esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Affecting Factors
### Effect of Armor Blocks  
Previous researchers have shown that armor blocks such as accropodes can effectively reduce the wave-induced impact force on breakwater [41]. This section is aimed to explore the effect of the armor blocks on enhancing the stability of the revetment breakwater by comparative study. Accropodes are laid on the revetment breakwater in Test 4, rubbles are laid in Test 5, accropodes and rubbles are jointly adopted in Test 6. Distribution of the maximum wave impact on the surface of the revetment breakwater under the wave loading in Test 4, Test 5 and Test 6 is shown in Figure 20. Comparison of the wave impact force on structure A and B measured in Test 3, Test 4, Test 5 and Test 6 is shown in Figure 21. It is found that the wave impact on the revetment breakwater is obviously small in Test 4 and in Test 6, especially for the wave impact on structure B. The wave-induced impact on the breakwater recorded in Test 3 and Test 5 is basically the same. It is indicated that the accropodes can help to dissipate the wave energy and protect the revetment breakwater; especially for structure B, the protection effect is more significant. Meanwhile, the rubble plays little role in dissipating the wave energy.  
Figure 19: Comparison of pore pressure recorded at P1, P4, P8 and P12 between Test 3 (\(T=3.0\) s) and Test 2 (\(T=2.2\) s).  
Figure 20: Distribution of the maximum wave impact on the surface of the revetment breakwater under regular wave loading in Test 4 (accropodes), Test 5 (rubble) and Test 6 (accropodes and rubble).  
Figure 21: Comparison of the wave impact force on structure A and B measured in Test 3 (no armor block), Test 4 (acroporodes), Test 5 (rubble) and Test 6 (acroporodes and rubble).  
Comparison of the pore pressure recorded at P1, P4, P8 and P12 among Test 3, Test 4, Test 5 and Test 6 is shown in Figure 22. It can be seen in Figure 22 that the pore pressure recorded in Test 6 is the smallest. Table 4 also shows similar phenomenon that the displacement of the revetment breakwater and the overtopping water measured in Test 6 are the smallest. Conclusions can be made that the accropodes will help to maintain the stability of revetment breakwater under wave impact. The protection effect for breakwater is more significant when accropodes and rubble is jointly adopted to dissipate the energy of waves.  
phenomenon indicates that pore pressure is more easily accumulated in relatively loose sand foundation. Combining the test results listed in Table 4, the settlement of structure B measured in Test 7 is only 3 mm, which is much smaller than that measured in Test 3. A conclusion can be made that the revetment breakwater is more stable if the dry density of its foundation is relatively dense.
## 4 Results and Discussion  
### Data Set Introduction and Analysis  
Figure 9 shows the change curve of parking occupancy in 30 min intervals in a week for PL1 to PL8. When we superimpose the trend on the figure, it can be seen that each parking lot has the same trend for the week, except individual times are different, and the overall law is not affected; namely the change from Monday to Sunday is similar. From 0:00 a.m. to 6:00 a.m., the occupancy of parking lots can be negligible. The occupancy of parking lots is increased from 6 a.m., and there are still some differences between these parking lots. The occupancy rate of the parking lots peaked from 7:00 a.m. to 8:00 a.m. From 11:00 a.m. to 12:00 p.m., the occupancy rate of parking lots decreased slightly. The peak value is basically maintained within working hours (from 8:00 a.m. to 6:00 p.m.), generally reaching the upper limit of the parking lot, indicating that the utilization rate of parking spaces in various regions is high during this period. The occupancy rate of the parking lot reached a high value between 7:00 p.m. and 8:00 p.m., but did not exceed the rate of the working hours' occupancy. This means people need to go out for entertainment, parties, or dinner, which increases the utilization rate of street parking space during this period of time. The trend of parking occupancy on Saturdays and Sundays is similar to that on weekdays. The peak value on Saturday is slightly lower than that on weekdays, and the peak value on Sunday is lower than that on Saturday. Therefore the parking occupancy can be predicted using the deep learning method.
## 1 Introduction
When the gravity is neglected, and when both wettability and the disorder of the porous media are involved, the competitive effects of capillary and viscous forces become more complex [23]. [PERSON] and [PERSON] [24; 25] emphasized the effect of wettability on two-phase flow in heterogeneous porous media, discovering that wettability heterogeneity and transitions significantly impact fluid displacement paths and invasion patterns. [PERSON] [26] used microfluidic cells to study displacement patterns under various wettability conditions. It was found that the increase in the wall surface contact angle leads to more uniform displacement fronts and higher displacement efficiency. Since the impact of gravity on two-phase flow cannot be entirely ignored in real scenarios, [PERSON] and [PERSON] [27; 28] studied immiscible displacement in heterogeneous porous media from the perspective of gravity's influence. By evaluating the effects of hypergravity and dimensionless analysis, the feasibility of using hypergravity models was systematically analyzed to assess CO\({}_{2}\) migration in geological contexts. Most scholars studying two-phase flow in porous media at the pore scale focus on oil reservoir extraction, with few examining the flow state of gas in coal seams at the pore scale. Therefore, in the field of coal mining, it is urgent to explore the relationship between coal wettability, gas extraction efficiency, and the fingering phenomena resulting from the displacement of multiphase media within pores during and after hydraulic measures. This study simulates coalbed methane extraction by utilizing the Navier-Stokes and Cahn-Hilliard equations to model the gas-water two-phase flow in porous media and solves these equations using the finite element method in COMSOL Multiphysics(tm) [29]. The model is subsequently applied to address gas-water displacement at the pore scale following hydraulic fracturing of coal seams. The viscous and capillary instabilities under homogeneous conditions as well as the effects of wettability and permeability on flow patterns and fluid saturation are investigated.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 1 Introduction
Feedback controllers include PID controllers, adaptive controllers, and sliding mode controllers, among others. [PERSON] [14] used the Maxwell model for the feedforward compensation of the PAM and designed two feedback controllers to control the force and position of the pneumatic muscle, respectively. [PERSON] [15] employed the three-element method to model friction in the PAM and, based on this, designed a fuzzy feedforward plus feedback control scheme to study the trajectory tracking control of pneumatic muscles. Scholars have proposed other control methods based on the hysteresis models of the PAM, such as feedback linearization, robust control, and sliding mode variable structure control. [PERSON] [16], using the three-element model, studied the trajectory tracking control of a mechanical arm composed of a PAM with a robust control strategy. [PERSON] [17] explored the sliding mode control of a linear axis driven by a PAM based on the LuGre model. Based on the above analysis, this paper proposes the FN-QUPI model to describe the displacement-pressure hysteresis characteristics of pneumatic artificial muscles. The model reduces the number of unknown parameters, and particle swarm optimization is employed for parameter identification, enabling the model to adapt to system dynamics while maintaining high accuracy. Additionally, a composite controller is established, where the QUPI inverse model is used as the feedforward control, and incremental PID-type iterative learning serves as the feedback control for trajectory tracking of the PAM. The experimental results demonstrate that the FN-QUPI model provides highly accurate displacement predictions for various input signals, confirming the model's generalization capability. Furthermore, the composite controller exhibits high precision and robustness in tracking control of different types of PAM-motion trajectories. The remainder of the paper is organized as follows: Section 2 introduces the FN-QUPI model and its inverse model, while Section 3 discusses the trajectory tracking control methods for the PAM. Section 4 presents an analysis of the experimental results of the FN-QUPI model under different signals and the tracking results of the PAM for various trajectories. Finally, Section 5 provides a summary of the paper.
## 4 Embedded Platform for the Decentralized Monitoring of Power Distribution Systems  
### The Implemented Decentralized Architecture  
The decentralized architectures for the monitoring of PDSs proposed in [7] are based on ESEMs that manage information and allow for the real-time monitoring of LV networks near the SMs, contrary to the centralized monitoring strategies currently used in DOCs. Among these decentralized architectures, the obtained theoretical results demonstrated that one with ESEMs in the secondary of all distribution transformers was the best in terms of the data volume and computational processing requirements. This architecture is illustrated in Figure 1.  
In this architecture, each ESEM installed in the secondary of a distribution transformer is responsible for the state estimation process of the LV network connected to that transformer. The input data of each ESEM module include the topology and the parameters and real-time measurements of the corresponding LV network. Based on these data, each ESEM executes a WLS SE to estimate the state variables of each LV network, i.e., the estimate of the voltage magnitude and phase angle of all buses of the corresponding LV network (each bus corresponds to a Customer Unit (CTU)). After that, each ESEM sends the DOC the load estimation of each distribution transformer (the estimate of the net real and reactive power injection of each distribution transformer). With this information, an SE can be executed in the DOC to estimate the state variables of all feeders and distribution substations.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 3 Receiver Algorithms
### Tr-Mc-Dfe  
Subarray processing can improve the performance of element-domain MC-DFE [6]. In some underwater platforms, small arrays with 6-8 elements may be possible for underwater acoustic communication [11]. In this section, TR-MC-DFE is proposed to improve the performance of the TR-DFE. In this system, the entire receive array is divided into several subarrays. The proposed receiver structure is shown in Figure 3. In Figure 3a, \(r_{k}(t)\) denotes the combination signal after TR processing as follows:  
\[r_{k}(t)=\sum_{m=1}^{K}y_{m}(t)\otimes\hat{h}_{m}^{*}(-t),\qquad k=1,\cdots Q, \tag{11}\]  
where \(K\) is the number of sensors per subarray, \(Q\) is the number of subarrays, and \(M=KQ\).  
Prior to the following MC-DFE, TR processing is conducted in each subarray to shorten the underwater acoustic channels. Because \(Q\ll M\), the parameters of the following low-order MC-DFE are reduced significantly.
## 5 Experiments
### Function Experiments  
#### 5.1.1 Magnetic Circuit Measurement  
The magnetic field between the rotary part and the stationary part of the actuator was measured by a digital Gauss meter with a measuring range of 0-2000 mT and resolution of 0.1 mT. In order to install the measuring probe, the air gap between the rotary part and the stationary part was extended to 2 mm and a Gauss Hall probe was adopted to measure the magnetic field intensity of the actuator with different driving voltages. Before measurement, the insertion depth of the Hall probe was changed. By observing the Gauss meter reading when the driving coil was not energized, the position of the maximum magnetic field intensity was found. At the end of each excitation operation, the Gauss meter reading had to be observed again to ensure that the Hall probe was in the position of maximum magnetic field intensity during the whole measurement process. During the experiment, the power supply voltage of the coil was adjusted from 12 V to 23 V and a change of maximum magnetic field intensity was observed, as shown in Figure 16. Two conclusions can be drawn from the experimental results: (1) when the driving voltage was adjusted in the range of 12-23 V, the relationship between the magnetic field intensity and the driving voltage was approximately linear and with the increased driving voltage, the magnetic field intensity in the actuator increased synchronously; and (2) when the supply voltage was adjusted in the range of 23 V, the magnetic field intensity increased monotonously all the time and there was no saturation of magnetic field intensity in the magnetic circuit, verifying the effectiveness of the internal magnetic circuit design of the actuator.
# Brain-Inspired Data Transmission in Dense Wireless Network
## 6 Discussion
As in the case of percolation analysis, it is possible to consider introducing some kind of node control (e.g., power control) here; however, it would also mean increasing the complexity of the network, but it is a promising topic for further research. Conclusions resulting from the presented results concerning the modeling of a dense wireless network are certainly the possibility of using percolation theory to check the network behavior (in a stochastic way). Secondly, in dense networks, we have the possibility of achieving higher reliability when we apply reduced requirements for data processing (simplification of transmission). This aspect is extremely important in relation to sensor networks where energy consumption is crucial, especially in the case of networks where we cannot replace the battery and each node is limited by the energy reserves it has, and energy harvesting solutions are considered in these networks. In this type of network, the presented simplification of transmission is the most promising. Of course, this requires further research--in particular, the analysis of the impact of the radio environment, the analysis of the aging of devices, and the analysis of failure rates. Device aging and failure frequency are often expressed as reliability versus time, which adds another dimension to the analyses presented in this article. Summarizing the overall results, one may conclude that, when the dense wireless network is considered, there is a space for the application of highly simplified transmission schemes while keeping the network reliability at an assumed level. Thus, there is a space for further research towards the implementation of other human-nervous-system-focused solutions. Conceptualization, A.K. ; methodology, L.K. and A.K. ; software, L.K. ; validation, L.K. ; investigation, writing--original draft preparation, writing--review and editing, and visualization, L.K. and A.K. ; and supervision, A.K. All authors have read and agreed to the published version of the manuscript. The presented work was funded by the National Science Centre in Poland within the \"BioNets\"project (No. 2016/23/B/ST7/03937) of the OPUS programme. Not applicable. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Ground Motion Propagation of Nonlinear Soil  
### Fitting Parameters and Implementation of Davidenkov Model in ABAQUS  
#### 3.1.1 Fitting Parameters of Soil  
Adopting the Davidenkov model for this study, the three key parameters \(A\), \(B\), and \(\gamma_{0}\) in Equation (1) were fitted through the experimental data using a numerical algorithm [45]. Taking three sets of existing experimental data of typical Shanghai soft soils (clay, sand, and muddy clay) as an example [46], the fitting effect is shown in Figure 5. As illustrated in Figure 5, the degradation of the shear modulus with strain and the hysteretic behavior and damping are critical characteristics of soil behavior under cyclic loading. The considered model is effective at capturing this behavior and predicting the values of shear modulus degradation and damping increase with strain. The fitting parameters of the typical Shanghai soft soils in this study are summarized in Table 1.  
Figure 5: Comparison between numerical model predictions and experimental data for (**a**) clay, (**b**) sand, and (**c**) muddy clay.  
The viscous damping effect of the soil is represented by Rayleigh damping [47; 48] and assumed proportional to the stiffness of the soil layers [39]:  
\[[C]=\frac{2\xi_{soil}}{\omega}[K], \tag{16}\]  
where \(\omega\) is the circular frequency and takes the fundamental frequency corresponding to the first mode (\(\omega_{1}\)), \(\xi_{soil}\) is the equivalent damping ratio of the soil, and the value of \(\xi_{soil}\) is between 1.5 and 4% for most soils [39]. Here, \([C]\) is assumed to be independent of the strain level, therefore the effect of hysteretic damping induced by nonlinear soil behavior can be separated from (but added to) viscous damping. The Davidenkov model was incorporated as an independent subroutine attached to ABAQUS (Dassault, Paris, France) [49] by the author to conduct nonlinear ground motion propagation analysis. Details and verification of this subroutine can be found elsewhere [45].
### Overview and Contribution  
In this study, we present a thorough experimental framework for both centralized and federated learning paradigms relating to deep transfer learning models for video-based fall detection. Leveraging the publicly available fall-detection dataset from Kaggle, our approach integrates MobileNetV2 and VGG16 as backbone architectures for the efficient and accurate classification of fall versus non-fall events. The proposed work explores and compares four training schemes:* Decentralized Federated Learning (Ring-based): Clients receive a copy of the global model, train locally, and sequentially update weights, which are averaged using FedAvg. This was implemented using both MobileNetV2 and VGG16.
* Centralized Federated Learning: A central server aggregates model updates from multiple clients through weighted averaging of parameters. This method was also evaluated with both MobileNetV2 and VGG16 to assess scalability and coordination benefits
* Transfer Learning: Models pretrained on ImageNet (MobileNetV2 and VGG16) were adapted to the fall-detection task. This transfer learning technique enhanced generalization performance and drastically decreased the requirement for large amounts of training data.
* Privacy-Preserving Architecture: By partitioning data across multiple simulated clients and avoiding central data storage, our federated setup respects data sovereignty and aligns with privacy regulations such as GDPR.  
The novelty of this study lies in the end-to-end deployment and benchmarking of federated fall-detection systems using both lightweight (MobileNetV2) and heavy (VGG16) architectures. Furthermore, the dataset was preprocessed using a label-aware cropping strategy to extract meaningful fall-related regions from YOLO-labeled images, which were then normalized and resized. This preprocessing pipeline not only reduced noise but also enhanced training convergence.  
The following is a summary of this study's primary contributions:  
* In order to detect falls in a video-based environment, we offer a unified framework that assesses both centralized and decentralized federated learning techniques.
* We implement a robust preprocessing pipeline converting YOLO-labeled bounding boxes into clean image patches, facilitating accurate binary classification.
* We compare the performance of MobileNetV2 and VGG16 under different federated setups, reporting metrics such as accuracy, loss, and confusion matrices for comprehensive evaluation.
* We provide reproducible code and systematic methodology that can be extended to other healthcare monitoring applications requiring real-time, privacy-aware decision making.
## 3 Materials and Methods
### The Proposal and Integration of DenseDDSSPP into the Network  
We propose a novel module called DenseDDSSPP and its integration into the DeepLabV3+ architecture, replacing the standard ASPP module in the encoder to achieve more accurate road extraction from satellite imagery (as shown in Figure 3). For a clearer understanding of the differences between ASPP and DenseDDSSPP, Figure 2 provides a side-by-side illustration of the two modules. Motivated by the work of [PERSON] et al. [20], DenseDDSSPP is designed using depthwise dilated separable convolution layers arranged in a cascade, with dilation rates increasing in ascending order. Unlike ASPP, where individual layers operate independently, DenseDDSSPP concatenates the output of each intermediate layer, computed through depthwise dilated separable convolutions with a selected dilation rate, with both the input feature map and the outputs from all previous layers. This dense arrangement facilitates the generation of comprehensive feature maps that integrate multi-scale contextual information. Such an arrangement of depthwise dilated separable convolution layers as a neural network module is a novel contribution in the current literature. The resultant concatenated feature map is sequentially passed to subsequent layers, with this iterative process performed for all convolution layers in the DenseDDSSPP module, resulting in the final dense feature map (depicted in Figure 3, within the encoder section, illustrated with different colored arrows). This approach leverages the computational efficiency of depthwise separable convolutions, as discussed in Section 3.2, to construct DenseDDSSPP as an efficient module. Tracing back to Equations (2) and (3), the layered approach in DenseDDSSPP is mathematically illustrated as follows:  
\[Y_{l}=D_{S,d_{l}}([Y_{l-1},Y_{l-2},\dots,Y_{0}]) \tag{4}\]  
where \(l\) denotes the layer index, \(d_{l}\) is the dilation rate for the layer \(l\), and concatenation is denoted by \([\dots]\). The expression \([Y_{l-1},\dots,Y_{0}]\) denotes the feature map resulted by merging the outputs from all preceding layers. Here, \(D_{S,d_{l}}\) represents the depthwise dilated separable convolution operation with the kernel size \(S\) and dilation rate \(d_{l}\). These above modifications and integration provide two explicit advantages, i.e., an efficient, denser  
Figure 2: Comparison of architectural designs: ASPP vs. DenseDDSSPP modules.  
feature map and larger receptive field, which are crucial for accurately interpreting complex road structures.