esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 5 Results and Analysis
### Impact of Dataset Partitioning Ratio  
To validate the performance of OI-EleAttG-GRU on different proportions of training and testing sets, we kept all other parameters unchanged and increased the original 70% training set to 80%. Subsequently, a comparison was made based on accuracy and F1-score metrics. The experimental results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Evaluation**} & \multicolumn{2}{c}{**Partition Ratio (Training Set: Test Set)**} \\ \cline{2-3}  & **(70\%:30\%)** & **(80\%:20\%)** \\ \hline Training\_accuracy & 98.58\% & 99.14\% \\ Training\_F1-score & 98.59\% & 99.15\% \\ Test\_accuracy & 97.52\% & 97.50\% \\ Test\_F1-score & 97.51\% & 97.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy and F1-score among different partition ratios (Training set: Test set) on the WISDM dataset.  
Figure 8: (**a**) Accuracy versus HAR task number. (**b**) Energy cost versus HAR task number.  
Table 2 indicates that having an 80% training set means there are more samples to learn the features of HAR data. Consequently, under the same number of training epochs, the model can better fit the data, resulting in higher accuracy on the training set. However, the difference in performance between the two sets on the testing set is small. The accuracy and F1-score of the 80% training set are 0.02% and 0.01% lower than those of the 70% training set. For the 70% training set, although there are relatively fewer training samples, the model might be more generalized, enabling better generalization to unseen data.
# A Bacterial Form I' Rubisco Has a Smaller Carbon Isotope Fractionation than Its Form I Counterpart
## 3 Results
Hypothesized evolutionary pathway is shown in black arrows, showing that ancestral dimers (L\({}_{2}\)) likely evolved to a common ancestral octamer (L\({}_{8}\)) [51] that then speculated into either Form I’ (L\({}_{8}\)) or Form I (L\({}_{8}\)S\({}_{8}\)) rubiscos [30]. Rubisco phylum is shown as shapes and references are shown in colors. Form II KLEs are from _Rhodospirillum rubrum_ or _Candidatus_ Endoriftia persephone [25; 44; 52; 53], Form I’ measurement  
is from _Candidatus_ Promineofilum breve (this study), all Form IB rubiscos are from _Synchococcus elongatus_ PCC6301 or 7942 (identical Rbcl and RbcS sequence) [25; 50] and this study, and Form IA KIE is from _Prochlorococcus marinus_ MIT9313 [24]. Error is reported as 95% confidence intervals for [24]; as standard deviation for this study and [50; 52; 53]; as standard error for [25]. See Supplemental Table S2 for literature values used, notes on variation between measurements, and rationale for which data was included and excluded. For recent compilation of all measured rubisco KIES, see [13]. (**B**) Compilation of additional KIE and specificity values in Form IC and ID rubiscos [25; 30; 36; 37; 52; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63], in addition to data shown in Figure 1A. Forms shown in shapes, references shown in the same colors as in Panel A. See Tables S2 and S3 for compilation of data used. Dotted line indicates original linear regression from [15]. Figure was prepared with the assistance of the _ggplot2_ package (v.3.3.66; [64]). Similarly, compared to prior Form II (L\({}_{2}\)) rubisco KIE measurements, the Form I\({}^{\prime}\) (L\({}_{8}\)) rubisco may fractionate less. Compared to Form I KIES, there is wider variation in previously measured Form II KIES, with the Form I\({}^{\prime}\) rubisco measured here overlapping in value with one Form II rubisco within uncertainty [53]. We note that all the Form II data presented here are largely from one species, _Rhodospirillum rubrum_, though the specific strain is not reported for all studies. Therefore, the variations may reflect experimental uncertainty with the exception of the measurement in [25], where MgCl\({}_{2}\) concentration was changed. Therefore, we are not confident concluding either way if the L\({}_{8}\) KIE is less than the L\({}_{2}\) KIE or not.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## Appendix A
### SNR with the Snow Properties  
SNR is also one of the most important output parameters of the CYGNSS observables. During our analysis, we also compared the relationship between snow depth and SNR (Figure 19). In order to reduce the influence of different land geophysical parameters, we also plotted the relationship for four different surface types, i.e., high-vegetation-covered area (a); moderate-vegetation-covered area (b); low-vegetation-covered area; (d) barren or desert area (c). We can see that the SNR also cannot reveal the relationship between the two parameters. More detailed analysis to retrieve this information for the detection of snow properties will occur on in our future research.
## 3 Results and Discussion
### Influence of the Aluminum Source Type on the Morphology of the Aluminum Oxides  
To further investigate the impact of the precursors on the microstructure of the aluminum oxide, SEM analysis was conducted on the products obtained after calcination of the precursors prepared from different aluminum sources. The test results are shown in Figure 9.  
Figure 9 clearly shows that there are significant differences in the crystal morphology of the aluminum oxides prepared from the different aluminum sources. The aluminum oxide product prepared from aluminum sulfate has fine particles, often appearing as aggregates, with individual crystallite sizes in the range of 60 to 80 nm. This is due to the small particle size of its precursor, which has higher reactivity. During calcination, the crystals tend to aggregate to reduce the surface energy, and the poor dispersibility of the precursor is a major contributing factor to this phenomenon. On the other hand, the aluminum oxide prepared from aluminum nitrate exhibited a fused appearance. This is because, during the preparation of the precursor, some ammonium nitrate is formed. The solubility of ammonium nitrate is much greater than that of ammonium chloride and aluminum ammonium sulfate. Ammonium nitrate readily adsorbs onto the precursor surface through hydrogen bonding or physical adsorption. Even after multiple centrifuge washes, some residue remains, leading to the role of a fluxing agent during calcination and causing the surface of the aluminum oxide to appear in a fused state [24].  
The N\({}_{2}\) adsorption-desorption results are shown in Figure 10. The curves in the N\({}_{2}\) adsorption-desorption isotherm plot exhibit a type IV adsorption branch with an H3 hysteresis loop. According to the BJH calculation, as shown in the pore size distribution curve, these shapes indicate the presence of a significant amount of mesopores. The specific surface area of alumina prepared with aluminum sulfate (68.54 m\({}^{2}\)/g) is higher than that  
Figure 8: The TG-DTG curves of alumina precursors prepared from different aluminum sources. (a) Aluminum sulfate; (b) aluminum chloride; (c) aluminum nitrate.  
Figure 9: SEM images of alumina prepared from different aluminum sources. (a) aluminum sulfate; (b) aluminum chloride; (c) aluminum nitrate.  
prepared with aluminum chloride (27.07 m\({}^{2}\)/g) and aluminum nitrate (6.34 m\({}^{2}\)/g). This indicates that alumina prepared with aluminum sulfate possesses abundant active sites and superior adsorption capacity.
## 3 Results
### Correlation of Jump Height in SLVHT with Distance Hop, IKDC SKF and KOOS  
In order to externally validate the digitally measured SLVHT, the jump height in the SLVHT with the injured leg at 6 months was tested against established measurement variables. The SLVHT results for the injured leg correlated significantly with the jump distance of the distance hop (r = 0.680, \(p\) = 2 \(\times\) 10\({}^{-16}\), y = 46.249 + 2.743 \(\times\) x; Figure 3A), while the jump height of the injured leg at 6 months significantly correlated with the IKDC SKF (r = 0.280, \(p\) = 0.0129, y = 67.563 + 0.507 \(\times\) x, Figure 3B) and the KOOS value (r = 0.200, \(p\) = 0.062, y = 71.120 + 0.505 \(\times\) x, Figure 3C).
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
###### Abstract  
Video anomaly detection is a crucial aspect of understanding surveillance videos in real-world scenarios and has been gaining attention in the computer vision community. However, a significant challenge is that the training data only include normal events, making it difficult for models to learn abnormal patterns. To address this issue, we propose a novel dual-generator generative adversarial network method that improves the model's ability to detect unknown anomalies by learning the anomaly distribution in advance. Our approach consists of a noise generator and a reconstruction generator, where the former focuses on generating pseudo-anomaly frames and the latter aims to comprehensively learn the distribution of normal video frames. Furthermore, the integration of a second-order channel attention module enhances the learning capacity of the model. Experiments on two popular datasets demonstrate the superiority of our proposed method and show that it can effectively detect abnormal frames after learning the pseudo-anomaly distribution in advance.  
generative adversarial networks; video anomaly detection; reconstruction Article
# Application of a Geotextile in the Treatment of Post-Subsidence in Karst Areas
## 2 Research Methodology
### Numerical Modeling  
To study the influence of the anchorage length and the buried depth of the geotextile on the treatment effect of subgrade subsidence in karst areas, a numerical model (shown in Figure 3) was established based on the prototype of subgrade subsidence. Triangular fifteen-node solid elements were used to represent the soil element and a Mohr-Coulomb constitutive model with a drainage condition was used. The geotextile was modeled as an elastoplastic using the line elements of the geogrid. In Table 3, the material parameters of the soil and geotextile are shown. The interface coefficient between the geogrid and soil was set as 0.9. The displacement of the subsided soil in the subsidence area beneath the geotextile was specified to simulate the subsidence shown in Figure 3. The size of the numerical model was 40 m \(\times\) 5 m (length \(\times\) depth) and the subsidence width was 1.5 m. To minimize the boundary effect, the length of the numerical model was greater than the scale model. The horizontal displacement was constrained at both lateral boundaries. Both the horizontal and vertical displacement were constrained at the bottom boundary.  
Table 4 lists a total number of 11 numerical models. The subsidence width \(B\) of 1500 mm, which is the same as the prototype, was used in the numerical modeling. In Table 4, to study effect of the anchorage length of geotextile on the treatment of subsidence, numerical models M1 to M6 were constructed with anchorage lengths of the  
Figure 3: Numerical model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**Unit Weight, \(\gamma\) (kN/m3)**} & **Elastic Modulus, \(E^{\prime}\)** & **Poisson’s Ratio, \(\
u^{\prime}\)** & **Cohesion, \(c^{\prime}\) (kPa)** & **Internal Friction Angle, \(\varphi^{\prime}\) (\({}^{\circ}\))** & **Tensile Stiffness, \(J\) (kN/m)** \\ \hline \multirow{2}{*}{Subgrade fill} & Mohr-Coulomb model & \multirow{2}{*}{17} & \multirow{2}{*}{30,000} & \multirow{2}{*}{0.3} & \multirow{2}{*}{1} & \multirow{2}{*}{33} & \multirow{2}{*}{-} \\ Reinforcement & Elastic model & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters used in the numerical modeling.  
Figure 2: Tensile force of model geotextile.  
geotextile (\(L\)) of 0 mm, 375 mm, 525 mm, 750 mm, 1125 mm, and 1500 mm, respectively. The corresponding anchorage ratios \(L/B\) for the six models were 0, 0.25, 0.35, 0.50, 0.75, and 1.00, respectively. To study effect of the buried depth of the geotextile on the treatment of subsidence, numerical models M7 to M11 featured different buried depths of the geotextile of 2000 mm, 3000 mm, 4000 mm, 5000 mm and 6000 mm, respectively.
## 3 Results and Discussion  
### Typical Fingering Structure Induced by Gel Formation  
Figure 2a shows the fingering pattern that emerges when aqueous citric acid solution displaces skim milk. The remarkable feature of this finger pattern is the presence of gel formation (red) that emerges at the interface between the skim milk and the aqueous citric acid solution. The gel formation acts as a continuous sidewall, preventing the finger from branching; thus, only the tip of the elongated finger continues to grow. This typical characteristic of the fingering pattern differentiates the fingering resulting from gel formation and the fingering caused by the viscosity difference [9].  
\begin{table}
\begin{tabular}{c c c c} \hline \(c\) [wt\%] & \(\rho\) [kg/m\({}^{3}\)] & \(\eta\) [mPa\(\cdot\)s] & \(T\) [\({}^{\circ}\)C] \\ \hline
0.0 & 1.00 & 1.17 & 18.8 \\
0.5 & 0.99 & 1.04 & 21.8 \\
1.5 & 0.99 & 1.12 & 21.1 \\
5.0 & 1.00 & 1.18 & 20.7 \\
15 & 1.05 & 1.59 & 22.8 \\ \hline \end{tabular}
\end{table}
Table 1: Measured values of density \(\rho\), viscosity \(\eta\), and temperature \(T\) at each concentration \(c\) of the citric acid used in the experiment.  
The gel film formation thickens with time at the interface between the aqueous citric acid solution and skim milk. Figure 2b shows the sequential thickening of the gel film. The gel film thickness increases logarithmically with time. The gel film thickens faster in the early stage and gradually becomes constant. The thickening of the gel film is caused by the diffusion of the citric acid through the gel film formation, resulting in further mixing of skim milk and citric acid. The relation between the film thickness \(\sigma\) growth over time \(t\) and the diffusion coefficient constant \(D\) is expressed as follows [41]:  
\[\sigma=\sqrt{4\pi Dt} \tag{1}\]  
We used the least-squares method to estimate the diffusion coefficient \(D\) that best fit the observed data, excluding two outlier data points \((\sigma>0.76)\). The estimated \(D\) is \(9.3\times 10^{-11}\) m\({}^{2}\)/s, with the coefficient of determination (R-squared), accounting for 72.5%. The estimated \(D\) is lower than the diffusion coefficient in a normal solution due to the bending of the diffusion path owing to gel formation. Moreover, unlike the diffusion phenomenon in uniform systems, the diffusion coefficient through the gel formation layer is not constant and depends on the solute concentration in the gel [42].
## 3 Results
### Evaluation with Real-World Data
#### 3.3.2 Evaluation with Garment Employee Productivity Data  
We have also tested the algorithm with data from the Garment Industry by [26]. This data set contains 1197 employee records, with 15 predictors for employee productivity (on a continuous 0-1 scale). We removed the variables wip (to avoid missing values) and date (for a simpler regression problem) and used dummy variables for department and date. This data set also has quite low correlations, with a median correlation of 0.03. Figure 7 depicts the distribution of variables between the parties.  
On average, the \(\beta\) estimates are close to the centralized analysis, although they do differ with a single run (see Figure 7). The effect of \(\varepsilon\) is similar to that for the forest fires analysis. The distribution is narrower for \(\varepsilon=10\).  
With respect to \(R^{2}\), Figure 8 depicts that the relation between \(\varepsilon\) and \(R^{2}\) is similar to those observed for the synthetic data and forest fires data. However, compared to the forest fires data set, more predictive power is preserved, which is related to centralized \(R^{2}\) of 0.24, This can be observed from the median \(R^{2}\) values, which are both higher and closer to the centralized results.  
Figure 6: \(R^{2}\) over \(\varepsilon\) for the forest fires data set (5 iterations, 100 repetitions, and \(\gamma=1.2\)).  
Figure 7: Centralized parameter estimates (black) for regression analysis with Garment employee productivity data set, with average coefficients and 95% confidence intervals for \(\varepsilon=1\) (orange) and \(\varepsilon=10\) (green), for \(\gamma=1.2\), 1000 repetitions. Parties are separated with background shading.
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 1 Introduction
Therefore, robust control [19], incremental nonlinear dynamic inversion (INDI) control [20; 21], and adaptive control [22] have been applied to improve the robustness of modeling uncertainties. Among these, active disturbance rejection control (ADRC) [23; 24] uses disturbance-observer-based control, treating model uncertainties and environmental disturbances as lumped disturbances. An extended state observer (ESO) is designed to estimate and compensate for the lumped disturbance, showing excellent robustness. The present study proposes a short-landing strategy utilizing thrust vector control, which features a continuous curved landing approach. To begin with, a detailed aircraft dynamics model is developed for landing simulation and performance analysis. To enhance the safety of the landing process, the attainable area of the linear and angular acceleration is determined based on the trim calculation to determine the reference values for the angle of attack and velocity during the short landing. Subsequently, a three-axis decoupled control law is designed using an extended state observer (ESO). The ESO is used to estimate and compensate for lumped disturbances, thus ensuring robustness against modeling uncertainties. To address the switching between different control modes during the landing process, the angular rate is selected as the control object of the inner loop, and the angular acceleration commands are allocated based on the different dynamics of the actuators. Furthermore, an overall control structure with multimode control is proposed which clarifies the control objectives for each phase of the landing process. Additionally, a flight-regime protection method is introduced to enhance landing safety by incorporating an additional angle of attack-command compensation. Finally, a nominal simulation and a Monte Carlo simulation are performed to assess the effectiveness and robustness of the proposed control strategy. The former involves a simulation without model parameters' perturbation, while the latter assesses the robustness of the control strategy against modeling uncertainties. This paper is organized as follows: Section 2 provides a detailed description of the configuration of the flying-wing UAV, and a dynamic model is developed to analyze the aircraft's behavior during the short-landing process. Section 3 presents the trim analysis results and defines the equilibrium points for the reference angle of attack and velocity, which are critical parameters for ensuring safe and efficient short landing. Section 4 comprehensively elaborates on the design of the control system, including the various control loops, allocation of control commands, and the overall control structure. In Section 5, a novel short-landing strategy incorporating lateral and vertical guidance is proposed. The effectiveness of the proposed strategy is evaluated through numerical simulations, which are presented in Section 6. Finally, in Section 7, the main findings of the paper are summarized, and conclusions are drawn.
## 6 Performance Evaluation
### Evaluation for the Analytical Model  
#### 6.2.1 Analysis Verification Simulation Setting  
In the verification process for the proposed analytical model, we select two lanes from six lanes in the protocol verification parts as an example. Here, we choose the lanes with the slowest speed and the middle speed, where vehicles drive from west to east. Let \(\rho_{1}\) and \(\rho_{2}\) denote the vehicular traffic density for the slow speed driving lane and the middle speed driving lane. For the driving speed, we assume that the default average speeds are defined as 70 and 90 km/h for the two driving lanes, while the maximum driving speeds are 90 and 120 km/h for the two driving lanes, respectively. The Krauss car-following mobility model is used with identical parameters setting as that in the protocol comparison simulations.
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 3 Experimental Results
### Real Detecting Data Experiment and Result Analysis
#### 3.2.1 Indoor Scene Experiment and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AG**} & \multicolumn{3}{c}{**ES**} & \multicolumn{3}{c}{**Time/ms**} \\ \cline{2-10}  & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** \\ \hline Bilinear & 2.34 & 1.27 & 0.65 & 23.94 & 13.81 & 7.33 & 4.1 & 5.3 & 6.7 \\ Bicubic & 3.22 & 1.69 & 0.85 & 30.38 & 18.08 & 9.56 & 4.2 & 6.4 & 7.3 \\ POCS & 5.06 & 2.17 & 1.11 & 34.96 & 22.36 & 11.93 & 16.2 & 23.8 & 31.5 \\ POCS [17] & 5.27 & 2.26 & 1.16 & 36.01 & 23.29 & 12.47 & 24.1 & 29.5 & 35.2 \\ Proposed & 5.46 & 2.35 & 1.22 & 37.41 & 24.16 & 12.82 & 27.2 & 32.4 & 39.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the evaluation indicators of the tank model range profile under different magnifications. Figure 7: Super-resolution rendering of the armored car model range profile with (**a**) the original armored car model range profile; (**b**) bilinear armored car model profile; (**c**) bicubic armored car model range profile; (**d**) POCS armored car model range profile; (**e**) POCS [17] armored car model range profile; and (**f**) the proposed armored car model range profile.
# Future of IoT Networks: A Survey
## 3 Current Market and Research Trend of IoT
### Research Programs
#### 3.2.1 European Union  
To expedite collaboration in the EU research and development program, Europe established the Cluster of European Research Projects on the Internet of Things (CERP-IoT) in 2010 to conduct research on IoT. The primary vision of the EU's IoT is based on smart things and objects. Starting from the interactions with the smart objects and things, the committee extended the IoT vision to accommodate the domain of smart energy, smart buildings, smart living, smart health, smart cities, and smart transport [26] and proposed a roadmap of IoT in 2011 [26]. Based on this roadmap, the EU government initiated the Horizon 2020 program in 2011 by announcing multiple IoT R&BD programs that were focused on implementing IoT smart life service [27]. Specific details of Europe's Horizon 2020's research goals are as follows. First, the program is not limited to IoT but also focuses on the future Internet, cloud computing, Big Data, and 5G networking. Each specific item of the program is designed not to work independently but to interact with each other. Second, the EU's research programs mostly fund small and medium enterprises (SME). With the funding emphasis on SME, many companies can discover innovative IoT business models and IoT applications to invigorate IoT services. Finally, to distinguish itself from the previous disseminated research and services, Europe's Horizon 2020 encouraged large global collaboration with other research institutions. Each specific member of Horizon 2020 should interact with the others and let the industry develop the business model, application, and services to enable IoT as a whole. The result [28] for first two years is that the number and the quality of applications about IoT had been rapidly increased. This results mean that the Horizon 2020 project is on a cruise to close its purpose.