esa-sceva/satcom-chunk-collection huggingface dataset:
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## Security Proofs
### Attacks on the Cryptographic Operations  
On the one hand, the security of the scheme depends on the chosen hash function. A collision attack on a cryptographic hash tries to find two inputs producing the same hash value. Another possible weakness can be a preimage attack, which tries to find a message that has a specific hash value. A cryptographic hash function should resist both preimage and collision attacks. In this work, the implementation uses the hash function SHA-3 for several operations. The algorithm behind SHA-3 is the Keccak function. With Keccak, it is possible to target a given security strength level by choosing the appropriate capacity, _i.e._, for a given capacity \(c\), Keccak is claimed to resist any attack up to complexity \(2^{\frac{1}{2}}\). This approach is similar to the one suggested to choose the security strength used in [32] by NIST (National Institute of Standards and Technology).  
On the other hand, the security of the proposal depends on the symmetric cipher used to encrypt each segment. The encryption used in the implementation is the basis of the security in LTE communications, called SNOW 3G. The SNOW 3G core is supplied as portable Verilog, with a Verilog Hardware Description Language (VHDL) version available, thus allowing customers to carry out an internal code review to ensure its security.  
Therefore, the security of cryptographic operations is guaranteed due to the used standards: SHA-3 and SNOW 3G. The SHA-3 algorithm is based on a permutation where collisions and preimages can be found for its cryptographic hash function in one query to the permutation. The sponge construction has been proven to be indifferentiable from a random oracle if the underlying permutation is assumed to be ideal [33], and this result applies to Keccak function. Optimal bounds have been obtained on collision resistance and on preimage and second preimage resistance for Keccak in the ideal permutation model [34]. Regarding SNOW 3G, it has linear time complexity, which guarantees efficiency during the encryption/decryption process. Furthermore, security proofs of SNOW 3G are based on the assumption that this encryption system behaves like a perfect random function of the key [35].
## 1 Introduction  
The development of technologies in industry moves along the path of complicating technological operations, creating flexible and robotic industries. At the same time, the requirements for the reliability of technological equipment are increasing. There is a trend towards a shift from preventive maintenance to predictive maintenance of equipment, which corresponds to the general direction of technological process development according to the concept of Industry 4.0 [1, 2].  
Today, virtual testing and virtual commissioning based on the behavior simulation of production systems are widely used. Such technologies rely on a model-based development [3, 4]. At this stage, the studies of maintenance and repair processes become essential, as they provide the organization of the predictive maintenance system at the time of actual product output. Another important aspect is the continuous monitoring of technological equipment in the production process to replace parts or serviced units in a timely manner, predicting failure or defect.  
For many applications in industry, discrete-event simulation is the most appropriate tool for studying technological processes and equipment properties. Many kindsof parts create specific difficulties in describing the models of machines, components, and assemblies. Due to the stochastic nature of the technological process, calculating unit performance is impossible. These circumstances lead to Petri nets' (PNs') mathematical formalism, aiming to build simulation models of production processes and maintainability evaluation [5; 6].  
This article discusses a method for constructing a hierarchy of simulation models based on timed colored PNs, describing processes in a set comprising:  
* [noitemsep]
* Performed production tasks, specified in the form of a Gantt chart;
* A set of technological units;
* Service stations for maintenance and repair.  
This article contains the following parts: Related works are presented in Section 2. The problem statement of evaluating maintenance and repair efficiency is discussed in Section 3. Section 4 briefly describes the definitions of colored Petri nets (CPNs) and timed colored Petri Nets (TCPNs). Section 5 presents the hierarchical Petri net model (HPN), and describes how this architecture is abstracted and modeled using TCPNs. Sections 6 and 7 provide an example of the use of HPN to simulate an autonomous agricultural vehicle system and discuss the results obtained. The future extensions to the proposed approach are presented in Section 8.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 1 Introduction
[36] combined the latest method of audiovisual feature extraction with the multi-resolution feature bag model to improve the feature extraction technology and proposed a unique audiovisual environment perception model based on compressed sensing theory [37] to improve the environment scene classification performance. As research endeavors in image and audio scene classification tasks advance, this interdisciplinary approach holds substantial promise. In this paper, we propose a novel two-stage fusion-based multimodal audiovisual classification network, aiming to explore the potential of using remote sensing images and ground environment audio for multimodal remote sensing scene classification. The audio, vision, and fusion modules make up the network. By combining ground environment audio and remote sensing aerial images, we can obtain more-accurate audiovisual scene classification results. The resilience of the classification method may also be improved via multimodal fusion, allowing it to perform well in challenging and noisy conditions. The following are the primary contributions of this study:  
1. A Two-stage Fusion-based Audovisual Classification Network (TFAVCNet) was designed in this paper. We simultaneously used ground environment audio and remote sensing images to solve the task of remote sensing audiovisual scene classification in order to enhance the performance of single-mode remote sensing scene classification. 2. The proposed approach makes use of two separate networks, which were, respectively, trained on audio and visual data, so that each network specialized in a given modality. We constructed the Audio Transformer (AiT) model to extract the time-domain and frequency-domain features of the audio signals. 3. In the fusion module, we propose a two-stage hybrid fusion strategy. During the feature-level fusion stage, we propose the adaptive distribution weighted fusion to obtain the fusion embedding. In the decision-level fusion stage, we retained the single-mode decision results for both images and audio and weighted them with the feature fusion results. This paper is organized as follows: Section 2 presents the related work. Section 3 presents the overall framework of the proposed audiovisual remote sensing scene classification network, as well as the audio module, visual module, and two-stage hybrid fusion strategy. Section 4 describes the details and results of each part of the performed experiments. Section 5 summarizes the paper and details the outlook for future research work.
## 4 Data Preparation
### Super-Resolution Enhancement Dataset for Lining Surface Cracks  
In the study of super-resolution enhancement for the detected crack image regions, the open-source image super-resolution dataset DIV2K [49] was first used to pretrain the Real-ESRAN model. This dataset contains 800 high-resolution images from various scenes and subjects, with resolutions up to 2048 \(\times\) 1080. The dataset also provides corresponding low-resolution images. When the pretraining method is used, the performances of the generator and discriminator models can be improved, enabling them to generate high-quality, high-resolution images from low-resolution images. The pretrained model is then used for transfer learning to further enhance the super-resolution restoration effect of the crack image regions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Facilities** & **Bolt Hole** & **Joint** & **Crack** & **Exfoliation** & **Leakage** & **Image** \\ \hline train & 5403 & 4676 & 1279 & 1367 & 891 & 463 & 1784 \\ valid & 786 & 624 & 248 & 233 & 155 & 96 & 254 \\ test & 1476 & 1335 & 404 & 407 & 256 & 121 & 509 \\ all & 7665 & 6635 & 1931 & 2007 & 1302 & 680 & 2547 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Division of the dataset.  
Figure 9: (**a**) Data acquisition equipment, (**b**) field experiments, (**c**) apparent grayscale image of metro tunnel lining.  
An open-source concrete-crack dataset was used to train the super-resolution model, especially since the apparent surface of metro tunnel linings is composed of concrete. The dataset used is Aft_Original_Crack_DataSet_Second [50], which contains 1625 high-resolution images of cracks on concrete backgrounds. After the image dataset was converted to grayscale, second-order degradation operations (e.g., blurring, down sampling, noise, and JPEG compression) were applied to obtain an equal number of low-resolution images (Figure 10). These images were used together as the model training dataset to learn the texture features of cracks. The crack morphology and detailed features were transferred to the image super-resolution model for detailed restoration of the crack areas.
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
## 2 Theoretical Analysis  
We break down the theoretical analysis into two sections in this paragraph. In Section 2.1, we provide a comprehensive formulation of the mathematical model of the modeling using the bidirectional atmospheric turbulence transmission channel, and in Section 2.2, we meticulously describe the flow of the proposed adaptive power transmission technique based on the reciprocity of bidirectional atmospheric channels.
## 2 Materials and Methods
### Characterization of BC Samples  
BCs were characterized using different techniques. The surface morphologies of as-prepared BC samples were observed via SEM (JEOL, JSM-6360 LV; Tokyo, Japan), and their elemental percentages [including carbon (C), nitrogen (N), oxygen (O) and sulfur (S)] were analyzed using energy dispersive X-ray spectroscopy (EDS). In order to explain the changes of CPP200 pre- and post- adsorption of MB, the CPP200 after MB adsorption was also analyzed via SEM and element mapping. An FT-IR (Model: Shimadzu IR Prestige 21; Japan) spectrophotometer within the wavenumber range 4000-400 cm\({}^{-1}\) and a resolution of 4 cm\({}^{-1}\) was used to observe all functional groups of BC samples as well as the changes in functional groups of CPP200 post-adsorption of various dyes. The specific surface areas (SSAs) of CPP200 and CPP500 were examined using a Kubo X1000 automated surface area analyzer (Beijing Builder Electronic Technology Co., Ltd., Beijing, China); the test accuracy of the instrument was \(\pm\)1% (\(\leq\)1 m\({}^{2}\)/g samples: \(\pm\)1.5%). In order to further confirm the chemical composition of CPP200, X-ray photoelectron spectroscopic (XPS) analysis was carried out on a Perkin Elmer PHI 5000 C ESCA instrument with Al K\(\alpha\) radiation operated at 250 W (Perkin-Elmer, Norwalk, CT, USA). The cation exchange capacity (CEC) of CPP200 was determined according to a simplified forced-exchange method [28]. Briefly, 100.0 mg of BC sample was added into a conical flask; then, 20.0 mL of 0.25 M BaCl\({}_{2}\) was added in. The mixture was oscillated in a shaker at 200 rpm for 2 h and then filtered. The concentrations of exchangeable ions (Na, Mg, K, Ca, Al, Mn, Zn) in the filtrate were detected usinginductively coupled plasma-optical emission spectrometry (ICP-OES). CEC was calculated by the sum of all cations in the BC sample (\(\rm{cmol}_{c}\)/g).  
Figure 1: SEM images of BC samples: (**A**) CPP200; (**B**) CPP250; (**C**) CPP300; (**D**) CPP350; (**E**) CPP400; (**F**) CPP450; (**G**) CPP500; (**H**) CPP200, post-adsorption of MB.
## 3 WebAssembly and IoT
### Practical Applications of WebAssembly in IoT
Additionally, Wasm enables the implementation of access control policies, ensuring that only authorized modules can be executed on the gateway and granting specific permissions based on the module's trust level. * **Gateway Management and Orchestration** Wasm can be leveraged in the management and orchestration of IoT gateways. Thanks to its modular and lightweight nature, Wasm modules can be used to implement gateway management functionalities such as configuration management, firmware updates, and remote monitoring. These modules can be dynamically loaded, enabling flexible and efficient gateway management without the need for complex software updates or downtime. * **Offline Operation and Local Decision Making** IoT gateways often operate in environments with intermittent or unreliable internet connectivity. Wasm enables the gateway to operate offline by executing critical logic and decision-making processes locally. By running Wasm modules on the gateway, it can continue to function autonomously even when disconnected from the cloud or experiencing network disruptions. This ensures that essential operations such as real-time control, event processing, and local data analytics can be performed without relying on constant internet connectivity. * **Intelligent Edge Analytics** Wasm allows IoT gateways to perform advanced analytics and machine learning tasks at the edge. By executing Wasm modules that incorporate pre-trained models or algorithms, the gateway can analyze sensor data in real time, identify patterns, detect anomalies, and derive valuable insights locally. This enables timely and intelligent decision-making at the edge, reducing the need to transmit raw data to the cloud for analysis and enabling faster response times for critical applications. * **Scalability and Flexibility** Wasm offers scalability and flexibility in IoT gateway deployments. The modular nature of Wasm allows developers to build and deploy custom functionalities and services as lightweight modules. These modules can be dynamically loaded or unloaded on the gateway, enabling easy customization and scalability based on specific use cases or changing requirements. Wasm's ability to run across different hardware architectures and operating systems further enhances the flexibility and compatibility of IoT gateway deployments. * **Over-the-Air Updates** Wasm simplifies the process of updating IoT gateway applications and functionalities. By delivering optimized Wasm binary modules, developers can perform over-the-air updates on the gateway, allowing seamless deployment of new features, bug fixes, and security patches without interrupting device operation. This ensures that the gateway remains up-to-date with the latest capabilities and improvements, enhancing the overall performance and security of the IoT ecosystem. Table 2 presents a comparison of practical applications in the context of Wasm in IoT.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 4 System Analysis under Double Constraints
### Analysis of Threads in Coarse Registration  
This part discusses the relationship between the thread and resources in coarse registration. Coarse registration requires two-dimensional FFT and IFFT operations. The FFT IP core that Xilinx provided consumed 48 DSPs. A total of 6 FFTs were needed to finish parallel processing. As the thread increased, the usage of FFT increased by double. Due to the limitation of the dual-channel DDR3 storage structure and the data width of 512 bits, the maximum thread of the coarse registration was 4. Similarly, time could be calculated according to the working clock. Resources and time are shown in Table 4.  
When the thread of interferogram generation was 4 (scheme \(c\)), the ideal time \(T_{g}\) was 167.77 ms. When the thread was 2 (scheme \(b\)), the \(T_{g}\) was 343.40 ms. As shown in Figure 6,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Thread** & \(R_{i}\) & \(R_{\text{DSP}}\) & \(R_{i}\)/\(R_{\text{DSP}}\) (\%) & \(T_{c}\) (ms) & \(T_{g}\) (ms) & \(T_{i}\) (ms) \\ \hline \(d\) & 1 & 288 & 3600 & 8 & 2.62 & 167.77 & 175.63 \\ \(e\) & 2 & 576 & 3600 & 16 & 1.31 & 167.77 & 171.70 \\ \(f\) & 4 & 1152 & 3600 & 32 & 0.66 & 167.77 & 169.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Theoretical resource usage and calculation time with different threads in coarse registration.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Thread** & \(R_{i}\) & \(R_{\text{LUT}}\) & \(R_{i}\)/\(R_{\text{LUT}}\) (\%) & \(T_{c}\) (ms) & \(T_{g}\) (ms) & \(T_{i}\) (ms) \\ \hline \(a\) & 1 & 10,477 & 433,200 & 2.42 & 7.86 & 671.09 & 678.95 \\ \(b\) & 2 & 20,914 & 433,200 & 4.83 & 7.86 & 335.54 & 343.40 \\ \(c\) & 4 & 41,723 & 433,200 & 9.63 & 7.86 & 167.77 & 175.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resource usage and theoretical calculation time in interferogram generation with different threads.  
\(\Delta c\_xx\) and \(\Delta b\_xx\) in the figure represented the results when the threads of interferogram generation were 4 and 2, respectively. \(\
abla G\) of \(\Delta c\_fe\) was over 12, which meant that many resources were used, but the time improvement effect was very small. So, it could be seen that scheme \(e\) was more balanced than \(f\). If scheme \(e\) was used as the initial state to analyze, \(\Delta c\_ed\) was greater than 3, which showed that scheme \(d\) was more balanced than scheme e. In other words, when the thread of interferogram generation was 4, the scheme with 1 thread in coarse registration was the balanced design.  
Similarly, under scheme \(b\), \(\
abla G\) was nearly doubled since the overall system processing time was almost doubled. It could be seen from \(\Delta b\_fd\) that reducing the use of resources by 24% only deteriorated the system processing time by less than 2%. So, as the processing time of the system increased, the scheme with 1 thread in coarse registration (scheme \(d\)) was still the balanced design among other schemes.
# Instability Risk Assessment for Deep Excavation of Soil-Rock Combinations Containing Groundwater
## 3 Case Study
### Exavation Risk Assessment
#### 3.2.1 Evaluation Index System for Excavation Instability Risk
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Evaluation Index** & & **I** & **II** & **III** & **IV** \\ \hline Soil stress C\({}_{11}\) & MPa & \(\leq\)6 & 6 & 15 & 24 \\ Microseismic grade C\({}_{12}\) & grade & \(\leq\)3 & 4 & 5 & \(\geq\)6 \\ Weak rock thickness C\({}_{13}\) & m & \(<\)6 & 6\(-\)12 & 12\(-\)18 & \(>\)18 \\ Microseismic frequency C\({}_{14}\) & grade & weak & medium & comparatively strong & strong \\ \hline Thickness of overburden C\({}_{21}\) & mm & \(<\)2 & 2\(-\)5 & 5\(-\)8 & \(>\)8 \\ Deformation of neighboring buildings C\({}_{22}\) & mm & \(<\)10 & 10\(-\)14 & 14\(-\)18 & \(>\)18 \\ Surface settlement C\({}_{23}\) & mm & \(<\)12 & 12\(-\)18 & 18\(-\)24 & \(>\)24 \\ Water table C\({}_{24}\) & mm & \(<\)500 & 500\(-\)1000 & 1000\(-\)1500 & \(>\)1500 \\ \hline Vertical deformation C\({}_{31}\) & mm & \(<\)18 & 18\(-\)24 & 24\(-\)30 & \(>\)30 \\ Transverse deformation C\({}_{32}\) & mm & \(<\)12 & 12\(-\)17 & 17\(-\)25 & \(>\)25 \\ Anchor cable axial force C\({}_{33}\) & MPa & \(<\)216 & 216\(-\)243 & 243\(-\)270 & \(>\)270 \\ \hline \end{tabular}
\end{table} 
Table 5: Risk evaluation index system.
# Rapid Parametric Modeling and Robust Analysis for the Hypersonic Ascent Based on Gap Metrics
## 4 Climbing Command and Track Controller Design
### Track Controller Design  
The control commands are identified to reach the desired cruise states. Furthermore, the linear time-invariant system of hypersonic vehicles can be considered as  
\[\left[\begin{array}{c}\dot{\mathbf{x}}_{\rm{P}}\\ \dot{\mathbf{x}}_{\rm{e}}\end{array}\right]=\left[\begin{array}{cc}A_{\rm{P}}& \mathbf{0}\\ -C_{\rm{P}}&\mathbf{0}\end{array}\right]\left[\begin{array}{c}\mathbf{x}_{\rm{P}}\\ \mathbf{x}_{  
Furthermore, the control law for Equations (34) and (35) is provided as  
\[\mathbf{u}=\mathbf{u}_{\text{trim}}+\Delta\mathbf{u}=\mathbf{u}_{\text{trim}}-\mathbf{K}_{x}\mathbf{x}- \mathbf{K}_{z}\mathbf{r} \tag{36}\]  
where \(\mathbf{u}_{\text{trim}}\) is the trim control inputs, and  
\[\mathbf{K}_{x}=\mathbf{R}^{-1}\mathbf{B}^{\text{T}}\mathbf{P} \tag{37}\]  
\[\mathbf{K}_{z}=\mathbf{R}^{-1}\mathbf{B}^{\text{T}}\Big{(}\mathbf{P}\mathbf{B}\mathbf{R}^{-1}\mathbf{B}^{ \text{T}}-\mathbf{A}^{\text{T}}\Big{)}^{-1}\Big{(}\mathbf{H}^{\text{T}}\mathbf{Q}\mathbf{M}+\bm {P}\mathbf{G}\Big{)} \tag{38}\]  
For Equation (36), the control gains can be determined by selecting \(\mathbf{Q}\) and \(\mathbf{R}\) to make the track errors close to zero. Hereto, we design an optimal controller for ascent tracking, whereas the robustness of the closed-loop system should be further discussed.  
The normalized coprime robust stability margin (NCSM), the gap metric stability margin, indicates closed-loop robustness to unstructured perturbations. Values of NCSM greater than 0.3 generally indicate good robustness margins. Furthermore, the open-loop dynamics vary dramatically due to trajectory flight. The gap metric has been introduced to perform LPV modeling. Moreover, the gap metric can also be applied to quantified open-loop model differences.  
Let \(\mathbf{A}_{i}\) denote the open-loop system of the \(i\)-th plant and \(\mathbf{K}_{i}\) denote the control parameters obtained based on \(\mathbf{A}_{i}\). The gap metric between the \(i\)-th plant and \(j\)-th plant is \(\
u(i,j)\), and the NCSM for the \(i\)-th plant with \(j\)-th controller is \(\mu(i,j)\). If \(\mu(i,j)>\
u(i,j)\), we can deduce the closed-loop system process robustness to deal with the varying of the plant. Herein, we assume that the control parameters are designed for the \(i\)-plant and remain constant for the other operating conditions. The gap margin is defined as the margin between the NCSM and the gap metric.  
\[\
u_{\text{m}}(i,j)=\mu(i,j)-\
u(i,j) \tag{39}\]  
Generally, the initial controller parameters are obtained at the initial point of ascent. The plant dynamics vary as the trajectory tracking flight. If we keep the controller parameters constant, \(\mu(i,j)\) decreases with an increase in \(\
u(i,j)\). Hence, we can locate the flight conditions where the gap margin vanishes to evaluate the closed-loop robustness.