esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.6 Adaptive Local-Fitting (Alf) Method  
The ALF model pushes the initial contour towards the object boundary using adaptive local fitting energy and regularization energy. This method shows an accurate way to separate the region of interest. The traditional methods assume that the intensities in the local region are constant; however, the LBF method finds an optimal solution by fitting the original image using an adaptive technique [52].  
The ALF method improves on the local binary fitting method. The energy function of the ALF is:  
\[\begin{split} E^{ALF}=&\int\sum_{i=1}^{N}\biggl{(} \kappa_{i}\int w^{x}(x-y)|I(y)-\mu_{i}(x)-\lambda(x)\bar{\sigma}(y)|^{2}\\ &\times M_{i}(\Phi(y))dy)dx\\ &+\beta\int\frac{1}{2}(|\
abla\Phi(x)|-1)^{2}dx+v\int\delta( \Phi(x))|\
abla\Phi(x)|dx\end{split} \tag{15}\]  
where \(w^{x}\) is a truncated weight function, \(\bar{\sigma}\) is an appropriate estimation to \(\sigma\) and \(M_{i}(\Phi(y))\) is a membership function satisfying:  
\[M_{i}(\Phi(y))=\left\{\begin{array}{l}1,y\in\Omega_{i}\\ 0,\text{ else}\end{array}\right. \tag{16}\]  
In Equation (27), when \(\lambda(x)=0\) or \(\bar{\sigma}(y)=0\), it is the same as the LBF model. So, the LBF model can be seen as special case of the proposed ALF model. Nevertheless, LBF discards the local information about intensity variance Despite the necessity to improve the efficiency of the method, the model can extract more details and performs robustly with regard to to intensity inhomogeneity and noises.
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 4 Results
### Sensitivity to Initial Guesses  
Finally, to assess the stability of the model, the parameter estimation was repeated using different initial values, specifically the lower and upper bounds of the considered ranges. Focusing on the teardown scenario, the analysis was performed on the diffusion coefficients, as these parameters span several orders of magnitude. The effect on the estimation of the parameters was examined: no significant difference was observed in the estimation of the active material fractions, while the variations in the diffusion coefficients themselves and reaction rates are reported in Figure 12. Given the non-convex nature of the objective function--namely, the presence of multiple local minima--there is no unique set of parameters to which the algorithm will necessarily converge. Nevertheless, the electrochemical-mechanical approach exhibits greater stability, tending to converge to similar values even when different initial guesses are used. In fact, the inclusion of mechanical measurements provides further information and constraints that reduce the sensitivity to initial parameter guesses, thereby lowering the risk of the algorithm converging to a local minimum that is less representative of the actual cell response.
## 4 Simulations
Figure 2(f) depicts the profile of the control inputs where the input saturation (IS) problem Was effectively compensated by the auxiliary system. The control inputs of the proposed method were in the specified region. Hence, in the case, the proposed FCONN path following control method was more effective and robust according to these simulation results. Figure 3: _Cont._  
Case 2: Similar to Case 1, another comparison is presented to verify the performance in the case of following a curvilinear path with the same design parameters. The desired path and speed assignment were chosen as \(\mathbf{P}_{d}=\theta,[10 sin(0.1\theta)]^{T}\) and \(u_{d}=0.5\) m/s. The initial states were given as \([x(0),y(0),u_{r}(0),v_{r}(0),r]^{T}=[0\) m, \(2\) m, \(0.01\) m/s, \(0\) m/s, \(0\) rad/s\(]^{T}\). The simulations results are shown in Figure 3(a)-g and the performance quantification indices are summarized in Table 2. As illustrated in Figure 3(a), although following the curvilinear path, the proposed FCONN method behaved almost the same in both control scenarios. As shown in Figure 3(b), the along- and cross-tracking errors oscillated to varying degrees for the poor performance of IAONN, whereas the position errors of FCONN could smoothly and steadily converge to a small neighborhood around zero within a short time. Moreover, the smaller IAE and ITAE metrical values verified it. Figure 3(c) shows the slight oscillation of the heading and surge velocity tracking error of IAONN. Moreover, in Figure 3(d), the poor performance of estimating ocean currents of IAONN undoubtedly degraded the tracking performance. Figure 3(e),f shows the exceptional performance of the dynamical uncertainties estimation and disturbance rejection \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Control Law**} & \multicolumn{2}{c}{**Line Path Following**} & \multicolumn{2}{c}{**Curvilinear Path Following**} \\ \cline{2-5}  & **IAE(-10\({}^{3}\))** & **ITAE(-10\({}^{3}\))** & **IAE(-10\({}^{4}\))** & **ITAE(-10\({}^{4}\))** \\  & \(\int_{0}^{t}|\mathbf{y}_{e}|d\tau\) & \(\int_{0}^{t}|\mathbf{y}_{e}|d\tau\) & \(\int_{0}^{t}|\mathbf{y}_{e}|d\tau\) & \(\int_{0}^{t}|\mathbf{y}_{e}|d\tau\) \\ \hline FCONN & 1.92 & 5.78 & 2.58 & 9.01 \\ IAONN & 4.18 & 13.12 & 7.32 & 20.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance indices in these two path following scenarios. Figure 3: Line path following results. of the FCONN method. In addition, the control inputs were in the specified region for introducing the auxiliary system, as depicted in Figure 3(g). Overall, the proposed control method achieved satisfactory performance and robustness in both cases with fast and accurate estimations of the unknown time-varying ocean currents and dynamical uncertainties, and satisfactory rejection of external disturbances. Figure 4: _Cont._
## 3 LiGME Model for Group Sparse Estimation
### LiGME of \(\ell_{2,1}\)-Norm  
For simplicity as well as for effectiveness in application to GSC and WGSC, we focus on the LiGME model of \(\|\cdot\|_{2,1}\) with an invertible linear operator \(\mathbf{W}\),  
\[\underset{\mathbf{x}\in\mathbb{R}^{n}}{\text{minimize}}\ J_{(\|\cdot\|_{2,1})_{ \mathbf{x}}\circ\mathbf{W}}=\frac{1}{2}\|\mathbf{y}-\mathbf{A}\mathbf{x}\|_{2}^{2}+\lambda(\|\cdot \|_{2,1})_{\mathbf{B}}\circ\mathbf{W}(\mathbf{x}). \tag{20}\]  
In this case, for achieving \(J_{(\|\cdot\|_{2,1})_{\mathbf{x}}\circ\mathbf{W}}\in\Gamma_{0}(\mathbb{R}^{n})\), we can simply design \(\mathbf{B}\in\mathbb{R}^{m\times n}\), in a way similar to ([31], (48)), as in the next proposition.  
**Proposition 4**.: _For an invertible \(\mathbf{W}\in\mathbb{R}^{n\times n}\), let_  
\[\mathbf{B}=\sqrt{\theta/\lambda}\mathbf{A}\mathbf{W}^{-1},\ 0\leq\theta\leq 1, \tag{21}\]  
_then for the LiGME model in (20), \(J_{(\|\cdot\|_{2,1})_{\mathbf{x}}\circ\mathbf{W}}\in\Gamma_{0}(\mathbb{R}^{n})\)._  
Proof.: By \(\mathbf{A}^{\top}\mathbf{A}-\lambda\mathbf{W}^{\top}\mathbf{B}^{\top}\mathbf{B}\mathbf{W}=\mathbf{A}^{\top }\mathbf{A}-\lambda\mathbf{W}^{\top}(\sqrt{\theta/\lambda}\mathbf{A}\mathbf{W}^{-1})^{\top}( \sqrt{\theta/\lambda}\mathbf{A}\mathbf{W}^{-1})\mathbf{W}=(1-\theta)\mathbf{A}^{\top}\mathbf{A} \succeq\mathbf{O}_{n\times n}\) and Proposition 1, \(J_{(\|\cdot\|_{2,1})_{\mathbf{x}}\circ\mathbf{W}}\in\Gamma_{0}(\mathbb{R}^{n})\) is ensured.  
Model (20) can be applied to many different applications that conform to group-sparsity structure.
## 2 Materials and Methods
### Deep Water Body Segmentation Network
#### 2.3.2 Encoder and Decoder  
The backbone network used in this study is MobileNetV2, whose core is composed of inverted residual blocks and depthwise separable convolutions. Each inverted residual block dynamically adjusts the number of channels through an expansion factor, successively generating multi-scale feature maps with 16, 24, 32, 64, 96, and 160 channels. Feature reuse is achieved through skip connections between various modules, enhancing the expressive ability of the model and providing a feature foundation that combines local details and global semantics for subsequent upsampling and multi-scale fusion, as illustrated in Figure 3.  
After downsampling through the deep MobileNetV2 encoder, the model utilizes an upsampling module that combines bilinear interpolation with a 3 \(\times\) 3 convolution to gradually restore the resolution of the feature maps and finally output the segmentation results. The upsampling module can dynamically adjust the input and output channels according to the feature scale. The concatenated multi-scale feature maps are further corrected and optimized through a 1 \(\times\) 1 convolution and a learnable Kalman filter module, effectively achieving the fusion of global semantics and local details and enhancing the model's ability to distinguish between water pollution and the interference of vegetation shadow occlusion.
## 5 Surface Accuracy and Topography Analysis  
### Diamond Grinding Wheel Topography after Texturing  
The curve between the actual and ideal section profile was compared in Figure 9, and the profile error was analyzed. The measured values on the textured grinding wheels are in reasonably good agreement with the target values. The profile error of the metal-bonded and resin-bonded diamond wheels were 15.45 \(\upmu\)m and 9.62 \(\upmu\)m, respectively. The maximum profile error in the metal-bonded diamond wheel occurred near the middle of the sinusoidal contour, mainly caused by the increased water pressure, and a more abrasive flow rate increased the wheel material removal at the bottom of the sinusoidal profile. The profile error of the grinding wheel is also connected to the effect of the particles' secondary impact, and the errors are confined to the allowable range. Two micro-textured grinding wheels and their corresponding microscopic morphology measurements are indicated in Figure 10. It can be seen from the figure that the bond material on the wheel surface was removed by MAWJ, leading to the emergence of a large proportion of abrasive grains with a certain protrusion height. It can be concluded that the texturing method, which uses MAWJ, obtains the desired textured-wheel profile and improves grinding performance. Comparing the results of two grinding wheels, it is proven that the resin-bonded diamond wheel has higher accuracy than the metal-bonded diamond wheels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Wheel Speed** & **Feed Rate** & **Depth of Cut** & \\  & **(m/s)** & **(m/s)** & **(\(\upmu\)m)** & **Grinding Coolant** \\ \hline Value & 20 & 0.17 & 5 & 5\% water-based \\  & & & & grinding fluid SC25 AC \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rough grinding process parameters.  
Figure 8: Experimental setup for combined grinding of CMA moulds.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Other Approaches  
As discussed in detail in Section 3, ILNP is an Identifier Locator (IL) approach to networking. Although there are other approaches to IL networking, they have varying architectures and, especially, approaches to addressing. Architecture and addressing are particularly important to consider, given that we have noted at the beginning of this section that we have a desire to focus on practical, deployable solutions. A survey of IL approaches to networking is provided in [27]. That study made qualitative comparisons between many proposals that use an IL approach. The comparison included discussion on state management and control plane functionality, as well as considering different OAM approaches, e.g., edge-core separation, centralised vs de-centralised, etc. The study included ILNP, HIP, LISP, MIPv6, and Shim6 (all compared in Table 1), as well as related proposals. Another review and assessment of IL approaches is given in RFC6115 [28]. That study took a practical view and considered global deployability, scalability, and compatibility with current Internet architecture as part of its analysis. Again, ILNP was included with HIP, LISP, and Shim6, and others, so there is overlap with the list of approaches examined in the survey noted above [27]. The RFC6115 recommended the use of ILNP as the preferred solution. Meanwhile, where general Internet-wide deployability is not a key consideration, there are possibilities for enterprise-oriented solutions for combined mobility and multihoming. These often leverage Virtual Private Network (VPN) technology for specific use cases, and two key examples are given below. A mature solution is IKEv2 Mobility and Multihoming Protocol (MOBIKE), where IKE is the Internet Key Exchange Protocol [29]. The use of MOBIKE is defined in [30] to provide both mobility and multihoming, and it builds upon a VPN-based system initially defined in [31]. The VPN-based approach is focussed on enterprise users. This means that it has an OAM overhead for overall deployment, and it is not suited for general-purpose use by Internet users, e.g., domestic users. As ILNP only requires unicast connectivity and uses a simple, end-to-end signalling protocol, it does not have the same constraints. Another enterprise-focussed approach, especially for providing interconnection between datacentres, is the Ethernet VPN (EVPN) [32]. As the name suggests, the main goal of this approach is to extend the _logical_ viewpoint of a single Ethernet network beyond its normal _physical_ constraints. EVPN is built upon the notion of a Virtual Private LAN Service (EVPLS) [33]. EVPN extends this capability to provide the logical viewpoint of the Ethernet beyond the boundary of a single network. EVPN uses extensions to the Border Gateway Protocol (BGP) along with Multiprotocol Label Switching (MPLS) [34; 35; 33] for state
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Challenges Necessating Automation
#### 3.1.3 Alert Fatigue  
The most well-known challenge that plagues SOCs is the volume of alerts that their analysts must triage [24, 42, 43]. Reasons for this include an increased number of tools, poor configuration, and anomaly detection systems, among others. This issue is exemplified by [39] who reported a true positive alert ratio of 1.59% when analyzing an alert dataset. A frequent discussion point is that of false positives, which [44] define as genuinely flagged security alerts that do not have an associated security event and are usually caused by loosely defined rules. According to the same authors, false positives are differentiated from benign triggers, which are true alerts that match a configured rule but are purposefully ignored (due to legacy systems, for example). The voluminous alert issue has knock-on effects. Firstly, not only does the flood of alerts need to be analyzed, but this information must also be stored and backed up. Secondly, the timely nature of response forces analysts to make quick judgments concerning which alerts require a deeper investigation [43]. Furthermore, human factor implications of desensitization after repeated exposure and a reduction in response accuracy as a result of an overwhelming amount of information processing are mentioned [45, 39]. Correlating with industry reports [3], security analysts state that the constant integration and maintenance of security monitoring tools has significant secondary effects, namely, how time-consuming it is and the resultant increase in alerts [31, 46]. Refs. [36, 47] both address how the continuous addition of tools can lead to analysts becoming overwhelmed and suffering from alert fatigue. Another study commented on the tedious tool integration process, given the number of APIs (averaging over 200 APIs per tool) and plugins that need to be accommodated [48]. Investigating the same intrusion, either in parallel by different analysts or sequentially by the same analyst, results in individual alerts being assessed in isolation instead of analyzed collectively concerning the same event. However, ref. [36] claim that not all tools can group associated alerts, rendering a large volume of alerts and their repeated investigation inevitable. The repeated investigation of alerts was also mentioned by [1] who found that security analysts often inspect the same domain on different occasions without arriving at a final solution. These challenges are amplified by the skills shortage that security operation centers are currently subject to [11, 49]. These challenges were mentioned in 31/49 (63%) of the articles analyzed, making alert fatigue and its sub-themes the most dominant issues experienced by security analysts.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 4 Experiments Description
### Experiments
#### 4.2.3 Fault Prediction  
We used the interval data set obtained after dimensionality reduction by the CIPCA method to train the CIPCA-BPNN fault prediction model. Since the interval data could not be directly used to establish the BPNN model, according to the instructions in Section 3.4, we extracted the \"minimum\" matrix and the \"maximum\" matrix from the flight interval data set. We then used BPNN to train the \"lower bound\" base learner and the \"upper bound\" base learner, respectively, and summarized the results to form an \"ensemble\" learner. The method of summarizing the results was the weighted average.  
In the failure prediction experiment, the label of the failure sample was 1 (\(y=1\)), and the label of the normal sample was 0 (\(y=0\)). We used two-thirds of all data as the training set, and the remaining one-third was the test set. We trained a single hidden layer BPNN with the number of hidden neurons of 10, 15, 20, 35, 30, 35, 40, and 45 to find the optimal model. The output function of BPNN was a linear function. We used the _nnet_ package in \(R\) to build the BPNN model on a computer with the AMD Ryzen 7 1700 Eight-Core Processor 3 GHz CPU and 32 GB RAM. In the comparative experiment of PCA-BPNN, the same number of principal components as CIPCA-BPNN was used. The setting of PCA-BPNN parameters was the same as CIPCA-BPNN.  
Figure 5: The correlation between variables of the data. The color is darker, the correlation between variables is higher. (The figure shows the correlation coefficient of the corresponding variable. Because the autocorrelation coefficient of the variable is 1, it is not shown in the figure.)
# Axial and Bending Bearing Capacity of Double-Steel-Concrete Composite Shear Walls
## 4 Derivation of the Axial and Bending Bearing Capacity  
### Basic Assumption  
Based on the experimental results and finite element analysis, the basic assumptions for calculating the axial and bending bearing capacity of SC walls are proposed.  
1. The failure of SC walls is that the outermost concrete in the compression zone reaches the ultimate compressive strain, and the corresponding bearing capacity is taken as the ultimate bearing capacity;
2. The surface steel plate and concrete are well combined without slippage;
3. The average strain distribution of the wall cross section is consistent with the plane assumption;
4. The tensile effect of the concrete is not taken into account;
5. The constitutive of the concrete with the confinement of a rectangular steel tube is introduced to describe the behavior of concrete under a uniaxial load, which was proposed by [PERSON] [35]. The ultimate compressive strain of concrete corresponded to a stress value of 0.5\(f_{cc}\) in the descending part of the stress-strain curve;
6. The ideal elastic-plastic constitutive is introduced to describe the behavior of steel plate, ignoring the strain hardening;
7. The concrete compressive stress distribution is replaced by equivalent rectangular stress distribution. The equivalent stress is \(\alpha_{1}\)\(f_{cc}\) and the equivalent compression zone height is \(x\) = \(\beta_{1}\)\(x_{n}\). \(\alpha_{1}\) and \(\beta_{1}\) are equivalent rectangular stress coefficients, which are related to the concrete grade. The values of \(\alpha_{1}\) and \(\beta_{1}\) are the same as ordinary concrete. \(f_{cc}\) is calculated using Equation (5) [34]. \[f_{cc}=\left[1+\left(-0.0135\xi_{1}^{2}+0.1\xi\right)\left(\frac{24}{f_{c}} \right)^{0.45}\right]\] (5) where \(f_{cc}\) is the compressive strength of confined concrete;\(f_{c}\) is the compressive strength of the concrete; and \(\xi_{1}\) is the confinement effect coefficient.  
The confinement effects of the column plate and the steel plate on concrete are different in SC walls. When the connectors between the surface steel plates are enough, \(\xi_{1}\) can be calculated conservatively using Equation (6).  
\[\xi_{1}=\frac{A_{80}f_{y}}{A_{c}f_{c}} \tag{6}\]  
Figure 5: Maximum concrete compression strain versus axial compression ratio of specimens at ultimate load.  
where \(A_{s0}\) is the total cross-section area of the steel plate in the SC wall (including the vertical partition plates); \(f_{y}\) is the yield strength of the steel plate; and \(A_{c}\) is the total cross-section area of the concrete in the SC wall.
## 2 Basic Principles
### Preprocessing  
In general, returned radio signals suffer from external incoherent influences, i.e., clutter and noise, and are, therefore, unsuitable for the training of machine learning-based classification methods. In addition to this aspect, which concerns _data quality_, the success, as well as the performance, of classification methods depends on the _data representation_, _data dimensionality_, and _information density_. Thus, it is necessary to apply signal-processing techniques in order to enhance the data properties prior to training and classification. The next subsection provides a brief description of common preprocessing methods.
## 4 Modeling and Results
### Results
#### 4.3.3 Experiment 2: Handwritten Kanji Recognition (200 Classes)  
According to the promising results from our previous experiment (Section 4.3.2), we expanded our evaluation to include 200 kanji classes (Table 6). Figures A6-A9 in Appendix A demonstrate the accuracy and loss measurements obtained through our proposed approaches. Similar convergence patterns are observed for the 200-class task (Figures A6-A9). Fast initial learning is followed by stabilization, with Algorithm 2 again demonstrating convergence to superior loss minima compared to those of the other approaches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Accuracy** & **mAP** & **MAA** \\ \hline Vanilla Preact ResNet-18 & 96.87\% & 94.47\% & 96.13\% \\ Preact ResNet-18 with data augmentation & 96.70\% & 94.17\% & 95.92\% \\ Algorithm 1 & 94.00\% & 90.68\% & 93.74\% \\ Algorithm 2 & **97.94\%** & **96.91\%** & **97.61\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance indicators for all the investigated approaches in case of the 150-class recognition.  
An in-depth evaluation of the separate kanji recognition performances showed the highest and lowest accuracy rates for the different training strategies, which are presented below:  
* Vanilla Preact ResNet-18: The lowest accuracy rate for % (77.14%) and the highest for \(\dashv\) and some others (100%);
* Preact ResNet-18 with data augmentation: The lowest accuracy rate for % (77.14%) and the highest for and the highest for and some others (100%);
* Algorithm 1: The lowest accuracy rate for % (71.80%) and the highest for and some others (100%);
* Algorithm 2: The lowest accuracy rate for % (80%) and the highest for and others (100%).