esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Non-Volatile Metabolites of FAA Tea at Four Harvest Times
#### 3.3.4 Kyoto Encyclopedia of Genes and Genomes (KEGG) Analysis of Differential Metabolites  
To investigate the potential pathways of the effect of the harvest period on FAA tea metabolites, functional enrichment analysis was performed using the KEGG database (Figure 4). A total of 154 differentially accumulated metabolites were mapped to 30 metabolic pathways. Among them, the biosynthesis of plant secondary metabolites exhibited the most significant impact, encompassing 20 differentially accumulated metabolites, including L-glutamic acid, succinic acid, L-arginine, L-glutamine, L-phenylalanine, L-tyrosine; L-malic acid; L-asparagine; citric acid; phenylpyruvate acid; L-valine; palmitic acid; nicotinic acid, prephenate; geranyl diphosphate; L-dopa; 4-hydroxyphenylpyruvate acid; campesterol; stigmasterol; and (S)-abscisic acid. [PERSON] et al. highlighted that amino acids, such as glutamic acid, aspartic acid, glutamine, and asparagine, contribute to fresh flavor, with glutamic acid being a key determinant of taste [45]. The enrichment of these metabolites underscores their potential role in shaping the flavor profile of FAA tea. Metabolicpathway analysis revealed the complex biological activities involved in metabolite accumulation and highlighted significant differences between FAA-3 and FAA-6. These differences were primarily associated with amino acid biosynthesis pathways, suggesting that the harvest period significantly affects metabolite pathways. In contrast, metabolite differences between FAA-4 and FAA-5 were minimal, consistent with the PCA and HCA results, which showed closer clustering of these groups. This indicates that intermediate harvest periods have less pronounced effects on the metabolite composition.  
Figure 4: (a–f) KEGG enrichment bubble chart (FAA-3 vs. FAA-4, FAA-3 vs. FAA-5, FAA-3 vs. FAA-6, FAA-4 vs. FAA-5, FAA-4 vs. FAA-5, FAA-4 vs. FAA-6, FAA-5 vs. FAA-6).
## 4 Preprocessing of the Measured Data
### Denoising  
The measured time-varying signal includes the movement of the victim under the debris, as well as undesired signals around the measurement area. These undesired signals can significantly degrade detection performance by masking the subtle motion patterns of the victim, potentially leading to missed detections and higher false alarm rates [66; 67].  
Figure 4: Analysis of the \(S_{21}\) signals measured for different orientation angles of the subject: (**a**) FT of the measured phase of \(S_{21}\) signals at 405 MHz. (**b**) Average standard deviation of the phase and magnitude at different frequencies.  
Therefore, a robust and targeted filtering of these components is essential to enhance the reliability and accuracy of the detection system. To increase the PD and reduce the probability of false alarms (FAs), signal components caused by the chaotic environment in the region and the noise that it creates should be filtered out. Wavelet-based noise reduction is a suitable solution for this purpose [68]. The orthogonality and tight support of the wavelet function result in good performance for signal denoising. This method aims to separate noise and signal by analyzing the frequency components of the signal at different scales. It provides a multi-resolution analysis that can decompose a signal into different frequency components at various resolutions. This capability enables the identification and removal of the noise at different scales and increases the clarity of the underlying signal among chaotic environmental conditions. It allows rapid processing of large data sets, which contributes to the timely and accurate detection of survivors in our case. The Wavelet Signal Denoiser tool in MATLAB2023 was used for the denoising of the measured data [69]. The 8 th-order Daubechies wavelet was used as the mother wavelet. The optimal decomposition level was determined to be seven, and the hard threshold rule was chosen.
## 3 Results  
### Time on Court, Distance Covered, and Running Pace in Offense and Defense  
The average time on court (_n_ = 1865) during the European Championship was 26.48 \(\pm\) 14.94 min, with 2.23 \(\pm\) 2.35 min spent standing, 18.84 \(\pm\) 10.64 min walking, 3.29 \(\pm\) 1.73 min jogging, 1.35 \(\pm\) 0.84 min running, 0.45 \(\pm\) 0.47 min running at high intensity, and 0.03 \(\pm\) 0.07 min sprinting. Significant differences were found between offense and defense (Figure 2) in time spent walking (8.92 \(\pm\) 5.74 min vs. 10.06 \(\pm\) 6.73 min; \(p\) < 0.001; ES = 0.19), jogging (2.15 \(\pm\) 1.24 min vs. 1.15 \(\pm\) 0.72; \(p\) < 0.001; ES = 0.89), running (0.75 \(\pm\) 0.49 min vs. 0.60 \(\pm\) 0.38 min; \(p\) < 0.001; ES = 0.32), and high intensity running (0.20 \(\pm\) 0.24 min vs. 0.24 \(\pm\) 0.25 min; \(p\) < 0.001; ES = 0.16). There were no significant differences for total played time (13.40 \(\pm\) 8.19 min vs. 13.27 \(\pm\) 8.59 min; \(p\) > 0.05) or time standing (1.19 \(\pm\) 1.67 min vs. 1.06 \(\pm\) 1.01; \(p\) > 0.05).  
The average distance (_n_ = 1865) covered during each game was 2105.78 \(\pm\) 1150.76 m, with 999.95 \(\pm\) 555.52 m covered walking, 544.32 \(\pm\) 286.79 m jogging, 356.44 \(\pm\) 225.67 m running, 153.59 \(\pm\) 166.86 m running at high intensity, and 15.19 \(\pm\) 30.80 m sprinting. Significant differences were found between offense and defense (Figure 3) in total distance covered (1217.48 \(\pm\) 699.33 m vs. 900.96 \(\pm\) 538.95 m; \(p\) < 0.001; ES = 0.48) as well as walking (564.03 \(\pm\) 361.56 m vs. 442.12 \(\pm\) 295.86 m; \(p\) < 0.001; ES = 0.35), jogging (370.01 \(\pm\) 210.58 m vs. 176.87 \(\pm\) 112.48 m; \(p\) < 0.001; ES = 1.02), running (190.99 \(\pm\) 128.28 m vs. 167.47 \(\pm\) 109.45; \(p\) < 0.001; ES = 0.19), high intensity running (67.31 \(\pm\) 84.54 m vs. 87.64 \(\pm\) 93.48 m; \(p\) < 0.001; ES = 0.23), and sprinting distances (6.34 \(\pm\) 15.37 m vs. 9.07 \(\pm\) 19.09 m; \(p\) < 0.001; ES = 0.17).  
Figure 2: Time spent in court in each locomotion category by offense and defense. * Significant differences between offense and defense (_p_ < 0.05).  
Locomotion activities were then normalized for each player according to the time they spent on court to obtain a true reflection of these demands, both for offense and defense. The total average running pace was 89.63 \(\pm\) 35.18 m/min during a game. The running pace was significantly higher in offense 96.53 \(\pm\) 22.57 m/min than in defense 82.72 \(\pm\) 43.28 m/min (\(p<0.001\); ES = 0.47).
# A Low-Complexity Pilot-Based Frequency-Domain Channel Estimation for ICI Mitigation in OFDM Systems
## 2 System Model
### OFDM Modulation and Sub-Carrier Coupling Matrix
Assuming that a given OFDM symbol has a pilot (\(X^{(m)}_{k}=\alpha_{k}\)) on sub-carriers that belong to the set \(k\in\mathcal{P}\), then the channel estimate, \(\hat{H}^{(m)}_{k}\), can be obtained with a LS criteria [27] as  
\[\hat{H}^{(m)}_{k}=\frac{Y^{(m)}_{k}}{\alpha_{k}}=H^{(m)}_{k}+\frac{W^{(m)}_{k }}{\alpha_{k}}. \tag{9}\]  
As can be observed, the second term, \(W^{(m)}_{k}/\alpha_{k}\), represents the estimation error. Since this error has zero mean, an averaging of the channel estimates over a time and frequency window (where the channel can be considered constant) is typically performed. Nevertheless, if the time and frequency coherence of the channel is smaller than the pilot density in frequency and time domains, the channel cannot be considered as constant, and thus,such an averaging cannot be done. Under such circumstances, the channel estimation is less accurate. Once channel estimates are available on OFDM symbol and sub-carrier with pilots, channel interpolation is needed in time and frequency domains for equalization purposes. In this work, we have considered the following interpolation methods: (i) Zero Order Hold (ZOH); (ii) linear; and (iii) spline. The former method leads to a less computational complexity at the expense of achieving a worst performance whereas the latter method achieves the best performance at a higher computational cost. Finally, once channel estimates are available at each \((m,k)\) pair with data for a group of OFDM symbols, 1-tap Zero Forcing (ZF) equalization is done as follows  
\[\hat{\chi}_{k}^{(m)}=Y_{k}^{(m)}/\hat{H}_{k}^{(m)}. \tag{10}\]
## 1 Introduction
(2020,21) divided the stored material inside the bunker into four regions based on the flow state: the mass flow region, the funnel flow region, the transition region between mass flow and funnel flow, and the wall adhesion region. [PERSON] et al. (2021) employed a similarity experiment and numerical simulation method to study the flow trend of particles during coal discharge, categorizing the particles inside the bunker into three zones and analyzing the flow characteristics of each zone. [PERSON] et al. (2013) utilized the finite element method (FEM) based on the Eulerian reference system to calculate the deformation rate, velocity field, and stress distribution during bunker discharge. [PERSON] et al. (2020) employed the EDEM program to simulate the coal bunker's discharge process, studying the flow patterns of coal particles and the distribution characteristics of wall pressure. Their findings indicated that coal material could be classified into mass flow and funnel flow patterns, and they explained the transition between these flow patterns and wall pressure distribution based on the arching principle. [PERSON] et al. (2015) predicted the material flow mode during discharge using the finite element method and studied the effect of the bunker hopper's inclination angle on the flow mode. [PERSON] et al. [26], [PERSON] et al. [27], and [PERSON] et al. [28] utilized the Discrete Element Method (DEM) to explore the impact of particle size and distribution on particle fluidity in silos. Current research primarily focuses on material flow characteristics and bunker wall pressure distribution under specific parameter conditions, while studies on the adhesive clogging characteristics of coal and their influencing factors remain limited. There is a lack of systematic quantitative research on the effects of raw coal moisture content, coal storage height, and coal bunker structural parameters on clogging behavior. Therefore, this study focuses on raw coal in coal bunkers as the research subject. By independently designing a raw coal shear test system and conducting shear tests, the effects of moisture content, compaction pressure, and interface effects on the adhesion parameters of raw coal are investigated. Based on the adhesion property parameters of raw coal, PFC2D software is employed to quantitatively analyze the influence of moisture content, coal storage height, and coal bunker structural parameters on the adhesive clogging characteristics of coal bunkers. This research aims to reveal the mechanisms of adhesive clogging in underground coal bunkers and provide scientific evidence and valuable guidance for optimizing solutions to this issue.
## Appendix C Proof of Lemma 2  
We first prove the first part of the lemma. Without loss of generality, we assume that only condition (a) is satisfied. From (31), the power of MUI can be rewritten as  
\[\begin{split} Q_{l}^{\text{MUI}}&=\sum_{k=1,k\
eq l }^{L}P_{k}\mathbf{w}_{D,l}^{\text{H}}\mathbf{C}_{k}^{\text{LoS}}\mathbf{w}_{D,l }+\sum_{k=1,k\
eq l}^{L}P_{k}\mathbf{w}_{D,l}^{\text{H}}\mathbf{C}_{k}^{\text{ NLLoS}}\mathbf{w}_{D,l}\\ &=Q_{l,\text{LoS}}^{\text{MUI}}+Q_{l,\text{NLLoS}}^{\text{MUI}}. \end{split} \tag{12}\]  
The first and second terms of right-hand side represent the MUIs contributed by the LoS and NLoS channels, respectively. By using the expression of \(P_{k}\) in Lemma 1 and (10), we can obtain (in the large \(\{N_{x},N_{y}\}\) limit)  
\[\frac{Q_{l,\text{LoS}}^{\text{MUI}}}{Q_{l}^{\text{U}}}\leq\frac{1}{N_{x}N_{y} }\sum_{k=1,k\
eq l}^{L}\frac{P_{k}}{P_{l}}\frac{\sin^{2}\left(N_{x}\pi\left( \rho_{x,l}-\rho_{x,k}\right)\right)}{N_{x}\sin^{2}\left(\pi\left(\rho_{x,l}- \rho_{x,k}\right)\right)}\frac{\sin^{2}\left(N_{y}\pi\left(\rho_{y,l}-\rho_{y, k}\right)\right)}{N_{y}\sin^{2}\left(\pi\left(\rho_{y,l}-\rho_{y,k}\right) \right)}. \tag{13}\]  
Note that the function \(\frac{1}{N}\frac{\sin(N\pi x)}{\sin(\pi x)}\) converges to standard sinc function \(\text{sinc}(Nx)\) as \(N\rightarrow\infty\)[43]. Thus, when condition (a) is satisfied, i.e., \(\left[\rho_{x,l}^{\min}\right.\),\(\rho_{x,l}^{\max}\right]\cap\left[\rho_{x,k}^{\min},\rho_{x,k}^{\max}\right]=\emptyset\), \(\forall l\
eq k\), we can obtain  
\[\begin{split}\frac{\sin^{2}\left(N_{x}\pi\left(\rho_{x,l}-\rho_{ x,k}\right)\right)}{N_{x}\sin^{2}\left(\pi\left(\rho_{x,l}-\rho_{x,k}\right) \right)}&=N_{x}\frac{\sin^{2}\left(N_{x}\pi\left(\rho_{x,l}-\rho_ {x,k}\right)\right)}{N_{x}^{2}\pi^{2}\left(\rho_{x,l}-\rho_{x,k}\right)^{2}} \\ &\leq\frac{1}{N_{x}\pi^{2}\left(\rho_{x,l}-\rho_{x,k}\right)^{2}} =\mathcal{O}\left(N_{x}^{-1}\right),\end{split} \tag{14}\]  
where the second step follows from the inequality \(|\sin x|\leq 1\). Moreover, the function \(\frac{1}{N}\frac{\sin(N\pi x)}{\sin(\pi x)}\) achieves its maximum at point \(x=0\), thus, we have  
\[\frac{\sin^{2}\left(N_{y}\pi\left(\rho_{y,l}-\rho_{y,k}\right)\right)}{N_{y} \sin^{2}\left(\pi\left(\rho_{y,l}-\rho_{y,k}\right)\right)}\leq N_{y}. \tag{15}\]  
Substituting (14) and (15) into (13), we can obtain \(\frac{Q_{\text{LoS}}^{\text{MUI}}}{Q_{l}^{\text{U}}}\leq\mathcal{O}\left(N_{x} ^{-2}\right)\). Using a similar procedure as that in the appendix of [44], we can prove \(\frac{Q_{\text{LNLLoS}}^{\text{MUI}}}{Q_{l}^{\text{U}}}\leq\mathcal{O}\left(N _{x}^{-2}\right)\). This completes the proof for the first part of Lemma 2.  
When both conditions (a) and (b) are satisfied, the proof is similar with that in appendix of [44]. Thus, it is omitted.
## 2 Literature Review  
### Mobile Robots  
The implementation of mobile robotics in industry offers a set of specific advantages that the traditional industrial robots, such as robots that are static or move on rails, lack--namely, task flexibility and robotic mobility [11]. The introduction of these robots in assembly lines also enables reduced duration of breakdowns, lower commissioning time, minimum need for human intervention due to their autonomous behavior and higher production variability [12]. Developments of mobile robots have led to various relevant research topics, such as methodologies that can make use of observations of mobile robots on shop floors and build up the fidelity of simulations based on those shop floors [13].  
For autonomous industrial mobile manipulators (AIMM), as they are called, it is easier to adapt to changes in the environment and are able to perform a wider variety of tasks. The usual architecture of these robots is a manipulator mounted on a mobile platform.  
An example of such a robot is the \"Little Helper\" [11], an experimental robot that utilizes a generic product platform architecture and universal communication language, by use of commercial off-the-shelf hardware components and software systems, focusing on flexibility and versatility in order to become a plug-and-produce solution. In [14], the Little Helper was used in experiments that helped validate the concept of general, self-asserting robot skills for manufacturing.  
Another similar robot is SwRI's \"Mr. Roam\" [15], which consists of a Vetex mobile platform integrated with a Motorman robot arm (SIA-20) and a Nikon metrology iGPS device to create an off-the-shelf manipulator. SwRI validated the industrial application of the robot in an experiment on an aircraft wing model.  
KUKA's \"KMR iiwa\" [16] combines a mobile, flexible platform with the sensitive LBR iiwa lightweight robot arm. The robot's advantages include flexibility, autonomy, omnidirectional agile movement, precision of up to +/ \(-\) 5 mm, intelligence in moving around obstacles and finding new paths, and easy operation.  
The \"KMR QUANTEC\" [17] is a combination of KUKA robots, mobile platforms and other industry-standard components. The variability in the components allows customized solutions according to each user's individual requirements. It consists of the KUKA  
Figure 2: Robotic perception for robot behavior adaptation concept.  
omniMove platform and a KUKA robot of the KR QUANTEC series, as well as software and a controller. It is safe, mobile, independent, multifunctional, adaptable and variable.
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Objective Functions  
The optimization and reconfiguration of UDNs incorporating VPPs should consider both the distribution network's stability and economy. Therefore, this paper proposes three optimization objectives: minimizing voltage deviation, minimizing network losses, and maximizing operational benefits.
## 1 Introduction
A similar comparison of hop oil composition for seven different hop cultivars of European and North American types based on GC/mass spectrometry (MS) results is given in reference [27]. Neither reference [29] nor [30] used phytochemical standards of the hop oil components for quantification purposes and the contents of individual components were comparatively assessed against the FID-derived total sum of peak areas [29] or the MS-derived total ion current [30]. To our best knowledge, so far, quantification of the components of different commercially available hop oils has not been performed basedon chemical or phytochemicalam standards, except for sporadic cases (such as the use of isobutyric, 2-methylisobutyric, and isovaleric esters, as reported in reference [24]). High-performance thin-layer chromatography in combination with effect-directed analysis (HPTLC-EDA) is an efficient tool to obtain the bio-profiles of samples with various matrices [31], including essential oils [32]. The results obtained can point out the separated compounds responsible for antimicrobial and/or enzyme inhibitory activities, thus allowing their further characterization and identification. So far, HPTLC has been used for the analysis of hops or peers in a few cases only. For example, an HPTLC method was developed to quantify xanthohumol in hops [33], and the antibacterial components of aqueous and ethanol hop extracts have been traced by TLC-directed bioautography using _Bacillus subtilis_ and _Escherichia coli_[34]. Demonstration of phenolic, antioxidant, antibacterial, and acetylcholinesterase inhibitory profiles of various beers was carried out utilising HPTLC and HPTLC-EDA [35; 36]. Compounds with an anti-obesity effect can be screened using HPTLC-\(\alpha\)-glucosidase [37; 38] and HPTLC-lipase [39] assays. The inhibition of \(\alpha\)-glucosidase delays the digestion of polysaccharides, thus reducing the release of glucose [40]. Pancreatic lipase inhibition results in reduced fat absorption [41]. Therefore, such inhibitors improve energy metabolism in obese individuals. The aim of this study was to compare the chemical and biological profiles of various hop oils. Using GC/MS and GC-FID, we compared the contents of seven terpen and methyl heptanoate, considered chemical markers, of seven commercial North American hop oils and six hop oils hydrodistilled from commercial hop pellets. To our best knowledge, these are the first quantification results with selected compounds contained in hop oils derived from seven North American hop varieties. For all commercial hop oils considered in this study, their antioxidant potential was evaluated with the use of electron paramagnetic resonance (EPR) spectroscopy, and their enzyme (\(\alpha\)-glucosidase and lipase) inhibitory and antibacterial profiles were revealed using HPTLC-EDA. The components in the active HPTLC zones were further characterized by HPTLC-TLC-MS Interface-SPME--GC-MS.
## 3 Implementation
By conducting these separate implementations with the above-mentioned tools, this study was able to explore and compare the effectiveness of different frameworks in automating the ensemble modeling process for electrical load forecasting. The use of the MAPE metric across all implementations ensured a consistent basis for comparison, highlighting the strengths and potential limitations of each framework's approach to ensemble creation within this specific application domain. The performance of the frameworks was evaluated using the mean absolute percentage Error (MAPE) as the key metric. AutoML by H2O.ai demonstrated the best performance with a MAPE of approximately 12.89%, indicating its strong accuracy in predictive tasks. FlaML followed closely with a MAPE of 13.95%, offering a competitive balance between accuracy and efficiency. AutoTS, while slightly less accurate, achieved a MAPE of 14.01%, making it a viable option depending on the specific requirements of the application. These results, presented in Table 1, highlight that while all three frameworks are effective, AutoML by H2O.ai may be the preferred choice when precision is critical. Moreover, despite the  
Figure 2: Industrial load profile. volatile energy consumption of the forecasted load, these frameworks could also create viable predictions as far as the energy trading business boundaries are concerned.
## 2 Materials and Methods
### Data Collection Campaigns
#### 2.2.2 Ground Truth Data  
Daily weather data of pan evaporation (mm/day), rainfall, maximum and minimum air temperature, relative humidity, and wind speed were collected from the meteorological observatory situated 500 m away. Root zone (0-20 cm) soil moisture content (SMC) was monitored using a soil moisture meter (ICT, MP-406, Armidale, Australia). These weather parameters were evaluated to study microclimate variations as a result of different irrigation treatments in okra. The determination of the relative water content (RWC) of leaves was conducted according to the specified protocol [12]. The ground data observations were utilized to classify and designate the images as crops that were experiencing stress and non-stress (Table 1).  
Figure 1: Experimental site and plot layout of irrigation type and treatment levels.  
Figure 2: Representative RGB (**a**,**b**) and thermal imagery (**c**,**d**) of okra under stress (25% Etc, sprinkler irrigation) and non-stress condition (100% Etc, flood irrigation).