esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this research, the authors developed a structural optimal parameter and uncertainty estimation method using the SCEM-UA algorithm, which mainly focused on uncertain parameter distributions. Parameter identification has been successfully investigated based on numerical simulations and laboratory experiments. The main conclusions of this research are:  
1. The SCEM-UA algorithm can effectively explore the feasible parameter space to locate the high probability density region and converge to the stationary posterior parameter distributions for structural systems under seismic excitation.
2. The sampling of the SCEM-UA algorithm requires no prior distribution, which can better estimate the posterior distribution of complex structural model parameters with very little prior information.
3. The SCEM-UA method shows slightly better global optimization capability compared with the other global optimization algorithms, and the SCEM-UA algorithm focused on model uncertainty analysis especially. The SCEM-UA algorithm not only searches the posterior distribution of model parameters but also deduces the fitting of a model's calculated and observed values under a certain confidence interval. Moreover, many parameter sets close to the global optimum behave with quite similar performances in reproducing the observed data.
4. Parameter uncertainties, including measurement uncertainty and model uncertainty, do exist in the structural system as posterior probability density distributions, and the results obtained showed wide demand prediction uncertainty bounds. Moreover, the prediction uncertainty bounds are wider in the nonlinear structural systems than in the linear structural systems due to the nonlinear model simplification.  
Figure 20: Prediction uncertainty ranges of acceleration histories in the nonlinear structural system: (**a**) At the 3 rd floor; (**b**) At the 5 th floor.  
However, this research only considered the uncertain source of insufficient observations with errors. In the future, the SCEM-UA approach presented here will be employed to assess the uncertainty in parameter estimates of real structural models including modeling, observation, and excitation uncertainties. In addition, if the independent errors assumption does not exist, the estimation of the parameter posterior distributions will be affected. Future studies will also address this issue.  
Conceptualization and Methodology, H.T.; Investigation and Validation, X.G.; Writing--Original Draft Preparation, H.T., and X.G.; Writing--Review and Editing, H.T., L.X. and S.X.  
This study was supported by the Ministry of Science and Technology of China (Grant No. SLDRCE19-B-02), the National Key Research and Development Program (2016 YFC0701800, 2017 YFC0703607) and the Natural Science Foundation of Shanghai (Grant No. 17 ZR1431900).  
The authors declare no conflict of interest.
# Overload Control in Smart Transformer-Fed Grid
## 2 Smart Transformer Concept and Control  
The smart transformer proposed in this work is a three-stage power electronics transformer [5; 20]. The ST adapts the voltage between the MV to the LV grid, and enables the DC grid connections at both voltage levels [21]. A reference system is shown in Figure 1. Several topology solutions can be implemented for each stage, although the control strategies do not differ substantially. The ST control strategy adopted in this work is shown in Figure 2.  
The MV side regulates the MV DC link voltage at the nominal value, controlling the active current absorption in the MV grid. The reactive power injection in the MV grid is controlled in order to provide reactive power support in MV grid. The ST has the possibility of working under constant power factor, or to support the voltage profile by injecting reactive power. The reactive power control can be local by means of a V/Q droop controller, or remote using a centralized controller.  
Figure 1: Smart transformer (ST) concept. LV: low voltage; MV: medium voltage; PV: photovoltaic; \(\mu\)T: micro-turbine; \(\mu\)W: micro-wind turbine.  
The DC/DC converter has the task of transforming the voltage from MV to LV and to control the LV DC link voltage. The DC/DC controls the power flow between the two voltage levels in order to keep the LV DC link voltage at its nominal value. The reference power is limited between the \(P_{max}\) (determined by the ST sizing) and \(P_{min}\). In order to prevent reverse power flow, \(P_{min}\) must be set to zero.  
The LV side controls the LV grid voltage waveform. It provides symmetrical voltage waveforms with constant amplitude and frequency, independently from the load power request. The current amplitude and shape is instead decided solely by the load. In this work, the voltage waveform reference is given by the overload controller (Figure 2, red box), explained in the next section.
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 3 Background and Challenges
### Workload Admission in Virtualized Environments  
The deciding factor for accepting or rejecting a new elastic service request is one of the key decisions of an infrastructure provider. Usually, at workload admission time, it is not known how the elasticity requirements will affect the future capacity needs of an application. Sometimes, admitting new services increases the risk of the already deployed ones failing. Similarly, a strict acceptance policy for incoming services may result in an increased number of rejections. The elasticity decisions, therefore, should ideally be able to forecast in advance a change in a load of a service. It should also be able to react to the unexpected load changes faster than the rate of change of the load [36]. This requires fast and reliable mechanisms for managing usage and future usage predictions.  
Figure 1 demonstrates a generalized view of a system virtualization architecture, in which a layer (the hypervisor or virtual machine monitor (VMM) is introduced between the guest operating systems and the underlying hardware. This virtualization technology is broadly employed in cloud data centers. Workload-admission and control services in virtualized environments decide whether a set of services can be admitted in a virtualized infrastructure. These schemes are based on a vast number of factors, including the services, infrastructure, QoS, policies, and costs, etc. In order to effectively utilize the underlying network resources, virtualized systems control and facilitate resource administration capabilities by:  
* Allowing the efficient use of resources.
* Initiating support for upcoming requests.
* Assigning requests their respective resource requirements.
* Mapping various user requests with different QoS parameters to VMs.
# Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm
## 3 Infrared Patch Tensor (IPT) Model
### Structural Tensor Description  
The structure tensor is commonly used in image processing fields, such as feature extraction and corner point detection, to obtain the gradient directions in a specified neighborhood at a point as well as the degree of coherence of these directions. Compared with gradients, the structure tensor not only enables structural analysis and direction estimation of images but also obtains richer local structural features [22]. Suppose the original image is \(D\), then the classical linear structure tensor is computed as:  
\[\begin{split}& J_{p}=K_{\rho}*(\
abla D_{\sigma}\otimes\
abla D_{ \sigma})\\ &=\left(\begin{array}{c}K_{\rho}*I_{x}^{2}K_{\rho}*I_{x}I_{y}\\ K_{\rho}*I_{x}I_{y}K_{\rho}*I_{y}^{2}\end{array}\right)\\ &=\left(\begin{array}{c}I_{11}I_{12}\\ J_{21}I_{22}\end{array}\right)\end{split} \tag{5}\]  
where \(K_{\rho}\) denotes a Gaussian kernel function with variance \(\rho\), \(*\) denotes the convolution operation, \(\otimes\) denotes the Kronecker product, \(\
abla\) denotes the gradient, \(D_{\sigma}\) denotes a Gaussian smoothing filter with variance \(\sigma\) on the original image, and \(I_{x}=\frac{\partial D_{\sigma}}{\partial x}\), \(I_{y}=\frac{\partial D_{\sigma}}{\partial y}\) denotes the gradient of \(D_{\sigma}\) in the (x,y) direction. The linear structure tensor is a positive definite symmetric. There are two non-negative eigenvalues of the structure tensor at each image element position in the image \(D\in R^{m\times n}\). For \(J_{\rho}\) in the image, the two eigenvalues \(K_{1},K_{2}\in R^{m\times n}\) are calculated as follows [22]:  
\[K_{1},K_{2}=(J_{11}+J_{22})\pm\sqrt{(J_{22}-J_{11})^{2}+4J_{12}^{2}} \tag{6}\]  
where \(K_{1}(x,y)=\lambda_{1},K_{2}(x,y)=\lambda_{2},\lambda_{1}>\lambda_{2}\); when \(\lambda_{1}\) and \(\lambda_{2}\) are different cases, the local characteristics of the reflected image are also different; when \(\lambda_{1}\approx\lambda_{2}\approx 0\), the image has a small change in the gray value around the point; when \(\lambda_{1}\gg\lambda_{2}\approx 0\), the change in the image in the vertical direction is much smaller than the change in this direction, and the difference is significant, which is consistent with the characteristics of the edge; when \(\lambda_{1}\gg\lambda_{2}\gg 0\), the image has a relatively large change in the gray value at the point, and there are two vertical directions, which are consistent with the characteristics of the corner point.  
Figure 2: Infrared tensor image construction process.
## 4 Experimental Validation
### Results and Discussion  
#### 4.2.1 Data Preprocessing  
Original high-dimensional AE signals are firstly processed by MVW with the moving step \(k\) of 8192. The window length \(l\) and moving stride \(s\) are set as 464. Therefore, each AE file is downsized to a shorter vector with a length of 8192. Let us recall the structure of the Generator, where two LSTM cells are utilized to capture the time-series correlations in the data. To fit the Generator, each input datum is divided into \(N\) segments with length \(M\), as described in Figure 4. We recommend that each segment should contain information about at least one entire axel revolution. In this way, LSTMs can capture the correlation of AE signals generated in continuous axel revolutions. For instance, the lowest axel rotation frequency in the present work is 254 rpm, i.e., for a 2-s recording, 8 complete rotations are captured. Therefore, the segment parameters \(N\) and \(M\) are defined as 8 and 1024, respectively.
## 4 Experimental Verification and Analysis
### Comparative Analysis
#### 4.5.4 Discussion  
1. Through the comparative analysis of the single-view information model CNN and the multi-view information fusion model CAM_MCFCNN, it can be concluded that the CAM_MCFCNN model has improved diagnostic accuracy and stability in both constant-speed and mixed-speed datasets, which indicates that comprehensive utilization of multi-view information can more comprehensively reflect the equipment operation status and improve the reliability of status monitoring and fault diagnosis. From the feature visualization results, it can be seen that the features learned by the CAM_MCFCNN model are more compact and centralized, and the clustering is more obvious. Compared with the features learned by the single-view CNN model, the features learned by the CAM_MCFCNN model have better discriminability, which can effectively improve the performance and accuracy of the fault diagnosis method.
2. Compared with other multi-view information fusion methods, the CAM_MCFCNN model achieved the best performance, which further proves that the weight scheme based on the attention mechanism is effective. At the same time, the CAM_MCFCNN model can automatically learn the weight of each perspective, which saves labor and is easier to promote and apply in practice.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 4 EAP for Bootstrapping  
EAP relies on a central Authentication Authorization and Accounting (AAA) server infrastructure. EAP authentication results in a different Master Session Key (MSK) for each device known only to the server. The server may then send keys derived from the MSK to a gateway or hub (referred to as an authenticator in EAP terminology). Indeed, in current Wi-Fi deployments, a Pairwise Master Key (PMK) derived from the MSK is sent to the Access Point (AP). APs can then use the key to verify that the devices are authorized to join the network and send security credentials for protecting group traffic. In addition to having this deployment flexibility and per-device keys, EAP makes the revocation of devices straightforward. Unlike the process of removing a Fibaro Z-Wave sensor from the network as documented in Section 2, the owners of devices using EAP authentication do not need physical access to them for deleting their network access credentials. Owners can simply delete any device credentials on the server for removing them from the network. EAP infrastructure also provides support for devices that are mobile and may need to connect to the Internet through different gateways as they move. Finally, EAP, together with protocols, such as RADIUS [28] and DIAMETER [29], provides the flexibility of deploying the AAA server on-site or remotely.  
Admittedly, the owners of devices discussed thus far cannot be expected to understand, let alone setup, the complexity of an EAP infrastructure. However, as we will see later on, the deployment can be simplified such that users only need to perform very basic tasks while adding new devices to their network. Rather than diverging from enterprise wireless deployments, EAP provides a stable standard with open source implementations that can be extended to home and small office networks. Therefore, we chose to utilize the EAP infrastructure as a critical building block for our bootstrapping protocol.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
## 2 Experimental Program  
### Material Properties and Specim Preparation  
As shown in Figure 1, the CFC panel used in this experiment had a structure consisting of a 1.0 mm thick carbon fiber core sandwiched between two 3.0 mm thick flexible boards that are made from cement and reinforcing fibers.  
The RC beams had a simple rectangular cross-section 120 mm in height and 100 mm in width with a concrete cover of 30 mm. The specimens had a length of 1200 mm and a span of 1000 mm between the supports. The reinforcements were arranged as shown in Figure 2, with 2\(\O\)10 mm longitudinal rebar at the bottom, 2\(\O\)6 mm compression reinforcing bars at the top, and \(\O\)6 mm stirrups spaced 100 mm apart. The purpose of the tests was to investigate the effect of using the CFC panels on the response of RC beams subjected to impact loading. Four different types of beam specimens were used: (a) conventional RC as a standard for comparison (RC), (b) RC fully reinforced with CFC panels at the bottom (RC_FB_CFC), (c) RC partially reinforced with CFC panels at the bottom and sides (RC_P3S_CFC), and (d) RC fully reinforced with CFC panels at the bottom and sides (RC_F3S_CFC), as shown in Figure 3.  
The CFC panels were installed using the following procedures, as shown in Figure 4: starting with preparation of the concrete surface by grinding, dusting, and priming the surface; placing the CFC panels and sealing the edge gaps between the concrete and the CFC panel with epoxy resin, named \"CFC putty\"; finishing with the epoxy resin injection and final polishing. The strengthened CFC panels were allowed to cure at room temperature for 1-2 days before starting the drop-weight impact tests.  
Figure 1: CFC panel in this study (unit: mm).  
Figure 2: RC beam specimen dimension in this study (unit: mm).  
Figure 4: Installation process of CFC panels in this study: (**a**) concrete surface preparation; (**b**) crack injection; (**c**) priming; (**d**) placing the CFC panels and putty application; (**e**) epoxy resin injection; (**f**) polishing.  
Figure 3: Types of specimens prepared in this study: (**a**) RC; (**b**) RC_FB_CFC; (**c**) RC_P3S_CFC; (**d**) RC_F3S_CFC (unit mm).  
The constants for each material are given in Table 1. On the 28 th day, the concrete was subjected to a material test according to JIS [29; 30], and its Young's modulus, mean compressive strength, and mean tensile strength are listed in this table. The SD295A rebar was subjected to a uniaxial tensile test in accordance with JIS [31], and its average yield strength and tensile strength were measured. For the mechanical properties of the CFC panel components, the material constants were also obtained by various tests according to JIS [32; 33] and JSCE [34] standards, but nominal values were adopted for CF injection.
## 3 Results and Discussion
### Self-Piercing Riveted Joint of Vibration-Damping Aluminum and CFRP  
In this section, the quality of self-piercing riveted joints formed between vibration-damping Al and thermoset CFRP are investigated. Unlike other metals, the CFRP panel could not be positioned on the bottom during the SPR process due to a lack of the ductility required for the material to deform and establish an interlock. The cross-sectional views of the joints between the two materials are shown in Figure 12. The figure shows that the rivet pierces the CFRP panel and forms an interlock with the upper sheet of the vibration-damping Al panel. However, the bottom Al sheet is joined only by the existing adhesive between the two Al sheets. The figures show that cracks within the CFRP are present near the rivet head, which could have an adverse effect on the fatigue performance under  
Figure 11: Failure modes of the self-piercing riveted joint formed between vibration-damping Al and GA590 DP with respect to the die type and the panel configuration: (**a**,**d**) die type **A**, (**b**,**e**) die type **B**, and (**c**,**f**) die type **C**. The bottom side of the top panel is shown for (**d**–**f**).  
cyclic loading [27; 28]. Additionally, non-uniform contacts between the rivet legs and the Al sheets are shown, resulting from an uneven deformation of CFRP within the rivet shank cavity.  
The measured geometrical indexes are shown in Figure 13a for the joints between CFRP and the vibration-damping Al panel. The results show that the interlock widths are relatively small compared with the joints formed using other materials, while the bottom depths are maintained at an acceptable value. To investigate the joint strength, the tensile shear loads were measured, and the results are shown in Figure 13b. As shown in the figure, a maximum tensile load of 2.26 kN was obtained for the joint formed using die type C, while tensile loads of 1.95 kN and 2.04 kN were obtained for the joint formed using die types A and B, respectively. The joint with the highest interlock width (namely, the joint formed using die type C) showed the highest tensile shear load but does not conform to the automotive joint standards. Finally, the failure modes of the tested joints were examined, and the results are presented in Figure 14. The figure shows that all of the joints show \"rivet pull-out failure\", and no bottom cracks were observed.  
Figure 14: Failure modes for the self-piercing riveted joint formed between vibration-damping Al and CFRP with respect to the die type: (**a**) die type **A**, (**b**) die type **B**, and (**c**) die type **C**.  
Figure 12: Cross-sectional images of a self-piercing riveted joint formed between vibration-damping Al and CFRP with respect to the die types: (**a**) die type **A**, (**b**) die type **B**, and (**c**) die type **C**.  
Figure 13: (**a**) Interlock width and bottom thickness, and (**b**) tensile shear loads for the self-piercing riveted joint formed between vibration-damping Al and CFRP with respect to the die type.
## Problem Formulation  
### Wind Output Characteristics  
#### 2.1.1 Variability  
Considering the wind power as a \"negative\" load, the net load of systems can be described as Formula (1). With the wind power participating in the scheduling plan, the climbing constraint of conventional units that responds to the peaking requirement of the net load fluctuation can be expressed as Formula (2).  
\[P_{net,t}=P_{load,t}-P_{wind,t} \tag{1}\]  
\[R^{dn}_{amp,t}\leq P_{net,t}-P_{net,t-1}\leq R^{up}_{amp,t} \tag{2}\]Depending on the fluctuation magnitude and direction of the wind output and the load demand, the variability of wind output can be divided into positive peaking characteristics and anti-peaking characteristics [15]. Figure 1 shows the typical daily load demand and wind output of an actual wind farm in central China.  
As we can see from Figure 1, the output of the wind farm exhibits obviously anti-peaking characteristics at most moments, especially at night, when the wind power output is high and the load is small. In which situation, the usual peaking strategy of systems is to on-off peaking units or curtail the wind power to meet the additional peaking demand that is caused by wind power integration, which aggravate the operation cost of conventional units and limit the benefit of wind farm owners at the same time.
# Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
## 5 Parameter Acquisition  
### Time-Temperature Superposition  
In dynamic modulus prediction models, the concept of \"time-temperature superposition\" (referred to as \"characteristic time\") is used to interpret the influence of temperature on material properties and predict their behaviour at different loading rates. This concept is important for asphalt mixtures, as they exhibit significant viscoelastic behaviour and are sensitive to temperature changes.  
The characteristic time (\(\tau\)) is the time scale used to shift the isothermal curves in the previous sections and generate the master curve. It is calculated as follows:  
\[Ln(\tau)=a+bT+cT^{2}, \tag{10}\]  
\(a,\ b\), and \(c\) are regression constants representing the material characteristics.  
In this part, the resulting regression constants are presented for the tested asphalt mixtures in Table 5. It is shown that the values of \"\(\mathbf{a}\)\" and \"\(\mathbf{b}\)\" were close to each other for the different reclaimed asphalt mixtures, but the values of \"\(\mathbf{c}\)\" were different.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**RA (\%)**} & \multicolumn{2}{c}{**0 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**10 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**20 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**30 \({}^{\circ}\)C**} \\ \cline{2-9}  & **a(T)** & **log(a(T))** & **a(T)** & **log(a(T))** & **a(T)** & **log(a(T))** & **a(T)** & **log(a(T))** \\ \hline
0 & 200 & 2.301 & 5 & 0.699 & 1 & 0 & 0.033 & \(-1.481\) \\
30 & 980 & 2.991 & 30 & 1.477 & 1 & 0 & 0.038 & \(-1.420\) \\
40 & 900 & 2.954 & 30 & 1.477 & 1 & 0 & 0.05 & \(-1.301\) \\
50 & 1500 & 3.176 & 30 & 1.477 & 1 & 0 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shift factor determination for asphalt mixtures with different RA contents.  
Figure 7: Master curves of asphalt mixtures with 0% RA, 30% RA, 40% RA, and 50% RA.