esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Resistivity Test Analysis
#### 3.3.2 The Effect of Porosity on Resistivity  
The pollution concentration and porosity of contaminated soil samples were controlled, and the relation diagrams of resistivity change of soil samples under different conditions were obtained (Figures 11 and 12).  
Figure 11: The resistivity of zinc-contaminated soil varies with the porosity.  
Figure 10: The resistivity of lead-contaminated soil varies with water content.  
Under the same water content conditions, the change in specimen resistivity shows a positive correlation with the increase in the porosity of the soil sample, resulting in an increase in resistivity. Under the same porosity conditions, the resistivity of the soil samples decreases continuously with increasing pollutant concentration. It is also noted that the magnitude of change in resistivity of the two contaminated clayey soils does not vary much with the increase in soil porosity.  
The reason for this phenomenon may be that in certain water content conditions, with the increase in soil porosity, pore water cannot fill the original pore space, causing the soil to produce excess pore space, so that the overall conductivity path of the soil body is reduced and the soil resistivity drop is elevated. With the further increase in porosity of the soil body and the pore water gradually discontinuing, the pore water resistivity on the overall resistivity of the degree of influence becomes larger, so that the soil resistivity tends to infinity. The resistivity of the two kinds of heavy metal-contaminated powdery clay with porosity change trend is basically the same. The reason for this is that the three heavy metal ions used (Zn, Pb, Cd) are all +2 valence metal ions with similar ionic activities [23].
## 3 Results
### Mass Concentration Comparison of Instruments  
The sampling frequency of the DustTrak DRX instrument resulted in 900 records from each measurement. Mass concentration was monitored in four channels, i.e., PM\({}_{1}\), PM\({}_{2.5}\), PM\({}_{\text{resp}}\) and PM\({}_{\text{10}}\). Data were averaged to be comparable with OPS.  
The mass concentration determined by OPS was averaged in each of the 16 channel midpoints for 10 treatments and 15 min of records in each treatment. Subsequently, a particular fraction of PM was calculated by summing the corresponding range of midpoint averages: PM\({}_{1}\) (from 0.337 to 1.007 \(\upmu\)m), PM\({}_{2.5}\) (from 0.337 to 2.421 \(\upmu\)m), PM\({}_{\text{resp}}\) (from 0.337 to 4.672 \(\upmu\)m), and inhalable fraction PM\({}_{\text{10}}\) (from 0.337 to 9.016 \(\upmu\)m).  
The normal distribution of data was tested by Shapiro-Wilk test. Figure 5 shows particulate mass concentration and comparison of used instruments conducted by paired \(t\) test.  
Figure 5: PM mass concentration measured by OPS and DustTrak DRX, and paired \(t\) test comparison of particle mass generated by sanding paper grits: (**a**) P60; (**b**) P120; (**c**) P240.  
The compared instruments showed no statistically significant difference for PM\({}_{\text{resp}}\). Other size categories of PM showed statistically significant differences. Therefore, the results from DustTrak DRX and OPS did not agree with PM\({}_{1}\), PM\({}_{2.5}\) and inhalable fraction PM\({}_{10}\).
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### 7.3.2 Complex Malicious Attack Protection  
The vast range of IWSN applications means that they have different and often conflicting priorities. These applications thus have dedicated network policies and protocols to this end and anomaly detection schemes should form part of this while maintaining their detection generality. The majority of the schemes proposed in the literature were designed to detect generic anomalies. Considering the fact that anomaly detection schemes should also be able to detect anomalies that result from malicious attacks, which might be concealed, this might not always be adequate. For instance, attackers might be able to compromise several nodes and then launch a series of unobservable attacks [81] to systematically shut the system down or cause irreparable damage. Generic detection schemes may not be able to detect these kinds of attacks if not trained adequately which could be disastrous in critical infrastructure applications. This is especially true with schemes that employ reinforced learning as the new behaviour might be falsely learned as the new norm. Given the lack of training data, how to protect these schemes against various complex malicious attacks while maintaining their detection generality is going to be in interesting research topic going forward.
# Laser Intensity Noise Suppression for Preparing Audio-Frequency Squeezed Vacuum State of Light
## 4 Experimental Results and Discussion  
### Suppression of 795-nm NIR and 397.5-nm UV Laser Intensity Noise  
The radio-frequency power (omitted in Figure 1) driving AOM is controlled by a servo system according the adjustment of Bragg diffraction of AOM to laser intensity which could be thus stabilized. Finally, laser power can be stabilized to a different level with disparate feedback bandwidth. The following two sets of feedback results for 795-nm NIR laser are obtained by changing the bandwidth of PD1 and adjusting parameters of the servo system, as shown in Figure 2a-d. For (a) and (b), the bandwidth of PD1 is set to DC-300 kHz, for (c) and (d), the bandwidth of PD1 is set to DC-100 kHz. The corresponding bandwidth of PD2 is DC-300 kHz, which can cover a wide range of frequencies that we care about. The optical power reached PD1 and PD2 are \(\sim\)2 and \(\sim\)5 mW, respectively. For Figure 2, the feedback bandwidth is mainly determined by the parameter of integral time in the servo control and selection of PD1's bandwidth. In addition, the response frequency of AOM and attenuator also plays an auxiliary role in the feedback bandwidth. Here, the effect of the AOM (Model 3080-122 @ 795 nm, Model 1-M110 @ 397.5 nm, Cooch & Housego, Somerset, UK) and attenuator (Model ZAS-3+, Mini-circuits, Brooklyn, USA) on the feedback bandwidth can be ignored as their response frequency is higher than MHz. We adopt a digital multimeter (Model DM3068, RIGOL, China) to record the output of PD2 for 30 min in time domain. To characterize relative laser intensity noise in frequency domain a FFT dynamic spectrum analyzer (Model SR785, SRS, USA) is employed.  
Figure 2: _Cont._  
Analyzing the above-mentioned experimental results, the narrower feedback bandwidth is, the more obvious the suppression effect will be. However, not all bandwidth can be suppressed due to the limitation of feedback loop. In addition, in order to map the laser intensity noise on the feedback detector to be more real, we try to let as much optical power as possible to reach the sampling detector to make feedback better, but it must be below the saturation level of the detector. Responses at five different sampling optical powers are shown in Table 1.  
According to the same principle, we set up a feedback system for 397.5-nm UV laser beam, as shown in Figure 1b. We changed the type of AOM and in-loop parameter setting. The results are shown in Figure 3a,b, and the corresponding 397.5-nm UV sampling power is -5.0 mW, the bandwidth and gain factor of PD1 are DC \(\sim\)100 kHz and 3, respectively.
## 7 Economic Assessment  
To further assess the economic performance of GSPCW walls, a simple economic evaluation of GSPCW for application in the selected super-high-rise building was performed. Note that this assessment was for the whole structure, including both the original structure and the parts of structure where the conventional RC core walls were replaced with GSPCW walls. Figure 23 and Table 8 present the results from this simple economic assessment. In the RC model and the GSPCW model, the proportions of concrete were 74.78% and 73.14%, respectively, while the proportions of steel were 25.22% and 26.86%, respectively. According to the statistical results, in terms of the GSPCW model versus the RC model, the amount of concrete was reduced by 1.64%, while the amount of steel was increased by 1.64%. In this regard, it can be seen that the overall increase in steel use was marginal when using GSPCW walls in the lower 1/3 of the building. Table 8 lists the material construction prices for each model (considering concrete to be 1100 JPY/ton and steel to be 12,000 JPY/ton). It shows that the material price for the GSPCW models is only increased by about 2.2% per unit area. However, due to the reduction in GSPCW wall thickness, the available architecture area of the GSPCW building increases by 1283.9 m\({}^{2}\), which is almost a 3% increment over the original area. Therefore, the economy of GSPCW walls can still be considered to be reasonable.  
Figure 22: Comparison of story force between the RC and GSPCW models: (**a**) story shear forces; (**b**) story overturning moments.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 2 Related Work
### Nonlinear Autoregressive with Exogenous Input Model  
A NARX model allows the combination of autoregressive modelling, regression on external series, and nonlinear basis functions, and are widely-used, versatile models for the analysis of complex time series [21; 22; 23; 24]. We deploy NARX with neural basis functions favouring their flexibility, although other options such as polynomial or wavelet bases, sigmoid networks, etc., would be equally viable [11].  
The typical NARX neural network model relates a target series \(\{y_{t}\}_{t\in\mathbb{N}}\) to an exogenous series \(\{x_{t}\}_{t\in\mathbb{N}}\) in an autoregressive manner,  
\[y_{t}=F^{\ell}(y_{t-1},\ldots,y_{t-n_{y}},x_{t},x_{t-1},\ldots,x_{t-n_{x}})+ \varepsilon_{t},\ t\in\mathbb{N}, \tag{1}\]  
where \(n_{y},n_{x}\in\mathbb{N}\) are the maximum lags for the series and \(\varepsilon_{t}\in\mathbb{R}\) denotes additive noise [11]. \(F^{\ell}\) represents the basis function--in this case, the neural network--with a nonlinearity degree of \(\ell\in\mathbb{N}\), defined by the complexity of the network.  
NARX neural network models have been applied to meteorological prediction tasks [25], simulation in health applications [26; 27], and anomaly detection in industrial monitoring [28; 29; 30]. However, previous anomaly detection schemes have used crude threshold exceedance-based criteria which can cope poorly with drifts and shifts in variance. Our combination of NARX with SCAPA (as described in the next section) adapts more readily to non-stationary data.
## 5 Case Study and Suggestion Regarding Work Index
### Development on Concrete Surface Crack Recognition  
In order to use the object detection method, a total of 20,000 photos showing concrete cracks in 224 \(\times\) 224 pixels were collected. Concrete cracks were acquired by photographing actual architecture, and the cracks on the image were cropped and saved. It took 200 h under one worker/hour to complete data acquisition and cropping. Special labeling was not required; thus, the work index was 1.4 when compared to a considerable amount of learning data. As a result of learning, the AI models could perceive cracks. While acquiring learning data, the images were amassed around horizontal or vertical cracks, so diagonal cracks were identified, but those intersecting in X were unrecognizable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Method**} & **Amount of Data** & \multirow{2}{*}{**Step**} & **Manpower** & **Working** & **Metrics per** & \multirow{2}{*}{**Avg.**} \\ \cline{1-1}  & & & **/Hour** & **Time** & **Data** & & \\ \hline A. Crack & Classification & 20,000 & Acquisition/labeling & 1 & 200 & 1.4 & 1.4 \\ \hline \multirow{2}{*}{B. Construction waste} & \multirow{2}{*}{Instant segmentation} & \multirow{2}{*}{866} & Acquisition & 3 & 48 & 6.0 & \multirow{2}{*}{3.3} \\  & & & Labeling & 4 & 180 & 1.2 & \\ \cline{3-8}  & & & Relabeling & 4 & 60 & 3.6 & \\ \hline \multirow{2}{*}{C. Rebar} & \multirow{2}{*}{726} & Acquisition & 3 & 48 & 5.0 & \multirow{2}{*}{4.1} \\  & & & Labeling & 4 & 110 & 1.65 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ‘Work index’ on the basis of required manpower and hours depending on the AI R&D method.
## 4 Results
### Analysis of Measured Data  
Figure 8 shows the data collected during the measurement campaign with RSSI values ranging from \(-80\) to \(-130\) dBm with their respective geographic location on the map. Thus, to reduce the signal attenuation effects caused by the wooded environment under analysis, the Pareto 8 front was selected as the most suitable for the scenario, prioritizing the maximization of the coverage area and minimizing the shading effects caused by forests with fewer gateways.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models
### Q-Learning Algorithms  
This study employs Q-learning, a reinforcement learning technique, to enable adaptive MCS index selection in C-V2X environments. The proposed methodology implements a continuous \"on-the-run\" learning process, synchronizing learning time steps with simulation time (\(T\)). Q-values are derived from corresponding action-state pairs, with the action space comprising 28 possible MCS index values (\(A_{k},\ k=1,\ldots,\ 28\)) specific to the C-V2X context. The calculated packet reception ratio (PRR) statistic defines the state function at each time step. Here, the PRR values are produced from the \(P_{rk}\) parameter as an output from the previous Sugeno-FIS routine. Because the \(P_{rk}\) values will be adaptively changed due to the Sugeno-FIS routine, the PRR values should also change over time. Thus, the state values would also be changed over time.  
During each iteration, the system selects an action, applies the chosen MCS index to the resource management calculation, and generates the PRR statistic for the following time step. This approach facilitates dynamic adaptation of the MCS index in response to changing network conditions, potentially enhancing the overall performance of C-V2X communications. The Q-learning function itself can be expressed as  
\[Q(S,A)_{t}\gets Q(S,A)_{t-1}+\alpha\times\Delta Q_{t}, \tag{3}\]  
where \(t\) is the time steps, \(S\) is the state, \(A\) is the action, \(\alpha\in[0\,1]\) is the learning rate, and \(\Delta Q_{t}\) is the difference between the value function target (\(Y_{t}\)) and the current Q-value. The value function target (\(Y_{t}\)) and \(\Delta Q_{t}\) can be expressed as  
\[Y_{t}=R_{t}+\gamma\underset{A}{\max}Q(S,A)_{t}, \tag{4}\]  
And  
\[\Delta Q_{t}=Y_{t}-Q(S,A)_{t-1}, \tag{5}\]  
respectively, where \(R_{t}\) is the reward function in current time step and \(\gamma\in[0\,1]\) is the discount factor.  
The corresponding reward function can be expressed as  
\[R_{t}=PRR_{t}-PRR_{t-1}, \tag{6}\]  
In the initial time step, the initial Q-value is set to equal to the current \(PRR_{0}\) based on the initial MCS index value \(I_{MCS_{0}}\). As the learning step runs, the appropriate action will be selected according to the \(\varepsilon\)-greedy exploration mechanism,  
\[\pi_{t}=\left\{\begin{aligned} & choose\ random\ action\ in\ A,\ \ if\ \xi_{t}<\varepsilon\\ & arg\underset{A}{\max}Q(S,A)_{t},\ \ otherwise\end{aligned}\right., \tag{7}\]  
where \(\xi_{t}\) is a random value between 0 and 1 and \(\varepsilon\in[0\,1]\) is the predefined epsilon value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CSI** & **d** & **VN** & **N** & **F** & **VF** \\ \hline \hline
**VL** & VL & L & L & M \\ L & L & M & L & M \\ M & M & H & M & H \\ T & H & H & M & VH \\ VT & H & M & M & VH \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rules.
## 6 Conclusions  
A fast and robust Gaussian fitting technique was adopted to analyze and parameterize the spatial distribution of the SUHI for a selected urban area. This technique provides a tool to perform quantitative comparisons between different SUHI radial maps useful for inter-city comparisons or for  
Figure 4: MODIS, August 19, 2009, 14:30. SUHI [K] correspondent to the vertical cross-section of the Gaussian surface along the major axis (left panel) and along the minor axis (right panel). The axes \(a_{x}\) and \(a_{y}\) for the two ellipses of area A_61% and A_1K are also reported.  
Figure 5: Landsat TM, August 6, 2009, 12:00. SUHI [K] correspondent to the vertical cross- section of the Gaussian surface along the major axis (left panel) and along the minor axis (right panel). The axes \(a_{x}\) and \(a_{y}\) for the two ellipses of area A_61% and A_1K are also reported.  
single city analysis over different time scales. The enclosed plotting procedure allows a further better understanding of the shape and orientation of the heat island for each image, and allows us to analyze the performance of the fitting in terms of correlation and reliability of the SUHI pattern representation.  
In particular, the results of the parameterization have been shown both for low-resolution and high resolution images. While low-resolution satellite data are adequate for studying the large-scale SUHI characteristics, it is useful for urban planners to use also high-resolution thermal data to monitor the heat island intensity on a smaller scale, with the aim to control or mitigate the effects of the SUHI in relation to population density and land cover.  
As expected, an increase of the extent parameters and magnitude and a reduction of the correlation coefficient were observed for the map with the higher spatial resolution. This fitting method is not influenced by localized high surface temperatures within the image, smoothing isolated peaks: this is especially true for the high resolution image, where a greater variability of the SUHI values is evident with respect to the smoothed Gaussian surface, although the effectiveness of the SUHI pattern representation is preserved for the urban area investigated.
## 5 Dynamic Loading Mitigation of Offshore Platform Using TMD  
### Metocean Conditions  
The offshore platform is located in a deep-water region off the coast of Zhejiang Province, China, as illustrated in Figure 8 (Map data in Figure 8 was obtained from Google Maps under academic fair use. Copyright 2024 Google LLC), where the marine environment is characterized by high-intensity typhoons and stratified seabed geology. Environmental parameters governing the platform design were derived from a combination of hindcast data and field measurements, with wave characteristics and current velocities for extreme return periods summarized in Table 1. The 100-year return period storm condition exhibits a significant wave height of 32.2 m paired with a spectral peak period of 18.9 s, while currents demonstrate marked asymmetry between flood and ebb phases.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Water Level (m)** & \(H_{s}\) (m)** & \(T_{p}\) (s)** & **Current Velocity (m/s)** \\ \hline
110.18 & 32.2 & 18.9 & 1.905 \\ \hline \end{tabular}
\end{table}
Table 1: Wave Parameters and Current Velocity for 100-year Return Period.  
Figure 8: Location of the offshore platform.  
Subsea geotechnical conditions, as shown in Table 2, established through a campaign of boreholes below the mud line, reveal a stratigraphic sequence critical to foundation design. From the seabed downward, four distinct sand-dominated strata are identified: an upper loose sand unit transitioning to medium-dense sands with gradual increases in effective unit weight and friction angle. These are underlain by three clay interlayers with undrained shear strength progressively intensifying from 96 kPa to 179 kPa. The basal soil unit comprises highly compacted sand, forming a competent bearing stratum for pile tip embedment.
## 5 Experimental Results  
### Imperceptibility  
Figure 6 shows the original objects and the watermarked ones. It can be seen that the distortion is negligible and cannot be noticed by the human eye thanks to the use mesh saliency in the embedding process. This means that the imperceptibility of the proposed method is enough to make the viewer hard to distinguish the original mesh from the watermarked one. Figure 10 sketches the visual impact of the watermark caused by the watermark embedding for Bunny, Bimba, and Cat meshes. It can be seen in Figure 10 that no perceptible distortion caused by the watermark embedding exists, especially in salient regions. Besides, according to Table 3, it can be concluded that the proposed method can achieve a high imperceptibility performance in terms of MRMS, HD, and MSDM. We guess that this is achieved thanks to the exploitation of mesh saliency to minimize distortions after watermark insertion. In fact, only wavelet coefficients corresponding to salient vertices are altered. It can also be observed that imperceptibility results in terms of MRMS, HD, and MSDM are not the same for the test 3D meshes. We believe this is due to the curvature nature of each one of these 3D objects. In order to evaluate the importance of using mesh saliency in the proposed work, we compared the imperceptibility performance in terms of MRMS, HD, and MSDM without and with saliency. The obtained results for six 3D meshes are listed in Table 4. According to this table, the proposed scheme based on mesh saliency gives higher imperceptibility scores than the scheme without using saliency, which illustrates the importance of saliency to improve the imperceptibility performance of the proposed scheme especially for the MSDM, which is more correlated with human perception.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MRMS (\(\mathbf{10^{-3}}\))** & **HD (\(\mathbf{10^{-3}}\))** & **MSDM** \\ \hline Flower & 0.43 & 3.23 & 0.24 \\ Vase & 0.32 & 2.86 & 0.34 \\ Cup & 0.78 & 2.58 & 0.31 \\ Ant & 0.41 & 3.87 & 0.38 \\ Cat & 0.49 & 0.78 & 0.18 \\ Bimba & 0.31 & 1.41 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Watermark imperceptibility measured in terms of maximum root mean square error (MRMS), HD, and mesh structural distortion measure (MSDM).
## 2 Material and Methods
### Design Parameters  
Four factors, including both flow and geometrical parameters, have been considered to perform a sensitivity analysis: the pressure difference across the microgrippers \(\Delta p\), the lateral hole inner diameter (\(d\)), the releasing mass length (\(L\)) and the microgrippers' body inner diameter (\(D\)). The other geometric quantities (\(l_{1}\) = 3.25 mm, \(l_{2}\) = 2.1 mm, \(l_{3}\) = 10 mm, \(D_{1}\) = 2.6 mm) shown in Figure 5 have been kept constant. A classic full factorial DOE design was adopted and three levels were chosen for each factor. Convenient numerical values have been established for each level in accordance with the size of the device inside a region of interest, as shown in Table 1.  
Twenty-seven geometric configurations, obtained from all possible combinations of geometric parameters' values shown in Table 1, were tested. Each configuration was studied for the three chosen values of \(\Delta p\) = 10, 20, and 30 kPa: 81 CFD simulations were performed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Main Factor** & **Low** & **Middle** & **High** \\ \hline d [mm] & 0.59 & 0.69 & 0.77 \\ L [mm] & 16.2 & 24.2 & 32.2 \\ D [mm] & 3.2 & 3.4 & 3.6 \\ \(\Delta p\) [kPa] & 10 & 20 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Factors and levels for the DOE analysis.  
Figure 5: The simplified gripper model for CFD analysis.