esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Drift Support Analysis
### Numerical Simulation  
RS2 software was used to analyze stresses, deformations, and induced plastic zones of surrounding rock. This program is based on finite element analysis using the Hoek-Brown failure criterion. The drift section is built as a three-core arch. The sectional dimensions of the \(-915\) m, \(-945\) m, and \(-960\) m levels are 4.3 m \(\times\) 3.5 m, 4.6 m \(\times\) 4.1 m, and 4.3 m \(\times\) 3.5 m, respectively, with an expansion factor of \"3\" for the fixed external boundary. Graded mesh with three-node triangular elements was chosen for mesh discretization and to increase density surrounding the drift. The constant field stress type was taken from in situ stress values presented in Section 3.2. Then, the drift section was analyzed and modeled, and the geotechnical parameters are shown in Table 5. Different input parameters were used before and after the support was implemented. The plastic zone was analyzed for the three study areas (Figure 8). Overall principal stress analysis was also performed for average rock mass (Figure 9). As per the instruction received from the client, the standard pattern of grouted rock bolts, 32 mm in diameter and 2.2 m in length, was used in this software. After the installation of support system, the radius of the influenced plastic zone was reduced. Figure 7: Estimated support categories based on the updated Q-support chart [48]. Based on a comparative analysis of the plastic zone in the study area, it can be concluded that, after the adoption of support, the surrounding rock exhibited stress redistribution of the drift, and the disturbance was significantly reduced, indicating that the support method improved the stress state of the surrounding rock. The plastic zone areas of the \(-915\) m, \(-945\) m, and \(-960\) m drift decreased by 8.882 m\({}^{2}\), 3.605 m\({}^{2}\), and 28.773 m\({}^{2}\), respectively. Detailed information on the plastic zones is presented in Table 8. The failure types of the drift show the coexistence of shear failure and tensile failure although the shear failure was the main failure, and the failure mostly occurred at the drift roof. Therefore, special attention should be paid to the drift roof position when supporting and monitoring the drift. From the comparative analysis of the principal stress before and after the support, as shown in Figure 9, the hot-color high-stress zone in the surrounding rock was far away from the drift roof before support, and after being supported, the hot-color high-stress zone shifted closer to the drift roof, indicating that the bearing capacity of the drift surrounding rock was significantly increased after being supported. The area of the cold-color low-stress zone in the drift surrounding rock was significantly reduced after support, especially in the roof of the drift, which also indicated that the unloading damage zone in the drift was reduced. The increase in the bearing capacity and the reduction in the damage zone of the surrounding rock indicated that the support system was effective in improving the stress state of drift surrounding the rock.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design
#### 3.1.2 Experimental Settings  
Our Study 1 utilized Pico 4 as the experimental platform. This device is equipped with two 2.56-inch fast LCD displays and features a proprietary high-precision four-camera environmental tracking system, combined with an infrared optical positioning system, enhancing tracking and positioning capabilities through optical sensors. The experimental application was developed using Unity 2021.3.27f1c2, along with the PICO Unity Integration SDK, version 2.5.0. The vibration haptic devices were operated via an application built in Unity 2019.4.40f1c1 using the bHaptics haptic plugin. This haptic control application was deployed on a Windows 11 desktop computer. The LED light strips were controlled via a Halo board using the MQTT (Message Queuing Telemetry Transport) protocol to publish commands, enabling synchronization between the vibration haptic devices and the LED light strips. An MQTT server, hosted on Tencent Cloud, was established using EMQX V4.0.4 running on a CentOS 7 server. Both the haptic and visual feedback control applications subscribed to the relevant pattern information and triggered the corresponding feedback modes upon receiving the data.
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 4 Experimental Analysis
### Comparison with AFLnet  
We evaluate the effectiveness of SATFuzz in comparison with a stateful grey-box fuzzer called AFLnet. Specifically, we compare the average invalid interaction rates and numbers of crashes exposed in a 12-h fuzzing campaign on LightFTP and Live555. To reduce the impact of randomness, we ran 10 isolated instances of AFLnet and SATFuzz for each subject. The results are shown in Figure 7.  
Figure 7a shows that, for LightFTP, the invalid interaction rate of AFLnet was 98.84% on average, while the invalid interaction rate of SATFuzz was only 44.95% on average, showing that the proposed approach avoided more than half of the invalid interactions. Figure 7b shows that, for Live555, the invalid interaction rate of AFLnet was 97.99% on average, while the invalid interaction rate of SATFuzz was only 50.68% on average, showing that the proposed approach avoided nearly half of the invalid interactions.  
The interaction processes of the FTP and RTSP include connection establishment. Only after the client establishes a stable and reliable connection with the server can the subsequent reasonable client requests be received and processed normally by the server. In contrast, establishing an FTP connection requires additional authentication, and the interaction process is slightly more complicated. The test cases constructed by mutation usually have difficulty satisfying the interactive conditions. Therefore, the invalid interaction rates of AFLnet for LightFTP and Live555 were close to 100%, and the invalid interaction rate obtained on LightFTP was slightly higher than that obtained on Live555.  
Figure 6: Comparative experimental results produced by the two test case filters.  
Figure 7: Comparison of the experimental results produced by AFLnet and SATFuzz. (**a**) LightFTP. (**b**) Live555.  
Moreover, the classification performance of the test case filter was better for LightFTP as the average request message length of the FTP was less than that of the RTSP, which means that the context correlation of the test case was closer, making it easier for the QRNN to learn. Figure 7a,b show that, for LightFTP and Live555, the vulnerability discovery efficiency levels of SATFuzz were 3.05-times and 1.45-times higher than those of AFLnet, respectively. The experimental results show that, compared with competitors of the same type, the method in this paper had clear advantages in improving the efficiency of vulnerability discovery and achieved the expected results.
## 2 VMD-ESN Model
### Synthesis of Predicted Subseries in VMD-ESN Model  
As shown in Figure 1, the VMD-ESN model synthesizes predictions by aggregating the predicted subseries of all decomposed modes through linear superposition. Low-frequency modes carry the fundamental vortex-shedding energy and govern macroscopic pressure evolution. Their spectrally concentrated characteristics align with ESN's memory capability, enabling accurate modeling with fewer neurons. High-frequency modes capture turbulent microscale fluctuations governing transient peaks, requiring sparser reservoir connectivity to resolve transient features. Each decomposed mode undergoes rolling prediction by ESN individually, and the final result is obtained by summing all predicted subseries, a process that harnesses the collective predictive power of the ensemble model. This aggregation not only enhances the robustness of the prediction by mitigating the inherent impact of noise in each subseries but also leverages the unique information captured by different VMD modes. This synthesis preserves spectral fidelity while balancing local mode accuracy and global signal consistency, which is critical for aerodynamic noise under high-Reynolds-number flows where energy spans broad frequency ranges. The combined result integrates VMD's adaptive decomposition with ESN's nonlinear dynamics modeling, enabling accurate predictions of transient noise behavior.
## 5 Discussion
### Limitations  
In the above data, most of the information of walls, rooms and doors are detected, but there are some errors in the detection process. In the process of wall restoration, although several given connection schemes are able to restore the missing walls, these schemes are too limited to represent all connection situations due to the complicated building structure. In the door extraction, the \"thick doors\" with occlusion can be extracted well, but the \"thin doors\" with a lot of occlusion cannot be extracted in this paper, as shown in Figure 17. Other point cloud information (e.g., density, intensity, color, etc.) can be tried for extraction. Secondly, this paper uses the plane as the building element for navigation element extraction. The reconstruction results will be different from the actual situation when facing building components with larger curvature or rooms with fewer planes. In addition, the laser scanner cannot accurately acquire data on the surface of the object in some scenes due to the influence of the object surface material. Therefore, the method in this paper is also not applicable to indoor scenes where a large number of objects are used that affect the data acquisition by the laser scanner.
## 1 Introduction: Crack Localization Based on Pressure Wave Propagation
### Objectives and Methodology  
The purpose of this work is to achieve a better physical understanding of the nature of the waves propagating through the capillaries. First, a numerical study is realized in which the flow through the leak is simulated by means of computational fluid dynamics simulations. As a second step, these results are compared to measurements obtained using a self-built experimental setup. The specificities of the present work are highlighted in the next points:  
* Previous experiments described in [10; 11] were performed on short ducts of around \(L=0.5\) m. In this work, longer ducts of \(L=3\) m are considered. In this way, a deeper understanding of the propagation behavior of the wave can be obtained. Next to that, it allows analyzing if the system can be used for larger components, where the effect of friction could become important due to the longer length of the capillaries and therefore influence the wave propagation speed and localization accuracy.
* The diameter and location of the capillary channels is of high importance in terms of structural integrity of the part. The inclusion of capillaries in the structure reduce its resistance and should as a consequence be optimized to the smallest possible dimension. Considering a trade-off between the actual 3D printing accuracy and the structural integrity of the component, the capillary diameter should not exceed 1.6 mm. For practical reasons, in the present study, a duct diameter  
Figure 2: Wave propagation velocity evolution as a function of wave amplitude \(p_{1}/p_{2}\).  
of \(D_{d}=11.45\) mm is considered. In this way, the physics behind the problem can be understood without having to tackle the difficulties implied by the real small-scale dimensions. As highlighted before, the wave propagation through larger scale ducts is also widely used for pipeline leak detection; see [21]. Therefore, this work will address research questions that are relevant for both leak localization techniques (small and large scale). According to [PERSON]'s equation [22] for choked flow, the diameter ratio \(D_{leak}/D_{d}\) is the only geometrical parameter affecting the amplitude of the propagating wave:  
\[\Delta P=0.37P_{atm}\left(\frac{D_{leak}}{D_{d}}\right)^{2} \tag{3}\]  
in which \(P_{atm}\) represents the static atmospheric pressure condition in the leak. To obtain the same amplitude in this study as for the real application with capillary channels, the diameter ratio is set at the same value; see Equation (4). The pressure jump of the wave is expected to equal \(\Delta P\approx 36\)Pa; see Equation (3). More information about the geometry is given in Section 2.1.1.  
\[\left(\frac{D_{crack}}{D_{cap}}\right)_{real}=\left(\frac{D_{leak}}{D_{d}} \right)_{study}=0.003 \tag{4}\]
## 2 Overview of Published Papers
The paper introduces a methodology to determine the minimum number of basis functions required for accurate representation and proposes an interpolation formula that optimally exploits non-redundant field samples. This work contributes to advancing the efficiency of field measurement techniques, crucial for applications where acquisition time is a limiting factor. [PERSON] et al. (contribution 45) tackle the complex problem of aperture-level simultaneous transmit and receive (ALSTAR) with a digital phased array. The authors propose an adaptive random group quantum brainstorming optimisation (ARGQBSO) algorithm to simplify the array design, addressing the isolation between transmit and receive apertures. This innovative algorithm demonstrates robust performance, reducing complexity and enhancing overall efficiency in ALSTAR systems. [PERSON] and [PERSON] (contribution 46) present a novel formulation based on Floquet spectral analysis for the almost-periodic modulation of massive finite and infinite strongly coupled arrays. The study has significant implications for applications such as dense-massive-MIMO, intelligent-surfaces, and future wireless technologies (5G and 6G). The numerical methods adopted, including the method of moments, pave the way for the advanced modelling of antenna structures in small areas with a large number of elements. Collectively, these contributions demonstrate the breadth and depth of contemporary antenna research, addressing challenges and pushing the boundaries of what is possible in diverse applications. The innovative methodologies and findings presented in this Topic contribute to the ongoing evolution of antenna technologies, playing a vital role in shaping the future of wireless communication and sensing systems.
## 1 Introduction
### Paper Motivation  
A review of the literature shows that various machine learning (ML) algorithms have been suggested to improve the detection performance of backscatter communication systems, such as DL and neural networks. However, there is no clear indication of which model is the most effective. Since 2006, DL has been a popular topic in the ML field due to the development of system processing power and data availability. DL models have been shown to be better than traditional ML models. These methods have become more popular due to their ability to reduce computation time and increase the convergence curve. Over the past few years, DCNNs have exhibited notable proficiency in several applications, such as estimation, classification, and detection, particularly for complex and high-dimensional data. DCNNs are well-suited to detecting patterns in complex and noisy signals, making them an ideal candidate for improving the detection performance of BC systems [17; 18; 19]. DCNNs are inspired by the way neurons transmit information in biological processes. Unlike traditional networks that only comprise input and output layers, DCNNs are based on multilayer perceptron with a fully connected architecture.  
DCNN is a well-known DL approach that has been proven to be effective because of its ability to train the hierarchical layers successfully. It requires minimal pre-processing and fine-tunes all the layers of the network. DCNN reduces the number of parameters by utilizing spatial relationships between features. DCNNs have the ability to automatically acquire progressively more intricate features from unprocessed pixel data, enabling them to capture and depict high-level concepts and abstractions. Fine-tuning a DCNN trained on one task for a related task can enhance its performance and decrease the amount of training data required. DCNNs are also highly parallelizable, which makes them suitable for processing vast amounts of image data simultaneously using multiple GPUs. As a result, this study employs a DCNN to improve the detection performance of backscatter communication systems.
## 5 Formulation of the 5-Layered Arm Model with an Internal Object  
In order to investigate the differences between scattered and transmitted fields in healthy and unhealthy human arms, an unhealthy arm model with an abnormal region (e.g., bleeding, clot or tumor) is created by placing a dielectric structure \(\mathcal{D}\) with a rectangular cross-section inside the concentric cylindrical arm model. The configuration is illustrated in Figure 1. Now, assume the unhealthy arm model is illuminated by a line source located outside the inhomogeneities. Let us introduce the field \(u_{0}(x)\), which would be observed in the absence of the body \(\mathcal{D}\). When the contribution of the unhealthy tissue \(\mathcal{D}\) is denoted by \(u_{s}(x)\), the total field \(u(x)\) can be expressed at any point as the following sum:  
\[u(x)=u_{s}(x)+u_{0}(x). \tag{22}\]The  
\[\
abla^{2}u_{s}(x)+k_{b}^{2}\,u_{s}(x)=-k_{b}^{2}\,v(x)\,u(x), \tag{23}\]  
where the function \(v(x)\) is the so called _object function_ and defined by  
\[v(x)=\begin{cases}\frac{k^{2}(x)}{k_{b}^{2}}-1,\text{ if }\quad x\in\mathcal{D}\\ 0,\text{ if }\quad x\
otin\mathcal{D}\end{cases} \tag{24}\]  
In this study, the object \(\mathcal{D}\) representing the abnormal tissue is placed in the innermost layer of the cylindrical structure; therefore, in numerical implementations, \(k(x)\) is arranged for a medium with the wave number \(k_{5}\), as depicted in Figure 2. The scattering problem under consideration can be formulated equivalently to the problem of solving the Lippman-Schwinger integral equation  
\[u(x)=u_{0}(x)+k_{b}^{2}\int\limits_{\mathcal{D}}G(x,y)\,v(y)\,u(y)\,dy,\quad x \in\mathcal{D} \tag{25}\]  
for \(u\), which is a second-kind Fredholm integral equation. The numerical solution of this equation can be obtained by a method of moments (\(MoM\))-based technique given in [28]. For the numerical evaluation, the dielectric object \(\mathcal{D}\) is divided first into \(M\) sufficiently small rectangular cells \(\mathcal{D}_{m}\), \(m=1,2,\ldots,M\) assuming the field \(u(x)\) constant over each cell. Consequently, the problem can be reduced to the solution of a system of linear equations for the unknown \(u(x_{n})\):  
\[u(x_{n})-k_{5}^{2}\sum\limits_{m=1}^{M}\,u(x_{m})\,C_{nm}=u(x_{n}),\quad n=1,2,\ldots,M \tag{26}\]  
where  
\[C_{nm}=v(y_{n})\int\limits_{\mathcal{D}_{m}}G(x_{m},y_{m})dy \tag{27}\]  
is substituted. In the implementation of the \(MoM\)-based method, the integration of the singular part \(H_{0}^{(1)}(k_{b}|x_{n}-y_{m}|)\) of the Green's function over the rectangular cells in \(\mathcal{D}_{m}\) can be obtained by the equivalent circular cell approach given in [28] as follows:  
\[C_{nm}=\begin{cases}1-\frac{i}{2}[\pi k_{b}aH_{0}^{(1)}(k_{b}a)+2i]\,v(y_{m}),\ n=m\\ \frac{-i\pi k_{b}a}{2}J_{1}(k_{b}a)H_{0}^{(1)}(k_{b}|x_{n}-y_{m}|)\,v(y_{m}), \ n\
eq m\end{cases} \tag{28}\]  
where \(a\) is given by  
\[a=\sqrt{\Delta x\,\Delta y/\pi} \tag{29}\]  
denoting the radius of the equivalent circular region where \(\Delta x\) and \(\Delta y\) represent the side lengths of a rectangular cell.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 4 Results
### Validation of Discrete Fracture and Matrix Model  
X-ray tomography images of saturation in the physical experiment are shown alongside with our simulation results in Figure 12. An excellent match is observed between the position of the wetting front as a function of time. Since the matrix is completely dry, spontaneous imbibition is strong and most of the injected water imbibes the matrix instead of filling the fracture (Figure 13). Subsequently, the wetting front in the matrix moves at a similar speed as that in the fracture. [PERSON] and [PERSON] [25] termed this behaviour 'filling' regime, to contrast it from that which typified faster injection rates where the wetting front in the fracture overtakes that in the rock matrix.  
Figure 14 quantifies matrix imbibition over time. The average matrix saturation curves have a distinct 'S' shape. Their first part represents an early time response when water enters the fracture. The middle part describes the spontaneous imbibition process during which FMT scales linearly with the square root of time. The plateau of the final part illustrates 'late-time' behaviour that begins when the wetting front reaches the sealed sample boundaries, and the rest of the air is trapped in the rock sample. The late-time response is characterised by a drop in FMT and nearly constant saturation gradients in the rock matrix. The outlet port produces mostly water.  
Our simulation captures these three evolutionary stages albeit with a small but constant offset in time. In the simulation, FMT ramps up earlier than in the physical experiment. There may be several reasons for this mismatch: Candidates are the constitutive relationships used for the fracture (the experimental study did not establish these and we just used Brooks-Corey functions), the geometry of the inlet fluid port that was not captured exactly or perhaps a minor delay between when the pump was switched on and when the fluid pulse arrived in the sample. A further possibility relates to the regression of the experimental results (cf., [PERSON] and [PERSON] [39]). However, the similarities in saturation patterns, average water saturation over time, and evolutionary stages, strongly suggest that the proposed fracture discretisation and nonlinear FMT scheme can realistically capture
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 5 Conclusions  
In this paper, we propose a multi-scale feature fusion pyramid network for spatially small target detection in complex environments. The feature pyramid is used by increasing the sensing field and using the attention mechanism in order to solve the problem of the difficult extraction of spatial small target features. The experimental results show that the algorithm in this paper is significantly better than existing methods for detecting spatial small targets in complex environments, and its average recognition accuracy for spatial small targets is higher, reaching 98.1%, which is 4% higher compared to the baseline method, and the average recognition accuracy was also improved compared to other mainstream algorithms with superior performance. In summary, the method in this paper has the potential to be widely used in spatial target research, especially for the high-precision recognition of small spatial targets and fuzzy targets.  
Conceptualization, H.X.; methodology, C.X.; investigation, Y.L.; writing--original draft, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data contained within the article.  
The authors declare no conflicts of interest.