esa-sceva/satcom-chunk-collection huggingface dataset:
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 1 Introduction
In contrast, complex-valued CNN (CV-CNN) has the natural ability to process complex-valued signals and can simultaneously learn the coupling relationship between amplitude and phase, which provides a new method for high-precision defect reconstruction [23; 24; 25; 26]. Based on the above background, this study proposes a defect reconstruction method based on CV-CNN for ECT signal enhancement and the classification and identification of carbon steel materials. By acquiring the eddy current signals of carbon steel standard defects at different frequencies, the high-precision extraction and reconstruction of defect features are achieved. The experimental results show that the method has excellent classification performance and strong generalization ability in multi-category defect recognition. The main contributions of this paper are summarized as follows:  
* A 2D defect reconstruction method based on complex-valued eddy current signals is proposed, which effectively enhances defect visualization and sharpens defect boundaries. * A defect classification model based on CV-CNN is designed and implemented, achieving superior accuracy and robustness in classifying 16 defect categories, with an overall accuracy of 85.0%, significantly outperforming the complex-valued fully CNN (CV-FCNN) model. * The complex-valued convolutional architecture jointly models magnitude and phase information, with a specially introduced magnitude pooling strategy, improving the model's ability to distinguish similar defects and resist noise, thereby enhancing the reliability of practical industrial inspection. The application of the complex-valued neural network (CVNN) provides a novel perspective for eddy current signal processing. The remainder of this paper is organized as follows. Section 2 reviews the related work on complex-valued neural networks (CVNNs) and their applications in processing complex-valued signals. Section 3 describes the Materials and Methods, including the experimental setup, dataset, architecture, and training and evaluation details. Section 4 presents the Results, including signal measurement results, defect reconstruction, and performance evaluation using precision, recall, and the F1-score, along with a comparison against CV-FCNN. Section 5 discusses the experimental results, analyzes the limitations and applicable scope of this study, and reflects on its practical implications. Section 6 concludes the paper by summarizing the main contributions and outlining directions for future research.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 4 Privacy-Preserved Trustworthy Seamless Service Agility  
In this section, we propose a connection establishment for secure service porting between service providers to reduce high loads from other service providers in overpowering traffic situations. Such events can accrue due to heavy user activity such as big-game days and sale-days in shopping malls. Such feature can also be a game changer enabling emergency user porting in disaster situations and infrastructure failures or service shutdowns. We propose an authentication delegation drone UAV that offers verified mutual authentication services for UEs in need of connection establishment assistance. UAVs can provide the UEs with the eNB/gNB ID from cooperative service providers that will be able to accommodate it's connection needs.  
We consider the assigned UAV to possess the 5G core network delegation warrant for the assumed \(X\) service provider and other delegation parameters. The UE in question will send an authentication challenge to the \(D_{i}\) UAV asking for the valid signature message, then \(D_{i}\) replies with a valid proxy signature message. Once the UE validates the UAV, the UE sends an eNB/gNB ID request to regain communication stability. The UAV then replies with the nearest eNB/gNB ID for the most available service provider. We use public-private key data encryption for communication between the UAV and UE during the Guest eNB/gNB ID assignment in the guest network service porting process. No further communication is required between the UAV and the UE; such an assumption is  
Figure 2: The cooperative service provisioning model.  
convenient to both the UAV as a limited resources device and, at the same time, reduce the communication messages between the UAV and UE.  
Figure 3 shows the detailed proposed authentication model. In this work, we rely on KPW proxy signature scheme [30], the KPW model is a delegation warrant-based proxy signature where the proxy key pair depends on the proxy signer's private key. Therefore, the proxy signer's identity is protected using the node's authentic key pair \((x_{i},y_{i})\). The KPW is considered a robust proxy signature model since it identifies both the original signer's identity through a Delegation Warrant \(w_{i}\) signed by its private key and the proxy signer's identity through its private key. Once a proxy signer creates its unique proxy signature, it can never repudiate his/her signature as no other node can replicate it.
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
## 6 Conclusions  
The paper has proposed an ordered subspace clustering method for complex and various non-rigid motion via 3D reconstruction. In the proposed model, we reveal the sequential property and intrinsic structure of the complex and various non-rigid motion based on the reconstructed 3D information. Specially, inspired by QOSC and CNRMS, we formulate the block-diagonal structure and sequential constraint to 3D representation generated by CNRMS model so as to obtain good representation for clustering. We verified the proposed method OSC-NRSfM on three public datasets. The experimental results demonstrated that the proposed method of this paper outperforms state-of-the-art methods.  
Conceptualization, W.D. and Y.S.; Methodology, J.L.; Software, F.W. and W.D.; Validation, W.D., Y.S. and Y.H.; Formal analysis, J.L.; Investigation, F.W. and W.D.; Resources, Y.H.; Data curation, W.D.; Writing--original draft preparation, W.D., J.L. and F.W.; Writing--review and editing, Y.S., W.D. and Y.H.; Visualization, W.D.; Supervision, Y.S. and Y.H.; Project administration, J.L. and Y.H.; Funding acquisition, Y.S. and Y.H.  
This research was funded in part by the National Natural Science Foundation of China under Grant 61876012, 61672066, 61772049, 61602486, in part by the Beijing Educational Committee (KM201710005022).  
Figure 6: Clustering results of “\(p3-meet-2\)”.
# A Survey of Deep Learning for Data Caching in Edge Network
## 3 Deep Learning Outline
### Deep Reinforcement Learning (DRL)
#### 3.5.3 Actor-Critic Model  
Generally, compared with policy-based approach, the value-based method is less stable and suffer from poor convergence since the policy is derived based on \(Q^{\pi}(s,a)\) approximation. But a value-based method is more sample efficient, while a policy-based method is easier to fall into local optimal solution because the search space is vast. The actor-critic model combines these two approaches, i.e., the agent contains two DNNs named actor and critic, respectively. In each training iteration, the actor considers current state \(s\) and policy \(\pi\) for deciding action \(a\). Then, the environment changes to state \(s^{\prime}\) and returns reward \(r\). The critic updates its own parameters based on the feedback from the environment and output a mark for the actor's action. The actor updates policy \(\pi\) depending on the critic's mark. Some typical algorithms have been proposed recent years, like Deep Deterministic Policy Gradient (DDPG) [55] and Asynchronous Advantage Actor-Critic (A3C) [56], as Figure 6 illustrates.  
Similar to Double DQN, DDPG also utilizes the idea of target DNN and experience replay memory. In DQN, the action is determined via trained DNN \(Q\), while, in DDPG, the action is selected by actor DNN. Initially, target critic \(Q^{\prime}\) and actor \(P^{\prime}\) are the replicas of \(Q\) and \(P\), respectively. In each training step, actor \(P\) plays with the environment and store its experience in-memory buffer \(D\). The target actor \(P^{\prime}\) samples batches of experiences randomly and selects action \(a^{\prime}\). Then, the target critic \(Q^{\prime}\) evaluates the expected reward \(q^{\prime}\) on action \(a^{\prime}\). Meanwhile, the trained critic \(Q\) also estimates the expected reward \(q\) but is based on the recorded action \(a\). The critic \(Q\) and actor \(P\) are updated via square error and policy gradient, respectively, while the target network \(Q^{\prime}\) and actor \(P^{\prime}\) are also updated in a little progress.  
In the scheme of A3C, the DNN structure is similar to dueling DQN. A3C does not apply experience replay memory but employ asynchronous training method. As shown in the right side of Figure 6, there are \(n\) global network's replicas interacting with their environments and each local DNN (including the actor and the critic) is trained independently. During the training episode, the local DNN does not update its own parameters but renovate the global. The local model synchronizes global DNN after several steps. So, the global network is the model we want and these independent local models accelerate the training process.  
Figure 6: Deep Deterministic Policy Gradient (DDPG) and Asynchronous Advantage Actor-Critic (A3C).  
As a conclusion of this section, we summarize these illustrated DL schemes in Table 2 with their advantages and disadvantages, classified by their training methods and DNN architectures.
## 2 Antenna Geometry and Free Space Performance  
A single-sided printed monopole antenna is easier to integrate on the surface of the upper part of the footwear or above the textile strips of a garment, without the requirement of another layer. An antenna optimized for this purpose is prototyped on a 0.2 mm single-sided FR4 layer and shown in Figure 1a. For simplicity the antenna is fed by coplanar waveguide (CPW) which is connected to a SMA connector and optimized for on-body performance from 2.4 GHz to 10 GHz. The antenna size is 40 \(\times\) 50 mm\({}^{2}\). A transition in the CPW is required for matching due to the connector flange. The width of the feed line and slot line under the connector is 1 mm and 1.2 mm respectively. Beyond the connector, these become 3.3 mm and 0.2 mm respectively. The antenna is then shaped with splined lines, resulting from the on-body optimization. The ground plane shape is also optimized to increase the bandwidth of the antenna with the dimension GP\({}_{1}\) = 13 mm, D\({}_{1}\) = 1 mm and D\({}_{2}\) = 0.8 mm. The parameters of the splines are given in Table 1.  
The antenna free-space S\({}_{11}\) is shown in Figure 1b. The simulated bandwidth of the antenna is -10 dB matched across 2.1-10 GHz. In the measurement there is a shift of the lower -10 dB frequency of 177 MHz. The measured -10 dB bandwidth for the free space antenna is within the range of 2 GHz to 8 GHz. The small disagreement between simulation and measurement is due to an imperfectly flat dielectric layer and non-uniform distance between the printed copper feed line on the dielectric of the CPW and the flange of the connector. A photograph of the prototyped antenna is shown in Figure 1c.  
Figure 1: **(a)** Antenna geometry; **(b)** comparison of simulated and measured S\({}_{11}\) for the antenna in free space; **(c)** photo of the prototyped antenna.
## 2 Configuration and Pollutant Dispersion Modelling  
### Problem Configuration  
Figure 1 presents the geometry of the computational domain around the isolated cubical building of this study, with exactly the same dimensions as the SILSOE cube. The SILSOE cube field experiment for the study of the airflow around it [43], with a 6 m height cube placed in an open area, has been used as a test case in several other studies [44]. This geometry remains unchanged in all the different simulated scenarios of this work.  
The global coordinate system origin is the frontal left edge of the computational domain and the distance between the frontal area of the computational domain and the building is 5 H. The distance between the lateral boundaries of the computational domain and the building's surface is also 5 H [45]. The distance from the rear surface of the building up to the outlet boundary of the computational domain is set at 12 H and the total height of the computational domain is 5 H. The resulting blockage effect value is approximately 1.81%. Under the recommendations of the German Association of Engineers (VDI), the blockage effect should be maintained below 10% during the simulations [46] and according to [46; 47] should be even lower than 3%, which is satisfied in the present study.  
The Reynolds number is kept constant at 4.03 \(\times\) 10\({}^{6}\), based on the height of the cube and the free stream velocity. The specified flow field at the inlet of the computational domain is in the form of a logarithmic profile with the no-slip condition at the ground. A passive source of pollutants is located on the floor of the computational domain at a distance \(H\) from the rear surface of the building, using as a pollutant methane (CH\({}_{4}\)) gas with a concentration and release of 600 ppm and 18.5 L/h, respectively. The dimensionless concentration coefficient \(K\) of the passive pollutant is defined as [48; 49]:  
\[K=\frac{(c_{Measured}/c_{Source})u_{H}H^{2}}{Q_{Source}} \tag{1}\]  
where \(c_{Measured}\) is the tracer concentration at any position, \(c_{Source}\) is the tracer concentration on the pollutant source, \(Q_{Source}\) is the release rate of the pollutant, and \(u_{H}\) is the velocity of the flow at the height of the building.  
In Scenarios B and C, the temperature is affecting the flow field through the usual Boussinesq approximation. In these cases, the Grashof number, i.e., the ratio of thermal buoyancy forces over viscous forces is defined as [50]:  
\[Gr_{H}=\frac{g\beta\Big{(}T_{Surface}-T_{\infty}\Big{)}H^{3}}{v^{2}} \tag{2}\]  
Figure 1: Computational domain and boundary conditions.  
where \(T_{Surface}\) is the temperature on the surfaces of the building, \(T_{\infty}\) is the bulk temperature, \(\beta\) is the coefficient of thermal expansion, and \(v\) is the kinematic viscosity of the fluid. In Scenarios B and C, the Grashof number based on the height of the building is \(2.43\times 10^{11}\) and \(7.68\times 10^{11}\), respectively. Thus, the buoyancy forces are intensive and the boundary layers close to the heated surfaces are highly turbulent.
# Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning
## 4 Experimentation
### Dissimilarity Space Mapping  
Dissimilarity Space stands for the strategy of mapping a given structural representation onto a feature-based one by computing pairwise dissimilarities between the elements of the dataset [19]. By performing this process a new dataset is derived in which the new features constitute the actual dissimilarity values to a subset of the data usually known as pivots. This process enables the use of certain data transformations which are unfeasible, or at least not well defined, in the structural space but it usually entails a drop in terms of classification accuracy due to the loss of representation capabilities.  
Mathematically, let again \(\mathcal{T}\) be a labeled set of prototypes such that \(\mathcal{T}=\{(x_{i}y_{i}):x_{i}\in\mathcal{X},y_{i}\in\mathcal{Y}\}_{i=1}^{ |\mathcal{T}|}\), where \(\mathcal{X}\) and \(\mathcal{Y}\) denote a structural space and a set of discrete classes, respectively. In order to map the prototypes of \(\mathcal{T}\) onto a feature space \(\mathcal{F}\), Dissimilarity Space methods seek for a subset \(\mathcal{R}\) out of the training set (\(R\subseteq T\)) by following a certain policy. The elements of \(\mathcal{R}\), which constitute the aforementioned pivots, are noted as \(r_{i}\) with \(1\leq i\leq|\mathcal{R}|\). Then, a prototype \(x\in\mathcal{X}\) can be represented in \(\mathcal{F}\) as a set of features \((v_{1},v_{2},v_{3},...,v_{|\mathcal{R}|})\) computed as \(v_{i}=d(x,r_{i})\), where \(d:\mathcal{X}\times\mathcal{X}\rightarrow\mathbb{R}^{+}\) represents a dissimilarity function in space \(\mathcal{X}\). This way, an \(|\mathcal{R}|\)-dimensional vector is obtained for each prototype in the initial space.  
Among the existing policies for selecting the mapping pivots, in our experimentation we consider the use of the RandomC strategy [24]. This approach selects a random subset of prototypes from each class for the mapping process. The number of prototypes selected from each class is exactly \(c\) (tuning  
Figure 1: Description of the experimental setup considered. Dissimilarity Space is used for obtaining the feature-based representation of the initial string data. In both string and feature-based representations, classification is performed both before and after the RHC Data Reduction process.  
parameter), being thus the total number of pivots \(|R|=c|Y|\). In our experiments different values of parameter \(c\) are considered to compare its influence in the overall performance.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### Noise Modeling  
The ambient noise underwater is Gaussian, not white, and possesses an unremitting power spectral density (PSD). The greatest and most noticeable base of ambient noise is waves driven by wind, thermal, shipping, and turbulence. [PERSON]'s model [157; 159; 160] suggests a method to compute the PSD in dB re.\(\mu\)Pa per Hz for different sources of these noises. It is estimated by the following:  
\[10 logN_{t}(f)=17-30 logf \tag{22}\]  
\[10 logN_{sr}(f)=40+20(sr-0.5)+26 logf-60 log(f+0.03) \tag{23}\]  
\[10 logN_{w}(f)=50+7.5w^{1/2}+20 logf-40 log(f+0.4) \tag{24}\]  
\[10 logN_{th}=-15+20 logf \tag{25}\]  
where, \(N_{t}\) denotes turbulence noise, \(N_{sr}\) specifies the noise induced by shipping, \(sr\) is the factor of shipping ranging between 0 and 1, \(N_{w}\) denotes the noise from wind-driven waves (as w is the wind speed in m/s), and \(N_{th}\) infers the thermally induced noise. Each of the noises dominates in a specific frequency domain due to the colored nature of ambient noise in the water. Turbulence noise is more evident in the low-frequency domain whereas thermal noise is evident in the higher-frequency zone [148]. Wind noise has 100 Hz to 100 kHz frequency range, while noise from shipping has 10 Hz to 100 Hz frequency range. The different noise variances have been shown in Figure 15. The entire PSD noise \(N(f)\) is determined using the following equation:  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{26}\]  
Figure 15: Different noise variances in UWC.
## 5 Case Study  
### Case Study Presentation  
The case study focuses on a mid-sized financial services company that specializes in processing loan applications for small and medium-sized enterprises (SMEs). The organization manages an average of 1500 loan applications per month, each requiring meticulous review, cross-referencing with client documentation, and the validation of compliance with financial regulations. The primary challenge lies in the time-consuming and error-prone nature of these processes, which are critical for maintaining regulatory compliance and customer satisfaction.  
The organization's operational team consists of 12 employees, working in three shifts to ensure 24/7 coverage. Despite their efforts, the manual nature of the process has resulted in several challenges:  
1. Inefficiencies in processing time: * Each application takes an average of 2 h to process fully, leading to delays in meeting customer expectations. * Bottlenecks often occur during peak periods, such as the end of fiscal quarters.
2. High error rates: * Manual data entry errors lead to incorrect assessments or rejections of valid applications. * Approximately 8% of applications require rework due to inconsistencies in documentation validation.
3. Strained employee workloads: * Employees report high levels of stress due to repetitive tasks and the pressure to meet tight deadlines.  
* Turnover rates have increased, with 3 employees leaving in the past year.
* Lack of scalability:
* As the company expands, the current processes struggle to keep up with the growing volume of applications.
* Management has expressed concerns about the long-term sustainability of the current approach.  
To address these issues, the company identified the need for a Robotic Process Automation (RPA) system with integrated human-computer interaction (HCI). This solution would provide the following benefits:  
* Automate repetitive tasks such as data entry, document validation, and compliance checks.
* Provide employees with intuitive dashboards to monitor and intervene in processes as needed.
* Reduce processing times while maintaining high accuracy levels.
* Enhance employee satisfaction by allowing them to focus on higher-value tasks.  
The case study aims to achieve the following:  
1. Analyze the inefficiencies and challenges in the current manual processes.
2. Evaluate the impact of implementing FRIDA on the development and deployment of an RPA system.
3. Compare the performance metrics before and after the framework's implementation, focusing on the following aspects:  
* Processing time per application.
* Error rates and rework percentages.
* Scalability and adaptability of the automation solution.
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 4 Results
Figure 7 also shows that the proposed algorithm outperformed the previous algorithms for each bitstream length. Figure 5: Detection probability with (a) BCH(15,5) code, (b) BCH(15,7) code when \(S=30\). Figure 6: Detection probability with Hamming (7, 4) code when (a) \(S=28\), (b) \(S=42\). In addition, for a fair performance comparison with Multinomial [6] and MDS [9], additional experiments were conducted with the number of attempts to construct the rank distribution at 1000. BCH (15, 7) and BCH (15, 11) was used to show superiority of the proposed algorithm even at high code rates. Figure 8 shows that our method improved even more with increasing number of trials compared to other methods. As the code rate increases, the difference in performance from other algorithms has decreased, but it can be seen that it still outperforms the other algorithms in terms of detection probability. Lastly, we calculated the run time for complexity comparison. Table 2 represents the time it takes to estimate the interleaver period once when \(S=30\) with BCH (15, 11) code. The false alarm control process was omitted, and the average time was obtained by measuring the time taken to estimate the interleaver period 100 times for each algorithm. Except for MDS [9] and Multinomial [6], Bernoulli [4], KLD [5], and proposed method have the similar execution speed as represented in Table 2. It can be seen that Bernoulli, KLD, and the proposed algorithm have a high proportion of the computational complexity of Gauss elimination process in exploiting the rank distributions. Since Multinomial [6] calculates the probability that each event will occur and finds the lowest probable event, it takes more times to estimate the interleaver periods. We can see that MDS has a relatively high computational complexity because MDS adopted the method of using vectors with a high probability of low errors before comparing two distributions. Figure 8: Detection probability (**a**) BCH(15, 7) code with \(S=35\), (**b**) BCH(15, 11) code with \(S=35\). Figure 7: Results with Hamming (7, 4) code when \(S=35\) (**a**) the detection probability, (**b**) the false alarm probability according to the length of bistreams.