esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 9 Conclusions  
In this paper, we proposed an optimal authentication scheme a through dual signature for the Internet of Medical Things. The proposed schemes achieve authentication while simultaneously providing data privacy, integrity, and nonrepudiation. We carried out a thorough performance analysis in comparison with the existing scheme, and the study reveals that our scheme is more efficient in terms of computational cost and communication overhead compared to the existing scheme. We have used the Scyther tool for formal verification of the newly designed scheme and the results show our scheme is secure and authentic. The limitation of this paper is that it utilized the genus 2 hyperelliptic curve, which still needs more computational power and will not be suitable for resource-hungry IoMT devices. In the future, we will design a more lightweight dual-signature scheme using the genus 3 hyperelliptic curve cryptography.  
**Author Contributions:** Conceptualization, Z.J., I.U., B.H., N.U.A. and M.A.K.; methodology, Z.J., I.U., M.A.K., P.L. and N.I.; software, I.U.; M.A.K. and P.L.; validation, Z.J., P.L. and I.U.; formal analysis, B.H., I.U. and M.A.K.; investigation, I.U., N.U.A., and M.A.K.; resources, P.L., N.U.A. and N.I.; data curation, M.A.K. and I.U.; writing--original draft preparation, B.H., I.U., M.A.K., P.L. and N.I.; writing--review and editing, M.A.K. and I.U.; visualization, P.L.; funds acquisitions, N.I.; supervision, I.U. and N.U.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scheme** & **Communication b/w IoMT and PDA** & **Communication b/w PDA and AP** & **Total** \\ \hline [PERSON] et al. [21] & \(2|q|=2\) * 160 = 320 & \(2|q|=2\) * 160 = 320 & 640 \\ \hline Proposed scheme & \(1|n|=80\) & \(1|n|=80\) & 160 \\ \hline Reductions & & 640 – 160/640 * 100 = 75\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of communication overhead (in bits).  
Figure 4: Comparison of the computation costs (in bits) of the proposed scheme to those of [PERSON] et al. [21].
## 2 Methodology
### Ground-Lining Interaction  
The ground spring for the ground-lining interaction is composed of the normal spring and the tangential one. The deformation characteristics of normal ground springs were determined based on the GRC, as shown in Figure 3. The shape of the GRC, which represents the non-linear characteristics of the ground, has been validated by the two-dimensional elasto-plastic finite element (FE) analysis [31]. Furthermore, the GRC has been applied to some similar targets successfully [36]. The GRC represents the normal ground-lining interaction from the active to the passive side, including ground self-stabilization.  
The GRC in the horizontal and vertical directions is formulated as follows:  
\[K_{\text{i}}(u_{\text{n}})=\left\{\begin{array}{lr}(K_{\text{i0}}-K_{\text{ i}\text{ min}})\text{tanh}\Big{(}\frac{a_{\text{i}}u_{\text{n}}}{K_{\text{i0}}-K_{\text{i }\text{ min}}}\Big{)}+K_{\text{i0}}&(u_{\text{n}}\leq 0)\\ (K_{\text{i0}}-K_{\text{i max}})\text{tanh}\Big{(}\frac{a_{\text{i}}u_{\text{ n}}}{K_{\text{i0}}-K_{\text{i max}}}\Big{)}+K_{\text{i0}}&(u_{\text{n}}\geq 0)\ (i=\text{h},\text{v})\end{array}\right. \tag{4}\]  
where \(K\) is the coefficient of earth pressure; subscripts \(h\) and \(v\) show the horizontal and vertical directions, respectively; and subscripts \(0\), \(\max\), and \(\min\) indicate \(K\) at rest and the upper and lower  
Figure 3: Ground reaction curves (GRC).  
limits of \(K\), respectively. Here, \(u_{\mathrm{n}}\) is the gap from the original excavated surface before excavation to the outer surface of the lining. It is noted that ground self-stabilization and the constant overcut can be represented using the gap instead of the displacement, as shown in Figure 4. \(a\) is the normalized subgrade reaction coefficient \(k\) by the initial overburden load at spring line \(\sigma_{\mathrm{v0}}\) at \(u_{\mathrm{n}}=0\). The coefficient of earth pressure in the normal direction, \(K_{\mathrm{n}}\), can be interpolated between \(K_{\mathrm{h}}\) and \(K_{\mathrm{v}}\) by the angle measured from the crown to the considered point (Figure 1b). Then, the normal earth pressure acting on the lining, \(\sigma_{\mathrm{n}}\), can be obtained. The tangential ground spring, which transmits the load between ground and lining tangentially, was assumed to be linearly elastic.
# Contrastive Learning Based on Transformer for Hyperspectral Image Classification
Secondly, since 3D CNNs require much more computational resources than 2D CNNs, we used a transformer model instead of convolutional neural networks as the feature extractor. The transformer architecture is well-designed for effectively processing and analyzing sequential data. We only adjusted the model parameters, which had little impact on the model computation. In this article, we introduce bootstrap your own latent (BYOL), a state-of-the-art contrastive learning framework, to hyperspectral image classification using a transformer architecture. After training under the unsupervised condition, the transformer model can extract features from the input hyperspectral image. We use a SVM model to obtain excellent classification performance with a small percentage of features and corresponding labels for training. The contributions of our work are as follows:  
1. We introduce contrastive learning to extract hyperspectral image features in the unsupervised situation, which removes the manual labeling costs. As many contrastive methods rely on large amounts of negative samples to work well, which greatly reduces the training efficiency, we use BYOL as the contrastive learning framework. BYOL avoids the need for negative examples. Moreover, we adjust the data augmentation methods to make it suitable for hyperspectral image classification. 2. We introduce a vision transformer into unsupervised hyperspectral image classification. The transformer model does not have any convolution or recurrent units. The 3D CNNs for hyperspectral image classification need much more computational resources than 2D CNNs for computer vision, and fail to process sequential data well. We adjust the transformer model parameters to ensure the transformer models are suitable for hyperspectral image processing. Additionally, the transformer used for common computer vision contains at least 12 layers. We apply a two-layer transformer model in this paper, which can reduce the model size for better computational efficiency. The 12-layer transformer for computer vision has 86 million parameters, while our model has much fewer parameters. 3. We combine a contrastive learning method and a transformer model as the framework of our unsupervised model. Our proposed model performs better than traditional representative methods in hyperspectral image classification. We organize the remainder of the paper as follows: A brief overview of the contrastive learning and transformer model is presented in Section 2. Our proposed model is introduced in Section 3. In Section 4, we provide experimental descriptions and result analysis. Finally, the conclusions are described in Section 5.
###### Abstract  
Polystyrene-organo-montmorillonite (PS-OMMT) nanocomposite particles were prepared via emulsion polymerization of styrene in the presence of montmorillonite modified with an anionic surfactant, sodium lauryl sulfonate (SLS), and its tribological properties as an additive to polyalphaolefin (PAO) were tested. The results of Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA) showed that SLS molecules resided in the montmorillonite (MMT) interlayer space. The effects of OMMT on the morphology and properties of the nanocomposites were also investigated. Gel permeation chromatography (GPC) and dynamic light scattering (DLS) demonstrate that the presence of OMMT can effectively reduce the average molecular weight and average particle size of PS. XRD and transmission electron microscopy (TEM) of the PS-OMMT nanocomposites indicate that exfoliated and intercalated structures form and that the MMT layers either are partly embedded inside the PS particles or remain on their surface. Compared with pure PS, the PS-OMMT nanocomposites possessed higher stability to thermal decomposition and higher glass transition temperatures. Adding nanocomposite particles reduces the friction coefficient, and thus, the antiwear properties of the PAO are significantly improved. The PS-OMMT-3 (3 wt % of OMMT based on styrene) particles have the best tribological performance and maintained a stable, very low coefficient of friction of 0.09.  
Keywords:organolx; emulsion polymerization; nanoparticles; morphology; tribological property +
Footnote †: journal: Applied Sciences
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Wavelength and Propagation Direction Calculation of \(n\)th Ocean Waves
#### 2.5.1 Wavelength Calculation of \(n\)th Ocean Wave  
The \(n\)th wave wavelength \(\lambda_{p}^{n}\) can be calculated from its corresponding wavenumber, namely,  
\[\lambda_{p}^{n}=\frac{2\pi}{\sqrt{k_{ao}^{n\ 2}+k_{ro}^{n\ 2}}} \tag{10}\]
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.7 Selective Forwarding (Gray Hole)  
A selective forwarding attack (SFA) is a special type of black hole attack in which the compromised node drops some selective packets instead of all packets. Invaders drop packets containing critical information, such as military information, without noticing them or allowing others that may contain non-critical information to pass. This can lead to worse effects and a decrease in network efficiency [34; 35; 36; 37]. Selective forwarding attacks impact network performance and consume limited energy resources.
## 5 Experimental Results and Analyses
### Pre-Training Experiments  
The idea of transfer learning [33] has been widely used in various fields. Using transfer learning, the transfer and application of knowledge between different fields can be realized, helping people solve problems such as insufficient label data. In our scenario, due to the inconsistency of the node embedding vector space between different networks, the node features of another network cannot be directly used in the training process of this network. However, this barrier can be broken down by transfer learning; a similar graph neural network pre-training process has been applied in [29] with remarkable results. Therefore, we conducted similar pre-training experiments to verify whether the bridge node features learned in the network can help the training process in other networks. We trained the BND-GCN model on network CA-HepTh and fine-tuned the model on the other four networks. With the fine-tuned model, we can successfully implement bridge node detection tasks on different networks. Figure 5 shows the changes in Loss, Accuracy, and F1 score of the model during training when using the pre-trained model and the directly trained model.  
By comparing the loss curve of the model before and after pre-training, it is obvious that our pre-training process accelerates the convergence rate in the early stage of model training and plays a good role in initializing the model. Through transfer-learning technology, we can transfer the bridge node feature information learned on another network to this network for use. This is very meaningful where there are few label resources available.  
Figure 4: Effects of different Bridge-Percentage on five social networks.  
Figure 5: _Cont_.
## 3 Experimental Results  
This study investigates the relationship between patient-related parameters (BMI, weight and age) and CT dose indices CTDI\({}_{\text{vol}}\), DLP, ED and SSDE using regression and correlation analysis. Table 2 displays the mean values and ranges within two standard deviations of each of CTDI\({}_{\text{vol}}\), DLP, ED and SSDE for each CT exam considered in the study. The table also provides the number of statistical outlier cases, defined as cases outside two standard deviations, with a maximum of seven outliers for each of the CT exams. The variation in mean dose indices is primarily influenced by the CT exam parameters and the relatively small size of the dataset.  
In this study, linear regression and Pearson correlation analysis were utilized to analyze the relationship between BMI, weight and age (as input variables) and each of CTDI\({}_{\text{vol}}\), DLP, ED and SSDE (as output variables). Additionally, multiple linear regression and correlation analysis models were employed to examine the relationship between all patient parameters as input variables and each of the dose indices. The evaluation results for each of the dose indices CTDI\({}_{\text{vol}}\), DLP, ED and SSDE are summarized in Sections 3.1-3.4, respectively. Section 3.5 presents the plots for the linear regression models generated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CT Exam** & **Statistics** & **DLP** & **CTDI\({}_{\text{vol}}\)** & **ED** & **SSDE** \\  & **(in mGy-em)** & **(in mGy)** & **(in mSv)** & **(in mGy)** \\ \hline \multirow{4}{*}{Chest} & Mean & 316.76 & 9.03 & 3.32 & 8.53 \\ \cline{2-6}  & Range & 69–1013 & 3.01–28.73 & 0.82–15.05 & 3.59–25.3 \\ \cline{2-6}  & Std. & 197.16 & 5.42 & 2.12 & 3.7 \\ \cline{2-6}  & Number of Outliers & 3 & 4 & 2 & 2 \\ \hline \multirow{4}{*}{Cardiac Angiogram} & Mean & 654.1 & 37.1 & - & - \\ \cline{2-6}  & Range & 222–1494 & 13.4–71.8 & - & - \\ \cline{2-6}  & Std. & 241.1 & 11.5 & - & - \\ \cline{2-6}  & Number of Outliers & 5 & 7 & - & - \\ \hline \multirow{4}{*}{Cardiac Calcium Score} & Mean & 44.5 & 2.93 & - & - \\ \cline{2-6}  & Range & 16–121 & 1.1–7.8 & - & - \\ \cline{2-6}  & Std. & 22.62 & 1.44 & - & - \\ \cline{2-6}  & Number of Outliers & 5 & 7 & - & - \\ \hline \multirow{4}{*}{Abdomen/Pelvis} & Mean & 1156.4 & 27.5 & 11.53 & 22.71 \\ \cline{2-6}  & Range & 58–2750 & 2.96–65.51 & 2.7–32.72 & 9.9–43.72 \\ \cline{1-1} \cline{2-6}  & Std. & 660.5 & 14.39 & 6.23 & 7.2 \\ \cline{1-1} \cline{2-6}  & Number of Outliers & 3 & 1 & 3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dose indices’ parameter range for each CT exam.  
Section 3.6 includes the regression and correlation analysis evaluation for each pair of the dose indices.  
It is worth mentioning that polynomial regression models were investigated. However, it was found that the linear regression models outperformed the polynomial models. Therefore, the results from the polynomial regression analysis were not included in this study.
## 1 Introduction  
With the development of new technologies, it is often necessary to modify the materials employed in these technologies. This also applies to the construction industry. Specifically, in the case discussed in this manuscript, in the production of large profile concrete sewer pipes (diameter of 1 m and more). Until recently, construction applications relied on the use of straight pipes with separate elements (especially shafts) installed to change the direction of the main sewer lines. Although not very flexible, this system is still commonly used in most construction works to this day, especially when planning sewers through irregularly built-up areas [1; 2; 3; 4]. In case of this classic technology, the major elements, i.e., straight concrete pipes, are produced using the vibro-pressing compaction technology. One of its advantages is that the concrete produced in this way, even with a relatively low cement content, is structurally very compact, strong, and sufficiently resistant to those aggressive actions it is usually exposed to in the given area of application [5; 6].  
The new technology, in combination with unique software, allows the production of curved pipes according to customer requirements but at the cost of a necessary inclusion of self-compacting concrete (SCC) in the curved section of a pipe. Due to higher cement (generally binder) paste content, self-compacting concrete is generally less resistant to most aggressive actions than vibro press compacted (VPC) concrete used in the straight section of a pipe [6]. This fact led to the basic aim of the experiment: to design a SCC that would at least match standard VPC concrete in terms of mechanical properties and durability. This project task has been properly achieved in cooperation with the company Prefa Brno a.s. as part of a project No. TH02020871 (see Funding) and resulted in the development of a SCC with the required parameters (even in several variants). Within the project, two unique test devices were constructed and utilized with corresponding methodologies also being developed at the same time. Thanks to the granted project for the development of a technology to produce pipes with controlled curvature, as well as the materials used here, the development and testing of SCC for the curved part of pipes could be completed.  
Complex durability tests followed and a part of them--durability in the sulfate environment--constitutes the core contents of this manuscript.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 4 Short-Term Frequency Support of a DFIG
### Proposed Short-Term Frequency Support Method of a DFIG
#### 4.2.2 Power Reference in the Acceleration Period of Short-Term Frequency Support Method  
To restore the rotor speed of DFIGs, the conventional method in [12] decreases its output power by 0.03 p.u. in a step-like manner after the rotor speed converges to Point C. With various settings of the reduced power, the time of the rotor speed restoration and size of SFD become different. This means that the output power decrease of more than 0.03 p.u. is advantageous for fast rotor speed restoration; meanwhile, it might act as a severe SFD after decreasing \(P_{r\!rf}\) to Point C. For speeding up the rotor speed restoration with a small SFD, a large power reduction from Point C should smoothly decrease. To this end, \(P_{STFS}\) in the proposed method smoothly decreases during \(\Delta t\) from the locus Point C to Point D (see in Figure 4), as defined in Equation (14). From Point D, \(\omega_{r}\) returns to \(\omega_{0}\) along with the MPT curve, as in the conventional method.  
\[P_{STSF}=P_{MPT}+\Delta P(\omega_{C})\times\{-\frac{1}{\Delta t}[t-t(\omega_{C })]+1\}, \tag{14}\]  
where \(\Delta p(\omega_{C})\) is \(P_{STFS}(\omega_{c})-P_{MPT}(\omega_{c})\); \(t(\omega_{c})\) is the beginning of the acceleration period; and \(\Delta t\) is the duration from Point C to Point D (see in Figure 4a).  
As in Equation (14), the first part--which is cube of \(\omega_{r}\)--increases with \(\omega_{r}\); the second part decreases with time and decreases to zero at \(t(\omega_{c})+\Delta t\) (Point D in Figure 4a). Thus, the performance in terms of the duration for the rotor speed restoration and size of an SFD critically relies on the setting of \(\Delta t\). If the value of \(\Delta t\) is close to zero, the recovery of the rotor speed is significantly rapid, but it leads to a severe SFD because of the rapid output decrease from the trajectory Point C to Point D in Figure 4a, and vice versa. Special attention should be paid to setting the value of \(\Delta t\).  
In this study, as a comparison, the value of \(\Delta t\) is set to a value so that the second frequency deviation in the proposed method is the same as in the conventional method. Note that \(\Delta t\) can be set to other values depending on the design objectives; to minimize an SFD, \(\Delta t\) can be set to a value as large as possible or to accomplish the rapid rotor speed recovery, \(\Delta t\) can be set to zero. However, this is out of the scope of this study.
## 3 Results and Discussion
### Effect of Ultrafine Calcium Silicate on Permeability  
The permeability of cement stone has an important influence on the sealing of cement slurry [20], and the cement sample with low permeability has a stronger annular sealing ability. The permeability of the cement sample was measured, and the results are shown in Figure 3. The ultrafine calcium silicate can reduce the permeability of cement samples, and the effect is more obvious as the content of ultrafine calcium silicate increases. The initial permeability of cement stone is 0.0426 mD. When the addition of calcium silicate is 8%, the permeability of cement stone is the lowest, only 0.0092 mD, and the permeability decreases significantly. This is because the particles of ultrafine calcium silicate are small. On the one hand, it promotes the hydration of the cement slurry to form a dense cement stone, and on the other hand, it can effectively fill the internal pores of the cement stone, thereby reducing the permeability of the cement.  
Figure 3: Permeability of ultrafine calcium silicate cement paste.  
Figure 2: Thickening time and water loss of cement samples.
# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 2 Methods
### Numerical Simulations  
The software package ABAQUS (SIMULIA Inc, Providence, RI, USA) was used to build the finite element model (FEM) of the steel frame used in the vibration experiments (Figure 2). The density, elastic modulus, Poisson's ratio, and modal damping were 7800 kg/m\({}^{3}\), 212 GPa, 0.028, and 0.02, respectively. It was assumed that the structural damage level was proportional to the reduction of the elastic modulus, i.e., the damage of 50% was simulated by reducing the elastic modulus of the rod to its half.  
Each of the 355 rods was regarded as an element. To obtain the training samples for various damage scenarios studied in this paper, python scripts were used to automatically analyze the damage scenarios and extract vibration signals in batch in ABAQUS [29]. The specific steps included: 1. Use ABAQUS to establish the FEM and specify each rod material properties, then generate the input file of the intact structure. 2. Read the input file of the intact structure and modify the material property of the damaged rod to generate the input file for a damage scenario and submit it for FEA. 3. Extract response signals (accelerations) of the concerned rods under the intact state and various damage scenarios.
## 2 Literature Review
### Industrial IoT (IIoT) Networks  
Advances in intelligent, machine-to-machine communication have enabled flexible manufacturing in Industry 4.0. However, increased interconnectivity exposes industrial systems to cyber threats such as remote sabotage, risking operational disruptions and financial losses. Ensuring execution integrity and system reliability is thus critical [58]. Although DLTs like blockchain have been considered to address these risks, they often face challenges with scalability, interoperability, data privacy, and environmental impact [14; 59].  
The decentralized nature of Holochain ensures that IIoT systems can operate effectively even in environments with intermittent connectivity. Devices can continue to function autonomously, updating the network when a connection becomes available, thereby reducing vulnerabilities associated with centralized systems. This resilience is particularly beneficial for applications like predictive maintenance, where uptime and real-time monitoring are essential. Holochain's flexible and agent-centric architecture enables the development of interoperable applications that integrate with existing industrial systems, supporting a wide range of IIoT use cases such as remote monitoring, quality assurance, and SC optimization. Its scalable design efficiently handles the high volumes of real-time data generated within Industry 4.0 environments, while its lightweight, energy-efficient framework ensures low operational costs, making it a sustainable and cost-effective solution for modern industrial applications.