esa-sceva/satcom-chunk-collection huggingface dataset:
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### UWB Positioning Techniques and Algorithms
#### 3.1.1 Triangulation Technique  
The triangulation technique is based on calculating angles. The location of a moving tag can be identified if the location of two fixed anchors is known, by estimating the angles between the movable tag and the antennas of the fixed anchors and intersecting the extension of the moving tag with the known line between the two fixed anchors, which enables the accurate determination of the location of the moving tag (see Figure 3).  
Figure 3: Triangulation IPS positioning technique.  
In Figure 3, the coordinates of anchor1 and anchor2 are \((x_{1},\ y_{1})\) and \((x_{2},\ y_{2})\), respectively, and the coordinates of the movable tag are \((x_{t},\ y_{t})\). Two angles were observed between the anchors, the movable tag, and the west-east line:  
\(\theta_{1}=\) A1 T E (the angle between anchor1 (A1), tag (T), and E horizontal line);  
\(\theta_{2}=\) A2 T E (the angle between anchor2 (A2), tag (T), and E horizontal line).  
The slope of the line A1 T can be calculated using the following formula:  
\[tan\theta_{1}=\frac{y_{t}-y_{1}}{x_{t}-x_{1}} \tag{1}\]  
We can repeat the same procedure for the line A2 T:  
\[tan\theta_{2}=\frac{y_{t}-y_{2}}{x_{t}-x_{2}} \tag{2}\]  
By solving Equations (1) and (2), we get  
\[x_{t}=\frac{(y_{1}-y_{2})+x_{2}tan\theta_{2}-x_{1}tan\theta_{1}}{tan\theta_{2} -tan\theta_{1}} \tag{3}\]  
and  
\[y_{t}=\frac{[y_{1}tan\theta_{2}-y_{2}tan\theta_{1}]-[(x_{1}-x_{2})tan\theta_{ 2}tan\theta_{1}]}{tan\theta_{2}-tan\theta_{1}} \tag{4}\]
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 2 Machine Learning Tools and Signal Processing
### Machine Learning Tools
#### 2.1.2 Extreme Learning Machine (ELM)  
An ELM is a feed-forward neural network with a single hidden layer which was first proposed by [PERSON] et al. [19]. ELM compute the network parameters analytically unlike MLP, which determine the weights based on the gradient descent training procedure [20; 21]. The norm of the output weights being minimum confirms that both the training error and norm of weights are minimum and hence guarantee the best generation performance and uniqueness of the solution [22]. ELMs are able to generalize well and can be trained faster than networks that are trained based on gradient descent back propagation [22].  
Mathematically, the network output model for separate samples of \(L\) arbitrary hidden layers \(\left\{x_{j},\ t_{j}\right\}_{j=1}^{L}\) is described using Equation (1),  
\[a\ t_{j} =\sum\
olimits_{i=1}^{L}\beta_{i}G\big{(}a_{i}\,\ b_{i}\,\ x_{j}\big{)}\,\ j=1,2,\ 3\ \ldots,\ M \tag{1}\]  
where \(X=\{(x_{i},\ t_{j})|xi\in R^{n},\ t_{i}\in R^{m},\ i=1,\ \ldots\,N\}\) is a training set for a standard single hidden layer feed-forward neural network (SLFN), \(L\) is the number of hidden nodes, \(G(x)\) is activation function,is the input weight vector relating to the \(i\)th hidden node, with the input nodes or the center of the \(i\)th hidden node, \(\beta_{i}\) is the weight vector connecting the \(i\)th hidden node and the output node, and \(b_{i}\) is the threshold or impact factor of the \(i\)th hidden node.  
The equivalent equation of the above is \(F\beta=T\), where F, \(\beta\), and \(T\) are as defined in Equations (2)-(4):  
\[F(a_{1},\ldots a_{L}\,\ b_{1}\,\ldots,\ b_{L}\,\ x_{1},\ldots\,\ x_{M})= \left|\begin{array}{ccc}G(a_{1}\,b_{1}\,x_{1}\ )&\ldots&G(a_{L}\,b_{L}\,x_{1}\ )\\ \vdots&\ldots&\vdots\\ G(a_{1}\,b_{1}\,x_{M})&\ldots&G(a_{L}\,b_{L}\,x_{M})\\ \end{array}\right|_{M\&L} \tag{2}\]  
\[\beta=\left[\begin{array}{c}\beta_{1}^{T}\\ \vdots\\ \beta_{1}^{T}\\ \end{array}\right]_{L\&m} \tag{3}\]  
\[T=\left[\begin{array}{c}t_{1}^{T}\\ \vdots\\ t_{M}^{T}\\ \end{array}\right]_{M\&m} \tag{4}\]  
In a case where number of neurons in the hidden layer and distinct training samples are equal, i.e., \(M=L\), the hidden layer output matrix of the network \(F\) will become a square invertible matrix and consequently, SLFNs will approximate the training samples accurately. As a result, ELMs aim to minimize both training error and the norm of the output weights according to the objective function given in Equation (5):  
\[\textit{Minimize}\ \left\|F\beta-T\right\|^{2}\ \textit{and}\ \left\|\beta\right\| \tag{5}\]  
Lastly, the original form of ELMs uses the minimal norm least square and output weights (\(\overline{\beta_{i}}\)) given by Equation (6):  
\[\overline{\beta_{i}}\textit{=}\ F^{\dagger}T \tag{6}\]  
where \(F^{\dagger}\) is the pseudo inverse of hidden layer output matrix \(F\).
## 7 Simulation Results
### Comparison with the Classical PID Controller
We achieved the graphical outcomes depicted in Figure 8 by using the conventional PID controller (104) and taking the feedback gain \(\mathcal{K}\) specified in (118) into consideration. When the lumped disturbance \(\Delta_{t}\) is administered over a duration of 2-7 s, the knee exoskeleton robot is able to be controlled and stabilized at the intended position. Nonetheless, when juxtaposed with the simulation outcomes depicted in Figure 6, the knee exoskeleton's reaction under the classical PID controller reveals unsatisfactory (degraded) results. Indeed, in Figure 8a, and compared to Figure 6a, the angular position error \(\phi\) experiences an important deviation/decrease below the zero axis. The same remark holds for Figure 8b compared to Figure 6b, where the angular velocity error \(\phi\) is decreased by around 50 [deg/s]. Moreover, these differences in the results of \(\phi\) and \(\dot{\phi}\), and even in the presence of the integral action, provoked the increase in the control effort applied to the exoskeleton robot, as seen in Figure 8c, compared to Figure 6b. As depicted in Table 6, at the beginning of the control process, the classical PID controller exhibits a control effort around \(-5\) [Nm], whereas the proposed affine state-feedback controller starts around \(-2\) [Nm]. When the knee exoskeleton moves and the lumped disturbance is absent, the PID controller's control effort reaches a maximum value of approximately 1 [Nm], which is relatively higher than the 0.25 [Nm] value achieved by the affine state-feedback controller. Furthermore, by evaluating various performance indicators (see Appendix A), the results shown in Table 7 were obtained when applying the classical PID controller with the fourth LMI design approach. These results include the following: \(\text{MAE}(\phi)=0.0417\), \(\text{MAE}(\phi)=0.1520\), \(\text{MSE}(\phi)=0.0134\), \(\text{MSE}(\phi)=0.1343\), \(\text{RMSE}(\phi)=0.1156\), \(\text{RMSE}(\phi)=0.3665\), \(\text{CEI}(u)=0.5182\), and \(\text{ MATI}(u)=4.8115\). Compared to the values obtained in Table 7 for the fourth design approach of the affine state-feedback controller, all of the indicators derived from the PID controller are relatively higher. Therefore, the affine state-feedback controller outperforms the classical PID controller in all aspects, as demonstrated by the simulation results presented in this section. These results validate the superior performance of the proposed controller over classical PD and PID methods, as also supported by previous experimental studies [17; 63; 64]. This lays the foundation for future practical implementation, where the proposed controller can be validated under real-world conditions.
## 3 System Model
### Transmission Capacity  
As we can see, if the cognitive relay is decided to assist to retransmit the primary signal, some price is needed to pay, such as the cost of booting energy consumption, hardware price, time-window value, etc. Hence, we assume that the secondary network needs to achieve the SU's QoS requirements, i.e., the minimum requirement transmits rate \(R_{\min}\), which is obtained by the third sub-time slot \((t_{3})\). The total duration of the time slot is \(t\). The total frame and time slot allocations are shown in Figure 2.  
For AF relay protocols, the duration of first and second sub-time slot are the same, i.e., \(t_{1}=t_{2}\). Therefore, the capacity of SU is given by  
\[R_{AF_{k}}\left(t_{2}\right)=t_{2}\text{log}_{2}\left(1+\frac{\gamma_{1}\gamma _{2}}{\gamma_{1}+\gamma_{2}+1}\right). \tag{5}\]  
The transmitted capacity of third sub-time slot is given by  
\[R_{R_{k}C_{k}}\left(t_{3}\right)=t_{3}\text{log}_{2}\left(1+\gamma_{3}\right), \tag{6}\]  
where \(\gamma_{3}=P_{k}G_{R_{k}C_{k}}/\sigma_{2}^{2}\).  
For DF relay protocols in the first two temporal sub-time slots, i.e., \(t_{1},t_{2}\), the transmitted capacity is given by  
\[R_{DF_{k}}\left(t_{1},t_{2}\right)=\min\left[t_{1}\text{log}_{2}\left(1+\gamma _{1}\right),t_{2}\text{log}_{2}\left(1+\gamma_{2}\right)\right], \tag{7}\]  
where \(\gamma_{1}=P_{S}G_{SR_{k}}/\sigma_{0}^{2}\), \(\gamma_{2}=P_{k}G_{R_{k}D}/\sigma_{1}^{2}\).  
Announcing Selection Results  
Figure 2: Time frame structure for auction-based selection and time slot design for DF and AF relay protocols.  
According to the above analysis, the proper secondary relay selection is key to obtaining both the enhanced primary capacity and sum capacity. There are two conventional schemes about relay selection. The first one is that the satellite can select a relay based on the knowledge of the instantaneous SNRs of the satellite-relay and relay-destination links, which can select the best relay theoretically. For practical application of such a complex scheme, CSI of all relay-destination and satellite-relay links is required at the satellite, which is provided by using feedback from the destination to the satellite. Due to the high latency in satellite systems, it is very difficult to provide CSI of all links at the satellite. Besides, the computational complexity is also quite higher for on-board processing satellite. Another simple relay selection scheme is that the satellite only requires the CSI feedback of the satellite-relay links from the relays, where the total capacity maximization can not be guaranteed. Those two schemes are centralized ways. Actually, the relays are aware of the CSIs of both relay-destination and satellite-relay links and the relays are isolated. In the overlay paradigm, the relays are competed with each other. If a proper scheme is implemented, the relays could tell the true ability in a distributed manner and the whole systems obtain the maximum capacity.
# Centrosymmetric Optical Vortex
## 3 Experimental Setup  
The schematic of the experimental setup is sketched in Figure 2. A reflective phase-only liquid-crystal spatial light modulator (SLM, HOLOEYE, Berlin, Germany PLUTO-VIS-016, pixel size: 8 \(\upmu\)m \(\times\) 8 \(\upmu\)m) is illuminated with an approximate flat-top beam (wavelength 532 nm), modified by a pinhole filter and an aperture. Importantly, the polarizer P1 used before SLM is due to the SLM responding only for a horizontally polarized beam. Furthermore, the polarizer P2 used after SLM is to eliminate the unmodulated light. Then, the modulated beam realizes Fourier Transform by a lens (\(f\) = 200 mm) and is recorded by a CCD camera (Basler Ahrensburg Germany acA1600-60 gc, pixel size of 4.5 \(\upmu\)m \(\times\) 4.5 \(\upmu\)m).  
Figure 1: Schematic of the centrosymmetric optical vortex (CSOV) generation. (**a1,a2**) are spiral phase patterns, (**b1,b2**) are intensity patterns, and (**c1,c2**) are decompositions of the orbital angular momentum (OAM) states, where \(l^{\prime}\)\(\in\)(-\(\infty\), \(\infty\)) is an integer reflected the single states.  
Figure 2: Schematic of the experimental setup. (**a**) phase mask written into the spatial light modulator (SLM) and (**b**) the intensity record by the CCD camera.  
In our experiment, we used the numerical axicon method to generate the CSOV beam [29]. The phase mask of the CSOV is shown in Figure 2a, which is produced using the following equation:  
\[t(\rho,\theta)=\exp\left[i\sum_{n^{\prime}=1}^{4}rect\left(\frac{2\theta}{\pi}- \frac{5}{2}+n^{\prime}\right)I_{n^{\prime}}\theta\right]\exp[-ik(n-1)\alpha \rho]\exp\left(\frac{i2\pi x}{D}\right), \tag{2}\]  
where \(D\) is the period of the blazed grating used in the mask.
# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 1 Introduction
However, challenges such as data availability, the \"black box\" nature of deep learning models, and the need for specialized expertise remain. Addressing these challenges is crucial for the wider adoption of deep learning in the construction industry. Consequently, applying deep learning to the Scan-vs-BIM framework presents a promising opportunity to overcome the limitations inherent in conventional approaches. The aim of this study is to develop a novel deep learning-based Scan-vs-BIM framework that directly compares As-Built scan data with As-Planned BIM models without the need for mesh conversion, thereby improving computational efficiency and accuracy in structural integrity evaluation. Specifically, we propose the distance deep neural network (Distance-DNN) model, which evaluates structural integrity by analyzing the distribution tendencies of distance values between the As-Built point cloud and the As-Planned BIM model. The remainder of this paper is organized as follows: Section 2 presents a literature review related to Scan-vs-BIM and deep learning. Section 3 describes the methodology and framework of the Distance-DNN-based Scan-vs-BIM process. A review of the model training process and the training results of Distance-DNN are presented in Section 4. Section 5 discusses the evaluation of Distance-DNN after applying it to an actual steel structure case. Finally, Section 6 summarizes the conclusions of the study.
## 2 Design Parameters of the Metamaterial and Simulation Setup
### Parametric Studies  
#### 2.2.1 Design Hierarchy  
The chronological development of the proposed metamaterial unit cell is shown in the Figure 2. The design architecture and its morphology are set up to achieve the highest performance possible. An iterative method is applied to record feedback of the unit cell in order to determine the transmission coefficient (S\({}_{21}\)). Design 2(a) comprises a split-ring resonator (SRR) along with an octagonal ring on the substrate layer. It yields resonance at 2.44 GHz, 8.67 GHz and 10.84 GHz. Another comparatively smaller octagonal ring of the same width is added to the first design, which is shown in the Figure 2b. In CST simulation, it exhibits quad-band resonance peaks at 2.51 GHz, 8.55 GHz, 9.50 GHz and 11.02 GHz. Again, to test the enhancement of the bandwidths, a small octagon with a width of 0.75 mm is placed at the center of the previous structure, which is shown in Figure 2c, with resonance at 2.50 GHz, 8.57 GHz, 9.49 GHz and 11.03 GHz. Moreover, as shown in Figure 2d, all the octagons are attached to one another with four metal bars with a length of 3 mm and a width 0.40 mm, exhibiting resonance at 2.51 GHz, 8.58 GHz and 11.05 GHz. Finally, four split gaps with a width of 0.40 mm are made in the three octagons, as shown in the Figure 2e.  
This reformed design yields quad-band resonance at 2.35 GHz, 7.72 GHz, 9.23 GHz and 10.68 GHz with magnitudes of \(-\)43.23, \(-\)31.05, \(-\)44.58 and \(-\)31.71 dB, covering the S, C and X bands with excellent bandwidths. This quad-band metamaterial also exhibits negative permittivity (\(\varepsilon\)), a negative refractive index and near-zero permeability (\(\mu\)). It is noteworthy that RT 6002 dielectric is used as a substrate for all the designs shown in Figure 2a-e. Therefore, the structure shown in Figure 2e is selected as a final unit cell for the proposed MM. The simulated results of the S\({}_{21}\) from Figure 2a-e are shown in the Table 2. Figure 3 shows the numerical results of S\({}_{21}\) for all design steps.
## 5 Discussion
### Validation of the Results  
The validation process was done by comparing the results obtained in Section 4 with the available standards. Table 11 summarises the available standards for each type of analysis performed.
# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Subsea Tunnel
#### 3.3.1 Structural Stability
The results show that the short-term structural response characteristics, the spatial location of different sensors, the internal temperature of the tunnel, and the external water pressure are the main factors affecting the development of the structure. At present, structural health monitoring (SHM) technology is the main means for structural stability assessment [84], but this technology started later than big data tech nology, and its application in various engineering fields is also more limited, and the monitoring data obtained by this technology have the characteristics of huge quantity, a wide distribution of sources, and a complex correlation between data, which make it difficult for time series models to describe the effective information in the data, and the prediction effect struggles to meet expectations. Therefore, researchers apply deep learning algorithms to SHM technology to predict the structural stability of undersea tunnels with multi-factor coupling.
## 2 Method
### Theoretical Basis of EFSDAF  
Like FSDAF, the input images of EFSDAF include, a MODIS image at \(T_{B}\), a Landsat image at \(T_{B}\) and a MODIS image at \(T_{P}\). The output of EFSDAF is a predicted fine resolution image (e.g., Landsat-like) at \(T_{P}\). Unlike FSDAF, EFSDAF considers more than one land cover type in both the Landsat images (30 m) and MODIS images (500 m). Therefore, the endmembers determination and abundances extraction of the Landsat image at \(T_{B}\) are firstly carried out in EFSDAF. EFSDAF includesfive main steps: (1) endmember determination and spectral unmixing for Landsat image at \(T_{B}\); (2) temporal prediction (\(F^{TP}\)) for no land cover change from \(T_{B}\) to \(T_{P}\); (3) spatial prediction (\(F^{SP}\)) for land cover change at \(T_{P}\); (4) residual distribution by using a new residual index (\(RI\)); and (5) final prediction of a Landsat-like image using neighborhood in a sliding window. The workflow of EFSDAF is outlined by the flowchart in Figure 1. Detailed descriptions of each step in EFSDAF are given below. The code of EFSDAF can be found at the URL [[https://github.com/max19951001/EFSDAF.git](https://github.com/max19951001/EFSDAF.git)]([https://github.com/max19951001/EFSDAF.git](https://github.com/max19951001/EFSDAF.git)).
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 1 Introduction  
Since its introduction in 1968, light-emitting diodes (LEDs) have become the most common optoelectronic components used in the industry. LED lamps have become more and more popular in daily lighting and have gradually replaced incandescent lamps. Compared with incandescent lamps, LED lamps are energy-saving and power-saving. However, it is necessary for LED lamps to improve the luminous efficiency and solve the disadvantages of heat dissipation. Only 15 to 20% of the input power for a LED lamp is converted into light energy, and the remaining power will be transformed into heat. Due to the high temperature generated by heat dissipation, the use of LED lamps in harsh environments may endanger the safety of human lives and property.  
With the introduction of graphene, which was isolated and characterized in 2004 by [PERSON] and [PERSON] at the University of Manchester, the application of graphene ink for heat dissipation has often been discussed for electronic devices or components such as LED lamps,virtual reality devices and printed circuit board assembly (PCBA). For electronic products, the coatings should be effective or appropriate for heat dissipation, electrical resistance and surface adhesion, etc. In this study, NDG ink and its application for the coating of LED lamps were studied. The NDG ink in this research was tuned with a high electrical resistance and suitable for coating the LED lamps. The cooling efficiency is discussed for LED lamps with different cooling mechanisms such as fins and NDG coating. In addition, in this study, the NDG powder was tested to verify its properties.
## 4 Discussion  
### Gap-Filling Temporal Approach and Measurements  
In this article, we employed multiple models to fit the photosynthetic light response curve and used it as a gap-filling method for GPP fluxes for full-day periodic chamber campaigns on a campaign-wise basis. Compared to year-wise approaches, the campaign-wise gap-filling strategy has been identified as the most robust for peatland areas [4]. Additionally, the campaign-wise approach has demonstrated a better performance with full-day measurements compared to midday values [27]. However, the reliability of the campaign-wise approach depends on the number of measurements and the range of temperatures and PAR measured during the CO\({}_{2}\) fluxes measurements [7; 27]. It is not always possible to capture enough PAR and temperature conditions when the number of replicates and sites is considerable while the hours of sun are limited. Due to the previously mentioned constraints, the campaign-wise gap-filling approach was modified in this article. The modification involves applying a conditional order whenever the number of measurements is fewer than eight (four NEE and four R\({}_{\text{eco}}\)). In such cases, a two-campaign moving window was implemented. Merging two campaigns, especially during winter, has partially solved the problem of lack of variability in PAR and temperature, allowing the retrieval of reliable results during the period with less than 10% error. However, the eight-measurement threshold is not always sufficient for a reliable fit (e.g., March and April campaigns), and the number of measurements selected as the limit should probably be increased to catch the saturation points of the light response curve.