esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 4 State-of-the-Art in IDS for IoT
### Signature-Based Approaches to Intrusion Detection  
Signature-based techniques for intrusion detection are well-established especially for conventional computing systems. However, one of the critical factors for effectiveness of such schemes is the availability of trustworthy dataset simulating patterns/signatures of misuse, which has proved to be a challenge for IoT systems. In this respect, [PERSON] et al. [53] presented an IDS framework for 6 LoWPAN focusing on detecting denial of service attacks by monitoring physical parameters of the device. The proposed IDS leverages existing IDS (Surricata) and monitors network traffic for both signatures and abnormal behaviour to identify malicious users to detect flooding attacks with an increased detection rate.  
[PERSON] et al. [54] propose leveraging Snort, a contemporary signature-based NIDS, to establish a portable, easy-to-use intrusion detection for IoT networks. The resultant IDS is packaged within a Raspberry Pi so it can be transported with the device, enabling it to work in collaboration to achieve detection of sophisticated attacks, such as those targeting network topology. Under this setup, an IDS node can request data captured by neighboring nodes to perform rigorous analysis and reduce false positives. In view of the increasing threats for IoT systems with respect to volume and sophistication, this observation will be crucial to an effective intrusion detection for IoT systems. [PERSON] and [PERSON] [55] propose a modular architecture for intrusion detection, which utilizes detection techniques, such as signature, anomaly and specification-based systems with network traffic to achieve effective detection. In particular, signature-based detection uses rules based on packet headers whereas anomaly-based detection aims to identify abnormal data patterns, and specification-based detection focuses on botnet detection. However, the proposed approach envisages intrusion detection at the edge router level which compromises the visibility of the system.
## 4 Conclusions  
This study investigated the ideal mix and resulting properties of alkali-activated slag ceramsite concrete hollow blocks. The investigated parameters were the water-to-slag ratio, sand ratio, silicate modulus, and Na\({}_{2}\)O content. The primary conclusions of this study are as follows:  
1. The compressive strength of alkali-activated slag concrete decreased with increasing water-to-slag ratio, silicate modulus, and Na\({}_{2}\)O content. The effects of the silicate modulus and Na\({}_{2}\)O content on the mechanical performance were quite obvious, and an optimum sand ratio for providing maximum compressive strength was observed in the range of 0.54-0.68.
2. The drying shrinkage of alkali-activated slag concrete increased as the water-to-slag ratio, silicate modulus, and Na\({}_{2}\)O content increased and as the sand ratio decreased.
3. The optimum mix for preparing the alkali-activated slag concrete hollow block contained a water-to-slag ratio of 0.35, sand ratio of 0.64, silicate modulus of 1.2, and Na\({}_{2}\)O content of 8%, providing optimal workability, high compressive strength, and low drying shrinkage. The hydration products of the selected mix were close-grained and symmetrical, which provided improved compressive strength.
4. The alkali-activated slag concrete hollow block made of Mix M1.2N8 can be classified as strength grade of MU15 and is appropriate for use as a load-bearing hollow concrete block.  
The proposed use of alkali-activated slag in hollow concrete blocks shows great promise for not only providing suitable material for future construction but also for reducing industrial waste and the emissions of the cement manufacturing industry.  
Additional mechanical properties, such as shear strength, bending tensile strength, axial tension strength, and compressive strength of the masonry with different grades of alkali-activated slag concrete hollow blocks, should be investigated to further examine the effects of the water-to-slag ratio, sand ratio, silicate modulus, and Na\({}_{2}\)O content in more detail.  
Conceptualization, Y.W. and W.Z.; Investigation, Z.J., W.H., and X.Z.; Writing--Original Draft Preparation, Z.J.; Writing--Review & Editing, Z.J.; Supervision, Y.W. and W.Z.; Funding Acquisition, Y.W.  
This research was funded by the National Natural Science Foundation of China [grant number 51478142].  
The authors declare no conflict of interest.
## 2 Problem Formulation
### Reactive Power Capability  
The reactive power capability for each PV system is determined by its apparent power rating \(S_{i}^{rated}\) and the active power rating \(P_{i}^{rated}\) for the \(i\)-th PV system, as shown in Equation (2), where kvarMax is the maximum value of the reactive power per unit that the inverter can provide to the grid. If the \(i\)-th PV inverter is less than \(~{}0.05\times P_{i}^{rated}\), it is not allowed to provide/absorb reactive power. In addition, it is not allowed to provide/absorb reactive power more than kvarMax. According to the IEEE 1547 standard, it is assumed that the magnitude of kvarMax is \(0.44\times S_{i}^{rated}\). Figure 3 shows the reactive power capability curve.  
Figure 2: Volt–var curve for each inverter.  
\[Q_{i,t}^{lim}=\left\{\begin{array}{ll}0,&0\leq P_{i,t}\leq 0.05\times P_{i}^{rated} \\ \frac{P_{i,t}}{P_{i,t}^{rated}}\times kvarMax,&0.05\times P_{i}^{rated}<P_{i,t} \leq 0.2\times P_{i}^{rated}\\ kvarMax,&0.2\times P_{i}^{rated}<P_{i,t}\end{array}\right. \tag{2}\]
# Detection of Decision-Making Manipulation in the Pairwise Comparison Method
## 3 Preliminaries
### Error Measurement  
In real-life scenarios, \(PC\) matrices created by experts are inconsistent. Most often, the value of the inconsistency index is insufficient to detect the pairwise comparisons that correspond to a high level of inconsistency (the Koczkodaj inconsistency index may be an exception [58]). For this reason, the concept of error is often considered [67].  
To define an error indicator, let us consider a \(PC\) matrix \(C\), where each entry \(c_{ij}\) represents an expert's subjective comparison between alternatives \(a_{i}\) and \(a_{j}\). The ranking vector \(w\), for \(C\), is given as \(w=\left[w(a_{1}),\ldots,w(a_{n})\right]^{T}(3)\); then, the error matrix [67] can be defined as follows:  
\[E(\mathbf{C})=\left[c_{ij}\frac{w(a_{j})}{w(a_{i})}\right],\quad\forall\,i,j\in \{1,\ldots,n\}. \tag{13}\]  
High error values identify highly inconsistent comparisons and, thus, potentially erroneous expert judgments. For a consistent \(PC\) matrix \(C\), all elements of the error matrix are 1, i.e., \((e_{ij}=1)\), where \(E(C)=[e_{ij}]\).
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 2 Modeling of Three Body Dynamics of Maglev Satellite
### Platform Subsystem Kinetic Energy
\begin{split} T_{pf}&=\tfrac{1}{2}M_{pf}\dot{\mathbf{X}}_{pf }^{\mathrm{T}}\dot{\mathbf{X}}_{pf}+\tfrac{1}{2}\mathbf{\omega}_{b}^{\mathrm{T}}\mathbf{J} _{pf}\mathbf{\omega}_{b}\\ &+\tfrac{1}{2}J_{rx}\hat{\theta}_{r}^{\mathrm{T}}+\mathbf{\omega}_{b} ^{\mathrm{T}}(M_{r}\tilde{\mathbf{r}}_{r}C_{g}\tilde{\mathbf{r}}_{rc}^{\mathrm{T}}+C_{ r}\mathbf{J}_{r})\mathbf{D}_{r}\hat{\theta}_{r}\\ &+\tfrac{1}{2}M_{r}\dot{\mathbf{r}}_{r}^{\mathrm{T}}C_{r}\tilde{\mathbf{g}} _{rc}^{\mathrm{T}}D_{r}\hat{\theta}_{r}\end{split}  \tag{18}\]  
According to Equation (15), it can be inferred that:  
\[\dot{\mathbf{r}}_{bc}=-\frac{M_{r}}{M}\mathbf{C}\tilde{\mathbf{g}}_{rc}^{\mathrm{T}}\mathbf{D}_{ r}\hat{\theta}_{r} \tag{19}\]  
Substituting Equation (19) into Equation (18) yields:  
\[ \begin{split} T_{pf}&=\tfrac{1}{2}M_{pf}\dot{\mathbf{X}}_{pf }^{\mathrm{T}}\dot{\mathbf{X}}_{pf}+\tfrac{1}{2}\mathbf{\omega}_{b}^{\mathrm{T}}\mathbf{J} _{pf}\mathbf{\omega}_{b}\\ &+\tfrac{1}{2}J_{rx}\hat{\theta}_{r}^{\mathrm{T}}-\tfrac{1}{2}\tfrac {M_{r}^{2}}{M}\mathbf{D}_{r}^{\mathrm{T}}\tilde{\mathbf{g}}_{rc}\tilde{\mathbf{g}}_{rc}^{ \mathrm{T}}D_{r}\hat{\theta}_{r}^{2}\end{split}  \tag{20}\]
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 2 Quasi-Static Approach for the Evaluation of Tire Forces  
Six equilibrium equations and additional assumptions were utilized for the evaluation of unknowns that include tire forces. This so-called quasi-static approach has been applied only to the two-axle types of vehicles tested in previous studies [2; 3; 10]. The present study, however, extended the procedure to trailers, which are more vulnerable to strong side winds [2].
## 2 Mathematical Model
### Angle Incidence (\(\theta\))  
The angle of incidence of the direct solar radiation on the tilt surface, \(\theta\), can be calculated by (2) [31]:  
\[\begin{array}{rl}\cos\theta=&\sin\delta\sin\phi\cos\beta-\sin\delta\cos\phi \sin\beta\cos\gamma\\ &\qquad\qquad+\cos\delta\cos\phi\cos\beta\cos\omega\\ &\qquad\qquad+\cos\delta\sin\phi\sin\beta\cos\gamma\cos\omega\\ &\qquad\qquad+\cos\delta\sin\beta\sin\gamma\sin\omega\end{array}, \tag{2}\]  
where \(\phi\) is the latitude of the site, \(\beta\) is the tilt angle of PV panel, \(\gamma\) is the azimuth angle, and \(\omega\) is the hour angle, which shifts with the sun movement.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Climate** & **Location** & **Latitude (\(\theta\))** & **Longitude (\(\theta\))** & **Elevation (m)** \\ \hline \(\mathrm{TZ}\) & Sanya & \(18^{-14}\)/ & \(109^{-31}\)/ & \(5.9\) \\ \(\mathrm{SZ}\) & Shanghai & \(31^{-2}\)/\(24^{\prime}\) & \(121^{-2}\)/\(29^{\prime}\) & \(6\) \\ \(\mathrm{WTZ}\) & Zhengzhou & \(34^{-4}\)/\(4^{\prime}\) & \(113^{-3}\)/\(39^{\prime}\) & \(110.4\) \\ \(\mathrm{MTZ}\) & Haehin & \(45^{-4}\)/\(45^{\prime}\) & \(126^{-4}\)/ & \(142.3\) \\ \(\mathrm{CTZ}\) & Mohe & \(53^{-2}\)/\(26^{\prime}\) & \(122^{-2}\)/\(31^{\prime}\) & \(433\) \\ \(\mathrm{TZ}\) & Lussa & \(29^{-4}\)/\(40^{\prime}\) & \(91^{-0}\)/\(8^{\prime}\) & \(3648.7\) \\ \hline \end{tabular}  
TZ = tropical zone; \(\mathrm{SZ}\) = subtropical zone; \(\mathrm{WTZ}\) = warm temperate zone; \(\mathrm{MTZ}\) = mid temperate zone; \(\mathrm{CTZ}\) = cold temperate zone; \(\mathrm{TPZ}\) = Tibetan plateau zone.  
\end{table}
Table 1: General information about selected six typical stations.  
Figure 1: General layout of the six major climates across China. \(\mathrm{TZ}\) = tropical zone; \(\mathrm{SZ}\) = subtropical zone; \(\mathrm{WTZ}\) = warm temperate zone; \(\mathrm{MTZ}\) = mid temperate zone; \(\mathrm{CTZ}\) = cold temperate zone; \(\mathrm{TPZ}\) = Tibetan plateau zone.
## 6 Application II: Fast Filtering of Time-Vertex Signals
### Numerical Experiments  
We consider the ideal lowpass filter (12) and the wave filter (13). We approximate \(h(\mathbf{L}_{G},\mathbf{L}_{R})\mathbf{X}\) for both filter functions, with \(T=1000\) observations, and for different graphs \(G\): gnp (\(N=500\)), saylr4 (\(N=3564\)) and a random sensor network (\(N=5000\)), the cumulative spectral densities of which are shown in Figure 2. In each case, we choose \(\mathbf{X}=\frac{1}{\sqrt{NT}}\sum_{i=1,\cdots,N;j=1,\cdots,T}\mathbf{v}_{G_{i} }\mathbf{v}_{T_{j}}^{\top}\), i.e., a constant vector in the joint spectral domain of \(\mathbf{L}_{G}\) and \(\mathbf{L}_{R}\), in order to test the average performance of the approximation methods over different combinations of eigenvalue pairs. With polynomial approximation orders ranging from \(K=1\) to \(30\), we follow the procedure described in Algorithm 3 to approximate \(h(\mathbf{L}_{G},\mathbf{L}_{R})\mathbf{X}\). We estimate the cumulative spectral density functions \(\tilde{P}_{\lambda}(z)\) with parameters \(T=10\), \(J=10\), and \(K_{\Theta}=30\). We use \(M=100\) in the spectrum-adapted polynomial regression/orthogonal polynomial expansion when finding the best polynomial approximation. We compare the proposed methods to the truncated Chebyshev expansion and the Lanczos method with the same polynomial order. For each method, we examine the convergence of relative errors in Frobenius norm as a function of \(K\) for graphs with various structures (and thus various distributions of Laplacian eigenvalues). The results are summarized in Figure 10. Similar to our observation in Figure 5, we see that the spectrum-adapted interpolation method performs well for lower polynomial orders \((K\leq 5)\), but tends to be unstable at higher polynomial orders.
###### Abstract  
Total hip arthroplasty (THA) is a widely performed procedure to restore hip function in patients with degenerative joint diseases. Traditional \"press-fit\" fixation methods rely on sufficient bone quality for stability, but additional screw fixation is often necessary for patients with suboptimal bone conditions. However, comprehensive studies utilizing predictive modeling to optimize screw placement strategies in THA remain limited. This study integrates finite element analysis (FEA) with deep learning (DL) to optimize screw fixation strategies, enhancing implant stability and reducing revision rates. The design optimization process was conducted to refine key implant parameters before training the deep learning surrogate model. By utilizing advanced simulation techniques--including Goodness of Fit analysis, Response Graphs, Local Sensitivity Analysis, and Spider Charts--critical factors influencing stress distribution and fixation stability were identified. The optimization process ensured that the dataset used for deep learning training consisted of well-validated simulations, thereby improving the predictive accuracy of stress-strain responses. The findings indicate that optimized screw placement significantly improves load distribution, reducing stress concentrations and enhancing long-term implant stability. The comparative analysis of FEA and DL results showed that the DL-FEA surrogate model successfully replicated deformation patterns, though with a mean squared error (MSE) of 24.06%. While this suggests room for improvement, the model demonstrates potential for streamlining surgical planning. A comparative assessment with traditional methods highlights the advantages of DL-FEA in reducing computational time while maintaining precision. Future improvements will focus on refining the DL model, increasing the dataset size, and incorporating clinical validation. These findings contribute to developing a computational protocol for personalized acetabular cup fixation, with implications for reducing revision rates and improving surgical outcomes.  
deep learning; machine learning; finite element analysis; design optimization; total hip arthroplasty; joint; replacement; optimized screw fixation +
Footnote †: journal: Applied sciences
# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 5 Defect Inspection
### Defect Classification  
Defect prediction is the first step of the inspection where the scan is classified as defective or defect-free. Image classification research and applications are growing rapidly due to advances in Machine Learning (ML) and deep learning. In recent decades, many classification models, techniques, and frameworks have been presented. Some works focused on developing new frameworks to reduce spatial redundancy in image classification tasks, saving computational cost, memory footprint, and power consumption [39]. [PERSON] et al. [40] introduced a framework, BetteR Accuracy from Concept-based Explanation (BRACE), to recognize candidate samples obtained from image repositories for data augmentation. Other works proposed new model architectures to deal with model scalability with high-resolution features such as the Global Filter Network (GFNet) [41]. [PERSON] et al. [42] proposed a batched gradient SIS to find sufficient salient supporting input features/subsets in complex datasets. Some other works focused on missing labels and incomplete annotation, such as [PERSON] et al. [43], who proposed a novel method named \"one-bit supervision\" for data annotation in image classification tasks with incomplete annotations. In the defect prediction/classification function, different state-of-the-art deep learning architectures are used as a backbone to perform binary classification of 2D images/3D volumes. Figure 15 shows the general framework of the architecture of defect prediction.  
The input image needs some preparations and pre-processing such as resizing and data augmentation such as random rotation, horizontal and vertical flip, and color jitter. Figure 16 shows an illustration of the classification framework.  
Figure 14: The full inspection flowchart in the CTIMS-Toolbox.  
For instance, the classification framework uses the **Resnet18**-based model for binary classification. The performance of the trained model, for 200 epochs using real CT dataset, is shown in Figure 17.  
On that note, during the data preparation, it was ensured that the class distribution of the dataset had a balanced number of images. Our contribution brings into light the application of existing deep learning architectures such as **Resnet18** on industrial CT scans for image classification.
## 3 Experiments and Results
### Synthetic Data
#### 3.1.1 Numerical Stability  
To analyse the numerical stability of our proposed method, we perform 50,000 trails. At each trail, we randomly select three 2D-3D point correspondences from synthetic data without any noise added to the 2D image points for our proposed method and [PERSON]'s method, and four 2D-3D point correspondences for GP4 Pf. We measure the log10 value of the relative error between the ground truth and the estimated focal length by our proposed method and GP4 Pf. The results are shown in Figure 5(a). We measure the log10 value of the error in translation by our proposed method, [PERSON]'s method and GP4 Pf, as shown in Figure 5(b).  
From Figure 5(a), the distribution of the log10 value of the relative error between the estimated focal length and the ground truth can be observed. We can see our proposed method is clearly superior in numerical stability over GP4 Pf which is the state-of-the-art 4-point solution.  
From Figure 5(b), the distribution of log10 value of error in translation can be observed. We can see our proposed method is clearly superior in numerical stability over GP4 Pf and  
Figure 6: (a) Relative error in focal length. (b) Error in translation.  
compared to [PERSON]'s method, it behaves in a very similar way, but [PERSON]'s method has a multi-solution phenomenon that is up to four solutions and needs a fully calibrated camera.
# Context-Aware Caching Distribution and UAV Deployment: A Game-Theoretic Approach
## 2 System Model and Problem Formulation
### Summary  
Besides the caching contents and service locations, we consider the impact of _LOS_ and _NLOS_ links on the performance of UAV-assisted networks. Meanwhile, the necessary formulations are presented in this part, which indicates that the tradeoff between the transmission overhead and successful communication probability should be balanced.
## 6 Conclusions  
Two channel estimation techniques based on implicit training and compressed sensing have been proposed for massive-MIMO sparse uplink channels. A comprehensive analysis based on the results obtained from computer based simulations of these techniques has been presented. Performance of the proposed schemes is evaluated on the basis of NCMSE and BER as the performance criteria. In order to prove the validity of simulation results, NCMSE and BER based performance comparison of the proposed schemes with that of a notable SiT-LS scheme has been presented. Moreover, effect of variations in channel sparsity parameter and training-to-information ratio has also been presented for the proposed techniques. It has been established that the proposed SiT-StOMP and SiT-GP techniques outperform the first-order statistics based SiT-LS in terms of NCMSE and BER for the case of sparse multipath channels. It has been shown that the proposed SiT-StOMP and SiT-GP can provide a performance gain of 18 dB and 13 dB, respectively, in terms of NCMSE at an SNR of 12 dB and channel sparsity of 80%, over SiT-LS. Similarly, for a BER of \(10^{-2}\), a gain of about 3 dB and 2.5 dB in SNR is achieved by SiT-StOMP and SiT-GP, respectively, over SiT-LS, for a channel sparsity level of 80%. Furthermore, in order to compare the performance of proposed SiT-StOMP and SiT-GP techniques, the NCMSE has been plotted against variations in channel sparsity and TIR. It has been demonstrated that, for sparser channels, SiT-StOMP achieves better performance in terms of NCMSE as compared to SiT-GP. However, as the channel becomes less sparse, the NCMSE for SiT-StOMP increases in comparison to that of SiT-GP. Furthermore, both of the proposed techniques perform better than SiT-LS even if the channel is less sparse. Moreover, it has been shown that SiT-StOMP performs better as compared to SiT-GP for lower as well as higher values of TIR.
## 3 Results and Discussion
### Simulation Results and Their Discussion  
Figures 10-12 show the results of the simulation of braking of the nominal set (b1) in the case of constant excitation (CE). There are presented time histories of vertical and horizontal forces, accelerations, velocities, wheels' angular velocities and their slip ratios. Figures 13 and 14 show selected results (horizontal forces and velocities) for other considered vehicle set variants. The histories presented in Figures 10-14 refer to cases in which the ABS was inactive. In the case of b1 assumed as the nominal (reference case), the force in the coupling reaches approximately 100 kN and neither wheel locks (with ABS disengaged). It results from the adopted parameters of the braking system and the selection of the maximum position of the main control valve positioner for the adopted simulation conditions b1. Figure 11a reveals a potential wheel lockup tendency in the form of a high circumferential deceleration gradient of the wheels. The deceleration of the set (in the phase of full braking) was 6.0 m/s\({}^{2}\), and the braking distance was 40.0 m. The other cases were analysed similarly. Table 4 presents synthetically the effects of the analysis of all tests with constant excitation (CE) in the form of quantitative and qualitative indicators describing the braking process of the set of vehicles. In a similar way, Table 5 presents the effects of the analysis for the simulations with the same deceleration (CD). Since, in this case, the quantitative measures of braking efficiency were similar for individual set variants (braking time approx. 4.7 s and braking distance approx. 50 m), the table is limited to the presentation of horizontal force in the coupling and the effect of wheel locking. Figure 12: Time histories for variant b1, CE, ABS off: (a) Angular velocities; (b) Slip ratios. Figure 13: _Cont._  
Figure 14: _Cont._  
Figure 13: Time histories of horizontal forces, CE, ABS off: (**a**) b2; (**b**) b3r; (**c**) b3f; (**d**) b4; (**e**) b5; (**f**) b7. Figure 14: Time histories of velocities, CE, ABS off: (**a**) b2; (**b**) b3r; (**c**) b3f; (**d**) b4; (**e**) b5; (**f**) b7. Figure 15: Time histories for variant b7, CE with ABS turned on: (**a**) Horizontal forces; (**b**) Velocities. A graphical interpretation of the above tables in terms of selected parameters of tractor-semitrailer set on the braking safety indicators is shown in Figures 16-18. The results in Tables 4 and 5 show that in all tests of the braking of the tractor-trailer combination--variants b1 to b8 for the case of the same position of the main control valve adjuster (CE) and the individual position of this adjuster at which the deceleration in the fully developed braking phase was c.a. 4.5 m/s\({}^{2}\) (CD), the horizontal force in the coupling is positive. The obvious result is that the combination with an unladen semitrailer (b2) shows the lowest value of the horizontal force in the coupling (up to a maximum of 17 kN). The horizontal force in the coupling is greatly influenced by the shift of the centre of gravity (b3r and b3f).