esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of Efficient 5G AKA Protocol for Light-Weight Environment
## 5 Conclusions  
The standardized 5G AKA protocol is vulnerable during authentication because the messages sent between the UE and SEAF are transmitted unencrypted. While 3 GPP places considerable emphasis on the privacy of subscribers, it was necessary to modify the 5G AKA protocol to compensate for the structural vulnerabilities that have been inherited. We also expect that if the protocol is inherited as it is in future 6G standard studies, it will have serious consequences as it inherits vulnerabilities arising from 5G. These standards should be improved in a way that can be operated in a fast and light-weight environment in consideration of the low-level device.  
To complement this vulnerability, we proposed an improved protocol that uses LEA for encryption the UE and the SN. In addition, instead of using AES algorithms recommended by existing standards, we speed up the protocol using LEAs that can be implemented in IoT in the future and can provide high speed in cloud environments [22]. In addition, LEA reduce the power consumed, it is efficient to use LEA in encryption and decryption section.  
Experiments demonstrate that using pkHN during encryption/decryption provides higher performance than protocols using conventional AES algorithms. We find the improved protocol valuable because it encrypts the information sent by plain-text and is increased the speed of the protocol and we can experiment with them directly using existing structures. In the experiment, we demonstrate an improvement in the speed of the encryption and decryption.  
The improved AKA protocol has been complemented the vulnerability and sped up the processing time. Using the LEA we speed up the processing time of the protocol and reduce consumed power. In addition, we don't change the framework or append the hardware equipment or logical structure, there is no burden on revising the standards. Since sustainable technology is important in the environment of the 5G and the next generation, we expect this protocol is effective.  
If we can continue our research, we want to design the protocol safe to address the MitM attack. In addition, we improve the encryption and decryption function to reduce the processing time. Further, we measure safety and speed even in multi-UE environments, and see how much higher performance, when implemented in cloud environments.  
C.Y. research paper for the 5G AKA protocol, S.C. design and experiment. [PERSON] supervised the study. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01343, Training Key Talents in Industrial Convergence Security).  
The funders had no role the study design; in the collection, analysis or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 2 Materials and Methods
### Reference MAR Networks  
To compare our iCTU-Net to other methods, we implement two deep learning MAR algorithms similar to those of [PERSON] et al. Both networks use pre-corrected NMAR inputs [17; 19]. One is based in the projection domain (U-Net Sino), and the other one in the image domain (U-Net Image). To ensure comparability, we use the same U-Net architecture in the iCTU-Net, U-Net Sino, and U-Net Image. In the U-Net Sino, the sinograms are first refined by a U-Net, and the result is then reconstructed using the FBP [17]. In the U-Net Image, the sinograms are first reconstructed with the conventional FBP and then refined with a U-Net [19]. These reference networks were chosen to allow a comparison of sinogram pre-processing, image post-processing, and reconstruction deep learning MAR techniques.  
The U-Net architecture is shown in Figure 1d and is similar to the original U-Net by [PERSON] et al. [29]. It has four en- and decoding blocks consisting of \(3\times 3\) convolutions, which are connected via skip connections. Zero-padding is used in the convolutions to ensure that the network output is the same size as the network input. The blocks of the top level have 32 channels, which are doubled with each encoding block until the lowest block has 512 channels. Downsampling in the contracting path is performed via \(2\times 2\) max-pooling with stride 2, while upsampling in the expansive path is accomplished using \(3\times 3\) transposed convolutions with stride 2. All convolutional layers are followed by a ReLU activation function.
# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 1 Introduction  
In recent years, point clouds have been researched in various fields, such as autonomous robot systems [1], three-dimensional (3D) face recognition [2], intelligence surveillance [3], and 3D modeling [4]. Unlike images captured by cameras, the point clouds are not likely to be influenced by lighting and color, and they maintain a high resolution. However, when using most existing neural network structures, point clouds are hard to process directly because of their irregularity.  
To solve this problem, researchers developed various methods to extract features from raw point clouds for existing deep learning structures, such as spin image (SI) [5], clustered viewpoint feature histogram [6], and view feature histogram [7]. As deep learning is gaining success in many fields, researchers also proposed or employed several deep learning structures for different tasks [8; 9; 10], e.g., 3D model retrieval. However, most deep learning structures contain a large number of parameters, which increases the time consumption of the whole system. It is also inconvenient to add new categories into a trained deep learning network or change the structure of a deep learning network, because thewhole network then has to be retrained. These problems make it difficult to apply deep learning methods in fields requiring real-time and massive data processing.  
Recently, the broad learning system (BLS) was developed and applied for image classification [11]. Compared to deep learning structures, the time consumption of BLS is decreased significantly, and there is no need to retrain the whole network upon changing the network structure. However, in BLS, it is difficult to directly process irregular point cloud data. Thus, a preprocessing of point cloud data is required to employ BLS in 3D object classification tasks. This paper proposes a 3D object classification system using BLS with a pretrained feature extractor for point cloud data. The pretrained feature extractor intends to change irregular point cloud data into regular features for BLS to process. Another goal of the feature extractor is to find more suitable descriptions for original objects, so as to improve classification accuracy. The proposed system was tested on the ModelNet40 dataset and achieved a recognition accuracy of 83.99%, while the time consumption for training and testing was about 30 s in total, which is far shorter than that of deep learning networks. Experimental results imply that the proposed method is applicable in the field of real-time processing of massive data, i.e., environment perception.  
The paper is organized as follows: Section 2 surveys related works in 3D object classification area; Section 3 introduces the proposed system; Section 4 describes the experiments and discusses the results; Section 5 gives the conclusion and the suggestions for future research.
## 1 Introduction
In recent years there have been a few attempts to develop weak adhesion detection in SLJ. Initial studies were conducted with simulated data to determine features and find the best visualization methods to detect this form of damage in [PERSON] et al. [13]. Further studies were conducted with simulated data and limited experimental validation such as in [PERSON] et al. [14], where machine learning was used together with LWs to detect damage using a limited number of classes, but no direct correlation to experimental data was used to increase accuracy nor was the damage limited to a small area of the SLJ and intensity found independent of its position. This paper proposes a new framework for the detection and classification of weak adhesion within SLJs through a fusion of LW data and machine learning techniques. The main contributions are as follows:  
* A finite element method (FEM)-based model of SLJs for properly modeling weak adhesion is developed. * An approach to validate the numerical model of SLJs subjected to weak adhesion using a small set of experimental data and machine learning algorithms is derived. * A machine learning algorithm for classifying weak adhesion based on simulated LWs data and neural networks is proposed, exhibiting remarkable potential in precisely predicting the extent of weak adhesion, and being robust even when the defect's location within the joint is altered. This paper is divided into sections. In Section 2, a numerical method is developed, able to generate LW data characterizing SLJs with different levels of weak adhesion. The numerical model is tuned and validated by comparing, via machine learning, the LW signals with those obtained from a real experimental setup. The validated model, being reliable, constitutes the basis upon which further algorithms are trained and tested for weak adhesion classification. In Section 3, large volumes of model-simulated data for multiple cases of weak adhesion are generated, and a suitable machine learning algorithm is developed, which is robust enough to detect the level of weak adhesion independent of where it is located within the SLJs. The results are reviewed and discussed. Finally, in Section 4, the main conclusions are drawn.
# Environmental Efficiency Indices in the Public Hospital Sector: A Proposal
## 3 Results and Discussion
### Global Efficiency Indices
#### 3.1.2 Environmental Efficiency Index per Category  
The results shown in the Table 4 present the global environmental efficiency indices calculated using the CML-IA methodology for each impact category based on 2019 data. In addition, the results obtained for each environmental aspect analysed in this study are included. For the index associated with the global warming potential for the year 2019, EEI\({}_{\text{GWPI100A}}\), a value of 6.22 \(\times\) 10\({}^{2}\) was obtained. All the environmental aspects analysed had an impact on this index; in particular, those related to transport, in addition to materials consumption and waste generation. [PERSON] [38] also noted the importance of transport in an analysis of the environmental impact of the hospital sector. On the other hand, [PERSON] et al. analysed the environmental impact of primary care in Switzerland, and also noted that 82.6% of CO\({}_{2}\) emissions were due to secondary activities necessary to provide primary care, including the transportation of employees and patients [39]. This showed how important it was to consider not only the environmental impact of the hospital itself, but also all the ancillary activities that were necessary to provide these services. From all the results obtained for the global environmental indices, transportation and materials consumption were the most significant aspects. Only in the cases of the EEI\({}_{\text{FWAE}}\) and EEI\({}_{\text{MAETP}}\) indices was it observed that the generated waste had a greater impact than materials consumption. [PERSON] et al. analysed different medical waste treatments and noted the relevance of heavy metal emissions in the FWAE category [40]. In particular, they highlighted the emissions of nickel and copper into the water, which can be generated both in their incineration process and during their relocation to a landfill. The most relevant finding of this work was that the environmental impact of transportation made the most significant contribution to the 11 indices per impact categories studied (Figure 4). In some cases, as with the EEI\({}_{\text{AD}}\), the sum of transport of employees and patients was responsible for over 90% of the global environmental index. The contribution of transport to the calculated global indices was very large due to the great intensity of trips that occur in a hospital on a regular day.
# Dynamic Dark Channel Prior Dehazing with Polarization
## 3 Experimental Setup
### Polarization Imaging System
#### 3.1.1 Optical Module  
A cascade prism is used as the beam splitter, which distributes the light from the scene to different detection channels [24], as shown in Figure 2a. The same type of CMOS camera is placed in each detection channel for imaging, and a polarization state analyzer is configured in each of them. Here, only the linearly polarized elements in the Stokes vector are considered, and therefore the orientation of the polarization axis of the polarization state analyzer in each detection channel is set to 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), respectively. The physical object of the cascaded beam splitter is shown in Figure 2b. Compared with the traditional discrete beam splitter shown in Figure 2c, the cascaded beam splitter is an integrated device that can effectively avoid the influence of the reflection phenomenon between the prism surfaces on the imaging process and facilitate the coaxial alignment of the prism.
## 6 RSS Difference-Aware Graph-Based Semi-Supervised Learning RSS Smoothing Method
### Estimation of \(\hat{R}_{d}(S_{i},S_{j})\)
#### 6.1.1 Offline Training Phase  
In the offline training phase, the coordinates of RPs \(S_{i}\) and \(S_{j}\), \(c_{i}\) and \(c_{j}\), are already given in the radio map and the location of AP \(c_{AP}\) can be calculated precisely using the CS-based method. Thus, the Euclidean distance \(d_{i}\) and \(d_{j}\) between the RPs and AP can be obtained. Finally, the RSS difference \(\hat{R}_{d}(S_{i},S_{j})\) can be calculated directly using (20).
## 2 System Model in the Absence of Non-Idealities
### Feasible SNR Region of a Multiple Access Scheme
The first one tries to make the system operational point lies on the bisector: in other words, \(\gamma_{1}=\gamma_{2}\) and, for this reason, this feedback power control strategy will be referred to as balanced SNR. The second selects the target SNRs so that the transmit energy consumption is minimized: in this case, it turns out that typically \(\gamma_{1}\
eq\gamma_{2}\) and, for this reason, this feedback power control strategy will be referred to as unbalanced SNR. In a general scenario with \(n\) sources, the balanced SNR feedback power control strategy will assume that all target SNRs are equal and properly select the common value, whereas the unbalanced SNR strategy will lead to different target SNRs over the links. Figure 2: Feasible region in a scenario with two sources and \(\rho=0.95\).
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 6 Open Issues and Challenges
To provide researchers with clear and direct insights for the development of underwater acoustic sensor network routing protocols, we show the development trends of UASN routing protocols in recent years, which can be found in Figure 6.  
In Figure 6, the \"T\" means the traditional non-cross-layer design routing protocol, the \"TC\" means the traditional cross-layer design routing protocol, and the \"IC\" means the intelligent algorithm-based routing protocol. From Figure 6, we can conclude the following development trends in UASN routing protocols: the traditional non-cross-layer design routing protocols were mainly used before 2010, while the traditional cross-layer design routing protocols developed from 2010 to 2014, and the intelligent algorithm-based routing protocols are being utilized since 2010. Figure 6 can give researchers a clear and direct viewpoint that in the future the most hot research areas in the field of underwater acoustic sensor network routing protocol design will be the cross-layer design methods and the intelligent algorithm-based methods. **Acknowledgments:** The research leading to the presented results has been undertaken within the SWARMs European project (Smart and Networking Underwater Robots in Cooperation Meshes), under Grant Agreement n. 662107-SWARMs-ECSEL-2014-1, partially supported by the ECSEL JU and the Spanish Ministry of Economy and Competitiveness (Ref: PCIN-2014-022-C02-02). **Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] wrote the paper. **Conflicts of Interest:** The authors declare no conflict of interest. Figure 6: The development trends of UASN routing protocols.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMC and PDSMC
#### 3.1.3 Low Pass Filter Characteristics  
It was mentioned in the previous chapter that PDSMC had low pass filter characteristics compared with SMC because the breaking frequency of PDSMC is smaller than SMC. It emphasized that during the sliding phase, when PDSMC was insensitive to high-frequency perturbation compared to conventional SMC. In Figure 8, the breaking frequency of SMC was 550 (rad/s, blue line), whereas the breaking frequency of PDSMC was 275 (rad/s, red line).  
To understand this phenomenon, the following scheme was made. External sine wave disturbances of different frequencies were introduced in the simulation, and the effect of this disturbance was observed on the sliding surface.  
Case 1  
An external sine wave whose frequency was 50 (rad/s) was introduced in the simulation. It was observed that this frequency was less than the breaking frequency of both controllers, so it affected the sliding surface of both controllers. Figure 9 shows the effect of disturbance on both sliding surfaces.  
Figure 8: Bode plot of sliding surface over perturbation.  
Figure 7: Sliding surface response of both schemes.  
Case 2  
The sine wave disturbance of frequency 300 (rad/s) was introduced since we know that the breaking frequency of SMC was 550 (rad/s) and PDSMC 275 (rad/s). This disturbance frequency was less than the breaking frequency of SMC, so it affected the SMC sliding surface whereas it did not affect PDSMC. In Figure 10, chattering is visible in the solid red line (SMC), whereas the sliding surface of PDSMC (dotted blue line) has no chattering during the sliding phase.  
Case 3  
In this case, disturbance frequency was greater than the breaking frequency of both controllers, so it did not affect the sliding surface of both schemes as shown in Figure 11.  
Figure 10: Effect of disturbance on sliding surface.  
Figure 9: Effect of disturbance on sliding surface.  
Table 3 below summarizes the whole phenomena.
## 2 Background and Existing Work
This literature discusses some key features (PTT values, PAT, PWV, PPG area, diastolic time, ECG time interval, and systolic time) to assess SBP, DBP, and mean absolute difference (MAD). The suggested models mentioned here are stepwise regression-based, and waveforms were collected using a multi-channel physiological instrument sample at 1 kHz within 10 mn. The mean error and the estimated STD during this experiment were under 6 \(\pm\) 6.5 mmHg for all of the BP. These values were derived from vital signals. However, vital information extraction is more difficult when signal morphology between samples varies by showing nonideal shapes. The feature selection can be automated by considering a full representation of the signals and making use of non-linear learning algorithms. While this approach shows better robustness for the variation in the shape of the signals, the resulting vectors are larger and require more samples for the model training, resulting in a huge computation. A significant number of researchers have developed techniques such as linear regression [27], CNN Wave-U-Net [28], supportive vector machines using a small real-world ECG, and PPG data [29]. And others developed artificial neural networks for predicting continuous BP by using only PPG with a multitaper method (MTM) for the feature extraction [30]. [PERSON] et al. even proposed BP derivation without the need for feature extraction using one-dimensional CNN and bidirectional long short-term memory (BiLSTM) network techniques. The raw PPG signal was directly fed into their proposed model after being preprocessed [31]  
The shortcoming of these techniques is that the signal-to-noise ratio (SNR) is low due to motion artifact. Therefore, it results in unreliable BP value estimation, which means the correlation between BP and PPG is still uncertain and more research needs to be performed for feature selection and representation of the signals.
# Recent Trends in AI-Based Intelligent Sensing
## 2 AI Methods for Intelligent Sensing
### Deep Learning Algorithms/Models in Intelligent Sensing  
Deep Learning is now dominating the industry and research spheres for the growth of a range of smart-world systems for good reasons. DL has shown considerable potential in approximating and reducing huge datasets into accurate predictive and transformational output, greatly facilitating human-centered smart systems. This section discusses deep learning models based on intelligent sensing. * Convolutional Neural Network--CNN is a robust supervised DL algorithm with better performance than other DL algorithms. IoT security is one of CNN's applications where the features of the security data can be automatically learned by the sensors [82]. Deep CNN-based learning is proposed in [83] to recognize human emotions using electrodermal activity (EDA) sensors. These devices capture emotional patterns from a group of persons. The paper [84] proposed a system that detects the physical activity of older people from wearable sensors. For rotation-invariant features, each feature triplet is extracted from the X, Y, and Z axes and reduced to one feature represented by a 3D vector. Other works similar to this also achieve high accuracy in the study of younger people. * Recurrent Neural Network--RNN is an important algorithm of DL in which present and past inputs depend on the output of the neural network. It is used to handle sequential inputs, which can be speech, text, or sensor data [85]. An RNN-based approach is discussed in [86], which is meant to interpolate sparse geomagnetic data from lost traces to reduce the time taken by linear interpolation approaches. The study in [87] discussed a mobile positioning method using RNN to analyze the strength of received signals. The authors experiment with the training of two RNNs separately