esa-sceva/satcom-chunk-collection huggingface dataset:
# A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks
## 2 Antenna Design Analysis
### Parametric Study of the Presented Design  
The presented design is finalized after performing several parametric optimizations on different variables, as shown in Figure 4. The first parametric study is performed on the length and width of the ground plane. By increasing the length of the ground plane \"Lg\" from 10 mm to 12 mm, the impedance matching of the antenna improves with suitable bandwidth, and when the width of the ground plane \"wg\" is varied from 9 mm to 11 mm, then the bandwidth of the antenna is increased from 4.1 GHz to 8 GHz. The next parametric study is performed on the width of the seedling \"wr\". Gradually increasing the width of the feedline improves the impedance bandwidth from 5.8-8 GHz. A parametric study of the C-shaped radiator is also performed. By varying the lengths \"L6\" and \"L3\", the bandwidth of the antenna is improved, as depicted in Figure 4.  
The surface current densities of the UWB antenna at different frequency bands are taken into consideration. This indicates that the antenna plays a significant role in making it resonate at the desired frequency bands. For example, the surface current density at 3.5 GHz is illustrated in Figure 5a. Most of the current seems to flow through the radiator at 3.5 GHz (see Figure 5b), while at 4.1 GHz, the current only flows through the C-shaped resonator and some amount of current through the feedline (see Figure 5c). At 8 GHz, the current flows through the outer lower edge of the printed monopole and some amount of current flow through the CPW ground at 10.5 GHz (see Figure 5d).  
Figure 4: _Cont._  
Figure 4: Different parameters optimization: (**a**) variation in “Lg”, (**b**) variation in “wg”, (**c**) variation in “wf”, (**d**) variation in “L6”, (**e**) variation in “L3”.  
Figure 5: Surface current density (**a**) at 3.5 GHz, (**b**) at 4.1 GHz, (**c**) at 8 GHz, (**d**) at 10.5 GHz.
## 5 Conclusions  
This study provides the life cycle assessment of solar-thermal, solar PV, and biogas systems on a common basis of 1 kWh of useful energy, combining cumulative energy demand and ReCiPe single-score results. The study concludes that manufacturing steps mirrored in CED by high fossil and biomass shares dominate endpoint impacts for all three systems, but the scale differs from approx. 1 mPt for solar-thermal, 72 mPt for PV, and 18 mPt for biogas, highlighting that battery intensive PV carries the largest single score burden despite near-zero operational emissions. However, the selection of an optimal system for a various area still greatly depends on their local context, infrastructure, policy support, and discussed trade-offs. Solar thermal suits industrial use in sunny regions like Africa, India, or South America, etc., for heating and cooking needs. Solar PV fits off-grid rural or remote areas due to low cost and modularity. Biogas systems are best suited in agrarian or livestock dense regions, converting organic waste into cooking fuel, electricity, and fertilizer, supporting sustainability and circular economy.  
Conceptualization, S.T., U.N.M. and V.K.; Methodology, S.T. and R.K.C.; Validation, S.T. and D.S.; Formal analysis, S.T. and U.N.M.; Investigation, S.T., D.S. and U.N.M.; Resources, D.S. and R.K.C.; Data curation, U.N.M.; Writing--original draft, S.T. and U.N.M.; Writing--review and editing, S.T., D.S., V.K. and R.K.C.; Visualization, U.N.M.; Supervision, V.K. and R.K.C.; Project administration, V.K. and R.K.C. All authors have read and agreed to the published version of the manuscript.  
The publication charges for this article have been funded by a grant from the publication fund of UiT the Arctic University of Norway.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This research acknowledges the support by the DEVISE (ERA-Net_SES/106231) and PEERS (UTF 2020/10131) at UiT the Arctic University of Norway. The authors also acknowledge the support of G. S. Sailesh Babu, DEI Agra and IIT Roorkee for providing the study facilities.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Scenario** & **Treatment Route** & **PV Modules (mPt/kWh)** & **Li-Ion Batteries (mPt/kWh)** \\ \hline BAS & Engineered landfill & 0.031 & 0.012 \\ OLR & Glass- and aluminum-only recovery & 0.018 (\(-\)41\%) & — \\ CLR & Full hydrometallurgical recycling & 0.014 (\(-\)55\%) & 0.005 (\(-\)58\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Endpoint impact of three end-of-life scenarios.  
**Conflicts of Interest:** [PERSON] was employed by Arctic Experts AS and was a part of DEVISE project. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 9 Conclusions  
This paper proposed a on-ground retracking method to reduce on-board tracking frequency. The sensitivity of the feature parameters of non-retracked waveform to sea surface height and wind speed are discussed. Compared to the aligned waveform, the sensitivity of non-retracked waveform to wind speed is decreasing. The proposed method was verified through real UK-DMC, UK-TDS-1 data and simulation. After retracking non-aligned waveform of UK-DMC, the MSEs between aligned and retracked waveform reduced 0.026 and 0.044 from 0.110 and 0.156 for 1 s and 3 s incoherently averaged waveform, respectively. For UK-TDS-1 data, the MSEs decreased from 161.01 and 227.34 to 70.10 and 61.80. Moreover, the sensitivity of retracked waveform to wind speed was improved. The retrieval performance of sea surface height and wind speed were analyzed. The results showed that the best performance was obtained using aligned waveform; for sea height retrieval, the standard deviation of the estimated height using retracked waveform could reduce over 5 and 10 times than using non-retracked waveform with the TPR of 1 s and 3 s, respectively; the RMES of retrieved wind speed using retracked waveform significantly are reduced compared to the non-retracked waveform. Finally, the dependence of retrieval accuracies using retracked waveform on the SNR was discussed. It was concluded that when the SNR is lower than 3 dB, the retrieval performance rapidly get worse. Above all, proposed method could be implemented on-ground to reconstruct delay waveform from the one corrupted by the dynamic of LEO and GNSS satellite.  
In fact, the most complete GNSS-R observable is two-dimensional Delay-Doppler Map (DDM). As mentioned as in [22], not only the delay, but also Doppler frequency varies with the motion of the LEO and GNSS satellite. Therefore, it is needful to adjust correlation window in delay and Doppler domain on board or to recover the DDM from distorted one. In future, our work will be to develop a two-dimensional motion distortion model of the DDM, and based on this, propose an approach to reconstruct DDM.  
The authors would like to thank Surrey Satellite Technology Ltd. for providing UK-DMC data in this paper and the reviewers for reviewing to improve the quality of the paper.  
[PERSON] proposed the approach and drafted the manuscript; [PERSON] put forward the architecture of the paper; [PERSON] processed the spcceborne data; [PERSON] derived the relationship between the DDCR and the Doppler difference of reflected and direct signals. All of the authors contributed to the result discussion and paper writing.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 16: Dependence of retracking performance for wind speed on SNR.
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 5 Detection of Anomaly Zones by Statistical Methods
### Anomaly Depth  
The ER of the natural ground in Section 1, where no anomaly zones existed, increased with the depth and exhibited a relatively constant value according to position for all depths, as shown in Figure 16a. The ER also increased with depth and was nearly constant according to position in Section 3, whereas the ER significantly changed at the location of the anomaly zone, as shown in Figure 16b. As a result, the positions of the anomaly zones can be found by comparing the trend of the cubic curves generated at each measurement position. However, the ER according to position cannot be expressed by a cubic curve because the ER was nearly constant for each measurement position at all depths. Therefore, the relative standard deviation (RSD) of the ER was used to identify the depth of the anomaly zone quantitatively.  
Figure 16: ER according to position at each depth. (**a**) Absence of anomaly zone (Section No. 1). (**b**) Existence of anomaly zone (Section No. 3).  
The RSD is generally used to compare the relative magnitudes of the standard deviation of data sets with different means. The RSD is calculated by dividing the standard deviation by the arithmetic mean as shown in Equation (2).  
\[RSD=\frac{\sigma}{\overline{x}} \tag{2}\]  
where  
\(RSD\) = relative standard deviation;  
\(\sigma\) = standard deviation;  
\(\overline{x}\) = arithmetic mean.  
The outliers were found by distributing the RSDs calculated by the means and standard deviations of the ER according to position on a box plot by depth. In Section 1, where no anomaly zones existed, no outliers were identified on the box plot, as shown in Figure 17a. However, in Section 3, where an anomaly zone existed, the depth of the actual anomaly zone was identified as the outlier, as shown in Figure 17b.  
In Section 2, where a single large anomaly zone was simulated, the measured depths of the anomaly zones occupied 3/7 of the total measured depths. In this case, the depth where an anomaly zone did not exist was plotted as an outlier, as shown in Figure 17c. In cases where several anomaly zones exist at various depths in the same position, even though this case was not prepared in the testbed, the depths at which an anomaly zone does not exist may also be presented as outliers on the box plot. Therefore, a sufficient number of measured depths should be determined to facilitate surveying of the locations where a large anomaly zone or several anomaly zones are expected.  
Figure 17: _Cont._
###### Abstract  
This paper presents a fully integrated, four-stack, single-ended, single stage power amplifier (PA) for millimeter-wave (mmWave) wireless applications that was fabricated and designed using 45 nm complementary metal oxide semiconductor silicon on insulator (CMOS SOI) technology. The frequency of operation is from 20 GHz to 30 GHz, with 13.7 dB of maximum gain. The maximum RF (radio frequency) output power (Pout), power-added efficiency (PAE) and output 1 dB compression point are 20.5 dBm, 29% and 18.8 dBm, respectively, achieved at 24 GHz. The error vector magnitude (EVM) of 12.5% was measured at an average channel power of 14.5 dBm at the center of the the 3 GPP/NR (third generation partnership project/new radio) FR2 band n258-i.e., 26 GHz--using a 100 MHz 16-quadrature amplitude modulation (QAM) 3 GPP/NR orthogonal frequency division modulation (OFDM) signal.  
power amplifier; stacked power amplifier; CMOS SOI; mmWave; 3 GPP; NR +
Footnote †: journal: Applied Sciences
# Photocatalytic Properties of Immobilised Graphitic Carbon Nitride on the Alumina Substrate
## 2 Materials and Methods
### Photocatalytic Tests  
To examine the photocatalytic activity of the immobilised photocatalyst \(g\)-C\({}_{3}\)N\({}_{4}\), the photodegradation of the MB dye (Table 2) with a concentration of 10 mg.L\({}^{-1}\) was investigated under simulated solar light irradiation. Osram's Ultra Vitalux lamp (300 W) was used as a light source. The MB dye aqueous solution and immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) photocatalyst were stirred in the dark for 30 min to achieve an adsorption-desorption equilibrium between the organic pollutant and the photocatalyst. All the photocatalytic experiments were carried out under the same conditions at room temperature. At defined intervals, 15 min, the absorbance was monitored by UV-Vis spectroscopy on a Lambda 950 UV/VIS/NIR spectrophotometer (PerkinElmer, Waltham, MA, USA) in the 200-750 nm range using a quartz cuvette (Hellma Analytics, Stuttgart, Germany) with a path length of 10 mm. Under identical experimental conditions, the activity of bare alumina substrate was also investigated and compared with immobilised photocatalyst \(g\)-C\({}_{3}\)N\({}_{4}\) on the substrate.  
The kinetics study of MB photocatalytic decolourisation and degradation is described by pseudo-first-order kinetic equation. The integrated form of the pseudo-first-order kinetic model is expressed in Equation (1) [25]:  
\[\ln(A_{0}/A_{t})=k\cdot t \tag{1}\]  
where \(A_{0}\) and \(A_{t}\) express the initial and remaining MB dye absorbance for a specific interval of time and \(k\) represents the first-order rate constant (min\({}^{-1}\)) of photocatalytic degradation of MB dye.  
To investigate the reusability of immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) catalyst, recycling experiments were carried out for three cycles under the same experimental conditions. After each cycle, the immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) catalyst was washed with ultrapure water and ethanol, and then dried at 60 \({}^{\circ}\)C.
## 4 Discussion
### The Influence of Scene Geometry
It is worth noting that although the alignment process based on the optical image in the georeferencing phase (collocation--see Section 2.3) intends to reduce the differencesbetween SAR-SLs derived from ASC and DESC tracks due to these scene geometry issues (e.g., shadow/layover--see Figure A7), it does not entirely eradicate them.
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 2 Excessive Transverse Deformation Cases in Ghuangzhou Metro
### Tunnel Reinforcement and Subsequent Monitoring Data Analysis  
In response to these findings, the relevant departments initiated a special reinforcement project. The specific steel plate reinforcement method for the affected tunnel includes the following steps: firstly, repairing the tunnel segment cracks in the tunnel; secondly, pasting aramid fiber cloth to the cracks, ensuring joint stress between the shield tunnel segment and the aramid fibers; and lastly, introducing an additional steel plate inside the tunnel as a structural reinforcement measure, enabling the combination of the steel plate and the shield tunnel segment to establish a composite structure. In order to guarantee the tunnel's structural integrity during the routine operation of the reinforced tunnel, it is imperative to periodically monitor the deformation and internal forces of the reinforced composite structure. Due to space limitations, only the results from ring No. 7 of the upstream line, which exhibits the highest ellipticity and most significant damage, are displayed. High-precision vibrating string surface strain gauges facilitate this monitoring, with stress-strain monitoring points positioned in five directions within each tunnel ring section. The variations in internal force at each point within the reinforced composite structure of ring No. 7 over time are depicted in Figure 5.  
During the first year after the reinforcement of the shield tunnel with steel plates, the subway tunnel operated normally. Figure 5 illustrates that the overall bearing characteristics of the concrete tunnel-steel plate composite structure remain relatively stable. Although some measurement points display minor stress fluctuations due to external influences, the overall stress level remains minimal. The monitoring data accounts not only for changes in the internal force of the composite structure but also monitors the tunnel's transverse deformation, settlement, and other parameters. All these values remain within 3 mm, indicating minimal deformation and confirming that they are within the safe range.  
Figure 5: Ring 7 of the uplink. Note: the positive stress value indicates tensile, while the negative value indicates compressive.  
This data underscores that the structure remained safe and stable throughout the period following the reinforcement.
# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 5 Results
### Experimental Analysis of Borehole Wall Defect Detection
#### 5.2.2 Quantitative Estimation of Crack Image  
The detection of a crack image is to obtain the specific characteristic parameter information of a crack, mainly including the width and length of the crack. In the actual detection, although the orbit is fixed, the distance between the detection module and the borehole wall will still change slightly in actual operation, resulting in a gap between the actual distance between the detection device and the borehole wall and the theoretical value. Therefore, it is necessary to first study the changing law of pixel equivalent calibration under variable distance, and then complete the detection of crack images.  
(1) Pixel equivalent calibration  
Pixel equivalent calibration is the actual physical size represented by a single pixel in an image. In this section, the relationship between the pixel equivalent calibration and the distance of the camera used in this case is measured by experiments, as shown in Figure 26.  
(2) Crack image detection  
Figure 25: Test process.  
Using the intrinsically safe camera selected above, borehole wall fracture images are obtained at distances of 300 mm, 350 mm and 400 mm from the borehole wall. According to the results of the above pixel equivalent calibration, \(\sigma\) is 0.35 mm/pixel, 0.41 mm/pixel and 0.46 mm/pixel. By quantitatively estimating different fracture images, the detection results of borehole wall fractures are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Original Image & & & \\ \hline Treatment result & & & \\ \hline Maximum width detection & & & \\ value/mm & & & \\ Measured value of maximum & & & \\ width/mm & & & \\ Accuracy & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shaft wall image processing algorithm.  
Figure 26: Pixel equivalent calibration fitting curve.  
According to the detection experiments of the above-mentioned crack defect images, the model has good accuracy (\(\geq\)92%) in calculating the width and length of cracks and can realize the detection of shaft wall defects.
## 3 Preliminaries  
### The FB-Based Programming Model  
The FBs in IEC 61499 are categorized as basic FB (BFB, state machines), service interface FB (SIFB platform dependent implementations), and composite FB (CFB)/subapplication (encapsulating function networks). Our work is based on BFB, so only the BFB model is introduced here.  
Definition 1: A basic function block, \(\mathsf{bfb}\), is defined as an 8-tuple:  
\[bfb=\langle E1,EO,DI,DO,INvar,\omega_{i},\omega_{o},ecc\rangle\]  
where E1 and EO are sets of event inputs and outputs, respectively; \(DI\), \(DO\), and \(INvar\) are sets of data inputs, data outputs, and internal variables, respectively; \(\omega_{i}\) and \(\omega_{o}\) are functions for associating events with data, \(\omega_{i}:EI\to 2^{DI}\) and \(\omega_{o}:EI\to 2^{DO}\); and \(\mathsf{ecc}\) is the execution control chart, which works in collaboration with the finite-state-machine behavior.  
Definition 2: An execution control chart, \(\mathsf{ecc}\), is defined as a 5-tuple:  
\[ecc=\langle ES,es_{0},ET,EA,\rho\rangle\]  
where \(ES\) is a set of execution control (EC) states; \(es_{0}\) is the initial state, \(es_{0}\in ES\); \(ET\) and \(EA\) are sets of EC transitions and actions, respectively; and \(\rho\) is an assignment function, \(\rho:S\to 2^{EA}\).  
Definition 3: An execution control action, \(\mathsf{ea}\), is defined as an ordered pair:  
\[ea=\langle algo,eo\rangle\]  
where \(algo\) is an algorithm and \(eo\) is an event output, \(eo\in EO\). Note that \(eo\) can be absent.  
Definition 4: An execution control transition, \(\mathsf{et}\), is defined as a triple:  
\[et=\langle ss,\sigma,ds\rangle\]  
where \(ss\) is a source EC state, \(ss\in ES\); \(\sigma\) is a condition function that calculates \(\sigma:(EI\cup\{1\})\times 2^{DI}\times 2^{DO}\times 2^{INvar}\to\{true,false\}\); and \(ds\) is a destination state, \(ds\in ES\). To guarantee the determinism of event-triggered systems, a time stamp is introduced to extend the FB-based model in [18], as defined below.  
Definition 5: An event model, \(\mathsf{e}\), is defined as a triple:  
\[e=\langle T_{dl},T_{last},P\rangle\]  
where \(e\in EI\cup EO\); \(T_{dl}\) is the deadline for the processing of this event, representing the worst-case execution time; \(T_{last}\) is the last time this event was updated, used to distinguish the order of events in the queue; and \(P\) is a priority, used for identifying simultaneous events. More exhaustive specifications can be found in [18].
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 4 The Proposed Filtering Algorithm Based on Chip
### DKF and PKF Filtering Algorithm  
DKF and PKF are an algorithm designed by \(\alpha\)-\(\beta\) filtering algorithm. According to different environments use different judgment conditions. Table 1 shows that the final-stable value of KG will affect the value of covariance \(\mathbf{R}\) and \(\mathbf{Q}\) according to a different environment, change judgment conditions. PKF uses the percentage of current KG with average of past eight KGs to make judgments. DKF uses the difference between current KG with average of past eight KGs to make judgments. On the basis of the results in Figure 3, the training lengths are 18, 21, and 23 for the variances of 5, 10, and 15, respectively. According to the simulation results, there is a tradeoff between accuracy and hardware cost, and the window size was eight for the proposed approaches. Namely, after convergence, the sequence values of 8 KGs are averaged in this article. For example, in terms for the variances of 5, the first average value is based on the sampling KG value of iteration number from 18 to 25 for the present state 25, and the second average value is based on the sampling KG value of iteration number from 19 to 26 for the present state 26. Users can choose to use PKF or DKF according to the current environment, and every one hundred points will be re-judged. It is not lead to inaccurate tracking due to environmental alteration. Under  
Figure 4: Flow diagram of the proposed approach.  
the environment with larger KG, use PKF, as shown in Equation (21). Conversely, if you encounter environment with smaller KG, use DKF, as shown in Equation (22). Selecting the appropriate judgment equation according to different environments can greatly reduce the tracking error and thus obtain more accurate position results.  
\[\{\mathbf{K}_{k}/[\left(\mathbf{K}_{k-1}+\mathbf{K}_{k-2}+\mathbf{K}_{k-3}+ \mathbf{K}_{k-4}+\mathbf{K}_{k-5}+\mathbf{K}_{k-6}+\mathbf{K}_{k-7}+\mathbf{K} _{k-8}\right)/8]\}\leq 1.005 \tag{21}\] \[\{\mathbf{K}_{k}/[\left(\mathbf{K}_{k-1}+\mathbf{K}_{k-2}+ \mathbf{K}_{k-3}+\mathbf{K}_{k-4}+\mathbf{K}_{k-5}+\mathbf{K}_{k-6}+\mathbf{K} _{k-7}+\mathbf{K}_{k-8}\right)/8]\}\leq\ 16 \tag{22}\]