esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
No significant differences over time were observed in the middle trapezius for the mean amplitude EMG\({}_{\text{RMS}}\) (\(F=0.352\); \(p=0.706\); \(\eta_{p}^{2}=0.019\)), maximal amplitude EMG\({}_{\text{RMS}}\) (\(F=1.729\); \(p=0.205\); \(\eta_{p}^{2}=0.088\)), and EMG\({}_{\text{MED}}\) (\(F=0.437\); \(p=0.524\); \(\eta_{p}^{2}=0.024\)). No significant interaction between time and group was found in the low trapezius when considering the mean amplitude EMG\({}_{\text{RMS}}\) (\(F=0.496\); \(p=0.515\); \(\eta_{p}^{2}=0.027\)), maximal amplitude EMG\({}_{\text{RMS}}\) (\(F=0.445\); \(p=0.585\); \(\eta_{p}^{2}=0.024\)), and EMG\({}_{\text{MED}}\) (\(F=0.640\); \(p=0.530\); \(\eta_{p}^{2}=0.034\)). Significant differences over time were observed in the low trapezius for the mean amplitude EMG\({}_{\text{RMS}}\) (\(F=4.388\); \(p=0.044\); \(\eta_{p}^{2}=0.196\)) and maximal amplitude EMG\({}_{\text{RMS}}\) (\(F=7.358\); \(p=0.006\); \(\eta_{p}^{2}=0.290\)), although no significant differences were found in EMG\({}_{\text{MED}}\) (\(F=0.483\); \(p=0.621\); \(\eta_{p}^{2}=0.026\)). Post hoc comparisons revealed a significantly smaller mean amplitude EMG\({}_{\text{RMS}}\) after 100 shots compared to baseline (258.3 vs. 375.0 \(\upmu\)V; \(p=0.014\)) and after 50 shots (258.3 vs. 408.5 \(\upmu\)V; \(p=0.029\)). Figure 3 shows the mean amplitude variations in the electromyography (EMG) root mean square (RMS) values which differed across all the regions analyzed across the three repeated measures.
## 5 Conclusions  
1. It can be seen from the SEM image that the PEF with the voltage amplitude of 25 kV, the processing time of 4.62 min., and the pulse duration of 60 us acts on _Rhizoctonia solani_, destroyed the morphological structure and accompanied the overflow of cell contents, which indicates that PEF can effectively destroy the structure of cell membrane and cell wall.
2. The cell membrane damage was detected by measuring the conductivity value of the mycelium suspension. It was found that the cell membrane damage rate of _Rhizoctonia solani_ was mainly related to the voltage amplitude and processing time (_p_\(<\) 0.01), while the current pulse duration of 30-60 us was not significant (_p_\(>\) 0.05).
3. The cell wall damage was detected by measuring the change of absorbance at 510 nm, and it was found that the absorbance value at 510 nm of _Rhizoctonia solani_ was mainly related to the change of electric field strength (voltage amplitude) (_p_\(<\) 0.01), while the current pulse duration of 30-60 us and the current processing time of 1-5 min. was not obvious (_p_\(>\) 0.05).
4. The optimal treatment conditions for PEF is as following: voltage amplitude 25 kV, processing time 4.62 min., and pulse duration 60 us. the cell damage rate of was 48.72%, and the absorbance value at 510 nm was significantly higher than that of CK (_p_\(<\) 0.01). When compared with CK, the cell damage rate of _Rhizoctonia solani_ was 47.91%, and the absorbance value at 510 nm was 0.0067, which were significantly higher than CK (_p_\(<\) 0.01).  
Conceptualization, X.F. and H.L.; methodology, X.F.; software, Q.J. and M.Z.; validation, X.F., W.Y. and J.J.; formal analysis, Q.J.; investigation, H.L.; resources, J.J.; data curation, Q.J.; writing--original draft preparation, Q.J and J.J.; writing--review and editing, X.F. and M.Z.; funding acquisition, X.F. and H.L.  
This research was funded by National Natural Science Foundation of China (Grant No. 51507081), the Fundamental Research Funds for the Central Universities (KJQN201623), the National Key Research and Development Program of China (2017 YFD0701300).  
The authors declare no conflict of interest.
## 4 Experiment
### Comparison Model  
To assess the effectiveness of the SPE propensity score estimation method in mitigating selection bias, we compared it with two baseline models, namely LFM and BiasSVD. Additionally, we included three existing propensity score estimation methods, namely IPS [11], DR [17], and MRDR [18], for comparison. By combining these methods with the LFM model, we constructed three additional comparison models.  
LFM-IPS: IPS is a framework for eliminating selection bias, which uses the marginal probability of the user's item selection as the estimation method of the propensity score and combines the framework and LFM model.  
LFM-DR: DR is a dual-robust estimator that integrates the estimation error and the propensity in a doubly robust manner to obtain unbiased performance estimates and mitigates the effect of propensity variance, combining this estimator with an LFM model.  
LFM-MRDR: MRDR is a more robust estimator, which further reduces the variance based on the dual robust estimator, combining this estimator with the LFM model.  
Considering that the models utilized in this experiment are all founded on matrix decomposition, their time complexity is denoted as O(tmnK). These algorithms exhibit no substantial disparity in terms of time complexity; the variance lies primarily in the number of iterations, while their time consumption remains within the same order of magnitude.
# Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling--An Oil and Gas Sector Case Study
## 2 Background and Related Work
### Threat Modeling Techniques
#### 2.2.4 Common Vulnerability Scoring System (CVSS)  
The CVSS [17,18] (Common Vulnerability Scoring System) Framework defines a standardized framework for quantitatively determining the severity of security vulnerabilities. This score helps to prioritize mitigation efforts by showing a vulnerability's potential impact and exploitability.  
CVSS scores vulnerabilities from 0.0 (least severe) to 10.0 (most severe) using three essential metrics:  
* The base score indicates the underlying severity of the vulnerability, considering aspects such as exploitability, scope, and the possible impact on the system's three core security services: confidentiality, integrity, and availability (CIA).
* Temporal Score: This score evaluates both the availability of exploits for vulnerability and the likelihood of successful exploitation. It is a dynamic component that may change over time as new exploits arise. It considers three factors: exploit code maturity, availability of remediating controls, such as a patch, and report confidence.
* Environmental Score: This metric considers the specific characteristics of the target system's environment that may influence the vulnerability's ultimate impact. It is adaptable to the asset's value, the perpetrator's privileges, and the existing security controls.  
Combining these three metrics provides both a comprehensive and useful standardized approach for evaluating vulnerabilities in legacy ICS systems. This allows security professionals to prioritize and focus their mitigation controls and resources on addressing the most critical vulnerabilities that pose the greatest risk.
# A New Method for Evaluating the Bearing Capacity of the Bridge Pile Socketed in the Soft Rock
## 2 Research Methodology
### Analysis of the Bearing Capacity Coefficient's Parameters  
Many factors affect the bearing capacity and restraint stiffness of a single rock-socketed pile, including design parameters, engineering geology conditions, and construction factors. The geological situation is set, as the embedded rock is moderately weathered soft rock, and the overlying rock is strongly weathered soft rock. According to the properties of soft rock, the parameters are valued in ranges as follows: the elastic modulus of the bearing stratum rock is valued between 2.0 and 5.0 \(GPa\), the elastic modulus of rock at the pile side is valued between 0.2 and 0.6 \(GPa\), the pile body modulus is valued between 20.0 and 40.0 \(GPa\), the pile length is valued between 6.0 and 18.0 m, the pile diameter is valued between 0.8 and 2.0 m, and the rock-socketed depth is valued between 1.0 to 9.0 \(m\).  
The ratio of the bearing capacity \(R_{a}\) (calculated using Equation (24)) to the constraint stiffness \(K_{\text{vs}}\) (calculated using Equation (25)), i.e., the bearing capacity coefficient \(\lambda\), is proposed. With the parameters valued in the above ranges, the relationships between the parameters and the bearing capacity coefficient were calculated and are shown in Figure 3.  
Figure 3: The bearing capacity coefficient curves with different parameters: (**a**) the pile foundation length, (**b**) the pile foundation diameter, (**c**) the rock-socketed length, (**d**) the pile foundation modulus, (**e**) the modulus of the bearing stratum rock, and (**d**) the modulus of rock at pile foundation.  
It can be seen from Figure 3 that the pile length, socketed depth, and pile modulus have a more significant impact on the bearing capacity coefficient, while the pile diameter and the bearing stratum rock modulus have a smaller influence. By comparison, the pile side rock modulus has no apparent impact on the bearing capacity coefficient, which is also the reason that rock-socked piles are mostly end bearing piles.
## 4 The Optimization Model
### Design Variables  
The geometric shape of the blade can be generated by chord distribution, twist distribution and the airfoil series. Eight control points are used for the thickness distribution, and the upper and lower bounds of the control points are set with adequate space, as shown in Figure 4. Nine control points are used for the chord and twist distributions, as shown in Figure 5. The first control point of chord distributions is at the root, the third control point is at the maximum chord station, and the ninth control point is at the blade tip. The thickness distribution is defined with a 9 th order Bezier curve, and the chord and twist distributions are defined with a 10 th order Bezier curve. For large scale wind turbine, a lower tip speed ratio would result in less efficient aerodynamic performance but lower cost of fabrication because of less aerodynamic loads by lower blade centrifugal stresses. The lower tip speed ratio can also reduce the tower cost because of the lower aerodynamic loads from the rotor. Thus, the tip speed ratio is set as a design variable in the design process.  
Figure 4: Thickness distribution of the blade.  
Figure 3: Comparison between the stiffness and mass per unit length distributions obtained by CLT code and actual blade.  
The typical airfoil and its internal structural configuration of a typical modern wind turbine blade are shown in Figure 6 (c is the chord length). The internal structure mainly consists of spar cap and shear webs. The spar caps at the maximum thickness resist the flap-wise loads and the two shear webs resist the torsional loads. In this work, the maximum thicknesses of the spar cap and trailing edge reinforcement are the design variables for the blade layup schedule which are shown in Figure 7. The shear webs distributions are also considered in the structural optimization design. The locations of each web at blade root and tip are set as design variables.  
Figure 5: Chord and twist distributions of the blade.  
Figure 6: Typical structural parameters for section of the blade.  
Figure 7: Design variables for spar cap and trailing edge reinforcement.  
Figure 8 presents the schematic of the tower structure. With increasing tower height, the optimization design needs to trade off between the energy production and the cost of the structure. The wind turbines used in low wind speed areas always have higher hub height to obtain better wind sources than those used in high wind speed areas. The tower heights are always much greater than 80 m in low wind speed areas. Thus, the relationship of the tower mass and cost scaling are not efficient [15]. Therefore, an actual 2.1 MW commercial tower is used in an optimization process instead of using an empirical equation for the tower cost. Foundations were scaled as a function of hub height and rotor swept area, which is directly proportional to the tower overturning moment. The top and base diameters of the tower and their thickness are taken as design variables. The variations of diameters and thickness across the length of the tower are assumed as a linear distribution.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Evidence Representation Based on Spatiotemporal Structure
#### 4.2.3 Spatiotemporal Feature Fusion Unit  
To represent the semantic features of information from multiple perspectives, the temporal semantic representations and spatial structural representations of evidence are selected and combined through a fusion gate to obtain a semantic representation of the spatiotemporal structure of evidence. Because the temporal semantic representation and spatial structural semantic representation of evidence are not in the same semantic space (\(2l\
eq Kq\)), it is necessary to convert them into the same semantic space (i.e., \(\mathbf{h^{\prime}},\mathbf{m^{\prime}}\in R^{h}\)).  
\[\begin{split}&\mathbf{h}_{i}^{\prime}=\tan h(\mathbf{W_{\text{h}}}\mathbf{h _{\text{i}}})\\ &\mathbf{m}_{i}^{\prime}=\tan h(\mathbf{W_{\text{m}}}\mathbf{m_{\text{i}}}) \end{split} \tag{10}\]  
Here, \(\mathbf{W_{h}}\in R^{h\times 2l}\) and \(\mathbf{W_{m}}\in R^{h\times Kq}\) denote the transformation matrixes from the different feature spaces of evidence \(i\) to an implied common space.  
Next, we used a fully connected layer with a sigmoid activation function to learn the importance of temporal sequence semantic representation and spatial structure semantic representation, as shown in Equation (11):  
\[\mathbf{z}=\sigma\big{(}\mathbf{W}_{z}\big{[}h^{\prime}_{i};\mathbf{m}^{\prime}_{i}\big{]} \big{)}, \tag{11}\]  
where \(\mathbf{W}_{z}\in R^{h\times 2h}\) is a learnable weight matrix. \(\mathbf{z}\in R^{\ h}\) is a weight vector to trade off the semantic representation from spatial and temporal structures. Its elements are in (0, 1). Finally, the two semantic representations are fused using different weights.  
\[\mathbf{x}_{i}=\mathbf{z}\odot\mathbf{h}^{\prime}_{i}+(\mathbf{1}-\mathbf{z})\odot\mathbf{m}^{\prime}_ {i} \tag{12}\]  
Here, \(\odot\) denotes elementwise multiplication. \(\mathbf{x}_{i}\in R^{h}\) is the information representation obtained through the fusion gate, and h is the dimension of the fusion gate output representation.
## 1 Introduction
In addition, some scholars at home and abroad had studied the freshness of other aquatic products through near-infrared hyperspectral imaging technology. [PERSON] et al. [16] distinguished between fresh and freeze-thawed cod fillets by using VIS/NIR hyperspectral technology combined with machine vision, by which high classification accuracy was acquired and the freshness of each fillet could be determined and the number of frozen days could be evaluated with a precision of 1.6 days. [PERSON] et al. [17] used VIS/NIR hyperspectral imaging technology (400-1000 nm) combined with classifier and spectral pre-processing technology to distinguish fresh, frozen (4 \({}^{\circ}\)C, 7 days) and frozen-thawed (20 \({}^{\circ}\)C, -40 \({}^{\circ}\)C, 30 days) grass carp slices. Four kinds of classifiers were used to establish classification models and classifier analysis combined with hyperspectral imaging could quickly distinguish between frozen, frozen-thawed, and fresh fish slices. The modeling results showed that the classification accuracy peaked at 92.29% and hyperspectral imaging technology combined with classifier analysis had tremendous potential for online inspection. As a new generation of inspection technology, NIR hyperspectral imaging technology is developing well for aquatic product quality inspection as it can simultaneously obtain unique spectral and image information for each pixel of an object's surface [18]. To our knowledge, studies have rarely utilized NIR hyperspectral imaging to accurately determine the shelf life of shrimp. Therefore, in this study, NIR hyperspectral imaging technology combined multivariate methods to identify the freshness of shrimp. The targets are as follows: (1) analyzing the changing trend and absorption peak distribution of full-band spectrum and average spectrum of white shrimps with different freshness; (2) selecting the best preprocessing method is identified by modeling and analyzing the full wavelengths with various preprocessing methods; (3) utilizing different characteristic wavelength extraction algorithms to extract the characteristic wavelengths of the spectral data processed by the best preprocessing method, and then various discriminant models are employed to identify the model performance of different characteristic wavelengths so as to work out the optimal model for freshness detection of white shrimps; (4) substituting the original random hyperspectral images into the optimal model to visualize the classification effect of white shrimp refrigerated for different days and the feasibility of the selected model is thus verified.
## 3 Methods and Results  
### Electro-Optical and Infrared  
#### 3.1.1 VHR Data  
Very-high-resolution (VHR) satellite imagery is now approaching the quality of airborne observations, which were used for many decades for sea-ice reconnaissance [1; 44]. Imagery acquired by VHR optical satellites can be used for qualitative image interpretation to identify various sea-ice features and is especially valuable when detailed ground validation is not available. Optical imagery has been used to map pressure ridges to verify bright features in SAR images [45], and imagery acquired by a VHR airborne camera were used for the operational planning of navigation through drifting ice [46]. An overview of the retrieval from VHR satellite imagery of five sea-ice parameters including melt pond coverage, open water fraction, ridge height, floe size and openings and closings [37]. Thus, an expert can easily identify EIFs in a VHR image. [PERSON], who had many years of expertise with EIFs in the Beaufort Sea and other areas, was consulted to assist in interpreting VHR satellite data and detecting EIFs.  
Ice islands in the Beaufort Sea appear \"striped\" in optical imagery and are more visible in winter when the snow is dry. One defining characteristic, along with their enormous size and tabular shape, is the rolling terrain of almost parallel hills and troughs of similar size and depth. Hills are typically parallel to prevailing wind direction and shore. The ridges of multiyear land-fast sea ice also give a striped appearance, but of a smaller scale than those of ice islands/ice shelves [47]. A typical ice island has a convex, wavy surface and is usually smoother than the surrounding ridged sea ice (Figure 2). Ice-island fragments are similar to ice islands but have two main differences. First, ice-island fragments have smaller sizes (we considered their size to be less than 100 m). Second, they very often appear as small features with sharp edges.  
The appearance of hummocks, including MYHFs, in VHR electro-optical and infrared images is provided in the following sections.  
Figure 2: Ice island (**a**) and ice-island fragments (**b**) in WorldView-2 image. Images © DigitalGlobe, 2014.
## 4 FTN Detection Scheme in Multipath Channels Based on Neural Network
### Proposed Deep-Bi-LSTM Architecture for FTN Signal Detection
#### 4.2.3 Reasons for Using Deep-LSTM Structure  
Like Deep Neural Networks (DNN) with a deep architecture, Deep-LSTM has been successfully used in fields such as speech recognition text generation, machine translation, speech recognition, generated image description, and video tagging. In fact, any single LSTM neural network is already a deep architecture, because it can be regarded as a feed-forward neural network unfolded in time, in which eachayer shares the same model parameters. Compared with traditional standard LSTM networks, Deep-LSTM networks provide another advantage. They can make better use of parameters by distributing parameters in multiplexers in space. For example, if the memory size of the standard model is increased by 2 times, there can be 4 ayers with roughly the same number of parameters, whicheads to more non-linear operations for the input of each time step. The simulation in Section 5.2 also proves that introducing a Bi-LSTM network with a suitable depth can improve the performance of the network for FTN signal detection, especially in multipath channel scenarios.  
All in all, the interference of FTN signaling mainly comes from the influence of adjacent symbols on the current symbol, which has something in common with the processing architecture of Deep-Bi-LSTM.
## 3 Method
### Bidirectional Flow Loss as Occlusion Mask  
To detect occlusion, we conduct the method recommended by [23] which is based on forward-backward consistency assumption to mask the occluded area. The forward flow \(F_{t}^{f}(u_{f},v_{f})\) and backward flow \(F_{t}^{b}(u_{b},v_{b})\) are computed by performing a second pass with the input images exchanged with shared weights and symmetric loss. Occluded pixels are marked whenever there is a significant mismatch between these two flows. The occlusion flag is set to be 1 when the following constraint  
\[\|F_{t}^{f}+F_{t}^{b}|^{2}<\alpha_{1}(|F_{t}^{f}|^{2}+|F_{t}^{b}|^{2})+\alpha_ {2} \tag{1}\]  
is against, and 0 otherwise. In our experiment we set \(\alpha_{1}=0.01\), \(\alpha_{2}=0.5\). Then the occluded pixel mask of frame \(M_{t}\) is calculated as \(O_{t}\).  
Forward-backward consistency between adjacent flows for the non-occluded region is applied during training, along with a constant penalty \(\lambda_{p}=3.5\) to all occluded pixels in case the whole scene happens to be occluded:  
Figure 1: Overview of our system. It consists of three task-specific sub-networks targeting at estimating monocular depth, optical flow, and camera motion. Rich geometric constraints are employed extracting from the natural structure of the scene. Here M denotes input image and P denotes point cloud.  
\[L_{fb}=\sum_{X\in M}(1-O_{t})\rho(\|F_{t}^{f}(X)+F_{t}^{b}(X)\|_{1})+O_{t}\lambda_ {p} \tag{2}\]  
with \(X\) denoting pixels of image \(M\), \(\rho\) denoting the robust generalized Charbonnier penalty \(\rho(x)=\sqrt{x^{2}+0.001^{2}}\).
## 5 Discussion
### Technical Analysis and Comparative Effectiveness of ML and DL Models
#### 5.2.2 Random Forest (RF)  
Technical Details: Random forest is an ensemble learning method that constructs multiple decision trees during training. It makes predictions based on the majority class for classification tasks or the average prediction of individual trees for regression tasks.  
Performance: According to [PERSON] et al. (2019) [14], random forest (RF) models are robust against overfitting and provide high accuracy for demand forecasting. In vehicle demand forecasting, the RF model showed a 10% improvement in accuracy compared to traditional regression models.