esa-sceva/satcom-chunk-collection huggingface dataset:
# Regional Seismic Damage Simulation of Corroded RC Frame Structures: A Case Study of Shenzhen City
## 3 Corrosion Simulation of Coastal RC Buildings
### Initiation Time  
There are many models concerning the calculation of the corrosion initiation time of reinforcements in concrete [38; 39; 40]. The ingress of chloride ions is one of the most important causes of corrosion initiation of reinforcing steel [41]. Most of the existing methods are based on [PERSON]'s law to simulate the chloride ion diffusion in concrete structures [42]. The corrosion initiation time is the time taken for the chloride ion concentration on the surface of reinforcements to reach the critical value [43]. Specifically, Equation (1) can be used to calculate the corrosion initiation time of RC structures [40].  
\[T_{\text{corr}}=\frac{X^{2}}{4D}\bigg{[}\text{erf}^{-1}\bigg{(}\frac{C_{\text {s}}-C_{\text{cr}}}{C_{\text{s}}-C_{0}}\bigg{)}\bigg{]}^{-2} \tag{1}\]  
where \(T_{\text{corr}}\) is the corrosion initiation time, \(D\) is the chloride ion diffusion coefficient, \(X\) is the cover of concrete, \(C_{\text{cr}}\) is the critical chloride ion concentration, \(C_{0}\) is the initial chloride ion concentration in cement paste, and \(C_{\text{s}}\) is the chloride ion concentration on the surface of concrete.  
The chloride ion diffusion coefficient \(D\) and the critical chloride ion concentration \(C_{\text{cr}}\) in Equation (1) can be determined according to the study by [PERSON] and [PERSON] [1]. The initial chloride ion concentration, \(C_{0}\), in cement paste can be determined according to the actual condition of coastal buildings. For example, if sea sand is used irregularly in concrete, the initial chloride ion concentration can be relatively high [2; 44]. Owing to the influence of sea breeze and sea fog, the chloride ion concentration on the surface of the coastal RC structures, \(C_{\text{s}}\), is related to the distance from the coastline \(d\). The chloride ion concentration on the surface of coastal RC structures, \(C_{\text{s}}\) (kg:m\({}^{-3}\)), can be calculated according to the work of [PERSON] (Equation (2)) [45], which is proposed based on the statistical results of 1158 bridges in coastal atmospheric environments.  
\[C_{\text{s}}(d)=\left\{\begin{array}{cc}2.95&d\ <\ 0.1\ \text{km}\\ 1.15\ -\ 1.81\times\ \log_{10}(d)&0.1\ \text{km}\ <\ d\ <\ 2.84\ \text{km}\\ 0.03&d\ >\ 2.84\ \text{km}\end{array}\right. \tag{2}\]  
Figure 4: Deterioration process of chloride-induced RC structure corrosion.
## 4 Experiments
### Experimental Settings
#### 4.1.2 Baselines  
The DESTformer is compared with five state-of-the-art long-term forecasting methods based on the transformer, including ARIMA [37], Informer [1], and LogTrans [21], as well as FEDformer [3] and Autoformer [2], which combine time series decomposition with the transformer.
## 3 Research Design
### Evaluation Methods  
RQ1: Can we offer new evaluation methods for GitHub project recommendation problem?  
We recommended the top-\(n\) highest-rated projects among the unknown projects to each developer. We then evaluated the recommendations using two methods we proposed. When recommending projects to developers, there should be a ground truth for evaluating the proposed projects. Unlike ordinary recommender systems, this is an unsupervised model. The evaluation criteria used in some studies related to this subject are set forth below.  
* A project-user rating matrix was split randomly into test and training subsets. Accuracy or recall scores were then calculated from the intersection between the _top-\(n\)_ scores of the test and train subsets [5]. However, another study argued that this method should not be used on platforms like GitHub where time is an important parameter, pointing out that problems would arise regarding predicting past activity with future data will occur when using k-fold cross-validation by randomly dividing the data [3].
* In another study, the accuracy of project recommendations was evaluated using the developer's past commits to the related project. A recommendation was assumed to be correct if the number of commits a certain developer made to the project exceeded a certain value. The average number of commits per project was set as the threshold value in the dataset [27].
* In a study predicting whether a developer would join a project in the future, the dataset was split into two different sets by time. In this way, the predicted result was verified with actual future data [25].
* In a survey-based study, the authors asked respondents which features could be used as a recommendation tool. Most of them stated that the languages in which developers already coded or with which they were familiar were important for recommendations [38].  
In this study, we used two evaluation methods to analyze our proposed developer metrics.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 2 Background
### Related Literature
#### 2.2.2 Circuit and Manufacturing Technology Solutions  
Several researchers have proposed technology and circuit solutions as an alternative to ECC. [PERSON] et al. have proposed a solution based on Temporal Majority Voting (TMV) and dark bits evaluation [10]. They also employ burn-in and aging effects to improve the PUF. Design changes to enable voting, along with uniqueness improvement through synchronous design, have also been proposed. However, the majority voting scheme proposed can only correct up to error rates of less than 8% stand-alone (with a 15-way voter), and additional techniques are mandatory to achieve practical application. [PERSON] et al. have also proposed a method to improve the reliability of the PUF cells by aging the cells and increasing the mismatch [16]. Similarly, [PERSON] et al. have proposed a technique to improve reliability through hot carrier injection (HCI) aging [17]. [PERSON] et al. also investigate the effect on aging in SRAM PUFs and effectiveness of data-dependent strategies to improve reliability [18]. [PERSON] and [PERSON] [19] proposed an 8T SRAM PUF with a PMOS latch and a low-power 7T SRAM cell with an embedded Magnetic Tunnel Junction (MTJ) to enhance the reliability of the PUF in the presence of environmental fluctuations during multiple power-ups. The authors also focused on lowering the leakage power. [PERSON] and [PERSON] [20] presented a differential circuit that captures the process mismatch and amplifies it to reduce the effect of noise. Once the difference has been sufficiently amplified, the differential outputs are latched to generate stable bits. [PERSON] et al. have proposed an alternative to error correction by pre-selecting the bit with greater mismatch to reduce error rate [21]. [PERSON] et al. implemented an SRAM PUF on a microcontroller and combined it with simple repetition codes to reduce the error rates. They also focused on checking the uniqueness of the PUFs and reliability over a wide range of temperatures. [PERSON] et al. have proposed a method by adapting voltage ramp-up time to ambient temperature to reduce the error rate of memory PUFs [22]. Though this technique improves the reliability, the auxiliary circuits needed for voltage ramp-up can be area intensive. In addition, shaping supply voltage is expensive for designs with large power delivery networks.  
While many prior works have focused on circuit, design and manufacturing technology based solutions to improve reliability of SRAM PUFs, we seek a low-cost alternative for low-cost systems that avoids expensive technology solutions. We elaborate on this in the next sections.
## 4 Numerical Results
### Comparisons and Discussion
The latter was generated between the extreme points in order to narrow down the search space to the region of interest. Figure 12b shows a comparison of the designs obtained using both routines (the plots present the responses obtained for the antenna model, represented using 30 parameters). As can be seen, the algorithm of Section 3 outperformed the benchmark method in terms of performance of the resulting designs. More importantly, the number of \(R\) simulations required to obtain the final designs using the proposed algorithm amounted to 1889, which was 81% less compared to the cost of MOEA operation. Even when the optimization was performed in a nested loop, the cost of the presented algorithm (4389 EM simulations) was over two times lower compared with the benchmark routine (while providing noticeably better responses characterized by a more even spread between the extreme designs and better in-band reflection). Figure 12: Comparison of the optimized antenna designs obtained using (**a**) different numbers of adjustable parameters, as well as (**b**) conventional MOEA and the presented nested algorithm (\(d_{4}=12\)). One should emphasize that the dimensions of the smallest Pareto-optimal antenna design were only 12.5 mm \(\times\) 18.4 mm (footprint = 230.4 mm\({}^{2}\)). The structure was compared against other compact UWB monopoles from the literature in terms of size and bandwidth. For the sake of fair comparison, the dimensions of the considered structures were expressed in terms of the guided wavelength \(\lambda_{g}\) calculated for the lower corner frequency and the electrical performance of the substrates on which the considered radiators were implemented. The results gathered in Table 2 indicate that the proposed structure was characterized by a competitive size while providing acceptable performance. Small dimensions make the design's potential use not only for a range of mobile UWB sensors and IoT-based localization services, but also for body of area network applications [54].
# An ADMM Based Parallel Approach for Fund of Fund Construction
## 2 Problem Formulation
#### Transforming the Inequality to Equality  
To solve the nonlinear problem (1), we consider the general formulation of the nonlinear problem (3). We start by transforming the convex problem (3) to a new problem without inequality constraints by introducing the slack variables to transfer the inequality constraints into the equality constraints. Let \(\tilde{x}\in\mathbf{R}^{m}\), the inequality constraints \(Ax\leq b\) and the equality constraint \(\mathbf{1}^{T}x=1\), which could be rewritten as  
\[\begin{bmatrix}A&\mathbf{1}\\ \mathbf{1}^{T}&\mathbf{0}\end{bmatrix}\begin{bmatrix}x\\ \tilde{x}\end{bmatrix}=\begin{bmatrix}b\\ 1\end{bmatrix},l_{i}\leq x_{i}\leq u_{i},i=1,\ldots,n\tilde{x}_{i}\geq 0,i=1, \ldots,m. \tag{4}\]  
Let \(\tilde{x}=\begin{bmatrix}x\\ \tilde{x}\end{bmatrix}\), then the problem (3) could be rewritten as  
\[\begin{array}{ll}\min\limits_{x\in\mathbf{R}^{m+n}}&\frac{1}{2}\tilde{x}^{T }\tilde{P}\tilde{x}+\tilde{q}^{T}\tilde{x}+\sum_{i=1}^{n}f_{i}(\tilde{x}_{i}) \\ \text{subject to}&\tilde{A}\tilde{x}=\tilde{b},\\ &l_{i}\leq x_{i}\leq u_{i},i=1,\ldots,n\\ &\tilde{x}_{i}\geq 0,i=n+1,\ldots,n+m.,\end{array} \tag{5}\]  
where  
\[\tilde{P}=\begin{bmatrix}P&\mathbf{0}\\ \mathbf{0}&\mathbf{0}\end{bmatrix},\tilde{q}=\begin{bmatrix}q\\ \mathbf{0}\end{bmatrix},\tilde{A}=\begin{bmatrix}A&\mathbf{1}\\ \mathbf{1}^{T}&\mathbf{0}\end{bmatrix},\tilde{b}=\begin{bmatrix}b\\ 1\end{bmatrix}\]  
By introducing the slack variable \(z\in R^{m+n}\), the problem (5) is equivalent to  
\[\begin{array}{ll}\min\limits_{x\in\mathbf{R}^{m+n}z\in\mathbf{R}^{m+n}}& \frac{1}{2}\tilde{x}^{T}\tilde{P}\tilde{x}+\tilde{q}^{T}\tilde{x}+\sum_{i=1}^ {n}f_{i}(\tilde{z}_{i})+\mathcal{I}_{\mathcal{C}_{1}}(x)+\mathcal{I}_{ \mathcal{C}_{2}}(z)\\ \text{subject to}&\tilde{x}_{i}-z_{i}=0,i=1,\ldots,n+m.\end{array} \tag{6}\]  
with variables \(x\in\mathbf{R}^{m+n}\) and \(z\in R^{m+n}\). \(\mathcal{C}_{1}=\{x|\tilde{A}\tilde{x}=\tilde{b}\}\), \(\mathcal{C}_{2}=\{z|l_{i}\leq z_{i}\leq u_{i}\), \(i=1,\ldots,n,z_{i}\geq 0,i=n+1,\ldots,n+m\}\). One can easily figure that the original problem (3) is transformed to problem (6) by two steps. The first step is the slack variable \(\tilde{x}\), which changes the inequality \(Ax=b\) to the equality \(\tilde{A}\tilde{x}=\tilde{b}\). The second step, with the help of the slack variable \(z\), transforms the problem (5) to problem (6) without any inequality constraints remaining.
# Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT
## 3 Numerical Experiments
### Force Inverter Design  
The force inverter design problem is demonstrated in Figure 9 where an input load is defined in the positive direction, and symmetric constraints are applied on top and side faces (two pink faces). In the case of force inverter design, the goal is to maximize the negative horizontal output displacement. The design domain is discretized by a 40 \(\times\) 20 \(\times\) 5 finite element mesh.  
Figure 10a shows that the reference design case converges at 1.195 mm after 131 iterations, and the self-supporting design case converges at 1.105 mm after 287 iterations. That is, a performance sacrifice of 7.53% is made to form the self-supporting topology suitable for AM. Compared to the reference topology (Figure 10b), self-supporting features are observed in the optimized topology with [PERSON]'s AM filter (Figure 10c).  
Figure 8: Convergence processes and optimized topologies with different critical overhang angles: (a) convergence processes, (b) reference, (c) 30\({}^{\circ}\), (d) 45\({}^{\circ}\), (e) 60\({}^{\circ}\).  
The compliant mechanism design problem itself is more challenging than the compliance minimization design problem in terms of sensitivity analysis. The problems of self-supporting design for a compliant mechanism in this study demand more accurate  
Figure 10: Convergence processes and optimized topologies solving the force inverter design case: (**a**) convergence processes, (**b**) reference, (**c**) self-supporting design.  
Figure 9: Design domain of force inverter design.  
sensitivity analysis than general compliant mechanism design problems. Therefore, the successful formation of the self-supporting topology for compliant mechanism design (Figure 10c) further validates the effectiveness of the sensitivity analysis method assumed in SEMDOT. More details on the mentioned sensitivity analysis method can be found in [16].  
Based on the results presented from Sections 3.1-3.4, it can be concluded that the generation of a self-supporting design needs a longer convergence process than its corresponding reference design. The similar phenomenon was also observed by the authors when 2D self-supporting topologies were explored [33; 34].
## 2 The Pancretan Stadium Crete
### Energy Consumption in the Stadium's Current Operation  
In the current operation of the Pancretan Stadium, the final energy needs coverage is based on electricity and diesel oil. Electricity is consumed for the indoor space heating and cooling with a central chiller and remote split units, the lighting of the outdoor and indoor space, the production of hot water with electrical resistances installed in four thermal storage tanks, the indoor swimming pools heating with two air-to-air heat pumps, the operation of the circulators and fans of the hot water hydraulic network and the air duct network and for smaller office devices. Additionally, diesel oil is also consumed exclusively for the production of hot water.  
The electricity consumption annual time series for 2016 is presented in Figure 5, as measured by the local electrical grid's operator. The total annual electricity consumption is calculated at 1,889,198 kWh, while the total annual procurement cost for the same year was 241,889 C, leading to the annual average electricity procurement price of 0.1277 C/kWh. The remarkably high power demand peaks observed in Figure 5, which almost doubled compared to the average hourly values, are due to the main Stadium's lighting system operation. Finally, the total annual reactive consumption was 388,407 kWh, imposing an annual, average power coefficient of 0.976. Generally, all the monthly average power coefficients are calculated higher than 0.965, obviously, due to the reactive consumptions support with a reactive power compensation panel.  
Beyond the electricity consumption, diesel oil is also consumed exclusively for the production of hot water. The annual oil consumptions were also provided by the Stadium's Management for the years 2016 and 2017 equal to 16,500 L and 16,430 L, respectively. For the purposes of this work, the annual oil consumption in the existing operation will be assumed equal to 16,500 L. The annual average diesel oil procurement price for 2017 in Crete was configured at 0.80 C/L (VAT not included). This price will be also adopted for this study, leading to an annual diesel oil procurement cost of 13,200 C.  
Figure 4: The involved in the calculation annual time series of the (**a**) wind velocity and (**b**) the seawater temperature.  
Figure 5: Electricity consumption annual fluctuation in Pancretan Stadium in the existing operation.  
In total, the annual energy resources procurement cost for the coverage of the energy needs in the Stadium's current operation, given the above facts, is calculated as presented below:  
* electricity annual procurement cost: 241,389 \(\upxi\);
* diesel oil annual procurement cost: 13,200 \(\upxi\);
* both energy resources annual procurement cost: 254,589 \(\upxi\).
# Design of Optical Tunnel Switching Networks for Big Data Applications
## 5 Network Performance and Its Applications
### Applications  
Figure 20 shows the application architecture of the Torus topology tunnel switching network in the mobile edge computing (MEC) of Telecom 5G. When high-speed, high-bandwidth, low-latency  
Figure 19: Traffic changed of two-plane optical tunnel switching network with 25 nodes.  
Figure 18: Traffic distribution for Method 2 of two-plane optical tunnel switching network with 25 nodes.  
applications are required, the traffic is directly transmitted to the server via the Optical Tunnel Switch (OTS), without being sent to the cloud data center for processing of big data. The architecture can provide up to 1764 servers to establish 2352 tunnels. Of course, the aforementioned architecture can also be used in the data center or telecommunications core network to handle big data.  
C2 TCP (Cellular Controlled Delay TCP) uses TCP to calculate the average delay time with RTT (Round Trip Time) of each packet to control the transmission traffic [13]. Therefore, the multi-plane Torus topology network architecture can also use TCP to calculate the average delay time with RTT of each packet using the delay time of transmitting ACK signals, and then estimate the traffic between nodes, and nodes via the SDN controller, according to the average delay time. When the traffic is changed, the number of network tunnels can be scheduled in time to balance the load traffic to avoid traffic loss.
# A Broadband Ultra High Frequency (UHF) Fat-Dipole Antenna for Digital TV Applications
###### Abstract  
A broadband and compact planar UHF antenna is presented for use in MIMO Digital TV indoor applications. The choice of the element and its physical implementation is oriented towards a low-profile, low-cost, and single-layer deployment. Since it is intended to be used in a 2 \(\times\) 1 MIMO array, two antennas were constructed and tested to minimize their coupling to provide larger MIMO gains. Regarding the excitation, two different structures were evaluated to observe their impact on the final impedance profile. The option of an infinite balun provided a 62% fractional bandwidth (394 MHz to 747 MHz), covering the desired band and with good isolation levels when analyzed in the array. Results regarding MIMO parameters are provided herein, such as Envelope Correlation Coefficient and Total Active Reflection Coefficient, proving the potential of the proposed antenna array to be used in MIMO Digital TV applications in covering the UHF band.  
broadband antenna; digital terrestrial TV broadcasting (DTTB); planar antenna +
Footnote †: journal: _Sci._  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Improved Control Method
### Vibration Suppression by the Feedback Controller
: _If the parameters \(a_{21},a_{22},c_{12}\), and a satisfy the relations \(a_{21}<0,a_{22}<0,c_{12}<0\), and \(a>0\), the system described by (29) is a BIBO system._  
Proof of Theorem 1.: According to **Lemma 2**, let the matrix \(\mathbf{Q}\) equal the unit matrix, \(\mathbf{Q}=\mathbf{I}\), and the solutions to Equation (39) can easily be obtained. \[\mathbf{P}= \begin{bmatrix}-\frac{a_{21}+a_{21}^{2}+a_{22}^{2}}{2a_{21}a_{22}}&- \frac{1}{2a_{21}}\\ -\frac{1}{2a_{21}}&\frac{1-a_{21}}{2a_{21}a_{22}}\end{bmatrix}  \tag{40}\]  
Consider the following conditions in the matrix. \[\frac{-a_{21}+a_{21}^{2}+a_{22}^{2}}{2a_{21}a_{22}}>0,\text{ and }det\ \mathbf{P}>0 \tag{41}\]  
Because \(\mathbf{P}\) is a positive matrix, asymptotic stability is achieved according to **Lemma 2**. Subsequently, if the state function of a system is asymptotically stable, it is a BIBO system according to **Lemma 1**. It is a practical notion of BIBO stability in the sense that tracking errors can be minimized. Thus, the state space function from the PI controller to the modified one described by (29) is a BIBO system. After the modified signal \(\dot{\theta}_{b}^{*}(t)\) is obtained, the performance of the closed-loop system depends on the correct selection of the PI controller parameters, \(K_{p}\) and \(K_{i}\). We can set these parameters on the basis of engineering experience or simulate and calculate the ideal controller parameters on the basis of the model. Herein, we employ the engineering method to adjust the parameters in four steps. First, the sine command is used as the input signal, in which case the changes in the amplitude and phase can be easily observed. Second, the proportional gain is increased until it reaches a critically stable state. In this state, the control system may produce weak vibrations, and occasionally, we could judge whether the system is close to the critical stable state by the weak vibration. Third, the proportional gain is reduced appropriately (i.e., by 0.6-0.8 times) and the integral gain is added. Finally, the steady-state error of the system is observed, and a larger integral gain is selected under the condition that the system does not diverge. If the system requirements for overshooting exist, the step signal can be used as the input, and the integral gain can be properly reduced.
## 5 Results and Discussion
### Packets Generated  
For each scenario with varying numbers of nodes, a variety of packets are generated over the span of the simulations. The number of packets created is directly proportional to the number of mobile nodes, as can be demonstrated in Figure 11.