esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2009**, \(9\), 9380-9397; doi:10.3390/s91109380  
_Article_  
**Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps**  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department Ingenieria Electronica, ETSI Telecomunicacion, Universidad Politecnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain; E-Mails: [PERSON]_ADDRESS] (A.A.); [PERSON]_ADDRESS] (Z.B.); [PERSON]DRESS] (J.M.G.); [EMAIL_ADDRESS] (J.C.V.); [PERSON]ADDRESS] (P.M.); [PERSON]DDRESS] (D.V.); [PERSON]_ADDRESS] (D.F.); [PERSON]L_ADDRESS] (E.R.); [PERSON]_ADDRESS] (J.B.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-915-495-700, ext 4224; Fax: +34-913-367-323.  
_Received: 9 October 2009; in revised form: 30 October / Accepted: 4 November 2009 / Published: 20 November 2009_  
**Abstract:** The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.  
**Keywords:** SCADA control system; cyber security; critical infrastructure; reputation system; countermeasure; security framework
# A Novel Method of Calibrating Micro-Scale Parameters of PFC Model and Experimental Validation
## 5 Application of Calibrated Model to Rock Fracture and Experimental Validation
### Stress-Strain Curves
As a result, the quantity of the formed micro-cracks grows significantly and the stress-strain curves at this point goes up, and then the stress decreases rapidly for the IS and SC models. After the peak, the number of the micro-cracks rises rapidly due to the coalescence of the cracks. The slopes of the curves after the peak are large, indicating that the models are quite brittle. It is further observed that the remarkable increase in the number of the micro-scale cracks of the models containing an opening occurs earlier than that of the intact model. The reason is that cracks are more likely to emerge at the stress concentration areas around the openings under loads. Table 7 lists the simulated uniaxial compress strength, Young's modulus, and peak strain of the models subjected to uniaxial compression. It is observed that the strength of the models embedded an opening is much lower than that of the intact model, and the weakening degree ranging from 21.14-31.97% is closely associated with the opening shape. Among the prehled samples, the uniaxial compressive strength of the model SD with a height-width ratio greater than 1 is the largest, while that of the model SS is the smallest. Interestingly, the strength of the model SD is larger than that of the model SC, which may be related to the height-width ratio. For the Young's modulus, the value of the prehled samples is slightly lower than that of the intact model, and the opening shape has little influence on it. Moreover, the peak strain of the prehled models decreases significantly compared with that of the intact model, but the difference in peak strain among the prehled models is not significant. Figure 10: Fabricated sandstone samples: (**a**) samples containing a circular opening; (**b**) samples containing a square opening, and (**c**) samples containing a D-shaped opening.
# Development of Ultrasonic Pulsed Plasma Jet Source for Remote Surface Treatment
## 3 Results and Discussion
### Evaluation of Hydrophilic Effect Dependence on Plasma Gas Type  
Figure 5 shows the results of hydrophilic treatment with plasma for each gas type. The horizontal axis represents the irradiation distance, and the vertical axis represents the contact angle. The contact angle was measured at five points and the average value was obtained. This procedure was repeated three times, and the mean value and standard deviation were further obtained. When nitrogen and carbon dioxide supersonic plasmas were irradiated at a distance of 5 mm, the contact angles were 33\({}^{\circ}\) and 53\({}^{\circ}\), respectively, and the maximum contact angle was 14\({}^{\circ}\) lower than that of the conventional system. For helium and argon supersonic plasmas, the contact angles were lower than those of the conventional system when the irradiation distance was \(\geq\)10 mm. In particular, in helium supersonic plasma, the contact angle decreased by a maximum of 20\({}^{\circ}\) when the irradiation distance was 10 mm, and the irradiation distance at which the hydrophilic effect appeared increased from approximately 20 to 40 mm. The sound velocity of helium is 1014 m/s, which is more than three times higher than those of nitrogen (352 m/s) and carbon dioxide (271 m/s). Therefore, compared with supersonic plasmas of other gases, the active species are ejected farther in a shorter time, which is thought to improve the hydrophilicity of the plasma. In contrast, when helium and argon plasma were irradiated at a distance of 5 mm, the contact angles were 58\({}^{\circ}\) and 55\({}^{\circ}\), respectively, in the conventional system, but increased to 61\({}^{\circ}\) and 68\({}^{\circ}\) in the supersonic plasma. This is because the plasma and atmospheric components did not react sufficiently, and the active species of oxygen and nitrogen that contribute to hydrophilicity were not generated. These results confirm the improved hydrophilicity of nitrogen, carbon dioxide, argon, and helium supersonic plasmas. Evidently, nitrogen supersonic plasma is effective in improving hydrophilicity for short-distance treatment, whereas helium supersonic plasma is effective for long-distance treatment.  
Figure 5: _Cont._
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 4 Experimental Results and Analysis
### Evaluation of Enhanced Semi-Supervised Learning Algorithm  
During the training phase of the proposed network, we observed that a subset of unlabeled data needed to be not effectively exploited in the first stage of base class training. Acknowledging the substantial investments in human effort and financial resources typically required to acquire high-quality labels, particularly for steel defects that are challenging to label, we resolved to implement and optimize an SSL algorithm based on pseudo-labels. For the purpose of enhancing data cleansing, local linear embedding (LLE) was applied to reduce the dimensionality of a dataset comprising 6047 training images of pits and scratches. This process yielded 159 images suspected of mislabeling or non-labeling, which were subsequently incorporated into an unlabeled dataset of 1801 images, enriching the dataset's diversity. Meanwhile, pseudo-labels were generated for these additions, thus reinforcing the training of the model. The final results of this experiment are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Pits/AP** & **Cracks/AP** & **Scratches/AP** & **Iron Oxide Scales/AP** \\ \hline YOLOv4 [45] & 44.3\% & 61.5\% & 66.4\% & 71.0\% \\ YOLOv5 [45] & 42.3\% & 69.3\% & 60.7\% & 68.2\% \\ Cascade R-CNN [45] & 60.8\% & 71.4\% & 68.4\% & 69.6\% \\ Ours & 76.17\% & 74.68\% & 78.24\% & 77.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results for the Severstal dataset.  
Figure 5: Comparison of pre- and post-improvement accuracy in single defect detection.  
Additionally, we implemented a random sampling strategy within the labeled dataset for pits and scratches, collecting images with increments of 1000. These images, along with 1960 pseudo-labeled images derived via the SSL algorithm, were fed into the network for model training. From the data presented in the table, it was apparent that the SSL algorithm successfully transformed the previously unexploited unlabeled data into valuable training assets. This transformation yielded a marked improvement in the efficacy of the small-sample detection algorithm: a surge in the mean Average Precision (mAP) from 76.58% to 78.21% was achieved when the complete dataset was engaged in training. This clearly suggests that the employment of SSL algorithms can lead to a significant increase in model precision, particularly in industrial settings where data are relatively challenging to label and acquire.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 6 Conclusions  
The focus of this paper is to realize high-precision measurement and low-BER communication under a Doppler frequency offset environment. We propose an integrated communication and measurement system with BOC-assisted OFDM under a Doppler frequency offset environment. The transmitter utilizes the orthogonality of the OFDM subcarriers and the spectral characteristics of the BOC signal to combine OFDM modulation with BOC modulation to produce an OFDM+BOC composite signal through power control.  
Figure 20: Flight speeds of the UAV node A.  
Moreover, the receiver adopts the high-precision Doppler frequency offset tracked by the BOC signal to assist in the demodulation of the OFDM signal. The results demonstrate that a significant improvement in the measurement accuracy is achieved. The ranging error is below \(10^{-2}\), and the Doppler frequency error is below 2 Hz. Compared with channel compensation, the BER of the composite signal with carrier Doppler compensation is lower by at least two orders of magnitude. Therefore, carrier Doppler compensation greatly decreases the influence of the Doppler frequency offset on the OFDM signal and improves the communication performance.  
Conceptualization, X.L. and L.X.; Data curation, X.Z.; Formal analysis, X.Z.; Investigation, L.X.; Methodology, X.Z.; Resources, X.Z.; Software, X.Z.; Validation, X.L. and X.Z.; Writing--original draft, X.Z.; Writing--review and editing, X.L. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (No. 2021 YFB3900300) and the Fundamental Research Funds for the Central Universities (No. 2022 CDJKYJH036).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ OFDM & Orthogonal Frequency Division Multiplexing \\ TC-OFDM & Time and Code Division-Orthogonal Frequency Division Multiplexing \\ PHY & Physical Layer \\ MB-OFDM & Multiband Orthogonal Frequency Division Multiplexing \\ TDOA & Time Difference of Arrival \\ BPSK & Binary Phase Shift Keying \\ BOC & Binary Offset Carrier \\ GPS & Global Positioning System \\ BDS & Beidou Navigation System \\ CNR & Carrier to Noise Ratio \\ S/P & Serial to Parallel \\ IFFT & Inverse Fast Fourier Transform \\ QPSK & Quadrature Phase Shift Keying \\ FFT & Fast Fourier Transform \\ P/S & Parallel to Serial \\ BER & Bit Error Rate \\ SPLL & Subcarrier Phase Locked Loop \\ NCO & Numerically Controlled Oscillator \\ FLL & Frequency-Locked Loop \\ PLL & Phase-Locked Loop \\ DLL & Delay-locked Loop \\ LS & Least Squares \\ AWGN & Additive White Gaussian Noise \\ SNR & Signal-to-Noise Ratio \\ PAPR & Peak-to-Average Power Ratio \\ CCDF & Complementary Cumulative Distribution Function \\ \end{tabular}
## 2 Methodology
### Analysis of Input Values for Local Fatigue Design Methods
#### 2.1.2 Requirements for TCD Method  
To apply the Theory of Critical Distances to threshold-based local fatigue assessment, one must evaluate the principal stress at the point at a distance \(a_{0}/2\) away from the notch tip along the bisector axis. For finite life assessment [37], the evaluation distance can be generalized to \(L_{m}(N)\), instead of the El Haddad length parameter. In practice, TCD-based finite life strength assessment requires evaluation distances on the order of millimeters, which is already in the range of the typical size of the defect of interest. Thus, the stress evaluation path must be evaluated in a manner dependent on the curvature of the defect.  
For TCD-based fatigue analysis, the following key values must be provided for each point of the imperfection contour:  
* Notch opening angle \(2\alpha\) (depending on the evaluated arc-length sector),
* Notch radius \(\rho\),
* Principal stress scalars along a path following the notch bisector and extending towards a macroscopic curvature orientation.  
In the case of TCD evaluation, the vector paths must be computed for arc-length values up to several millimeters, significantly extending the required geometric dataset of the  
Figure 3: Numerical study of the applicability of peak stress method for sharp notches: (**a**) Parameter range; and (**b**) Comparison of the non-dimensional ratio \(K^{*}_{FE,I}\) and modified \(K^{*}_{FE,I,mod}\).  
notch opening angle and notch radius, as previously defined for N-SIF evaluation only in the vicinity of the notch tip.  
In pre-processing, fixed points given by the curvature-dependent path may be added to the mesh definition before simulation. As such additional result query points may be placed anywhere in the vicinity of the defect--or even within a region not covered by bulk material, as in the case of adjacent imperfections--it is beneficial to aim for interpolation-based stress results along the TCD evaluation paths.
## 3 Results
### Correlation between the Outcome of Treatment and the Levels of Salivary Inflammatory Markers  
Assessing the treatment outcome using PA radiographs showed a significant correlation at 1-year post-treatment follow-up, with the salivary levels of VCAM-1 (\(p\) = 0.01); the better the outcome, the lower the value of VCAM-1. No significant correlation was evident with CBCT or the 2-year treatment outcome.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Control** & **Treatment** & **p-Value** \\ \hline \multicolumn{3}{c}{Median (IQR)} & Median (IQR) \\ \hline
**FGF-23** & 20.44 (10.71–31.26) & 13.23 (8.09–20.17) & 0.089 \\ \hline
**IL-1\(\beta\)** & 354.14 (181.2–577.2) & 398.01 (178.3–772.1) & 0.257 \\ \hline
**IL-8** & 797.57 (370.2–1860) & 511.09 (117.3–1547) & 0.172 \\ \hline
**Pentraxin 3** & 4217.6 (2131–8020) & 2093 (1286–29,057) & 0.143 \\ \hline
**TNF-\(\alpha\)** & 40.15 (20.28–88.88) & 22.96 (9.79–43.46) & 0.029 \({}^{*}\) \\ \hline
**ICAM-1** & \
\begin{tabular}{c} 25,883.02 \\ (13,974–46,718) \\ \end{tabular} &
\begin{tabular}{c} 15,975.95 \\ (9041–35,671) \\ \end{tabular} & 0.144 \\ \hline
**IL-6** & 31.17 (22.74–59.01) & 20.68 (13.44–32.85) & 0.005 \({}^{**}\) \\ \hline
**MMP-8** &
\begin{tabular}{c} 82,732.46 \\ (18,560–269,023) \\ \end{tabular} & 84,431.78 & 0.136 \\ \hline
**E-selectin** & 95.22 (65.03–217.3) & 138.86 (68.16–291.4) & 0.182 \\ \hline
**VCAM-1** & 6412.39 (4205–23,601) & 3059.68 (1557–9886) & 0.005 \({}^{**}\) \\ \hline
**hs-CRP** & 192.04 (100.9–502.2) & 372.35 (166.6–1324) & \textless{}0.001 \({}^{**}\) \\ \hline
**MMP-2** & 974.52 (537.9–1413) & 638.66 (251.3–1617) & 0.127 \\ \hline
**MMP-9** & 4446.12 (921.1–15,776) & 8100.99 (2157–33,650) & 0.057 \\ \hline \hline \end{tabular} \(p\)-value—difference between median of control and disease group using the Mann–Whitney test; \({}^{*}\) (\(p\) < 0.1); \({}^{**}\) (\(p\) < 0.01); \({}^{**}\) (\(p\) < 0.001); IQR: Interquartile range. FGF: Fibroblast Growth Factor; IL: Interleukin, TNF: Tumor Necrosis Factor; ICAM: Intercellular adhesion molecule; MMP: Matrix Metalloprotelease; VCAM: Vascular cell adhesion molecule; hs-CRP: high-sensitive C-reactive protein  
\end{table}
Table 2: Salivary inflammatory biomarker level comparison between control and treatment groups at baseline.
# Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges
## 2 Materials and Methods
### Fuzzy Logic and Fuzzy Weighted Geometric Mean-Theoretical Background
\begin{array}{c}\left(y_{L}\right)_{\alpha}=\text{Min}\ \exp\left(\frac{\sum_{i=1}^{n}w_{i} \ln\left[\left(x_{L}\right)_{i}\right]_{\alpha}}{\sum_{i=1}^{n}w_{i}}\right) \\ \left[\left(w_{L}\right)_{i}\right]_{\alpha}\leq w_{i}\leq\left[\left(w_{U} \right)_{i}\right]_{\alpha},\quad i=1,\ldots,n\end{array}  \tag{13}\]  
\[ \begin{array}{c}\left(y_{U}\right)_{\alpha}=\text{Max}\ \exp\left(\frac{\sum_{i=1}^{n}w_{i} \ln\left[\left(x_{U}\right)_{i}\right]_{\alpha}}{\sum_{i=1}^{n}w_{i}}\right) \\ \left[\left(w_{L}\right)_{i}\right]_{\alpha}\leq w_{i}\leq\left[\left(w_{U} \right)_{i}\right]_{\alpha},\quad i=1,\ldots,n\end{array}  \tag{14}\]  
where exp() is an exponential function. Let \(z=\frac{1}{\sum_{i=1}^{n}w_{i}}\) and \(u_{i}=zw_{i}\) for \(i=1,\ldots,n\), models (13) and (14) can be transformed into:  
\[ \begin{array}{c}\text{Min}\ z_{1}=\sum\limits_{i=1}^{n}u_{i}\ln\left[\left( x_{L}\right)_{i}\right]_{\alpha}\\ u_{1}+u_{2}+\ldots+u_{n}=1\\ \left[\left(w_{L}\right)_{i}\right]_{\alpha}\cdot z\leq u_{i}\leq\left[\left( w_{U}\right)_{i}\right]_{\alpha}\cdot z,\quad i=1,\ldots,n\\ z\geq 0\end{array}  \tag{15}\]  
\[ \begin{array}{c}\text{Max}\ z_{2}=\sum\limits_{i=1}^{n}u_{i}\ln\left[\left( x_{U}\right)_{i}\right]_{\alpha}\\ u_{1}+u_{2}+\ldots+u_{n}=1\\ \left[\left(w_{L}\right)_{i}\right]_{\alpha}\cdot z\leq u_{i}\leq\left[\left( w_{U}\right)_{i}\right]_{\alpha}\cdot z,\quad i=1,\ldots,n\\ z\geq 0\end{array}  \tag{16}\]  
Models (15) and (16) are calculated using linear modelling which was easily conducted in _Matlab_. Let \(z_{1}^{*}\) i \(z_{2}^{*}\) be optimal objective values of models (15) and (16), than \(\left(y_{L}\right)_{\alpha}=\exp\left(z_{1}^{*}\right)\) and \(\left(y_{U}\right)_{\alpha}=\exp\left(z_{2}^{*}\right)\). By defining different \(\alpha\)-cuts, different sets of \(\alpha\)-cuts for \(\widetilde{y_{G}}\) can be generalized based on which \(\widetilde{y_{G}}\) can be expressed as follows:  
\[\widetilde{y_{G}}=\cup_{\alpha}\alpha\cdot\left(y_{G}\right)_{\alpha}=\cup_{ \alpha}\alpha\left[\left(y_{L}\right)_{\alpha},\left(y_{U}\right)_{\alpha} \right],\quad 0<\alpha\leq 1 \tag{17}\]  
Suppose a bridge component has \(n\) elements, \(EL_{i}\) (\(i=1,\ \ldots,n\)), and each element is undergoing under visual evaluation of an expert. Let \(\widetilde{R}_{EL_{i}}=\left(\left(R_{EL_{i}}\right)_{L},\left(R_{EL_{i}} \right)_{M^{\prime}}\left(R_{EL_{i}}\right)_{U}\right)\) be a fuzzy grade of \(i\)-th element, and \(\widetilde{w}_{EL_{ij}}=\left(\left(w_{EL_{i}}\right)_{L},\left(w_{EL_{i}} \right)_{M^{\prime}}\left(w_{EL_{i}}\right)_{IJ}\right)\)\(j=1,\ \ldots,\ n\), is a fuzzy weight of \(i\)-th element by \(j\)-th expert, according to defined fuzzy grade of the element. \(L\) is lower boundary of set, \(M\) is middle value and \(U\) is upper boundary of set. Based on the assumptions, grades of bridge components (superstructure, substructure, and equipment) can be defined using the following steps.
## 3 Materials and Methods
### Statistical Analysis  
The scheme for all gathered data types is shown in Figure 5. For statistical analysis of the calibration distance measurements, the data comparison between different point locations (\(\Delta_{\text{D1}\ldots 4}\)) was evaluated. The same principle was applied for precision data analysis, where comparisons of calibration (\(\Delta_{\text{K1}\ldots 4}\)) data sets were evaluated. Furthermore, comparisons were made between the positional data of marker point precision values (\(\Delta_{\text{Q}}\)) for each setting. Additionally, a comparison between each trueness data point location (\(\Delta_{\text{V1}\ldots 4}\)) in different settings and positions was estimated.  
Data sets were subjected to the Shapiro-Wilk normality test with additional considerations of the results presented with quantile-quantile (Q-Q) plots. To verify the homogeneity of variance between data sets, [PERSON]'s test was used. For data comparison the non  
Figure 4: Marker (\(\Delta\)Q) and camera (\(\Delta\)F) data variations gathered during video recording (**left**). Measurement scheme from recorded video (**right**). Black dots represent marked characteristic physical points, and green points are average coordinates of Kanade–Lucas–Tomasi algorithm results (green crosses). Red lines annotate distance between green and black points (\(\Delta\)V) and the numbers indicate the respective data gathering position.  
Figure 5: Scheme of gathered variables in each experimental situation. Numbers in \(\Delta\)D, \(\Delta\)K, and \(\Delta\)V variables annotate location in dental model (see Figures 1 and 2).  
parametric Kruskal-Wallis test was used followed by [PERSON]'s post-hoc test. For all used statistical tests, the level of significance \(\alpha\) = 0.05 was chosen.
# Vision Transformer-Based Tailing Detection in Videos
## 4 Experiments and Analysis
### Discussion on Tailing Detection Performance with Model Size  
We also performed a scaling study of different models. Since the recall is more important factor in tailing detection, we compared recalls of these models. The model set includes: five variants of CNN: (i) Simple-CNN, (ii) Simple-ResNet, (iii) ResNet50/512, (iv) ResNet101/512, (v) ResNet152/512; and six variants of TSViT: (i) TSViT-S/256, (ii) TSViT-B/256, (iii) TSViT-L/256, (iv) TSViT-B/512, (v) TSViT-S/512, (vi) TSViT-L/512. In the case of TSViT, the recall drops as the network size increases, except for TSViT-S/256 (see Figure 7). We believe that this is related to the fewer attention heads and smaller number of hidden layers in TSViT-B/256 and TSViT-B/256, compared with TSViT-S/256. On the other hand, for CNN variants, ResNet-101 shows very little improvement over ResNet-50 considering the increase of its model size. However, further increasing the network to ResNet-152 reduces the performance, as shown in Figure 7.  
Generally, CNN-based methods perform better when they are fed with contextual information, and CNN makes the relationship between context and the tasks in the deeper layers. However, in our implementation, we only use one-hot encoded frames to represent location, which contains little contextual information. Moreover, the granularity of the location information degrades, because of the presence of max-pooling layers in the CNN, and thus resulting in performance degradation with deeper CNNs. Figure 8 illustrates the phenomena of increasing receptive field as we go deeper into the network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision** & **Recall** \\ \hline TSViT with Sliding Window & 71.03 & 0.7860 & 0.7362 \\ TSViT with ORS & **73.90** & 0.7506 & **0.8732** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between proposed ORS and the sliding window-based sampling method.  
Figure 7: Performance of each network according to model size.  
On the other hand, TSViT performance drops when the attention heads are increased. According to [34], attention distance is analogous to receptive field size in CNN. As depth increases, attention distance increases for all heads. This leads to using more context information, and thus negatively affects the performance of the TSViT.