esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FT-IR Spectroscopic Studies
#### 3.1.2 Guaiacol  
The FT-IR spectra obtained for pure G and immobilized on the surface of CeO\({}_{2}\) are shown in Figure 2. The absence for G/CeO\({}_{2}\) of the absorption band at 1364 cm\({}^{-1}\), which corresponds to \(\beta\)(OH) [62] (Table 2), as well as the decrease in the intensity of absorption at 1261 cm\({}^{-1}\) (\(\
u\)(CO) [62]), indicates the participation of the OH group of G in the interaction with CeO\({}_{2}\).  
Figure 2: Fourier transform-infrared (FT-IR) spectra of pure CeO\({}_{2}\) (a), samples of G/CeO\({}_{2}\) with different contents of G (0.1: b, 0.3: c; 0.6: d; 0.9: e; 1.2 mmol/g: f) and pure G (g).  
At the same time, for these samples, the bands of symmetric (\(\
u_{\rm s}\)) and asymmetric (\(\
u_{\rm as}\)) valence vibrations of the C-O-CH\({}_{3}\) groups, which were registered for guaiocal at 1024 and 1225 cm\({}^{-1}\)[63], shifted to 1018 and 1217 cm\({}^{-1}\), respectively. In addition, absorption bands in the range 1444-1469 cm\({}^{-1}\), corresponding to the \(\beta\)(CH\({}_{3}\)) vibrations [62; 63], were transformed. In particular, the peaks at 1444 and 1469 cm\({}^{-1}\) disappeared, and the maximum at 1458 cm\({}^{-1}\) shifted to 1456 cm\({}^{-1}\). These changes indicate the interaction of the methoxyl groups of G with CeO\({}_{2}\).  
The new bands at 1288 and 1323 cm\({}^{-1}\) in the spectra of G/CeO\({}_{2}\) samples were a sign of the formation of chemisorbed G complexes [62]. The absorptions for \(\
u\)(CH\({}_{3}\)) at 2843 cm\({}^{-1}\) partially remained in the spectra of G/CeO\({}_{2}\).Therefore, the interaction of the COCH\({}_{3}\) group with the oxide surface probably occurred through an oxygen atom without cleavage of CH\({}_{3}\). At the same time, it is possible that several methoxyl groups may have been freed.  
Thus, the obtained data (Figure 2) indicate the formation of G complexes on the nanoceria CeO\({}_{2}\) bound to the oxide surface through the phenolic and methoxyl groups simultaneously, as well as separately through each of these groups.  
In addition, the shift of the band of C = C vibrations may also indicate the interaction of the aromatic ring G with the oxide surface [62]. Figure 2 shows that the C = C band, which for pure G is about 1502 cm\({}^{-1}\), for the G/CeO\({}_{2}\) samples is shifted to 1498 cm\({}^{-1}\). According to [62], such a shift may be a sign of the presence on the CeO\({}_{2}\) surface of weakly bound G complexes, which occur due to the simultaneous formation of hydrogen bonds between the CH-group of the aromatic ring and the surface hydroxyl as well as OH group of G and the surface cerium atom.
## 3 Data Collection
### Binder and Mixture Aging Test  
The asphalt binders were aged by a Thin-Film Oven Test (TFOT) to simulate short-term field aging according to AASHTO T179. The TFOT aged binders were further aged by Pressure Aging Vessel (PAV) to simulate long-term field aging based on AASHTO R28.  
Regarding the asphalt mixtures, the short-term aging was conducted by placing loose mixtures in a stainless steel pan, which was then heated for 4 h at 135 \({}^{\circ}\)C in a traditional oven. Within the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Testing** & **MSCR, Binder** & **Performance** & **Viscosity, Binder** & **Monotonic** & **SCB, Mixture** & **IDT, Mixture** \\ \hline Conditions & 20, 25, 30 \({}^{\circ}\)C & \(-\)24 to 100 \({}^{\circ}\)C & 65, 135 \({}^{\circ}\)C & 20, 25, 30 \({}^{\circ}\)C & 30 \({}^{\circ}\)C, & 30 \({}^{\circ}\)C, \\ \hline Parameter & \(R_{32}\) & \({}^{\circ}\)C & Viscosity & Fracture Energy & IFT & Strength, work density \\ \hline \multirow{2}{*}{References/standards} & AASHTO & AASHTO & \multirow{2}{*}{AASHTO T316} & \multirow{2}{*}{[7]} & \multirow{2}{*}{[10]} & \multirow{2}{*}{AASHTO T322} \\  & & TP70/M32 & & MP1/M322 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Laboratory Testing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Aggregate Gradation**} & \multicolumn{2}{c}{**Mix Design**} \\ \hline
**Sieve Size, mm** & **Percentage Passing, \%** & **Parameter** & **Value** \\ \hline
16.0 & 100.0 & G\({}_{\text{sb}}\) & 2.854 \\
13.2 & 94.7 & VMA & 13.3\% \\
9.5 & 76.2 & VFA & 69.8\% \\
4.75 & 45.7 & AC & 5.0\% \\
2.36 & 29.7 & G\({}_{\text{mm}}\) & 2.706 \\
1.18 & 24.2 & V\({}_{\text{be}}\) & 10.0\% \\
0.6 & 18.4 & N\({}_{\text{des}}\) & 126 gyrations \\
0.3 & 12.7 & - & - \\
0.15 & 8.7 & - & - \\
0.075 & 6.1 & - & - \\ \hline \hline \end{tabular} Note: \(G_{\text{sb}}\): aggregate bulk specific gravity. VMA: voids in mineral aggregate. VFA: voids filled with asphalt. AC: asphalt content. G\({}_{\text{mm}}\): theoretical maximum specific gravity. \(V_{\text{be}}\): volume of effective asphalt. \(N_{\text{ds}}\): determined based on 30-100 million design Equivalent Single Axle Load (ESAL).  
\end{table}
Table 2: Aggregate Gradation and Mix Design Parameters.  
procedure, mixtures were stirred every 60 min to maintain uniform aging conditioning. In contrast, the long-term mixture aging was performed by utilizing specimens after gyratory compaction and shaping in an oven at 85 \({}^{\circ}\)C referring to AASHTO R30. The aging duration was set as two days (48 h), five days (120 h), and eight days (192 h), respectively. To avoid the large deformation or collapsing of specimens, all the specimens were wrapped in foil paper and tied with rubber bands, as seen in Figure 1. The foil paper was stabbed to generate small vents to allow heated air to circulate around the specimens.
## 5 Results  
This section presents the optimal satellite constellation configurations derived from the simulation-based GA optimization, along with the algorithm's convergence behavior. The analysis is structured by communication technology (LoRa, NB-IoT, and Mioty) and orbital altitude (VLEO at 200 km, VLEO at 300 km, and LEO at 600 km).  
Key evaluation metrics include the satellite configuration (total number of satellites, number of planes, and satellites per plane), communication performance (revisit time and data transmission volume), and economic efficiency (cost based on satellite count). These metrics collectively support a comprehensive assessment of system trade-offs.
## 2 Materials and Methods  
### System Model  
#### 2.1.1 Network  
Figure 1 illustrates a cellular wireless network, where each hexagon corresponds to one BS coverage area. UEs are distributed as a Poisson point process \(\Phi_{UE}\) with density \(\lambda\) in each BS coverage area. We denoted UEs by \(u\in U=\{1,2,\ldots,N_{ue}\}\) which \(N_{ue}\) is number of UEs. BS are denoted by \(b\in B=\{1,2,\ldots,N_{bs}\}\) which \(N_{bs}\) is the number of BS. Each BS uses orthogonal frequency-division multiplexing (OFDM) transmission method to provide UEs with data transmission services. With OFDM, UEs connect to the same BS are orthogonal with each other and no intra-BS interference exists. We assume that all BSs reuse the same frequency and bandwidth resources, which lead to inter-BS interference. Let \(c\in C=\{1,2,\ldots,N_{c}\}\) denote reused orthogonal sub-channels of each BS, with \(N_{c}\) is the number of orthogonal sub-channels.  
Thus, path loss of UE \(u\) between BS \(b\) on channel \(c\) is  
\[PL_{u,b}^{c}=32.4+21 log_{10}(d_{u,b})+20 log_{10}(f_{c}) \tag{1}\]  
where \(d_{u,b}\) is the distance between UE \(u\) and BS \(b\), and \(f_{c}\) denote the frequency of sub-channel. We compute the channel gain at time t of the transmission between UE \(u\) and BS \(b\) over sub-channel \(c\) as follows:  
\[g_{u,b}^{c}=|h_{u,b}^{c}|PL_{u,b}^{c} \tag{2}\]  
where \(h_{u,b}^{c}\) is the small-scale fading component. Moreover, each UE selects BS which provides the max RSRP as service BS, and sets the value of \(\eta_{u,b}\) to 1, which indicates the association between UE \(u\) and BS \(b\).
## 3 Experimental Bench and Theoretical Model
### Theoretical Model  
#### 3.2.1 Global Sensitivity Analysis  
The Morris sensitivity analysis technique is commonly employed to evaluate the sensitivity of input factors globally. It determines the influence grade of input factors on the output results by calculating sensitivity coefficients for each factor. It is considered a highly effective method for identifying sensitive and insensitive parameters. In this study, the impact of operating parameters on the optimization objectives based on the Morris method is obtained to guide the efficient control of the air supply system used in HFCVs.  
In the Morris sensitivity analysis, the utilization of elementary effects can be used to quantify the influence grade of a single perturbation on the objectives. Equation (4) allows for the calculation of the elementary effect of the \(\mathsf{i_{th}}\) input parameter [26]:  
\[S_{i}=\frac{y(x_{1},x_{2},\ldots,x_{i}+\Delta x,\ldots,x_{n})-y(x_{1},x_{2}, \ldots,x_{n})}{\Delta x} \tag{4}\]  
The average value \(\mu_{i}\) determines the assessment of the input parameter influence on the optimization objectives. A higher \(\mu_{i}\) signifies a greater impact of the input parameters on the objective. Therefore, the average value can be expressed as [24]:  
\[\mu_{i}=\frac{1}{m}{\sum_{j=1}^{m}S_{ij}} \tag{5}\]  
Taking into account the impact of negative values on the average, a corrected average is used to represent sensitivity. Therefore, the corrected average can be expressed as [24]:  
\[{\mu_{i}}^{*}=\frac{1}{m}{\sum_{j=1}^{m}}{\left|S_{ij}\right|} \tag{6}\]  
where \(S_{i}\) is the elementary effect of the \(\mathsf{i_{th}}\) input factor, \(y(x_{1},x_{2},\ldots,x_{n})\) is the initial output result, \(y(x_{1},x_{2},x_{i}+\Delta x\ldots,x_{n})\) is the output result after the perturbation of the input factor, \(\Delta\mathsf{x}\) is the perturbation of the input factor, and \(m\) represents the quantity of autonomous random pathways utilized in the global sensitivity analysis.
## 5 Experiments
### Diagnosis by Adaptive Filtering Based Envelope Analysis  
To show the superiority of the proposed method, the envelope demodulation method in the order domain, based on adaptive filtering, is selected for comparison. The following steps can be used:  
1. Adaptive filtering is adopted to reduce noise and harmonic components which are not the concerns in this research [33].
2. Order tracking. The same as (1) in Section 5.3.
3. The envelope demodulation method based on the Hilbert transform is applied to achieve the envelope order spectrum.  
Then, the envelope order spectrum is obtained, as shown in Figures 22 and 23.  
Figure 22 shows the envelope order spectrum of the fault signal of the intershaft bearing's outer ring. In this spectrum, the highest amplitude order is 2.7, and an order of 15.88 is the second high-amplitude component. An order of 15.88, corresponding to a fault in the outer ring, can be seen, but not the dominant component.  
Figure 21: Envelope order spectrum of inner ring fault (envelope demodulation based on VMD).  
Figure 20: Envelope order spectrum of outer ring fault (envelope demodulation based on variational mode decomposition (VMD)).  
Figure 23 shows the envelope order spectrum of the fault signal of the intershaft bearing's inner ring. In this spectrum, the dominant orders are 1 and its harmonics. An order of 18.1, corresponding to the fault of the inner ring, is completely buried by noise.  
The analysis results for the above two sets of data show that it is very difficult for the algorithm to extract the weak fault features of intershaft bearing, especially that of the inner ring.
# A Secure Median Implementation for the Federated Secure Computing Architecture
## 2 Preliminaries
### Secure Multiparty Computation  
In MPC, a set of two or more parties participate in an encrypted peer-to-peer network to jointly compute a function \(y=f(D_{1},\ldots,D_{m})\) without revealing each party's private input. That means that each party only receives the exact result \(y\) of that function and nothing more (see Figure 1). The function considered in this work is the identification of the \(k\)th-ranked element in a dataset. In other words, in an ordered set \(S\subset\mathbb{F}\) the value \(x\in S\) which has rank \(k\). Specifically, we are interested in the median value, i.e., finding the value that is ranked \(k=\left\lceil|S|/2\right\rceil\). To achieve this goal, MPC relies on protocols defining a set of rules and procedures for the parties involved to follow. These protocols are designed to ensure that the parties can interact with each other securely and reliably, even in the presence of adversaries. By following the protocol, the parties can compute the desired function while preserving the privacy of their inputs and maintaining the security and integrity of the computation.
## 3 Results and Analysis
### Spatial Variability of Soil Nutrients
#### 3.3.3 Spatial Clustering Analysis of Soil Nutrients  
By considering each administrative village in Fengtai County as an evaluation unit, the average Kriging interpolation results within these villages were incorporated. The local Moran's \(I\) index was subsequently determined, culminating in the LISA clustering map depicted in Figure 6. The insights gleaned from the analysis are as follows: High pH values predominantly surface in the southern regions, punctuated by pockets of low-high anomalies and a sparse presence in the northwest. Low pH values are largely centralized, with occasional appearances in the northwest. The spatial patterns of organic matter, total nitrogen, and alkali-hydrolyzed nitrogen are roughly analogous. They are largely scattered. Regions with lower values for these nutrients are chiefly situated in the southeast and sparingly in the north. Areas with higher concentrations appear in fragmented patches, primarily in the central-west and northeast regions. Both the low-high and high-low anomaly zones are interspersed within these clusters. Available phosphorus showcases distinct spatial clustering. Its low-value regions are centered in the south-central areas, accompanied by minute high-low anomaly patches. In contrast, the high-value territories are dominant in the northeast with minor representations in the central-west region. Quickly available potassium displays a patchy spatial layout for both its high and low-value clusters. The zones with lower values are mainly positioned in the northwest, occasionally in the central-west and east regions. High-value pockets are dispersed across  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Model** & \begin{tabular}{c} **Nugget** \\ **(C\({}_{0}\))** \\ \end{tabular} & \begin{tabular}{c} **Still** \\ **(C\({}_{0}\) + C)** \\ \end{tabular} & \
\begin{tabular}{c} **Nugget/Still** \\ **C\({}_{0}\)/(C\({}_{0}\) + C)** \\ \end{tabular} & **Range (km)** & **Moran’s I** &
\begin{tabular}{c} **Standard Z** \\ **Value** \\ \end{tabular} \\ \hline pH & Gaussian & 0.236 & 0.255 & 0.926 & 0.165 & 0.871 & 24.962 \\ Organic matter & Spherical & 22.80 & 25.59 & 0.891 & 0.462 & 0.682 & 19.563 \\ Total N & Spherical & 0.117 & 0.119 & 0.988 & 0.415 & 0.783 & 22.464 \\ Alkailhydrolyzed & Exponential & 412.0 & 412.9 & 0.998 & 0.777 & 0.876 & 25.095 \\ Nitrogen & Exponential & 266.3 & 311.1 & 0.856 & 0.756 & 0.884 & 25.353 \\ Olsen-P & Exponential & 941.2 & 1311 & 0.718 & 0.199 & 0.562 & 16.136 \\ Available K & Exponential & 3670 & 3772 & 0.973 & 0.237 & 0.729 & 20.921 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the semivariogram for soil nutrients in Fengtai County and the global Moran’s I.  
the north, central, and southern parts, encircled by sporadic anomaly regions. The low-value clustering of slowly released potassium is broken up, emerging in the northeast, northwest, and central regions, with infrequent high-low anomaly zones. Its high-value concentrations are predominantly in the south and sparingly in the central-west region.
## 6 Experimental Results and Discussion  
### Training Strategies  
The proposed TG2-CAFNet model was implemented using the PyTorch 2.0 deep learning framework. We divided the dataset into a training set and a testing set in a 7:3 ratio. To fully leverage the advantages of the fusion network, we employed a fine-tuning strategy. Specifically, we first trained TG2 Net, built with TG2, separately on RT and TD maps using the Adam optimizer with an initial learning rate of 0.0001 for 60 epochs. These pre-trained TG2 Net models can be considered as pre-trained networks. By removing their head layers, they serve as the fine-tuning part of TG2-CAFNet. Then, the remaining parts were trained with a learning rate of 0.0002 using the Adam optimizer. This training strategy ensured that TG2 optimally extracted features from individual spectrograms, allowing us to further improve the network performance by fusing the networks on the basis of optimal feature extraction modules. It also avoided a decrease in accuracy due to increased network complexity when training TG2-CAFNet directly.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
###### Abstract  
Managing sediments dredged from reservoirs of recharge dams is an environmental issue, however, these sediments can be an abundant and economical source of fine-grained fill soil. This experimental investigation quantifies the geotechnical properties of a reservoir sediment used to improve engineering properties of a poorly graded dune sand in Oman. The binary mixes were prepared with different percentages (5, 10, 20, 50, 75, 90, 95%) of sediment with sand. Laboratory tests such as gradation, consistency limits, compaction, and unconfined compression tests were performed to measure the engineering characteristics of the binary mixtures. The results showed that the maximum dry density increases up to a sediment content of 50% and then decreases with further increase in the sediment content. The optimum water content increases with the increase in sediment content from 17% for pure sand to 22.5% for pure sediment. The optimum moisture content shows a good correlation with the plastic limit of the binary mixture of sand and sediment. The unconfined compressive strength substantially increases with sediment content up to 75% and then decreases with further increase in the sediment content. The binary mixture of sand sediment is sensitive to moisture, however, the order of strength stability against moisture is dune sand mixed with 75, 50, and 20% sediments. The addition of sediment to dune sand improved the uniformity coefficient to some extent with an increase in the maximum and minimum void ratios as well. The elemental analysis of the sediment confirms that the material is non-contaminated and can be employed in geotechnical engineering applications as a sustainable and environmentally friendly solution.  
s 2021 1 1 1620. [[https://doi.org/10.3390/app11041620](https://doi.org/10.3390/app11041620)]([https://doi.org/10.3390/app11041620](https://doi.org/10.3390/app11041620))
# Test-Retest Reliability of Kinematic Parameters of Timed Up and Go in People with Type 2 Diabetes
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics included mean and standard deviation (SD) of age, weight, glycated haemoglobin, years since T2 DM diagnosis, body mass index and vibration threshold were calculated for the whole sample and divided into women or men. Parametric and non-parametric tests were conducted based on the results of Shapiro-Wilk and Kolmogorov-Smirnov tests.  
Differences between test and retest were evaluated using the paired samples t-test or Wilcoxon signed rank test when appropriate. The time spent to complete the TUG, the duration of phases and the forces registered by the force platforms were included in those analyses to compare the test and retest results.  
Reliability analyses were conducted in accordance with the recommendations of [PERSON] [18]. An intraclass correlation coefficient (ICC) of 3.1 (two-way mixed, single measures) with a 95% CI for test and retest [19] was selected. Both absolute and relative reliability were computed. The standard error of measurement (SEM) was calculated as \(SEM\ =\ SD\ \sqrt{1-ICC}\) where SD is the mean SD of the three repetitions, while the smallest real difference (SRD) was \(SRD\ =\ 1.96\ \times SEM\ \times\ \sqrt{2}\). These measures were converted into percentages (%SEM and %SRD, respectively) to enable comparisons with other investigations.
## 4 Simulation Results and Discussion
### Simulation Conditions  
Preconditions in the simulation are as follows.  
1. It is supposed that power consumption and heat load in the smart home can be forecasted. The assumed power consumption of uncontrollable loads is shown in Figure 6(a). NN is used for forecasting the insolation and the PV output which is calculated by Equation (2) from forecasted insolation, is depicted in Figure 6(b) where the forecasted error based on Figure 6 is added into the forecasted PV output.
2. In regard to the heat load, which is as follows. Three people use 30 L and 150 L hot water as shower from 7:00 to 8:00 and 19:00 to 22:00. When the water temperature in the storage tank is lower than 40 \({}^{\circ}\)C at 8:00 and 22:00, the water is heated by the HP.
3. Normal people often use EV as drive mode for a few hours of the day. Thus, we assume that the EV is used in drive mode from 8:00 to 18:00 and discharges 700 W/h outside. Except, for the drive time the EV connected to the smart home and has the function of both vehicle-to-grid (V2G) and grid-to-vehicle (G2V).