esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation
### Validation of the Proposed Power Loss Model
#### 4.2.3 Comparison with the Results from the Power Analyzer  
In order to further validate the accuracy of the proposed losses model, the power analyzer is used to measure the inverter power losses through the comparison of the dc input power and the ac output  
Figure 13: Comparison of two losses models. (**a**) The torque rise. (**b**) The torque drop.  
power. It should be noted that the power losses of the inverter consist mainly of six parts at the current of 500 (Arms), as shown in Table 4.  
The equivalent resistance can be obtained by the experiment measurement. The specific losses calculation is introduced as follows:  
1. The power losses of IGBT and FWD are calculated by the proposed losses model.
2. The power losses of the bus-bar in the internal IGBT modules are calculated in Equation (54). The equivalent resistance represents the circuit resistance of switching tubes. \[P_{1}=l^{2}R=500^{2}\times 0.75\times 0.001\times 3=562.5\] (54)
3. The power losses of the dc bus-bar are calculated in Equation (55). The equivalent resistance means the equivalent resistance in the dc circuit and the dc current is 300 A. \[P_{2}=l^{2}R=300^{2}\times 0.53\times 0.001=47.7\] (55)
4. The ac bus-bar exists outside the three-phase IGBT modules and the equivalent resistance of every phase is 0.4 m\(\Omega\), so the power losses can be calculated as: \[P_{3}=l^{2}R=500^{2}\times 0.4\times 0.001\times 3=300\] (56)  
Through the power analyzer, the power losses of the inverter are 4000 W. The losses estimation error is:  
\[\varepsilon=\frac{4000-3962.2}{4000}=0.95\% \tag{57}\]  
Figure 14 shows the power loss distribution of the inverter, and the error is 0.95%, which shows the proposed losses model can measure the chips losses with good accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Power Losses** & **Equivalent Resistance (m\(\Omega\))** & **Values (W)** \\ \hline IGBT chips & & 2340 \\ FWD chips & & 660 \\ Bus-bar in the internal IGBT modules & 0.75 & 562.5 \\ Dc bus-bar & 0.53 & 47.7 \\ Dc-link capacitance & & 16 \\ External ac bus of three-phase IGBT modules & 0.4 & 300 \\ Total & & 3926.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power losses of the inverter.  
Figure 14: Power loss distribution of the inverter losses.
## 2 Materials and Methods
### Methods
#### 2.3.2 ISC Density Classification Method  
Using ISC pixel proportion data, the ISC area of each city in the Beijing-Tianjin-Hebei region was extracted by city level and divided by its administrative area to obtain the ISC density value (ISD) for each city. Since the resulting ISC density values are relatively small, subsequent normalization operations were performed to enhance data comparability and better reflect regional differences. The formula is as follows:  
\[T=\ \frac{ISD-ISD_{min}}{ISD_{max}-ISD_{min}}\]  
where \(T\) is the normalized ISC density value of the image pixel, with values ranging from 0 to 1. The larger the value, the higher the corresponding ISC density; \(ISD_{max}\) and \(ISD_{min}\) represent the maximum and minimum values of the study ISC density, respectively.  
Traditional methods (such as equal interval division) may be affected by extreme values. To objectively classify ISC density into grades, the mean-standard deviation method is used for density segmentation. The mean-standard deviation method is based on data distribution characteristics and can more adaptively distinguish high, medium, and low density areas. The density segmentation results of different regions are paired to facilitate analysis of spatial differentiation. The density segmentation formula is as follows:  
\[T=A\pm X\cdot SD\]  
where \(T\) is the ISC density classification threshold; \(A\) is the average ISC density of the study area; \(X\) is the standard deviation multiplier; and \(SD\) is the standard deviation of ISC density in the study area.  
According to Table 2, the ISC density is classified to obtain Figure 3. This graded density map reflects to a certain extent the differentiated urbanization development in different regions of Beijing-Tianjin-Hebei. High-density areas are concentrated in the developed cities in the eastern part of Beijing-Tianjin-Hebei, centered on Beijing and Tianjin, and include Langfang and Cangzhou; medium-low density areas are concentrated in the western part of Beijing-Tianjin-Hebei, and low-density areas are concentrated in the mountainous areas in the northern part of Beijing-Tianjin-Hebei.
## 5 Experimental Results
### Baselines  
We compared our proposed FS approach MBHO with four baseline approaches for selecting the most dominant subset of features over the fourteen datasets considered in our study. These baselines are both filter-based and wrapper-based FS approaches. The baselines are detailed as follows.  
**(i)**: **Binary Cuckoo Search (BCS) [37]:**: Cuckoo Search (CS) is an optimization algorithm inspired by the reproductive behavior of cuckoo birds. These birds lay their eggs in the nests of other birds, increasing the chances of their own eggs hatching. To avoid detection, cuckoos mimic the appearance of the host bird's eggs. Additionally, cuckoo eggs tend to hatch before the host's eggs, allowing the chicks to assert dominance and secure a larger share of the food provided by the host bird.
**(ii)**: **Mutual Information Maximization (MIM) [38]:**: MIM is a filter-based FS approach inspired by the idea of MI. It computes the MI among the class label and the selected features. In other terms, MIM aims to select features that are highly informative about the target class while minimizing the redundancy, selecting the most relevant features that maximize the MI from the original dataset.
**(iii)**: **Joint Mutual Information (JM) [39]:**: JM is a filter-based FS approach that computes the amount of shared information among at least two variables. It is an extension of MI. In other words, JMI tends to measure the relevance of features according to their joint information with the class label.
**(iv)**: **minimum Redundancy Maximum Relevance (mRMR) [40]:**: mRMR is a filter-based FS approach widely used in ML and applied mathematics, aiming to select a subset of features that maximizes the correlation with the target class while minimizing the redundancy among the selected features.
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 3 Results and Discussion
### Interface Bond-Slip Model and Recommended Value for Stiffness
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Group** & \(\tau_{0}\) & \(K\) & \(s_{u}\) & **Correlation** & **Linear Segment** & **Ultimate Bond** & **Linear Growth** & \(\tau_{u}/\tau_{u}\) & **Linear** \\
**No.** & **/MPa** & **/MPa** & **/mm** & **Coefficient** & **P\(\tau_{u}/\)MPa** & **/MPa** & **/MPa** & **/MPa** \\ \hline Z-C35-T & 0.10 & 11.60 & 0.09 & 0.92 & 1.14 & 1.13 & 100 & 8.68 & 12.67 \\ Z-C35-T & 0.17 & 11.29 & 0.18 & 0.96 & 2.84 & 2.90 & 97.93 & 5.83 & 8.61 \\  & 1.45 & 4.19 & 0.33 & 0.88 & & & & & \\  & 0.34 & 10.49 & 0.25 & 0.94 & 3.10 & 3.15 & 98.41 & 11.02 & 9.12 \\ Z-C45-S & 2.73 & 1.09 & 0.34 & 0.92 & 3.46 & 3.50 & 98.85 & 8.17 & 10.18 \\ Z-C55-S & 0.28 & 9.34 & 0.34 & 0.94 & 3.71 & 3.73 & 99.53 & 5.13 & 8.43 \\ Z-C55-S & 0.19 & 8.01 & 0.44 & 0.96 & 3.71 & 3.73 & 99.54 & 4.04 & 28.65 \\ X-C35-W & 0.23 & 27.51 & 0.20 & 0.97 & 5.73 & 5.76 & 99.54 & 4.04 & 28.65 \\ X-C35-M & 0.30 & 22.11 & 0.40 & 0.97 & 9.15 & 9.24 & 98.98 & 3.32 & 22.88 \\ X-C35-W & 0.28 & 21.31 & 0.36 & 0.89 & 7.95 & 8.39 & 94.73 & 3.45 & 22.08 \\ X-C35-S & 0.27 & 22.57 & 0.45 & 0.94 & 10.43 & 11.52 & 90.53 & 2.59 & 23.17 \\ X-C45-Y & 0.34 & 25.46 & 0.54 & 0.92 & 14.09 & 14.47 & 97.35 & 2.40 & 26.09 \\ X-C55-S & 0.37 & 25.31 & 0.60 & 0.94 & 15.55 & 15.54 & 100 & 2.35 & 25.92 \\ \hline \hline \end{tabular} Note: (1) “Linear segment bond strength \(\tau_{u}\)” in the table refers to the end point bond stress of linear regression. For specimens that used multiple line segment regressions, the end point of the last curve is taken as the linear segment bond strength of the specimens. (2) Linear stiffness, \(\overline{K}\), represents the linearly increasing stiffness, and the ratio of the linear segment bond strength to the corresponding slip is used. (3) Ultimate bond strength, \(\tau_{\max}\), denotes the average bond strength of the adhesive interface (see Table 5 for specific values). (4) The column of “Linear growth proportion” in the table indicates the ratio of the linear segment bond strength to the ultimate bond strength, namely, Linear growth proportion(\(\%\)) = \(\frac{\tau_{\max}}{\tau_{\max}}\times 100\%\), which is used to measure the degree of linear growth of the interfacial adhesion–slip rising segment.  
\end{table} 
Table 6: Parameters related to the interface bond–slip curves.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 2 Related Work
Existing fire segmentation algorithms are proficient in identifying flame pixels in video images, but accurately perceiving intricate contour semantics of flames presents a challenge, limiting the exploration of fire situation information. To address the problem, this paper employs SEA attention to capture long-range dependencies between flame pixel semantics and a fusion block to fuse low-level spatial and high-level semantic information. This refined representation of the flame's contour enables a more detailed exploration and analysis of fire situation information.
## 2 The \({}^{3}\)ColStar Satellite Mission
### Preliminary Design
#### 2.3.4 Attitude Determination and Control System  
The ADCS module is in charge of the attitude and orientation control for the CubeSat, hence meeting a variety of operational requirements. These consist of spinning control to reduce orbital disturbances, pointing control for efficient communication with the IoT payload, and detumbling control to manage high rotational speeds after deployment. The performance of the ADCS is not heavily taxed by the space weather sensor, but the IoT payload requires accurate pointing control in order to communicate. The main goal of environment survival is energy production, which calls for deployable solar panels that need pointing control. However, because of the CubeSat's small size, there are few issues with gravity gradient torque or solar pressure. Figure 7(a) shows the centralized architecture, where OBC is in charge of ADCS execution. Figure 7(b) presents the distributed architecture, where a specialized controller will be in charge of the ADCS execution, both MCU or FPGA technicolic choises will be considered.  
The attitude determination will be carried out by the TRIAD algorithm [85] and attitude control by the control law developed by the group SpaceTech of Universidad de los Andes AES IEEE. The magnetorquers to be used will be those developed by the SpaceTech AESS IEEE group (TRL = 4) of the Universidad de los Andes and the general development of the ADCS will be supported by the previous developments made by the same group [85].  
An engineering model of the ADCS is currently being constructed using an FPGA controller under the platform HiLeS Designer 2.0, a proprietary and widely-used platform to develop embedded systems, offering advanced features for real-time control and data processing. This platform facilitates the design and implementation of complex control algorithms on FPGA hardware, providing a flexible and efficient solution for aerospace applications such as satellite ADCS [85; 86; 87].
## 3 Kalman Filtering for GPS/BDS Float Ambiguity Estimation
The one-step state transition and measurement equations of the discrete Kalman filter can be written as:  
\[\mathbf{X}_{k+1}=\mathbf{F}_{k,k+1}\mathbf{X}_{k}+\mathbf{W}_{k} \tag{17}\]  
\[\mathbf{Z}_{k+1}=\left[\mathbf{\Psi}_{\mathrm{GPS}}^{T},\mathbf{\Psi}_{ \mathrm{BDS}}^{T},\mathbf{\rho}_{\mathrm{GPS}}^{T},\mathbf{\rho}_{\mathrm{SDS}}^{T} \right]^{T}=h\left(\mathbf{X}_{k+1}\right)+\mathbf{V}_{k+1} \tag{18}\]where \(\mathbf{F}_{k,k+1}\) is the one-step state transition matrix, \(\mathbf{Z}\) is the DD carrier phase and pseudorange measurement vector, \(\mathbf{W}\) and \(\mathbf{V}\) are process and measurement noises respectively, the non-linear function \(h()\) as given in Equations (10) and (11) represents the relationship between the measurements and the states. The one-step state transition matrix is an identity matrix as follows:  
\[\mathbf{F}_{k,k+1}=\mathbf{I}_{m+n+3} \tag{19}\]  
By linearizing \(h()\), the measurement matrix \(\mathbf{H}\) can be obtained as given in Equation (20):  
\[\mathbf{H}_{k}= \begin{bmatrix}\mathbf{H}_{\mathrm{bl},k}^{\mathrm{GPS}}& \mathbf{D}^{\mathrm{GPS}}\\ \mathbf{H}_{\mathrm{bl},k}^{\mathrm{BDS}}&\mathbf{D}^{\mathrm{BDS}}\\ \mathbf{H}_{\mathrm{bl},k}^{\mathrm{GPS}}&&\mathbf{O}\\ \mathbf{H}_{\mathrm{bl},k}^{\mathrm{BDS}}&&\mathbf{O}\end{bmatrix}  \tag{20}\]  
where \(\mathbf{H}_{\mathrm{bl},k}^{\mathrm{GPS}}\) is the coefficient matrix of the baseline for GPS measurements, \(\mathbf{O}\) represents a zero matrix and other matrices are similarly defined. Furthermore, based on Equation (12) and omit the subscript, we get Equation (21) where \(\mathbf{a}_{j,x}\) is the normalized LOS vector:  
\[\mathbf{H}^{\mathrm{GPS}}= \begin{bmatrix}-\lambda_{\mathrm{GPS}}^{-1}\left( \mathbf{a}_{1,r}^{\mathrm{GPS}}-\mathbf{a}_{i,r}^{\mathrm{GPS}}\right)^{T} \\ \vdots\\ -\lambda_{\mathrm{GPS}}^{-1}\left(\mathbf{a}_{i-1,r}^{\mathrm{GPS}}-\mathbf{a }_{i,r}^{\mathrm{GPS}}\right)^{T}\\ -\lambda_{\mathrm{GPS}}^{-1}\left(\mathbf{a}_{i+1,r}^{\mathrm{GPS}}-\mathbf{a }_{i,r}^{\mathrm{GPS}}\right)^{T}\\ \vdots\\ -\lambda_{\mathrm{GPS}}^{-1}\left(\mathbf{a}_{m,x}^{\mathrm{GPS}}-\mathbf{a }_{i,x}^{\mathrm{GPS}}\right)^{T}\end{bmatrix}  \tag{21}\]  
In Equation (20), \(\mathbf{D}\) is the SD to DD transformation matrix for either GPS or BDS. It should be noted that the reference satellite cannot be a single satellite for both constellations because they have different signal carrier frequencies and their measurements cannot be mutually operated. As a consequence, we have to select two separated reference satellites from each constellation and double-difference the measurements within the single system itself. Take GPS as an example, if there are \(m\) common satellites and we choose the \(i\)th as the reference, then the \(\mathbf{D}^{\mathrm{GPS}}\) matrix can be written as Equation (22), and \(\mathbf{D}^{\mathrm{BDS}}\) can be derived similarly:  
\[\mathbf{D}^{\mathrm{GPS}}= \begin{bmatrix}1&&-1&&\\ &1&&-1&&\\ &&\cdots&\vdots&\cdots&\\ &&-1&&1&\\ &&-1&&1\end{bmatrix}
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 3 Materials and Methods
### Experiment Design and Procedure
#### 3.3.2 Stimuli Selection and Categorization  
To evaluate neural responses to various architectural styles, we initially referred to 25 distinct design styles proposed in previous research [145]. These styles were chosen based on (1) diversity (covering cultural, regional, and temporal variations), (2) comparability (visually distinctive characteristics enabling comparative analysis), and (3) popularity and recognition. For each of the 25 styles, we used Google Images API (SerPApi) to crawl approximately 10 candidate images per style, resulting in an initial pool of 250 images.  
To ensure the visual consistency and reliability of EEG data, we applied the following screening procedures:  
* Only high-resolution images of at least 2 megapixels were selected to maintain visual clarity.
* Brightness, luminance, and contrast were standardized to eliminate unnecessary visual biases.
* Images were presented in a random order to mitigate learning effects.
* Duplicate or low-quality images were excluded to enhance data reliability.  
Additionally, each image was verified by two architecture experts to ensure it accurately represented its intended category (e.g., visible eco-friendly elements for Sustainability, deteriorated facades for Urban Condition). Any image that did not clearly exhibit category-specific attributes or that showed irrelevant content was removed.  
As a result, we finalized 100 images (four images per style on average) for the EEG experiment. However, for illustrative clarity, Figure 4 shows eight representative styles--Sustainability, Urban Condition, Luxury, Minimalist, Materiality, Historical Style, Decrepit Buildings, and Modern and Contemporary--along with typical features characteristic of each. By covering a broad spectrum of aesthetic and functional attributes, these styles can elicit varying degrees of liking or disliking among users, making them well suited for EEG-based affective analysis.  
\begin{table}
\begin{tabular}{c c} \hline
**Age Range (Years)** & **No. Participants** \\ \hline
18–20 & 8 \\ \hline
21–30 & 30 \\ \hline
31–35 & 1 \\ \hline \(\geq\)36 & 1 \\ \hline Total & 40 \\ \hline \end{tabular}
\end{table}
Table 10: The age distribution of the participants.  
Figure 4: Representative examples of the eight architectural categories used as stimuli: (**a**) Sustainability; (**b**) Urban Condition; (**c**) Luxury; (**d**) Minimalist; (**e**) Materiality; (**f**) Historical Style; (**g**) Decrepit Building; and (**h**) Modern and Contemporary.
## 1 Introduction
The expected benefits of 5G for emergency communications are higher density (e.g., for large incidents or events), higher performance (e.g., for large number of high-quality video flows), availability, and guaranteed services thanks to slicing technology (versus other less critical verticals). This paper presents the design of capabilities and interfaces to enable the secure integration of innovative multimedia applications with smart devices and sensors (e.g., smart biometric sensors and environmental sensors), AR devices (e.g., glasses and HMDs), smart watches, and flexible displays with new approaches such as artificial intelligence for assisted decision-making to First Responders, and augmented situational awareness over the Katana slicing framework. This paper presents a mobile 5G platform for supporting both the communication needs of emergency units in the field, and enhanced services and applications, by utilizing network slicing. The following sections are as follows: Section 2 presents the proposed tentative 5G platform architecture, which offers service slicing capabilities for different responder teams and their correspondent services. Section 3 introduces the novel networking manager Katana, to support the different slicing scenarios envisioned for FR operations. Next, Section 4 presents an energy evaluation of different service slicing types in a commercial off-the-shelf (COTS) 5G NR system, integrated and extended appropriately for the needs of the experiments. Section 5 presents, in detail, related work and how the proposed tentative mechanism goes beyond that. Finally, Section 6 concludes the paper and draws future lines for the network slicing, regarding emergency communications.
# Hybrid Harmonic Suppression Method at DC Link of Series-Connected 18-Pulse Rectifier
## 4 Capacity of the Proposed Harmonic Reduction Circuit  
To estimate the harmonic suppression cost of the proposed hybrid method, the capacity of the injection transformer and the loss of the switches are analyzed. From Figure 7, the injection voltage \(u_{\text{FP}}\) and \(u_{\text{ST}}\) are calculated as  
\[u_{\text{FP}}=\left\{\begin{array}{ll}0\left[\frac{k\pi}{3},\frac{\pi}{2}+ \frac{k\pi}{3}\right]\cup\left[\frac{4\pi}{27}+\frac{k\pi}{3},\frac{2\pi}{9}+ \frac{k\pi}{3}\right]\\ 0.0079u_{0}\left[\frac{\pi}{27}+\frac{k\pi}{3},\frac{4\pi}{27}+\frac{k\pi}{3} \right]\\ -0.0079u_{0}\left[\frac{5\pi}{27}+\frac{k\pi}{3},\frac{8\pi}{27}+\frac{k\pi}{ 3}\right]\\ -0.0079u_{0}\left[\frac{2\pi}{3}+\frac{k\pi}{3},\frac{(k+1)\pi}{3}\right]\\ \end{array}\right.u_{\text{ST}}=\left\{\begin{array}{ll}0\left[\frac{\pi}{ 9}+\frac{k\pi}{3},\frac{5\pi}{27}+\frac{k\pi}{3}\right]\cup\left[\frac{8\pi}{ 27}+\frac{k\pi}{3},\frac{(k+1)\pi}{3}\right]\\ 0.0079u_{0}\left[\frac{5\pi}{27}+\frac{k\pi}{3},\frac{8\pi}{27}+\frac{k\pi}{3} \right]\\ -0.0079u_{0}\left[\frac{(k+1)\pi}{3},\frac{\pi}{9}+\frac{(k+1)\pi}{3}\right] \\ \end{array}\right. \tag{23}\]  
From Figures 1 and 7, the injection current \(i_{\text{x1}}\) and \(i_{\text{x2}}\) are calculated as  
\[i_{\text{x1}}=3\left\{\begin{array}{ll}\frac{9L_{\text{x}}}{\pi}x+\frac{l_ {\text{x}}}{3},\frac{4\pi}{27}+\frac{k\pi}{3}\end{array}\right. \tag{24}\]  
From Equations (23) and (24), Equation (25) describes the injection transformer capacity.  
\[S=u_{\text{FP}}i_{\text{x1}}+u_{\text{ST}}i_{\text{x2}}\approx 0.019u_{0}i_{0} \tag{25}\]  
From Equation (25), the injection transformer capacity is only 1.9% of the output power. As discussed in [11; 12; 13; 14; 15; 16], the injection transformers capacity of the existing methods was higher than 2% of load power. Therefore, the circuit loss of the proposed suppression method is reduced to a certain extent.  
According to Figure 9, the current across the secondary side of the injection transformer is 0.0118 times of the primary side. Assuming the load power is 1500 W and the drain source on-state resistance of the switch is 500 m\(\Omega\), the loss of the switch is calculated as 5 \(\times\) 0.0118 \(\times\) 0.5 = 0.0295 W. Therefore, the switch loss of the switch can be approximately neglected.
## 3 Architectural Layers and Digital Twin Integration for Headlamp Systems
### Abstraction Levels: L0, L1, L2, L3, and L4  
To enable systematic virtual validation and efficient integration of DT technology into automotive headlamp systems, we define a multi-level abstraction model that structures the gradual evolution of the system from conceptual design to real-world deployment.  
This layered approach provides a framework for organizing validation strategies according to system maturity and complexity. These levels reflect the increasing degree of system realism, from early behavioral modeling to full-scale DT deployment, and their ability to represent the real ECU and its physical environment. Figure 4 illustrates this progression, showing how DT involvement expands as the system moves from concept to test drives and finally to a fully integrated digital replica:  
* L0 (conceptual validation) uses abstract behavioral models with minimal realism, focused on early functional exploration. DT involvement is limited, serving only basic traceability.
* L1 (Software-in-the-Loop) introduces executable code within a virtualized ECU, allowing early logic validation. The DT begins supporting behavioral tracking and input simulation.
* L2 (Hardware-in-the-Loop) blends virtual and real components, enabling validation of real-time behavior on physical hardware. The DT support increases, contributing to environmental modeling and prediction.  
Figure 3: System-software architecture with digital twin integration for headlamp management.  
* L3 (test drive) integrates the DT with real-world driving tests, enabling parallel monitoring and scenario-based diagnostics.
* L4 (full DT integration) represents a fully bidirectional connection between virtual and physical domains, supporting high-fidelity simulations, predictive maintenance, and scalable validation of advanced lighting features.
## 2 BESS and Frequency Regulation Service
### Frequency Regulation Service
#### 2.2.1 Dynamic Regulation (dReg)  
dReg can dynamically follow the grid frequency and actively provide corresponding power every second based on the current grid frequency to help maintain the stability of the power system frequency. Such a service is required to respond within a second.  
Depending on the corresponding system frequency range and operating power, it can be divided into two types: dReg0.25 and dReg0.5. To be more specific, the operating frequency range of dReg0.25 is from 59.75 to 60.25 Hz, while that of dReg0.25 is from 59.5 to 60.5 Hz. Figure 1 shows the response curve of dReg, and Figure 2 shows its corresponding parameters [9]. The blue area in Figure 1 is the deadband, where the BESS can charge and discharge to maintain the SOC.