esa-sceva/satcom-chunk-collection huggingface dataset:
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 3 Proposed Technique 2: Stacked CAE Network for Audio Cough Classification
### Softmax Classifier  
For multi-class classification, a commonly utilized classifier is the softmax classifier [39]. Assuming the description of training datasets as \(\left\{\left(a^{(1)},b^{(1)}\right),\ldots,\left(a^{(N)},b^{(N)}\right)\right\}\), where one of the input vectors of the softmax classifier is expressed as \(a^{(S)}\in R^{c},s=1,2,\ldots,N\) and the corresponding label is represented as \(m=1,2,\ldots,N\). If the training datasets are assumed to belong to \(k\) different classes, then \(k\) different values are obtained by the labels, and \(b^{(s)}\in\{1,2,\ldots,k\}\). The output vector \(o^{(s)}\) for every input vector \(a^{(s)}\) is expressed as follows:  
\[o^{(s)}=\theta_{a}^{(s)} \tag{32}\]  
where for the softmax classifier the parameter matrix is expressed as \(\theta\in R^{k\times c}\) and is defined as  
\[\theta=\begin{bmatrix}\theta^{T}\\ \theta_{2}^{T}\\ \vdots\\ \theta_{k}^{T}\end{bmatrix} \tag{33}\]  
The probability of \(a^{(s)}\) belonging to class \(q\) (i.e., the probability of \(b^{(s)}=q,q=1,2,\ldots,k\)) is defined as follows:  
\[p\Big{(}b^{(s)}=q|a^{(s)};\theta\Big{)}=\frac{e^{\theta_{a}^{T}a^{(s)}}}{\sum _{v=1}^{k}e^{\theta_{v}^{T}a^{(s)}}} \tag{34}\]  
The probability of \(a^{(m)}\) belonging to each class is estimated by the hypothesis \(h_{\theta}(a^{(m)})\) and is expressed as follows:  
\[h_{\theta}\Big{(}a^{(s)}\Big{)}=\begin{bmatrix}p\Big{(}b^{(s)}=1|a^{(s)}; \theta\Big{)}\\ p\Big{(}b^{(s)}=2|a^{(s)};\theta\Big{)}\\ \vdots\\ p\Big{(}b^{(s)}=k|a^{(s)};\theta\Big{)}\end{bmatrix}=\frac{1}{\sum_{v=1}^{k}e^ {\theta_{v}^{T}a^{(s)}}}\begin{bmatrix}e^{\theta_{1}^{T}a^{(s)}}\\ e^{\theta_{2}^{T}a^{(s)}}\\ \vdots\\ e^{\theta_{k}^{T}a^{(s)}}\end{bmatrix} \tag{35}\]  
In between the target and the predicted class labels, the error has to be described, and the cost function of the softmax classifier \(J(\theta)\) is defined as follows:  
\[J(\theta)=\frac{1}{N}\left[\sum_{s=1}^{N}\sum_{q=1}^{k}1\{b^{s}=q\}\log\frac{e ^{\theta_{q}^{T}a^{(s)}}}{\sum_{v=1}^{k}e^{\theta_{v}^{T}a^{(s)}}}\right]+ \frac{\gamma}{2}\|\theta\|_{2}^{2} \tag{36}\]  
To prevent overfitting, the second term, also called the weight penalty term, is used. To control the significance of the penalty term, the penalty term parameter used is \(\gamma\). To procure the optimal parameter matrix \(\theta\), the training of the softmax classifier is also performed like the training of CAE so that by means of mixing the art function with the back propagation (BP) algorithm a suitable transfer function is established between the input and the target labels. Every element of \(\theta\) is updated after each iteration as follows:  
\[\theta_{ji}=\theta_{ji}-\varepsilon_{2}\frac{\partial J(\theta)}{\partial \theta_{ji}} \tag{37}\]  
where the learning rate is expressed as \(\varepsilon_{2}\). \(\theta_{ji}\) indicates the elements of \(\theta\) in the \(j^{th}\) row and \(i^{th}\) column.
# Theory of Edge Effects and Conductance for Applications in Graphene-Based Nanoantennas
## 3 Numerical Results and Discussion
In some cases, where the electrochemical potential varies adiabatically slow in time, it also produces a modulation of the THz emission. Figure 5: The spatial distribution of the conductance in the units \(G_{Drude}/Ll\). \(L=800\) nm, \(\mu=1.0\) eV. Figure 6: The spatial distribution of the conductance in the units \(G_{Drude}/LI\) for different values of length and \(\mu\) = 0.1 eV; (**a**) \(L\) = 2 nm (single-mode regime; edge mode); (**b**) \(L\) = 10 nm (single-mode regime; bulk mode); (**c**) \(L\) = 50 nm (three-mode regime; all modes are of bulk type). Figure 7: The spatial distribution of the conductance in the units \(G_{Drude}/LI\) for different values of length. \(\mu\) = 0.3 eV; (**a**) \(L\) = 2 nm; (**b**) \(L\) = 10 nm; (**c**) \(L\) = 50 nm. The concept of the optical conductance in this paper allows formulating the effective boundary conditions for electromagnetic field at the surface of graphene sheet in the form  
\[[\mathbf{n},[\mathbf{H}_{z=+0}-\mathbf{H}_{z=-0},\mathbf{n}]]=G^{Drude}\Theta(x )[\mathbf{n},\mathbf{E}]. \tag{30}\]  
Their using leads to modification of integral equations of antenna theory and methods of their solution. The considered scenario changes dramatically with shortening of the sheet, as shown in Figures 6-8 (each figure corresponds to a different value of the length for the same value of electrochemical potential). One can clearly see the enhancement of oscillations, which makes the Drude model invalid for such values of parameters. In fact, it becomes impossible to introduce the conductivity concept in its ordinary meaning. The physical mechanism for the conductance oscillations is the interference between the pseudo-spin modes due to the reflection from the sheet boundaries. The important features are demonstrated in the single-mode conductivity (Figure 6a,b). For the rather small electrochemical potential the active mode is an edge type. The sign of the conductivity is negative, which corresponds to its inductive origin. The increase in the electrochemical potential leads to the transformation from inductive to capacitive one (sign exchange) due to the transformation from the edge mode to the bulk one.
## 3 Three Designs of Vapor-Phase Monomer Deposition on Substrates in iCVD Process  
D-growth refers to Droplet growth, which is the growth method designed based on the pressure ratio for deposition-type polymers. D-L growth, on the other hand, signifies a two-step growth mode of droplet condensation on the substrate of polymer film after formation. The growth process begins with the deposition of a polymer layer, followed by the subsequent Droplet growth on the polymer film. As shown in Table 1, both D-growth and D-L-growth occur when the partial pressure of the monomer is greater than the saturated vapor pressure. The difference lies in their choice of substrate. D-growth typically involves the direct synthesis of PNDs on a silicon substrate on the thermoelectric cooling (TEC) surface. On the other hand, D-L-growth involves synthesizing fluorinated polymeric films with a low surface energy, such as 1H,1H,2H,2H-perfluorodecyl methacrylate (PFDMA), 1H,1H,2H,2H-perfluorodecyl acrylate (PFDA) [32], and 1H,1H,2H,2H-perfluorocyl acrylate (PFOA) [33] on the substrate before following the two-step method for PNDs synthesis in the same manner as D-growth.  
However, it is worth mentioning that Franklinsubjected pPFDA to an annealing treatment to achieve a smoother layer for PNDs. The pPFDA films were placed in an oven set to 80 \({}^{\circ}\)C for 1 h. An annealing thermal treatment can enhance surface smoothness by eliminating organized crystalline domains responsible for surface protrusions. The polymerization monomers chosen for droplet growth can be HEMA (hydroxylethyl methacrylate), DVB (divinylbenzene), and 4 VP (4-vinyl pyridine).  
Layer growth refers to the one-step deposition of polymer solid-state films, accompanied by the simultaneous introduction of a decomposable initiator (TBPO), monomer, and carrier gas at specific flow rates.  
Monitoring during the process of layer growth allows for in situ determination of the growth rate and for film growth to be terminated when a precise thickness is reached. High deposition rates (>1 \(\upmu\)m/min) and film thicknesses (>200 \(\upmu\)m) have been reported, as well as ultrathin pinhole-free CVD polymer layers (<10 nm).
## 2 Materials and Methods
### Selection of Main Urban Road  
The city's road infrastructure was developed from the historic center and international border crossing; primary roads connect this area with population nuclei to the south, east, and west, following a semi-concentric pattern of urban organization. Three primary roads were identified: Adolfo Lopez Mateos Boulevard (BLM), which has a daily vehicular demand of 48,661 units (annual daily average) and crosses the city from northwest to southeast; Manuel Gomez Morin road (formerly Periferico Oriente), with a vehicular demand of 25,598 units, which encircles the city; and Lazaro Cardenas Boulevard (BLC), with a vehicular demand of 68,013 units, that is another important road that connects the city in the east--west and west-east directions, which also has the highest vehicular flow [30; 31].  
The main study road was selected based on the following criteria:  
1. Being a primary urban road;
2. High vehicular volume in relation to other roads;
3. Presenting a variability in terms of vehicle classification;
4. Presenting connectivity with other primary and secondary urban roads, as well as with a variety of urban land uses;
5. Being susceptible to infrastructure and operation changes.  
Based on these criteria, we selected BLC and established 11 vehicle volume measurements (VVM) along an 11.4 km line (Figure 3). The points were not placed at intersections but at intermediate points, representing vehicular flow in selected BLC segments.  
Figure 2: Geographic location of Mexicali, Baja California, Mexico. Source: Google Maps.  
Congestion in BLC is largely due to its main function of connecting commercial, industrial, residential, and school areas. The activities in the area are related to jobs, housing, education, and culture, among others, as reported by monitorization studies on nine Mexican cities for which air quality data are systematized and analyzed. These cities have the highest levels of air pollutants in the country, exceeding official standards. Comparable pollutant levels are reported for Mexico City, Guadalajara, and Mexicali since their main emission sources are combustion processes, such as vehicle emissions and agricultural burning [32]. Land use and vehicles also favor the emissions generated by tire friction and tire erosion due to the unpaved urban roads. These cities present intense cargo vehicle traffic, especially those in arid regions; similar arterial road problems can be observed around the world [33].
# Multi-Agent Dynamic Fog Service Placement Approach
## 3 Service Placement Orchestrator Implementation
### Starting New Service  
Service requests are generated when a new end-device appears, or the current end-device is moving from one place to another. End-devices at the current fog node are disconnected and they need to send a request to a closer fog node. These end-devices are identified by their end-point address such as _coap://192.168.0.24/temp_ for a temperature device or _coap://192.168.0.24/led_ for lights, which can be changed or adjusted as required. Please see Figure 3 for more details.  
Figure 3: Starting a new service.  
Figure 2: Remote service redeployment.  
The light agent works as a light sensing element. If the light level outside falls below a certain threshold, it sends a request to its Service agent to start a new service. The Service agent defines what that service should be like. When that is a light service, it can define what intensity of the light is needed. The Service agent afterwards sends a defined request to the Bulb agent. It uses the PUT method to communicate with the end-device to set a required level of light. As the level is adjusted, the outcome is synchronized.
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 2 Silk Material Proteins
### Sericin  
Sericin is a natural protein produced from the middle silk gland of the silkworm [41]. It coats the fibroin fiber and acts as an adhesive to hold two fibroin fibers together [41]. It is composed of 18 amino acids and serine, aspartic acid, and glycine in proportions of approximately 33.4%, 16.7%, and 13.5%, respectively [42; 43]. Sericin is a hydrophilic protein mainly composed of an amorphous random coil  
Figure 1: Scanning electron microscopic view of cocoon fiber. (**a**) The silk mat is composed of interconnecting strings. Each string has two main fibers (F) and surrounding bonding proteins (S). (**b**) After the removal of sericin (S), two fibers composed of silk fibroin (F) remain.  
structure and less \(\beta\)-sheet structure [44]. Sericin has been considered to be a by-product of the silk cocoon and discarded by the degumming process. However, a recent study has shown that sericin has favorable biologic properties and the potential to be used as a biomaterial component [45]. Sericin has biocompatibility, biodegradability, antibacterial activity, and resistance to oxidative stress and ultraviolet radiation [46; 47]. The immunologic response is a major concern when considering using sericin as a biomaterial. However, sericin is known as a biocompatible polymer and induces a very weak immunologic response when used alone [48]. Sericin is now used in various fields, including pharmacologic, cosmetic, and biomedical applications [45].  
Sericin has been suggested as a biomaterial for tissue regeneration [41]. Sericin is more hydrophilic than fibroin and can more effectively induce collagen synthesis in dermal tissue. It is a good candidate for use in tissue engineering scaffolding as a dressing material or skin substitute [49]. Sericin film increases the proliferation of human skin keratinocytes and fibroblasts, and it accelerates wound healing [49]. Sericin also has antimicrobial, antioxidant, and photo-protective ability from ultraviolet radiation, so it has been effectively used as a dressing material for skin protection [50; 51]. Sericin shows potential as a bone tissue engineering scaffolding material. Hydroxyapatite (HA) and sericin composite improves cell viability and increases the differentiation of osteogenic cells [52; 53], and sericin coating on a titanium implant induces the adhesion, differentiation, and proliferation of osteoblast cells on the implant's surface [54]. It may enhance osseointegration of titanium implants and bone healing, Silk cocoon membranes containing fibroin and sericin have been studied as GBR membranes [20]. Silk cocoons may be separated into different layers, and the composition of sericin gradually increases from the inner layer to the outer layer [20]. Low concentration and release of sericin from the middle layer of silk cocoons induce increased osteogenic gene expression and affect the new bone regeneration without immune reaction in vivo [20; 55].
## 4 Experimental Evaluation of Performances
### Community Anomaly Detection in Real Datasets
#### 4.2.1 Disney Network  
The Disney network was extracted from the Amazon co-purchase network considering only Disney DVDs. The network consists of 124 nodes and 334 edges, and each product in  
\begin{table}
\begin{tabular}{l l l l} \hline \hline CODA & Communities number (K) = 8 & Percentage of anomaly (r) = 0.05 & link importance (\(\lambda\)) = 0.01 \\ \hline ConSub & Interval size (K) = 10 & The number of Monte Carlo iterations (M) = 150 & The significance level (\(\alpha\)) = 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Models Parameter setting.  
the graph is described by 30 attributes, such as product prices, review ratings, and so on. Even though the network is small, it is an interesting dataset due to its complex graph and attribute structure, and it is used to evaluate most of the anomaly detection models. For this real-world dataset, the ground truth of objects as anomalies or normal is not available. A user experiment was carried out to provide the ground truth of the dataset where high school students manually labeled each object as either an anomaly or normal.  
Figure 3 illustrates the results of our model on the Disney network compared with CODA and ConSub. The results show that the CAnom model achieves good-quality results, with an AUC equal to 0.92 (see Figure 4). In comparison, the other models obtain lower-quality results, with AUCs equal to 0.82 and 0.50 for ConSub and CODA, respectively. The ConSub model achieves good-quality results compared with the CODA model, which achieves the worst-quality results. The runtime evaluation is presented in Figure 3, which shows the runtime for the three models. The CAnom model shows the best runtime with 0.05 s, followed by the CODA model with 6.05 s. The ConSub model is the slowest model, at 8.93 s. From this experiment on the Disney network, we can conclude that, among the three models, the CAnom model achieves the highest-quality results for detecting community anomalies. In addition, the CAnom model is the fastest of the three. While the ConSub model achieves good-quality results, it suffers from a slow runtime, and the CODA model is considered the worst-performing model for identifying community anomalies in a real-world network.
## 4 Experimental Results and Analysis
### Evaluation Metrics  
To evaluate the performance of the Fed-DTB framework, we utilised several key metrics to assess model accuracy, robustness, efficiency, and adaptability. Accuracy reflects the proportion of correctly classified instances, providing an overall performance measure. Precision, recall, and F1 Score capture the model's ability to accurately classify positive instances and balance false positives and negatives. ROC-AUC evaluates the system's capability to distinguish between positive and negative classes. Loss measures the error during optimisation, with lower values indicating better model performance. Timing metrics were employed to measure computational efficiency, including the time required for local training, communication between clients and the central server, and trust management operations. Trust scores were tracked across rounds to monitor the framework's ability to adapt to client behaviour dynamically and maintain reliability. Client exclusion metrics quantified the number of excluded clients in each round, highlighting the system's robustness against adversarial behaviours and its capacity to preserve overall performance despite the presence of untrustworthy participants. Finally, we confirmed that the framework is not overly sensitive to the local-epoch count \(E\); varying \(E\) between 1, 3, and 5 changed final accuracy by less than 0.8 percentage points and altered convergence by no more than two global rounds.  
Figure 8: Data before and after injection.
## 2 Background
### First-Order Gradient Descent and [PERSON]'s Accelerated Gradient Descent Methods  
The gradient descent (GD) method is one of the earliest and simplest gradient based algorithms. The update vector \(\mathbf{v}_{k+1}\) is given as  
\[\mathbf{v}_{k+1}=-\alpha_{k}\
abla E(\mathbf{w}_{k}). \tag{3}\]  
The learning rate \(\alpha_{k}\) determines the step size along the direction of the gradient \(\
abla E(\mathbf{w}_{k})\). The step size \(\alpha_{k}\) is usually fixed or set to a simple decay schedule.  
The Nesterov's Accelerated Gradient (NAG) method [6] is a modification of the gradient descent method in which the gradient is computed at \(\mathbf{w}_{k}+\mu_{k}\mathbf{v}_{k}\) instead of \(\mathbf{w}_{k}\). Thus, the update vector is given by:  
\[\mathbf{v}_{k+1}=\mu_{k}\mathbf{v}_{k}-\alpha_{k}\
abla E(\mathbf{w}_{k}+\mu_ {k}\mathbf{v}_{k}), \tag{4}\]  
where \(\
abla E(\mathbf{w}_{k}+\mu_{k}\mathbf{v}_{k})\) is the gradient at \(\mathbf{w}_{k}+\mu_{k}\mathbf{v}_{k}\) and is referred to as [PERSON]'s accelerated gradient. The momentum coefficient \(\mu_{k}\) is a hyperparameter chosen in the range (0,1). Several adaptive momentum and restart schemes have also been proposed for the choice of the momentum [26; 27]. The algorithms of GD and NAG are as shown in Algorithm 1 and Algorithm 2, respectively.  
```
0:\(\varepsilon\) and \(k_{max}\)
0:\(\mathbf{w}_{k}\in\mathbb{R}^{d}\).
1:\(k\gets 1\)
2:while\(||E(\mathbf{w}_{k})||>\varepsilon\) and \(k<k_{max}\)do
3:while\(||E(\mathbf{w}_{k})||>\varepsilon\) and \(k<k_{max}\)do
4:\(\mathbf{w}_{k+1}\leftarrow-\alpha_{k}\
abla E(\mathbf{w}_{k})\)
5:\(\mathbf{w}_{k+1}\leftarrow\mathbf{w}_{k}+\mathbf{v}_{k+1}\)
6:\(k\gets k+1\)
7:endwhile
```  
**Algorithm 1** GD Method  
```
0:\(0<\mu_{k}<1\), \(\varepsilon\) and \(k_{max}\)
0:\(\mathbf{w}_{k}\in\mathbb{R}^{d}\) and \(\mathbf{v}_{k}\) = 0.
1:\(k\gets 1\)
2:while\(||E(\mathbf{w}_{k})||>\varepsilon\) and \(k<k_{max}\)do
3: Calculate \(\
abla E(\mathbf{w}_{k}+\mu_{k}\mathbf{v}_{k})\)
4:\(\mathbf{v}_{k+1}\leftarrow\mu_{k}\mathbf{v}_{k}-\alpha_{k}\
abla E(\mathbf{w}_ {k}+\mu_{k}\mathbf{v}_{k})\)
5:\(\mathbf{w}_{k+1}\leftarrow\mathbf{w}_{k}+\mathbf{v}_{k+1}\)
6:\(k\gets k+1\)
7:endwhile
```  
**Algorithm 2** NAG Method
## 2 SAR Data and SAR Data Archives
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Satellite \({}^{\bullet}\)** & **Band** & \begin{tabular}{c} **Acq.** \\ **Mode \(\bullet\)** \\ \end{tabular} & **Pol.** & \begin{tabular}{c} **Resolution** \\ **R/A \(\%\) (fm)** \\ \end{tabular} & \begin{tabular}{c} **Image Size** \\ **R \(\times\) A \(\%\) (km)** \\ \end{tabular} & \begin{tabular}{c} **Scene Center** \\ **Coordinate** \\ \end{tabular} & \
\begin{tabular}{c} **Swath/Track** \\ **/Bcam 4** \\ \end{tabular} & **Pass LD \({}^{\bullet}\)** &
\begin{tabular}{c} **Look** \\ **Angle (\(\uparrow\))\({}^{\ddagger}\)** \\ \end{tabular} & **First Image Date** & **Last Image Date** & **No.** \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 19.8 \(\times\) 26.1 & 21.25\({}^{\circ}\)S & 66 & DL & 52.5 & 20150/1/31 & 2015/10/24 & 5 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 21.5 \(\times\) 26.0 & 21.25\({}^{\circ}\)S & 67 & DL & 59.6 & 2014/08/21 & 2019/07/04 & 12 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.98 & 22.7 \(\times\) 26.0 & 21.25\({}^{\circ}\)S & 68 & DL & 65.1 & 2014/12/16 & 2019/10/15 & 11 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.98 & 23.2 \(\times\) 26.0 & 21.25\({}^{\circ}\)S & 157 & AL & 68.4 & 2014/10/04 & 2019/12/07 & 27 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.98 & 22.4 \(\times\) 26.0 & 21.25\({}^{\circ}\)S & 158 & AL & 63.6 & 2014/08/28 & 2019/11/28 & 24 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.98 & 21.1 \(\times\) 26.0 & 21.25\({}^{\circ}\)S & 159 & AL & 57.7 & 2014/08/19 & 2019/12/03 & 35 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.98 & 19.2 \(\times\) 26.1 & 21.25\({}^{\circ}\)S & 160 & AL & 50.2 & 2014/08/24 & 2019/12/08 & 29 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 16.2 \(\times\) 26.2 & 21.25\({}^{\circ}\)S & 161 & AL & 40.4 & 2014/09/26 & 2019/12/13 & 25 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 11.6 \(\times\) 26.2 & 21.25\({}^{\circ}\)S & 162 & AL & 27.7 & 2014/09/03 & 2019/12/14 & 20 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 15.4 \(\times\) 26.3 & 21.25\({}^{\circ}\)S & 166 & AR & 30.9 & 2014/11/8 & 2017/01/0 & 2 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 18.0 \(\times\) 26.3 & 21.25\({}^{\circ}\)S & 167 & AR & 46.1 & 2019/02/10 & 2019/12/15 & 4 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.98 & 20.4 \(\times\) 26.3 & 21.25\({}^{\circ}\)S & 168 & AR & 54.6 & 2015/01/23 & 2019/12/06 & 11 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 21.9 \(\times\) 26.2 & 21.25\({}^{\circ}\)S & 169 & AR & 61.0 & 2015/01/14 & 2019/11/27 & 19 \\ \hline ALOS2 & L & SPT & HH & 1.43/0.97 & 22.9 \(\times\) 26.2 & 55.71\({}^{\circ}\)E & 170 & AR & 66.2 & 2014/04/27 & 2019/12/02 & 15 \\ \hline ALOS2 & L & SPT & HH & 1.43/1.95 & 39.3 \(\times\) 72.7 & 21.35\({}^{\circ}\)S & 59 & DR & 43.5 & 2015/01/24 & 2019/02/16 & 13 \\ \hline ALOS2 & L & SMI & HH & 1.43/1.92 & 30.8 \(\times\) 72.7 & 21.19\({}^{\circ}\)S & 60 & DR & 31.5 & 2015/01/29 & 2018/02/22 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._