esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Correction of the Influence of Long Measuring Path Irregularity in Antenna Tests
## 1 Introduction  
At present, various microwave systems are being developed, which require high stability of the oscillation signal parameters. Such systems include, for example, active electronically scanned arrays (AESA) [1, 2], providing a narrow radiation pattern (RP). On the other hand it can be monopulse antenna arrays, which have a high value of difference pattern null slope, and therefore a high resolution. Thus, it expands the accessible amount of information about the propagation of electromagnetic waves from sources of radiation or reflection in the space around. However, these advantages are realized due to high identity of the channels output signals that is subject to various destabilizing factors.  
All measurement errors arising under the influence of destabilizing factors can be grouped into systematic and random ones. Systematic errors can be partially removed by introducing some compensating correction during the initial setup, whereas random errors cannot be systematized. To take into account the influence of random errors on the output signal variation of long microwave transmission lines, it is necessary to create special methods and algorithms for their determination and compensation.  
Radio engineering systems, which include antennas and antenna arrays, require increased accuracy of test procedures [3]. Ensuring high-precision measurements of the antenna technology parameters allows one to increase the development efficiency of the complex devices, potentially improve the technical performance of systems used and verify the characteristics of off-the-shelf products [4, 5, 6, 7].  
Antenna measurements are carried out in the near [8] or far field [9] regions using specialized test facilities such as anechoic or reverberation chambers [10], free-space testranges [11] or other equipment types. At the same time, not only the measured data themselves are of interest, but also issues related to the estimation of the measurement error [12], as well as the measurement reproducibility [13]. It is often necessary to provide the relative movement between the measuring antenna and the antenna under test (AUT) during a measurement process, so measurements are not static in the general case. An example is AUT rotation in the azimuthal plane when measuring cross-sections of RP [14] or near-field scanning to obtain the amplitude-phase distribution with the probe [12]. The effect of changes in test cable characteristics due to twisting or bending is usually not taken into account in such measurements.  
The aims of the work were to systematize the theoretical aspects of taking into account the influence of random factors on the characteristics of long measurement paths, develop an error correction mechanism and verify the proposed calibration method.  
As a result, relations were obtained indicating the need to correct random errors at the output of long microwave transmission lines found in both AESA feeding systems and measuring systems.
## 1 Introduction  
Though spatially small (5% of the terrestrial biosphere) compared with most other ecosystems [1,2], peatlands play a significant role in carbon (C) and water metabolism of the World. Understanding and quantifying C dynamics of peatlands are crucial to prediction of responses to global climate change and rehabilitation of peatlands under the increasing magnitude and rate of human-induced disturbances. Eddy Covariance (EC) sensors are one of the most direct ways to measure and estimate turbulent carbon dioxide (CO\({}_{2}\)), water vapor and energy fluxes exchanged among soil, vegetation, and atmosphere within the atmospheric boundary layer. The use of the EC method and sensors on a long-term and continuous basis across the World has led to the establishment of an integrated global network for standardization of flux tower activities (called FLUXNET) and a network for standardization and development of spectral sensors toward bridging the gap between remote and proximal sensing (called SpecNET).  
Our study area in Turkey, the Yenicaga peatland area that at one time occupied 240 km\({}^{2}\), has diminished to less than 30 km\({}^{2}\) due to drainage, cultivation, afforestation, and peat mining [3,4]. There is a lack of information about C metabolism of peatland ecosystems in Turkey, and this study is the first to comprehensively determine C dynamics and components in one of the remaining major peatlands. The objective of this study was to quantify the rate, magnitude, and timing of CO\({}_{2}\) exchange between the atmosphere and Yenicaga peatland using the EC sensors and multiple (non-)linear regression-M(N)LR-models.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### XAI Frameworks
#### 4.1.3 SHAPs  
SHapley Additive exPlanations (SHAPs) comprise a framework designed with a clear objective: to elucidate the prediction of a given instance by evaluating the contribution of each feature to that prediction. Similar to LIMEs, SHAPs operate under a local-based, post hoc, and model-agnostic approach. The technique employed by SHAPs leverages coalitional game theory to compute Shapley values, with each feature value of a data instance acting as a coalition member. These Shapley values provide insights into the fairness of the prediction distribution across different characteristics. Unlike LIMEs, SHAPs do not necessitate the establishment of a local model; instead, they utilize the same function to calculate Shapley values for each dimension [28; 48].
# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
## 2 Materials and Methods
### Experimental Setup  
For the testing activities, the closed impellers were fitted into an in-house test bench that was designed to test and evaluate the hydraulic performance of the pumps, developed by COMOTI (Bucharest, Romania). The test bench allowed temperature control on the pump support and on the working fluid (both under vacuum conditions). The effective integration into the bench was performed with a dedicated pump demonstrator, a modular mechanical assembly that is capable of accommodating numerous geometries by using adapters. The test bench is presented in Figure 12. A National Instruments data acquisition system (DAQ) was used with LabView 2020 software for data interpretation. The DAQ  
Figure 11: Boundary conditions for the centrifugal pump.  
Figure 10: Grid representation for the main components of the centrifugal pump.  
was set to 10 parameters/s data recording. Pressure data were recorded from 13 sensors installed on the pump casing.  
A general overview of the experimental procedure followed during the pump testing is as follows:  
1. Assembly of the device under test (DuT)--The pump is carefully assembled, ensuring all components are correctly fitted and sealed to avoid any leaks during testing. Calibration of sensors and measurement devices is also performed at this stage to ensure accurate readings throughout the process.
2. Filling the test bench with working fluid--The test bench is filled with the specified working fluid, ensuring proper circulation and venting of air to avoid any airlocks that might interfere with the performance measurements.
3. Determining the required rotational speed--The pump is operated at different rotational speeds, in line with the test conditions. These speeds are selected based on the operational parameters and the objectives of the study, ensuring that each test point reflects the desired performance range.
4. Recording data at each operational point--Data, including flow rates, pressures, and power consumption, are recorded for each operational point.
5. Emptying the test bench--After completing each set of measurements, the working fluid is carefully drained from the test bench. This ensures that no fluid remains in the system, preventing contamination or cross-contamination of fluids in subsequent tests.
6. Disassembling the DuT--Once all necessary data have been collected, the pump is disassembled for inspection and cleaning. This step ensures that no damage occurs to the components during the testing process and allows for any necessary maintenance before performing additional tests.
## Materials and Methods
### Bioinformatics Analysis  
We first used fastp [(23)] to remove the reads that contained adaptor sequences, low quality bases, and undetermined bases (Ns). The sequencing quality was then assessed using FastQC ([[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)]([http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/))). All downstream analyses were based on high quality, clean sequencing read data. De novo assembly of the transcriptome was performed with Trinity 2.4.0 with default parameter [(24)]. All assembled unigenes were used as queries to search the non-redundant (Nr) protein database of all included species ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/))) by blastx with a threshold of E <0.00001. RSEM was used to determine the expression levels for unigenes by calculating TPM [(25)]. The differentially expressed unigenes were selected using the criteria of log2 (fold change) >1 or log2 (fold change) <-1, and the statistical significance (p-adjust value <0.05) was determined using the DEGseq package [(26)]. Venn diagrams were drawn using an online tool ([[http://bioinformatics.psb.ugent.be/webtools/Venn](http://bioinformatics.psb.ugent.be/webtools/Venn)]([http://bioinformatics.psb.ugent.be/webtools/Venn](http://bioinformatics.psb.ugent.be/webtools/Venn))). GO and KEGG enrichment analyses were then performed on the differentially expressed unigenes using Goatools [(27)] and KOBAS software [(28)], respectively. Benjamini-hochberg correction was applied for GO and KEGG enrichment and significance set to adjusted P-value < 0.05. We used the MapMan tool [(29)] for a graphical overview of the biotic stress response by default parameters. We used the pfam domains PF01397 and PF03936 to assess terpene synthase (TPS) genes in the _P. acerifolia_ transcriptome assembly using the HMMER package [(30)] with a threshold of 0.01. Phylogenetic trees were constructed using the neighbor-joining method as implemented in MEGA6.06 [(31)] with 1000 bootstrap replicates.
## 4 Experimental Components
### Overview of the Metaheuristic Optimization Algorithms
#### 4.3.1 Particle Swarm Optimization (PSO)  
The PSO is a population based-metaheuristic optimization algorithm, belonging to the class of Swarm Intelligence [42]. It models solutions to an optimization problem as a swarm of particles having specified positions and velocities. These particles also possess memory for storing their local best position and some means of communication of the global best position:  
\[v_{i}^{t+1}=v_{i}^{t}+\alpha\varepsilon_{1}[g^{*}-x_{i}^{t}]+\beta\varepsilon_{ 2}[x_{i}^{*}-x_{i}^{t}] \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S/N** & **Name** & **Number of Instances** & **Number of Features** & **Missing Values?** \\ \hline
1 & CAR & 1728 & 6 & NO \\
2 & Breast Cancer & 569 & 32 & NO \\
3 & Adult & 48,842 & 14 & YES \\
4 & Letter & 20,000 & 16 & NO \\
5 & Cover Type & 581,012 & 54 & NO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the UCI dataset.  
Figure 2: Some of the images present in the MNIST dataset.  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{5}\]  
where \(v\) and \(x\) represent the velocity and position of a particle, respectively. The subscripts \(t\) and \(i\) are the iteration and an individual particle, respectively, whereas \(\alpha,\beta,\ \varepsilon_{1},\ \varepsilon_{2}\) are the learning parameters used to fine-tune the algorithm per specific problem.
## 5 Error Analysis
### Penetration Method and the Pulse Reflection Method  
To analyze the error in the double-probe penetration detection method in crack detection, the pulse reflection method was used to detect the depth and angle of prefabricated cracks in flat plate specimens, and the detection accuracy of crack depth and angle under the two methods was compared. The detection errors under the two methods are shown in Figures 7 and 8. As shown in Figure 7, the relative error of the pulse reflection method for crack depth detective was less than 25%, and the mean square deviation (SD) of the two methods was 3.39 and 3.41, respectively. There were no significant differences between them. For deep cracks, the accuracy of the penetration method was greater than that when using the pulse reflection method. In Figure 8, the relative error of the pulse reflection method for the crack angle was less than 35%, and the mean square deviation (SD) of the detection results was 12.29. Compared with the penetration method, the detection accuracy of the pulse reflection method was lower and the detection results were more discrete.  
The results of crack width detection using both methods are shown in Figure 9. From Figure 9a, the test results arising from the use of the pulse reflection method were affected by the crack width. When the crack width increased, the crack detection accuracy first decreased, then increased. The detection precision at a crack width of 0.1 mm was similar to that at 0.2 mm: that at a width of 0.5 mm was the lowest. The crack width detection results, achieved using the penetration method, are shown in Figure 9b: the position of the incident point for penetration detection and wave height curves of the three kinds of width cracks was similar. It can be assumed that the crack width did not influence penetration detection. The effect of crack width could be neglected when using the double-probe penetration detection method.  
Figure 8: Error analysis of crack angle measurement. (**a**) Pulse reflection method and (**b**) penetration detection method.  
Figure 7: Error analysis of crack depth measurement. (**a**) Pulse reflection method and (**b**) penetration detection method.
## 1 Introduction
By excluding the influence of PAR, the resulting apparent SIF yield (SIFy) exhibits greater sensitivity to water stress than does the SIF [15]. Consequently, [PERSON] et al. proposed the use of the diurnal variation characteristics of SIFy, referred to as the noonmorning ratio (NMR), to monitor the daily water stress in vegetation and successfully tracked daily variations in the water status of maize [12]. Recent studies have also indicated that \(\Phi\)F\({}_{\text{canopy}}\) may be more sensitive to water stress [17; 18; 19]. Researchers have employed the spectral invariance theory to derive the NIRv and FCVI, which approximates the product of \(f_{\text{esc}}\) and fPAR [20; 21]. They aimed to further isolate \(\Phi\)F\({}_{\text{canopy}}\) that contains only physiological information and effectively captured the suppression of agricultural drought [22]. Additionally, [PERSON] et al. successfully tracked daily changes in water stress in forests via the afternoon-morning ratio (AMR) of \(\Phi\)F\({}_{\text{canopy}}\)[23]. Therefore, the diurnal variation characteristics of \(\Phi\)F\({}_{\text{canopy}}\), which contains solely physiological information, may be more sensitive to changes in the daily water status. However, on a daily scale, the canopy structure also undergoes changes, such as leaf rolling and variations in leaf angle distribution (LAD), which can affect the absorption of light energy by the canopy [10]. [PERSON] et al. reported that fPAR significantly decreases at midday under water stress [24]. Additionally, other researchers have indicated that the far-red SIF is highly influenced by structural factors, leading to considerable uncertainty in drought monitoring [25]. Consequently, there is no consensus on whether monitoring the daily water stress is more effective via the diurnal variation characteristics of \(\Phi\)F\({}_{\text{canopy}}\), which exclusively contains physiological information, or via the diurnal variation characteristics of SIFy, which contains both structural and physiological information. To investigate the relationship between water stress and the diurnal patterns of vegetation structural and physiological traits, and to evaluate the effectiveness of the use of vegetation indices with varying information contents to monitor the daily water stress, we obtained continuous observational data of the active fluorescence indicative of leaf physiological information and leaf rolling measurements representing structural information from a maize canopy. The canopy SIF and corresponding meteorological data were continuously monitored. Specifically, we aimed to address the following research questions: (1) How are the diurnal variation characteristics of the vegetation structure and physiology manifested under different water stress conditions? (2) Which approach is more effective for monitoring the daily water stress, i.e., the use of the diurnal variations in \(\Phi\)F\({}_{\text{canopy}}\), which reflects only physiological dynamics, or the use of the diurnal variations in SIFy, which contains both vegetation structural and physiological information?
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 4 Simulation Experiments and Comparison of the Fusion Algorithm
### Standard Test Function Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **GA-DWA** & **PSO-DWA** & **SSA-DWA** & **NRBO-DWA** \\ \hline \multirow{3}{*}{\(F_{1}\)} & BEST & \(3.08\times 10^{-61}\) & \(7.90\times 10^{-4}\) & \(9.71\times 10^{-114}\) & \(1.05\times 10^{-295}\) \\  & Average & \(3.17\times 10^{-55}\) & \(1.53\times 10^{-2}\) & \(1.12\times 10^{-98}\) & \(1.68\times 10^{-293}\) \\  & STD & \(2.91\times 10^{-54}\) & \(2.45\times 10^{-1}\) & \(4.73\times 10^{-97}\) & \(2.01\times 10^{-292}\) \\ \hline \multirow{3}{*}{\(F_{2}\)} & BEST & \(1.01\times 10^{-40}\) & \(6.88\times 10^{-5}\) & \(1.10\times 10^{-83}\) & \(4.65\times 10^{-161}\) \\  & Average & \(2.96\times 10^{-33}\) & \(4.15\times 10^{-3}\) & \(8.20\times 10^{-70}\) & \(5.84\times 10^{-146}\) \\  & STD & \(1.67\times 10^{-32}\) & \(2.62\times 10^{-3}\) & \(1.58\times 10^{-68}\) & \(5.02\times 10^{-145}\) \\ \hline \multirow{3}{*}{\(F_{3}\)} & BEST & \(3.11\times 10^{-16}\) & \(1.76\times 10^{2}\) & \(8.66\times 10^{-144}\) & \(3.94\times 10^{-289}\) \\  & Average & \(6.18\times 10^{-12}\) & \(6.61\times 10^{2}\) & \(1.20\times 10^{-118}\) & \(1.17\times 10^{-271}\) \\ \cline{1-1}  & STD & \(2.40\times 10^{-11}\) & \(1.11\times 10^{2}\) & \(6.19\times 10^{-117}\) & \(1.08\times 10^{-270}\) \\ \hline \multirow{3}{*}{\(F_{4}\)} & BEST & \(0\) & \(3.69\times 10^{1}\) & 0 & 0 \\  & Average & \(0\) & \(9.31\times 10^{1}\) & 0 & 0 \\  & STD & \(0\) & \(4.65\times 10^{1}\) & 0 & 0 \\ \hline \multirow{3}{*}{\(F_{5}\)} & BEST & \(1.47\times 10^{-16}\) & \(7.95\times 10^{-3}\) & \(4.44\times 10^{-16}\) & \(4.44\times 10^{-16}\) \\  & Average & \(1.47\times 10^{-14}\) & \(5.97\times 10^{-2}\) & \(4.44\times 10^{-16}\) & \(4.44\times 10^{-16}\) \\ \cline{1-1}  & STD & \(2.32\times 10^{-12}\) & \(2.98\times 10^{-1}\) & 0 & 0 \\ \hline \multirow{3}{*}{\(F_{6}\)} & BEST & \(0\) & \(1.30\times 10^{-2}\) & 0 & 0 \\ \cline{1-1}  & Average & \(0\) & \(1.15\times 10^{-2}\) & 0 & 0 \\ \cline{1-1}  & STD & \(0\) & \(1.43\times 10^{-1}\) & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of different algorithms on benchmark functions. comparison algorithms. Although other algorithms occasionally achieve the minimum values, the convergence curve of the fitness function shows that NRBO-DWA converges faster, requiring fewer iterations. The final optimization performance ranking, from highest to lowest, is as follows: NRBO-DWA, SSA-DWA, GA-DWA, PSO-DWA.
## 3 Use Case Description and Problem Statement
### Opportunistic Sharing of GSO/NGSO Satellite and Terrestrial Systems in Downlink Ka-Band  
This use case concerns GSO and NGSO satellite systems and terrestrial systems in the downlink Ka-band (17.7-19.7 GHz) and is shown in Figure 1a. The terrestrial systems are mainly 5G FWA and MW links and it is assumed that they use frequency division multiple access (FDMA) for their transmission. The geostationary earth stations (GSO-ES) and non-geostationary earth stations (NGSO-ES) are mobile ESIMs, and they represent trains, ships, cars, etc. Furthermore, we consider that a control channel (CC) exists between different entities (satellites, ESIMs, GSs, and terrestrial networks). Since all the geostationary satellites (GSO-SAT), non-geostationary satellites (NGSO-SAT) and the terrestrial network operate in the same band, their transmissions can have an impact on the ESIMs. For the downlink operation of GSO and NGSO satellites in the presence of the terrestrial network, the following interference components are considered:  
* GSO-SAT towards NGSO-ES;
* NGSO-SAT towards GSO-ES;
* Terrestrial network towards GSO-ES;
* Terrestrial network towards NGSO-ES;* GSO-SAT towards terrestrial network;
* NGSO-SAT towards terrestrial network.  
The goal here is to protect the communication of incumbent terrestrial networks, manage the interference towards GSO and NGSO ESIMs coming from GSO-SAT, NGSO-SAT, and the terrestrial network and improve the performance (spectrum utilization, throughput, PER, latency, etc.) by applying DSS techniques.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.3 Remote Sensing-Based GPP Products  
Global Solar-Induced chlorophyll Fluorescence (GOSIF), FLUXCOM, and GLASS GPP products are widely utilized in terrestrial ecosystem productivity studies. The GOSIF GPP product ([[http://data.globalecology.unh.edu/data/GOSIF_v2/](http://data.globalecology.unh.edu/data/GOSIF_v2/)]([http://data.globalecology.unh.edu/data/GOSIF_v2/](http://data.globalecology.unh.edu/data/GOSIF_v2/)), accessed on 14 February 2025) is derived from Orbiting Carbon Observatory-2 (OCO-2) Solar-Induced chlorophyll Fluorescence (SIF) observations and MODIS reflectance data, providing global coverage at 0.05\({}^{\circ}\) spatial resolution and 8-day temporal resolution [33]. FLUXCOM (Flux Communication) GPP ([[https://www.fluxcom.org/CF-Download/](https://www.fluxcom.org/CF-Download/)]([https://www.fluxcom.org/CF-Download/](https://www.fluxcom.org/CF-Download/)), accessed on 14 February 2025), generated by integrating FLUXNET site observations with machine learning algorithms and remote sensing data, offers monthly estimates at 0.5\({}^{\circ}\) spatial resolution, representing a fusion of multiple modeling approaches [34]. The GLASS GPP product ([[https://glass.hku.hk/archive/GPP/](https://glass.hku.hk/archive/GPP/)]([https://glass.hku.hk/archive/GPP/](https://glass.hku.hk/archive/GPP/)), accessed on 14 February 2025), developed using a light use efficiency model and improved remote sensing inputs, delivers measurements at 0.05\({}^{\circ}\) spatial resolution with 8-day temporal intervals [35].