esa-sceva/satcom-chunk-collection huggingface dataset:
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 1 Introduction
To do so, firstly, we have used a simulation environment in a laboratory with real equipment. This environment has allowed us to simulate attacks to the network without compromising the normal operation of a real smart city. We have then used the data received from the sensors to compare and evaluate two widely known machine learning techniques (one-class support vector machines (OC-SVM) and isolation forests). This analysis has shown that it is possible indeed to use machine learning to implement anomaly detection systems for complex environments such as smart cities. Secondly, our work has shown that the use of these algorithms in this type of scenarios is complicated, and several things must be taken into account to avoid drawing wrong conclusions. Applying machine learning techniques is not as simple as calling a method of a specialised library. The complexity lies not only in the need to understand how to use a certain machine learning algorithm, but also in taking into account the difficulties associated with their application in a smart city context. In this paper, we present the difficulties posed by the use of machine learning in these scenarios and the challenges that lie ahead: the availability of anomaly-free data, the use of the right machine learning technique for each situation, the aggregation of data in appropriate time windows, among others. The rest of the paper is structured as follows: Section 2 contains background and related work; Section 3 describes our laboratory simulation environment; Section 4 shows the attacks performed in the laboratory; Section 5 provides details of the proposed anomaly analysis to detect the attacks. In Section 6, we evaluate the experiment results, discuss the testbed experience and we highlight main challenges in this context. Finally, Section 7 concludes the paper.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 4 Experiment  
The experiment aims to evaluate the proposed method (Figure 2) and assess its effectiveness through three aspects: viewpoint image dataset construction, parameter sensitivity analysis, and image-based viewpoint estimation performance evaluation. Our algorithms are implemented in MATLAB (2018). Each run is executed on a single thread on a personal desktop computer, and the computer configuration is Intel (R) Core (TM) i9-9700 CPU @ 3.00 GHz processor and 16 GB RAM per run. The runtime for each parameter setting is measured as the average execution time (in seconds) of five repeated experiments.
# Diatomaceous Soils and Advances in Geotechnical Engineering--Part I
## 3 Review of Progress in Geotechnical Lines
### Shear Strength of Diatomaceous Soils
The interest in the mechanical properties of DSs and diatomites, particularly their shear strength, has led to the study of variables such as the effects of intermediate principal stresses on the response of \"soft rocks\" (<20 MPa uniaxial compressive strength) under complex stress states (\(\sigma 1>\sigma 2>\sigma 3\)). Thus, Ref. [83] developed a series of numerical simulations of triaxial tests in drained (CD) and undrained (CU) consolidated conditions. From the simulations of the triaxial test conducted in the (CD) condition in soft diatomaceous rocks, it was concluded that the peak (qp) and residual (qr) strengths increase by about 30% when the effect of the intermediate principal stress is considered. The resistances (qp) and (qr) are lower in the \(\sigma 2=\sigma 1\) condition with respect to the \(\sigma 2=\sigma 3\) condition. Additionally, Ref. [83] assessed the response of a strong rock core with intermediate layers of soft diatomaceous rock by applying two-dimensional finite difference simulations. From this, it was determined that the rock pillar's strengths (qp) and (qr) are significantly influenced by the effect of the intermediate principal stress, especially when the intermediate diatomaceous rock layer approaches verticality. Tests carried out on \"diatomaceous shale\" samples have shown that, after the peak strength, the deviatoric stress decreases with increasing axial strain. The stress ratio n (q/p\({}^{\prime}\)) increases during the strain-softening process because of the excess pore pressure generated by a negative dilatancy phenomenon. The effective stress paths in undrained triaxial tests lie within the yield surface of the Cam Clay model. It is not possible to clearly distinguish the elastic behavior of plastics. It is then recognized that diatomaceous shales behave as typically overconsolidated clay. The compressive strength of diatomaceous sediments in unconfined undrained conditions is four times lower (Su = 0.1) than the unconfined, undrained shear strength determined in triaxial tests (Su = 0.4). If a soil sample is considered close to an unconfined situation, the results obtained in the laboratory will underestimate the actual properties of this type of deposit [44]. As the content of diatom frustules increases within an artificial mixture (diatomite-kaolin), the mixture's viscosity tends to decrease drastically, since the clay particles provide less cohesion and the frustules generate a weak interaction. However, at high shear rates (rheometer), the breakage and rearrangement of the frustules increase the viscosity, since the interaction between the particles is enhanced. Samples with low diatom contents (20%) report a higher shear strength than a 100% kaolin sample. This attribute indicates that a small amount of frustules reinforces the structural cohesion provided by the clay matrix [14].
## 3 Theoretical Background
### Dielectric Properties in the NIR \(\rightarrow\) UV Region  
The indium pnictides (InP, InAs, and InSb) are direct-bandgap semiconductors and crystallize in the zb (T\({}_{\mathrm{d}}^{2}\)) crystal structure with F43m space group. In Figure 1, we reproduced the electronic energy-band structure E\({}_{\mathrm{j}}\)(\(\stackrel{{\rightarrow}}{{\mathbf{q}}}\)) of InAs by [PERSON] et al. [28] along the high-symmetry directions in the BZ using a full-potential linearized augmented Slater-type orbital (LASTO) method. The locations of inter-band transitions labeled by vertical arrows in Figure 1 for InAs are equally applicable to InP and InSb materials, as well. These CP energy transitions have played important roles in the analysis of the optical spectra.  
Following [PERSON] [37; 38], we calculated the dielectric functions \(\widetilde{\varepsilon}(\mathrm{E})\) of the binary InP and InSb materials by using the sum of different terms, each of which has an explicit function of the critical-point energies (Sections 3.3.1-3.3.3). In the NIR \(\rightarrow\) UV region, the CPs that provided major contributions in the simulations of continuous model dielectric functions for InP and InSb are linked to the inter-band transitions E\({}_{0},\)E\({}_{0}+\Delta_{0},\)E\({}_{1}\)  
Figure 1: Electronic energy-band structure of direct-bandgap InAs (Ref. [28]) along high-symmetry directions in the Brillouin zone. The calculations are performed using a full-potential linearized augmented Slater-type orbital (LASTO) method (see text).  
\(\mathrm{E_{1}}+\Delta_{1},\mathrm{E_{0}^{\prime}},\mathrm{E_{0}^{\prime}}+\Delta_{0} ^{\prime},\mathrm{E_{2}}\) and \(\mathrm{E_{2}^{\prime}}\) (Figure 1) of their band structures \(\mathrm{E_{i}}\!\left(\stackrel{{\rightarrow}}{{\mathbf{q}}}\right)\)[28]. In the following subsections, we have briefly summarized the appropriate expressions used for evaluating \(\stackrel{{\sim}}{{\varepsilon}}(E)\), which required three fitting parameters: (a) the energy, (b) strength, and (c) broadening parameters per CP of InP and InSb [37; 38].
## 3 The Relay Experiment and Parameters Analysis
### Prediction Effect Evaluation of Each Model  
Three evaluation indices of _AD_, _RMSE_, and R2 are used to evaluate the prediction effect of the three models. The evaluation results are shown in Table 11.  
Table 11 presents that the _AD_ and _RMSE_ values in GNN model are the largest of the four prediction models, indicating that the prediction error of the GNN model is larger. Similarly, the decision coefficient _R2_ of the GNN model is 0.81 and is the smallest of the four models, indicating the input and output fits of the GNN model are worse. The _AD_ and _RMSE_ values of FWA and IFWA are obviously better than the GNN model. The FWA-GNN and IFWA-GNN have the highest coefficient _R2_ of determination. The fitting effect is better than the GNN model. The prediction error of the model is reduced by optimizing the parameters of the GNN model.  
The analysis shows that fitting effect of IFWA-GNN model and FWA-GNN model are better than the other two models. The _AD_ value of IFWA-GNN model is 14.73% smaller than the _AD_ value of FWA-GNN model; the _RMSE_ value of IFWA-GNN model is 6.75% smaller than the _RMSE_ value of FWA-GNN model. Hence, the prediction effect of the IFWA-GNN model is better than the other three models.
## 3 Model and Parameter Identification
### Comparative Study of Optimization Methods  
In this study, the above five optimization methods were used to identify the nine ECMs, respectively, then the appropriate models and algorithms were selected by comparing the identification accuracy and time cost.  
Figure 4a shows the RMSE of model error in the entire SOC area. It can be seen that the PNGV had a great accuracy advantage. For the nRC model, the model accuracy of the MFO increased with the increase of \(n\), and the accuracy of the other optimization algorithms did not increase or even decrease when the order was greater than the second. Moreover, MFO had an obvious accuracy advantage for the high-order RC model. The model error distribution in the low SOC area (0-20%) is shown in Figure 4b, indicated that the PNGV model had obvious advantages in the low SOC area, and the FMIN, PSO, and MFO had almost the same identification accuracy. The error distribution comparison between the PNGV model and the 2 RC model in the low SOC area is shown in Figure 5. It is clear that the PNGV model had a good accuracy in the low SOC area. The error distribution in the high SOC area (20-100%) is shown in Figure 4c. It can be seen that the second- and higher-order RC models had high accuracy using PSO and MFO, and the accuracy of MFO was slightly higher. The identification time of each identification algorithm is shown in Figure 6. It can be seen that the FMIN had the shortest identification time, and PSO had the second shortest identification time (approximately one-sixth of the time taken by the other algorithms). Therefore, these two algorithms are suitable for on-line identification.  
Based on the above analysis, we could see that the PNGV model was the best choice, and the matched identification algorithm was FMIN in the low SOC area. In the high SOC area, if it was on-line identification, 2 RC plus PSO was the best choice for the balance between accuracy and time cost. If it was off-line identification, 4 RC plus MFO was the best choice for the highest accuracy.  
Figure 4: Cont.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Type** & **Algorithm Name** & **Inspiration** & **Year of Proposal** \\ \hline Nonlinear programming & Find minimum of constrained nonlinear (FMIN) & N/A & 1951 \\ Evolution-based & Genetic Algorithm (GA) [20; 24] & Biological evolution & 1992 \\ Physics-based & Simulated annealing algorithm (SA) [25] & Solid annealing & 1983 \\ Swarm-based & Particle Swarm optimization (PSO) [26] & Bird flock & 1995 \\ Nature-inspired & \begin{tabular}{c} Both-flame optimization (MFO) [23] \\ \end{tabular} & \
\begin{tabular}{c} Both \\ \end{tabular} &
\begin{tabular}{c} 2015 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization algorithms.  
Figure 4: Comparison of identification results. (**a**) In the entire state-of-charge (SOC) area; (**b**) in the low SOC area (0-20%); and (**c**) in the high SOC area (20-100%).  
Figure 5: The error distribution comparison between the PNGV model and the 2 RC model.  
Figure 6: Comparison of identification time.
## 5 Simulation Study
### Transient Stability Analysis  
In the transient stability analysis, a symmetrical three line to ground (3 LG) fault at the transmission line is considered as network disturbance, as shown in Figure 20. Temporary fault (Case 1) and permanent fault (Case 2) are considered in the simulation study to show the effectiveness of the proposed control. In Case 1, the fault occurs at 0.1 s; the circuit breakers (CBs) on the faulted line are opened at 0.2 s, and at 1.0 s the CBs are re-closed. In Case 2, the fault occurs at 0.1 s; the CBs on the faulted line are opened at 0.2 s, and at 1.0 s the CBs are re-closed, and at 1.1 s the CBs are opened again because the fault is still continuing. In this transient analysis, the wind speeds for the wind generators are kept constant at the rated speed (12 m/s), assuming that the wind speed does not change in short time duration.  
The simulation results for the transient stability analysis in Case 1 and Case 2 are shown through Figures 21-27. Figures 21 and 22 show responses of reactive power output of PMSG and IG, respectively. It is seen that, by using the proposed controller for PMSG, the grid side converter of PMSG can provide necessary reactive power during 3 LG fault. Therefore, the terminal voltage at PCC can return back to the rated value quickly as shown in Figure 23. The rotor speed of IG can also become stable as shown in Figures 24. Figures 25 and 26 show responses of the active power output of PMSG and IG, respectively. The active power can be controlled more effective by using Fuzzy-PI Controller. Figure 27 shows the DC-link voltage response of the PMSG during the fault. From these results, it is clear that the stability of performance of the wind farms can be improved more effectively in the case of PMSG with the Fuzzy-PI controller than that with just PI controller. Moreover, in Case 2, the system becomes unstable when the converter with PI Controller is used for PMSG.  
Figure 23: Terminal voltage at PCC. (**a**) case 1; (**b**) Case 2.  
Figure 24: Rotor speed of IG. (**a**) case 1; (**b**) Case 2.  
Figure 25: Active power output of PMSG. (**a**) case 1; (**b**) Case 2.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 5 Conclusions  
This paper proposes a constellation encryption design based on chaotic sequence and the RSA algorithm. The method uses the parameters transmitted by the RSA algorithm to generate chaotic sequences, and then uses the chaotic sequences to make the modulation constellation points inconsistent with the original meaning, which further improves the security of satellite communication without increasing the complexity of the original system. After simulation analysis and verification, the bit error rate of the non-cooperating party will be greatly improved, and at the same time, it will not affect the receiving performance of the cooperating party, so this algorithm has good confidentiality performance and ensures the communication security of the system. Therefore, it is suitable for satellite security communication.  
Conceptualization, methodology, and writing--original draft preparation, M.Q. and Q.J.; writing--review and editing, M.Q. and B.B.; supervision, J.Z. and C.J. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the National Natural Science Foundation of China (grant No.62171468).  
The authors claim that the data used in this article are provided by our simulations and the data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare that they no conflicts of interest.  
Figure 11: Simulation diagram of the bit error rate of 16 QAM scrambling constellation encryption algorithm.
# Chronological Evaluation of Gait Ability and Posture Balance after Adult Spinal Deformity Surgery
## 1 Introduction  
In the world, the population is rapidly increasing because of increased longevity, improved medical care, and declining natality [1, 2]. These global population shifts have affected the prevalence of musculoskeletal diseases such as adult spinal deformity (ASD). ASD is highly prevalent in persons older than 65 years, affecting more than 30% of the total population [3, 4]. ASD is caused by spinal malalignment, which results in not only severe low back pain and neurological dysfunction but also reflux esophagitis, cosmetic disorders, and mental disorders [5, 6].  
A large number of reports support the effectiveness of surgical treatment over conservative treatment in severe cases [7,8]. The goal of ASD surgery is to restore the global spinal balance without any complications, especially sagittal alignment [9,10]. However, it needs several months for the elderly ASD patients to adopt a new posture. The rehabilitation after ASD surgery is very important to regain normal daily life activity for the ASD patients to avoid falls because of spinal imbalance [11]. There are many important reports that describe excellent postoperative results for ASD surgery [12,13].  
The systematic review revealed that complex rehabilitation reduces short and long-term disability and fear-avoidance behavior following lumbar fusion surgery [14], and the rehabilitation for ASD results in significant functional improvements [15]. Recently, gait analysis, including a timed up and go test (TUG), was reported as a very useful tool to evaluate patients' dynamic balance [16, 17, 18]. However, there is no report that describes a chronological evaluation of gait ability and spinal balance after ASD surgery. We made a hypothesis that patients' gait ability and posture balance will recover gradually and independently after surgery. With those results, the rehabilitation staff can provide patients with appropriate rehabilitation programs according to the recovery of gait ability and posture balance. The present prospective study aimed to determine their recovery times for gait and spinal balance ability after ASD surgery.
# Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion
## 2 Case Study to Test the Procedure
### Response Spectrum and Spectrum-Consistent Earthquakes  
The reference response spectrum for the Life Protection (LP) limit state is plotted in Figure 2, as obtained according to [1]. A medium-seismicity zone characterized by a peak ground acceleration of 1.6 m/s\({}^{2}\) (for the LP) was considered, with reference to the values provided by Annex B of the Italian Building Code [1]. Consistent with the spectrum in Figure 2, seven couples of records in the x and y directions are obtained through the software REXEL [19], according to code rules [1,2]. Earthquake name, date of occurrence, magnitude M\({}_{\text{w}}\), and distance \(\Delta\) from the epicenter are listed in Table 5.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Performance Check [1]** & **Variables for the Target Matrix** \\ \hline Bending moment hierarchy rule & \(y_{1}=\sum M_{c,Rd}-\gamma_{Rd}\sum M_{b,Rd}\) \\ Shear vs. moment on columns & \(y_{2}=\frac{\gamma_{Rd}\left(\frac{M_{b,Rd}^{2}+M_{b,Rd}^{2}}{l_{c}}\right)}{l _{c}}-V_{p,Ed}\) \\ Beam mid-section bending moment & \(y_{3}=\ M_{b,mid,Rd}-M_{b,mid,Sd}\) \\ Beam end-section bending moment & \(y_{4}=\ M_{b,mid,Rd}-M_{b,end,Ed}\) \\ Shear vs. moment on beams & \(y_{5}=\ V_{b,Rd}-\left(\frac{2\ \gamma_{Rd}M_{b,Rd}}{l_{b}}+\frac{(G+\gamma_{b,2})l_{b}}{2}\right)\) \\ Shear on beam-column node (x direction) & \(y_{6}=\ 0.6\ \eta\ f_{cd}\ b_{jx}\ h_{jx}\ \sqrt{1-\frac{N_{c,Rd}}{\eta}}-\left[\gamma_{Rd} \big{(}A_{b,1x}+A_{b,2x}\big{)}f_{yd}-V_{p,Ed}\right]\) \\ Shear on beam-column node (z direction) & \(y_{7}=\ 0.6\ \eta\ f_{cd}\ b_{jz}\ h_{jz}\ \sqrt{1-\frac{N_{c,Rd}}{\eta}}-\left[\gamma_{Rd} \big{(}A_{b,1x}+A_{b,2x}\big{)}f_{yd}-V_{p,Ed}\right]\) \\ \hline Symbols: \(M=\) bending moment; \(V=\) shear; \(\gamma=\) resistance factor; \(l=\) length; \(N=\) normalized axial load; \(l_{l_{c}}=\) distance between column’s longitudinal bars; \(b_{j}=\) length of the node; \(f=\) strength; \(A=\) area of bars; a = area of stirrups; \(G=\) gravity load; \(\psi_{2d}=\) reduced variable load; \(f_{cd}=\) concrete design strength; \(\eta=\left(1-\frac{f_{cd}}{250}\right)\) with \(f_{cd}\) in MPa. Subscripts and quotes: \(c=\) column; \(b=\) beam; \(d=\) design; \(R=\) resistance(capacity); \(y=\) yield; k = characteristic; \(st=\) stirrup; 1 = bottom section; 2 = top section; \(E=\) calculated (demand). \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural checks for the network target matrix.  
Figure 2: (a) Target response spectrum and (b) spectra of the records in the x and y directions.
## 4 Solution Derivation
By substituting Equation (43) into Equations (33) and (36), the solutions for the excess pore-water pressures and average degree of consolidation can be obtained as the following expressions:  
\[u(z,t)=q_{0}\sum\limits_{m=1}^{\infty}\left[\phi(t)+\frac{1}{2}\sum\limits_{j =1}^{2}\frac{A_{j}}{x_{j}}\left(1-e^{x_{j}T_{\rm v}}+\alpha_{j}\right)\right] C_{m}\sin\left(\frac{M}{H}z\right) \tag{44}\]  
\[U_{p}(t)=\phi(t)-\frac{1}{1+\zeta/2}\sum\limits_{m=1}^{\infty}\frac{C_{m}}{M} \left[\phi(t)+\frac{1}{2}\sum\limits_{j=1}^{2}\frac{A_{j}}{x_{j}}\left(1-e^{ x_{j}T_{\rm v}}+\alpha_{j}\right)\right] \tag{45}\]  
where  
\[\alpha_{j}=\frac{x_{j}}{\left(2\pi/T_{\rm T}\right)^{2}+x_{j}^{2}}\left[ \frac{2\pi}{T_{\rm T}}\cos\left(\frac{2\pi}{T_{\rm T}}T_{\rm v}\right)+x_{j} \sin\left(\frac{2\pi}{T_{\rm T}}T_{\rm v}\right)-\frac{2\pi}{T_{\rm T}}e^{x_{ j}T_{\rm v}}\right] \tag{46}\]  
Using the method of generalized Fourier series, \(\phi(t)\) for a general cyclic loading shown in Figure 3, can be expressed as  
\[\phi(t)=\frac{1}{2}a_{0}+\sum\limits_{n=1}^{\infty}\left[a_{n}\cos\left( \frac{2n\pi}{T}t\right)+b_{n}\sin\left(\frac{2n\pi}{T}t\right)\right] \tag{47}\]  
Figure 3: Dimensionless function for general cyclic loading. Figure 2: Dimensionless function of sinusoidal loading. The Fourier coefficients are given by  
\[\left\{ \begin{array}{ll}a_{n}=2/T\int_{0}^{T}\phi(t)\cos(2n\pi t/T)dt,&n=0,1,2 \cdots\\ b_{n}=2/T\int_{0}^{T}\phi(t)\sin(2n\pi t/T)dt,&n=1,2\cdots\end{array} \right. \tag{48}\]  
By substituting Equation (48) into Equations (33) and (36), the solutions for the excess pore-water pressures and average degree of consolidation can be obtained as the following expressions:  
\[u(z,t)=q_{0}\sum\limits_{m=1}^{\infty}\left\{\phi(t)+\sum\limits_{j=1}^{2} \frac{A_{j}}{x_{j}}\bigg{[}\frac{1}{2}a_{0}\Big{(}1-e^{x_{j}T_{\rm v}}\Big{)}+ \omega_{j}\bigg{]}\right\}C_{m}\sin\left(\frac{M}{H}z\right) \tag{49}\]  
\[U_{p}(t)=\phi(t)-\frac{1}{1+\zeta/2}\sum\limits_{m=1}^{\infty}\frac{C_{m}}{M} \left\{\phi(t)+\sum\limits_{j=1}^{2}\frac{A_{j}}{x_{j}}\bigg{[}\frac{1}{2}a_{ 0}\Big{(}1-e^{x_{j}T_{\rm v}}\Big{)}+\omega_{j}\bigg{]}\right\} \tag{50}\]  
where  
\[\omega_{j}=\sum\limits_{n=1}^{\infty}\frac{x_{j}}{(2n\pi/T_{\rm v})^{2}+x_{j }^{2}}\left\{a_{n}\Big{[}x_{j}\cos\Big{(}\frac{2n\pi}{T_{\rm v}}T_{\rm v}\Big{)} -\frac{2n\pi}{T_{\rm v}}\sin\Big{(}\frac{2n\pi}{T_{\rm v}}T_{\rm v}\Big{)}-x_ {j}e^{x_{j}T_{\rm v}}\Big{]}+b_{n}\Big{[}\frac{2n\pi}{T_{\rm T}}T_{\rm v} \Big{)}+x_{j}\sin\Big{(}\frac{2n\pi}{T_{\rm T}}T_{\rm v}\Big{)}-\frac{2n\pi}{ T_{\rm T}}e^{x_{j}T_{\rm v}}\Big{]}\right\} \tag{51}\]