esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
A two-tailed Student's \(t\)-test was used to analyze the results for glass tube attractancy assays. A two-tailed paired \(t\)-test was used to compare the collections of the pared baits in semi-field attractancy bioassays. All statistics were evaluated as significant when there was less than 5% chance of error (\(p<0.05\)). Statistical analysis was conducted using RStudio [29] and Excel.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Compound** & \multicolumn{2}{c}{**Area \%**} & & **References** \\ \hline
**RRI** & **Name** & **Hydrodistillate** & **DHS** & **Identification** & **References** \\ \hline
960 & 6-methyl-2-heptanone b & & 1.0 & MS & [10; 30; 31] \\ \hline
993 & 2-octanone a & 0.9 & 0.7 & RRI, MS & [32] \\ \hline
1005 & octanal a & & 1.8 & RRI, MS & [8] \\ \hline
1078 & 1-octanol a & & 7.0 & RRI, MS & [30; 31] \\ \hline
1079 & \(p\)-cresol a & 1.8 & & RRI, MS & [30; 31] \\ \hline
1100 & linalool & & 1.7 & RRI, MS & [30; 33] \\ \hline
1106 & nonanal a & & 15.3 & RRI, MS & [8] \\ \hline
1120 & 3,5-dimethyl-1,2_A_- & & & & \\  & trithiolane (one of the isomers) a,b & 2.1 & & MS & \\ \hline
1128 & 3,5-dimethyl-1,2_A_- & & & & \\  & trithiolane (one of the isomers) a,b & 3.7 & & MS & [30; 31] \\ \hline
1196 & 5,6-dihydro-2,4,6- & & & & \\  & trimethyl-4_H_-1,3,5- & & & & \\  & dithiazine (thialdine, base peak 163) a & 86.6 & & RRI, MS & \\ \hline
1200 & dodecane & & 5.8 & RRI, MS & [34] \\ \hline
1207 & decanal & & 1.1 & RRI, MS & [30] \\ \hline
1284 & bornyl acetate b & & 1.4 & MS & [32; 35] \\ \hline
1300 & tridecane a & & 16.2 & RRI, MS & [30; 36] \\ \hline
1392 & longifolene a & & 10.7 & RRI, MS & [34] \\ \hline
1399 & tetradecane a & & 13.6 & RRI, MS & [30] \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of sheep wool hydrodistillate, and DHS volatile collection analyzed by gas chromatography–mass spectrometry (GC–MS).
## 5 Conclusions  
The following conclusions are obtained from this study:  
1. During the back-calculation of the rock-mass quality (\(RMR\)* and \(Q\)*), the difference between the rock-mass quality obtained from the rock bolt spacing (\(RMR_{1}\)* and \(Q_{1}\)*) and fiber-reinforced shotcrete thickness along with the tunnel span (\(RMR_{2}\)* and \(Q_{2}\)*) are responsible for the weightage effect. The weightage effect is more pronounced when the difference between the back-calculated rock-mass quality from the rock bolts spacing and shotcrete thickness is high and vice versa.
2. So far, the back-calculation approach is used for the extension of empirical classification systems to highly stress-jointed rock-mass environment and due to low difference between the back-calculated rock-mass quality through different support media, the weightage effect is small. However, using this approach for the extension of \(RMR\) and \(Q\) systems to other ground behaviors during tunnel construction and stability, selection of appropriate weightage is the key to evaluate the contribution of rock bolt and shotcrete.
3. By increasing the weightage of the shotcrete along with the tunnel-span-based back-calculated rock-mass quality (\(RMR_{2}\)* and \(Q_{2}\)*), the \(RMR\)* and \(Q\)* values are high if \(RMR_{2}\)* \(>RMR_{1}\)* and \(Q_{2}\)* \(>Q_{1}\)* and vice versa. Owing to the structure of \(RMR\) and \(Q\) value calculation in the two systems, the consequence of low weightage effect is higher in the \(Q\) system as compared to the \(RMR\) system.  
In the case of the \(Q\) system, on using the five weightage cases, the plot trend of the stress reduction factor \(SRF_{Q}\) versus the relative block size \(RQD/J_{n}\) is the same for all the three ranges of strength-stress ratio (\(\alpha_{c}/\sigma_{1}\)). These trends show that the rate of \(SRF_{Q}\) increases with the relative block size.  
[PERSON] supervised the research. [PERSON] and [PERSON] have developed the proposed research concept. [PERSON] contributed to reviewing the final paper and made important suggestions and recommendations for paper revision. [PERSON] helped in writing and re-checking the paper technically as well as grammatically.  
This research was funded by Ministry of Land, Infrastructure and Transport of the Korean government grant number 195 CIP-B089413-06.  
This research was supported by the \"Development of Design and Construction Technology for Double Deck Tunnel in Great Depth Underground Space (19 SCIP-B089413-06)\" from Construction Technology Research Program funded by Ministry of Land, Infrastructure and Transport of the Korean government. The authors ([PERSON], [PERSON] and [PERSON]) are extremely thankful to the Higher Education Commission (HEC) of Pakistan for HRDI-UESTPs scholarship.  
The authors declare no conflict of interest.
###### Abstract  
The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data.  
Keywords:handwritten text; handwritten pattern; adult and child classification; machine learning; sequential forward floating selection approach +
Footnote †: journal: Applied sciences
## 4 Experimental Setup  
### Dataset  
We used the MRI datasets offered by the Brain Tumor Segmentation (BraTS) Challenge in 2020 and 2021 for this study. The BraTS 2020 Challenge dataset was utilized to create a segmentation model for detecting the tumor area. There were 369 MRI images available in four different modalities: fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1w), T2-weighted (T2w), and T1-weighted contrast-enhanced (T1-weighted contrast-enhanced) (T1 wCE). The images and extraction patterns both are provided in NIfTI format and coronal orientation. Non-tumor, non-enhancing tumor core, peritumoral edema, and enhancing tumor were among the four classifications supplied by the masks. The second dataset is BreakHis, one of the most widely used and important datasets in the medical imaging field. It is used for the classification and segmentation of different illnesses to analyze Breast cancer. BreakHis, a Histopathological dataset of 7936 pictures of 82 individuals at various magnification settings, is one of the most extensively utilized. These two primary groups are further separated into sub-categories (four malignant breast tumors: Carcinoma (DC), Mucinous Carcinoma (MC), Papillary Carcinoma (PC), and Lobular Carcinoma (LC), and benign breast tumors: Tubular Adenona (TA), Fibroadenoma (F), Phylodes Tumor (PT) and Adenosis (A)). The third dataset is the NIH chest X-ray dataset included 30,805 patients with disease-specific data. The image is 120 \(\times\) 120 pixels in size, with 15 distinct classes.
# Tuning of a Viscous Inerter Damper: How to Achieve Resonant Damping Without a Damper Resonance
## 2 Governing Equations of Motion
### Inerter Damper Model  
Inerter dampers can generally be constructed as an inerter (with inertance \(m\)) placed in series with an arbitrary damper element, which contains a viscous dashpot \(c\) in combination with springs and potentially additional inerters. This dynamic combination, placed in series with the front-end inerter \(m\), is in the present (general) inerter damper model denoted \(D(\omega)\); see Figure 2. This general inerter damper model is also used in Figure 1a when acting inside the building model. Furthermore, the figure shows two examples in (b) and (c), where the pure viscous inerter damper in Figure 1b is represented by \(D(\omega)=i\omega c\), with \(c\) denoting the tunable viscous dashpot coefficient. In Figure 1c, the viscous dashpot is extended by a spring \(k_{0}\) to (in principle) form a tuned inerter damper (TID) by the damper function \(D(\omega)=i\omega c+k_{0}\). However, in the present case, the pure viscous damper function in Figure 1b is designed to operate resonantly by creating modal interaction between the two fundamental modes of the building structure, while \(k_{0}\) in (c) is only introduced in the numerical example in Section 4 to avoid the drift of the damper stroke during operation.  
Figure 1: Shear frame building model with an inerter damper (with inertance \(m\)) between the ground and the first floor. (**a**) General inerter damper with damper function \(D(\omega)\), (**b**) viscous inerter damper with viscous coefficient \(c\), and (**c**) viscoelastic inerter damper with viscous coefficient \(c\) and parallel spring stiffness \(k_{0}\) introduced to avoid drift in dashpot deflection.  
In the present section, the general inerter damper is assumed to be attached to the flexible dynamic structure, as shown for the building model in Figure 1a. The governing equation for the general inerter damper in Figure 2 is written in flexibility format as  
\[\bigg{(}\frac{1}{-\omega^{2}m}+\frac{1}{D(\omega)}\bigg{)}f=u \tag{6}\]  
where the displacement \(u\) in Figure 2 represents the full stroke across the inerter damper. For the building model in Figure 1, this entire stroke \(u\) is determined by the connectivity vector as  
\[u=\mathbf{b}^{T}\mathbf{q} \tag{7}\]  
which, therefore, by construction, is the energy conjugate displacement to the damper force \(f\).  
For the general inerter damper, the undamped case is retrieved by \(D\to 0\), in which case \(f=0\) in (6). On the other hand, for \(D\to\infty\), the damper link, inside the blue-dashed box in Figure 2, will become fully rigid, thus ideally transferring the force, whereby \(f=-\omega^{2}mu\) will add pure inertia to the structure. These two limiting cases will be important in the dynamic system reduction applied next.
## 5 Scene-Driven Remote Sensing Image Object Detection  
As shown in Figure 6, scene-level object detection has, in recent years, been gradually applied in scientific research due to how can extract segmented image information to improve the accuracy of object detection. The scenes trained for deep networks are gained through sliding scenes. By comparing the labeled scene and unlabeled scenes, the training scenes can be labeled, and the object detection result can be achieved. There are several methods used to extract multi-layer features of images by using deep networks. One is based on region nomination, such as Region-based Convolutional Neural Networks (R-CNN) [83], Spatial Pyramid Pooling Net (SPP-net) [84], Fast R-CNN [85], and Region-based Full Connection Network (R-FCN), and the other is end-to-end, which does not require region nomination, including You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD) [86]. Furthermore, the feature maps from higher layers of the deep neural network display semantic abstracting properties [87].
## 2 Materials and Methods
### How Cognitive Radio Engage with the Industrial IoT?
#### 2.1.2 Cognitive LPWAN  
LPWAN technology is a low range revolutionary system for IIoT low-powered sensor devices that operate in a short distance area [48]. Thus, LPWAN technologies are up-and-coming for low power consumption, coverage, and low costs. Cognitive LPWAN operates multiple LPWAN technologies as a combination in one backbone network that achieves efficient user experiences using AI services [49]. It can provide stable communication between people and things together in heterogeneous IIoT. Cognitive LPWAN expands subscribers with more performance and relief smart services by enhancing cognitive LPWAN technologies for various applications of smart cities, Internet of vehicles, and smart home with the AI mechanisms [50].  
Figure 5: Mixed LPWAN for IIoT architecture [32; 33; 34].  
The cognitive devices or users need to detect the spectrum before the unused spectrum's occupation using a detection technology to determine an inactive frequency spectrum. After access to the available spectrum, cognitive users need to perceive other channels to prepare the primary users to achieve transparent transfer and prevent cognitive-communication [51; 52; 53]. The benefit of using cognitive radio in LPWAN is to use sufficient spectrum and efficient energy and resource availability when using the unused opportunistic licensed spectrum [10]. Smart sensors that use a cognitive radio can access the IIoT network through LPWAN base stations responsible for receiving, storing [54], and processing data from other nodes and distributing the different requests on the network. Smart sensor nodes use a specific low power, low-speed, long-range network--typically LPWAN networks [55]. In long-range and lower power consumption applications and cellular LPWAN technologies such as LTE, NBIoT is used. A dynamic spectrum access scheme is used in such networks to allow unlicensed users to access unused licensed bands dynamically. Cognitive radio with dynamic spectrum access provides a solution for increasing spectrum utilization in cognitive LPWAN [56].  
The fusion of different LPWA technologies offers cognitive-LPWAN (C-LPWAN), an entirely new set of the application domain, as it can make intelligent services more comfortable and efficient for the users. The C-LPWAN based on an AI-enabled engine has been presented in [57]. According to this study, C-LPWAN can ensure superior advantages over known LPWAN technologies by minimizing energy consumption and delay. The architecture of C-LPWAN incorporates different LPWAN technologies, and by utilizing precise calculation, analysis, and distributed cloud support, it can choose the best suitable technology for each application [58].
# Energy and Distance-Aware Hopping Sensor
## 2 The Proposed Protocol to Relocate Hopping Sensors
### Components and Brief Descriptions of the Proposed Protocol
\[f(\text{node\_i,w})=w\text{ *}(c\_i/c\_ini)+(1-w)\text{*}((d\_max-d\_i)/d\_max)\] (1) where \(d\_i\) is the distance between the member sensor node _node_i_ and the cluster header (CH2) of the zone to be moved, \(c\_i\) is the number of movable hops (residual energy) of the node _node_i_, \(c\_ini\) is the initial value for the number of movable hops, and \(d\_max\) is _max_ {_d_i_}. If \(w\) is close to 0, the distance to the moving zone has more weights in the selection of the candidate hopping sensors to move. On the other hand, if \(w\) is close to 1, the residual energy has more weights. In Figure 4, the member sensor node M1 is selected considering the node position only, and the cluster header CH1 commands the member node M1 to move to the neighboring cluster  
In order to recover cluster zone 3, which is a sensing hole as shown in Figure 6, the relay relocation strategy between clusters is shown as follows. The cluster header CH3 transmits a message requesting a single sensor to the relay node R2. 1. Relay node R2 forwards the received message to the cluster header CH2 of cluster zone 2. 2. Cluster header CH2 selects M3 as a movable sensor node in its own zone and sends a move command to cluster zone 3. 3. The cluster header CH2 predicts that its zone may also be a sensing hole and sends a message requesting one sensor to relay node R1, sequentially. 4. The relay node R1 delivers the message REQ to the cluster header CH1 like 2. 5. Cluster header CH1 selects M2 among the sensor node members in its zone and commands M2 to move. As a result, one sensor is assigned appropriately within each cluster zone so that all cluster zones can be recovered from sensing holes, sequentially. In addition, the sensor nodes M2 and M3 that have moved to the new zone can communicate with the cluster headers of each zone and receive a new ID. This procedure is out of the scope of this paper, so the details are omitted and Reference [25] can be referred to.
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 2 Design Input and Flow Chart of Mirror  
The optical system introduced in this paper is derived from the infrared high-sensitivity remote sensing load of deep space exploration, which is limited by the constraints of the platform, and the envelope volume of the optical load needs to be strictly controlled. The optical system is shown in Figure 1. In order to reduce the distance between primary and secondary mirrors, the primary mirror of the optical system bears a large focal power.  
According to the design requirements of the optical system, the quantitative index requirements of the primary mirror are set, as shown in Table 1.  
The larger the steepness of the mirror, the more difficult it is to design and manufacture. For a mirror of such high steepness, it is necessary to study a design method that can quickly complete the initial structure evaluation and verification.  
Figure 2 provides a flow chart for the design of a high-steepness lightweight elliptical mirror. Firstly, an equivalent method to design an elliptical mirror plate with high steepness is proposed to quickly select the mirror diameter thickness ratio. By combining topology shape optimization and theoretical calculations, a mirror structure suitable for SPDT turning was established. Then, the rationality of the design was verified by a finite element simulation analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index Parameters** & **Requirement** \\ \hline shape & elliptical mirror \\ radius of curvature (mm) & 433 \\ conic & \(-1\) \\   F number & long axis: 0.53 \\  & short axis: 0.70 \\   mass & \(<\)3.5 kg \\   first order model (Hz) & \(\geq\)180 Hz \\   surface accuracy requirement (RMS) & \(1/10\)\(\lambda\) (\(\lambda\) = 632.8 nm) \\   surface roughness (nm) & \(<\)5 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirement of main mirror index.  
Figure 2: Flow chart of design scheme of a high-steepness lightweight elliptical mirror.
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 3 Methodology
### Damage Geometry Generation  
The main three steps followed to generate the damage geometry on the basis of photos are shown in Figure 5. First, photos of a checkerboard pattern are used to calibrate the camera. Intrinsic parameters and the radial distortion coefficients, which are required for 3D reconstruction, respectively for undistorting images and their segmented masks, are defined by this calibration. With all intrinsic parameters identified, the OpenSfM library in Python was then used to 3D reconstruct a dense point cloud based on the inspection images up to scale. The process of generating the point cloud may be accelerated by separating the images into clusters to generate point cloud submodels. Furthermore, this modification limits the need to segment unnecessary images used to reconstruct the scene and could be used to reduce the density of submodels without defect geometries.  
Second, among the outputs of the resulting 3D reconstruction are the estimated pose of the camera for each inspection image taken (i.e., 3D rotation and translation) as well as its depth map. This additional information concludes the full knowledge of all variables in the perspective projection equation for each image, which could be utilized to project the classified spalling pixels in the selected images of choice backwards into the 3D space of the reconstructed scene [48; 49]. The regions of interest classified as spallings in the binary maps of the selected defect images could be then converted from pixel units into 3D world coordinates. Figure 6 shows the point cloud with the marked damage points on the left. These points are triangulated to retrieve the surface of the defect.  
Figure 4: Exemplary segmentation of a spalling defect. (**a**) cropped part of the inspection photo. (**b**) spalling map. (**c**) overlay of the indentified spalled region and the inspection photo.  
Figure 5: Process for generating the geometry.  
Third, to reconstruct the shape of the spalling, the unit vector for the extrusion direction is derived from the arithmetic mean of all conformed normals pointing into the direction of the camera for all vertices of each segmented spall patch in the point cloud. Each main patch is defined by merging overlapping labeled patches from different images via a boolean union operation after filtering out duplicated vertices, if they exist. Extruding along that unit vector for a distance significantly larger than the depth of the hosting damaged beam ensures the generation of a valid defect shape with an outer surface always protruding from the surface of the hosting building element. Figure 6 shows the resulting damage geometry after the step of extruding. Overdimensioning the subtraction geometry avoids the possibility of erroneous boolean difference operations when creating the voids that was reported by [PERSON] et al. [41]. On the most right of Figure 6, the subtraction is illustrated. This step requires further semantic modeling.