esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results of the Proposed Fast Analysis of Wave Speed (FAWS) Method  
To validate the capability of the proposed FAWS method in the CS framework, geophones were randomly chosen to acquire the data based on the Bernoulli matrix. Figure 7a shows the random distribution of the active geophones. The total number of the geophones in the non-even-spaced array is 20 instead of 50 geophones in the whole 25-m measurement length. The locations of the activated geophones are randomly selected based on Bernoulli matrix as mentioned in the previous section. The black block represents a geophone at the corresponding location; by contrast, the white block means no geophone is arranged there. The bandwidth and the filter spacing of the bandpass filter bank is 0.5 Hz and 1 Hz, respectively. Thus, there are 98 filters in the desired range of 3 to 100 Hz.  
Considering the impulsive wave propagation admits only a few modal wave number at each frequency in the dispersion image, the sparse _f-k_ spectrogram is reconstructed based on the \(\ell_{1}\)-norm minimization algorithm of CS theory (Equation (10)). The reconstruction results are shown in Figure 7. The two modes can be obviously separated in the wavenumber-frequency spectrum. The reconstructed results are demonstrated in an image whose amplitudes is mapped by a white-red colortable: the higher magnitude, the darker red color. It can be seen the wavenumber-frequency spectrum have two peaks: one peak is observed at 50 Hz with the wavenumber of 1 m\({}^{-1}\), the other is in the range of 50 Hz to 60 Hz with the wavenumber around 2 m\({}^{-1}\), which are in good agreement with the conventional 2D-FFT algorithm.  
Meanwhile, as shown in Figure 7c, the two dispersion curves of the phase velocity of surface wave are accurately identical in the whole range, which means the proposed method transform can be used to identify the properties of the two modes. Thus, in the application of Shenyang metro line 9, a total number of 20 geophones are random selected from 50 geophone space array for measuring the seismic wave to evaluate the mechanical properties of the soil. This proposed technique reduces the acquisition time to achieve high speed in characterization of the shallow subsurface by removing 60% of the Nyquist sampling grid.  
Figure 6: Identification results of the soil properties computed by even-spaced geophone array: (**a**) _f-k_ spectrum obtained by the two-dimensional fast Fourier transform (2D-FFT) method; and (**b**) dispersion image of the soil properties.  
Figure 7: _Cont_.
## 3 Results and Discussion  
### Properties of the PGPB Inoculum  
_Kocuria rhizophila_ (GenBank Accession No. OM 743439) and _Bacillus wiedmannii_ (GenBank Accession No. OM 743440) strains have demonstrated the possession of three of the most interesting plant growth-promoting (PGP) properties. In particular, both strains have demonstrated the ability to produce IAA, siderophores and ammonia. The _K. rhizophila_ strain was also capable of inorganic phosphate solubilization, while the _B. weidmanii_ strain formed biofilm in vitro. [PERSON] et al. (2016) observed that _K. rhizophila_, associated with citric acid, increased _Glycine max_ L. plant biomass by approximately 38.73% compared to uninoculated plants. Furthermore, the _Kocuria rhizophila_ Y1 strain isolated from the rhizosphere of _Zea mays_ has been shown to solubilize phosphate and produce IAA ([PERSON] and [PERSON], 2016; [PERSON], 2016). Inoculating _Zea mays_ with 10% salinity increased biomass production, photosynthetic capacity, antioxidant levels and chlorophyll accumulation compared to uninoculated plants ([PERSON], 2016). Lastly, in a recent study ([PERSON] et al., 2016), isolation and selection of Pb and Cd-tolerant PGPB from the roots of _Helianthus petiolaris_ were performed. One of these strains (_Bacillus wiedmannii_ ST29), inoculated into _Helianthus annuus_ plants, reduced the bioaccumulation of Cd by 40%.
## 5 Experiments and Performance Evaluation
### WADGPS Implementation Procedures  
The WADGPS system implemented in this paper is based on that of the NSTB which is operated by FAA in the United States. Therefore, the NSTB archive data are used to verify the WADGPS performance. On the other hand, the GPS receivers used in the e-GPS observation stations might be different, for ease of data processing, the common GPS observation data format, the Receiver INdependent EXchange format (RINEX), is adopted for this work. Before the WADGS MS can use the observations to generate the WADGPS messages, the RINEX data needs to be decoded and organized in a proper format. Figure 9 shows the experiment setup. A computer is used to collect the RINEX data and NSTB archive data for data pre-process which transforms them into the WADGPS data format. This computer then sends the GPS observations to the WADGPS MS _via_ the Internet to execute the WADGPS MS algorithms to generate the corresponding WADGPS messages. Finally, the WADGPS messages are sent to the WADGPS users _via_ the Internet.  
To evaluate the performance of the WADGPS developed in this work, a WADGPS user monitor is developed based on WAAS MOPS [8] and its flow chart is depicted in Figure 10. The operating system (OS) of the WADGPS user monitor is FreeBSD [18] and the process is developed using C language and Open Motif [19]. After receiving and decoding the WADGPS messages, the WADGPS user applies the vector corrections to the GPS measurements according to WAAS MOPS. In addition, the protection level (PL) is calculated based on the received integrity messages [8]. The Horizontal Protection Level (HPL) calculation is also defined in WAAS MOPS [8]. For the convenience of monitoring the WADGPS MS processes, this work also develops a Graphic User Interface (GUI) to show the WADGPS MS status. Figures 11 to 13 depict the master station monitor and control GUI. In Figure 11, the first row describes the GPS time and the message types generated by the master station. The GPS satellites corrections status window also includes the satellite position error corrections in the ECEF coordinate, satellite clock error corrections, the UDRE and the health flag for each satellite. For the ionospheric grid corrections, Figure 12 shows the ionospheric vertical delays and their GIVE values at the grid points. Figure 13 exhibits the reference stations status which includes their positioning results and the satellites in view. The other status windows include the reference stations distribution map, the ground tracks of the satellites in view, and the WADGPS system status.  
Figure 10: The procedures of the WADGPS user software.  
Figure 9: Experiment setup.  
Figure 11: The WADGPS satellites corrections status window.  
Figure 12: The WADGPS ionospheric grid corrections status window.
## 1 Introduction
Based on the advantages and current status of multi-agent intelligent routing, this paper integrates the advantages of establishing intelligent routing based on SDN [15] and combining deep learning with reinforcement learning. According to the Markov process, an Elastic Routing Algorithm based on Multi-Agent Deep Deterministic Policy Gradient (ERA-MADDPG) is established; when confronted with topology changes, network routing can adapt more flexibly and intelligently through collaborative learning among multi-agent systems. According to the state mapping, action mapping, and reward value mapping, the routing performance is judged to achieve flexible routing based on multi-agent. The main contributions of this paper are as follows:  
* Proposes a novel SDN-based three-layer architecture for resilient routing, comprising a multi-agent layer, a controller layer, and a data layer. The architecture enables dynamic collaboration among agents to process topology changes through a streamlined workflow (information collection \(\rightarrow\) state input \(\rightarrow\) policy generation \(\rightarrow\) delivery), addressing the limitation of single-agent models in distributed network environments. * Develops the ERA-MADDPG algorithm by integrating the actor-critic framework with CNN networks. This innovation enhances training efficiency by 20.9-39.1% compared to traditional methods (MADQN, SR-DRL) through centralized training of multi-agent experiences and CNN-based high-dimensional feature extraction, while stabilizing learning through policy gradient optimization in continuous action spaces. * Demonstrates superior adaptability to topology dynamics: Through simulation experiments, the algorithm achieves re-convergence speeds over 25% faster than baselines under 5-15% node/link changes, validating its robustness in dynamic networks. The multi-agent collaborative mechanism reduces reliance on centralized retraining, providing a scalable solution for real-time routing optimization. The remainder of this paper is organized as follows. Section 2 presents the system architecture of the proposed method, including the three-layer SDN-based architecture and the interaction mechanism between agents and the environment. Section 3 details the ERA-MADDPG intelligent routing solution, covering the algorithm and its interaction with the environment. Section 4 conducts experimental evaluations, comparing the performance of the ERA-MADDPG algorithm with other related algorithms in terms of convergence speed, adaptability to network changes, and performance metrics such as delay, throughput, and packet loss rate. Finally, Section 5 concludes the paper, summarizing the research achievements and highlighting the advantages of the proposed algorithm.
## 5 CSUH Model Iterative Inversion Parameters
### Rockfill Material
#### 5.3.3 CU Triaxial Compression Test  
Figure 15 compares the CU test results of the rockfill material under different confining pressures with the CSUH model predictions. From Figure 15a, it can be observed that under different confining pressures, the rockfill material exhibits strain hardening. Figure 15b shows that as axial strain increases, pore pressure initially rises rapidly and then gradually decreases, indicating a tendency for the rockfill material to undergo initial shear contraction followed by shear dilation in undrained conditions. Additionally, higher confining pressures result in greater pore pressure buildup, which offsets part of the confining pressure. As a result, for the same initial confining pressure, \(p_{0}\), the undrained shear strength, \(q_{i}\), is lower than the drained shear strength.  
A comparison between Figures 14b and 15b reveals that under an initial confining pressure of 3500 kPa, the rockfill material undergoes continuous shear contraction in the drained test, whereas in the undrained test, it also exhibits dilation. The good agreement between the experimental results and the CSUH model predictions demonstrates that the CSUH model can quantitatively describe the differences in dilation behavior of the same material (rockfill) under different drainage conditions.  
Figure 14: Comparisons between the CD test measured results of the rockfill from the Changhe dam and the CSUH predictions [(23)]: (**a**) Axial Strain vs. Deviatoric Stress; (**b**) Axial Strain vs. Volumetric Strain.
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 4 Result
### Overall Performance  
Table 1 presents the overall detection performance comparison between the baseline YOLOv11n and WT-YOLO. WT-YOLO achieved improvements across all IoU thresholds,  
with mAP50-95 increasing from 0.2397 to 0.2515, mAP50 from 0.5519 to 0.5675, and mAP75 from 0.1624 to 0.1836, demonstrating enhanced localization accuracy. Notably, precision improved from 0.58 to 0.6279, while recall slightly decreased from 0.5453 to 0.5400. The integration of WTConv introduces multi-scale frequency domain analysis, enabling WT-YOLO to capture both global and local texture patterns through multi-scale feature extraction. This capability is particularly beneficial for detecting fine-grained defect features, as it suppresses noise and amplifies meaningful structural details. The increased precision indicates that WTConv enhances the model's ability to differentiate defects from the background, reducing false positives. However, the slight drop in recall suggests that some defects with subtle intensity variations may not be fully captured. This is because frequency decomposition can alter spatial information.  
For 1400 defect images, WT-YOLO completed the detection in 28 s, averaging approximately 0.02 s per image, while the baseline YOLOv11n took 36 s, averaging about 0.026 s per image. This demonstrates the potential of WT-YOLO as a viable approach for real-time defect detection in industrial applications.
## 3 Space Wave Propagation for LEO Downlink Analysis
We assume that the elevation angle of the LEO satellite is \(\theta_{gs}\) = 10\({}^{\circ}\), and the main lobe is steered at an observation point of 78.23\({}^{\circ}\) latitude and 15.408\({}^{\circ}\) longitude. Figure 6a shows the normalized received power in free space, whereas Figure 6b shows the result in the space environment. This clearly demonstrates that there is a difference between the observation point and the location of the maximum received power due to the bore-sight error in the space wave propagation. Figure 4: Geometrical optics model in a space environment.
# Coupled Elastic-Plastic Damage Modeling of Rock Based on Irreversible Thermodynamics
## 4 Validation
### Eastern Yunnan-Western Chongqing Shale  
#### 4.2.1 Project Case  
The bedding angle of shale significantly affects its mechanical behavior. To test the applicability of this model at different bedding angles, experimental data from Eastern Yunnan-Western Chongqing shale were used for validation. The Eastern Yunnan-Western Chongqing area is one of China's key shale gas exploration and development regions, possessing abundant shale gas resources [107]. This region is rich in Silurian hydrocarbon-bon source rocks, having undergone multiple phases of complex geological structural activities, and has long been in a state of uplift and erosion [108]. From a petrophysical evaluation perspective, the Silurian shales in the Eastern Yunnan-Western Chongqing area provide high-quality regional cap rocks for hydrocarbon reservoirs, characterized by strong continuity, large thickness, good sealing properties, high organic content, and well-developed maturity. The proven total natural gas resources reach 1.74 \(\times\) 10\({}^{12}\) m\({}^{3}\)[109]. The mechanical properties of shale are crucial for optimizing hydraulic fracturing techniques and addressing wellbore instability issues during extraction. Thus, [PERSON] [110] conducted a detailed investigation of the shale in this area. As shown in Figure 7, the sampling site is located in Qiliao Village, Liutang Township, Shizhu County, Chongqing (108\({}^{\circ}\)17\({}^{\prime}\)11.97\({}^{\prime\prime}\) E, 29\({}^{\circ}\)52\({}^{\prime}\)43.83\({}^{\prime\prime}\) N), a region geologically situated at the eastern margin of the Sichuan Basin, at the junction of the Sichuan Basin steep structural belt and the Eastern  
Figure 5: Stress–strain curves of theoretical and experimental for La Biche shale [18].  
Figure 6: Volumetric–axial strain curves of theoretical and experimental for La Biche shale [18].  
As shown in Figure 8, to ensure that the rock samples retain their original physical and mechanical properties during collection, samples were directly drilled from the rock using mechanical methods, or first, a cylindrical core with a diameter of 150 mm was drilled and then samples at different dip angles were extracted from it. Samples at angles of \(0^{\circ}\), \(22.5^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\) to the bedding planes were taken. After sampling, the samples needed to be quickly dried, labeled, wrapped in cling film, then wrapped with kraft paper, isolated with plastic, wax-sealed with numbers, and finally, boxed and transported to the laboratory. Subsequently, [PERSON] [110] conducted indoor microstructural analysis and triaxial compression tests on these samples, systematically studying the microstructure, elastic modulus, and compressive strength of the Eastern Yunnan-Western Chongqing shale.
## 3 Methodology  
Generally, the adaptive classifier has two states: classifying a new arrival data stream according to the current model and adapting the model to the change in data distribution. The first state needs low computational resources, while the second state may require the model's reconstruction and therefore more resources. Considering that there is no control over the time and order of the arrival of the stream data and the change in its distribution, the classification process requires the provision of high computational resources all the time. The utilization of the cloud as a computational platform represents an efficient solution for adapting the classifier as follows:  
1. Scalability of cloud resources can provide a fast training process even when an unexpectedly large amount of data arrives from patients. The resource scheduling in the cloud environment provides a reduction in the execution time by choosing the optimal virtual machines.
2. In the cloud, an on-demand service with auto-scale resources can reduce the cost of the classification task by utilizing the high computational resources only in the classifier adapting process.  
The goal of the proposed model is to precisely classify the EEG signal for detecting epileptic seizures. The proposed model, entitled Adaptive Random Forest in the cloud (ARFC), can be used to protect the patient from sudden epileptic seizures, which can cause injury or more critical situations. It also provides more time for the medical staff to take the appropriate medical procedure. The model contains two major stages: (1) the stream data preprocessing stage, which is performed on the patient's side, and (2) the building of an adaptive classifier stage in the cloud environment. Figure 1 illustrates all steps of the proposed model. The paradigm can utilize the advantages of both Edge computing and adaptive ML. It includes two classifiers: (1) a global classifier in the cloud master node, which receives the data stream from all edge nodes for adaptive training processes, and (2) a local classifier in each edge node that contains the latest version from the global classifier. The local classifier is used to classify the local node's stream data rows then send them to the master node. The global classifier applies machine learning imputation for retrieving the missing label values. It adapts itself with the change in the data distribution from all nodes, and finally, updating the local classifier with the newest version will be performed.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 3 Proposed Method  
In the proposed method, illuminant estimation is achieved by a serial of linear or nonlinear transformation of suitable image features. The proposed method also can be easily used to assess the illuminant estimation performance by utilizing different features to construct more efficient feature substitutes. The flowchart of the proposed framework is illustrated in Figure 1.  
In the training phase, the training dataset is classified into different clustering using the KFCM algorithm. For each cluster, the illuminant estimation models \(M_{1},M_{2},\cdots,M_{K}\) are obtained using a non-negative least square regression (NLSR) method. For a given test image that has been removed dark level and saturation level in the linear RGB space, the corresponding image features will be extracted and then the final estimated illuminant will be produced by fuzzy weighting all outputs of multiple regression models.
## 3 Results
### Estimated Post-Operative Hip Muscle and Joint Contact Forces
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**CAT 1**} & \multicolumn{4}{c}{**CAT 2**} & \multicolumn{4}{c}{**SODN**} & \multicolumn{4}{c}{**SOUP**} \\ \cline{2-13}  & & \(F_{X}\) [N] & \(F_{X}\) [N] & \(F_{Z}\) [N] & \(F_{X}\) [N] & \(F_{Y}\) [N] & \(F_{Z}\) [N] & \(F_{X}\) [N] & \(F_{Y}\) [N] & \(F_{Z}\) [N] & \(F_{Y}\) [N] & \(F_{Z}\) [N] \\ \hline Hip Joint Content Force & & 40.2 & 577.6 & –146.9 & 257.9 & 1141.7 & –106.5 & –1129.7 & 561.3 & –601.1 & –1009.5 & 578.6 & –568.8 \\ \hline _Proceed Muscle_ & _MTU_ & & & & & & & & & & & \\ \hline Ropyn Femoris Lengboel & BPHPH & 0.5 & –15.1 & 1.6 & 0.0 & 0.0 & 0.0 & 307.0 & –66.3 & 156.9 & 261.3 & –72.4 & 142.4 \\ \hline \multirow{2}{*}{Extural Obliques} & E010 & 0.0 & 0.0 & 0.0 & –0.1 & 7.4 & 1.5 & 3.8 & 23.9 & 0.4 & 3.5 & 246.0 & 0.1 \\ \cline{2-13}  & E012 & 0.0 & 0.0 & –11.3 & 25.2 & 0.2 & -12.7 & 92.4 & –18.0 & –13.3 & 82.3 & –16.8 \\ \hline \multirow{3}{*}{Glutes Measures} & CIMAX1 & 2.3 & –54.4 & 27.3 & 0.1 & –2.0 & 1.0 & 5.6 & –134.2 & 67.3 & 1.6 & –38.3 & 19.2 \\ \cline{2-13}  & CIMAX2 & 0.3 & –44.9 & 25.9 & 0.0 & 0.0 & 0.0 & 1.7 & –231.1 & 133.3 & 1.1 & –150.8 & 86.9 \\ \cline{2-13}  & CIMED1 & –45.5 & –110.5 & 7.0 & –88.6 & –206.8 & 9.4 & –0.4 & –21 & 0.4 & –0.3 & –1.6 & 0.3 \\ \hline \multirow{3}{*}{Ghteteens Medians} & CIMEM2 & –6.7 & –82.7 & 30.9 & –7.5 & –83.2 & 30.6 & 0.2 & –2.0 & 0.9 & 0.1 & –1.4 & 0.6 \\ \cline{2-13}  & CIMED3 & 2.2 & –6.01 & 40.7 & 1.0 & –37.7 & 25.7 & 4.7 & –16.0 & 11.2 & 29.4 & –104.1 & 75.8 \\ \hline \multirow{3}{*}{Internal Obliques} & K02 & 27.7 & 11.5 & –17.6 & 0.4 & 0.2 & –0.3 & 0.6 & 0.2 & –0.4 & 0.7 & 0.3 & –0.5 \\ \cline{2-13}  & K04 & 0.2 & 0.1 & 0.0 & 18.3 & 11.9 & 4.0 & 85.8 & 49.5 & 4.3 & 72.5 & 45.3 & 2.8 \\ \cline{2-13}  & ICPS & 20.9 & 5.2 & 10.3 & 20.0 & 7.2 & 9.4 & 98.5 & 39.0 & 26.6 & 74.6 & 32.0 & 19.5 \\ \hline \multirow{3}{*}{Pectures} & PUCT & –0.4 & –0.5 & 0.4 & –0.4 & 0.3 & 29.0 & –339.2 & 122.0 & 17.9 & –26.3 & 86.4 \\ \cline{2-13}  & RCTEM & 0.0 & -0.1 & 0.0 & –13.5 & –145.3 & -16.4 & 375.0 & -107.2 & 184.4 & 378.7 & –139.5 & 137.5 \\ \hline \multirow{3}{*}{Restus Abnormals} & RCTEM, ADD & 0.3 & 0.4 & 0.1 & 0.3 & 0.5 & 0.2 & 0.3 & 0.4 & 0.2 & 0.4 & 0.5 & 0.2 \\ \cline{2-13}  & SART & –0.1 & –3.5 & –0.5 & –3.1 & –18.1 & –5.5 & 23.9 & –12.5 & 4.9 & 32.9 & –20.4 & 7.5 \\ \hline \multirow{3}{*}{Semiembranous} & SEMEMEM & 7.7 & –87.4 & –4.4 & 0.0 & 0.0 & 0.0 & 0.2 & 0.0 & 0.1 & 36.3 & –11.1 & 13.0 \\ \cline{1-1} \cline{2-13}  & SEMEMEM & 9.1 & –95.2 & -3.3 & 0.0 & 0.0 & 0.0 & 30.5 & –6.9 & 10.8 & 69.3 & –20.5 & 26.5 \\ \hline \multirow{3}{*}{Tensor Facise Latte} & TPL & –0.5 & \(-4.6\) & 0.7 & –33.3 & –179.3 & 10.6 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \end{tabular}
\end{table} 
Table 2: Estimated hip muscle and joint contact forces for each of the seven external load cases.