esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Rockfill Materials Based on Fractal Theory
## 2 Grading Scale Methods  
The commonly used scale methods for the grading of rockfill materials include screening, similar gradation, equivalent substitution, and mixing. Screening refers to the removal of particles that exceed the permissible maximum particle size. Thus, the content of fine particles smaller than 5 mm is relatively increased with the mechanical properties of granular materials. Screening is suitable only when few particles exceed the permissible maximum particle size. Self-similarity gradation remains the coefficient of curvature of gradation, and the nonuniformity coefficient remains unchanged with the increasingly larger content of fine particles and the smaller content of coarse particles, thus changing the mechanical properties. Equivalent substitution ensures that the content of coarse particles and that of fine particles are unchanged even with changes in the grading, nonuniformity, and curvature coefficients of the coarse particles. In mixing, the particle size is reduced by self-similarity gradation at a certain scale, and the equivalent substitution is used. Whatever scale method is chosen, the relationship of the coarse and fine particles in the filling process changes markedly, and the mechanical properties of the rockfill materials change significantly before and after scaling. Therefore, how a reasonable scale method should be chosen awaits further study.  
To investigate the influences of the fractal dimension \(D\) on the mechanical properties of rockfill materials, this paper chooses the grading curve designed in [28] and the grading curves obtained from the four scale methods; the maximum particle size after scale is 60 mm. The scale methods are equivalent substitution, screening, self-similarity gradation, and mixing, which are denoted by DL, TC, XS, and HH, respectively. The grading curve designed for rockfill materials and the gradation characteristic curve after scaling are shown in Figure 1.
# Silver Nanoparticles-Loaded Exfoliated Graphite and Its Anti-Bacterial Performance
## 3 Results and Discussion  
### XRD and FTIR Analysis  
To improve the synthesis of Ag-EG composite, a series of characterization techniques were applied. Figure 1a shows the X-ray diffraction (XRD) patterns of the graphite, GICs, and Ag-GICs. The (002) diffraction peak of graphite appears at 26.56\({}^{\circ}\), GICs at 26.06\({}^{\circ}\), the slight change in the location of the (002) diffraction peaks relative to graphite is ascribed to the oxygen-containing groups on the surface of GICs [32]. In the insert picture, the peaks at 38.1\({}^{\circ}\), 44.4\({}^{\circ}\), 64.5\({}^{\circ}\), and 77.5\({}^{\circ}\) can be assigned to the (111), (200), (220), and (311) planes of silver with the face-centered cubic (fcc) structure (space group: Fm3m), respectively (JCPDS card, No. 04-0783), which indicates that the silver nanoparticles are composed of pure crystalline silver [33].  
Figure 1b shows the FTIR spectra of GICs, Ag-GICs, and Ag-EG. For GICs, there is a strong absorption band at 3430 cm\({}^{-1}\), which corresponded to the O-H stretching vibrations. It also exhibits bands around 1720 cm\({}^{-1}\) due to the C=O stretching, 1630 cm\({}^{-1}\) due to aromatic C=C, as well as bands due to carboxy C-O (1450 cm\({}^{-1}\)), and a strong band at 1050 cm\({}^{-1}\) attributed to C-O stretching vibrations. It has been reported previously that these groups are situated at the edges of the graphite nanosheets [34]. However, an obvious change of the absorption bands of the oxygenated functional groups was seen in the FT-IR spectrum of the Ag-GICs, and the peak at 1720 cm\({}^{-1}\) corresponds to the C=O groups. This could be attributed to both the reduction of NaBH\({}_{4}\) and the existence of the AgNPs on the surface of GICs [33; 35]. The oxygenated functional groups of Ag-EG was further decreased. The spectrum also presents a C=C peak at 1630 cm\({}^{-1}\) corresponding to the remaining sp\({}^{2}\) character from the unoxidized graphitic domains. From the FT-IR spectra, it could be deduced that functional groups provide ideal nucleation sites for Ag nanoparticles; powerful interactions would occur between Ag nanoparticles and the un-reduced oxygen-containing groups on the surface of the GICs and that of the EG. Therefore, Ag nanoparticles can strongly attach to the exfoliated graphite surface [23].
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
## 5 Evaluation
### Privacy Compliance
#### 5.1.7 Third Parties  
The banks did quite well (92% compliance) in terms of specifying which third-parties they share the collected personal information with. However, only 47% of the mobile money services complied with this principle.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & **GSMA** & & & **FDIC** & & & **GSMA \& FDIC** \\ \hline  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline Banks & 100 & 16 & 56 & 96 & 84 & 84 & 92 & 72 & 88 & 96 & 56 \\ \hline Mobile & & & & & & & & & & \\ money & 87 & 20 & 27 & 83 & 83 & 73 & 47 & 50 & 67 & 83 & 43 \\ service & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall percentage (%) of investigated banks and mobile money services complying with each of the GSMA and FDIC principles.
## 2 Theory of UWA Communication Using CCVTRM
### Principles of Detection and Channel Estimation  
The key to implementing CCVTRM is detecting the probe pulse signal and estimating the composite channel. From the signal processing point of view, copy-correlation can be viewed as a matched filter that can achieve a maximum SNR under ideal conditions. In this paper, copy-correlation technology is used to detect the probe signal and estimate the composite channel.  
In the absence of multipath, the output probe signal of the VSA can be expressed as:  
\[p_{r}(t)=A_{1}p(t-\tau_{1})+n_{p}(t) \tag{17}\]  
where \(n_{p}(t)=\sum\limits_{i=1}^{M}n_{pi}(t)\).  
After copy-correlation processing, the output is:  
\[\begin{array}{ll}R(t)&=\int p_{r}(\tau)p(\tau-t)d\tau\\ &=A_{1}\int p(\tau-\tau_{1})p(\tau-t)d\tau+n_{p}^{\prime}(t)\end{array} \tag{18}\]  
where \(n_{p}^{\prime}(t)=\int n_{p}(\tau)p(\tau-t)d\tau\), denotes the noise component of the output.  
Because the probe pulse signal and the noise are uncorrelated, the noise component is weak. Consequently, a correlation peak can be easily found at \(t=\tau_{1}\), which means the probe pulse signal is detected.  
Taking multipath into account, Equations (17) and (18) can be modified as:  
\[p_{r}(t)=\sum\limits_{k=1}^{N}A_{k}p(t-\tau_{k})+n_{p}(t) \tag{19}\]  
\[\begin{array}{ll}R(t)&=\int p_{r}(\tau)p(\tau-t)d\tau\\ &=\int\left[\sum\limits_{k=1}^{N}A_{k}p(\tau-\tau_{k})\right]p(\tau-t)d\tau+ \int n_{p}(\tau)p(\tau-t)d\tau\\ &=\sum\limits_{k=1}^{N}A_{k}\int p(\tau-\tau_{k})p(\tau-t)d\tau+n_{p}^{\prime} (t)\end{array} \tag{20}\]  
It can be seen that the output of copy-correlation contains multiple correlation peaks and the positions of the peaks corresponding to time delay of the multiple paths. Considering the effects of noises, we set a threshold and keep the peaks that higher than the threshold. Then the channel impulse response is obtained.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data
### MERRA-2 and Global and Regional Land Data Assimilation Projects  
The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) is a reanalysis product suite developed by the NASA's Global Modeling and Assimilation Office (GMAO) [19]. MERRA-2 includes new observation types in the assimilation that are not available in the previous version (MERRA) along with updates in the Goddard Earth Observing System (GEOS) model and analysis scheme [19]. Meteorological variables (e.g., winds, temperature, pressure, humidity), which are crucial for weather and climate analysis in the tropics, are available in MERRA-2. The spatial coverage of MERRA-2 is global (90\({}^{\circ}\) N-S). The temporal resolution varies from hourly, 3-hourly to monthly. The spatial resolution is 0.625 deg. \(\times\) 0.5 deg. in longitude and latitude. The MERRA-2 product suite begins from 1 January 1980, to the present. Over the years, various MERRA-2 assessment activities have been conducted (e.g., precipitation [34]).  
The NASA land data assimilation projects produce global and regional land datasets that consist of quality-controlled, spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities [35], by ingesting satellite- and ground-based observations and using advanced land surface modeling and data assimilation techniques in the regional and global land data assimilation systems (LDAS) [35]. The temporal and spatial resolutions vary and so do the spatial coverage and the data availability (Table 3).
# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
## 2 Materials and Methods
### Data Collection  
The SOC710 portable hyperspectral imager (wavelength range 400-1000 nm, resolution 4.6875 nm), produced by Surface Optics Corporation (11555 Rancho Bernardo Road San Diego, CA 92127, USA) of the United States, is used for spectrum measurement. The dark box of the optical experiment (task cabin) is placed in dark room conditions. The bottom area of the box is 50 * 60 cm and the height is 100 cm with a movable base. The interior of the dark box has a diffuse reflection coating. Four sets of surround-type built with 70 W halogen light source with an incident angle 45\({}^{\circ}\) was used. It has a cooling device (in accordance with the requirements of the SOC710 hyperspectral imaging spectrometer). The instrument is placed vertically and directly above the target with the exposure distance of 300 mm, preheated for 15 min, and then used to measure spectral information. We put the rape seeds in 16 circular dishes. In every dish, 5 non-overlapping rectangular areas (see Figure 1) are randomly selected as the region of interest (ROI) and then 80 ROIs can be obtained. In each ROI, we randomly measure the spectral reflectivity five times and average them as the final spectral reflectivity value of the ROI. In this manner, 80 spectral reflectivity can be obtained that is labelled as 1-80. Table 1 shows the statistical characteristics of rapeseed oleic acid content.  
On the other hand, all of the rapeseeds in every ROI are grinded to measure the fatty acid content of rapeseed and to further to obtain rapeseed oleic acid data by using Agilent 7890 Inductively Coupled Plasma Mass Spectrometer in the Oil Research Institute of Hunan Agricultural University, where the indoor temperature is 16 \({}^{\circ}\)C and the relative humidity is 40%. The experimental environment is as follows:  
* Chromatography column: DB-5 Chromatography column (30 m \(\times\) 0.25 mm \(\times\) 0.25 mm, 5% Phenyl-95% Polydimethylsiloxane);
* Temperature programmed condition: The initial temperature is 50\({}^{\circ}\). The temperature is then raised to 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held there for 10 min. The temperature is then raised to 300 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held there for 10 min again;
* Vaporization chamber temperature: 300 \({}^{\circ}\)C;
* Detector temperature: 300 \({}^{\circ}\)C;
* Carrier gas: high-purity nitrogen, gas flow ratio 100:1;
* Column flow rate: 1 mL/min;
* Cracking furnace temperature: 350 \({}^{\circ}\)C;
* Interface temperature: 300 \({}^{\circ}\)C;
* Fatty acid methyl esterification: 400 mL potassium hydroxide methanol solution and 800 mL ether petroleum ether solution.  
From the above process, we obtained 80 samples of spectral reflectivity and oleic acid data, out of which 64 were used as the training samples and the leftovers were used as the test samples.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 4 Twin System Modeling and LSTM-DQN-APF Algorithm
### Algorithm Validation Test and Analysis
#### 4.3.4 Experiment 3: Algorithm Validation Test for Urban Warfare Scenario
The green line is the flight path of the virtual UAV without disturbing wind, and the purple line is the actual flight trajectory of the virtual UAV under the influence of wind. As shown in Figure 17, the UAV starts from (0,0,0), the target point location is (5,25,0), and the flight speed is 1m/s. The red represents LSTM-DQN-APF moving towards the target point; Ge-Apf and APF initially keep circling under the influence of wind. In real situations, such flying behavior of the UAV is very dangerous, and the APF falls into a local minimum after 15 s.  
Figure 16: The effect of wind on the virtual UAV generated. Figure 17: UAV flight path. Table 7 compares the average flight time, average path length, and task completion rate of APF, Ge-Apf, and LSTM-DQN-APF under windy and non-windy conditions. The LSTM-DQN-APF algorithm shows significant advantages in average flight time, average path length, and task completion rate under both windy and non-windy conditions. In contrast, the APF algorithm has many issues in both environments. Under windless conditions, when applying APF, the UAV reached an unreachable target state after flying for 20 s, resulting in increased experimental time and path length. In windy conditions, the APF algorithm causes the UAV to be affected by the wind field, deviating from the target point. After flying for 15 s, it gets trapped in a local minimum. Under the influence of the wind field, the virtual UAV in the simulation environment experiences multiple collisions and fails to find the correct path for an extended period. This not only increases the experimental time and flight path length but also potentially introduces safety risks in practical applications. While the Ge-Apf algorithm outperforms APF in certain aspects, its overall performance is still inferior to LSTM-DQN-APF. This indicates that the LSTM-DQN-APF algorithm has better performance and adaptability in path planning and flight control for UAVs, especially in complex environments (such as wind fields), providing more reliable algorithmic support for the application of UAVs in real-world complex environments.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 4 Keystroke Recognition Using Wi-Fi Sensing
### Experimental Setup  
In a standard office building within the range of commercially operating Wi-Fi networks, we setup our experimental apparatus as depicted below.  
In Figure 4, we depict the experimental setup for human keystroke recognition using Wi-Fi CSI. We positioned a Wi-Fi Transmitter (Tx) on one side of a standard keyboard, which broadcasts ping packets at a rate of 1300 Hz. On the other side, we placed a Raspberry Pi 4B Receiver (Rx) equipped with the Nexmon CSI extraction firmware [12]. This is denoted by the red circle in Figure 4. This device is manufactured by Raspberry Pi Ltd. (Cambridge, UK), and was sourced online within Australia via. Element 14. The Nexmon firmware activates monitor mode on the Raspberry Pi 4B Network Interface Card (NIC) and then captures CSI for every received packet. Given that Tx is propagating packets at 1300 Hz, Rx will capture 1300 CSI snapshots per second. All communications occur on channel 2 of the 2.4 GHz Wi-Fi spectrum, with a bandwidth of 20 MHz and CSI sampled for 56 subcarriers. The experimental subject (pictured) pressed the set of keys Q, Z, H, M and P a total of 100 times in the training phase and 50 times in the testing phase. Without loss of generality, we chose this subset of keys since they are evenly spread across the keyboard and allow for simpler analysis compared to a full keyboard.
## 5 Results and Discussion
### Output Power  
In order to investigate effect of the bluff body's shape on the output power, the variation of output power with wind velocity for WEHs with different arc angle bluff bodies is shown in Figure 7. The tail length of the bluff body is almost identical. The WEH starts to work at a wind velocity of 2 m/s. The output power increases with the increase of the wind velocity. The bluff body with shape 18 had the most significant output power. The max output power was 2 mW at a wind velocity of 7 m/s. For the bluff body with  
Figure 6: Kriging model for the average output power in (**a**) three-dimensional and (**b**) two-dimensional cartesian.  
a large arc angle, such as shapes 30 and 42, the output power was far lower than that of shape 18, and the max output power was only 0.5 mW at 7 m/s. It can be concluded that the output power tends to decrease with the increase of the arc angle of the bluff body.  
The variation of output power with wind velocity for WEHs with different tail length bluff bodies is shown in Figure 8. As can be seen, the tail length of shape 18 was between those of shapes 17 and 19. However, the output power of the WEH with shape 18 was the maximum. The WEH with shape 17 almost ceased to work. The output power of the WEH with shape 19 was minimal for all wind velocities and was only 0.5 mW at a wind velocity of 7 m/s. Compared with the WEH with too long and too short a tail length, an optimal tail length can provide the maximal output power.  
The variation of output power with wind velocity for WEHs with prism and arc-plate-shaped bluff bodies is shown in Figure 9. The aerodynamic shapes of the arc-plate-shaped bluff bodies are within the region of the Kriging surrogate model showing the maximum output power. The WEHs with the prism- and arc-plate-shaped bluff bodies have considerable energy output. The critical galloping wind speeds of bluff bodies with different sections were all around 2 m/s. When the wind speed was 7 m/s, the output power of the WEH with shape 11 and the prism bluff body was 2.3 mW and 1.1 mW, respectively. Compared with the WEH with a prism bluff body, the output power of the WEH with shape 11 doubled, indicating the significance of op  
Figure 8: Variation of output power with wind velocity for WEHs with different tail lengths bluff bodies.  
Figure 7: Variation of output power with wind velocity for WEHs with different arc angle bluff bodies.  
shape of the bluff body section. The aerodynamic optimization of this study can, therefore, be directly applied in piezoelectric WEHs, to increase power generation significantly.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Opportunistic Routing Protocols
#### 5.2.2 ORD  
In 2014, [PERSON] et al. proposed ORD [12], which is an improved version of ORW. The initial operation of ORD is similar to that of ORW. The core idea of this protocol is that if a forwarder node holds two data packets for the same destination, it will aggregate (Figure 15) the packets and transmit them as one packet in order to reduce the number of transmissions. To obtain more packets, the forwarder node waits for a specific amount of time. In this way, this protocol handles the tradeoff between energy consumption and latency. ORD can control the packet-holding time based on the application requirements. After choosing the forwarder set by EDC, the protocol decides the packet-holding time depending on the residual energy and hop count to the sink node.  
In this protocol, the author used in-network aggregation to reduce the number of transmissions; however, in contrast, the forwarder node increases the frequency of the duty-cycle in order to obtain more packets, which will increase energy consumption considerably. Moreover, the packet holding time can increase the latency.
# A Dynamic Parameter Tuning Strategy for Decomposition-Based Multi-Objective Evolutionary Algorithms
## 3 Suggested Strategies for Improvement  
### Basic Concept  
The aim of this paper is to tackle the issue that the penalty parameter is a fixed value in the traditional PBI decomposition method, which affects the convergence speed of the population to some extent, so we propose a boundary-crossing strategy to adaptively adjust the penalty parameter. The core idea of the dynamic tuning strategy is that the penalty parameter affects the probability of the offspring being retained to a certain extent, thus affecting the convergence and diversity of the population. According to the PBI formula, the larger the penalty parameter, the less likely the offspring will be retained. Therefore, at the start of each iteration, the primary objective is to bring the population as close as possible to the Pareto front, so it is necessary to make the penalty parameter low, a large number of substitution operations occur in the offspring, and population turnover; as the number of iterations increases, the penalty parameter is gradually increased, so that the population is diversified and dispersed. To cover the entire Pareto front, it is necessary to include all relevant options. Based on this approach, the penalty parameter is adaptively adjusted according to the current distribution of solutions around the weight vector. When the distribution of solutions around the weight vector is too wide, the penalty parameter decreases to increase the convergence of the population; when the distribution of solutions around the weight vector is too small, the penalty parameter increases to increase the diversity of the population.