esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems
## 5 Simulation and Discussion
Therefore, the curve of our proposed method in Figure 5 becomes worse than that in Figure 2. However, the curves of the two LS methods and our proposed method still overlap in Figure 5. This shows the channel estimation efficiency of our proposed method is still similar to those of the LS methods [10,11], even if the number of maximum channel delay points is greater than \(N_{p}/2\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Complex Multiplication**} & \multicolumn{2}{c}{**Stored Complex Number**} \\ \cline{2-5}  & **Number** & **Example** & **Number** & **Example** \\ \hline \multirow{2}{*}{Our method} & \(N_{p}^{out}N_{p}^{I}+(N_{p}/2)\text{log}_{2}(N_{p})\) & 2695 & \(N_{p}^{out}N_{p}^{I}\) & 2247 \\ \hline
[9,11] & \(0.5N_{p}N_{p}^{out}\) & 6848 & \(0.5N_{p}N_{p}^{out}\) & 6848 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of different methods on computational complexity and memory resource usage.
## 4 Experiment
### 3D JPS Algorithm Trajectory Optimization Experiment Based on Open Dataset
The paths optimized based on the parent node transfer law are 4.8% to 8.1% shorter than those obtained by the original 3D JPS algorithm, and the total turning angle of the paths is reduced by 51.6% to 93.2%, with a significant effect on the overall optimization of the paths. In terms of the time and space complexity of the algorithm, the newly added functional modules of the algorithm will inevitably lead to an increase in the running time of the algorithm and an increase in memory usage. The optimized algorithm based on the parent node transfer law increases the computing time by 1.4% to 3.2% and the memory usage by 0.0045% to 0.012% over the original 3D JPS algorithm. In order to ensure the continuity of position, velocity, acceleration, and other information of the final path at each path interruption point, the optimized path based on the parent node transfer law needs to be further optimized by polynomial interpolation. The seventh-order polynomial-interpolation method based on minimum snap used in this paper achieves the optimal smoothness and continuity in the path without affecting the overall computing time and memory consumption of the algorithm as much as possible. As can be seen in Table 1, the paths optimized by the parent node transfer law and polynomial interpolation only increase in computing time by 2.5% to 5.4% and memory usage by 0.0014% to 0.0018% compared to the 3D JPS algorithm.
## Appendix A  
When the three attitude angles of yaw, rolling and pitch angle are all 0, the satellite body coordinate system coincides with the orbit coordinate system. When the attitude angle is not zero, the orbit coordinate system \(OX_{o}Y_{o}Z_{o}\) is converted to the satellite body coordinate system \(OX_{b}Y_{b}Z_{b}\) by three coordinate rotations, and the conversion order is \(Z(\psi)-X(\theta)-Y(\theta)\), the transformation matrix \(\mathbf{C}_{orbit2 star}\) can be expressed as:  
\[\mathbf{C}_{orbit2 star}=\begin{bmatrix}\cos\theta&0&-\sin\theta\\ 0&1&0\\ \sin\theta&0&\cos\theta\end{bmatrix}\begin{bmatrix}1&0&0\\ 0&\cos\theta&\sin\theta\\ 0&-\sin\theta&\cos\theta\end{bmatrix}\begin{bmatrix}\cos\psi&\sin\psi&0\\ -\sin\psi&\cos\psi&0\\ 0&0&1\end{bmatrix} \tag{1}\]  
where \(\psi\) is the yaw attitude angle, \(\theta\) is the rolling attitude angle, and \(\theta\) is the pitch attitude angle.  
The angular relationship of the antenna coordinate system \(O_{a}X_{a}Y_{a}Z_{a}\) with respect to the satellite body coordinate system \(OX_{b}Y_{b}Z_{b}\) is given by stand-alone precision measurement (or theoretical design value). The Table 1 below gives the angle measurement of the three coordinate axes of the antenna coordinate system and the three coordinate axes of the satellite body coordinate system.  
According to the precision measurement result, the conversion matrix \(\mathbf{C}_{star2 antenna}\) of the satellite body coordinate system \(OX_{b}Y_{b}Z_{b}\) to the antenna coordinate system \(O_{a}X_{a}Y_{a}Z_{a}\) is:  
\[\mathbf{C}_{star2 antenna}=\left[\begin{array}{ccc}\cos\theta_{1}&\cos\theta _{2}&\cos\theta_{3}\\ \cos\theta_{4}&\cos\theta_{5}&\cos\theta_{6}\\ \cos\theta_{7}&\cos\theta_{8}&\cos\theta_{9}\end{array}\right] \tag{2}\]
## 8 Application Example: 2023 Bangladesh Flooding Season
### Total Annual Flood Duration Analysis  
How long a particular patch of land was inundated is a relevant metric for assessing the impact of flooding on agricultural crop yields, building integrity, transportation infrastructure, and other landscape functions. Furthermore, it can help in the assessment of the evolution of an inundation event and can be used to support disaster mitigation and prevention by indicating areas most severely affected by floods [15].  
We estimated the _total annual flood duration (TAFD)_from our 2023 HydroSAR HYDRO30 data time series by following the approach published in [15]. According to this approach, the TAFD is computed per pixel using all data generated within a calendar year. To provide an accurate count of the inundation days, we allowed for the possibility that a pixel was covered by more than one flood event, accounting for flood-free periods in between. Therefore, similar to [15], we first calculated the duration of each of the \(n\) (\(n\geq 1\)) inundation events separately and then summed all \(n\) event durations to arrive at the total flood duration in days:  
\[TAFD_{i}=\sum_{i=1}^{n}D_{d,i}-D_{c,i} \tag{4}\]  
where \(D_{c,i}\) is the first day and \(D_{d,i}\) is the last day inundation was observed for an event \(i\).  
Figure 19(a) shows the results of this analysis for the 2023 flood season. For ease of visualization, the TAFD results were divided into four categories: white pixels were never inundated; green areas were inundated for less than 30 days; red areas were inundated for \(\geq\)30 and \(<\)100 days; and blue pixels were water-covered for \(>\)100 and \(<\)200 days.  
It can be seen that large areas of the low-lying Sylhet division in northeastern Bangladesh were water-covered for more than 100 days. Long-lasting inundation can also be observed  
Figure 18: Total annual flood duration and flood—impacted agriculture areas. (**a**) Total annual flood duration (TAFD) per pixel, as calculated from HYDRO30 time series. Data were categorized into four classes as shown in the legend. (**b**) Extent of flood—impacted agriculture areas as a function of time, calculated by intersecting HYDRO30 information with agriculture extent information extracted from the Copernicus Global Land Monitoring Service.  
for some areas of the southwestern Khulna division. Note that permanent water was included in this category.  
Areas affected by short-duration (less than 30 days) flooding can be found throughout the country but occur most prominently in the Rajshahi and Rangpur divisions, as well as in the Mymensingh, Barisal, and Sylhet divisions.
# Evaluation of Glottal Inverse Filtering Techniques on
## Appendix C  
This appendix includes the results of the statistical analyses conducted to evaluate to what extent the obtained error metrics for each GIF method and evaluated scenario are statistically significant or not, according to the Wilcoxon signed-rank paired test with Holm-Bonferroni correction.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{**Male**} & \multicolumn{6}{c}{**Female**} \\ \hline
**Error** & **GIF Method** & **IOP-IAIF** & **GFM-IAIF** & **QCP** & **ST-QCP** & **IOP-IAIF** & **GFM-IAIF** & **QCP** & **ST-QCP** \\ \hline \multirow{4}{*}{RMSE} & IAIF & n & * & * & * & * & * & * & * \\  & IOP-IAIF & & * & * & * & * & * & * \\  & GFM- & & & * & * & & * & * \\  & IAIF & & & * & & & * & * \\  & QCP & & & * & & & * \\ \hline \multirow{4}{*}{NAQ} & IAIF & * & * & * & * & * & * & * & * \\  & IOP-IAIF & & * & * & * & * & * & * \\  & GFM- & & & * & * & & * & * \\  & IAIF & & & * & * & & * & * \\  & QCP & & & * & & & * & * \\ \hline \multirow{4}{*}{H\({}^{1}\)H\({}^{2}\)} & IAIF & * & * & * & * & * & * & * \\  & IOP-IAIF & & * & * & * & * & * & * \\  & GFM- & & & * & * & & * & * \\  & GFM- & & & * & * & & * & * \\  & GFM- & & & * & * & & * & * \\  & IAIF & & & * & & & * & * \\  & QCP & & & * & & & * & * \\ \hline \multirow{4}{*}{HRF} & IAIF & * & * & * & * & * & * & * \\  & IOP-IAIF & & * & * & * & * & * & * \\ \cline{1-1}  & IGFM- & & & * & * & & * & * \\ \cline{1-1}  & GFM- & & & * & * & & * & * \\ \cline{1-1}  & QCP & & & * & & & * & * \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wilcoxon statistical analysis for errors by F0 range, where \(l\,m\,h\) denote low, mid and high, * show statistical significant differences with \(p\) > 0.05, and n represents non-significant differences.
# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 4 Imagery Style Evaluation (ISE) Method for Cross-Category Industrial Product Forms
### Testing the Generalization Ability of the ISE Model on Different Industrial Products  
#### 4.3.1 Experimental Program  
To comprehensively demonstrate the applicability of the ISE method, this study evaluates two sets of product form images to verify the positive correlation between customer-perceived imagery style and the imagery style assessed by the deep learning model. Group A images contain 20 types of industrial products from the enhanced YOLOv5 testing sets, including car wheel, car, helmet, hairdryer, humidifiers, drill, lamps, vases, chairs, coffee machines, shoes, loudspeaker, swivel chairs with five-star bases, computer mice, air fryer, rice cooker, medical device, oven, washing machine, and refrigerator. Group B images include another 20 types of industrial products such as mugs, cameras, watches, water coolers, shavers, water bottles, printers, headphones, projectors, irons, trash, exercise bikes, and luggage. The study uses the root mean squared error (RMSE) for evaluation, as expressed in the following formula:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}} \tag{20}\]  
where \(y_{i}\) is the imagery style value given by customer perception for product form, \(\hat{y}_{i}\) represents the style value obtained from the model test using the ISE method, \(i\) is the test sample number, and \(n\) denotes the total number of evaluated samples. If there is no difference between these two values, the RMSE approaches 0; otherwise, a higher RMSE value indicates a greater discrepancy.
# Dye Schedule Optimization: A Case Study in a Textile Industry
## 3 Structuring the Problem Assessment Model
### Application  
For the study of setup time scheduling, the traveling salesman problem methodology was chosen, as it was seen the opportunity to employ a classic problem, which consists of searching for a circuit that has the shortest distance, passing through \(n\) cities, starting from any city, visiting all the others once and returning to the city of origin. In the study in question, the SKUs will represent the cities, and the setup time between pairs of SKUs will represent the distance between cities.  
Considering that the analysis aims to create a model that guides the prediction of setup time gain, comparing the optimized method with the empirical method, the tests were performed within a set of configurations, parameterized by factors that are subject to change, and which may influence the result of the optimization.  
The factor is:  
* Machine group: despite not being input to the problem, it deserves attention because the analyzed samples were processed in two groups of machines: slasher dye, being more complex and larger machines for more elaborate dyeing, and loop dye are smaller machines with a more straightforward conception and low-cost dyeing, so the two groups must be analyzed separately.
* Number of SKU: this factor is susceptible to seasonality and can vary significantly depending on fashion trends and market demand.
* Programming horizon: the further we can see the demand, the greater the optimization opportunities, so for each composition of machine group and SKU number, tests were performed for different horizons, ranging from one to four weeks.
## 2 Methodology
### The Dragon Transform and Fault Severity Quantification
The time-frequency decomposition shows the expected trajectory of the first harmonic during the inverter-controlled startup and the subsequent steady-state operation (see Figure 1c). The predicted trajectories of the fault-related harmonics are also observed during the transient and stationary operation. As the motor slip is low during the motor operation (see Figure 1b), these harmonics' trajectories develop close and parallel to the first harmonic (see predicted trajectories in Figure 1b), which makes impossible their observation and quantification by tools such as the Short Time Fourier Transform or Complex Wavelet Transform [3]. However, the excellent time and frequency resolutions of the Dragon Transform represents them as very thin lines, with no interference from the first component energy. Figure 1: Analysis with the Dragon Transform of the stator current of an inverter-fed induction motor with a broken rotor bar during a linear startup: (**a**) Stator current; (**b**) Motor slip; (**c**) Theoretical trajectories of the first component and the bar breakage harmonics; (**d**) Time-frequency distribution of the stator current given by the Dragon transform. As the trajectories of fault-related harmonics are correctly observed as thin lines in the time-frequency plane, their energy can be captured so fault severity can also be quantified. Figure 2 shows the quantification curves corresponding to the analysis of the signal shown in Figure 1. These time curves represent the energy density along the trajectories of broken bar harmonics shown in Figure 1d. The two curves correspond to the energy of the harmonics on both sides of the first component. Therefore, this quantification technique could be used for fault detection and diagnosis during an inverter-controlled IM startup following any profile in time.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 4 Cell Adhesion Molecules
### Cell-Cell Adhesion Molecules
#### 4.2.2 Cadherins  
Hyafil and Peyrieras first reported cadherin in the 1980s using the name \"Uvomorulin\" in mice [82]. Since then, numerous subtypes have been identified in different species, resulting in more than 100 members of the family now being recognised. The cadherins are transmembrane glycoproteins that promote cell-cell adhesion, which is highly dependent on calcium ion association [82]. Cells make use of cadherins not just for cell-cell adhesion during morphogenesis, but also to regulate cell signalling and control the fate, polarity and proliferation of cells [83]. The activation of cadherins mainly occurs due to the formation of transponds at the contact site between two cells. Once this connection has been formed, cadherins regulate cell-cell attachment in one of three ways; (1) reducing local tension; (2) actomyosin signalling; and (3) establishment of mechanical coupling [83]. The cadherin classification system includes several subtypes: type I classical cadherins such as E-cadherin, N-cadherin and P-cadherin, type II classical cadherins such as VECadherin and OB-cadherin, desmosomal cadherins such as the desmocollins, seven-pass transmembrane cadherins such as flamingo, FAT atypical cadherin (FAT) and dachosus group cadherins such as FAT cadherin and protocadherins such as C-protocadherin (C-Pcdhs) [82]. Despite their many classes, all cadherins are composed of three main domainsincluding an extracellular domain, a single-pass transmembrane domain and a cytoplasmic domain [82]. The presence of a peptide cadherin domain containing around 110 amino acids also serves to facilitate homophilic, calcium dependent interactions between different cadherin molecules [84].
###### Abstract  
Fluctuations in illumination are one of the major sources for SIF retrieval errors during temporal continuous field measurements. In this study, we propose a method for evaluating the quality of SIF based on simultaneous measurements of photosynthetically active radiation (PAR), which are acquired using a quantum sensor at a sampling frequency higher than that obtained using spectral measurements. The proposed method is based on the coefficient of variation (known as relative standard deviation) of the high-frequency PAR during a SIF measurement to determine the quality of the SIF value. To evaluate the method, spectral and PAR data of a healthy maize canopy were collected under various illumination conditions, including clear, cloudy, and rapidly fluctuating illumination. The SIF values were retrieved by 3 FLD, SFM, and SVD. The results showed that SFM and 3 FLD were sensitive to illumination stability. The determination coefficients (\(R^{2}\)) between PAR and SIF extracted by SFM and 3 FLD were higher than 0.8 on clear and cloudy days and only approximately 0.48 on the day with rapidly fluctuating illumination. By removing the unqualified data using the proposed method, the \(R^{2}\) values of SFM and 3 FLD on the day of rapidly fluctuating illumination significantly increased to 0.72. SVD was insensitive to illumination stability. The \(R^{2}\) values of SVD on days with clear, cloudy, and rapidly fluctuating illumination were 0.73, 0.76, and 0.61, respectively. By removing the unqualified data, the \(R^{2}\) values of SVD were increased to 0.66 on the day with rapidly fluctuating illumination. The results indicated that the quality assessment method based on high-frequency PAR data can eliminate the incorrect SIFs due to unstable illumination. The method can be used to extract more accurate and reliable SIF datasets from long-term field observations for the study of the relationship between SIF and vegetation photosynthesis.  
SIF; high-frequency PAR; illumination stability; data quality +
Footnote †: journal: remote sensing
## 5 Review of Case Studies: Flying Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Problem Addressed** & **Objective(s)** & **Optimization Approach** & **Key Contributions** \\ \hline [PERSON] and & Capacitated VRP (CVRP) for & Minimize total travel cost, waiting & & Efficient and effective solutions for the 2-CVRP, consideration of customer \\ [PERSON] [12] & distributing perishable products & times, and carbon dioxide emissions & NSGA-II & & 2-CVRP, consideration and environmental \\  & & & & satisfaction and environmental \\  & & & & sustainability \\ \hline [PERSON] et al. [19] & Multiple depot vehicle distribution & Minimize risk of product spoilers by & & Optimal route planning, reduced risk of \\  & routing problem (MDVRP) for & determining shortest route & & product deterioration \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline [PERSON] et al. [61] & UAV-vehicle joint delivery model for & Improve delivery efficiency, reduce & & & \\  & logistics distribution in mountainous & & total delivery route length & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline [PERSON] et al. [62] & Optimization of emergency logistics & & & & Reduced total cost compared to \\  & distribution using collaborative delivery model with vehicles and UAVs & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline [PERSON] et al. [63] & Optimization of drone delivery & & & & Insights into logistics, investment \\  & & & & & feasibility, and resource requirements for \\  & & & & & establishing an aerial delivery system \\ \hline [PERSON] et al. [64] & Joint optimization framework for & & & & \\  & UAV-routing and UAV-route scheduling problems in UAV-assisted & Minimize travel time and makespan & Hybrid genetic and simulated & & Optimized UAV-routing and UAV-route scheduling, outperformed existing \\  & delivery systems & & & & modelling algorithm \\ \hline [PERSON] and & Safe and efficient path planning for & & & & \\  & autonomous UAVs in target coverage & & & & \\  & & & and disposal effects & & \\  & & & & \\  & & & & \\ \hline [PERSON] et al. [71] & Optimization of scheduling for parcel & & & & \\  & delivery using drones & & & \\  & & & & \\ \hline [PERSON] et al. [72] & Environmental and cost-saving & & & & Reduced energy consumption and \\  & & & & & emissions, cost-effective, and \\  & & & & environmental friendly last-mile \\ \hline [PERSON] et al. [73] & Optimization of pickup and delivery & & & & \\  & with UAVs in intermittent & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 3 Passive Phased Arrays with Integrated Metallic and Dielectric Waveguide Phase Shifters
### High-Performance Metallic Waveguide Phase Shifter
For this lab-scale demonstrator [64], a voltage range of \(-200\) V \(\leq V_{b}\leq 200\) V was chosen, resulting in an internal electric field of almost \(E_{b}=200\) V/mm. The electrical biasing configuration uses five electrodes on each side, where parts of the electrodes has to be equipped with \(\uplambda\)/4-stub lines and stepped impedance low-pass filters to suppress the coupling of the TE\({}_{10}\) mode into stripline modes between the waveguide walls and the biasing electrodes. The waveguide itself was built up in a split-block design made of brass, in which the LC container is inserted. The split block has small gaps for the biasing electrodes to be lead out. This concept has been also proved and demonstrated for W-band frequencies in [67; 143]. The reflection is below \(-10\) dB, the insertion loss is below \(-3\) dB, and the differential phase shift is between 300\({}^{\circ}\) and 320\({}^{\circ}\); hence, the FoM is in between 116\({}^{\circ}\)/dB and 148\({}^{\circ}\)/dB from 95 to 105 GHz for magnetic biasing only, nevertheless revealing the potential of this technology also for W-band applications. Electrical biasing with electrodes made of chromium and processed on a 20 \(\upmu\)m thin PET film reacts very sensitively on the metal evaporation and photolithography process, causing microscopic cracks. These processing problems could not be solved sufficiently up to now. Figure 15: Schematic of the director distribution in a partially LC-filled waveguide phase shifter for both extreme tuning states parallel (**left**) and perpendicular (**right**) to the RF field \(\overset{\rightarrow}{E}_{RF}\), using a configuration of three electrode pairs. The values below are given for the LC TUD-566. Reprinted from [2], Cambridge University Press, 2020.