esa-sceva/satcom-chunk-collection huggingface dataset:
# Rheology, Hydration, and Microstructure of Portland Cement Pastes Produced with Ground Acai Fibers
## 2 Materials and Methods
### Experimental Procedures  
The rheological tests were conducted using a Haake MARS III (Thermo Fisher Scientific, Waltham, MA, USA) rheometer equipped with a parallel-plate geometry with a hatched surface to prevent wall slip. The geometry diameter and the axial gap were 35.00 and 1.00 mm, respectively, and the temperature was kept at 23.0 \(\pm\) 0.1 \({}^{\circ}\)C. The rheological tests started 10 min after the first contact between the dry materials and water. The flow curves were obtained by increasing the shear rate from 0.1 to 100 s\({}^{-1}\) in 10 steps and subsequently decreasing it down to 0.1 s\({}^{-1}\) in the same steps. In each step, the shear rate was kept for 30 s (which was enough to reach a steady-state flow), and the final 3 s of each step were recorded. The rheological properties, i.e., yield stress \(\tau_{0}\) (in Pa) and viscosity \(\mu\) (in Pa.s), were determined using Equations (1) and (2) respectively, where \(\tau\) is the shear stress (in Pa), \(\dot{\gamma}\) the shear rate (in s\({}^{-1}\)), \(K\) and \(n\) the consistency and pseudoplastic parameters of the Herschel-Bulkley model, and \(\dot{\gamma}_{\max}\) the maximum shear rate applied (in s\({}^{-1}\)).  
\[\tau=\tau_{0}+K.\dot{\gamma}^{n} \tag{1}\]  
\[\mu\ =\ \frac{3K}{n+2}\cdot(\dot{\gamma}_{max})^{n-1} \tag{2}\]  
The cement hydration kinetics was evaluated through isothermal calorimetry, conducted using a TAM Air (TA Instruments) calorimeter at 23 \({}^{\circ}\)C. Immediately after mixing, about 10 g of fresh paste was placed into the calorimeter container, and the heat release was recorded for up to 160 h. The calorimetry results (i.e., the heat flow and cumulative heat) were normalized for the weight of dry material. After the calorimetry test, the samples were removed from the container, ground until passing through a 75 \(\upmu\)m-opening mesh, and immediately tested through XRD under the testing conditions detailed in Section 2.1. At this age, a broken piece of paste was also analyzed through SEM using the equipment and conditions detailed in Section 2.1.
## 3 Materials and Methods
### Preprocessing  
The very first step of data processing is denoising. For this purpose, we used a second-order Chebyshev type-I filter [49] with a cutoff frequency of 0.001. The mentioned filter rejects noise very well and provides a signal with a good signal-to-noise ratio (SNR) at the output. Figure 2 represents the noisy and denoised signals for the \(x\), \(y\), and \(z\) channels of the magnetometer for the walking activity for all three datasets. A similar approach was followed to denoise the data obtained from other sensors corresponding to other activities. We chose the walking activity to plot because walking is a common activity among all three datasets, thus supporting a good comparison among datasets. Figure 2a-c shows the results for the MobiAct_v2.0 magnetometer. Figure 3a-c represents the denoising results for the Real-World HAR magnetometer, and Figure 4a-c shows the denoising results for the Real-Life HAR magnetometer.  
Figure 1: The architecture of the proposed smartphone sensors remote data classification (SSRDC) system.  
Figure 2: Signal denoising using second-order Chebyshev type-I filter with a cutoff frequency equal to 0.001. (**a**) MobiAct_v2.0 magnetometer x-channel, (**b**) MobiAct_v2.0 magnetometer y-channel, and (**c**) MobiAct_v2.0 magnetometer z-channel.
## 4 Results  
### Summary Statistics and Survey Results  
We prepared descriptive statistics of our response data (\(n=290\)). The summary statistics are presented in Table 2. The table contains summary statistics from our data gathered through the questionnaire. In total, the data represent 290 responses (\(n=290\)). Mean values for all variables, including the transformed binary variable, indicate where the center of the responses is located. It can be seen that for most of our observed variables, the mean values are above 3 but below 4,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Questions**} & \multicolumn{5}{c}{**Response**} \\ \cline{2-6}  & **Strongly Agree** & **Agree** & **Neutral** & **Disagree** & **Strongly Disagree** \\ \hline _Input Questions (1, 2, 3... 20)_ & 5 & 4 & 3 & 2 & 1 \\ Structure & 5 & 4 & 3 & 2 & 1 \\ Rules & 5 & 4 & 3 & 2 & 1 \\ Control & 5 & 4 & 3 & 2 & 1 \\ _Outcome Questions (1, 2, 3... 20)_ & 5 & 4 & 3 & 2 & 1 \\ \hline Total Questions 40 & \multicolumn{5}{c}{Total responses 290} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Survey questionnaire.  
i.e., mean response towards the BPR measures of structure, rules, and control lies between Neutral to Agree. This result shows not a very strong agreement towards the BPR measures, though there is not a total disagreement as well. The mean for the outcome is the highest, 3.879621, reflecting a comparatively better level of agreement amongst our surveyed population regarding the effectiveness of the BPR regime. This trend is also reflected as we observe the minimum and maximum range of response, with a minimum of 2.24 as compared to a minimum response for all other observed variables. Observation shows a more significant skewness in the case of rules, meaning that most of the respondents agree to the rules of BPR. The results also indicate a higher kurtosis in the case of structure, reflecting greater neutrality amongst the respondents regarding the effectiveness of the structural changes brought in by the BPR. This result is indicative of the confusion or lack of understanding or even lack of training necessitated by the structural reforms.  
Figure 1 plots the summary of the location of responses on a Likert scale of 1 to 5, whereas Figure 2 gives individual variable response distribution on a comparable scale. We can observe a larger concentration of responses between neutral (3) to agree (4). This trend may be indicative of a lack of enthusiasm towards the changes brought in by the BPR, although it also shows lack of an active animosity towards the changes. We insist on the importance of inclusiveness and proper training to raise both awareness as well and eagerness towards the acceptance and implementation of the BPR.
# Experimental Study on Drying Characteristics of Alfalfa Hay Bales Using Hot Air Convection
## 2 Materials and Methods
### Experimental Procedure  
The experiment started on 14 July 2024 and ended on 3 August 2024, lasting a total of 20 days. The bales awaiting the drying process were stored in a well-ventilated, shaded  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Technical Parameters** \\ \hline Bale size (L \(\times\) W \(\times\) H) & 0.85 \(\times\) 0.45 \(\times\) 0.36 (m) \\ Quantity of dry bales in a batch & 2 \\ Fan flow rate & 4600 m\({}^{3}\)/h \\ Fan power & 3 kW \\ Air outlet size & 500 \(\times\) 160 (mm) \\ Full pressure & 1460 Pa \\ Drying air temperature & 70 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical parameters of the hay bale drying and test equipment.  
Figure 2: Diagram of the measurement and control system for the baled forage drying test bench.  
area with an average ambient temperature of 20 \({}^{\circ}\)C and relative humidity of 60% (w.b.). The bales were placed on pallets to ensure proper air circulation and prevent moisture absorption from the ground. These storage conditions were selected to simulate typical pre-drying storage practices and maintain the initial moisture content of the bales before the drying experiments.  
At the beginning of the experiment, the initial state of the hay bales was measured, including their weight, density, and initial moisture content. The fan and electric heater were then activated to begin the drying process, and the experiment time and drying conditions were recorded. Every hour, the fan was stopped to measure the real-time moisture content of the hay bales, and the weight of the bales was monitored and recorded. After the measurements, the fan was restarted to continue the drying process. The drying conditions, including drying air temperature and drying air velocity, were measured and recorded, along with the drying time. After a certain period, the drying state of the hay bales was assessed again. The drying process continued, and the above experimental operations were repeated until the drying of the hay bales was completed.  
After completion of the drying, each hay bale was unbundled and sliced into 7-8 uniform sections (thickness = 5 cm) along the longitudinal axis using a hydraulic cutting machine (Jianke, Ningbo, China) with a precision of 0.2 cm. The slicing process aims to analyze cross-sectional moisture distribution (Figure 3).
## 4 Results and Discussion
### Effect of Fin Design Parameters
#### 4.3.4 Fin Thickness  
The effect of fin thickness on both the hot (solid curves) and cold side (dash curves) are illustrated in Figure 10. For the hot side, the pressure drop increased but heat transfer decreased with fin thickness rising. For the cold side, fin thickness has no influence on pressure drop; however, the increase in fin thickness had a negative impact on heat transfer area. It seems that 0.10 mm and 0.50 mm are reasonable choices for fin thickness on the hot and cold side, respectively.  
Figure 8: Effect of fin spacing on heat transfer area and pressure drop of PFHE.  
Figure 9: Effect of fin height on heat transfer area and pressure drop of PFHE.
###### Abstract  
Drought disasters jeopardize the production of vegetation and are expected to exert impacts on human well-being in the context of global climate change. However, spatiotemporal variations in drought characteristics (including the drought duration, intensity, and frequency), specifically for vegetation areas within a growing season, remain largely unknown. Here, we first constructed a normalized difference vegetation index to estimate the length of the growing season for each pixel (8 km) by four widely used phenology estimation methods; second, we analyzed the temporal and spatial patterns of climate factors and drought characteristics (in terms of the Standardized Precipitation Evapotranspiration Index (SPEI)), within a growing season over vegetation areas of the northern hemisphere before and after the critical time point of 1998, which was marked by the onset of a global warming hiatus. Finally, we extracted the highly drought-vulnerable areas of vegetation by examining the sensitivity of the gross primary production to the SPEI to explore the underlying effects of drought variation on vegetation. The results revealed, first, that significant (\(p<0.05\)) increases in precipitation, temperature, and the SPEI (a wetting trend) occurred from 1982 to 2015. The growing season temperature increased even more statistically significant after 1998 than before. Second, the duration and frequency of droughts changed abruptly and decreased considerably from 1998 to 2015; and this wetting trend was located mainly in high-latitude areas. Third, at the biome level, the wetting areas occurred mainly in the tundra, boreal forest or taiga, and temperate coniferous forest biomes, whereas the highly drought-vulnerable areas were mainly located in the desert and xeric shrubland (43.5%) biomes. Our results highlight the fact that although the drought events within a growing season decreased significantly in the northern hemisphere from 1998 to 2015, the very existence of a mismatch between a reduction in drought areas and an increase in highly drought-vulnerable areas makes the impact of drought on vegetation nonnegligible. This work provides valuable information for designing coping measures to reduce the vegetative drought risk in the Northern Hemisphere.  
vegetation; growing season; Standardized Precipitation Evapotranspiration Index; drought characteristics; vulnerability 2020 12 2146 10.3390/w12082146 10.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 2 Materials and Methods
### Computation Offloading Method Based on Minimum Cost Maximum Flow  
According to the computation offloading model constructed in Section 2, the proposed problem is a MINLP problem [27]. The main methods for solving MINLP problems are deterministic and stochastic methods. However, the large computational cost of deterministic methods such as branch and bound methods will cause high delay. Stochastic methods such as genetic algorithms may have a time-consuming search process and are easy to get trapped in local optimal solutions. None of the above methods are applicable to resource-constrained GEO/LEO hybrid satellite networks that have high real-time requirements. In this paper, we transform the computation offloading problem in GEO/LEO hybrid satellite networks into the MCMF problem and propose the SSPCO method based on the successive shortest path algorithm to solve it in polynomial time.
# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 3 Proposed Architecture
### ESS Power Management for Optimal Energy Trading
If the value of \(H\) is equivalent to 1 and the surplus energy of the \(k\)th nano-grid during time interval \(t\) is not greater than the energy stored in the battery at time interval \(t\) (i.e., \(\text{extra}_{k}(t)\leq\text{battery}_{k}(t)\)), then the discharging mode is initiated. Furthermore, the surplus power of the nano-grid at time \((k+1)\) can be determined by subtracting the surplus energy of the \(i\)th nano-grid house during time interval \(t\), denoted as \(\text{extra}_{k}(t)\), from the energy stored in the \(k\)th nano-grid at timestamp \(t\), represented as \(\text{battery}_{k}(t)\). Alternatively, if the surplus energy of the \(m\)th nano-grid at timestamp \(t\) is lower than the energy stored in the \(k\)th nano-grid's energy storage system (ESS), the charging mode is activated. Consequently, the excess energy for the \((i+1)\)th nano-grid during the time interval \(t\) is determined by subtracting the surplus energy of the \(i\)th nano-grid house from the energy stored in the ESS of the \(i\)th nano-grid at time \(t\), denoted as \(\text{extra}_{k}(t)-\text{battery}_{k}(t)\). The operational limitations for entity \(e_{i}\) throughout the charging process exhibit variations when the value of \(NG\) is -1 instead of +1. The aforementioned limits are contingent upon the energy usage and availability of each nano-grid. The term traded energy \(\text{sold}_{k}(t)\) is defined as:  
\[f(x_{i})=\text{sold}_{k}(t):\max(\text{remained}_{k}(t)-\text{remained}_{ \text{min},k}+\text{extra}_{k}(t),0) \tag{9}\]  
The given equation is subject to the following limitations:  
\[-\text{remained}_{k}(t)\leq\text{battery}_{k}(t)\leq 0\]  
Moreover, it is imperative to ensure that the exchange of energy between nano-grids with contrasting values, wherein one nano-grid possesses a negative value while the other has a positive value, does not exceed the quantity of energy the latter nano-grid has at its disposal for trading purposes. For a detailed explanation, we also provide the methodology of the proposed study in the form of a detailed algorithm shown in Algorithm 1.
These measurements allow for the estimation of losses under specific test conditions. This process involves calculating the AC/DC conversion efficiency and analyzing how the converters perform under different loads, temperatures, and other climatic factors. Unlike the previous standards, EN 50530:2010 does not address issues related to power quality. The IEEE 1547-2018 standard does not specify precise test conditions regarding the voltage, current, or power supplied to the input of the PV inverter, which directly depends on solar irradiance and other environmental conditions. The research presented here aims to evaluate PV inverters by analyzing the DC injection and output current distortion at different MPPs of the PV array. The measurements are performed at the MPPs chosen according to the test specifications for the conversion and static MPPT efficiency of the EN 50530:2010 standard [28] and compared with the limits prescribed by IEEE 1547-2018 standard [26]. Additionally, the DC injection and output current distortion during MPPT are measured from the instance when the DC voltage was switched on to the inverter input until the MPP was reached. These analyses are crucial because, under actual operating conditions, the position of MPP is not constant and depends on environmental factors. Additionally, the illumination of PV modules can change due to variable irradiance, leading to fluctuations in the MPP. It is essential to assess how these variations affect harmonic generation. This manuscript is organized as follows: The second section summarizes the IEEE 1547-2018 [26], IEC 61727:2004 [27], and EN 50530:2010 [28] standards. The IEEE 1547-2018 standard is summarized concerning only aspects of power quality, especially DC injection and current distortion. The third section describes the laboratory for microgrids and renewable energy sources, including test setup and instrumentation used for testing and evaluating the PV inverters. The fourth section presents the experimental results, including DC injection measurements and current distortion for three single-phase dual-stage PV inverters with 3 kVA, 3.3 kVA, and 6 kVA rated power. Measurements are conducted at different input voltages and power levels and represented numerically. The time evolution of the current distortion during MPPT is given graphically. The fifth section discusses the results, while the sixth one provides conclusions.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 5 Experimental Setup and Results
### Speed Measurement  
Figure 10 shows the measurement results for two different speeds. The speed of a single target (\(0.03\times 0.03\) m\({}^{2}\)) is set to 2.34 m/s in Figure 10a, while Figure 10b shows the result for a single target (\(0.015\times 0.02\) m\({}^{2}\)) moving at 5 m/s. The assembler, which contains the target mounted on a moving belt, is 0.26 m long and is placed 0.67 m from the receiving antenna. We can notice that both speed and distance can be identified, but due to the smaller size of the moving target, the received signal strength in Figure 10b is weaker than in Figure 10a.  
The result in Figure 10c are obtained by simultaneously placing a static target at 0.6 m and a moving target (\(0.015\times 0.02\) m\({}^{2}\)) at 2.34 m/s located between 0.67 m to 0.9 m. It is observed that the setup can accurately measure the speed in different arrangements within the speed resolution of 0.79 m/s and the range of the static target within the range resolution of 0.042 m. Measurements for the speed values in our experiment required an interval of 1.96 ms to capture sinusoidal variations caused by the Doppler frequency. Multiple reflections from the moving target cause echoes with higher speeds, as indicated in Figure 10a. The \(\pm\) speed in the results is due to the use of a double-sideband W-band mixer for up/down conversion in the experiment. The \(\pm\) speed ambiguity can be removed by using single sideband devices such as IQ modulators.
# Tribological Analysis of Steels in Fuel Environments: Impact of Alloy Content and Hardness
## 2 Materials and Experimental Procedures
### Tribological Testing  
Tribological tests were conducted in two different fuels, ethanol and decane, using a Phoenix TE-77 tribometer (Phoenix Tribology Ltd., Hampshire, UK). The effect of a chemically inert counterbody was investigated by comparing the tribological behavior of steels in contact with a 10 mm AISI 52100 ball and a 10 mm Al\({}_{2}\)O\({}_{3}\) ball with roughness below 200 nm with both ethanol and decane as lubricants.  
The reciprocating tribometer is a widely utilized instrument for studying the friction and wear behavior of materials under realistic operating conditions as shown in schematics in Figure 1. It provides precise control over testing parameters and enables accurate measurement of wear rates. Table 3 contains detailed information regarding the specific testing parameters used in this study. During the test, each steel sample was subjected to reciprocating sliding motion against the selected counterbody material, with the solvent environment serving as the lubricating medium. The solvents were continuously replenished to ensure full submersion of the samples for the whole duration of the tests. Each test was repeated at least 3 times.  
To determine the specific wear rate, the material loss of each sample after testing was measured and normalized by considering the sliding distance \(L\) and applied load \(W\):  
\[\begin{split} Wear\ rate=\frac{wear\ volume}{L\ W}\end{split} \tag{1}\]  
This normalization allowed for a direct comparison of the wear rates among different samples and under varying test conditions. Ethanol provided a polar alcohol environment similar to E-85, and decane provided an alkane environment similar to the main components of gasoline and kerosene jet fuels. Additionally, comparing the wear behavior with different counterbody materials, hardened AISI 52100 steel and Al2O3, provided insights into the tribological compatibility and wear mechanisms of the steels.
## 4 Experiment Results and Analysis
### Design of \(A(z)\) for the Feedforward Enhancement  
As described in Section 2.3, \(A(z)\) of the feedforward enhancement in the additive ASP algorithm is defined as the magnitude of the delayed inverse of the nominal secondary path \(\left|S_{0,\Delta}^{-1}(z)\right|\) from Equation (9). Thus, in this study, at first, the optimal FIR filter of \(S_{0,\Delta}^{-1}(z)\) with length \(I\) was obtained using the Wiener filter design process with the modeling delay \(\Delta\) approach. A number of computations were carried out to determine the optimal \(I\) and \(\Delta\), which minimize the mean square value of the modeling error. The filter length \(I\) was varied from 1000 to 6000 samples with an interval of 1000 samples. In each \(I\), the mean square errors (MSEs) were calculated with various \(\Delta\) from 0 to 1.2 times of \(I\) with 10 samples interval. Consequently, the optimal \(I\) and \(\Delta\), which provided the minimum MSE were attained by 4000 (See Figure 9a) and 1290 (See Figure 9b) samples, respectively. The optimally designed FIR filter coefficients, which is the impulse response, of \(S_{0,\Delta}^{-1}(z)\) is illustrated in Figure 10.  
Figure 9: (**a**) Minimum MSE with (**b**) the optimal \(\Delta\) at each length \(I\) of the FIR filter.  
Then, \(A(z)\) was obtained by taking the modulus of \(S_{0,\Delta}^{-1}(z)\), which was attained by transforming the designed impulse response in the z-domain. Figure 11 shows the comparison of the magnitude responses of \(S_{0}(z)\) (a dashed line) and \(A(z)\) (a dotted line), which are transformed from the two impulse responses displayed in Figures 6 and 10, respectively, and the magnitude of \(S_{0}(z)A(z)\) (a solid line) is also plotted in Figure 11 together. It was found that the magnitude response of \(A(z)\) was well-matched with that of \(S_{0}(z)\) at most frequency range since that of \(S_{0}(z)A(z)\) was almost 0 dB except for about 100 Hz.  
The designed \(A(z)\) was implemented in the additive ASP algorithm using a lookup table.