esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Frame Structure  
We consider a time-slotted frame structure with a slot duration of \(T_{s}\), as shown in Figure 2. Consider the \(k\)th V2I link. After performing the beam alignment between the RSU and the vehicle at the beginning of the time slot \(i\), the RSU transmits data to the vehicle during \(t_{\text{tx}}^{k}(\varphi_{i}^{k})\) seconds, where \(t_{\text{tx}}^{k}(\varphi_{i}^{k})\) is given by \(\tau_{i}^{k}-t_{\text{tt}}^{k}(\varphi_{i}^{k})\), \(\tau_{i}^{k}=L_{i}^{k}\cdot T_{s}\), and \(t_{a}^{k}(\varphi_{i}^{k})\), which is the beam alignment time, is obtained from (2). The beam alignment between the RSU and the vehicle proceeds again after \(L_{i}^{k}\) time slots, i.e., \(L_{i}^{k}\) is the beam alignment interval at time slot \(i\). Hence, the parameters, namely the antenna beamwidth \(\varphi\) and the beam alignment interval \(L\), affect on the throughput. Selecting a narrow antenna beamwidth results in higher antenna gain, but at the cost of a longer beam alignment time \(t_{a}\) and a shorter beam alignment interval \(L\). In other words, the narrower antenna beamwidth increases the received SNR at the vehicle but decreases the data transmission time.  
The V2I communication link in a highway scenario is most likely to be a line-of-sight (LOS) link [43]. We use the WINNER+ channel model for modeling the path loss between the RSU and a vehicle. The WINNER+ channel model of [44] provides a comprehensive framework for modeling wireless communication channels in various scenarios. The WINNER+ channel models have been used by the 3 GPP [45; 46]. The path loss for the LOS channel in the WINNER+ channel model is as follows [44]:  
\[\text{PL}(d)=40.0\log_{10}(d)+7.56-17.3\log_{10}(h_{\text{RSU}})-17.3\log_{10 }(h_{\text{vehicle}})+2.7\log_{10}(f_{c}), \tag{3}\]  
where \(h_{\text{RSU}}\) is antenna height of the RSU, \(h_{\text{vehicle}}\) is antenna height of the vehicle, \(d\) is distance, and \(f_{c}\) is carrier frequency.  
Figure 2: Time slot structure.  
The received SNR can then be expressed as  
\[\text{SNR}^{k}=\frac{P_{\text{tx}}G^{k}(\theta)\text{PL}(d^{k})}{\sigma^{2}}, \tag{4}\]  
where \(\text{PL}(d^{k})\) represents the path loss at the distance \(d^{k}\) between the RSU and the vehicle.
## 3 Results
### Light Use Efficiency  
The constant values of \(\varepsilon_{max}\) used in the MOD17A2H GPP algorithm varied for the different types of land cover; each biome type had an individual \(\varepsilon_{max}\) value. By using Equations (1) and (3) in Section 2.1.1, we recalculated the inferred \(\varepsilon_{max}\) for each land cover type and then directly compared them to the MOD17A2H \(\varepsilon_{max}\) (Figure 7, Table 5). The results showed that the inferred \(\varepsilon_{max}\) was higher than MOD17A2H \(\varepsilon_{max}\) by 31% for ENF, 21% for EBF, 72% for DBF, 61% for MF, 31% for GRA and 25% for CRO. Moreover, from the perspective of a single site, the inferred \(\varepsilon_{max}\) was higher than MOD17A2H \(\varepsilon_{max}\) at almost all sites except AU-Whr (bias = 0.093).  
Figure 7: Eight-day site estimated GPP (gC-m\({}^{-2}\)-8-d\({}^{-1}\)) against the product of GLASS FPAR, tower PAR, VPD\({}_{\text{s}}\) and T\({}_{\text{s}}\) for (**a**) ENF, (**b**) EBF, (**c**) DBF, (**d**) MF, (**e**) GRA and (**f**) CRO. APAR (absorbed photosynthetic-active radiation) = FPAR \(\times\) PAR.
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 3 Solution Method
### Bayesian Optimization
\tag{9}\]  
The predicted distribution or posterior probability distribution is given by:  
\[Z^{\prime}\big{|}\{(\mathbf{A}_{i},Z_{i})\}_{i=1}^{n},\mathbf{A}^{\prime}\sim N \Big{(}\mu_{n}\big{(}\mathbf{A}^{\prime}),\sigma_{n}^{2}\big{(}\mathbf{A}^{ \prime}\big{)}\Big{)}, \tag{10}\]  
\[\mu_{n}\big{(}\mathbf{A}^{\prime}\big{)}=cov\Bigg{(}\stackrel{{ \rightarrow}}{{\mathbf{Z}}},Z^{\prime}\Bigg{)}^{T}\Sigma^{-1} \Bigg{(}\stackrel{{\rightarrow}}{{\mathbf{Z}}}-\stackrel{{ \rightarrow}}{{\mathbf{\mu}}}\Bigg{)}+\mu_{0}\big{(}\mathbf{A}^{ \prime}\big{)}, \tag{11}\]  
\[\sigma_{n}^{2}\big{(}\mathbf{A}^{\prime}\big{)}=cov\big{(}Z^{\prime},Z^{ \prime}\big{)}-cov\Bigg{(}\stackrel{{\rightarrow}}{{\mathbf{Z}}},Z^{\prime}\Bigg{)}^{T}\Sigma^{-1}cov\Bigg{(}\stackrel{{ \rightarrow}}{{\mathbf{Z}}},Z^{\prime}\Bigg{)}. \tag{12}\]  
Here, \(\mu_{n}(\mathbf{A}^{\prime})\) denotes the estimated value of \(Z^{\prime}\) given \(\{(\mathbf{A}_{i},Z_{i})\}_{i=1}^{n}\) and \(\mathbf{A}^{\prime}\), and \(\sigma_{n}^{2}(\mathbf{A}^{\prime})\) represents the variance of the estimation error introduced into the function observation. Based on the posterior information, the acquisition function can determine the evaluation direction. The acquisition function is formulated considering both the estimated objective function value (i.e., the expectation in Equation (12)) and the uncertainty information (i.e., the variance in Equation (13)) over all of the feasible regions. Let \(af\) represent the acquisition function; then, the determination problem can be expressed as:  
\[\mathbf{A}_{next}=\underset{\mathbf{A}}{\text{argmax}}\ af(\mathbf{A}). \tag{13}\]  
The design of the acquisition function is a significant part of BO. In this paper, the expected improvement (EI) function is applied. Let \(Z_{best}^{+}\) denote the current best value of the objective function. EI maximizes the expected improvement over \(Z_{best}^{+}\). It has a closed-form expression:  
\[af_{EI}(\mathbf{A})=\big{(}\mu(\mathbf{A})-Z_{best}^{+}\big{)}\Phi\Bigg{(} \frac{\mu(\mathbf{A})-Z_{best}^{+}}{\sigma(\mathbf{A})}\Bigg{)}+\sigma( \mathbf{A})\phi\Bigg{(}\frac{\mu(\mathbf{A})-Z_{best}^{+}}{\sigma(\mathbf{A}) }\Bigg{)}. \tag{14}\]  
Here, \(\Phi\) represents the cumulative distribution function of the standard normal distribution, and \(\phi\) denotes the probability density function of the standard normal distribution. \(\mu(\mathbf{A})\) and \(\sigma(\mathbf{A})\) can be computed with Equations (12) and (13), respectively. Compared with the original objective function, the acquisition function has relatively simple forms and is easier to optimize. The algorithm of BO for solving the calibration of the link performance function is summarized as follows (Algorithm 2).
## 4 Analysis and Requirements  
In today's frontline firefighting practices, voice communication is the most important, and on many occasions the _only_ tool for coordination. Voice communication is essential, independent of a mission's nature (_i.e._, routine or firefighter emergency). For ICs and other personnel outside the IDHL, this type of communication is currently the only possibility to acquire knowledge about the ongoing events and mission process. Other methods such as watching movements of the fire hose only offer limited information.  
However, voice communication over radio is far from perfect and suffers from numerous problems. Communication frequently breaks down due to interferences, operating error while working with the SCBA (e.g., pressing the speak button on the radio by mistake) or insufficient maintenance (e.g., dead radio battery). Additionally, using the radio incurs immense cognitive and physical load. Speaking while wearing the SCBA means that the current work has to be paused, the radio has to be grasped, the speak button has to be located and pressed and respiration has to be adapted to talk clearly enough that the receiver can understand. Firefighters rely on that communication. Even though it is known that it may break down, there is no replacement or complementary technology available to provide support in these cases.  
A conflict becomes apparent from the discussion: the incident commander requires as much verbal information as possible to complete his task, but the firefighters who provide that information are hindered by the act of provision. This can even come to the point where queries by the incident commander are not answered because the current task is too demanding. The lack of response may be interpreted as an emergency by a commander waiting outside, which can in turn trigger a very complex process that involves new resources and efforts. This in turn causes the superfluous deployment of personnel and vehicles, escalating the situation unnecessarily, incurring significant costs and risk to firefighters and civilians.  
The weight of this cascade of events implies that the incident commander needs to be certain that the decision is correct. The incident commanders have experience with communication breakdowns and are aware that queried firemen may be too busy to respond, this decision can be delayed based on the experience of the commander.  
The discussed issues show the complexity of firefighting work. They represent an insight into the complex collection of interrelated strategies that firefighters use to deal with their tasks. However, firefighters themselves recognize that the current tools limit their monitoring and therewith also their commanding possibilities. Based on these issues, in the following subsections requirements for the implementation of a pervasive assistance system are discussed.
## 6 Performance Evaluation
### Experimental Set Up
#### Parameters  
Both AT-FTSP and A2T-FTSP are based on the concept of FTSP, and thus, the configurable parameters [3] are basically the same. Table 2 summarizes the parameters that have been selected for the experiments in a single-hop network.
## 4 Results
### Extreme Values for Different Return Periods
#### 4.2.1 Minimum Streamflows \(Q_{V}\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Probability Dist.**} & \multicolumn{2}{c}{**Fitting Method**} & \multicolumn{4}{c}{**Statistics**} \\  & & \(\mathbf{X^{2}}\) & \(\mathbf{C_{\texttt{P}}}\) & \(\mathbf{C_{\texttt{s}}}\) & \(\mathbf{C_{\texttt{k}}}\) \\ \hline \multirow{3}{*}{Weibull} & MV & 9.56 & 0.411 & 0.308 & 2.81 \\  & MM & 9.56 & 0.041 & 0.279 & 2.79 \\  & MVA & 1.26 & 0.43 & 3.6 & 51.3 \\ \multirow{3}{*}{GEV} & MM & 4.37 & 0.401 & 1.63 & 8.51 \\  & MMP & 2.81 & 0.454 & 4.54 & 14.3 \\  & MVA & 1.26 & 0.346 & 1.14 & 2.4 \\ \multirow{3}{*}{Gumbel} & MM & 3.33 & 0.401 & 1.14 & 2.4 \\  & MMP & 2.3 & 0.385 & 1.14 & 2.4 \\ \multirow{3}{*}{Normal} & MVA & 9.04 & 0.401 & 0 & 3 \\ \multirow{3}{*}{Log-normal} & MVA & 1.26 & 0.362 & 1.13 & 5.36 \\  & MVA & 3.33 & 0.4 & 2.17 & 12.4 \\ \multirow{3}{*}{Three-parameter log-normal} & MM & 4.37 & 0.401 & 1.63 & 8.1 \\  & MV & 1.78 & 0.353 & 0.707 & 3.75 \\ \multirow{3}{*}{Gamma} & MM & 5.41 & 0.401 & 0.802 & 3.96 \\ \multirow{3}{*}{Inverse Gamma} & MV & 2.3 & 0.37 & 1.72 & 9.38 \\  & MV & 209.3 & 0.433 & 3.94 & 91.3 \\ \multirow{3}{*}{Generalized Gamma} & MM & 219.5 & 0.401 & 1.63 & 8.53 \\ \multirow{3}{*}{Pearson type III} & MV & 3.85 & 0.38 & 1.46 & 6.19 \\ \multirow{3}{*}{Log Pearson type III} & MM & 5.41 & 0.401 & 1.63 & 7.01 \\  & SAM & 2.81 & 0.404 & 2.4 & 17.4 \\ \multirow{3}{*}{Log Pearson type III} & BOB & 3.85 & 0.393 & 1.54 & 7.76 \\  & WRC & 3.33 & 0.42 & 2.7 & 21.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of probability distributions used in the analysis of observed valley flows from 1972 to 1999. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Tr (Years)**} & \multicolumn{4}{c}{**\(\mathbf{Q_{V,Tr}}\) (m\({}^{3}\)/s)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Before the Operation of**} & \multicolumn{2}{c}{**After the Operation of the Hydropower Plant**} \\  & **the Hydropower Plant** & & & \\ \cline{2-5}  & **1970–1999** & **2000–2021** & **2000–2010** & **2011–2021** \\ \hline
500 & 25.00 & 12.30 & 35.40 & 117.00 \\
100 & 30.30 & 21.90 & 39.50 & 120.00 \\
50 & 33.30 & 28.20 & 41.80 & 122.00 \\
20 & 38.50 & 39.80 & 45.50 & 124.00 \\
10 & 43.70 & 52.00 & 49.20 & 126.00 \\
5 & 51.00 & 68.90 & 54.30 & 129.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Valley flows for return periods of 1970–1999 and 2000–2021. the entire period of 2000-2021 should be neglected since the hydropower plant had two operational regimes that were considered for hydrological analysis. Figure 11: Recorded valley streamflow versus projections for different return periods: **(a)** from 1970 to 1999 and 2000 to 2021; and **(b)** from 2000 to 2010 and 2011 to 2021.
## 2 State of the Art
The dataset, however, solely comprises data from an IIoT architecture and does not include any traffic, data, or threats from IoT-based devices. As a result, this dataset is insufficient for assessing IoT-based intrusion detection systems (IDSs) [19]. [PERSON] et al. [20] have created a dataset that can be used to assess and train deep learning and machine learning-based intrusion detection systems (IDSs) for IoT systems. The dataset employs the Industrial Internet Reference Architecture (IIRA), which has multiple levels as follows: edge level, platform level, and enterprise level, and deploys different industrial standards, cloud computing, and attack tools. The attack records were created with three separate frameworks: CKC, MALC, and ATTACK, and the dataset contains records from five distinct procedures: MQTT, TCP, Modbus, CoAP, and SMTP. The dataset was analyzed using seven different machine learning algorithms: KNN, DT, NB, SVM, LR, GRU, and DNN. The decision tree (DT) method performed best, with 99.54% performance for binary classification and 99.49% performance for multi-classification. One of the most important research needs noted in this article is the need for more investigation of cyber threats that directly influence the features of PLCs, such as false commands or data. Although the existing X-IIoTID dataset is an essential addition to IIoT security research, it does not include these forms of threats. Another exploratory study is the lack of preset data separated into training and testing datasets, which is required to improve federation capabilities and assist researchers in making fair comparisons. While K-fold cross-validation partially addresses this restriction, an empirical investigation employing several data-splitting strategies is required to select the best one for splitting the dataset. Furthermore, the researchers state that the X-IIoTID dataset contains minority attack types that need assistance with data preprocessing approaches. Table 1 outlines the compilation of prominent IoT datasets that have been extensively utilized for research purposes in recent times.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 5 Results and Discussion
### Throughput  
Compared with iM-SIMPLE and M-ATTEMPT, TSAER has a higher throughput. Figure 6 presents the throughput analysis. The throughput is related to the network stable period. A long stable period means that there are more active nodes, and the continuous work of these nodes continuously send data to the sink. Based on the previously analyzed network lifetime and stability period, the temperature state perception adopted by TSAER buffers the process of nodes forming hot nodes in the network, improves the utilization of available nodes, and improves the link quality. Moreover, TSAER has a relatively longer lifetime and stable period; therefore, the simulation results show that it has the highest throughput. The stable period of M-ATTEMPT is shorter, and its throughput decreases as the number of available nodes decreases. Therefore, M-ATTEMPT is lower than the other two protocols.
# Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology
## 5 Experimental Results
### Data Description
According to preliminary tests, there was an obvious relationship between the square and interaction of input variables and output variables. This research also analyzed second order terms of all input variables. This meant that the 12 input variables produced 90 terms (12 original variables, 12 squares of variables, and 66 interactions between variables). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Input Variables**} & \multicolumn{3}{c}{**Output Variable**} \\ \hline \(X_{1}\) & Moving speed & 8.2, 6.8 & \(Y_{1}\) & Ratio of resin & 50.3, 38.1 \\ \(X_{2}\) & Epoxy resin density * & 1.19, 1.15 & \(Y_{2}\) & Gel time 2 & 175, 134 \\ \(X_{3}\) & Oven temperature 1 * & 170.0, 143.2 & & \\ \(X_{4}\) & Oven temperature 2 * & 179.5, 168.0 & & \\ \(X_{5}\) & Position of doctor roller 1 & 665, 370 & & \\ \(X_{6}\) & Position of doctor roller 2 & 665, 370 & & \\ \(X_{7}\) & Fan speed 1 & 1150, 1000 & & \\ \(X_{8}\) & Fan speed 2 & 1700, 1166 & & \\ \(X_{9}\) & Fan speed 3 & 1719, 1150 & & \\ \(X_{10}\) & Fan speed 4 & 1800, 550 & & \\ \(X_{11}\) & Revolution speed & 0.00, \(-\)2.99 & & \\ \(X_{12}\) & Gel time 1 * & 346, 309 & & \\ \hline \hline \end{tabular}
* indicates the input variables are hard to adjust.  
\end{table} 
Table 1: The input and output variables and their corresponding minimal and maximal values.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 2 Methodology
### Review of Some Basic Principles of Electromagnetism
#### 2.1.3 [PERSON]'s Principle  
[PERSON]'s principle [10] states that the diffraction pattern from an opaque body is identical to that from a hole of the same size and shape, except for the overall forward beam intensity. The concept is expressed graphically in Figure 7 [11].  
[PERSON]'s principle can be used to compute the electric field diffracted when the signal emitted by the transmitter's image is diffracted by the light-gray opaque area in the z = 0 plane (the white area being \"transparent\"), and when it is diffracted by the white opaque area in the z = 0 plane (the light-gray area now being \"transparent\").  
Figure 6: Absolute value of \(F(v)\). Negative \(v\) indicates that the line-of-sight (LoS) trajectory is unobstructed, while positive \(v\) indicates an LoS obstruction (see Figure 5).
## 6 Results
### Speech LSTM Models Based on nEMO Dataset
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Models**} & \multicolumn{6}{c}{**Emotions**} \\  & **Average** & **Anger** & **Fear** & **Happiness** & **Sadness** & **Surprised** & **Neutral** \\ \hline
**Stacked Time Distributed 2D CNN** & & & & & & & \\ -**LSTM** & & & & & & & \\ Accuracy [\%] & 72.20 & 86.67 & 87.84 & 30.67 & 77.92 & 61.19 & **88.89** \\ Precision [\%] & 77.70 & 63.73 & 63.11 & **100.00** & 92.31 & 77.36 & 69.90 \\ Recall [\%] & 72.20 & 86.67 & 87.84 & 30.67 & 77.92 & 61.19 & **88.89** \\ F1-Score [\%] & 70.80 & 73.45 & 73.45 & 46.94 & 84.51 & 68.33 & 78.26 \\ \hline
**Stacked Time Distributed 2D CNN** & & & & & & & \\ -**Bidirectional LSTM with Attention** & & & & & & & \\ Accuracy [\%] & 71.30 & **90.67** & 86.49 & 36.00 & 90.91 & 44.78 & 79.01 \\ Precision [\%] & 75.90 & 75.56 & 58.18 & 93.10 & 81.40 & 81.08 & 65.98 \\ Recall [\%] & 71.30 & **90.67** & 86.49 & 36.00 & 90.91 & 44.78 & 79.01 \\ F1-Score [\%] & 69.90 & 82.42 & 69.57 & 51.92 & 85.89 & 57.69 & 71.91 \\ \hline
**Parallel 2D CNN** & & & & & & & \\ -**Transformer Encoder** & & & & & & & \\ Accuracy [\%] & **84.60** & 86.67 & **91.89** & **84.00** & **98.70** & **62.69** & 83.95 \\ Precision [\%] & **85.00** & **89.04** & 83.95 & 79.75 & 84.44 & **82.35** & **90.67** \\ Recall [\%] & **84.60** & 86.67 & **91.89** & **84.00** & **98.70** & **62.69** & 83.95 \\ F1-Score [\%] & **84.50** & **87.84** & **87.74** & **81.82** & 91.02 & **71.19** & **87.18** \\ \hline
**Parallel 2D CNN** & & & & & & & \\ -**Bidirectional LSTM with Attention** & & & & & & & \\ Accuracy [\%] & 79.40 & 84.00 & 86.49 & 69.33 & 92.21 & **62.69** & 81.48 \\ Precision [\%] & 80.00 & 75.00 & **88.89** & 81.25 & **94.67** & 54.55 & 85.71 \\ Recall [\%] & 79.40 & 84.00 & 86.49 & 69.33 & 92.21 & **62.69** & 81.48 \\ F1-Score [\%] & 79.50 & 79.25 & 87.67 & 74.82 & **93.42** & 58.33 & 83.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Models evaluation for emotion speech recognition on Mel spectrograms based on polish nEMO dataset.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 2 Related Works
### Cnn-Based Methods  
Recently, CNN-based methods [10; 11; 12; 33; 13] have shown impressive results and can outperform classical approaches by large margins. [PERSON] et al. [34] proposed the first CNN-based background subtraction method for specific scenes. First, a temporal median operation is performed on the video frames to generate a fixed background image; then, for each frame in the sequence, an image patch frame and background image centered on each pixel are extracted from the current frame. [PERSON] et al. [17] proposed a new background image generation and motion detector, which produces a more accurate background image. [PERSON] et al. [35] proposed a new computer vision algorithm to improve detection accuracy by using spatiotemporal features. Further optimization will reduce feature dimension by using principal component analysis to detect anomalies in a given video sequence.  
[PERSON] et al. [16] proposed a multi-scale cascaded convolutional neural network structure for background subtraction without the need to construct a background image. Recently, [PERSON] et al. [10] proposed a semantic segmentation-based encoder-decoder neural network with triple CNN and transposed CNN configurations, which has achieved state-of-the-art performance. In the subsequent studies [9, 11, 13, 33], the semantic segmentation-based strategy was used in the motion detection task and was able to achieve state-of-the-art performance. Although these semantic segmentation-based methods can achieve high accuracy, their inference efficiency is limited.
## 5 Damage Characterization, Objective Functions, and Optimization Algorithm
### Damage Characterization  
The first important step of a model-based method is the parameterization of the damage. The effect of damage on the structure model is introduced with a scalar variable or index as [143],  
\[d_{e}=1-\frac{k_{e}^{d}}{k_{e}} \tag{1}\]  
where \(d_{e}\) is the damage index for a certain element, \(k_{e}\) is the structure property value of the element in its intact status, and \(k_{e}^{d}\) is the value of the element damaged. With respect to a single damage or discrete ones, the stiffness matrix of the element is assumed to decrease uniformly [144]. As for distributed damage, instead of adjusting the stiffness properties of all the elements separately, [PERSON] et al. [145] proposed the damage function to reduce the number of unknown variables and to determine the stiffness distribution over the FE model. The damage parameters are the multiplication factors of the damage functions. This method is useful for structures whose damage pattern can be represented by a reduction factor of the element stiffness.  
The parameter definition and selection depend on the level of damage identification and uncertainty level. In the preliminary level, the parameters can be defined as global physical properties such as the Young's modulus, material density, section area, and the moment inertia since they are directly related to the element stiffness. In this level, several elements can share the same parameter value or one super element can be used in the case that several close elements are expected to have similar values [146; 147]. As the identification becomes quite local, the damage parameters could be the width of cracks, the stiffness of joints, and physical properties of the uncertain elements. When the damage is located at one element, this specific element can have multiple parameters by dividing the element into small ones with different properties. The parameter selection requires a considerable physical insight into the target structures. In general, only sensitive parameters are selected, otherwise the process may be ill-conditioned since insufficient information is available to estimate the parameters accurately.