esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Method  
To address the aforementioned issues and extract more discriminative pedestrian features, we propose an edge-enhanced global feature extraction and inter-part relationship-based network. The network combines various features, including global features, edge features, and inter-part dependency features to enhance feature representation. The network architecture is illustrated in Figure 2. Specifically, we employ a pre-trained fully convolutional network (FCN) to extract pedestrian masks. Subsequently, we extract pedestrian features through two modules:  
**Edge-enhanced Feature Extraction Module (EFEM)**. We utilize the Canny algorithm to generate pedestrian edges corresponding to the mask regions, then fuse these edges with the RGB image, and extract the features of the fused image through the feature extraction network.  
**Inter-part Relationship Extraction Module (IREM)**. Combined with semantic segmentation, we divide the features of the foreground region into multiple parts and capture the relationship between different parts through an RNN network.  
The features extracted by the two modules are fused to generate the final pedestrian representation, which is then classified using a classification layer.  
Below, we will provide a detailed introduction to various components of the network.
# Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network
## 2 Method
### Signal Model and Learning-Based Approach
However, the method we presented breaks the bottleneck, it uses neural network to approximate function \(g\) so that target scattering coefficient can be estimated directly from echo signal. \[\overset{\wedge}{\beta}=g_{\Theta}\left(S_{b}\right), \tag{6}\]  
where \(\Theta\) is the set of all possible parameters. Here they can be learned from a training set each of which pairs up a known original target \(\beta^{d}\) and the corresponding echo signal \(S_{b}^{d}\), where \(d=1,2,\ldots,D\), enumerates the total \(D\) different training data pairs. Thus, this parametric reconstruction process can be expressed as  
\[g_{learn}=\underset{g_{\theta},\theta\in\Theta}{\arg\min}\sum_{d=1}^{D}L\left( \beta^{d},g_{\theta}\left(S_{b}^{d}\right)\right), \tag{7}\]  
where \(g_{\theta}(\cdot)\) stands for the target of recovery from the imaging network under parameter \(\theta\), \(L(\cdot)\) is the loss function. As can be seen from Equation (4), the large-scale reference-signal matrix creates a heavy computational burden. Due to the short wavelength and precise resolving ability of THz waves, the imaging area is divided into smaller grids, which means that the more the number of elements in \(M\) and the more complicated calculation of reference-signal matrix. As a result, it is time-consuming to solve the target scattering coefficient through the algorithms consist of iterative-based processes as shown in Equation (5). Therefore, it is very necessary to use the parameter reconstruction algorithm as shown in shown Equation (6) to achieve fast TCAI. In contrast, the proposed approach is in significant ways different. It does not need some time-consuming operations like previous imaging technique. Instead, it demands plenty of data set which includes the groundtruth image and corresponding echo signal and spends some time in training network, but these can be done in advance. Once the training procedure is completed, the designed imaging network can blindly reconstruct target from echo signal.
# Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
## 2 Problem Statement and Formulation
### Motion Primitives  
The set \(s[n]\in\mathcal{S}\) is a dynamical system composed of the 3-\(D\) position, acceleration, and velocity: \(s[n]=[p[n],v[n],a[n]]^{\mathsf{T}}\). According to [28], the differential flatness of the UAV allows us to build control inputs from a 1-\(D\) time-parameterized polynomial trajectory that is independently specified on each axis. The discrete state over the entire task period can be \(\mathbf{P}=[p[1],p[2],\cdots,p[N]]^{\mathsf{T}}\), \(\mathbf{V}=[v[1],v[2],\cdots,v[N]]^{\mathsf{T}}\), and \(\mathbf{A}=[a[1],a[2],\cdots,a[N]]^{\mathsf{T}}\). The control variable input for the system is the jerk: \(\mathbf{J}=[j[0],j[1],\cdots,j[N-1]]^{\mathrm{T}}\). According to the equation of motion, the following correlations can be acquired:  
\[\begin{split}& p[n+1]=p[n]+v[n]\cdot\delta+\frac{1}{2}\cdot a[n] \cdot\delta^{2}\\ &+\frac{1}{6}\cdot j[n]\cdot\delta^{3},n=0,\cdots,N-1\end{split} \tag{12}\]  
\[\begin{split}& v[n+1]=v[n]+a[n]\cdot\delta\\ &+\frac{1}{2}\cdot j[n]\cdot\delta^{2},n=0,\cdots,N-1\end{split} \tag{13}\]  
\[a[n+1]=a[n]+j[n]\cdot\delta,n=0,\cdots,N-1 \tag{14}\]  
The matrix forms are as follows:  
\[\mathbf{P}=\mathbf{T}_{p}\cdot\mathbf{J}+\mathbf{B}_{p}, \tag{15}\]  
\[\mathbf{V}=\mathbf{T}_{v}\cdot\mathbf{J}+\mathbf{B}_{v}, \tag{16}\]  
\[\mathbf{A}=\mathbf{T}_{a}\cdot\mathbf{J}+\mathbf{B}_{a}, \tag{17}\]  
where  
\[\mathbf{T}_{p}=\left[\begin{array}{cccc}\frac{1}{6}\delta^{3}&0&0&\cdots&0 \\ \frac{1}{6}\delta^{3}&\frac{1}{6}\delta^{3}&0&\cdots&0\\ \vdots&\vdots&\vdots&\cdots&0\\ \frac{3N+1}{6}\delta^{3}&\cdots&\frac{3(N-n+1)\cdot(N-n)+1}{6}\delta^{3}& \cdots&\frac{1}{6}\delta^{3}\end{array}\right]_{N\times N}, \tag{18}\]  
\[\mathbf{B}_{p}=\left[\begin{split}& p[0]+v[0]\delta+\frac{1}{2}a[0]\delta^{2}\\ & p[0]+2v[0]\delta+\frac{2^{2}}{2}a[0]\delta^{2}\\ &\vdots\\ & p[0]+Nv[0]\delta+\frac{N^{2}}{2}a[0]\delta^{2}\end{split} \right]_{N\times 1}, \tag{19}\]  
\[\mathbf{T}_{v}=\left[\begin{array}{cccc}\frac{1}{2}\delta^{2}&0&0&\cdots&0 \\ \frac{3}{2}\delta^{2}&\frac{1}{2}\delta^{2}&0&\cdots&0\\ \vdots&\vdots&\vdots&\cdots&0\\ (N-\frac{1}{2})\delta^{2}&\cdots&(N-n+\frac{1}{2})\delta^{2}&\cdots&\frac{1}{2} \delta^{2}\end{array}\right]_{N\times N}, \tag{20}\]  
\[\mathbf{B}_{v}=\left[\begin{split}& v[0]+a[0]\delta\\ v[0]+2a[0]\delta\\ &\vdots\\ v[0]+Na[0]\delta\end{split}\right]_{N\times 1}, \tag{21}\]\[\mathbf{T}_{a}=\left[\begin{array}{ccccc}\delta&0&0&\cdots&0\\ \delta&\delta&0&\cdots&0\\ \vdots&\vdots&\vdots&\cdots&0\\ \delta&\delta&\delta&\cdots&\delta\end{array}\right]_{N\times N}, \tag{22}\]  
\[\mathbf{B}_{a}=\begin{bmatrix}a[0]\\ a[0]\\ \vdots\\ a[0]\end{bmatrix}_{N\times 1}. \tag{23}\]  
in which \(p[0]\) is the initial position, \(v[0]\) is the initial velocity, and \(a[0]\) is the initial acceleration.
# A Query Understanding Framework for Earth Data Discovery
## 6 Results and Discussion
### Qualitative Evaluation on a Sample Query  
The parsing results of query \"sea surface temperature modis level 2 Pacific Ocean in March 3 rd, 2004\" is achieved with the geo tab showing the latitude and longitude of the northeast and southwest points of the Pacific Ocean as a spatial bounding box to filter datasets that are located inside the spatial range (Figure 8). \"3 March 2004\" in the query is converted to \"03/03/2004:00:00:00\" for time comparison. Three phrases (i.e., \"sea surface temperature\", \"modis\", \"level 2\") are detected from the query, and similar phrases to these phrases are added to the query. Phrases in the original query are rewritten to \"sea surface temperature\" or \"sst\" or \"ghrsst\" or \"ocean temperature\" and \"modis\" and \"level 2\" to improve the search recall and precision. The tool also found that the query consists of three entities, in which \"sea surface temperature\" is a variable, \"level 2\" is a processing level, and \"modis\" is a sensor. The three entities are automatically applied to filters to narrow the search scope.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Types of WSNs  
Depending on the deployment environment on land, underground, or underwater, there are several types of WSNs [10] (see Figure 3):  
* _Terrestrial_. This kind of WSNs is intended to be deployed in a terrestrial space. In these networks, hundreds of thousands of sensors are deployed randomly or deterministically over a given area. This type of WSN is much more useful in the field of environmental monitoring and natural phenomena; however, it represents a challenge for the sustainability of the network in terms of energy management [34].
* _Underground_. These networks consist of very special sensor nodes known for their high cost and the logistics necessary for their implementation and maintenance. In this type of network, the sensor nodes are installed in the ground. They find their application in agriculture or in minefields, where they are used to monitor conditions in the soil. However, in this type of network, there is a ground node that has a role in transmitting the information detected by the underground nodes to the base station [35].
* _Underwater_. This particular type of WSNs is networks hidden under the sea. Because of the hostility represented by the environment of their deployment, this type of network remains an interesting research challenge. These nodes are more expensive than terrestrial sensors, wireless communication is acoustic, bandwidth is limited, signal loss is recurrent, and propagation delays and synchronization problems are frequent [36; 37].
* _Multimedia_. These WSNs are designed to monitor and trace data such as images, videos, and sound. These sensors are equipped with cameras and microphones. This type of network requires good bandwidth and good quality of service, which implies high power consumption for data processing and compression. Preplanning is required for the deployment of these sensors [38].  
Figure 2: Architecture of a wireless sensor.  
* _Mobile_. This is the most recent type of WSN, and it uses mobile nodes that can autonomously reposition and reorganize the network. After initial deployment, the nodes disperse to gather information. There is also a hybrid network consisting of a combination of mobile and fixed sensors [12; 25; 26; 37; 39; 40; 41; 42].
## 4 Discussion
Our results showed promising activity of licensing to suppress NO release from LPS-induced RAW264.7. The 25% reduction of NO by licensing at a concentration of 20 \(\upmu\)g/mL (Figure 6b) was comparable to 20 \(\upmu\)M of nefering [33]. Furthermore, licensing also notably decreased the protein expression of COX-2 and iNOS. The trend of iNOS and COX-2 inhibition shown by liensinine was similar to that shown by glucosamine (a commercially available anti-inflammatory drug) at a concentration of 2.5 to 10 mM in LPS-induced RAW264.7 cells [39]. IL-6 is a well characterized inflammatory mediator and it is released by VSMC after the induction of potent stimulants such as TNF-\(\upalpha\)[40] and angiotensin II [41]. IL-6 is crucial in vascular remodelling and it, along with ox-LDL, is a potential prognostic marker in predicting cerebral vascular and cardiovascular disorders [42]. In our ELISA result (Figure 5), licensing suppressed the TNF-\(\upalpha\) induced IL-6 level in VSMC by approximately 50% at 30 \(\upmu\)g/mL concentration. During atherosclerosis, the vascular lumen is narrowed by a fibrous cap composed of (among many other things) VSMC and extracellular matrix. Various growth factors and cytokines produced by endothelial cells and inflammatory cells contribute to the proliferation and migration of VSMC leading to fibrous cap formation [29; 43; 44]. Cytokines like PDGF can induce proliferation of VSMC to a significantly high level, whereas TNF-\(\upalpha\) is known to stimulate the migration of VSMC from tunica media to the site of a lesion by increasing MMP-9 expression. MMP-9 is a key gelatinolytic enzyme responsible for the degradation of the elastic lamina barrier of the extra cellular matrix [45; 46]. In our previous publications, we have shown that PDGF and TNF-\(\upalpha\) promote the proliferation and MMP-9-dependent migration of VSMC [11; 12; 14]. In our result, liensinine inhibited the PDGF-BB induced proliferation/growth of VSMC, revealing its potent anti-proliferating activity. At a 30 \(\upmu\)g/mL concentration of liensinine, the proliferation of VSMC was almost completely inhibited to the level of the control (without PDGF) (Figure 3). The potent anti-proliferative activity of liensinine at a concentration of 30 \(\upmu\)g/mL is comparable to 50 \(\upmu\)M of epigallocatechin-3-O-gallate [47] and 20 mM of carosine [48]. Likewise, liensinine at a concentration of 10, 20 and 30 \(\upmu\)g/mL significantly attenuated the expression of MMP-9 induced by TNF-\(\upalpha\) (Figure 4). We speculate that the notable inhibition of MMP-9 enzymatic activity by _Nelumbo nucifera_ leaf extract in our previous study, at a concentration of 250 \(\upmu\)g/mL, was in part exerted by liensinine [23]. Taken together, our results provide the mechanistic pathway to attenuate the progression of atherogenesis via controlling vascular inflammation by liensinine possibly by targeting VSMC proliferation, MMP-9 expression and inflammatory mediators released by macrophages (Figure 7).
## 2 Model Development
### Demand-Side Model  
This study uses the time-series decomposition method to forecast the monthly short-term electricity demand in Zambia. The multi-seasonal time series Auto-regressive Integrated Moving Average (SARIMA) method was proposed. The seasonal ARIMA model consists of modeling the number of yearly observations while considering both the non-seasonal part of the model and the seasonal part of the model. As stated, it has additional seasonal terms which are often multiplied by the non-seasonal terms. This methodology considers the differences and seasonal lags to fit the seasonal patterns, which are specified in the expression below [20]:  
\[\begin{split}\big{(}1-\phi_{1}&\mathrm{A}-\dots- \phi_{\mathrm{P}}\mathrm{A}^{\mathrm{P}}\big{)}\Big{(}1-\Phi_{1}\mathrm{A}^{ \mathrm{s}}-\dots-\Phi_{\mathrm{P}}\mathrm{A}^{\mathrm{S}\mathrm{P}}\Big{)}(1- \mathrm{A})^{\mathrm{c}}(1-\mathrm{A}^{\mathrm{s}})^{\mathrm{C}}\mathrm{D}_{ \mathrm{t}}^{0}\\ &=\big{(}1-\theta_{1}\mathrm{A}-\dots-\theta_{\mathrm{q}}\mathrm{A }^{\mathrm{q}}\big{)}\Big{(}1-\Theta_{1}\mathrm{A}^{\mathrm{n}}-\dots-\Theta_{ \mathrm{Q}}\mathrm{A}^{\mathrm{SQ}}\Big{)}\mathrm{d}_{\mathrm{t}}\end{split} \tag{1}\]  
In the equation above, the \(\mathrm{D}_{\mathrm{t}}^{0}\) represents the fitted historical monthly electricity demand at time index \(\mathrm{t}\). The ordinary autoregressive and moving average parameters are \(\phi_{\mathrm{P}}(\mathrm{A})\) and \(\theta_{\mathrm{q}}(\mathrm{A})\) with orders \(\mathrm{p}\) and \(\mathrm{q}\), respectively. The seasonal autoregressive and moving average parameters are \(\Phi_{\mathrm{P}}(\mathrm{A})\) and \(\Theta_{\mathrm{Q}}(\mathrm{A})\) of orders \(\mathrm{P}\) and \(\mathrm{Q}\). The ordinary seasonal component is represented by the variable \(\mathrm{c}\) and the seasonal difference component is represented by \(\mathrm{C}\). A represents the backward shift operator. The additional parameters are \(\mathrm{d}_{\mathrm{t}}\), which refers to the error term and has a significance of mean 0 and variance 1. \(\mathrm{s}\) is the number of seasonal periods. Finally, the model coefficients are \(\phi\), \(\Phi\), and \(\Theta\).  
The initial stage also consists of differencing the series, then considering the autocorrelation function (ACF) and the partial autocorrelation function (PACF) plots. The ideal one is selected using model selection criteria such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) after following the steps of the framework shown in Figure 4. The historical energy demand data is shown to be non-stationary with some seasonality; therefore, the ACF and PACF of the seasonally differenced should show significant spikes. The observed time series is decomposed into yearly seasonality as a result of monthly demand data. To find the appropriate SARIMA model based on the ACF and PACF of the model, the seasonality of the significant spikes at specific lags of both seasonal and non-seasonal terms are evaluated. Using the Box-Jenkins methodology, the residuals (forecast errors) are checked to be white noise.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 5 Simulations and Analysis
### Performance Analysis
#### 5.2.2 Energy Efficiency  
The energy consumption of the pre-processing circuit is related to the scale and number of Gaussian convolution unit. Here, we take MNIST (28 \(\times\) 28) images as the example, which means that the number of convolution operation is N\({}_{\text{con}}\ =\ 28\ \times\ 28\ =\ 784\). Assuming the convolution mask is \(5\times 5\) in size, every Gaussian unit contains N\({}_{\text{multi}}\ =\ 25\) multipliers, which consume P\({}_{0}\ =\ 25\ \times\ 1.8\ \text{V}\ \times\ 1\ \mu\text{A}\ =\ 45\ \mu\text{W}\) in total. One unit works at speed of V\({}_{0}\ =\ 3.3\ \text{MOPS/s}\) should take T\({}_{0}\ =\ \text{N}_{\text{pixel}}\ \div\ \text{V}_{0}\ =\ 237.58\ \mu\text{s}\) to finish the convolution. Thus, the total energy consumption is E\({}_{\text{total}}\ =\ \text{P}_{0}\ \times\ \text{T}_{0}\ =\ 10.69\ \text{nJ}\) per image and the energy efficiency is N\({}_{\text{con}}\ \times\ \text{N}_{\text{multi}}\ \div\ \text{E}_{\text{total}}\ =\ 1.83\ \text{TOPS/J}\).  
As concluded in Table 2, image classification circuit mainly contains multiplication, non-linear activation and current mirror units that respectively consume the energy of 9.23 uJ, 1.65 nJ and  
Figure 9: (a) Settling time of Gaussian convolution units; (b) Settling time of MLP.  
5.93 \(\upmu\)J. Total energy consumption is 15.16 \(\upmu\)J, with the operation number of 7.96 \(\times\) 10\({}^{4}\) that including multiplications, additions, etc. Thus, the energy efficiency of the flexible MLP circuit is 5.25G OPS/J.
## 5 Numerical Analysis according to Tunnel Size  
### Analysis Conditions  
As shown in Figure 2, the target ground comprised landfill, weathered soil, weathered rock, and hard rock layers, and the material properties of each stratum are listed in Table 2. For numerical analysis, each case was classified into a total of six types according to the NATM widening tunnel size and TBM pre-excavation tunnel size, as shown in Figure 15. In the TBM and NATM parallel construction method, only the outer shell hole was blasted, and the same load as that of the expanded hole described in Section 4.1.2 was applied. The blasting vibration measurement position was measured at the rear of the TBM device at the top of the pilot tunnel and at the TBM excavation surface. The measurement points are shown in Figure 16.
## 3 Results and Analysis
### Temperature Variation Curves during the Elapsed Time
Figure 7c shows that the surface temperature of specimens reached 24.9-46.5 \({}^{\circ}\)C even at an applied voltage of 20 V and an electrode spacing of 120 mm, a 4.9-26.5 \({}^{\circ}\)C increase compared to the initial temperature, while Figure 7d reveals that the surface temperature of specimens reached 34.3-73.7 \({}^{\circ}\)C at an applied voltage of 20 V and an electrode spacing of 40 mm, a significant 14.3-53.7 \({}^{\circ}\)C increase compared to the initial temperature. As shown in Figure 7e, the surface temperature of specimens reaches 30.1-65.9 \({}^{\circ}\)C at an applied voltage of 30 V and the electrode spacing of 120 mm, a significant 10.1-45.9 \({}^{\circ}\)C increase compared to the initial temperature. As shown in Figure 7f, the surface temperature of specimens reaches 77.0-145.1 \({}^{\circ}\)C at an applied voltage of 30 V and an electrode spacing of 40 mm, a 57.0-125.1 \({}^{\circ}\)C increased compared to the initial temperature. In particular, the most remarkable increase occurred with 145.1 \({}^{\circ}\)C in the ECM-CF125 specimen mixed with 1.25% CF. Such an increase in heating performance (by about 7.3 times compared to the initial temperature) seems to be related to the electrical conductivity, because it is proportional to temperature. It was determined that the heating performance of the ECM specimens mixed with CF improved as the applied voltages and fiber contents increased. The surface temperature of all specimens increased rapidly up to 20 min, then slowed down but gradually increased thereafter. In all specimens, it was confirmed that the temperature increase was similar for 40 min, and it was found that heating at 77 \({}^{\circ}\)C or higher was possible. This is in accordance with previous research results [38]. Figure 6: _Cont._  
Figure 6: Temperature variation of ECM specimens mixed with SF vs PM specimen: (**a**) 10 V and 120 mm, (**b**) 10 V and 40 mm, (**c**) 20 V and 120 mm, (**d**) 20 V and 40 mm, (**e**) 30 V and 120 mm and (**f**) 30 V and 40 mm. Figure 7: Temperature variation of ECM specimens mixed with CF vs PM specimen: (**a**) 10 V and 120 mm, (**b**) 10 V and 40 mm, (**c**) 20 V and 120 mm, (**d**) 20 V and 40 mm, (**e**) 30 V and 120 mm and (**f**) 30 V and 40 mm.
# Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System
## 2 Related Works
When creating MEP facilities with BIM, a study analyzed requirements and cases for facility management [18]. This study focuses on As-Built MEP modeling work. Another study focused on the effect of reverse engineering based on 3D scan image [19]. This study focuses on the analysis of the effects of Scan-to-BIM reverse engineering applied through real case studies. Another study investigated scan and BIM requirements using point cloud for automated construction progress monitoring [20]. This study identifies building construction quality factors and defines the related point cloud quality variables. One study proposed a method for Scan-vs-BIM deviation detection [21]. This study is about quality inspection methodology. Another study focused on the Scan-to-BIM methodology [22]. This study points out that Scan-to-BIM is a very difficult and time-consuming task. This study also investigates related automation methods such as tools and technologies. There has been a study to optimize the scan-to-BIM work path planning and scan location [23]. This study focuses on optimizing the scan plan. Another study proposed a 6D (XYZ + RGB)-based Scan-to-BIM methodology [24]. This study improves the accuracy of searching for BIM objects with similar features through 6D data on the interior walls of buildings. One study proposed a methodology to describe the major steps of the Scan-to-BIM process [25]. This study proposes a classification system, a level of detail (LoD), a scan parameter, a tool, and an analysis method as a framework. There was also a study on the use of scanning technology for HBIM construction [26]. This study focuses on case analysis. Another study focused on converting scan data into graph format [27]. This study focuses on the representation of interconnected geometry. An alternative study on the method of developing a deep learning training model using the point cloud obtained from BIM was also reviewed [28]. There was a study on the management of MEP facilities based on 3D reverse engineering [29]. This study focuses on facility management technology using scan and MEP BIM. Another study delved into the automatic inspection method between BIM and scan data [30]. This study focuses on the automatic inspection technology between the model and the point cloud. The previously investigated studies deal largely with Scan-to-BIM guidelines for specific applications, quality control methods, work performance methodology including tools, and generation algorithms for some shape types. This study proposes a rule-based Scan-to-BIM Mapping Pipeline method that can support application-oriented Scan-to-BIM process variability, automation, and recycling.