esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Rotational Viscosity Results  
The data presented in Figures 6 and 7 highlight the workability of asphalt binders, both in their original form and after undergoing the Rolling Thin Film Oven (RTFO) test. These figures reveal the effects of nanomaterials--NT, NA, and NS--on binder viscosity at varying temperatures: 115 \({}^{\circ}\)C, 135 \({}^{\circ}\)C, and 155 \({}^{\circ}\)C. Initially, a clear increase in viscosity with the addition of nanomaterials is observed, as shown in Figure 6. At 115 \({}^{\circ}\)C, viscosity starts at 2268 mPa-s for the neat binder and increases with the nanomaterial content, notably peaking at 5416.7 mPa-s for NS at an 8% dosage.  
Figure 5: Effect of nanomaterials on storage stability.  
Figure 6: Effect of nanomaterials on original binder RV.  
After RTFO aging, as indicated in Figure 7, there is an overall increase in viscosity, reflecting the stiffening effect of thermal exposure. Yet, even at the highest nanomaterial content, the viscosities remain below the threshold of 3000 mPa-s at 135 \({}^{\circ}\)C set by [41], ensuring the binders' suitability for paving operations. A review of the average viscosities across all dosages, detailed in Table 6, shows significant differences among the nanomaterial-modified binders. At 135 \({}^{\circ}\)C, NS records the highest average viscosity at 1150.1 mPa-s, a notable increment of about 33% compared to the neat binder's average of 863.3 mPa-s. In contrast, NT and NA exhibit lower average viscosities of 863.3 mPa-s and 936.56 mPa-s, representing increases of 16.6% and 8.5%, respectively. This suggests that NS's distinct morphology and high surface area significantly contribute to the viscosity increase. Furthermore, the chemical interactions between the NMs and asphalt components alongside the homogeneous distribution of nanoparticles, as evidenced in their storage stability, also play essential roles in these viscosity changes.  
Post-RTFO aging, the viscosity increase for NS is the most pronounced, indicating a heightened sensitivity to aging. At the 135 \({}^{\circ}\)C measurement, NS's average viscosity climbs  
\begin{table}
\begin{tabular}{c} **After** \\ **TFOT** \\ \end{tabular} \\ \hline
115 \({}^{\circ}\)C & 2730.12 & 3634.92 & 2937.5 & 3742.9 & 3554.46 & 4742.9 \\
135 \({}^{\circ}\)C & 863.3 & 1157.78 & 936.56 & 1215.32 & 1150.1 & 1498.1 \\
155 \({}^{\circ}\)C & 285.04 & 380.48 & 315.82 & 406.06 & 397.24 & 520.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of nanomaterials on average RV values.  
Figure 7: Effect of nanomaterials on aged binder RV.  
by 33% from its original state. Meanwhile, NT and NA show less significant aging effects, with increases of 25% and 21%, respectively. Despite these changes, it is recognized that post-aging viscosities are not subject to the same AASHTO M320 threshold as the original binders, and the modified asphalts are still expected to have adequate workability for practical paving applications.
# Room-Temperature Formation of Hard BC\({}_{\rm x}\) Films by Low Power Magnetron Sputtering
## 3 Results and Discussion
### Film Structure, Composition, and Bonding Types
The peaks with binding energies in the region of 191-192.6 eV can be attributed to boron atoms bonded to carbon and oxygen atoms simultaneously; a component with a lower binding energy corresponds to the BC\({}_{2}\)O environment [64; 65; 66], with higher E\({}_{\text{b}}\) to BCO\({}_{2}\)[65; 42; 66]. Thus, low-power films have a strongly oxidized surface. According to the EDX analysis data, these films contain 19 at.% oxygen. It should be noted that the survey spectrum also contained the N1s peak, which indicates the presence of nitrogen in the films. Thus, a component near 191 eV can also be associated with B-N bonds [67]. The component with a binding energy of 193.4 eV apparently refers to boron atoms bound with oxygen [67; 68]. In the C1s spectra, the two most intense peaks with binding energies of 283.0-283.7 eV and 285.3 eV are observed. The peak in the region of 283-284 eV corresponds to carbon atoms bound to boron [63; 69]. The peak centered at 285 eV is, apparently, associated mainly with the C-C bonds. The high energy components (287.0 eV, 289.2 eV) could be attributed to the presence of carbon in the composition of different oxygen-containing groups. For the B75 and A150 samples, obtained with and without the carbon target, respectively, the peak intensity ratios corresponding to the C-C and C-B bonds are close. The decrease in the target power to 50 W led to the more significant contribution of the C-C bonds in comparison with the C-B bonds in the C1s spectrum. The O1s spectra can be fit to components at 532.5 eV, 533.2 eV, and 533.6 eV, assigned to O-C [70], O-B [71], and -OH [72] bonds, correspondingly. These values of E\({}_{\text{b}}\) are typical for analogous systems [68]. However, it should be noted that the analysis of O1s spectra is associated with several difficulties, the main of which is the proximity of  
Figure 6: (**a**) B1s, (**b**) C1s, and (**c**) O1s core-level spectra of A50, A150, and B75 films. the energies of various oxygen-containing bonds in the studied system. As a result, the deconvolution into spectral components can only be qualitative.
###### Abstract  
Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.  
spatiotemporal data fusion; data blending; spatial resolution; temporal resolution; satellite images +
Footnote †: journal: Remote Sensing
## 6 Conclusions  
This research has achieved significant advancements in maximizing EE within HetNets to meet the growing demands of high-speed data networks. The study found that the proposed D-DOSS strategy resulted in a 60% to 80% increase in active small-cell BSs compared to state-of-the-art techniques during low to medium traffic densities [42]. Additionally, the EUE of the network showed substantial improvements, particularly with increasing numbers of mobile users, where the EUE ratio significantly rose. For instance, during high data traffic loads, the EUE under D-DOSS outperformed other sleep strategies, showcasing its ability to dynamically adapt to traffic variations and optimize energy utilization.  
Despite the inherent trade-offs associated with distributed sleep approaches compared to centralized schemes, D-DOSS offers notable advantages in terms of implementation ease and manageability. By integrating D-DOSS and addressing diverse constraints, including delay, coverage probability, and SINR thresholds, this study represents a milestone in advancing HetNet technologies.
## 3 Frequency Design Formula and Approximate Analytical Solution for NTMD
### Damping-Based Design of NTMD Parameters  
Having verified the correctness and accuracy of the complex variable averaging method, we now demonstrate the accuracy of the nonlinear design formulation for the NTMD frequency derived from the modulation-demodulation equation. By considering the nonlinear stiffness as \(\alpha_{2}\) and the structural mass ratio as \(\alpha_{1}\), we compare the optimal TMD design frequencies obtained by the numerical method and the complex variable averaging method for different parameter combinations, as shown in Table 1.  
The results indicate that the nonlinear design formulas for NTMD frequencies, derived using the approximate analytical method, closely match the results obtained by the numerical method with only minimal errors. This ensures the correctness of the nonlinear design formulas and demonstrates the good accuracy of the calculation results, thus confirming their suitability for subsequent analysis.  
Figure 2: Frequency amplitude plots obtained using the complex variable averaging method and numerical method.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 3 Method
### CBAM Attention Mechanisms  
CBAM is an attention mechanism designed for Convolutional Neural Networks to enhance their performance [33]. It consists of two essential submodules: channel attention and spatial attention (Figure 7).  
Channel attention involves weighted average pooling of the feature for each channel. The resultant vector undergoes two fully connected layers to obtain a weight vector using the Sigmoid function. This weight vector is then multiplied with the input original feature and its residual feature, thereby providing attention weighting to distinct channels. To sum up, the channel attention is computed as:  
\[M_{\text{C}}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))) \tag{1}\]  
In Equation (1), \(M_{\text{C}}(F)\) denotes channel attention mechanism operation, where \(F\) is the input feature, \(\sigma\) indicates the Sigmoid function, and \(MLP\) is a multi-layer perceptron.  
Spatial attention involves weighted average pooling of pixel features, followed by two fully connected layers to yield a weight vector. This weight vector is multiplied with the original feature to produce a weighted feature. CBAM attention mechanisms enhance model performance by exploiting the interplay between channels and spatial dimensions. This mechanism has demonstrated substantial success in various large-scale image classification tasks. The spatial attention is computed as:  
\[M_{S}(F)=\sigma\left(f^{7\times 7}([AvgPool(F_{1});\ MaxPool(F_{1})])\right) \tag{2}\]  
where \(M_{S}(F)\) denotes spatial attention mechanism operation, \(f^{7\times 7}\) denotes a convolution operation with the filter size of \(7\times 7\), and \(F_{1}\) denotes the feature output through the channel attention operation.
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 2 Methods  
Figure 1 illustrates a schematic of the gait rehabilitation system for post-stroke hemiplegic patients. As the patient is incapable of generating sufficient muscle force and range of motion in the paretic side, the muscle activities induced by FES are designed to assist the patient to generate a natural gait motion with sufficient muscle force and range of motion.  
In the proposed rehabilitation system, the ground reaction forces on the unaffected and paretic sides were measured using force-sensitive resistors (FSR) to determine the gait phase and walking speed on the paretic side. With the gait phase and walking speed, the reference EMG envelopes for the muscles on the paretic side were determined. As it is difficult even for the patient's unaffected side to perform the muscle activities yielding normal, natural gait patterns, the reference EMG data were acquired from healthy subjects. Subsequently, the envelopes of the EMG signals were functionalized for various walking speeds. With the functionalized EMG envelope as the input, a DNN model predicted the  
Figure 1: Schematic of the FES control algorithm.  
joint torque for a specific gait phase, which enabled natural ankle movement of the paretic side. Using the linear model between the joint torque and FES intensity, the amplitude of the FES was determined based on the joint torque estimated by the DNN model. The FES signal applied to the muscles on the paretic side was controlled based on the foot-ground contact information.
## 3 Results and Discussion
### Incorporating Waters into the Computations
From the calculations, we obtain a correlation with the mode-based fluctuations \(\rho_{W,FL}\) equal to 0.84, a correlation with the displacements from the application of forces to the entire structure \(\rho_{W,F,F,ALL}\) equal to 0.85, a correlation with the displacements from the application of forces only to the water molecules \(\rho_{W,F,F,WAT}\) equal to 0.63, and a correlation with the displacements due to the perturbations only on the surface \(\rho_{W,F,EXT}\) that varies with the shrink factor and reaches a maximum of 0.83 for the optimal shrink factor of 0.3. In this case, it is remarkable that by applying random perturbations on only 31% of the nodes (corresponding to a shrink factor of 0.3), we obtain a high correlation with the experimental data (0.83 Pearson coefficient). It should be noted that this correlation is found by comparing the experimental B-factors of all protein residues (both on the surface and within the core) to the computed average displacements due to the application of perturbations only on the external part of the elastic network. Thus, it follows that even perturbing a small portion of the protein surface (31%) allows us to predict fairly accurately the fluctuations of the entire protein. This result has its origin in the strong coupling throughout the elastic network model: since the ENM is a highly cooperative model, the perturbation of only a small part of the structure can indeed generate fluctuations over the entire protein. This arises from the specific features of the three-dimensional protein structure and all of the internal connections, which are both built into the ENM. Figures S6-S10 in the Supplementary Material show similar results as those reported in Figure 9, but for different exponents \(p\) in the water-augmented ENM, namely, \(p=1\), 2, 4, 6 and 12. Despite some differences in the numerical values of the correlation coefficients, similar conclusions are reached as for those in Figure 9.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 4 Circuit Implementation
### Off-Chip LC Equivalent Model  
In the frequency range of 300 to 600 MHz, the RF signal wavelength varies from approximately 1 m to 0.5 m. As a result, the size of the quarter-wavelength transmission line becomes impractically large, contributing significantly to both the design cost and chip area. To address this challenge, the simplest approach involves using off-chip lumped LC equivalents. For this purpose, a CLC network incorporating a single inductor is employed. Specifically, considering the inductive effect of bonding wires, an 8 nH wire-wound ceramic chip inductor with a Q factor of 80 is chosen to minimize passband loss. The inductance value is adjusted to modulate the transmission line's eigenfrequency, while the capacitances \(C_{T1}\) and \(C_{T2}\) in the CLC network are controlled on-chip through multiplier capacitor arrays. To achieve the desired filter performance, these capacitors have values ranging from 1 to 63 pF, adjustable in 1 pF steps.
# Control-Flow Integrity: Attacks and Protections
## 5 Protection Techniques
### Control-Flow Integrity (CFI1)  
[PERSON] et al. [51] proposed CFI, the first CFI proposal for control-flow security implementation. To avoid confusion we call this technique as CFI1. They have implemented inlined CFI for windows on the \(\times 86\) platform. Their work [52] suggests initiating a Control flow graph (CFG) before program execution. The CFG monitors run-time behavior; hence, any inconsistency in the program results in a CFI exception being called and terminate the application. The CFI instrumentation does not affect direct function calls, but only indirect calls require an ID-check. Functions that are called indirectly, such as virtual methods, need insertion of an ID. This CFI technique ensures that program control is transferred to a valid destination. Thus, whenever a machine code transfers its control, the destination needs to be valid as well as predefined by CFG.  
However, [PERSON] et al. [50] pointed out three important limitations of this technique. First, the source code is not always available. Second, binaries lack the required debug information and finally, it causes high execution overhead because of dynamic rewriting and run-time checks. It is also unable to cope with dynamic code or binaries which contravene compiler conventions [53]. In addition, [PERSON] et al. [20] shows that the CFG is unable to determine if a function returns to the current caller. The problem also occurs when a function is called from more than one place, for instance, in a regular application, a function can be called from one place but if the return address is overwritten then the called function may return together with another CFG edge to an unknown call point.
## Materials and Methods
### Chemical Analysis
#### 4.3.3 High-Performance Liquid Chromatography Analysis of Phenolics Compounds  
With the aim of further investigating the polyphenic fraction of the extract, a high-performance liquid chromatography-diode array detection (HPLC-DAD) analysis was carried out.  
A Shimadzu Prominence LC 2030 3D instrument equipped with a Bondapak(r) C18 column, 10 \(\upmu\)m, 125 A, 3.9 mm \(\times\) 300 mm column (Waters Corporation, Milford, MA, USA) was used.  
Water + 0.1% \(v/v\) formic acid (A) and acetonitrile + 0.1% \(v/v\) formic acid (B) were used as mobile phase. The following program was applied: B from 10% at 0 min to 25% at 20 min, then B 50% at 26 min.; flow was set at 0.8 mL/min. Chromatograms were recorded at 280 nm.  
Analyses were performed using 10 \(\upmu\)L of extract; rosmarinic acid, caffeic acid, chlorogenic acid, quercetin, apigenin, luteolin, rutin and hyperoside were used as external standard. Calibration curves were established using reference standards ranging from 0.008 mg/mL to 0.500 mg/mL. The correlation coefficient (R\({}^{2}\)) of each curve was \(>\) 0.99.
## 2 Materials and Methods  
### Study Area  
The SRB, located in West Africa, covers 1.6% of the continent and spans four countries, with total basin size of about 337,000 km\({}^{2}\). The Senegal River is a 1086-km-long river that forms the border between Senegal and Mauritania; it flows through large expanses of scarce Sahelian vegetation, with seasonally varying water levels [12]. We conducted field missions in the SRB seven times between 2016 and 2019, during which we visited 32 water access sites where human schistosomiasis transmission is known to occur, and collected various ecological and epidemiological data from 16 villages within the geographic coordinates 15.898277-16.317924\({}^{\circ}\)N and 15.767996-16.431078\({}^{\circ}\)W. Ten water access sites across six villages were located along the Senegal river and connected tributaries near the city of Saint-Louis, while the remaining 22 water access sites across 10 villages were located along the shores of Lac de Guiers, a 170 km\({}^{2}\) freshwater lake located about 50 km east of the river sites and which overflows into the Senegal River (Figure 3).  
Figure 2: (**a**) Typical vegetation types found at water access points in the SRB, as seen from the shoreline (**b,c**) through drone imagery and (**d**) satellite imagery (WorldView-2). Specific vegetation types are a key indicator of snail presence and abundance and extend far beyond the area safely sampled by trained technicians, thus limiting accurate estimates for human disease risk.
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 4 Suspension Time-Delay Control Parameter Optimization
### Time-Delay Control Parameter Optimization Based on Adaptive Weighted Particle Swarm Algorithm
#### 4.2.1 Principle of the Optimization Algorithm  
Because the large inertial factor is advantageous for jumping out of the local minimum point, it is convenient for global search. The smaller inertial factor is conducive to the accurate local search of the current search area, which uses algorithm convergence. Therefore, the PSO algorithm is prone to prematurity, and the oscillation phenomenon easily occurs near the global optimal solution in the late stage of the algorithm. The nonlinear dynamic inertia weight coefficient formula is used in this paper. The specific expression is as follows:  
\[w=\left\{\begin{array}{cc}w_{\min}-\frac{(w_{\max}-w_{\min})(f-f_{\min})}{(f_ {avg}-f_{\min})},&f\leq f_{avg}\\ w_{\max},&f>f_{avg}\end{array}\right. \tag{20}\]  
where \(w_{\max}\) and \(w_{\min}\) respectively represent the maximum and minimum values of W inertial weight coefficient, \(f\) represents the current objective function value of particles, and \(f_{avg}\) and \(f_{\min}\) represent the average and minimum target values of all particles at present, respectively. In the above equation, the inertia weight changes automatically with the objective function value of the particle, so it is called the adaptive weight.  
When the target value of each particle is generally consistent or locally optimal, it will increase the inertia weight. When the target value of each particle is relatively dispersed, it will reduce the inertia weight. At the same time, for particles whose target function value is better than the average target value, the factor of the corresponding inertial weight is small and protects the particle. For the particle whose target function value is different from the average target value, its corresponding inertial weight factor can make the particle closer to a better search area.
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 4 Evaluation
### Detection of Motion Direction
#### 4.2.3 Impact of Antenna Distance  
In Figure 15(b), we evaluate the impact of the antenna spacing distance on the detection accuracy. We change the antenna spacing distance \(L\) from 20 cm to 50 cm with the step 10 cm and conduct the experiments 50 times for obtaining the average results. As we can see, the accuracy is close to 100% when the distance is no less than 30 cm. The case of \(L=20\) cm sees a slight decline. The reason is that a large dist  
Figure 16: Detection accuracy with different antenna angle and distance.  
Figure 15: Detection accuracy of time-slot-based sensing model.  
better distinguish moving in and moving out (different directions) from the time domain. We suggest that the distance be set to 30 cm or larger in practical use.