esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Presentation of Proposed FPO-Guided FMLT Method
### FMLT for TFR Enhancement
With the \(f_{b(\tau,f)}(t)\), the transforming kernel can be written as:  
\[P_{(\tau,f)}(t)=e^{j2\pi\int_{-\infty}^{t+\infty}f_{b(\tau,f)}(t)\text{d}t}=e^{ j\pi\int_{f_{\text{s}}(\tau)}^{t^{\prime}}(t)}(t^{2}-2\tau t) \tag{18}\]  
However, the transforming kernel pair, i.e., \(P_{(\tau,f)}(t)\) and \(P^{*}_{(\tau,f)}(t)\), with modified frequencies may be not orthonormal any more. For this reason, a new transforming kernel denoted by \(K_{(\tau,f)}(t)\) is introduced. The LT can then be re-formulated as:  
\[X(\tau,f)=\int_{-\infty}^{+\infty}x_{win(\tau)}(t)K^{*}_{(\tau,f)}(t)\text{d}t \tag{19}\]  
According to ref. [18], the base set of \(P_{(\tau,f)}(t)\) may be non-orthogonal; however, it has to satisfy the following conditions:  
\[\left\{ \begin{array}{l}\int_{-\infty}^{+\infty}P_{(t,f)}(t_{n})K^{*}_{(t,f) }(t_{m})\text{d}f=\delta(t_{n}-t_{m})\\ \int_{-\infty}^{+\infty}P_{(t,f_{n})}(t)K^{*}_{(t,f_{m})}(t)\text{d}t=\delta( f_{n}-f_{m})\end{array} \right. \tag{20}\]  
where \(\delta(t)\) is the delta Dirac function. Solving Equation (20), one can obtain the transforming kernel \(K_{(\tau,\tau)}(t)\), which is expressed as:  
\[K_{(\tau,f)}(t)=\pi\frac{f^{\prime}_{\text{s}}(\tau)}{f_{\text{s}}(\tau)}(2t -\tau)e^{j\pi f\int_{f_{\text{s}}(\tau)}^{t^{\prime}_{\text{s}}(\tau)}(t^{2}- 2\tau t)} \tag{21}\]  
Then, the FMLT can be obtained:  
\[X(\tau,f)=\int_{-\infty}^{+\infty}x_{win(\tau)}(t)\frac{\pi{f^{\prime}}_{ \text{s}}(\tau)}{f_{\text{s}}(\tau)}(2t-\tau)e^{-j\pi f\int_{f_{\text{s}}( \tau)}^{t^{\prime}_{\text{s}}(\tau)}(t^{2}-2\tau t)}\text{d}t \tag{22}\]  
where \(f_{\text{s}}{}^{\prime}(\tau)\) can be obtained by calculating the derivative of \(f_{\text{s}}(t)\), which is extracted from STFR-resulting TFR (without enhancement) using the FPO algorithm. The FMLT can then be performed and the TFR can be enhanced for a more accurate IF estimation. The constructed bases of FMLT are shown in Figure 2(c). It can be observed that the frequencies of bases match the IF trajectories. The FMLT-resulting TFR of the simulated signal defined by Equation (12) is exhibited in Figure 2(d), where the IF ridge is quite sharp and concentrated. To further test the effectiveness of the proposed method in TFR enhancement, a multicomponent signal is constructed as:  
\[x_{multi}(t)=\sin\!\left(2\pi\!\int_{0}^{t}f_{\text{s}}(\tau)\text{d}\tau \right)+1.5\sin\!\left(2\pi\!\int_{0}^{t}1.3f_{\text{s}}(\tau)\text{d}\tau \right)+1.2\sin\!\left(2\pi\!\int_{0}^{t}1.6f_{\text{s}}(\tau)\text{d}\tau\right) \tag{23}\]  
where \(f_{\text{s}}(\tau)\) is defined by Equation (13). White Gaussian noise is also added
# Geometric Methods and Computational Tools for Switching Structured Linear Systems
## 4 Control and Observation Problems
### Disturbance Decoupling by Output Feedback
: _Let Problem 3 be stated for a switching structured linear system \(\Sigma_{\sigma}((G,\mathcal{E}_{\sigma}),G_{\sigma}^{u}\cup G_{\sigma}^{d},G_ {\sigma}^{y}\cup G_{\sigma}^{z})\) with two modes, where \(G=\{x_{1},x_{2},x_{3},x_{4},x_{5},x_{6}\}\), \(\mathcal{E}_{1}=\{(x_{1},x_{4}),(x_{2},x_{5}),(x_{3},x_{6}),(x_{5},x_{3})\}\), \(\mathcal{E}_{2}=\{(x_{1},x_{4}),(x_{2},x_{5}),(x_{3},x_{6}),(x_{5},x_{1})\}\), \(G_{1}^{u}=G_{2}^{u}=\{x_{1},x_{3}\}\), \(G_{1}^{d}=G_{2}^{d}=\{x_{2}\}\), \(G_{1}^{u}=G_{2}^{u}=\{x_{5}\}\), and \(G_{1}^{z}=G_{2}^{z}=\{x_{4},x_{6}\}\)._  
_The computation of \(V^{*}(\mathcal{E}_{\sigma},G_{\sigma}^{u},G\setminus\cup_{h\in\mathcal{H}}G_{ h}^{z})\) and \(S^{*}(\mathcal{E}_{\sigma},G_{\sigma}^{y},\cup_{h\in\mathcal{H}}G_{h}^{d})\), performed according to Algorithm 1 and Algorithm 2, respectively, gives_  
\[S^{*}(\mathcal{E}_{\sigma},G_{\sigma}^{y},\cup_{h\in\mathcal{H}}G_{h}^{d})=V^{ *}(\mathcal{E}_{\sigma},G_{\sigma}^{u},G\setminus\cup_{h\in\mathcal{H}}G_{h}^ {z})=\{x_{2},x_{5}\}.\]  
_Hence, the condition of Theorem 3 is satisfied and the problem is solvable. A switching essential output feedback \(\mathcal{L}_{\sigma}\), with \(\mathcal{L}_{1}=\{(x_{5},x_{3})\}\) and \(\mathcal{L}_{2}=\{(x_{5},x_{1})\}\), achieves disturbance decoupling, since it cancels those edges with the tail in vertices connected to the measurable output and the head connected to the control inputs contained in the paths originally connecting the disturbance input with the to-be-decoupled output._  
_The directed graph of the given system \(\Sigma_{\sigma}((G,\mathcal{E}_{\sigma}),G_{\sigma}^{u}\cup G_{\sigma}^{d},G_ {\sigma}^{y}\cup G_{\sigma}^{z})\) and of the devised switching essential output feedback \(\mathcal{L}_{\sigma}\) are shown in Figure 9 (left). The directed graph of the compensated system \(\Sigma_{\sigma}((G,\mathcal{E}_{\sigma}\setminus\bigcup\mathcal{L}_{\sigma}), G_{\sigma}^{u}\cup G_{\sigma}^{d},G_{\sigma}^{y}\cup G_{\sigma}^{z})\) is shown in Figure 9 (right)._  
Figure 9: An illustrative example for disturbance decoupling by output feedback.
## 3 Results and Discussion
### Analysis of Features
#### 3.3.2 ANOVA Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**H-C \({}^{\ast}\)**} & \multicolumn{1}{c}{**Cases**} & \multicolumn{1}{c}{**SS \({}^{\ast}\)**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**MS \({}^{\ast}\)**} & \multicolumn{1}{c}{\(F\)**} & \multicolumn{1}{c}{**\(p\)**} & \multicolumn{1}{c}{**VS-MPR \({}^{\ast}\)**} & \multicolumn{1}{c}{\(\eta^{2}\)**} & \multicolumn{1}{c}{\(\eta^{2}\)**} & \multicolumn{1}{c}{\(\omega^{2}\)**} \\ \hline None & MDS\_Coded & \(6.724\times 10^{6}\) & 3 & \(2.241\times 10^{6}\) & 4.369 & 0.006 & 11.878 & 0.109 & 0.109 & 0.083 \\  & Residuals & \(5.488\times 10^{7}\) & 107 & 512,919.89 & & & & & & \\ BF \({}^{\ast}\) & MDS\_Coded & \(6.724\times 10^{6}\) & 3 & \(2.241\times 10^{6}\) & 4.383 & 0.006 & 11.818 & 0.109 & 0.109 & 0.083 \\  & Residuals & \(5.488\times 10^{7}\) & 99.414 & 552,058.505 & & & & & & \\ Welch & MDS\_Coded & \(6.724\times 10^{6}\) & 3 & \(2.241\times 10^{6}\) & 3.885 & 0.014 & 6.303 & 0.109 & 0.109 & 0.083 \\  & Residuals & \(5.488\times 10^{7}\) & 56.059 & 979,014.002 & & & & & & \\ \hline \hline \end{tabular} \({}^{\ast}\)H-C = Homogeneity Correction; SS = Sum of Squares; MS = Mean Square; BF = Brown–Forsythe; VSWPR = Vovk–Selke Maximum p-Ratio.  
\end{table} 
Table 12: The ANOVA analysis of CMS and MTBF_Est. (e.g., contamination control), significantly improves the MTBF of fluid power systems. In contrast, the impact on other performance metrics was not documented. Lastly, using instruments for condition monitoring of fluid power systems proposes similar findings. Specifically, the post hoc pairwise comparison suggests the difference between the CMS1 and CMS4 (\(t_{bmferroni}=-3.391\), \(p_{bmferroni}=0.006\)), ultimately showing that contamination control instruments affect the fluid power system effectiveness, particularly in increasing the MTBF metric.
###### Abstract  
The effect of airflow rate, biomass moisture content, particle size, and compactness on the surface properties of biochar produced in a top-lit updraft gasifier was investigated. Pine woodchips were studied as the feedstock. The carbonization airflow rates from 8 to 20 L/min were found to produce basic biochars (pH \(>\) 7.0) that contained basic functional groups. No acid functional groups were presented when the airflow increased. The surface charge of biochar at varying airflow rates showed that the cation exchange capacity increased with airflow. The increase in biomass moisture content from 10 to 14% caused decrease in the pH from 12 to 7.43, but the smallest or largest particle sizes resulted in low pH; therefore, the carboxylic functional groups increased. Similarly, the biomass compactness exhibited a negative correlation with the pH that reduced with increasing compactness level. Thus, the carboxylic acid functional groups of biochar increased from 0 to 0.016 mmol g\({}^{-1}\), and the basic functional group decreased from 0.115 to 0.073 mmol g\({}^{-1}\) when biomass compactness force increased from 0 to 3 kg. BET (Brunaur-Emmett-Teller) surface area of biochar was greater at higher airflow and smaller particle size, lower moisture content, and less compactness of the biomass.  
biochar; surface properties; functional groups; gasification; TLUD
## 3 Magnetic Field Measurements of Ceramic Fragments by 3 Fluxgate Sensors in 3 Orthogonal Axes
The field flow in the excitation coil of the vertical sensor, \(\mathrm{B_{i}}^{\perp}\) (i = xy,z), is smaller than that of the parallel (\(\mathrm{B_{i}}^{\parallel/\parallel}\approx\lambda.\mathrm{B_{i}}^{\perp}\)) sensor, for the measurement of the same component \(\mathrm{B_{i}}\) of the field. The normalization factor \(\lambda\approx 2\), for the correction of their readings (Appendix B.5), has a similar value in non co-belonging fragments with different magnetization M due to the different orientation of the excitation coils on the specimen surface. The radii \(\alpha^{i}\), \(\alpha^{i}\)/ increase with the magnetization magnitude, M, increase and display similar values in co-belonging shells. * The sensor readings display similar values when the ratios \(\mathrm{r/\ell}\), of the cylindrical detecting areas of the ceramic material by the sensors, as does the measured axial field at the end of the solenoids with the same current and number of spirals, where the ratio of the radius, \(r\), to their height, \(\ell\), is kept constant. The measured magnetic field \(\mathbf{B}\) of the ceramic cylindrical areas is approximated by the magnetic field of a solenoid with the same dimensions. * If \(\mathrm{D\geq d=L.tan\gamma}\) (Figure 11a), the height, \(\ell=\mathrm{L/cosy}\), depends on the thickness, L, of the fragment and the components, \(\mathrm{M_{i}}\) (i = xy,z), are approximated by the sufficient length relationships: \[\mathrm{M_{i}}\ =\ \frac{2\mathrm{B_{i}}}{\mu_{o}}.\sqrt{1+(\frac{\alpha_{i}^{ \perp,//\cdot\cos^{2}\gamma}}{\mathrm{L}})^{2}}\] (4)
* If \(\mathrm{D\leq d}\) (Figure 11b), the height \(\ell\)=D/sin\(\theta\) depends on the distance, D, of the measurement position from the fragment edge in the magnetization direction, M. The components \(\mathrm{M_{i}}\) (i = xy,z) are approximated by the insufficient length relationships: \[\mathrm{M_{i}}\ =\ \frac{2\mathrm{B_{i}}}{\mu_{o}}.\sqrt{1+(\frac{\alpha_{i}^{ \perp,//\cdot\sin^{2}2\gamma}}{2\mathrm{D}})^{2}}\] (5)  
Since \(\alpha_{xy}\)/\(>\)\(\alpha_{\perp}\)/, the computed deviation, \(\theta_{B}\), of the field from the vertical (Figure 11c) at the measurement is less than the angle \(\gamma\) of the magnetization \(\mathbf{M}\). The horizontal components, \(\mathbf{B_{xy}}\), \(\mathbf{M_{xy}}\), of the measured field, \(\mathbf{B}\), and magnetization, \(\mathbf{M}\), diverge at the same angle \(\varphi\) from the horizontal direction of the \(x\)-sensor. The quantities \(\lambda\), \(\alpha^{i}\), \(\alpha^{i}\), \(\gamma\), M, \(\mathrm{M_{i}}\), (i = xy,z) were calculated with remarkable accuracy using the above relationships from the sensor readings and also by the thickness, L, and distance, D, measurements (Appendix B.6) in base specimens of vases 4 and 6.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 4 Pavement System Modular Work Order and Budget Analysis
### Maintenance Dispatch Cost Analysis  
This study analyzes six repair methods based on actual cases from the road management system, focusing on dispatch area and cost to calculate unit repair prices. In the future, cost analysis will be integrated into the Road Information Management Center. This will optimize road maintenance budgets by comparing repair methods, damage types, and costs, improving decision making and resource allocation (Table 5).  
A Road Management System (RMS) employs a systematic approach to routinely collect, store, and respond to decision-making needs, maximizing benefits at minimal cost. This system determines the optimal maintenance strategy by analyzing what treatments provide the best benefits, when and where they should be applied, and which construction methods are most suitable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Type/Severity** & **Low** & **Medium** & **High** \\ \hline
**Pothole** & No treatment; partial or full depth repair & Partial or full depth repair & Full depth repair \\ \hline
**Alligator Cracking** & No treatment; surface sealing; overlay & Partial or full depth repair; overlay; reconstruction & Partial or full depth repair; overlay; reconstruction \\ \hline
**Linear Cracking** & No treatment; seal cracks \(>1/8\) inch wide & Crack filling & Crack filling; partial depth repair \\ \hline
**Block Cracking** & Seal cracks \(>1/8\) inch wide; surface sealing & Crack filling; surface rejuvenation; hot milling with overlay & Crack filling; surface rejuvenation; hot milling with overlay \\ \hline
**Heaving and Settling** & No treatment & Cold milling; shallow, partial, or full depth repair & Cold milling; shallow, partial, or full depth repair; overlay \\ \hline
**Patch and Pipeline** & No treatment & No treatment; replace patch & Replace patch \\ \hline
**Manhole Cover** & No treatment & No treatment; replace manhole and level with road surface \\ \hline \hline \end{tabular}
\end{table}
Table 4: Seven Types of Damage and Corresponding Repair Solutions by the New Taipei City Government Maintenance Engineering Office.  
Figure 12: Dispatch Management Page (Illustrative).  
For example, considering cost as the primary factor, three different scenarios can be identified:  
Zone 1: A 20 m road segment contains a 1-square-meter pothole.  
Zone 2: A 20 m road segment has three linear cracks, each 1 m in length.  
Zones 3 and 4: A 40 m road segment exhibits alligator cracking over an area measuring 2 m wide by 10 m long.  
By utilizing the system's database for cost-benefit analysis, the cost difference between preventive maintenance and routine maintenance is illustrated in the Figure 13. Additionally, the system can serve as a basis for analyzing the effectiveness of various construction methods in addressing pavement distress (Table 6).
## 1 Introduction  
CO\({}_{2}\) is the primary substrate for photosynthesis, and it plays an essential role in crop growth, development, and productivity. Using an anthropogenic method to increase the CO\({}_{2}\) concentration in an enclosed greenhouse called CO\({}_{2}\) fertilization [1, 2] increases the growth and yield of the crop plants [3]. As an essential gas fertilizer, CO\({}_{2}\) has been widely used in greenhouses for the last century [4, 5]. Because of the characteristics of CO\({}_{2}\) diffusion, CO\({}_{2}\) fertilizer has mainly been used in protected cultivation facilities. However,many research findings have indicated that elevated CO\({}_{2}\) concentrations have appropriate positive effects on field crops such as soybeans, rice, and maize [6; 7; 8]. [PERSON] et al. [9] summarized the results of the published observations 30 years under free-air CO\({}_{2}\) enrichment (FACE), which indicate that long-term CO\({}_{2}\) enrichment has different impacts between the C\({}_{3}\) and C\({}_{4}\) crops. There is an urgent need to conduct studies on the implementation of CO\({}_{2}\) enrichment for field crops in open fields.  
Micro-nanobubbles (MNBs) are bubbles with sizes smaller than 50 \(\upmu\)m, giving them diameters between those of microbubbles and nanobubbles (NBs) [10]. In comparison to normal bubbles, MNBs have many specific characteristics, such as a long storage time, strong gas solubility, strong adsorption [11], a relatively large specific surface area, and a large pressure [12; 13]. In recent years, MNBs have been of great value in water treatment, medical and health care, precision chemical reactions, and agricultural activities [14; 15; 16; 17]. [PERSON] et al. [18] reported that air, oxygen (O\({}_{2}\)), nitrogen (N\({}_{2}\)), and CO\({}_{2}\) could be used as reactive oxygen species (ROS) in nanobubbles and influence seed germination and plant growth. In addition, the oxygenation by air and the O\({}_{2}\) MNBs in water, which can increase the oxygen content, have been shown to increase the yield and quality of crops, as well as the irrigation water use efficiency [19; 20]. [PERSON] et al. [21] conducted contrast tests to prove that water with CO\({}_{2}\) MNBs and air MNBs could positively impact vegetable growth. However, the formation mechanism and propensity of CO\({}_{2}\) MNBs in water remain unclear.  
Therefore, this work presents the preparation process and characteristics of CO\({}_{2}\) MNB water, in which CO\({}_{2}\) is injected into a generator by utilizing the dissolved gas release method to produce large amounts of CO\({}_{2}\) MNBs. The influences of the vapor-liquid ratio, MNB generator running time, and water temperature on the CO\({}_{2}\) content in the MNB water were studied. Primarily, the optimization experiments were conducted to obtain the optimal range of CO\({}_{2}\) in the MNB water. Additional research was conducted on the particle size distribution, pH changes, life cycle, and content changes of the CO\({}_{2}\) MNB water, which will assist in better understanding the CO\({}_{2}\) MNB water.
## 4 Discussion
### Effectiveness of SuDS  
The evidence obtained from this study suggests a mixed response to the effectiveness of SuDS. The ultrasonic sensors, installed at the street scale to quantify the volume of runoff attenuated by two SuDS tree pits, recorded a statistically significant decrease in manhole flow level downstream of the deployed interventions equating to a 25-30% reduction in mean flow level. However, the data obtained was patchy, with significant gaps arising as a consequence of signal obstacles and challenges with installing monitoring equipment in a retrofit scenario. Furthermore, any effect on the frequency of CSO spills, reductions in peak flow, or reductions in discharge rate were not evaluated and, therefore, it is difficult to ascertain the full impact of the interventions. Whilst there were challenges with quantitatively evidencing the benefits of SuDS, there was significant support for partnership working and knowledge sharing throughout the project, particularly in relation to innovative approaches and activities which could be promoted, or co-delivered, between networks and partnering organisations. The sharing of case studies such as this, is vital for capacity building and identifying learning opportunities for best practice. A study of this scale was only possible due to the collaboration and buy-in of stakeholders with adequate authority to grant access to both the physical infrastructure and the communities with whom the SuDS interventions would directly affect.  
At the property scale, quantitative evidence for the performance of the rear-yard planners was not part of the study design. Therefore, no data were collected on the quantity and/or quality of water attenuated or discharged from the planners. This is a recommendation for future study. Qualitative evidence was obtained through DCC engaging with local residents and stakeholders, and findings suggest that whilst there was a high level of support for these types of interventions, communication around their maintenance and purpose could be improved. The effectiveness of future SuDS schemes is dependent on understanding both the successes, and also the challenges encountered, in deploying SuDS. Many of the difficulties encountered in monitoring the SuDS interventions illustrate some of the reasons why evaluation of SuDS is not as extensive as researchers and practitioners would like. Dissemination of studies, such as this, provide a valuable opportunity to address this.
# Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
## 2 Data and Methods
### Swarm Observations and Preliminary Analysis
We used the non-orthogonal Quasi-Dipole (QD) system of magnetic coordinates [74; 75; 76], as is common practice when studying processes that are determined or crucially influenced by the geomagnetic field. The position of the Sun was accounted for by using Magnetic Local Time (MLT) instead of UTC time. For this purpose, we used a procedure based on the work of [PERSON] et al. [75], which essentially works in two steps: (1) it firstly transforms geographical coordinates and satellite altitude to geodetic coordinates; then, (2) it performs the transformation from geodetic coordinates to QD-MLT coordinates [75]. This preliminary analysis allowed us to grid data in bins of QD magnetic latitude versus MLT.
# Experimental Study of the Volumetric Error Effect on the Resulting Working Accuracy--Roundness
## 4 Discussion  
In Figure 18, a dependence can be seen between the roundness error of the workpiece and the machined diameter for machining circular parts (Figure 18a). Furthermore, with WP2, a relatively constant value of roundness can be observed for all inspected diameters. With WP1, an improvement in RONt can be seen with the decrease of machining diameter. Circularity errors DBB1 and DBB2 then show a linear relationship between the size of the error and the machined diameter. The size of the circularity error increases with the increase in machined diameter.  
Figure 18b shows a graph of the improvement in percentage versus the volumetric accuracy (VA), circularity (DBB) and roundness (WP) before the volumetric compensation and after the volumetric compensation of the machine. With outside milling technology and diameters of 300 and 200 mm, a strong linear relationship can be seen between WP-DBB-VA with determination indexes I2 equal to 0.9998 (300 mm) and 0.992 (200 mm), respectively. On the 100 mm diameter, even a 4% deterioration of roundness can be seen. This deterioration can be attributed to another type of machining technology and clearance specification due to system stiffness. A description of this behaviour will be the subject of further research.  
Figure 19 shows the dependence between the squareness measured in the XY machining plane without load from the Ballbar device (DBB1 and DBB2) and the RONt obtained from workpiece measurement (TH1 and TH2). A higher correlation rate can be seen for DBB1 and TH1. This can be attributed to a higher squareness error value, which also results in a higher rate on the workpiece. In contrast, the squareness error of DBB2 was reduced by 72% compared to DBB1. Here, squareness error does not have such a dominant effect on the resulting RONt, which is also reflected by the correlation between DBB2 and TH2. The difference between DBB2 and TH2 may be due to the fact that, with less geometric error, the proportion of the error wedged from the cutting forces increases.  
Figure 19: Description of roundness and squareness errors by machined diameter size.  
Figure 18: (a) Diagrams of the dependence between machine circularity error and (b) workpiece roundness error.  
The acquired knowledge about the behaviour of machine tools in terms of geometric accuracy can be further used to predict the dimensional and shape errors of workpieces. The results are applicable to finishing technologies where machining forces are negligible and do not burden the machine with deformation from static compliance.