esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 1 Introduction
### Organization of the Paper  
This paper is organized as follows. Section 2 highlights some information about IS development, architecture, and main attributes. Section 3 summarizes THz channel modeling used to assess wireless communication system performance. Section 4 shows some IS-aided THz applications. Section 5 includes a brief comparison with other emerging technologies in 6G networks, a discussion about hardware and cost considerations, open issues, highlights, future trends, and research directions related to IS, THz, and both. Lastly, Section 6 closes the paper with its conclusion, main contributions, and content summary.
# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
## 2 Related Work  
The selection of the order for SVD directly impacts the effectiveness of signal denoising. An excessively high-chosen order of the singular spectrum may inadvertently retain noise information in the filtered signal, thereby impeding the desired denoising outcome. Conversely, an excessively low-chosen order of the singular spectrum might mistakenly treat valid signal components as noise, leading to their unintentional removal during the filtering process. Traditional SVD-based noise reduction is typically carried out by directly examining changes in singular values, and the optimal denoising order corresponds to the first singular value that undergoes a substantial alteration. Subsequently, the denoising order is determined by selecting the singular value order associated with the initial significant change in difference values. Singular values beyond this chosen order are then set to zero, effectively achieving the denoising objective [14].  
Researchers have introduced various effective order selection methods. [PERSON] et al. proposed a method based on the singular value curvature spectrum [15]. [PERSON] et al. employed the SVD algorithm in conjunction with the principle of minimum mutual entropy to distinguish noise from the genuine signal with the aim of denoising [16]. [PERSON] et al. introduced an innovative denoising algorithm that combines segmented SVD with the lifting wavelet transform (LWT) based on the ensemble empirical modal decomposition (EEMD) [17]. [PERSON] et al. combined SVD with variational mode decomposition (VMD) to develop a novel denoising method [18]. In the work of [PERSON] et al., a novel denoising methodology was introduced, amalgamating intrinsic time scale decomposition (ITD) and permutation entropy (PE)-based dual noise reduction techniques with SVD [19].
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 6 Discussion
Additionally, because of the infeasibility of gathering a complete dataset of anomaly-free samples even in the most favorable circumstances, it is important that the detection techniques chosen to discover anomalies are capable to build the models from datasets which may include a certain amount of outliers (or abnormal samples), as is the case of the techniques used in this paper. The challenge here lies in being able to obtain data that can be considered anomaly-free with a high degree of certainty when the smart city's WSNs are already operating. New techniques or methodologies should be devised to solve this because otherwise there is no way that one can use any anomaly detection technique based on machine learning in this context. **Aggregation and definition of time windows. ** As we have seen in Figure 7, attacks have obvious effects when aggregating some variables in 4-h window intervals. Nonetheless, although this is practical to visually detect the attacks, it becomes a problem when training models. Using large time periods to aggregate data implies reducing the amount of samples that can be included in the datasets. Hence, this might become a problem due to a lack of samples to train, validate and test the models. In order to mitigate this problem, in addition to reducing the aggregation time intervals, analysts must consider using sliding windows (instead of fixed-interval windows as we did in our work), and also using mechanisms such as cross validation, as explained in Section 5.2.3, instead of creating different datasets for training and validation as it is being done in other scenarios with more available data.
## 2 Background and Literature Review
### SDN Model for NDN  
The division of the network control from the data plane is the fundamental feature of SDN. As illustrated in Figure 3, SDN designs are composed of three layers: the infrastructure layer, the control, and the application layer. On conventional networks, the traffic will be distributed or transmitted, preventing them from having a comprehensive overview of the whole network. On the other hand, because they have logically centralized control, the SDN controller will retain the global perspective of the network.  
Figure 2: Interest vs. data processing approach in NDN [19].
## 2 Materials and Methods
### Computation of Estimates of Probabilities of Correct and False SC Recognition  
The optimal SC detection threshold is found by estimating the probabilities of correct and false SC recognition. Let us consider an approximate scheme for calculating the indicated probabilities.  
Let us analyze a 1-year time series of 1-minute-sampled normalized data \(H_{N,rp}(T(i))\), \(i=1,\ldots,N_{f}\) using the sub-intervals \([i_{1l},\,i_{2l}]\), with \(i_{1l}=1+\,\Delta i_{0}\,\,(l-1)\), \(i_{2l}=\,\Delta i_{0}l\), \(l=1,\ldots,l_{f}\), and \(l_{f}\Delta i_{0}=N_{f}\). Next, we assume that \(N_{\text{SC}}\) real SC events occurred within these \(l_{f}\) intervals, and that \(N_{RDSC}\ \leq\ N_{\text{SC}}\) events have been identified with the method described above to be real. Let us assume that the number of local intervals for which there are no SC events is \(N_{\text{0 SC}}\) and the number of false recognitions turned out to be \(N_{FDSC}\). Then,  
\[\beta_{RD}^{\circ}=N_{RDSC}/N_{\text{SC}},\,\,\alpha_{FD}^{\circ}=N_{FDSC}/N_{ \text{0 SC}}. \tag{6}\]  
are the estimated probabilities for a correct and a false SC identification. Obviously the estimates of the probabilities \(\beta_{RD}^{\circ}\) (\(H_{M,0}\)) and \(\alpha_{FD}^{\circ}\) (\(H_{M,0}\)) (6) depend on the comparison threshold \(H_{N,0}\). The choice of the optimal threshold will be reduced to a conditional optimization problem. Let us introduce a limitation on the probability of false recognition \(\alpha_{0}\) and set \(H_{N,d,\,=}\left\{H_{N,d,\,=}:(0\leq\alpha_{FD}^{\circ}(H_{M,0})\leq\alpha_{0})\right\}\) as the value for which the probability of false detection is \(\leq\alpha_{FD}^{\circ}\). The optimal threshold is thus given by the conditional optimization problem  
\[H_{N,d,\,0}^{\circ}=\arg\{\max_{H_{N,d,0}\in H_{N,d,\,=}}\beta_{RD}^{\circ}(H_{ N,d,\,0})\}, \tag{7}\]  
which can be solved iteratively. Let \(H_{N,d,0 mx}H_{N,d,0 mm}\) be the maximum and minimum bounds of iterations within the maximum number of iteration steps \(m_{f}\). Discrete threshold values are then defined as follows: \(H_{N,d,0}(m)=H_{N,d,0 mm}+\,\Delta H_{N,d}(m-1)\), \(m=2,\ldots,m_{f}\), where the iteration step is \(\Delta H_{N,d}=(H_{N,d,0 mx}-H_{N,d,0 mm})/(m-1)\).
## 4 Results and Discussion
### Aerodynamic Damping
#### 4.3.1 Modal Responses in the Vacuum  
The non-aerodynamic damping \(\xi_{m}\) is calculated using the decay rates extracted from the time history of the response of the blade displacement in a vacuum. Obviously, this is closely related to the Rayleigh damping constants used to accelerate the convergence of the structure. Figure 9 shows the responses of the tip leading-edge displacement in a vacuum to different Rayleigh damping constants for the first bending modal excitation. The Rayleigh damping constants used in Figure 9a are defined by the frequency range of 0 to the first natural mode, which correspond to the larger \(\beta\). In Figure 9b, the constants are defined by frequency range of the first natural mode to the third natural mode, which correspond to the smaller \(\beta\). The amplitude of the excitation force is 0.15 N, which results in noticeable blade tip displacement, but does not plastically deform the blade. The maximum blade displacement due to the bending excitation is small enough (less than 1% of the chord) that the nonlinear aerodynamic effects is negligible.  
According to Figure 2, the response envelope for the blade vibrating in a vacuum is \(Ae^{(-\xi_{m}\omega_{n}(t-t_{0}))}+A_{\infty}\), where \(t_{0}\) and \(A_{\infty}\) are the offsets of time and amplitude. In conjunction with Figure 9, the non-aerodynamic damping of \(\xi_{m}\) is 0.03167 and 0.00729, which correspond to the Rayleigh damping constants of \(\alpha=0,\ \beta=1.77\times 10^{-5}\) and \(\alpha=4.49,\ \beta=3.67\times 10^{-6}\), respectively.  
The blade tip displacement in the vacuum for the second bending modal excitation corresponding to the Rayleigh damping constants of \(\alpha=0\) and \(\beta=6\times 10^{-6}\) is shown in Figure 10. The Rayleigh damping constants are defined by the frequency range of 0 to the second natural mode. Obviously, this is a multi-frequency response; the spectrum analysis is shown Figure 11. The effect of the rotation is taken into account by employing centripetal and Coriolis acceleration terms in the body force. The non-symmetrical nature of the response for the second bending excitation is due to the first bending mode still being present in the response. The response caused by second bending modal excitation is only obtained by filtering the signal. The filtered displacement response for only the second bending modal excitation is shown in Figure 12. The non-aerodynamic damping \(\xi_{m}\) achieved by the response envelope is 0.03374.  
Figure 9: Tip leading-edge displacement in a vacuum for the first modal excitation.
# Transfer Reinforcement Learning for Combinatorial Optimization Problems
## 2 Background
### Sequential Ordering Problem  
The sequential ordering problem is an NP-hard combinatorial optimization problem and a variation of the asymmetric traveling salesman problem (ATSP), with the addition of precedence constraints [39; 47]. Thus, as with the asymmetric TSP, the SOP also aims to find a Hamiltonian cycle of a minimum cost in a directed graph [39]. Thus, the search for the optimal solution to this problem becomes impractical, resulting in the need to apply algorithms to find the best approximate solutions [39].  
Consider that Figure 1 is a representation of an SOP problem. In this case, Figure 1a shows the graph that depicts this problem. Figure 1b shows the constraints of this problem. Assuming that the agent starts its trajectory at vertex \(A\), according to the problem constraint, some valid routes are \(A\to B\to C\to D\to E\to A\) or \(A\to E\to B\to C\to D\to A\). On the other hand, routes \(A\to C\to D\to B\to E\to A\) and \(A\to D\to E\to B\to C\to A\) are some invalid routes, as they do not respect the problem's precedence restriction.  
In the SOP, \(c_{ij}\) also represents the cost of the arc. When \(c_{ij}\geq 0\), this value will represent the cost of moving from city \(i\) to city \(j\). When \(c_{ij}=-1\), this value indicates that there is a precedence constraint. In this case, city \(j\) must precede city \(i\)[39].  
In [37], the authors presented a formulation for the SOP based on the TSP formulation in [59]. This adds the following restriction to the TSP formulation presented previously, according to Equation (7):  
\[c_{ij}\geq 0\lor c_{ij}=-1\wedge c_{ji}\geq 0(\forall i,j=1,\cdots,N) \tag{7}\]  
This new constraint aims to satisfy the existing order of precedence restriction in the SOP.
## 3 Security Certification of Services
### Model-Based Testing  
Model-based testing targets the representation and certification of complex Web Services. Services can be modeled as finite state automata and transition systems for the automatic generation of test cases, and the evaluation of correctness of tested services [10, 11]. [PERSON] _et al._[10], authors proposed Symbolic Transition System (STS) [12] as a suitable solution for the certification of complex Web services that involves communications, allowing the specification of typed variables, guards (_i.e._, constraints on state transitions), and actions (_i.e._, function calls).  
[PERSON] _et al._[9] proposed an extended version of STS suitable for generating tests based on container and service behavior. Specifically, Web Service Description Language (WSDL) specification is used for the creation of STS models. The definition of service interface, describing operations and input and output data models can be exploited to build a specific model describing three states for each described operation:* _initial state_, no inputs have been received;
* _intermediate state_, the inputs have been received but the outputs have not been produced yet;
* _final state_, the outputs have been generated and returned to the counterpart.  
The modeling can also be extended to the Web Services Conversation Language (WSCL) specification, where WS-Conversation policies and constraints are defined and applied. In particular, WSCL regulates the communications between users and services, which could be modeled in STS to take into consideration data exchanges [9]. Also, the WSCL specification can be extended to integrate implementation details describing the internal functioning of the services.  
Finally, the model can handle container certifications; STS service models can be extended to include interactions between the customer and the service, which are outside the service implementation but part of the security specification flow (e.g., key exchange for data integrity).  
A more lightweight approach was introduced by [PERSON] _et al._[13]. They model the behavior of the Web services and their communications employing constraints to automate and evaluate the correctness of the architecture and ultimately the reliability of the designed system (e.g., data validity and correctness). This approach can be extended to support a wide class of non-functional properties including security properties.
# Cognitive Radio Network Technology for IoT-Enabled Devices +
Footnote †: The exponential development of wireless applications has increased problems in the spectrum. The unlicensed frequency spectrum is becoming highly saturated, in order to support the conditions of new radio devices with increasing data rates. The spectrum that has previously been allocated is also unused. As a consequence of these developments, scientists have been putting a lot of effort into developing an approach to the issue of the limited spectrum that could make it possible to create a more effective utilization of it. As cognitive radio permits opportunistic use of the licensed spectrum in less crowded areas, it has been proposed as a solution to this challenge. This paper provides an overview of the cognitive radio environment, including a dynamic spectrum access strategy, as well as additional information on the cognitive capabilities operating in combination IoT communication technologies. We investigate the utilization of cognitive radio in the Internet of Things along with the significant role that cognitive radio plays in making the Internet of Things possible. Cognitive radio will provide a comprehensive examination of spectrum sensing, which will cover the many types of sensing, sensing that is based on machine learning, as well as open topics that still need to be addressed further in this sector. This research paper is written in a way that provides detailed instructions for the purpose of assisting new researchers in the area of Cognitive Radio Networks.
## 4 Failure Pattern and Analysis of the Energy Consumption
### Fragment Size Distributions and Crushing Energy Dissipation of Specimens  
The above sections explain the effect of rock crushing only from the perspective of energy release, while, in practical mining engineering, the problem of the particle size of crushed rock is a fundamental issue. Therefore, investigating the relationship between crushing size and energy dissipation has great significance. Representative specimens were selected from each group of crushed sandstone, and then the water-saturated fissured sandstone fragments under different strain rate impact loads were sieved by STSJ-4 digital high-frequency vibrating sieve machine according to 0.15, 0.3, 0.6, 1.18, 2.36, 4.75, 9.5, and 13.2 mm standard square hole gravel sieves. The crushed pieces were moved up and down the sieve and passed through the corresponding sieve holes according to their size. Subsequently, the retained fragments were weighed in each class of aperture sieve using a high-sensitivity electronic balance. The average particle size of the crushed sandstone was  
Figure 12: Variation in energy dissipation density with strain rate in water\(-\)saturated fissured sandstone specimens. calculated, which can aid in visualizing the degree of sandstone crushing; Equation (8), for the average particle size of the specimen crushing, is as follows:  
\[d_{s}=\frac{\sum r_{i}d_{i}}{\sum r_{i}} \tag{8}\]  
where \(d_{s}\) denotes the average crushed particle size of the specimen, \(d_{i}\) denotes the average size of the rock fragments retained in the different sizes of the vacuolar sieve, and \(r_{i}\) denotes the percentage of mass of the rock fragments corresponding to \(d_{i}\). The results of the fragment sieving tests after impact damage of the sandstone specimens are shown in Table 4:  
The relationship between the crushing average grain size of water-saturated fissured sandstone and the crushing energy dissipation density is presented in Figure 13.  
Figure 13 clearly shows that the crushing average particle size of water-saturated fissured sandstone after dynamic impact decreases gradually as the crushing energy dissipation density increases. This implies that the higher the energy absorption of the rock specimen, the higher the crushing energy dissipation during the deformation and destruction of the specimen, which leads to the formation of more cracks interacting with each other and cross-expanding, resulting in the rock specimen being broken into small fragments or even powder form. The smaller the crushing block scale of the sandstone specimen, the larger the crushing surface area per unit volume of rock, the larger the required specimen crushing energy dissipation, and, therefore, the more drastic the degree of crushing. The average particle size of specimen crushing is linearly and negatively correlated with the crushing energy dissipation density, reflecting a certain rate correlation; the fitting relationship is shown in Equation (9) with a positive correlation coefficient of 0.9981. \[d_{s}=10.593E_{V}{}^{2}-49.83E_{V}+36.3(R^{2}=0.9981) \tag{9}\]
## 3 Results and Discussions  
### Mechanical Properties  
The bending strength and tensile strength are the most important indicators revealing the mechanical property of SiCw/hydro-epoxy composites. The relationship between mechanical properties and SiCw content is shown in Figures 2 and 3. From Figure 2, it can be found that, with the increase in SiCw content, the bending strength of the SiCw/hydro-epoxy composites increases when the SiCw content is less than 12 wt %. The bending strength of the bulk specimen is 72.2 MPa, and the bending strength of sample with 12 wt % SiCw increases by 64.1% compared to the 0 wt %. Figure 3 reveals that the tensile strength of SiCw /hydro-epoxy composites increases first and then decreases with the increase in SiCw content. When the content of the SiCw content is less than 8 wt %, the tensile strength of the SiCw/hydro-epoxy composites increases linearly with the increase in SiCw content. However, the tensile strength increases significantly when the SiCw content rises to 12 wt %, and the tensile strength increases about 77.16% compared with the bulk specimen. However, the tensile strength decreases when the SiCw content increases to 16 wt %. This shows that the SiCw content should be such that the mechanical properties of the SiCw/hydro-epoxy composites are improved.  
Considering the effect of SiCw reinforcement, the bending strength of the composites will increase [29]. However, it was noted that the bending strength of the sample with 16 wt % SiCw decreased significantly; this is due to the proper amount of SiCw can not only strengthen the matrix but also make up the micro defects present in the pure epoxy resin, which can effectively transfer the stress, prevent the crack growth, and improve the mechanical properties of the pure epoxy resin.  
Figure 1: Shape memory model.  
However, too many SiCws will increase the viscosity of epoxy resin. It is easy to produce bubbles and other defects and destroy the continuous structure of epoxy resin matrix.
## 2 Background  
### Autoencoder  
Autoencoder, a particular type of deep learning network, is trained to replicate the input data. Anomaly detection, text generation, picture generation, image denoising, and digital communications are some areas where autoencoders outperform conventional engineering approaches in terms of accuracy and performance [20]. Autoencoder is an unsupervised learning traditionally consisting of two parts: encoder and decoder. The encoder compresses the data into a latent space vector, while the decoder tends to convert the latent space vector into the original input.  
If the input to an autoencoder is a vector, \(x\in\mathbb{R}^{D}\), then it will convert into another vector \(z\in\mathbb{R}^{D}\) as  
\[z=h(wx+b)\]  
where \(h\) is a transfer function f the encoder, \(w\) is a weight matrix, and \(b\) is a bias vector. Then, the decoder converts an estimate of the original input vector, \(x\), from the encoded representation, \(z\), as follows:  
\[x=h^{\prime}\big{(}w^{\prime}z+b^{\prime}\big{)}\]  
where the quotation marks represent the other layer. The autoencoder learns to recognize and replicate the \"standard\" or \"healthy\" condition of the structural signals. When new data are introduced, the autoencoder tries to reconstruct them based on its understanding of the normal state. If the new data reflect an undamaged state, the autoencoder should accurately recreate them, resulting in a low reconstruction error. If the signal is damaged, containing ambiguous terms, then the autoencoder's attempts to rebuild the data using what it has learned from the undamaged pattern will result in a more significant reconstruction error [21].
## 3 Results and Discussion
### Results of Tooth Segmentation
#### 3.2.3 Measurement Accuracy Results  
[PERSON] et al. [28] proposed an effective registration technique, which we used for the registration of the three data types. After the registration, we used Geomagic software (2012, [PERSON], [PERSON], Morrisville, NC, USA) to compare the distances between the data. We set the ground truth data as a mesh and the data segmented by each method as point clouds. The data segmented by each method were then sampled for a constant number of points. We classified the results into four tooth types, namely, incisor, canine, premolar, and molar, and measured the average mean  
Figure 21: Tooth pin model.  
Figure 22: (**a**) Data obtained through segmentation obtained using the proposed and (**b**) conventional methods.  
distance between the point cloud and the mesh. The table below presents the results (Table 3). The conventional method indicates the manual segmentation using the snake. The proposed method indicates segmentation by using GAN.  
The mean distance between the point cloud and the mesh for each method is displayed in Table 3. The above results show that the proposed method is approximately 0.004 mm more accurate than the conventional method, and this difference was significantly different (\(p=0.033\)).