esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
Figure 3 shows the designed framework for statistical and hydrological evaluation of GPM-era IMERG and GSMaP SPPs in both near- and post-real-time versions at sub-daily time scales. First, two TRMM-era TMPA (3B42 RT and 3B42V7), three IMERG (IMERG-E, IMERG-L, and IMERG-F), and three GSMaP (GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE) SPPs from 1 April 2014 to 31 December 2016 were compared against the ground precipitation observations by using several diagnostic indices. Such assessments were statistically conducted at sub-daily time scales (6 h for daytime precipitation and 12 h for night precipitation). Afterward, based on the statistical evaluations of the eight SPPs, the best SPPs in near- and post-real-time versions and the rain-gauge-based precipitation data were used to drive the Grid-based Xinanjiang (GXAJ) hydrological model to perform 3-hourly discharge simulations at the five hydrological stations (Figure 1) from 1 April 2014 to 31 December 2016. Hydrological simulations were performed under two model parameter setup scenarios (rain-gauge-calibrated parameters and SPP-specific recalibrated parameters). Finally, the simulated 3-hourly streamflow time series from the rain-gauge- and SPP-based model runs were evaluated against the observed streamflow with several statistical indicators. The detailed descriptions of statistical indices for precipitation and hydrological assessment, the GXAJ model, and the streamflow simulation schemes under two model parameter setup scenarios are given in the following sub-sections.
## 2 Materials and Methods
### Characterization Techniques  
The quantitative chemical composition of the catalysts was performed by inductively coupled plasma atomic emission spectroscopy (ICP-AES) on an ICP-OPTIMA 2100 DV Perkin Elmer instrument. The particle size distribution was determined by transmission electronic microscopy (TEM) in a FEI Tecnai T-200 operated at 200 keV. Aberration corrected scanning transmission electron microscopy images for selected samples were acquired using a high angle annular dark field detector in a FEI XFEG TITAN electron microscope operated at 300 kV equipped with a CETCOR Cs-probe corrector from CEOS Company allowing formation of an electron probe of 0.08 nm. The geometric aberrations of the probe-forming system were controlled to allow a beam convergence of 24.7 mrad half-angle to be selected. Elemental analysis was carried out with an EDS (EDAX) detector, which allows EDX experiments to be performed in scanning mode. The samples were suspended in ethanol, under sonication, and 3 drops of each sample were carefully drop-casted onto Ni mesh grids. Specific surface area of the fresh and used catalysts were determined by N\({}_{2}\) adsorption isotherms and the BET equation was measured with the aid of an instrument Micromerities Gemini V2.00-2380.  
Fresh and used catalysts were additionally characterized by X-ray diffraction (DRX, Shimadzu model XD-D1 with a copper source radiation, CuK\(\alpha\)). The Raman spectra were recorded using a LabRam spectrometer (Horiba-Jobin-Yvon) equipped with a CCD detector and a 532 nm excitation wavelength (laser power set at 30 mW) and a coupled Olympus confocal microscope.  
Oxidation states of copper were determined by temperature programmed reduction experiments carried out up to 500 \({}^{\circ}\)C in H\({}_{2}\)/Ar 5% v/v ambient, with a temperature heating ramp of 10 \({}^{\circ}\)C-min\({}^{-1}\). A Micromerities AutoChem II-2920 equipment with a TCD detector was used for temperature programed reduction (TPR) analysis. The hydrogen consumption was referred in all cases to 50 mg of each dry sample placed in a U-shaped quartz tube reactor. The TPR profiles of the fresh samples were obtained after the calcination treatment, described in Section 2.3.2. For used samples, a first TPR (TPR-I) profile was performed. Then, after TPR-I, the same sample was re-oxidized in pure oxygen for 30 min at 500 \({}^{\circ}\)C and a second TPR (TPR-II) was obtained. All the TPR experiments were carried out by heating the samples at 10 \({}^{\circ}\)C-min\({}^{-1}\) up to 500 \({}^{\circ}\)C in H\({}_{2}\)/Ar 5% v/v flowing mixture.  
Differential Scanning Calorimetry (DSC) was used to analyze the thermal decomposition of the unreacted precursors present in the catalysts. A Mettler Toledo instrument with a DSC821e module was employed. Samples of 30-40 mg placed inside a 40 \(\mu\)L aluminum crucible were heated from 30 \({}^{\circ}\)C to 500 \({}^{\circ}\)C in air flow (50 cm\({}^{3}\cdot\)min\({}^{-1}\)) employing a heating rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\).
# Adaptive Mutation Dynamic Search Fireworks Algorithm
## 4 Simulation Results and Analysis
### Simulation Results and Analysis  
#### 4.2.1 Study on the Mutation Probability \(p\)  
In AMdynFWA, the mutation probability \(p\) is introduced to control the probability of selecting the Gaussian and Levy mutations. To investigate the effects of the parameter, we compare the performance of AMdynFWA with different values of \(p\). In this experiment, \(p\) is set to 0.1, 0.3, 0.5, 0.7, and 0.9, respectively.  
Table 2 gives the computational results of AMdynFWA with different values of \(p\), where 'Mean' is the mean best fitness value. The best results among the comparisons are shown in bold. It can be seen that \(p=0.5\) is suitable for unimodal problems f1 \(-\) f5. For f6 \(-\) f20, \(p=0.3\) has a better performance than the others. When \(p\) is set as 0.1 or 0.9, the algorithm obtains better performance on f21 \(-\) f28.  
The above results demonstrate that the parameter \(p\) is problem-oriented. For different problems, different \(p\) may be required. In this paper, taking into account the average ranking, \(p=0.3\) is regarded as the relatively suitable value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Type** & **Function Number** & **Function Name** & **Optimal Value** \\ \hline \multirow{6}{*}{Unimodal Functions} & 1 & Sphere function & \(-\)1400 \\  & 2 & Rotated high conditioned elliptic function & \(-\)1300 \\  & 3 & Rotated bent cigar function & \(-\)1200 \\  & 4 & Rotated discus function & \(-\)1100 \\  & 5 & Different powers function & \(-\)1000 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Basic Multimodal \\ Functions \\ \end{tabular} } & 6 & Rotated rosenbrock’s function & \(-\)900 \\  & 7 & Rotated schaffers F7 function & \(-\)800 \\  & 8 & Rotated Ackley’s function & \(-\)700 \\  & 9 & Rotated weierstrass function & \(-\)600 \\  & 10 & Rotated griewank’s function & \(-\)500 \\  & 11 & Rastrigin’s function & \(-\)400 \\  & 12 & Rotated nastrigin’s function & \(-\)300 \\  & 13 & Non-continuous rotated nastrigin’s function & \(-\)200 \\  & 14 & Scheweef’s function & \(-\)100 \\  & 15 & Rotated schewef’s function & 100 \\  & 16 & Rotated katsuura function & 200 \\  & 17 & Lunacek Bi\_Rastrigin function & 300 \\  & 18 & Rotated Lunacek Bi\_Rastrigin function & 400 \\  & 19 & Expanded griewank’s plus rosenbrock’s function & 500 \\  & 20 & Expanded scalfer’s F6 function & 600 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Composition Functions \\ \end{tabular} } & 21 & Composition function ( N = 5) & 700 \\  & 22 & Composition function ( 2 N = 3) & 800 \\  & 23 & Composition function 3 ( N = 3) & 900 \\  & 24 & Composition function 4 ( N = 3) & 1000 \\  & 25 & Composition function 5 ( N = 3) & 1100 \\  & 26 & Composition function 6 ( N = 5) & 1200 \\  & 27 & Composition function 7 ( N = 5) & 1300 \\  & 28 & Composition function 8 ( N = 5) & 1400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CEC2013 test set.
## 2 Working Principle
Firstly, the depth of penetration (\(\Delta\)) of the eddy currents is increased by a few millimeters, as expressed by  
\[\Delta=\sqrt{\frac{1}{\pi f\mu\sigma}} \tag{6}\]  
where,\(f\) is the excitation frequency, \(\sigma\) is the electrical conductivity, and \(\mu\) is the magnetic permeability of the sample. The depth of penetration of eddy currents is limited to a fraction of a millimeter of mild steel owing to its high permeability. However, magnetizing the sample can increase it by a few millimeters due to the reduced permeability. Figure 2: (**a**) The schematic representation of the magnetization yoke, and (**b**) the simplified equivalent magnetic circuit using lumped parameters reluctance network. Secondly, the eddy currents field, set up in the sample due to the time-harmonic magnetic field of the coil, will be affected by the perturbations in the permeability caused by the corrosion defects. Consequently, a variation in the impedance of the coil will be experienced due to the corrosion of the sample. The impedance of the coil (\(Z=R+X_{L}\)), which is the resistance to the flow of the alternating current in the coil, is composed of two components. The resistance (\(R\)) of the coil is a function of the electric field set up by the eddy currents and is sensitive to the variation in the conductivity (\(\sigma\)) of the sample. The imaginary part of the impedance, called reactance (\(X_{L}\)), is the opposition due to magnetic fields induced by the time- varying currents and is affected by the permeability changes of the test sample. Theoretically, the expression for calculating the perturbation in the coil impedance (\(\Delta Z\)) placed above the samples with different metallic composition is given as follows [29; 30]:  
\[B=\mu_{0}(H+x_{m}H)=\mu_{0}H(1+x_{m})=\mu H \tag{1}\]0  
where \(K\) and \(P\) are the constants related to the coil parameters; and \(A(\alpha)\), and \(\varnothing(\alpha)\) are the magnetic scalar potential and flux density set up by the coil, respectively, and are a function of \(\alpha\), which is related to both conductivity and permeability of the test sample. \[B=\mu_{0}(H+x_{m}H)=\mu_{0}H(1+x_{m})=\mu H \tag{1}\]1  
Under DC magnetization, the conductivity effect is found to be negligible and the real part of the impedance can, therefore, be ignored for the sensor optimization study [22; 26].
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Channel Response Models under Test
#### 4.2.2 Frequency Selective Channel  
The proposed channel is a synthetic channel that was defined by the PRIME Alliance during the PRIME 1.4 PHY technology design process to test the performance of the system in a representative situation of a channel with a resonator in the center of the PRIME band. It is a filter of constant group delay: it introduces a distortion in amplitude depending on the frequency, but not in phase.  
This filter was particularly designed to attenuate 10% of the carriers 20 dB or more in the center of the band. More precisely, the filter features a maximum attenuation at 62.5 kHz (considering a sampling frequency of 250 kHz). It has an attenuation of 20 dB for carriers between 60.4 and 64.5 kHz (9 carriers) and an attenuation of 40 dB for carrier between 61.8 and 63.1 kHz (3 carriers) and an attenuation higher than 60 dB to 1 single carrier. The frequency characterization of this filter is represented in Figure 3.  
The frequency selective channel is defined by the impulse response \(h[n]\) in Table 2. It is a Finite Impulse Response (FIR) filter of order 31. This impulse response in represented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(n\) & \(h[n]\) & \(n\) & \(h[n]\) & \(n\) & \(h[n]\) & \(n\) & \(h[n]\) \\ \hline
0 & \(-2.66375\times 10^{-5}\) & 4 & \(8.897\times 10^{-6}\) & 8 & \(-5.562\times 10^{-6}\) & 12 & \(2.36223\times 10^{-5}\) \\
1 & 0.0972533 & 5 & 0.0570675 & 9 & 0.072636 & 13 & \(0.0814326\) \\
2 & \(3.06855\times 10^{-5}\) & 6 & \(1.60698\times 10^{-5}\) & 10 & \(-3.19252\times 10^{-5}\) & 14 & \(-1.51506\times 10^{-5}\) \\
3 & \(-0.0478325\) & 7 & \(-0.0654797\) & 11 & \(-0.0780915\) & 15 & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Impulse response for a frequency selective channel.  
Figure 4: Impulse response of the frequency selective channel.  
Figure 3: Frequency selective channel response module.
## 3 Results and Discussions
### Exergy Analysis
Moreover, [PERSON] and [PERSON] [73] have reported that only a slight difference was observed among the destruction ex  
Figure 6: Sustainability index (SI) and entropy generation (\(\dot{S}_{gen}\)) values of the tested fuels. biodiesels with respect to diesel fuel and also concluded that the fuel type was not effective on the irreversibilities fraction. The comparison between the energy and exergy efficiency (i.e., energy and exergy outlet work) shown in Figure 8a-e demonstrates that both efficiencies had similar findings, approximately 26%, and 24%, respectively, with a small variation between them. The exergy efficiencies were slightly lower than the corresponding energy efficiency. This is a consequence of the different chemical exergy of the tested fuels (\(\varphi\)), as shown in Equations (11) and (12). The specific chemical exergy is higher than the LHV of the tested fuels. Thus, the total inlet exergy is always greater than the total inlet fuel energy, which relies on the fuel volumetric fractions (i.e., fuel blend proportions). As a result, the exergy efficiency is always slightly lower for fuel than the corresponding energy efficiency [47]. Moreover, [PERSON] et al. [69] stated that this decrease in the exergy efficiency is because of the exergy destruction during the combustion of the fuel. It was observed that the addition of the biofuels (ethanol and biodiesel) to diesel fuel reduced both energy and exergy efficiencies, while the substitution to F-T diesel has also slightly reduced both efficiencies. This result is in agreement with the findings of [PERSON] and [PERSON] [26] utilizing blends of diesel/biodiesel/ethanol and [PERSON] et al. [27] with diesel/biodiesel/butanol, as compared with results using diesel fuel. Figure 8: _Cont._  
Figure 7: Exergy balance of the engine for each tested fuel. Table 6 shows the summary of the energy and exergy efficiencies of the current work. The comparison of the present results with those by previous researchers, presented in Table 1, for different fuel types and engine operating conditions were either lower, nearly the same, or higher than that of the current work. The main reasons for the difference in these results are the fuel type, blend composition, and properties (e.g., cetane number, heating value, etc.) and the experimental conditions at which the diesel engines were evaluated.
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
###### Abstract
All this information is usually aggregated, meaning that it is impossible to identify an individual user's QoE. With the latest advances in information technologies, it is now possible to analyze massive volumes of information by using Big Data Analytics (BDA) techniques [22]. In mobile networks, BDA can improve the reaction time of management systems, allowing actions in real time and in a proactive way to improve the monitoring, control and optimization of QoE [21]. Connection traces are one of the main sources of information in mobile networks. Traces systematically register all events associated with a specific cell/user in some period of time, becoming a powerful tool for automated network performance analysis, monitoring and control [6]. In this work, a novel automatic method is presented to tune QoS thresholds in classical analytical QoE models by analyzing radio connection traces in an Long Term Evolution (LTE) system. The proposed method relies on the fact that users tend to shorten their connections when QoE is not satisfactory. Thus, the values of QoS thresholds can be inferred by detecting the loss of traffic volume for each connection as a result of unsatisfied users. The method consists of two stages: first, connections are segregated per service, based on QoS Class Identifiers (QCIs) and hierarchical clustering from connection descriptors; then, the value of QoS thresholds is estimated for each service by analyzing traffic descriptors on a per-connection basis. Method assessment is carried out by using a real trace dataset from two live LTE networks. Unlike previous approaches, the proposed data-driven method (a) can be fully automated, eliminating the need for subjective tests when deploying a new service; (b) can deal with the large diversity of system and human factors, which cannot be taken into account in lab environments; and (c) can be executed periodically to detect changes in user trends in large geographical regions. The rest of the work is organized as follows. Section 2 introduces the use of utility functions for QoE characterization. Section 3 outlines the trace collection process in mobile networks. Section 4 describes the proposed method to adjust QoS threshold parameters in classical QoE models based on network traces. Section 5 shows the results obtained with a trace dataset taken from real LTE systems. Finally, Section 6 presents the main conclusions.
# Experimental Study on Drying Characteristics of Alfalfa Hay Bales Using Hot Air Convection
## 4 Conclusions  
This study systematically clarifies the drying behavior of alfalfa bales under hot air convection, indicating that drying efficiency and uniformity are significantly influenced by multiple factors, including the hot air temperature, air velocity, and initial moisture content of the hay bales. Specifically, we found that:  
Figure 9: Optimization of the alfalfa hot air drying scheme: (**a**) increase the driving force; (**b**) reduce the resistance.  
Temperature-efficiency tradeoff: At 70 \({}^{\circ}\)C and an air velocity of 2 m/s, bales with 25% initial moisture (w.b.) reached the target moisture (17% w.b.) within 1.5 h, achieving a drying efficiency of 26.67 kg/h. While higher temperatures (>70 \({}^{\circ}\)C) may further accelerate drying, they risk nutrient degradation.
2 Density-dependent airflow limitation: Increasing the bale density from 127.95 to 144.15 kg/m\({}^{3}\) extended drying time by 50%, indicating the relationship between density and airflow resistance. This necessitates density control (<130 kg/m\({}^{3}\)) during baling to match dryer capacity.
3 Directional drying heterogeneity: Horizontal air supply caused severe moisture gradients, with central regions exceeding 20% (w.b.) despite surface dryness. Vertical airflow (top-bottom) partially mitigated this, suggesting directional optimization as a priority for industrial dryers.  
These results demonstrate that efficient drying of dense alfalfa bales requires balancing thermal input (70 \({}^{\circ}\)C), airflow penetration (2 m/s), and preprocessing control (moisture \(\leq\) 25%, density \(\leq\) 130 kg/m\({}^{3}\)). Future work should focus on the optimization of the drying process by increasing the driving force and reducing bale density to lower resistance for drying combined with the precise control of the key parameters, thereby improving the quality and storability of alfalfa hay bales.  
Conceptualization, J.D.; methodology, J.D. and C.L.; software, C.L.; validation, J.D. and C.L.; formal analysis, C.L.; investigation, C.L.; resources, J.D.; data curation, C.L.; writing--original draft preparation, J.D.; writing--review and editing, C.L.; visualization, C.L.; supervision, J.D.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Program Project of Inner Mongolia Autonomous Region, grant number 2022 YFDZ0022.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors are grateful for the funding support of Science and Technology Program Project of Inner Mongolia Autonomous Region (2022 YFDZ0022) for carrying out this research work.  
The authors declare no conflicts of interest.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 2 Structure and Production of Pectins
### Structure Classification
#### 2.2.2 Degrees of Acetylation and Amidation  
The degree of acetylation (DAC) is defined as the percentage of galacturonosyl residues esterified (on the hydroxyl group) with acetyl (Figure 1). Acetylation prevents gel formation but increases the stabilizing and emulsifying effects of pectins [26; 27; 28]. The presence of multiple acetyl groups on sugar beet pectin gives it a surfactant behavior that can be used to stabilize emulsions [29; 30]. Pectins having a high degree of acetylation (DAC up to 25%) do not have good gelling properties [11].  
Figure 3: Partially methylated galacturonans. (**A**) Low-methoxy and (**B**) high-methoxy pectin structures [11].  
Amidated pectins are synthetized through the reaction of pectin carboxymethyl groups (-COOCH\({}_{3}\)) with ammonia [31; 32]. The degree of amidation (DA) is the percentage of carboxylic groups in the amide form. It mainly concerns weakly low-methoxy amidated pectin (LMAP). The amidation of pectin enables it to withstand more calcium variation and be more thermoreversible [31]. It also increases the water solubility of pectins [32].
# Two-Layer Network Caching for Different Service Requirements
## 3 Model  
### Statistical Model of Content Requests  
Most of proposals on cache management have been analyzed by using the Independent Reference Model (IRM) [13; 27]. According to it, contents belong to a fixed catalogue and their popularity is constant over time. In addition, customers' requests form a sequence of i.i.d. random variables and the request arrival process is memory-less.  
For what concerns the size of items, due to the common practice of chunking large contents if their size exceeds a given value, in many papers it is modeled as a constant value [13]. In this paper, we consider both constant and variable content size. The reason of considering also variable content size is that in some situations it can be requested to cache all content chunks or nothing.  
For what concerns the catalog size, considering it fixed in the era of Big Data could be considered an excessive simplification. However, if we limit the analysis to the set of contents that collects a high percentile of the popularity distribution, the number of contents that are worth of being considered is relatively small. For example, by using (1) for \(q=8\), \(M=10^{6}\), and \(\alpha=1.3\), it results that \(10^{5}\) and \(4\times 10^{5}\) contents account for a percentile of probability of about 97% and 99%, respectively.