esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 2 Methods
### Feature Fusion of Multi-Scale  
Most methods primarily rely on advanced features for prediction to expedite detection. The feature map after multi-layer convolution has an expansive receptive field for the entire image, making global perception more prominent with rich semantic information. However, it is difficult to capture the spatial information of the target simultaneously, making systems that rely solely on high-level features less effective for detecting small-sized targets, particularly dim and small ones. In contrast, low-level features contain precise spatial information. Therefore, we fuse high-level features with low-level features to provide the final detection system with both ample semantic and spatial location information, improving the detection and precise positioning of dim and small targets in infrared imaging.  
As shown in Figure 3, the input from the local region is fused with the convolutional layer of the global region input, facilitated by the backbone's convolutional layer. Essentially, the output channels are interconnected through a concat (channel connection) layer, which links the channels together. Afterward, the feature channels are processed through a \(1\times 1\) convolutional layer, which updates the focus weights for the dim and small target regions while eliminating superfluous information.  
Figure 2: Vision focus simulation with local input. Dim and small targets are marked with small red rectangle.  
As shown in Figure 4, the architecture of the branch input closely resembles that of the global input. The feature map from each local input is fused with the global feature map after passing through each convolution module, facilitated by the concat layer. The concat layer achieves feature fusion by increasing the number of channels rather than adding new feature maps. Due to the concat layer's output concatenating the input channel data, we apply a 1 \(\times\) 1 convolutional layer after the concat layer to integrate the features of both the target and the background. Each convolution is followed by a SiLU activation. During the feature extraction stage at each branch scale, the feature map is downsampled using a 3 \(\times\) 3 convolution with a stride of 2 and is then passed to the concat layer along with the other input to achieve multi-scale feature fusion. The final feature map in the backbone contains the largest receptive field, enriched with both semantic and spatial information. Equation (2) represents the process:  
\[F=SiLU\big{(}f_{1\times 1}\big{(}concat\big{(}F_{i};F_{g}\big{)}\big{)}\big{)} \tag{2}\]  
where \(f_{1\times 1}(\cdot)\) denotes the convolutional kernel of size 1 \(\times\) 1, and \(SiLU\big{(}\cdot)\) denotes the sigmoid linear unit operation. \(F_{I}\) and \(F_{g}\) correspond to the features of the local input and global input after the convolutional layer, respectively.
# Towards Trust and Friendliness Approaches in the Social Internet of Things
## 4 Classification of Trust- and Friendliness-Based Approaches in the Social IoT
### Service Search  
As mentioned earlier, thousands of objects in the IoT are connected to each other, and hence, each object looks among its friends for a service provided by another object. This is called a service search.  
According to [PERSON] et al. [14], a service search in the IoT is a challenging task. The motivation of the social IoT is to boost object discovery and the composition of services by objects that have access to the real world [14]. To conduct a service search for objects, a network must be navigable [14]. [PERSON] [45] is one of the famous scientists who have developed, and devoted significant effort to, this topic [14]. He defined it as a feature that is linked to the number of short paths that exist among pairs of nodes in a network [45]. Many studies discussed different formal definitions [46; 47], and all of them agree on the fact that, for a network to be navigable, all or most of the paths must be connected. This may happen only if a giant component exists in the network, and there also exists a shortest path between each pair of nodes. These features are necessary for global network navigability; for example, a network selection of paths between node i and node j is done by a central node that has information about the entire network structure. In local network navigability, node i selects next-hop k on the basis of local information, which is typically related to its adjacent nodes. After that, node k proceeds with its local information using its own direct contacts, and elects the next-hop node that it considers closestto node j. This procedure is known as decentralized search. We will discuss it in the next sections in detail by using various studies and comprehensive examples. According to [PERSON] [45], a network is navigable when it contains a complete connection among all edges, or a network is considered navigable when a giant component exists. The service search class is further divided into distributed search and centralized search.
_Appl. Sci._**2013**, \(3\), 299-313; doi:10.3390/app3010299  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Recent Developments in Experimental Techniques for Measuring Two Pulses Simultaneously**  
**[PERSON] \({}^{\star}\) and [PERSON]**  
School of Physics, Georgia Institute of Technology, 837 State Street, Atlanta, GA 30332, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-404-385-1223; Fax: +1-404-894-9958.  
_Received: 10 December 2012; in revised form: 7 February 2013 / Accepted: 1 March 2013 / Published: 18 March 2013_  
**Abstract:** As many high-intensity ultrafast-optical measurements involve more than one pulse--typically one to excite a medium under study and another to probe it--a technique for measuring two pulses simultaneously is highly desirable. In two decades, two-pulse measurement techniques have advanced from ambiguity-laden to a recently developed technique that can measure a pulse pair with arbitrary central wavelengths, complexities and bandwidths. Here, we review recent efforts to simultaneously measure two ultrashort laser pulses using a single device.  
**Keywords:** ultrashort pulses; ultrafast measurement; FROG
# Collision Detection Method Using Self Interference Cancelation for Random Access Multiuser MIMO
## 2 Proposed Method  
### Basic Concept of Proposed Method  
In this paper, we consider an IEEE802.11n/ac-based wireless LAN system. When the number of antennas in the AP is \(N\), and the number of antennas at the UTs is one, the AP makes a group with \(N\) users. Hereinafter, we assume that \(N\) is 4. In the case of wireless LAN, the process of carrier sense prohibits the transmission by other UTs. MIMO and MU-MIMO are introduced to the IEEE802.11n/ac-based wireless LAN. We focus on the interference signals due to packets that are synchronously transmitted from each UT, after each UT employs a carrier sense in the IEEE802.11n/ac-based wireless LAN.  
Figure 1 shows the transmit sequence of conventional and random access MU-MIMO. In conventional MU-MIMO, all the packets of the synchronous transmitted multiuser group are generated, and the channel state information (CSI) between the AP and UTs is estimated. After acquisition of CSI, the conventional MU-MIMO downlink transmission starts. As shown in Figure 1, since the waiting time of the first generated packet in conventional MU-MIMO transmission is long, the transmission efficiency of conventional MU-MIMO is very low. On the other hand, random access MU-MIMO realizes _asynchronous_ MU-MIMO. After one packet of the synchronous transmission multiuser group is generated, the CSI between the AP and all the UTs in multiuser group is immediately estimated. The data packets are (DATA1 and DATA2 in Figure 1) instantaneously transmitted in random access MU-MIMO. Therefore, since this procedure reduces not only the overhead of CSI estimation but also the transmission wait time for each data generation, random access MU-MIMO improves the transmission efficiency [13].  
As can be seen in Figure 1, for the random access MU-MIMO, the proposed method utilizes the fact that the number of data packets for a certain period is less than the number of _idle_ antennas, which are not necessarily required for transmission at the AP. Figure 2 shows an example of the  
Figure 1: Transmission sequence by conventional and asynchronous MU-MIMO.  
data sequence in random access MU-MIMO transmission and a basic idea of the proposed method. As can be seen in Figure 2a, the data packets are transmitted after gathering the downlink signals. Hence, the proposed method focuses on this feature, and the receive antennas are prepared to detect self-interference cancellation between the AP antennas. As can be seen in Figure 2, only Data 1 and 2 for UT 1 and 2 are transmitted. Hence, at least two antennas are required for MU-MIMO transmission. In the proposed method, the antennas #3 and #4 at the AP are used for collision detection. Since at least three, two, and one antenna are required at the AP in the periods 2, 3, and 4, respectively, as shown in Figure 2a, the unused antennas are prepared for realizing collision detection.
## 4 Proposed Method
### Modified Hourglass Network
#### 4.1.2 Spatial and Channel Attention Modules
Similar to the SENET, the feature maps are initially refined by using global max pooling to lower the feature map's dimension from \(C\times H\times W\) to \(C\times 1\times 1\). After that, it is passed through an adaptive kernel, the size of which describes the neighborhood size, as it slides across this space. It is a one-dimensional convolutional, with a kernel size \(k\) that is defined by the following formula and adaptive to the global channel space C:  
\[k=\psi(C)=\left|\frac{\log_{2}(C)}{\gamma}+\frac{b}{\gamma}\right|_{old} \tag{5}\]  
\[ \begin{split}\text{M}_{\text{c}}(\text{F})&=\sigma \big{(}f^{3\times 3}([\text{AvgPool}(\text{F});\text{MaxPool}(\text{F})])\big{)}\\ &=\sigma\big{(}f^{3\times 3}\Big{(}[\text{F}_{\text{avg}}^{ \text{s}};\text{F}_{\text{max}}^{\text{s}}]\Big{)}\Big{)}\end{split}  \tag{6}\]  
where \(\text{AvgPool}(\text{F})\) is the average pooling function and \(\text{MaxPool}(\text{F})\) is the maximum pooling function, and \(|t|_{old}\) denotes the nearest odd number of t. We have set \(\gamma\) to 2 and b to 1, in that order. In other words, when mapping the per-channel attention weights, k basically gives the size of the local neighborhood area that will be utilized to record cross-channel interaction. In essence, the function \(\psi(C)\) uses the closest odd number as the 1D convolutional kernel size. Because of this, high-dimensional channels interact over a greater range with this mapping \(\psi\), while low-dimensional channels interact over a shorter range with a nonlinear mapping. The subsequent phase bears similarities to spatial attention in that it involves subjecting the features to sigmoid activation. This yields the final channel attention map, which is subsequently multiplied by the initial feature map to obtain the updated feature map. The model can learn \"what\" and \"where\" to look in the image by applying the attention modules, which will result in the best performance. Figure 12: Spatial attention module.
## 2 Related Work
### Routing Protocols for UWSN
This routing algorithm tries to minimize the end-to-end delay in underwater networks by sending the same packet through different routes in a 2-hops neighbourhood using relay nodes. This algorithm achieves lower end-to-end delay than VBF with a better packet delivery ratio.
## 3 Results and Discussions
### Total Polyphenol Content (TPC) and Antioxidant Capacities (FRAP and TEAC)  
The TPC and antioxidant capacities measured in the Jimbee and Okashi musets were similar to those found in other studies performed in fresh melon [28; 29]. The effects of the alcoholic fermentation during fruit winemaking in TPC and antioxidant capacities are shown in Tables 1 and 3. During the winemaking process, both melon cultivars showed the same trend, with TPC increasing by about 13%, duplicated FRAP level, and decreasing TEAC value. These differences between antioxidant assays (FRAP and TEAC) are due to the different chemical reactions that each assay involves. The FRAP method constitutes an approach based on electron transfer, specifically based on the reducing action of antioxidants. In this method, iron in the oxidized form of Fe\({}^{+3}\) is reduced to Fe\({}^{+2}\) at acidic pH [30]. TEAC measures the relative ability of antioxidants such as Trolox to scavenge the ABTS generated in the aqueous phase, the assay is based on a quencher of peroxyl radicals [31]. Furthermore, this difference in both antioxidant assays could be due to the relative differences in the phenolic transformation that occur during the winemaking process. [32] reported the same trend in antioxidant capacity between the must to wine process (13%).  
Jimbee and Okashi wines exhibited TPC levels of 406 and 242 mg GAE/L, in the range of the 350 mg GAE/L reported by [PERSON] et al. [10] in melon wines. These results were higher than in apple wine (223 mg catechin equivalent/L) but lower than in blackcurrant (420 mg catechin equivalent/L), principally because that fruit is rich in anthocyanins, thus increasing its TPC and antioxidant capacity [33]. Nevertheless, when we compare the TPC of melon wines versus Sauvignon blanc wines (242-278 mg GAE/L), the results are higher than or similar. Concerning the antioxidant capacity (TEAC), the melon wines presented 1.60 and 1.80 mmol TEAC/L; these levels were slightly higher than for Sauvignon blanc wines (1.26-1.50 mmol TEAC/L) [18].
## 2 SPME-GC/MS Methods
### Analysis of PFBOA-Derivatives
\begin{table}
\begin{tabular}{l l} \hline Sample volume & 100 \(\mu\)L. \\ Vial volume & 4 mL \\  & 200 \(\mu\)L IS _o_-chlorobenzaldehyde, 3.4 mg/L in ethanol/water solution; 1 mL of PFBOA 2 g/L \\ Derivatization conditions & aqueous solution, volume adjusted to 2 mL with water \\  & 65-\(\mu\)m poly(ethylene glycol)/divinylbenzene (PEG/DVB) \\  & Addition to the sample & 50 mg NaCl \\ Sample heating & 50 \({}^{\circ}\)C for 20 min under stirring \\  & Extraction temperature and time & 50 \({}^{\circ}\)C for 5 min \\  & Desorption temperature and time & 240 \({}^{\circ}\)C for 1 min \\  & Fiber cleaning & 250 \({}^{\circ}\)C for 5 min \\  & GC column & HP-INNowax (30 m \(\times\) 0.25 mm i.d; 0.25-\(\mu\)m film thickness) \\  & Carrier gas & Helium, column headpressure 16 psi \\  & Injector & T = 240 \({}^{\circ}\)C, sample volume 0.5 \(\mu\)L, splitless injection \\  & Oven program & 60 \({}^{\circ}\)C for 5 min, 3 \({}^{\circ}\)C/min to 210 \({}^{\circ}\)C, held 5 min \\  & PICI mode using methane as reagent gas (flow 1 mL/min), ion source at 200 \({}^{\circ}\)C, damping gas \\  & 0.3 mL/min, simultaneous SCAN (range _m_/\(\sim\) 40–660, 1.67 scan/s) and MS/MS \\ CID experiments & Collision gas He, excitation voltage 225 mV \\  & Recorded signals at _m_/_z_ 240 for acetaldehyde, _m_/_z_ 282 for diacetyl, _m_/_z_ 266 for acetoin, _m_/_z_ 336 for _o_-chlorobenzaldehyde (I.S.) \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental conditions used for PFBOA derivatization and SPME-GC/MS analysis (ion trap and positive chemical ionization) of the main wine carbonyl compounds. Adapted from [PERSON] _et al._, 2005[46].
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 3 Elaboration of Sensor Materials
### Conductive Materials
#### 3.1.2 Carbon-Type Materials  
Commonly used carbon-type nanomaterials, including CB, CNTs and graphene, usually have excellent electrical properties and chemical stability [63; 64].  
CB is an amorphous 0D conductive particle. The conductivity of CB is influenced by the particle size, structure and roughness of CB (Figure 6a). Compared with CNTs and graphene, CB is much cheaper. So far, CB-based strain sensors have been manufactured by screen printing a CB-silicon elastomer composite on a fabric to produce a series of wearable flexible sensors [65]. Nonetheless, CB is easy to agglomerate and difficult to disperse because of its tiny particle size. In this case, a high content is required to obtain good conductivity, but this leads to reduction in the stretchability. In addition, the conductive path amongst CB particles is easily damaged and reconstructed when the flexible sensor is deformed, resulting in unstable sensing performance [66].  
CNTs are 1D conductive nanofibers with a low percolation threshold and excellent mechanical properties (Figure 6b). They are divided into single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) [67; 68]. The cost of MWCNTs is just one-tenth of that of SWCNTs. Moreover, the purity of MWCNTs in solutions can reach 99%--higher than the 90% purity of SWCNTs. Therefore, MWCNTs have much lower cost and higher purity than SWCNTs. Strain sensors based on MWCNTs have excellent sensing characteristics and a large working range. The curved structure of CNTs allows easy winding around each other in the substrate to form many contact points, which is helpful to improve the piezoresistive property of the composites [67; 68]. Although CNTs have high stretchability, CNT-filled elastic polymer composites usually have relatively low GF under a low strain level andnonlinear GF under a large strain level. In addition, CNT-based strain sensors often exhibit resistance hysteresis under cyclic loading owing to the irreversible change and degradation of the conductive network. Accordingly, structural stability is very important in realizing high performance, and many strategies have been employed to resist the buckling and binding of CNT networks during cyclic deformation [69].  
Graphene is a hexagonal 2D carbon nanomaterial with a honeycomb lattice composed of hybrid orbital carbon atoms (Figure 6c). It has a high transmittance of more than 90%, a large specific surface area (2630 m\({}^{2}\)/g) for a large sensing area per unit volume under strain and outstanding mechanical and thermal properties [70]. In particular, graphene has the advantages of high ductility, high recovery, high elastic stiffness (340 N/m) and good carrier mobility at room temperature, making it very suitable for flexible strain sensors with good sensing performance and stability [71,72].
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Attention-Based Model
#### 5.10.3 Hierarchical Attention Networks (HAN)  
Hierarchical Attention Networks (HAN) have demonstrated promising results at both the document and sentence levels, especially in detecting cyberbullying. HANs are neural networks that concentrate on significant portions of the input text by using an attention mechanism that enables them to collect the most relevant features for classification. HANs have been used to understand the common tone of a social media message and the presence of certain bullying behaviors at the sentence level [61; 118; 119].
# Skull Thickness Calculation Using Thermal Analysis and Finite Elements
## 2 Materials and Methods
### Mesh Generation Using the Finite Element Method  
The finite element (FE) method is a numerical technique used to analyze various engineering problems [28; 29; 30; 31]. This technique separates a large system into smaller and simpler parts, called finite elements. This method is increasingly frequently being applied to analyze bone structures [32]. FE can also be used to model complex geometries such as 3D solid models. The aim is to obtain reliable results with the least error and minimum calculation time, regardless of mesh size and density. Simulations were executed on a high-performance workstation with an Intel Core i7-6700 processor (3.40 GHz) and 16 GB RAM. Firstly, the created models are transferred to FE-based ANSYS. While designing the models, the anatomy of the skull was taken into account [33]. Then these solid models are meshed to prepare thermal analysis. The curvature and element size are adjusted to fit the model mesh, since the solid model has a curved structure, in order to maximize resemblance to the skull. The number of nodes and elements in a solid model varies depending on the size of the mesh. By using the sizing option under the mesh menu, the size of the element is selected as 1, 2, and 3 mm. As the mesh size gets smaller, the number of nodes and elements also increases. Table 2 shows the number of nodes, the number of elements, and the study time required, depending on the different element sizes of the meshed model. FE mesh models of three types with different levels were generated. During the analyses, it was determined that FE model-3 had the largest mesh size, but provided a reliable solution in terms of both time and number of elements. Therefore, we preferred the FE model-3 over the other two FE models (1 and 2), and used it in the subsequent analyses. Thanks to the improvement of the network and the use of the options available in the software, the network quality was increased, and optimal results were obtained. Figure 2 shows the meshed solid models with different numbers of nodes and elements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** &
\begin{tabular}{c} **Young’s Modulus** \\ **(MPa)** \\ \end{tabular} & **Poisson Ratio** & **References** \\ \hline [PERSON] (2006) & 300 & 0.19 & [25] \\ [PERSON] et al. (2009) & 238 & 0.20 & [26] \\ [PERSON] et al. (2009) & 380 & 0.22 & [27] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of skull bones were obtained by different authors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model No.** & **Mesh Size (mm)** & **Node** & **Elements** & **Study Time** \\ \hline
1 & 1 & 7635 & 1395 & 3 min, 8 s \\
2 & 2 & 1435 & 213 & 1 min, 17 s \\
3 & 3 & 637 & 80 & 0 min, 29 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: FE solution information against different mesh models.
## 2 Stiffness Identification Method Based on Joint Simulation
### Principle of Joint Simulation Method Based on Matlab and Ansys  
When the object is subjected to a resonant excitation force \(F\), the differential equation of vibration of the system is given as  
\[m\ddot{x}+c\dot{x}+kx=F \tag{1}\]  
The Laplace transform of both sides of Equation (1) leads to Equation (2):  
\[\Big{(}ms^{2}+cs+k\Big{)}x(s)=F(s) \tag{2}\]  
Therefore, the transfer function \(H(s)\) is expressed as  
\[H(s)=\frac{x(s)}{F(s)}=\frac{1}{ms^{2}+cs+k} \tag{3}\]  
Let \(s=j\omega\); the Laplace transform is transformed into the Fourier transform, and the transfer function \(H(s)\) is transformed into the frequency response function \(H(\omega)\) in the frequency domain.  
\[H(\omega)=\frac{1}{-\omega^{2}m+j\omega c+k} \tag{4}\]  
The frequency response function responds to the relationship between the steady-state output and input of the system for a given frequency, and the natural frequency \(\omega\) of the system can be identified according to the frequency response function, as shown in Equation (5):  
\[k=m\omega^{2} \tag{5}\]  
As long as the stiffness \(k\) is known, the natural frequency \(\omega\) can be obtained; thus, the joint simulation in Matlab and Ansys is proposed to identify joint stiffness. The spring element of Ansys (combin14) is applied to simulate the joint, and the stiffness \(k\) is selected randomly by the genetic algorithm of Matlab. The stiffness value is substituted into the spring element, and then calculated natural frequencies are obtained by modal analysis. Experimental natural frequencies are obtained by modal test. Comparing the calculated natural frequency \(f_{c}\) with the experimental natural frequency \(f_{test}\), when the difference  
Figure 1: Equivalent model of joint.  
converges, the values of the stiffness at this time are joint stiffnesses. The identification method of joint stiffness is shown in Figure 2.