esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The replaceable links controlled the seismic behavior of the structure better [21]. Numerous experimental studies were carried out on the seismic behavior of EBF links [22; 23; 24; 25; 26; 27]. These investigations show that EBF links typically exhibit excellent bearing capacity and deformability [28; 29; 30; 31; 32]. The link is designed as part of a frame beam, which results in the dimension of other components to increase with the enlargement of the links. In addition, a report on a series of cases shows that the plastic deformation region of the structure can hardly be located under large earthquakes [33; 34; 35; 36]. The EBF was designed for an anti-seismic structure, where the replaceable link is separated from the frame beams as a link that can be repaired conveniently to extend the service life of the overall structure [37]. Additionally, the end-plate connection was joined to the frame beams to form an eccentrically braced steel frame with a replaceable link. This not only concentrated the plastic deformation in the replaceable link during the large earthquake and reduced the cost of the overall structure, but also made the maintenance even easier after the earthquake. Previous investigations [38] have shown that elastic bracing can solve the problem of the lateral bracing of structural members and further research should discuss its applicability to EBFs. Based on the actual force of the links, a loading device was designed based on shear effect, consisting of a central \"fuse\" link connecting to steel beam segments at its two ends. This paper focused on a series of quasi-static tests with the goal of examining the seismic behavior and analyzing damage modes of links. A total of eight specimens with different parameters were loaded under cycle loading both with the welding access holes and no welding access holes to allow direct comparison between the two types. The influence of geometric parameters such as length ratio, stiffener spacing, and welding access hole on the seismic performance of links was determined by parameter analysis. Experimental studies and the related damage analyses reveal that cracks are the major causes of damage to the EBF, and there is a lack of research on real-time monitoring of the onset and development of these cracks in EBF structures in the literature. As a future work, this paper proposes a piezoceramic patch transducer-based active sensing approach to monitor the onset and development cracks in the EBF subjected to dynamic loadings.
# Prussian Blue: A Safe Pigment with Zeolitic-Like Activity
## 4 Adsorption Properties of PB  
The open structure of the crystal lattice of PB makes it capable of taking up and trapping other molecules or ions in the cavities of its lattice. The surface area analysis using N2 adsorption and desorption measurements of PB samples at 77.350 K afford  
Figure 1: (**a**) Cubic lattice of insoluble Prussian blue. Colors: Fe (II) yellow; Fe (III) violet; C gray; N blue; O red; H white. (**b**) Visual aspect of powder of Prussian blue. Figure 1a reproduced with permission from [5].  
significant information about the porosity, BET (Brunauer-Emmett-Teller) specific surface area, pore size, and pore volume. The size and morphology of PB nanoparticles influences the adsorptive properties. The small- (-20 nm) and medium-sized particles (around 100 nm) exhibited much higher BET surfaces areas than the large ones (in excess of 200 nm), probably due to the hindrance of removing water molecules during degassing caused by the larger size of the particles and dense crystal structure. In addition, the medium-sized particles showed the highest porosity [18]. The insoluble PB synthesized by us has a specific surface area of 370 m\({}^{2}\) g\({}^{-1}\), and its pore volume is 0.537 cm\({}^{3}\) g\({}^{-1}\). The adsorption-desorption isotherm is showed in Figure 2. This isotherm belongs to IV-type isotherm (Figure 2a) according to the IUPAC classification [19], since a distinguishable hysteresis loop appears at high relative pressure (P/P\({}_{0}\) > 0.8), which demonstrated that PB is essentially of mesoporous structure. The pore diameter distribution is centered at 17.3 nm.  
Due to uniform pore diameter, large pore width, and good surface area, PB and PBA are capable of hosting small molecules (water, gases, hydrocarbons) and ions (metal cations) in their crystal lattice spaces. The adsorption ability of cations by PB is influenced by the Stokes radium of the hydrated ion. Some Stokes radii are found to be smaller than the corresponding crystal radii, contrary to expectation for hydrated ions [20]. For instance, the Stokes radii of the alkali ions are Rb\({}^{+}\) (0.118 nm), Cs\({}^{+}\) (0.119 nm), K\({}^{+}\) (0.125 nm), and Na\({}^{+}\) (0.184 nm). The adsorption ability is quantified by the adsorption capacity defined as the amount adsorbed expressed in mg by the amount of PB in g. Besides the adsorption ability, the adsorbent capacity can be expressed in terms of the distribution coefficient (K\({}_{d}\)). The comparison among the different PB-composites is difficult since the obtained values are variable. They depend on the different morphology of the PB-composites, the temperature of mixing, the time of stirring of the solution with the sorbent (equilibrium time), the pH, the ratio ion/orbent, etc. Finally, it is necessary to perform a reusability test.
## 6 Results and Discussions  
In this section, the analytical results obtained from the derived outage probability expression (5) are presented and verified using simulation results. Results are provided for the fading scenarios discussed in Section 4. We observed in all plots that the analytical results were in accordance with the results obtained from the Monte-Carlo simulations.
## 2 Materials and Methods
### Clustering Analysis
#### 2.5.1 K-Medoids and CLARA  
The k-medoids clustering method (Partitioning Around Medoids (PAM)) helps compensate for the disadvantages of outlier-sensitive k-means analysis. Unlike k-means, the k-medoids clustering takes actual data points as the expected value [36]. The surrounding values form clusters according to the nearest representative value. These analytical methods have the advantage of being significantly more stable and less affected by noise when there are outliers in the data [37]. However, k-medoids clustering has inherent instability problems in large-scale data. This study used a sample-based method called Clustering LARge Application (CLARA) to improve instability [38; 39; 40]. It extracts only a limited amount of data, selects a representative value, and performs PAM to calculate the optimal median value.
## 3 Proposed Protocol
### Key Revocation Mechanisms  
Key revocation is an important aspect of key management, especially in UWSNs where sensors may be compromised or decommissioned. Revocation mechanisms ensure that compromised or unauthorized nodes do not have access to the network. There are several mechanisms for revoking keys, including Certificate Revocation List (CRL) and other revocation mechanisms.
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 2 Power System State Estimation
This requires complete modeling of all components of the power system including uncertain components such as RE sources. The authors of [11], one of whom also published the state estimation textbook [22], acknowledges that machine learning/deep learning-based state estimation is another direction that state estimation research can go to due to its ability to provide estimates without the need for power system models and not being vulnerable to assumptions that are not true for realistic cases. As [11] also mentions, deep learning-based state estimators can also be developed for dynamic state and parameter estimation. As will be mentioned in Section 3, the estimate is only true for the situations the network is trained in. This paper assumes a stance of static state estimation and is only trained for steady state measurements; therefore, should a fault or a slow acting stability issue occur, the _current trained model_'s estimates will not be valid. However, should the proposed method be trained for dynamic state estimation--where generator rotor angles are also estimated--then these issues should be addressable as well.
## 2 Materials and Methods
### Mobility Indicator Design
#### 2.2.1 Spatial Dependency  
We use spatial dependency (SD) to describe how much the movement of nodes is affected by each other. It is the speed similarity of two nodes that are not too far apart, which can be defined as follows:  
\[D_{spatial}(x,y,t)=RD\Big{(}\overset{\rightarrow}{v}_{x}(t),\overset{ \rightarrow}{v}_{y}(t)\Big{)}*VR\Big{(}\overset{\rightarrow}{v}_{x}(t), \overset{\rightarrow}{v}_{y}(t)\Big{)} \tag{6}\]  
When node \(x\) and node \(y\) move in approximately the same direction and at almost similar speeds, the value of \(D_{spatial}(x,y,t)\) is higher. However, if the ratio of relative direction or speed decreases, the value of \(D_{spatial}(x,y,t)\) will decrease.  
Since the motion of nodes rarely depends spatially on nodes that are farther away, the following conditions are added:  
\[l_{x,y}(t)>\alpha*R\Rightarrow D_{spatial}(x,y,t)=0 \tag{7}\]  
As can be seen from [27], for a value of \(a=2\), \(D_{spatial}(x,y,t)\) can very clearly distinguish different mobility patterns. The average value of \(D_{spatial}(x,y,t)\) at a specific time of a node pair is calculated as follows:  
\[\overline{D_{spatial}}=\frac{\sum_{t=1}^{T}\sum_{x=1}^{N}\sum_{y=x+1}^{N}D_{ temporal}(x,y,t)}{P} \tag{8}\]  
where \(P\) is the number of tuples \((x,y,t)\) that make \(D_{spatial}(x,y,t)\
eq 0\). Therefore, if the mobile nodes move independently of each other, the \(D_{spatial}(x,y,t)\) value of the mobility model will be smaller. On the other hand, if node movements are coordinated by a central entity or influenced by nearby nodes, making them move in similar directions and speeds, the \(D_{spatial}(x,y,t)\) value of the mobility model will be higher.
## 2 Aviation Fuel Emissions
### LCA of CO\({}_{2}\) Emissions
Specifically, the emissions for HEFA, CH, FT, hydroprocessed depolymerized cellulosic jet (HDCJ), direct sugar to hydrocarbons (DSHC), and ATJ are 3.1-40.0, 21.2-39.3, \(-\)1.6-18.2, \(-\)2.7-49.5, 22.0-80.2, and \(-\)27.0-117.5 g CO\({}_{2}\)-eq/MJ, respectively. Additionally, SAFs produced using the same process but different feedstocks also exhibit varying CO\({}_{2}\) emissions. For instance, ATJ fuels made from sugar cane, corn grain, switchgrass, and poplar emit \(-\)27.0-19.7, 47.5-117.5, 11.7-89.8, and 32.0-73.0 g CO\({}_{2}\)-eq/MJ, respectively [47]. Figure 2: GHGs emissions over the full life cycle of SAFs. Data are sourced from [47]. The green dashed line represents 0 g CO\({}_{2}\)-eq/MJ, while the red dashed line indicates 90 g CO\({}_{2}\)-eq/MJ, corresponding with the GHG emissions of conventional aviation fuel. The six bar graphs depict the GHG emissions for HEFA, CH, FT, HDCJ, DSHC, and ATJ fuels.
## 3 Proposed Approach
### Low Dimensional Representation
#### 3.4.1 Random Fourier Features  
The first method that we use is an approximate kernel method called random Fourier features (RFF) [48]. It is an unsupervised approach, which maps the input data to a randomized low dimensional feature space (Euclidean inner product space) to get an approximate representation of data in lower dimensions \(D\) from the original dimensions \(d\). More formally:  
\[z:\mathcal{R}^{d}\rightarrow\mathcal{R}^{D}\,. \tag{3}\]  
In this way, we approximate the inner product between a pair of transformed points. More formally:  
\[f(x,y)=\langle\phi(x),\phi(y)\rangle\approx z(x)^{\prime}z(y). \tag{4}\]  
In Equation (4), \(z\) is low dimensional (unlike the lifting \(\phi\)). Now, \(z\) acts as the approximate low dimensional embedding for the original data. We can use \(z\) as an input for different ML tasks like clustering and classification.
## 3 Supervised Learning of Inter-Floor Noises
### Network Architecture
Given the sparsity of the inter-floor noise dataset, it is difficult to train models with many weights. In such conditions, a CNN can be trained using transfer learning, as suggested in [33]. Transfer learning initializes a CNN with weights that are pre-trained on a large dataset (_source_) and fine-tunes them on a _target_ dataset. The method assumes that the internal layers of a CNN can extract mid-level descriptors from a _source_ that explain the distribution of the _source_. The distribution of a _target_ dataset can be learned by sharing the mid-level descriptors [33; 34]. In this study, a large image dataset is used as _source_ for training a CNN via transfer learning. Usually, image and sound representations are considered different. However, low-level notions of images [34] such as edges and shapes can be found in **P** and changes of lighting can be comparable to acoustic pressure changes in **P**. Hence, **P** can be considered to be an image with size of \(H\times W\) and the descriptors of the source could be shared to learn the distribution of the target. This approach for ASC can be found in [35; 36]. The weights of the three CNNs are initialized using weights that are pre-trained on ImageNet (_source_). The pre-trained weights used in this study are from [37] for AlexNet and VGG16, and from [38] for ResNet V1 50. Because the three CNNs are designed for ILSVRC, each output from the three CNNs have size of \(l=1000\). The adaptation layer reduces the number of output dimensions in the lower layer to \(C\), where \(C\) is the number of the inter-floor noise types/positions. The weights between the output of the CNNs and the adaptation layer are randomly drawn from a normal distribution with a fixed standard deviation of 0.01. This initialization is used in [26; 27; 39]. The bias \(\textbf{b}\in\mathbb{R}^{C}\) is initialized with 1, as in [26]. In a classification problem, the output values are normalized using a softmax function (i.e., softmax classifier) to convert the output elements to pseudo probabilities. For a given \(\textbf{x}\in\mathbb{R}^{n}\), the softmax function is defined as  
\[\text{softmax}\left(\textbf{x}\right)_{i}=\frac{\exp(x_{i})}{\sum_{j=1}^{n} \exp(x_{j})}. \tag{3}\]  
Let \(z_{c}\) be a \(c\)-th output node of the adaptation layer  
\[z_{c}=\sum_{i=1}^{l}w_{i,c}\sigma_{i},\ c=1,2,\cdots,C, \tag{4}\]where \(w_{i,c}\) is a weight between the \(i\)-th node of the former layer \(\sigma_{i}\) and \(c\)-th node of the adaptation layer. The predicted probabilities of given inter-floor noises with \(C\) type/position categories are \(\hat{y}_{c}=\text{softmax}\left(\mathbf{z}\right)_{c}\). The loss function \(L\) selected for optimization of three CNNs is cross entropy loss and \(L_{2}\)-regularization of \(w_{i,c}\)  
\[L=-\sum_{i=1}^{C}y_{i}\log\hat{y}_{i}+\lambda\left\|\mathbf{w}\right\|_{2}^{2}, \tag{5}\]  
where \(\lambda\) is a regularization strength and \(\mathbf{y}\in\mathbb{R}^{C}\) is a one-hot encoded true label.
# Charge Carrier Distribution in Low-Voltage Dual-Gate Organic Thin-Film Transistors
## Materials and Methods
### Method of the Device Simulation  
Device simulation in finite element method (FEM) was carried out to investigate the carrier distribution in single- and dual-gate p-type transistors by using the mathematics module of COMSOL Multiphysics (KESCO Ltd., Tokyo, Japan) [14]. The simulation was based on the Poisson equation and the continuity equation.  
\[\overrightarrow{\
abla}\cdot\left[\overrightarrow{\
abla}(x,y) \right]=\frac{e}{\varepsilon}[n_{\rm f}(x,y)n+n_{\rm t}(x,y)-n_{0}] \tag{1}\] \[\overrightarrow{\
abla}\cdot\left[en_{\rm f}(x,y)\mu \overrightarrow{\
abla E_{\rm F}}(x,y)\right]=0 \tag{2}\]  
Here \(V\) is the electrostatic potential, \(e\) is the elementary charge, \(\varepsilon\) is the dielectric constant, \(n_{\rm f}\) is the free hole carrier density, \(n_{\rm t}\) is the trapped hole carrier density, \(n_{0}\) is the carrier density in the neutral state, \(\mu\) is the mobility tensor, and \(E_{\rm F}\) is the Fermi energy. These equations contain two independent variables: \(V(x,y)\) and \(E_{\rm F}(x,y)\). Carrier densities \(n_{\rm f}\) and \(n_{\rm t}\) were approximated as  
\[en_{\rm f}(x,y)=D_{\rm v}exp(-\frac{E_{\rm F}(x,y)-E_{\rm v}}{k_{\rm B}T}) \tag{3}\]\[n_{\rm t}(x,y)=\int_{-\infty}^{+\infty}\frac{\frac{D_{\rm t}}{E_{\rm t}}exp[\frac{ E_{\rm v}-E}{E_{\rm t}}]}{1+exp[-\frac{E_{\rm F}(x,y)-E}{k_{\rm B}T}]}dE\approx D_{ \rm t}exp[-\frac{E_{\rm F}(x,y)-E_{\rm v}}{k_{\rm B}T}] \tag{4}\]  
Here \(D_{\rm v}\) is the effective density of states in the valence band, \(E_{\rm v}\) is the top energy of the valence band, \(k_{\rm B}\) is the Boltzmann constant, \(T\) is the temperature, \(D_{\rm t}\) is the total density of trap states, and \(E_{\rm t}\) is the average energy of traps with exponential density of states.  
The parameters used in the simulation were summarized in Table 1. Channel width, channel length, channel thickness, dielectric constant of insulator (\(\varepsilon_{\rm i}\)), and dielectric thickness were measured directly by experiments. The dielectric constant of semiconductor (\(\varepsilon_{\rm s}\)) and temperature were set at typical values. The effective density of states in the valence band (\(D_{\rm v}\)) was estimated from a typical effective mass of organic semiconductor, \(3m_{0}\), and has little effect on the simulation results. Here \(m_{0}\) is the mass of a free electron. The other parameters, mobility, work function, ionization energy, density of trap states (\(D_{\rm t}\)), average energy of trap states (\(E_{\rm t}\)), and carrier density in the neutral state (\(n_{0}\)), were arbitrarily set to fit the experimental data. In the simulation, either work function or ionization energy does not matter as long as the difference between the two does not change. We assumed anisotropic mobility because it is a common nature of polymeric semiconductors as reported [26].
## 3 Results
### Research Questions' Answers
#### 3.2.1 Spatial Scales Analyzed  
Following [PERSON] et al. (2018), the spatial networks could be classified into three categories, namely community, urban city, and regional. Community-scale assessments focus on networks with a typical length of less than 5 km, urban city scale assessments cover residential networks with a typical length of between 5 and 200 km, and regional scale denotes large-scale networks with a typical length of more than 200 km. The type of network analyzed in the region/area and the impact range are used to assign the mentioned spatial scale ranges. The majority of articles being reviewed in this SLR are focused on urban networks, and any publication that examines highway networks is categorized as being at the regional scale. However, estimating the indirect impacts of a flood on a particular transportation network could be challenging due to the variety of conditions and damages (such as economic and environmental impacts), as well as due to the interdependent nature of transportations connecting different modes, especially in urban contexts ([PERSON], 2018). Figure 4 shows the frequency of the different spatial scales analyzed in the literature of indirect impacts of flood on transportation. As it is depicted, the majority of the articles analyzed a spatial scale of the urban city, while only 4% and 30% of articles considered community and regional scales for their analysis, respectively. This could be attributed to the fact that for assessing the impacts of flooding on traffic, the cascading effect of the disruption is non-negligible which leads to considering a larger spatial scale. On the other hand, by considering a larger spatial scale the computational cost of analysis grows too. Therefore, when selecting the appropriate spatial scale for a study, it is important to not only consider the type of network and affected area, but also weigh the pros and cons of the chosen spatial area for the case study.
## 4 Results and Discussions
### Fragility Analysis  
Fragility analysis is used widely for assessing the seismic performance of structures. Several theories can be used to carry out the fragility analysis and one of them is incremental dynamic analysis (IDA), which involves numerous structural analyses corresponding to different ground motion records [58]. Each recorded earthquake is scaled into various intensity levels as long as the whole range from elastic to collapse of the structure can be covered.  
This section describes the probability of failure of the structure based on its damage state, which is characterized by an engineering demand factor and intensity measure. There are several types of intensity measures such as PGA, PGV, etc., which can be used to estimate the probability of failure of a structure via fragility curves [11; 13; 19; 21; 59]. In this research, PGA is used as a good indicator for time history analysis [60; 61]. As reported by the Nuclear Regulatory Commission [25], the electrical cabinet contains many electrical devices. Thus, the acceleration response at the top of the cabinet is considered as the limit state (LS) to develop fragility curves in this study. The LS value is defined when the zero period acceleration (ZPA) at 2% damping comes to 1.8 g.  
The fragility function is defined by a lognormal cumulative distribution function as [62]:  
\[P(C|IM=x)=\Phi\left[\frac{\ln\left(\frac{x}{\theta}\right)}{\beta}\right] \tag{8}\]  
where \(P(C|IM=x)\) is the probability that a ground motion with \(IM=x\) will cause the collapse of the structure; \(\theta\) is the mean value of the fragility curve, corresponding to the \(IM\) level with 50% probability  
Figure 12: Spectral acceleration responses (5% viscous damping) at the top of the cabinet (without TMD): (**a**) High-frequency; (**b**) low-frequency.  
Figure 13: Spectral acceleration responses (5% viscous damping) at the top of the cabinet (with TMD): (**a**) High-frequency; (**b**) low-frequency.  
of exceedance and \(\beta\) is the standard deviation of \(\ln IM\). These parameters are defined by Equations (8) and (9) [62].  
\[\ln\theta=\frac{1}{n}\sum_{i=i}^{n}\ln IM_{i} \tag{9}\]  
\[\beta=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(\ln\left(IM_{i}/\theta\right) \right)^{2}} \tag{10}\]