\theta_{L}=\left(\frac{\pi}{180}\right)\cos^{-1}\frac{n_{z}}{|N|} \tag{3}\]  
Here, \(\overrightarrow{N}\) is the normal vector (\(n_{x},n_{y},n_{z}\)). The normal vector (\(\overrightarrow{N}\)) of the fitted plane was obtained using the least-squares method [26]. The fitted plane has the smallest distance between the plane and the points of clusters. The least-squares method calculates the plane as follows:  
\[\text{M}=\;\frac{1}{n}\sum_{i=1}^{n}(P_{i}-\;\overline{P})(P_{i}-\;\overline{P })^{T},\;\;P_{i}\;\epsilon\;P^{k}. \tag{4}\]  
Figure 5: Illustrated definition of leaf angle (\(\theta\)).  
LAD is commonly proportioned by the number of PCDs, which means that the estimated leaf angle in the region of dense point clouds is likely to dominate the entire computation of LAD. It has been stated that the leaf angle should be weighted by a triangular leaf area for the calculation of the LAD, as the distribution of PCDs is not spatially uniform [10]. However, implementing this is difficult in terms of computational complexity. Instead, the information for calculated leaf angles stored in PCDs was weighted and averaged by cubic cell (0.1 cm) because it was approximately equal to the leaf area. All of the angle values stored in the LAD were calculated with f(\(\theta\)), as follows:  
\[\text{f}\big{(}\theta_{j}\big{)}=\ \theta_{j}\times\ N_{j}/\ N. \tag{5}\]  
Here, f(\(\theta_{j}\)) is a function of the LAD that is used to describe the proportion of the leaf angle in the \(j\)th interval (\(\%\)); \(\theta_{j}\) represents the angle range in the \(j\)th interval (\(5^{\circ}\) is assigned at each interval); \(N_{j}\) is the number of cubic cells containing the angle of the \(j\)th interval range; and \(N\) is the sum of cubic cells in the complete range of intervals.