esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Experiments Results and Discussion
### Establishment of Real-Time Experiment Platform  
The key to validation of the dynamical model and the designed controller is to establish a reliable real time control system which has the characteristics of rapid response speed, flexibility, and low hardware cost. MATLAB provides a real-time development environment to achieve system simulation and product rapid-prototype by adopting the RTW (Real-Time Workshop) toolbox. RTW can automatically transform the model into dynamic system model code running on the hardware to realize real-time simulation and control.  
xPC target environment adopts the Host-Target pattern. The Host is used for running Simulink model, compiling and generating executable code and then downloading it to the Target, it can also monitor and control the Target. The target is used for running executable code by installing highly optimized real-time kernel of 32-bit protected mode. The Host communicates with the Target using RS232 serial port or ethernet [32; 33]. The real-time control experiment platform based on RTW xPC target environment is shown in Figure 8.  
In the experiment, an Advantech PCL-726 analog output card is used for output of the control signal, and an MCX312 motion control card is used for collecting the position signal from the encoder. PLC-726 can be directly driven by the corresponding module in the I/O module library of xPC target. MCX312 isn't included in the module library and its driver must be written. In the target system, the device driver must be written using C MEX S function which is a kind of S function written using C. Figure 9 shows the display of the RTW real-time experiment platform.
## 3 The Semantic Social Network of Things Middleware (S2 NeTM)
### Service Discovery (SD)  
The SD component in the S2 NeTM is responsible for discovering and locating available services within the SIoT network. This component enables devices and users to discover the services offered by other devices and users in the SIoT network, and to advertise their own services. The SD component allows the S2 NeTM to provide automated discovery services, making it easier for devices and users to connect and collaborate.  
The SD component uses a variety of protocols and techniques to enable service discovery. These include the simple service discovery protocol (SSDP), the service location protocol (SLP), and the domain name system-service discovery (DNS-SD) protocol. These protocols provide a standard mechanism for service discovery in the SIoT network, ensuring that services can be discovered regardless of the device or platform being used.  
In addition to protocol-based service discovery, the S2 NeTM also supports semantic-based service discovery. This approach uses the semantic descriptions of services to enable more precise and context-aware service discovery. The service descriptions are based on the S2 NeTM ontology, which defines the concepts and relationships related to SIoT services.  
The service discovery component also supports dynamic service composition, which enables the automatic composition of services to create new and more complex services. The component uses semantic reasoning techniques to identify compatible services and create new service compositions based on the user's requirements and preferences. This approach enables the S2 NeTM to provide more tailored and personalized services to users, based on their specific needs and preferences.  
Therefore, the SD component is a critical component of the S2 NeTM, enabling the automated discovery, composition, and delivery of services in the SIoT network. It allows devices and users to seamlessly connect and collaborate, providing a more intelligent and interconnected system.
## 2 Data and Methods
### Methods
#### 2.2.3 Multilayer Denoising Autoencoder  
The autoencoder [46], as a type of feedforward neural network, aims to minimize the difference between its output and input values through the backpropagation algorithm. Typically, an autoencoder consists of an input layer, hidden layers, and an output layer, with the network structure depicted in Figure 2. The dimensions of the input and output layers are the same as the dimensions of the input data. The specific principles are represented by Equations (1) and (2):  
\[Z=f\theta(X)=\delta(WX+b) \tag{1}\]  
\[X^{\prime}=g\theta(Z)=\delta\big{(}W^{\prime}Z+b^{\prime}\big{)} \tag{2}\]  
In the equations, \(W\) represents the weight matrix, \(b\) represents the bias, and d is the activation function. Initially, data are passed through Equation (1) to obtain their hidden representation and then through Equation (2) to map them back to their original form. During the network training process, in order to better reconstruct the input data, the network [36] utilizes the learned effective features from the input data as the output of the hidden layer, which is subsequently used for reconstructing the input data. Therefore, in this study, the output of the hidden layer of the autoencoder network is utilized as the input data for the subsequent water quality inversion model.  
Due to the limitation of traditional autoencoders in effectively extracting information from input data containing noise, this study proposes a denoising autoencoder based on this concept. During the training process, Gaussian noise is added to the input data to train the encoder network to extract useful information from noisy data and identify and eliminate the noise present in the input data. Specifically, noise is first added to the input data to generate contaminated data samples, which are then fed into the autoencoder. The encoder gradually compresses the data through multiple hidden layers, extracting the main features that are insensitive to noise. The decoder then reconstructs the original data, which is close to being noise-free, based on these features. In this way, the autoencoder can eliminate noise from the input data and extract effective data information. The input data during the final network training process are represented as Equation (3).  
\[x^{\prime}=x+\varepsilon \tag{3}\]  
Figure 2: Network structure of the AE.  
In the equation, \(x\) represents the original input data, and \(\varepsilon\) represents Gaussian noise. Additionally, to enhance the model's capability of extracting effective features, this study opts to construct a multilayer denoising autoencoder, aiming to improve model performance. The final model network hyperparameters are presented in Table 2. In this study, the PyTorch library ([[https://pytorch.org/](https://pytorch.org/)]([https://pytorch.org/](https://pytorch.org/)) (accessed on 24 October 2024)) in Python 3.10 will be used to construct a multilayer denoising autoencoder model.
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
###### Abstract  
Girth weld cracking of long-distance oil and gas pipelines yields substantial harm to pipeline safety and may cause serious accidents. As of today, non-destructive testing has been one of the most common methods for predicting potential faults and ensuring safe operation. Classical pipeline non-destructive testing methods include magnetic flux leakage testing and the use of ultrasonic testing by electromagnetic acoustic transducers. However, they are incapable of identifying the defects in complex surfaces like girth welds. Magnetic flux leakage testing exhibits poor anti-interference abilities and low space resolution. Ultrasonic testing by electromagnetic acoustic transducers suffer from low conversion efficiency and poor signal quality. In order to overcome the disadvantages of conventional pipeline non-destructive testing methods, we propose an embedded eddy current testing system by leveraging image processing and neural networks. Hough transform and the contour extraction technique are employed to extract the characteristic features from the two-dimensional (2D) eddy current impedance image. Experiment results show that the system can effectively identify the girth weld defects, featuring an accuracy of up to 92%. The low power consumption and compactness of the proposed system makes it a great candidate for pipeline inner inspection.  
girth welds; eddy current NDT; image-processing; neural network +
Footnote †: journal: Applied Physics Letters
## 5 Method
Performance is evaluated using the following metrics:  
* Data delivery rate: the total number of received data messages divided by the total number of sent data messages;
* Data delay: the average time a data message takes to reach its destination;
* Control overhead: it includes IT-SDN control messages (flow request, flow setup, source routed flow setup, acknowledgment, neighbor discovery, controller discovery, and neighbor report messages), in addition to the AA approach control messages (statistical messages, calculated metrics messages, rescheduling request messages, rescheduling response messages, and new scheduling messages);
* Energy consumption: it is represented by the average energy consumed by the node;
* Control delivery rate: the total number of received control messages divided by the total number of sent control messages;
* Control delay: the average time a control message takes to reach its destination.
# AR in the Architecture Domain: State of the Art
## 4 AR Virtual Content
### Reality-Based 3D Models  
AR in the architecture domain is strongly connected to the digitization process (Figure 9). In the past 20 years, the development of 3D acquisition methodologies has allowed for an enormous increase in heritage digitization. The digitization activity is central in supporting the protection, maintenance, conservation, and promotion activities; It allows for the democratization and accessibility of much cultural heritage [61], encouraging digital heritage and digital humanities to meet [62].  
In the early 2000s, range-based systems were very limited in terms of sampling volume and scanning time, limiting their application largely to sculptal elements inside survey projects set out with topographic or photogrammetric techniques [63; 64; 65; 66]. In 2005, the introduction of CW-AM systems led to a drastic reduction in the survey time,  
Figure 8: Block diagram of the main features of the virtual content creation.  
Figure 9: Diagram of 3D digital content creation for AR application. The 3D modeling passages and surface normal control are highlighted in red.  
preserving a similar resolution, accuracy, and uncertainty [67], and allowing the first complex 3D acquisition. In the past decade, the advancements in the scanning domain have been concentrated mainly on the reduction of equipment dimensions. Moreover, new methodological approaches have experimented with a multiresolution integrated pipeline [68],and the generation of correct, reality-based models [69; 70].  
In the passive system domain, the use of ever more advanced and cheap digital cameras has been accompanied by the evolution of software for image analysis, supporting photogrammetric processing, and the construction of 3D models [71]. The introduction of these automatic or semi-automatic processes, such as Structure from Motion (SfM) in the computer vision field [72; 73], has gained these photogrammetric applications for the architectural survey and relative comparison works [74; 75; 76]. This trend, amplified in recent years by the increasing use of RPASs equipped with digital cameras [77], has supported the drive to build reality-based models referring to complex scenarios [78].  
Today, the massive integration of active and passive systems makes it possible to obtain ever more accurate and complete surveying campaigns, ensuring redundancy data for integration and validation activity, heading definitively towards a multiresolution approach. Triangular polygons characterize reality-based models. The surface normals are calculated during the meshing step, matching the relative RGB data with the vertices or the polygons. The final 3D model's reliability is dependent on accuracy and uncertainty concerning the acquisition instrument and the reverse modeling process applied [79]. In the end, the point clouds or polygonal models can be manually or automatically classified [80], depending on the ontologies present in the architecture detected and in the analysis process. This subdivision can be highlighted and managed separately during visualization in AR.
## 4 Cluster Formation Algorithm Evaluation
### Covering Domain  
Figure 8 illustrates the percentage of area that is covered by the random deployment in terms of node density. As it is shown in the figure, for covering 95% of the area, a dense deployment of 300 nodes is required. As the figure shows, the rate of increment of the covered area for low node densities is faster than for high node densities. This indicates that after a new node is added in a dense deployment, low new coverage area is obtained.  
For example, the first 100 nodes cover 75% of the field, but the next 100 nodes will only cover 15% of new area. The conclusion is that dense networks are able to cover high areas at the cost of high overlapping and sensing redundancy, but this overlapping can be used for improving reliability if nodes belonging to the same cluster work in a coordinated manner. Furthermore, the existence of obstacles produces a reduction of the sensing area because of FoV occlusion effect, [7]. So, employing dense networks of low-cost, low-resolution and low-power multimedia sensor nodes instead of sparse networks of high-power, high-resolution sensors (e.g., PTZ) will be more beneficial.  
Applications that are interested in multiple views will also benefit from this situation, since there will be several nodes monitoring the same area from several perspectives. Applications that are interested in detecting objects and are not interested in having an instantaneous multiple-view of the object may benefit from collaborative node processing in terms of energy savings. For the first set of applications clustering of nodes may serves as an indicator of triggering simultaneous multi-perspective pictures. For the second set of applications, clustering may serve as a baseline framework for collaborative node scheduling avoiding redundant sensing and processing and thus increasing network lifetime. Other applications that are interested in correlated data (e.g., Distributed Video Coding, DVC) may use clustering in order to exploit multi-view correlations to build joint encoders [24].  
Figure 7: The cluster size cumulative distribution function as a function of node densities (\(\gamma=0.5\)).
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 2 Related Work
### Image Translation in Medical Imaging  
Alternatively, GANs [13] have been used to generate realistic images that boost the performance of vision tasks, such as segmentation. Moreover, several studies show that GANs are able to synthesize high-quality images by learning the data distribution in order to augment the training data. Recently, [PERSON] et al. [30] used the pix2 pix [31] framework for ultrasound bone image generation and improved segmentation performance. [PERSON] et al. [32] used an unpaired CycleGAN-based approach for brain computed tomography (CT) image synthesis from magnetic resonance (MR) images with notable improvements for the task of cross modality-synthesis. Although image synthesis has been applied to various problems in the medical imaging domain [33; 34; 35], there are a few works that address catheter synthesis and segmentation in X-rays. A notable work is that of [PERSON] et al. [36]; the authors showed the feasibility of using synthetic data to segment catheters via a transfer learning approach. [PERSON] et al. [37] proposed simulated catheter data generation and then used recurrent neural networks to process multi-scale inputs for catheter segmentation. [PERSON] et al. [38] used similar approaches for synthesizing endotracheal (ET) tubes in chest X-ray images with CNNs used for the classification and segmentation of ET tubes. However, these approaches may fail to detect the catheter or tube due to the large domain shift between the simulated training images and actual X-ray test images.
# A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction
## 2 Materials and Methods
### Materials
#### 2.1.2 Data Processing  
Here, we first preprocessed the TEC data by filtering. In this paper, mean filtering is used to filter the data. In order to ensure that the filtered data waveform is close to the original data waveform and to achieve the effect of suppressing interference and supplementing missing data points, we may use the data in an appropriate time window to carry out mean filtering. For each TEC data, the three values before and after the TEC data and itself were taken, and their mean value was calculated as the filtered data.  
\[\text{TEC}_{i}^{f}=\left(\sum_{k=1}^{3}\text{TEC}_{i-k}+\text{TEC}_{i}+\sum_{k =1}^{3}\text{TEC}_{i+k}\right), \tag{1}\]  
where \(\text{TEC}_{i}^{f}\) is the filtered TEC, and the \(\text{TEC}_{i}\) is the original TEC. The processing in the following text is based on filtered TEC.  
After the mean filtering of the original data, the missing data in the data set was supplemented with the fitting data of cubic spline interpolation, and the scatter plot was drawn and connected to compare with the original data, as shown in Figure 2. It can be found that, compared with before denoising, the fluctuation amplitude of the data after denoising is reduced, supplementing the missing data points in the dataset.  
For F10.7, a scatter plot is drawn in time order and connected. TEC data and F10.7 were drawn on the same image, as shown in Figure 3a. Similarly, the same processing was performed on SSN, as shown in Figure 3b. It can be found that the overall variation of TEC data is strongly similar to the F10.7 data and SSN.  
Figure 2: Comparison between original and filtered TEC data.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 3 System Design and Components  
The QESIF is proposed to address the critical security challenges posed by quantum computing to IoT-based smart city systems. The QESIF is a lightweight, scalable, and adaptable architecture that integrates QKD with existing IoT infrastructures to provide end-to-end security. Unlike conventional cryptographic solutions, the QESIF leverages  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Limitation** & **QESIF’s Contribution** \\ \hline QRHE [10] & No entropy adaptation; lacks real-time anomaly detection & QESIF introduces entropy-weighted key generation and quantum-aware IDS for enhanced threat detection \\ \hline ML-QKD [12] & Focuses only on anomaly detection; lacks secure key lifecycle management & Combines ML-inspired quantum monitoring with entropy-weighted QKD key generation and session scoring \\ \hline QIoT Model [14] & Conceptual only; lacks implementation or protocol-level integration for IoT & Implements practical deployment over MQTT/CoAP with real-world datasets and IoT gateways \\ \hline QML-SIEM [15] & Centralized threat analytics but no device-level protection & Deploys lightweight edge-layer Q-IDS with real-time detection at device and gateway levels \\ \hline CR-QKD [17] & High power demand; not optimized for IoT devices & Introduces energy-normalized key rate function and adaptive hybrid communication mode \\ \hline HyQuSec [21] & Static protocol; no adaptability to channel conditions & QESIF uses hybrid protocol stack with dynamic switching based on QBER and key rate thresholds \\ \hline QuIDS [22] & Limited scalability and lacks energy efficiency metrics & QESIF delivers edge-integrated Q-IDS and energy-aware communication across constrained devices \\ \hline QLSN [23] & High computational cost and latency due to lattice operations & QESIF reduces latency using lightweight quantum key scoring and efficient entropy management \\ \hline QuantGAN [24] & Integrates quantum-enhanced risk analysis with GAN-based digital twins & Evaluation is limited to controlled IoT environments with restricted scalability. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing approaches and QESIF’s contributions.  
the physical principles of quantum mechanics for key exchange, ensuring provable security against eavesdropping. Furthermore, the QESIF combines a hybrid protocol stack that is capable of dynamically transitioning between quantum-secured and conventional communication channels based on prevailing network conditions and device constraints. Another novel component of this framework is the Q-IDS, an anomaly detection module that leverages quantum-level variations such as elevated QBER. The integration of quantum key generation, adaptive encryption mechanisms, and real-time threat surveillance collectively positions the QESIF as an advanced security architecture for large-scale urban IoT infrastructures.
## 3 The Control Strategy of FMSS for Maximum Consumption of DG
### The Objective Function for Consumption of DG Is Established
#### Objective Function Weight Coefficient Selection Method  
This paper combines the analytic hierarchy process and the entropy weight method to determine the index weight of the objective function in Equation (12).  
The analytic hierarchy process (AHP) is a multi-objective optimal weight coefficient allocation method that combines qualitative and quantitative analysis. Its advantage is that it can quantify the experienced judgments of decision makers, which is more practical in the case of complex target structures and the lack of necessary data [21]. The entropy weight method is an objective method to judge the weight coefficient of each index, and determine the weight of each index according to the variability of the index, and can make the value of each index into a dimensionless form.  
As AHP is based on expert experience and subjective scores, the influence of the expert's rich experiences on index weight cannot be ignored. However, the entropy weight method is an objective evaluation method, which calculates the weight according to the objective data, reflects the intensity of relative competition of the index data, and its influence on index weight cannot be ignored. Therefore, the combination of the analytic hierarchy process and the entropy weight method can make the final weight coefficient consider both subjective and objective factors, and unify the dimensions of each sub-function when determining the weight coefficient in this paper.  
Assuming the weight factor obtained by the analytic hierarchy process is \(\omega_{j}\) and the weight factor obtained by the entropy weight method is \(k_{j}\), then the calculation method of the comprehensive weight coefficient determined by combining the analytic hierarchy process and the entropy weight method is shown in (13).  
\[\lambda_{j}=\frac{\omega_{j}k_{j}}{\sum\limits_{j}^{m}\omega_{j}k_{j}} \tag{13}\]