esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 4 Experiments
### Results of Experiments and Analysis
#### 4.3.2 Comparative Experiment and Analysis  
To verify the performance of the neural network proposed in this paper, it is compared with other action recognition algorithms. The results are shown in Table 1.  
Table 1 shows some advanced action recognition algorithms on UCF101 data set and HMDB51 data set. We can see that the action recognition accuracy of the TLE network proposed by [PERSON], [PERSON] et al. [66] on the UCF101 data set has reached an amazing 95.6%, which is due to its expansion of the advanced 2D CNN network architecture into 3D CNN network, which can extract more in-depth Spatio-temporal features. Other works that achieve greater accuracy use more input [67] (75 frames) or a new network architecture [68]. The lower half of Table 1 shows the accuracy of the action recognition algorithm based on a two-stream convolution network. We can observe that our BS-2 SCN network achieves the best performance on both data sets, and the accuracy on the UCF101 data set is 89.9%. The accuracy of the HMDB51 data set has reached 71.3%, which is the highest accuracy among all the comparison methods in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Data Set**} \\
**UCF101** & **HMDB51** \\ \hline Slow Fusion [69] & 65.4 & - \\ BiLSTM [70] & 70.0 & 39.8 \\ ST-D LSTM [55] & 75.7 & 44.4 \\ P3D ResNet [71] & 88.6 & - \\ Transformations [68] & 92.4 & 62.0 \\ MiCT-Net [67] & 94.7 & 70.5 \\ TLE [67] & **95.6** & 71.1 \\ \hline \hline \multicolumn{3}{c}{Method based on Two Stream Convolutional Networks (2 SCN)} \\
2 SCN (VGG) [20] & 86.9 & 58.0 \\
2 SCN (ResNet) & 87.5 & 59.6 \\
2 SCN (Conv Pooling) [72] & 88.2 & - \\
2 SCN (LSTM) [72] & 88.6 & - \\
2 SCN (Fusion) [73] & 89.6 & 57.6 \\
2 SCN (Hidden) [74] & 89.8 & - \\ BS-2 SCN (proposed) & **90.1** & **71.3** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy comparison of action recognition algorithms on UCF101 dataset and HMDB51 dataset (%).
## 3 Results and Discussion
### FTIR Study  
The existence of functional groups in the samples was confirmed by Fourier-transform infrared spectroscopy, which also revealed evidence of intramolecular and intermolecular interactions. Figure 6 showed the FT-IR spectral peaks of green technique manufactured ZnO-NPs in the range of 4000-500 cm\({}^{-1}\). The typical vibrational mode of Zn-O bonding is given to a band at 634.5 cm\({}^{-1}\). The asymmetry and symmetry vibrations of the -COOH group and C-N stretch, respectively, correspond to the peaks near 1413.6 cm\({}^{-1}\) and 1064 cm\({}^{-1}\). C=O stretching could be linked to the band at 1603 cm\({}^{-1}\). The absorbed moisture [27] is responsible for the large peak at 3400 cm\({}^{-1}\). The activity of _P. juliflora_ leaf extracts identified a strong and high-intensity peak of Zn-O band generated by green manufactured nanoparticles.  
Figure 5: Schematic model of a wurtzitic ZnO structure with lattice constants a, b and c.  
Figure 6: FTIR spectra of ZnO-NPs.
###### Abstract  
We present a first prototype of a wideband microwave tomography system with potential application to medical imaging. The system relies on a compact and robust printed monopole antenna which can operate in the 1.0-3.0 GHz range when fully immersed in commonly used coupling liquids, such as glycerine-water solutions. By simulating the proposed imaging setup in CST Microwave Studio, we study the signal transmission levels and array sensitivity for different target and coupling liquid media. We then present the experimental prototype design and data acquisition process, and show good agreement between experimentally measured data and results from the CST simulations. We assess imaging performance by applying our previously proposed two-dimensional (2-D) DBIM TwIST-algorithm to both simulated and experimental datasets, and demonstrate that the system can reconstruct simple cylindrical targets at multiple frequencies.  
printed monopole antenna; wideband; array; microwave tomography; DBIM; medical imaging
# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Subsea Tunnel
#### 3.3.3 Water Inrush Risk Prediction
Starting from the surrounding rock state of the aquifer structure and the degree of accumulation of seawater pressure, effective treatment of sudden water disasters can be realized. Restricted by the harsh underwater construction environment and high construction cost, the number of undersea tunnels in China currently under construction is relatively small, the design scheme is still being optimized continuously, and the number of training samples that can be used for deep learning algorithms is also relatively small. Hence, the existing research mainly simulates the marine environment indoors and builds a neural network model with experimental data as the sample data to realize the prediction of tunnel durability and risk assessment.
## 4 Results and Discussion
### Results and Analysis
#### 4.3.4 Cross-Stock Domain Transfer Evaluation  
To evaluate the model's generalization ability across different assets within the same market domain, a domain transfer experiment was conducted. The agent was trained exclusively on AAPL stock data and directly evaluated on MSFT data without any fine-tuning. All features and preprocessing procedures were kept consistent. The evaluation metrics included the total profit gain, average reward, policy stability, \(R^{2}\) correlation, and Sharpe ratio. Table 9 summarizes the performance of various models under this cross-stock generalization scenario.  
The results demonstrate that the proposed method achieved superior cross-stock generalization performance. Despite being trained on AAPL, the model exhibited a high \(R^{2}\) score (0.85) and low action standard deviation (0.42) when deployed on MSFT, indicating consistent policy stability and accurate reward prediction. This supports the model's capability to generalize across assets within the same market domain without fine-tuning.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Std. of**} & \multirow{2}{*}{**R2**} & **Sharpe** & **Max** \\  & **Profit** & & & & & **Ratio** & **Drawdown** \\  & **Gain** & & & & & \\ \hline w/o All (Only Backbone) & 0.19 & 3.42 & 0.91 & 0.65 & 0.66 & 0.45 \\ w/o Perturbation & 0.23 & 3.88 & 0.75 & 0.72 & 0.72 & 0.37 \\ w/o Profit Backtracking & 0.26 & 4.10 & 0.68 & 0.77 & 0.76 & 0.32 \\ w/o Strategy Stabilizer & 0.29 & 4.21 & 0.57 & 0.80 & 0.80 & 0.27 \\ Proposed (Full Model) & 0.34 & 4.78 & 0.41 & 0.88 & 0.86 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study of key modules in the proposed method.
## 3 Results and Discussion
### Influence of Bends (R/D Ratio of 0.6) on Pressure  
In Figure 6, pressure measurements obtained with straight and bent ducts are compared. Pressures were measured at three different locations: near to the vessel (P1); in the  
Figure 4: Influence of bends (R/D ratio 0.6) on flame speed between position 1 and 2 for each dust.  
Figure 5: Trend in flame speed along the duct for Al dust using bends (R/D ratio 0.6) calculated from position 1 to 2 and from 2 to 3.  
middle of duct, between the second and third bend (P2); and near to the valve after the third bend (P3) (Figure 3). For Al and MS dusts, the more obstacles there were, the higher the pressure was in the three locations reaching similar values. Regarding the increase in pressure due to the bends, this can be explained by the reflected shock wave. When a shock pressure wave hits a solid surface, a new shock wave appears, called a reflected wave, and moves in the opposite direction. According to [PERSON], reflected waves are important because they usually cause a greater overpressure than the original one [24,27].  
Figure 6: Influence of bends (R/D ratio of 0.6) on pressure for each dust: (**a**) pressure in the vessel (P1), (**b**) pressure in the middle of the duct (P2), (**c**) pressure near the valve (P3).  
Regarding the results of WF dust, it can be seen that the pressure fell when using bends. This can be explained by the slower burning rate, which might have meant that the pressure propagation wave was also weaker [28]. Thus, it can be concluded that the effect of bends on explosion dynamics depends on the reactive properties of the dust.  
In addition, as expected for all tests (with and without bends), the pressure in the valve increased as the blast travelled along the duct (the pressure near the flap valve was about twice that in the vessel) [26].  
Concerning the influence on the flap valve design and assessing its functionality, the potential rise in pressure due to the use of bends should be considered. In addition, some researchers consider that the residual pressure produced by the bends can affect the venting efficiency, thus it must be also considered in this regard [29].
## 3 Optimization with Consideration of the Reliability Criteria  
In accordance with dockets from the reliability commission of MOTIE (Ministry of Trade, Industry and Energy) in South Korea, the reliability constraints were amended to prevent the BESS from overloading; the constraints were mandated as in Table 1.  
The other reliability constraints referring to the renewables were organized as in Table 2. The total output measured at its PCC could not exceed 70% of the nominal capacity of the renewables installed or coordinated. Otherwise, the AO became ineligible for the weighted settlement with the energy multiplier discharged from the coordinated BESS.  
The incentive criteria were mandated as in Table 3. In accordance with the incentive settlement amended by the reliability commission, the eligible AOs for the incentive were those AOs with operating standards or logics adhering to the reliability criteria mandated.  
Depending upon the installation site, the additional incentive varies. For the resources coordinated and installed outside the structure, the maximum SoC was limited to 90%, and 3% of the additional incentives could be added to the energy discharged regardless of the scheduling periods.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l} \hline \hline
**Acronym** & **Description** & **Acronym** & **Description** \\ \hline
3 GPP & Third Generation Partnership Project & AB HARQ & Adaptive Byte-Level Hybrid Automatic Repeat reQuest \\ \hline ABC MAC & Adaptive Beacon Control MAC & AC & Access Category \\ \hline ACB MAC & Authentication Cluster-Based MAC & ACFM & Adaptive Collision-Free MAC \\ \hline ACK & Acknowledgment & \begin{tabular}{l} AHT MAC \\ \end{tabular} & \begin{tabular}{l} Adaptive High-Throughput \\ MAC \\ \end{tabular} \\ \hline ASAS & \begin{tabular}{l} Adaptive TDMA Slot Assignment Strategy \\ \end{tabular} & AVeMAC & Adaptive Vehicular MAC \\ \hline BB MAC & Black Burst MAC & BEB & Binary Exponent Back-Off \\ \hline BER & Bit Error Rate & BFP & Broadcast Feedback Phase \\ \hline BSS & Basic Service Set & BT & Broadcast Table \\ \hline BTMA & Busy Tone Multiple Access & \begin{tabular}{l} CAC \\ MAC \\ \end{tabular} & \begin{tabular}{l} Cellular Automata to Form CAR \\ MAC \\ \end{tabular} \\ \hline CAH & Cooperative Ad Hoc MAC & CAM & \begin{tabular}{l} Cooperation Acceptance \\ Message \\ \end{tabular} \\ \hline CAP & Contention Access Phase & CaSSaM & \
\begin{tabular}{l} Context Aware System for Safety \\ Messages \\ \end{tabular} \\ \hline CB MAC & Cluster-Based MAC & CBCH & Contention-Based Sub-Channel \\ \hline CBP & Contention-Based Period & CBR & Channel Busy Rate \\ \hline CCH & Control Channel & CDMA & Code Division Multiple Access \\ \hline CF MAC & Collision-Free MAC & CFP & Contention-Free Phase \\ \hline CFP & Contention-Free Period & CFR MAC & Collision-Free Reservation MAC \\ \hline CH & Cluster Head & CI & Control Interval \\ \hline CoMACAV & Cooperative MAC for Autonomous Vehicle & CR & Cooperative Relaying \\ \hline CRM & Cooperation Request Message & CRMAC & Cooperative Relay-Based MAC \\ \hline CRP & Channel Request Packet & CSMA/CA &
\begin{tabular}{l} Carrier Sense Multiple \\ Access/Collision Avoidance \\ \end{tabular} \\ \hline \hline \end{tabular}
## 2 Deployment and Proposed Transmission/Reception Timing Schemes
### Deployment and Environments
Transmitting the data gathered in the sink node is not considered here. The case shown in Figure 3a is simpler than the case shown in Figure 3b because interference also comes from the opposite side of the sink node, in the case shown in Figure 3b. Therefore, a transmission/reception timing scheme that is good for the case shown in Figure 3b shall be found and applied to the case shown in Figure 3a. In the paper, the case shown in Figure 3b, which is more complicated, is analyzed. Let us find the methods for efficiently packing the time axis. Because each node is a half-duplex, when a node operates fully, it should spend a half of the operating time for transmission and the remaining half of the operating time for reception. That is, the maximum time efficiency for transmission is 1/2.
## 3 Results  
### Surface Treatments of Montmorillonite  
#### 3.1.1 FTIR Analysis  
The FTIR spectra of MMT, PMMT, P-SLS-MMT, and P-KH570-MMT are given in Figure 2. In the spectra of MMT, the broad absorption bands at 3446.4 and 1643.4 cm\({}^{-1}\) corresponded to the -OH stretching vibration and H-O-H bending vibration of adsorbed water within MMT interlayer, respectively. The characteristic absorption peak at 3632.5 cm\({}^{-1}\) was attributed to the stretching vibration of the Al-OH and Si-OH in the silicate layers, and this -OH group was responsible for the reaction with the silane coupling agent [26]. The bands at 1036.1 and 793.4 cm\({}^{-1}\) were representative of the stretching vibration of Si-O and Si-O-Al, respectively. In addition of these characteristic peaks of MMT, novel vibration bands related to surfactants and/or silane coupling agents appeared in the spectra of OMMT (Figure 2B-D). For the P-MMT, the asymmetric and symmetric stretching vibration of -CH\({}_{2}\) of phosphonium surfactants was observed at 2928.2 and 2854.0 cm\({}^{-1}\), respectively. This is direct evidence for the successful intercalation of TTPC surfactants into the interlayer space of MMT. In comparison with the P-MMT, the intensity of the absorption bands at 2928.2 and 2854.0 cm\({}^{-1}\) increased for the P-SLS-MMT, which can be reasonably attributed to the introduction of the SLS surfactants. Besides, the absorption band at 1090.2 cm\({}^{-1}\) corresponded to the asymmetric stretching vibration of -SO\({}_{3}\)H group of SLS surfactants. These results indicated that the TTPC and SLS surfactants were synergistically incorporated into the interlayer space of MMT. The spectrum of P-KH570-MMT not only had the characteristic bands of P-MMT, but also exhibited a novel band at 1723.9 cm\({}^{-1}\), which was assigned to the stretching vibration of the C=O in the KH570 molecules. In addition, higher intensity of the absorption bands at 2928.2 and 2854.0 cm\({}^{-1}\) were also found for the P-KH570-MMT sample, implying that KH570 was successfully grafted on the surfaces and edges of the silicate layers [27]. These observations suggested that the MMT was doubly organo-modified by a phosphonium surfactant and a silane coupling agent. It is worth mentioning that a significant decrease of the relative intensity of the bands at 3632.5-3446.4 cm\({}^{-1}\) and 1639.8 cm\({}^{-1}\) was observed for the OMMT compared with the MMT, which indicated that the surface of OMMT changed from a hydrophilic to a hydrophobic character and the content of water over OMMT decreased. The above results indicated that the target doubly organo-modified MMT was successfully synthesized.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme  
A concrete description of our enhanced privacy-preserving authentication scheme is presented in this section. In our scheme, the vehicles are also classified into three types: law executors (LEs), mistrusted vehicles (MVs) and trusted vehicles (TVs) as displayed in Figure 1. The LEs are equipped with TPD, but the normal vehicles such as TV and MV are not equipped with TPD. Our improved scheme consists of nine procedures: initialization, registration, login, password change, general authentication, trust-extended authentication, secure communication, key update and revocation. The notations used in this section are also briefly defined in Table 1.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Propagation Models for Simulation
#### 3.15.2 Three-Log Distance Propagation Model  
This approach works well in densely populated areas. The model calculates the route loss across a greater distance and range of communication [29]. This model calculates the route loss caused by the distance between a transmitter and a receiver. The model assumes an exponential path loss across the distance between the transmitter and receiver.  
\[L=L_{o}+10n\text{log}(\frac{d}{d_{o}}) \tag{9}\]  
The path loss is designated by \(L\), the length is indicated by \(d\), and the reference distance is represented by \(d_{o}\); the path loss for the reference distance is defined as \(L_{o}\), and the path loss exponential is marked by \(n\).
## 3 Experiments and Results
### Cross-Dataset Generalization Evaluation  
To evaluate the cross-domain generalization ability of YOLOv11-OAM, we performed an external validation experiment using a publicly available tree species detection dataset from the Roboflow Universe platform. The dataset, collected and curated by user \"andreas-sdrm\" ([[https://universe.roboflow.com/andreas-sdrm](https://universe.roboflow.com/andreas-sdrm)]([https://universe.roboflow.com/andreas-sdrm](https://universe.roboflow.com/andreas-sdrm))) (accessed on 19 July 2025). contains 3130 annotated instances of tropical tree species, including banana, coconut, mango, false ashoka, gulmohar, jackfruit, and mahogany. All annotations follow the YOLO format, providing bounding box coordinates and class labels.  
Without any domain adaptation or fine-tuning, the YOLOv11-OAM model--trained solely on UAV imagery from southern Xinjiang--was directly evaluated on this heterogeneous dataset. It achieved an overall mAP of 0.902, precision of 0.909, recall of 0.902, and an F1-score of 0.819. Class-wise performance was also competitive, with APs of 0.904, 0.911, and 0.918 for coconut, mango, and mahogany, respectively. Notably, for banana trees, despite the limited number of samples (\(n=156\)), the model maintained high detection accuracy (AP = 0.894, recall = 0.906).  
These results underscore the robustness and transferability of YOLOv11-OAM when applied to out-of-distribution data featuring significant variations in species composition, imaging conditions, and environmental contexts. The model's consistent performance across previously unseen categories highlights its potential for broader applications in forestry monitoring and smart agriculture systems. Similar cross-domain generalization has also been demonstrated in remote sensing scene classification. For instance, [PERSON] et al. showed that a ResNet-50 model trained on one domain retained classification accuracies above 99% when applied to a different target domain without fine-tuning. Subsequent studies further indicate that domain adaptation techniques can enhance performance across heterogeneous forestry datasets [46]. Our cross-dataset results align with these findings, reinforcing the feasibility of species-level generalization in UAV tree detection.
## 4 Results and Discussion  
In this paper, the proposed pattern reconfigurable antenna was simulated by HFSS software (HFSS13.0, Anosoft, Pittsburgh, PA, USA), and the results are presented in Figure 6b. Photographs of the antenna are shown in Figure 7. The proposed antenna was measured by the network analyzer Agilent PNA N5442A (10 MHz-43.5 GHz) (Agilent, Santa Clara, CA, USA).
# A Hybrid Discrete Memetic Algorithm for Solving Flow-Shop Scheduling Problems
## 3 A Review of Classic and State-of-the-Art Approaches
### Heuristic or Meta-Heuristic Algorithms
#### 3.2.2 DBMEA-Based Approaches  
The simple discrete bacterial memetic evolutionary algorithm (DBMEA) is a specific variant of the memetic algorithm used for optimization problems. Memetic algorithms combine elements of both evolutionary algorithms (EAs) and local search to efficiently explore the solution space and find high-quality solutions. The \"Bacterial\" aspect in DBMEA is inspired by the behavior of bacteria in nature. The algorithm uses a population-based approach where each candidate solution (individual) is represented as a \"bacterium\". These bacteria evolve over generations using mechanisms similar to those found in evolutionary algorithms, such as selection, crossover, and mutation. The \"memetic\" aspect indicates that each bacterium undergoes a local search process to improve its quality within its neighborhood. This local search is typically a problem-specific optimization procedure that helps the algorithm fine-tune the solutions locally. The \"discrete\" in DBMEA suggests that the problem domain is discrete in nature, meaning that the variables or components of the solution are discrete and not continuous. According to our investigations, it could not generate satisfactory results based on the Taillard [23] benchmark results. Therefore, we combined it with other algorithms, producing hybrid solutions that use the DBMEA as a global search algorithm. The discrete bacterial memetic evolutionary algorithm with simulated annealing (DBMEA + SA) [3] uses the simulated annealing as a local search algorithm, while the DBMEA poses as a global search algorithm to walk the domain space similar to genetic algorithms.
## 3 Processing Method
### The Proposed Method
#### 3.2.3 Radial Acceleration Estimation  
As shown in Equation (15), the linear term of \(\varphi_{m}(\eta)\) is determined by the radial acceleration. By performing a linear fit to \(\hat{\varphi}_{m}(\eta)\), the results of its fit are obtained:  
\[\hat{\varphi}_{m}^{\prime}(\eta)=p_{m,0}+p_{m,1}\eta\ \ \,m=1,2,\ldots,M-1 \tag{17}\]  
where \(p_{m,0}\) is the estimate of the constant term in \(\varphi_{m}(\eta)\) and the estimation of radial acceleration is denoted as  
\[\hat{a}_{c}=\frac{\lambda v_{a}p_{m,1}}{4\pi d_{m}} \tag{18}\]