esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study  
In this section, to verify the effectiveness of each strategy in GNNDRS, we conducted ablation studies. We specifically excluded the doc-s edges in the heterogeneous graph interaction network, as shown in Section 3.3, and the dynamic detection strategy for argument roles, as presented in Section 3.5. The experimental results are shown in Tables 5 and 6.  
According to the results in Tables 5 and 6, we can draw the following conclusions: (1) Incorporating edges between documents and sentences leads to an increase of 7.3% and 0.9% in the F1 scores. By incorporating document node features, the heterogeneous graph interaction network adeptly captures the holistic context of the entire document, employing enhanced input features. This integration empowers the model to engage in comprehensive global reasoning while reinforcing semantic dependencies among sentences through interaction with sentence nodes. Ultimately, this cultivates an enriched understanding of the intricate relationship between arguments and events. (2) It is noteworthy that employing our proposed dynamic sorting strategy for argument role detection increases by 0.3% and 0.8% in the F1 scores. In our model, argument roles associated with fewer candidate arguments are prioritized for processing. These relatively fewer arguments are the most crucial components of an event. Consequently, prioritizing their handling enhances the accuracy of identifying and extracting vital information, thus improving the precision of the extraction outcomes. (3) The above two points demonstrate that introducing document nodes into the heterogeneous graph and modeling the interaction between documents and sentences contribute significantly to our model.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 3 Results
### Experiment Setup  
Front-end hardware: As shown in Figure 9, the prototype system proposed in this paper consists of a pair of LoRa transceivers. The LoRa transmitter consists of Arduino Uno and Semtech SX1276 modules and is equipped with an antenna. LoRa nodes transmit signals in the 915 MHz band with a 125 kHz channel bandwidth and transmit power of 20 dBm. The receiver is a USRP X310, receiving signals at a sampling rate of 1 kHz and equipped with two antennas. The LoRa transceiver is equipped with three-directional antennas with a gain of 8 dpi.  
Back-end implementation: The USRP X310 was connected to a Lenovo ideapad 700 laptop with an Intel Core i5-6300 HQ CPU and 8.0 GB RAM via a network cable. We used Labview software to collect LoRa signals, and MATLAB was used to process the collected signals.  
Experiment environment: As shown in Figure 9, we conducted the evaluation experiment in a laboratory environment with a length of 12 m and a width of 6.5 m. The distance between the LoRa transceiver devices was 1 m, and the height of the transmitting antenna was set to 0.8 m. The height of receiving antenna 1 was set to 0.8 m, and the height of receiving antenna 2 was set to 1.3 m. The target travelled back and forth in the LOS direction perpendicular to the position 5 m away from the transceiver.
## 2 Materials and Methods
### NDVI and LST
#### 2.3.3 \(Lst\)  
In [29], the \(LST\) calculation was defined by the ratio between the operated satellite spectral radiance in the thermal band and the emissivity, given by Equation (9).  
\[LST=\frac{K_{2}}{ln(\frac{\chi_{NB}\times K_{1}}{L_{\lambda}}+1)} \tag{9}\]  
where  
\(T_{S}\) = Earth's surface temperature;  
\(K_{1}\) and \(K_{2}\) = Calibration constants for the thermal band;  
\(\varepsilon_{NB}\) = Emissivity;  
\(L_{\lambda}\) = Spectral radiance in watts/m\({}^{2}\)\(\times\) sr \(\times\) um.  
The used thermal band calibration constants are available in the metadata of the captured images.  
For [17], emissivity is obtained from the ratio between the energy emitted by the surface of a given material, and the energy emitted by a mass of the same temperature. Therefore, determining the emissivity of each _pixel_ in the spectral domain in thermal band is given by Equation (10).  
\[\varepsilon_{NB}=0.97+0.0033\times LAI \tag{10}\]  
where  
\(\varepsilon_{NB}\) = Emissivity;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Thematic Classes** & \(NDVI\) **Intervals** \\ \hline Water & \textless{}0 \\ Exposed soil & 0–0.2 \\ Low vegetation & 0.2–0.4 \\ Medium vegetation & 0.4–0.6 \\ Dense vegetation & 0.6–0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(NDVI\) classes for land use and occupation.  
\(LAI\) = Leaf area index.  
The leaf area index is an indicator of the biomass in each image _pixel_; it is defined by the ratio between the leaf area of all the vegetation per unit and the area used by that vegetation. The \(LAI\) is obtained from Equation (11) [17].  
\[LAI=\frac{ln(\frac{0.69-SAVI}{0.59})}{0.91} \tag{11}\]  
where  
\(LAI\) = Leaf area index;  
\(SAVI\) = Soil-adjusted vegetation index.  
The \(SAVI\) is an index used to smooth out the \"background\" effects of the soil. It is calculated according to soil type and given by Equation (12) [30].  
\[SAVI=\frac{(1+L)\times(\rho_{\lambda IV}-\rho_{\lambda V})}{(L+\rho_{\lambda IV }+\rho_{\lambda V})} \tag{12}\]  
where  
\(SAVI\) = Soil-adjusted vegetation index;  
\(L\) = Adjustment factor depending on soil type.  
The constant soil type (\(L\)) in the region is 0.5, because it is Latin-American soil [31].
## 5 Results and Discussion
### Manoeuvring Tests
#### 5.2.3. Spiral Test  
The main purpose of the spiral test was to evaluate the SFRM's ability to control the rudder plane and maintain linear stability. In the simulation, the rudder plane was immediately pulled to a certain angle, e.g., \(25^{\circ}\), and the angle was gradually reduced until the stable yaw rate was reached. This was repeated to identify the relationship between the rudder plane angle and the yaw rate. Figure 17 illustrates the distributions of the spiral tests for different rudder plane angles at three sailing speeds: \(U=0.5\), \(1\), and \(1.5\) m/s. Specifically, Figure 17 displays changes in yaw rate and rudder plane angles when the rudder plane was gradually reduced from \(25^{\circ}\) to \(-25^{\circ}\) by \(5^{\circ}\) each time. From the simulation results, the yaw rate exhibited a relatively linear trend with different rudder plane angles and sailing speeds. For example, a larger rudder plane angle indicated a faster yaw rate. In other words, it explained that the yaw rate at each increment is dependent on the initial sailing speed. The trend also revealed that for small rudder angles, the rate of turn would depend upon whether the rudder angle is increasing or decreasing ([PERSON] _et al._, 2018), especially in case of high sailing speed.
# Specific Mutations in Aph1 Cause \(\gamma\)-Secretase Activation
## 2 Results
### A\(\beta\) Production by Mutant Aph1 in Yeast Microsomes  
We analyzed A\(\beta\)-producing activity using the yeast microsomal fraction. The cleavage of APP\({}_{\text{C55}}\) was tested in vitro by incubating the solubilized microsomes (Figure 4) [30]. A\(\beta\) was observed in the presence of \(\gamma\)-secretase with the wild-type Aph1 (Figure 4a, lanes 1-4). The amount of A\(\beta\) increased in the presence of PC, and diminished with L685,458, a \(\gamma\)-secretase-specific inhibitor. With the Aph1 L30F/T164A mutant, the intensities of the A\(\beta\) bands increased in different conditions (Figure 4a, lanes 6 and 7, and Figure 4b), indicating that the mutation enhances A\(\beta\)-producing activity. The intensities of different A\(\beta\) species, A\(\beta\)38, A\(\beta\)40, A\(\beta\)42, and A\(\beta\)43, also increased with L30F/T164A (Figure 4c, lanes 2, 3, 6,  
Figure 2: \(\beta\)-Galactosidase activity of Aph1 active mutants in the absence of NCT. Wild-type (WT) or mutant Aph1 was introduced into yeast with PS1, FLAG-Pen2, and APP\({}_{\text{C55}}\)-Gal4p; Gal4p cleaved from APP\({}_{\text{C55}}\)-Gal4p activates the reporter gene, _lacZ_, encoding \(\beta\)-galactosidase. \(\beta\)-Galactosidase activity was assayed in cells containing Aph1 double and single mutants (**a**-**g**). One unit of \(\beta\)-galactosidase activity corresponds to 1 nmol O-nitrophenol \(\beta\)-galactosidase hydrolyzed per minute; it is expressed as units/mg lysate protein. Activity was normalized as relative to the activity of control cells without NCT (52.0 units/mg protein). Results from three independent assays are shown with their standard deviations. Statistical analyses were performed using one-way analysis of variance (ANOVA) followed by [PERSON]’s multiple comparison test. Asterisks on each bar indicate \(p<0.001\) (***), \(p<0.001\) (***), \(p<0.01\) (*) or \(p<0.05\) (*)(**a**-**g**).  
and 7, and Figure 4d); the ratio of each species was similar (Figure 4e), except that the ratio of A\(\beta\)38 was increased with the mutant. The ratio of long A\(\beta\) species (A\(\beta\)42 and A\(\beta\)43) with A\(\beta\)40 was similar. These results suggest that L30F/T164A enhances A\(\beta\) production but its A\(\beta\) trimming activity is similar to the WT.  
One possible explanation for the increased A\(\beta\) production in the Aph1 L30F/T164A mutant is that the mutations increase complex formation. To test whether the Aph1 L30F/T164A mutants form a complex with other components, \(\gamma\)-secretase in yeast microsomes was immunoprecipitated with a PS1 antibody against the loop region (GIL3) (Figure 5). NCT, PS1, and Aph1 were coimmunoprecipitation with wild-type Aph1 or the L30F/T164A mutant (Figure 5, lanes 3 and 4). The recoveries of Aph1 and NCT were similar between the WT and the mutant, suggesting that the mutant Aph1 did not affect \(\gamma\)-secretase complex formation.
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
## 2 Experimental Section
### Preparation of the TiO\({}_{2}\) Inverse Opal Structure  
The TiO\({}_{2}\) sol was prepared by mixing 0.1 mL deionized water, 6 mL anhydrous ethanol, 6 mL diethanolamine, and 1 mL titanium tetraisopropoxide were homogeneously mixed in an Erlenmeyer flask to obtain a stable and transparent sol [25; 26]. Several droplets of the TiO\({}_{2}\) sol were dropped onto the PS colloidal crystal array using a quantitative pipette, which could infiltrate the interstices between microspheres. Then, the sample was exposed to the atmosphere so the hydrolysis reaction could be completed within 2 h. Finally, the well-filled samples were sintered in an oven (the heating ramp was 5 \({}^{\circ}\)C/min) at 500 or 600 \({}^{\circ}\)C, held for two hours, and cooled down naturally along with the oven. In this process, the polystyrene spheres were removed by sintering, and the amorphous phase of titania could transform to a crystalline phase (anatase or rutile). The phase transition leads to a change in optical properties, such as the refractive index, the absorbance, or the fluorescence.
# A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal
###### Abstract  
The presence of control signal feedback to the reference microphone in feedforward active control systems deteriorates the control performance. A four-stage method is proposed in this paper to carry out online feedback path modelling with the control signal. It consists of controller initialization, feedback path modelling using decorrelation filters, active control operation, and feedback path change detection for maintaining the control operation. In contrast to the existing auxiliary noise injection method, the proposed method uses five switches and three thresholds to control and maintain the system stability by avoiding the interference between control operation and feedback path modelling, and adaptive decorrelation filters are used to increase the feedback path modelling performance. Simulation results reveal that the proposed method is capable of tracking feedback path changes without injecting any auxiliary noise and maintaining the noise reduction performance and stability of the system.  
active noise control; online feedback path modelling; decorrelation filter; convergence speed; stability Article
# Experimental Study of Erosion Prevention Model by Bio-Cement Sand
## 1 Introduction
While some researchers have attempted to evaluate the strength of this shell-like protective layer using micro-beam bending tests [23], hardness testers [6; 24], and other methods, there is still a lack of unified measurement techniques and evaluation standards. Further clarification is needed regarding the relationship between protective layer strength, water stability, and erosion resistance indices. Currently, research conducted by scholars on microbially induced mineral precipitation primarily emphasizes the strength and permeability characteristics of rock and soil stabilized by microorganisms. In contrast, investigations into the anti-erosion properties of microbially mineralized rock and soil predominantly focus on wind erosion in desert environments. Studies addressing microbial stabilization against water erosion are relatively scarce, with most attention directed towards hydraulic erosion occurring during rainfall events. A significant challenge in exploring the anti-erosion properties of microbially stabilized slopes lies in establishing a clear relationship between calcium carbonate content, shear strength, and critical initiation shear velocity within the context of water erosion processes. In this study, a microbial slurry was applied onto the sand surface of the sandbox to prepare model samples of microbiologically reinforced cemented bodies with varying degrees of reinforcement. Penetration tests were conducted to investigate the relationship between penetration resistance and calcium carbonate content in the microbiologically reinforced sand models. Additionally, erosion tests on microbial cemented soil were performed using a self-designed self-flowing open channel flume. The relationship between critical starting velocity, erosion rate, and calcium carbonate content in microbial cemented soil was examined, leading to the establishment of a critical starting formula for microbial cemented soil. The quantitative relationship between the critical starting velocity and calcium carbonate was established through model tests utilizing microbially induced calcite precipitation (MICP). The findings of this study can provide valuable field guidance for the reinforcement of silty slopes, as well as for erosion prevention and dike stabilization. This research aims to assess the effectiveness and applicability of microbial slurry for protecting silt slopes and provide scientific evidence for applying microbial soil consolidation protection technology in slope protection and soil-water conservation engineering in silt areas.
## 4 Target Model and Frequency Offset Extraction Method
### Target Detection Processing Method
#### 4.2.3 Interference Suppression
For antennas \(i\) and \(i+1\) using the same oscillator, the signal ratio can be expressed as follows [26]:  
\[h^{i}(t)=\left[h^{i}_{s}(t)+h^{i}_{d}(t)\exp(j2\pi f_{d}t)\right]\exp[j\theta( t)] \tag{17}\]  
For a rotating target, it is approximately fixed in a short period, during which channel states \(h^{i}_{s}(t)\) and \(h^{i}_{d}(t)\) are constant. It can be seen that \(R(t)\) and \(\exp(j2\pi f_{d}t)\) have the same periodicity, so the CASR method can remove phase noises while utilizing the periodic changes in phase to obtain Doppler frequency shift information.
## 3 Geocasting Based on Local Coverage Areas
### Geocasting Using Local Coverage Areas
#### 2) Forwarding and Halting Decisions:  
With the forwarding decision a node determines if there _might_ be child nodes or descendant nodes further down the routing tree that are within the area of interest specified in the query. If there are, the node should forward the query to its child nodes, which in their turn decide if the query needs to be propagated. When the coverage area is represented by a convex hull, a node \(i\) cannot determine with certainty that there is indeed any node that is located in the subtree of node \(i\) and within the polygon \(\mathbf{R}\)  
Figure 2: Routing decisions: (a) node \(i\) executes query if \(p_{i}\) inside \(\mathbf{R}\), (b) node \(i\) forwards query to children if \(\mathbf{H}_{\text{i}}\) overlaps with \(\mathbf{R}\), and (c) node \(i\) halts forwarding query to children if \(\mathbf{H}_{\text{i}}\) and \(\mathbf{R}\) are disjoint.  
because positions are lost for the nodes located within the convex hull. However, a node is able to decide with certainty that further in its part of the routing tree no node is present within the region \(\mathbf{R}\). When \(\mathbf{R}\) is disjoint from the node's convex hull \(\mathbf{H_{i}}\) in the later case, the node does not forward the query and it consequently does not spend energy on transmitting the query and also saves resources from its child nodes.  
The forwarding decision is taken based upon \(\mathbf{R}\) extracted from the query and the local coverage area \(\mathbf{H_{i}}\). Checking if \(\mathbf{R}\) overlaps with \(\mathbf{H_{i}}\) (_i.e._, determining if two convex polygons intersect) can be easily performed in \(\mathcal{O}(\log{(m+n)})\) as described in [32], where \(m\) and \(n\) are the numbers of vertexes of the two polygons, respectively. We assume that the target region is a circle in our simulations. Checking if circle \(\mathbf{R}\) overlaps with \(\mathbf{H_{i}}\) is done by \"partially\" checking the circle against edges [33], and against vertexes. If the area of interest and the local coverage area intersect, the node forwards the query (see Figure 2(b)). Otherwise, it halts the forwarding of the query (see Figure 2(c)).
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 5 Experiment Results and Analysis
### Datasets and Experimental Setting  
Following the dataset used in [18], we choose the DAVIS2017 dataset [36] as the training set of the proposed network. The DAVIS2017 dataset has 90 different scenes and a total of 6208 frames with two resolutions: 480 \(\times\) 894 and 1080 \(\times\) 1920. These video scenes are cut into video cubes of 8 \(\times\) 256 \(\times\) 256. Then, we use the binary random masks \(\left\{\mathbb{C}_{k}\right\}_{k=1}^{B}\) used in [18] to modulate these eight consecutive frames (\(B=8\)) into one frame snapshot measurement \(Y\) according to Equation (1). The compression ratio of the binary random mask is 50%. In this way, the original video is compressed into one frame at a compression rate of 1/8, and each frame is modulated with a 50% ratio. We group each video cube and corresponding snapshot measurement into a data pair and collect 13,000 data pairs as the training set. We test the trained reconstruction network on both the simulation datasets and the real dataset. The simulation datasets contain Kobe, Traffic, Runner, Drop [8], Crash and Aerial [17]. We adopt the same setting as in the training set to generate the snapshot measurements. The real data Wheel [7] is captured by the SCI camera. We use PyTorch to train the proposed network on the RTX 3090 GPU and use the Adam optimizer [37] to minimize the loss function. The initial learning rate is 2 \(\times\) 10\({}^{-4}\) and we reduce the learning rate by 10% every 10 epochs. The epoch number is set as 100. The values of the hyper parameters \(\lambda\) and \(\gamma\) of Equation (20) are both set as \(1\times 10^{-1}\).
## 2 Guidelines for Using Recorded Ground Motions in Response History Analysis
### Selection of Ground Motions  
ASCE 7-10 guidelines [12] recommend that when performing a linear response history procedure for a structure, the ground motions should consist of a selected horizontal acceleration time history from the recorded ground motions. The selection of ground motion time histories is based on factors such as magnitude, distance from the fault, and earthquake rupture mechanism. To select the ground motion acceleration time histories, the recorded ground motions should be selected for events that are consistent with the seismic magnitude and fault distance controlling the ground motion. The selected ground motions must be scaled to be compatible with the spectrum over the natural period range of significance of the structural response. However, in low to moderate seismicity regions where strong earthquakes rarely occur, the recorded ground motion data for risk assessment are insufficient and require excessive scaling or modification. If the ground motion time history is excessively modified, leading to a significant difference in earthquake intensity, the actual earthquake characteristics of the seed ground motions may be lost. Alternatively, ground motions recorded in regions with high seismicity may be used to select the appropriate intensity for the input seismic loads. These ground motion data do not reflect the site-specific characteristics of low to moderate seismicity regions. Differences in seismicity between high and low to moderate regions are caused not only by the mechanism of fault rupture but also by the attenuation characteristics from the earthquake source to the site. These differences support the proposal in the guidelines and standards that input ground motions based on recorded ground motions are necessary for a seismic risk assessment.
## 2 Material and Method  
### Experimental Materials  
In this study, we selected the silty clay from the Qinghai-Tibet Plateau, China (34\({}^{\circ}\)54.2\({}^{\prime}\) N and 92\({}^{\circ}\)56.4\({}^{\prime}\) E). To eliminate the effect of salt on experiment results, the natural saline soils were washed by deionized water salts over 10 times [25]. Then, the non-saline soils were dried, pulverized, and sieved with a 2 mm sieve. The particle size (R) distribution of the experimental soil was analyzed by a Master Sizer-2000 Laser particle size analyzer (Figure 1). It was found that the silty clay consisted of 30.14% clay (R < 0.005 mm), 61.53% silt (0.005 < R < 0.05 mm), and 8.33% sand (0.05 < R < 2 mm). The uniformity coefficient and curvature coefficient of the silty clay were 8.49 and 2.96, respectively. The physical properties of Qinghai-Tibet Plateau silty clays are summarized in Table 1.  
X-ray diffractometer (XRD) tests were conducted using a D/Max-3B X-ray diffractometer (Tokyo, Japan), with a scanning rate of 4\({}^{\circ}\)/min. According to mineral component identification experiments by XRD, the mineral composition of silty clay mainly included illite, kaolinite, quartz, potash feldspar, plagioclase, calcite, and hematite. Their contents were 18.6, 5.3, 42, 7.0, 11.3, 13.4, and 2.4 percent, respectively.  
In this study, the additive NS particle used was spherical, the diameter was about 15 nm, the purity was 99.5%, the specific gravity was 200 m\({}^{2}\)/g, and the bulk density was 0.05 g/cm\({}^{3}\).