esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 3 GPP Standardization for URLLC
### QoS Monitoring to Support URLLC  
As mentioned in Table 2, URLLC services require high reliability and very low latency. Such requirements pose quite a challenge for 5G systems, because such requirements could affect E2E QoS performance. In the current 5G system, QoS notification control (NC) is maintained by a 5G access network (AN) to monitor GBR. Though the 5G-AN mechanism supports the guaranteed flow bit rate (GFBR) [49], which might not be sufficient for URLLC E2E services, features such as packet loss, jitter, and packet latency will also play a major part for URLLC services. To counter such QoS relates issues, 3 GPP has branched out further aspects of studies, which include specification of the UE requirements for URLLC and improvement of QoS monitoring ability of the defined mechanisms.  
In Release 16 [49], a number of solutions to handle the mentioned issues were purposed and evaluated. The idea that server supporting user equipment should be kept close to the user (i.e., topologically, geographically) was proposed. With this idea, the transmission latency between the server and the base station could be minimized. In order to achieve high consistency, backhaul reliability was suggested for further improvement.
Using a Preoperative Scan Digital Impression and a Digital Index to Build Immediate Interim Full-Arch Implant-Supported Prosthesis. A Case Report and Proof of Concept  
[PERSON]  
1 Department of Surgical Sciences, University of Torino, Via Nizza 230, 10126 Torino, Italy;
itation could present several issues when a fully digital workflow is to be used. In order to transfer information, such as preoperative OVD, between the preoperative situation to the postoperative one through a digital process, stable points of references are required. Due to the surgical treatment both the hard and soft tissue are going to remodel, resulting in dynamical changes through all the healing phases. Due to this process, a stable point of references for the digital process from the presurgical to the postsurgical situations are difficult to find. In fact, to provide a reliable maxilla-mandibular three-dimensional relationship the main problem to overcome is to transfer information between different stages of implant supported rehabilitation due to the lack of stable points of reference in the lower jaw. In fact, in the upper jaw the palatal rugae provide us with a stable anatomical structure over the different surgical and prosthodonic stages. In fact, they are a useful human identification marker for forensic purposes [8]. [PERSON] et al. demonstrated that not even palatal expansion in children could affect their stability [8].  
On the lower jaw no such anatomical structure exists, thus complicating the transfer of information that could be gathered preoperatively, i.e., tooth position, vertical dimension of occlusion and maxilla-mandibular relationship.  
In recent years, some authors have addressed these problems [9; 10], but several issues are still far from being solved. In 2019, [PERSON] et al. [11] proposed a digital technique to fabricate an interim fixed complete-arch restoration based on the exact occlusion relationship, tooth position and tooth contour of the original dentition. Despite its high quality, this procedure may be considered invasive and at a high biological and economic cost to the patients, due to the insertion of the mini-screw and the virtual surgical planning on cone-beam computed tomography (CBCT) images. In 2020, [PERSON] et al. [12] proposed a similar method using pins instead of a mini-screw, but without solving the previously cited invasive procedure [12].  
The purpose of the present clinical report is to describe a novel digital workflow based on the use of a digital index that allows a precise superimposition of the mandibular presurgical scan with the postoperative impression aiming at fabricating an interim fixed complete-arch restoration. The protocol entails recording the vertical dimension of the occlusion, occlusal plane and intermaximillary relationship in implant-supported complete rehabilitations before the surgical insertion of the fixtures. Successively, information is transferred to the impressions with the scan abutments to be able to produce accurate immediate provisional prosthetic rehabilitation.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 3 Architecture and Key Challenges
### Challenges
It is also essential to have redundant nodes in the network to improve the reliability of the sensor network. However, the total deployment cost also needs to be minimized. Most of the literature on sensor network deployment deals with two dimensional (2D) environments, whereas the deployment environment in the case of UAV-enabled IoT platform is 3D, which is even more complex.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 3 SOH Prediction Based on Transformer-LSTM Fusion Model
### Transformer-LSTM Fusion Model
#### 3.1.2 LSTM Module  
To better capture the trend, dynamic changes, and complex nonlinear relationships of battery performance degradation, we used the output of the transformer module as the input of the LSTM module, aiming to process battery degradation sequence data and effectively capture long-term dependencies. The LSTM model consists of a cell state, forget gate, input gate, and output gate, as shown in Figure 9. During the training process, the cell state is used as long-term memory to save the key information in the input sequence; the forget gate determines which data should be deleted from the cell state to prevent information overload; the input gate controls which new information needs to be added to the cell state and updates the memory content; the output gate determines which data to output to the next time step based on the current cell state and input information. Through this mechanism, LSTM optimizes the weights of each gate via backpropagation during training, thereby improving model's prediction accuracy.  
Figure 8: The structure of the transformer model.  
Figure 9: The structure of the LSTM model.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 2 Materials and Methods  
The performance of AutoDock 4 and Vina was measured using the Directory of Useful Decoys-Enhanced (DUD-E). DUD-E contains a large collection of decoys and ligands that can be used for benchmarking ligand/decoys discrimination in virtual screening tests. The DUD-E dataset has been widely used to validate data from other open source such as Dock [47; 48] and commercial programs such as Gold, Glide, Surflex, and FlexX [48]. It is also frequently used to validate the development of new consensus scoring functions [38; 49; 50; 51; 52; 53; 54]. An overview of the 102 protein-targets in DUD-E can be found in Figure 1, and Table 1 specifies the types of protein targets and the number of ligands and decoys in the dataset. DUD-E contains a wide variety of protein target types, including 26 kinases, 15 proteases, 11 nuclear receptors, 5 G protein-coupled receptor (GPCR), 2 ion channels, 2 cytochrome P450s, 36 other enzymes, and 5 miscellaneous proteins. About 18 of these proteins contain metal atoms, while the other 84 do not. Proteases, kinases, and metalloenzymes are the largest groups present in the DUD-E dataset and are the ones that were emphasized on in the discussion. We also included GPCRs, since this large class of proteins was structurally very similar and was the focus of many other studies. The results presented in this study here might guide the selection of the most adequate docking software for these specific families. Figure 1: Overview of the protein target families of the Directory of Useful Decoys–Enhanced (DUD–E) dataset.
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
## 2 Materials and Methods  
A choke ring antenna cover was designed to protect the dedicated (delivered by the manufacturer) GNSS antenna from the negative effects of the signal multipath phenomenon. The research was conducted with the use of the SwiftNav DURO receiver (Figure 1) [36].  
The DURO GNSS receiver was launched in 2017. It tracks the following signal frequencies: GPS L1/L2C, GLONASS G1/G2, BEIDOU B1/B2, GALILEO E1/E5b, and signals: GPS L1/L2, GLONASS G1/G2, BeiDou B1/B2, Galileo E1/E5b, SBAS (WAAS, EGNOS, GAGAN, MSAS). In addition, it has a built-in GSM modem and supports RTK formats: RTCM 3.x. [36].
# Robust and Efficient Corner Detector Using Non-Corners Exclusion
## 4 Experimental Results and Analysis
### Computational Time Performance  
All the timing experiments are operated on Matlab R2014a with a 3.20 GHz Pentium(R) dual-core central processing unit (CPU) and 2 GB random access memory. To get more reasonable results, the five corner detectors are executed 100 times and mean execution times are measured. The time is obtained through the timing function in Matlab (tic/toc). Table 3 and Figure 13 show the execution time of the RECD compared with other corner detectors. The speedup is relative to the performance of the RECD which represents the ratio of each referenced detector to the RECD. The computing method of the speedup is shown as (5):  
\[\textit{speedup}=\frac{t_{1}}{t_{2}} \tag{5}\]  
where \(t_{1}\) represents the execution time of the RECD and \(t_{2}\) represents the execution time of one of the five corner detectors. The smaller the value of speedup, the more obvious the speed advantage of RECD over this compared method.  
Figure 13 is Contrast of execution time of the five corner detectors. The RECD is the fastest among the five corner detectors while the conventional Harris corner detector is the slowest. The speed of RECD is more than 10 times faster than the traditional Harris angle detector.  
As can be seen in Figure 13, compared with other detectors, the speed advantage is also obvious: the execution time of RECD is only 20.6% and 10.6% of Moravec and SUSAN respectively. Even with the closest FAST-9, our method has more than doubled the speed. This is enough to show that the speed improvement of RECD is significant. Note that the FAST-9 detector is different from the original FAST detector. It involves a machine-learning step to refine the result after the FAST step with 9 contiguous pixels. That is why the FAST-9 is slower than RECD.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Detector**} & \multicolumn{5}{c}{**Time (s)**} & \multirow{2}{*}{**Speed up (\%)**} \\ \cline{2-2} \cline{4-7}  & **Block** & **Flower** & **House** & **Airplane** & **Lab** \\ \hline RECD & 0.100 & 1.014 & 0.102 & 0.138 & 0.814 & 100.0 \\ Moravec & 0.561 & 6.166 & 0.559 & 0.977 & 2.251 & 20.6 \\ SUSAN & 1.010 & 12.002 & 1.111 & 1.856 & 4.391 & 10.6 \\ Harris & 1.239 & 16.155 & 1.307 & 2.280 & 5.266 & 8.2 \\ FAST-9 & 0.293 & 2.427 & 0.340 & 0.449 & 1.326 & 44.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Execution time of the five corner detectors.  
Figure 13: Contrast of execution time of the five corner detectors.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 2 Related Work
### Traffic Problems  
Traffic forecasts can predict flow, speed, and demand, categorized into road-level, region-level, and station-level. Road-level forecasts use sensor or GPS data on road networks to predict traffic flow [10]. Station-based issues concentrate on the layout of metro or bus stations, analyzing connectivity and passenger flow [11]. Region-level problems deal with larger areas, using nodes in a graph to represent regular or irregular regions and extracting spatial dependencies based on land use purposes. Each category requires specialized approaches to effectively model and understand the traffic patterns specific to its context.
## 3 Verification of Noise Reduction Method by Combined Use of Improved Wavelet and EMD
### Denoising Analysis of Defect Signal
#### 3.4.2 Noise Reduction of Electromagnetic Signal with Wire Rope Break  
The conveyor belt steel wire rope break electromagnetic signal containing the noise is shown in Figure 9a, the noise includes 10 dB gaussian white noise and non-stationary noise with frequency and amplitude modulations, the noise function is shown in Equation (11). Figure 9b is the normalized spectrum diagram of wire rope break electromagnetic signal with noise, it can be seen that the noise function has the same frequency band as the signal before denoising. The denoised result by the improved threshold wavelet is shown in Figure 9c, while the denoised result by combined use of improved wavelet and EMD is shown in Figure 9d.  
The eigenvalues of 11 IMF components are \(\lambda_{i}\), the ratio of \(\mathrm{K}_{i}\) and \(\Delta_{i}\) is shown in Table 3.  
According to Table 3, \(\Delta\)6 is smaller than 1.0. Thus, the selected IMF components are IMF1 to IMF7. The reconstruction result of seven IMF components is shown in Figure 9d, two denoising methods are compared, as shown in Table 4. According to Table 4 and Figure 9c,d, the proposed method obtained by combined use of the improved wavelet and EMD has the better noise reduction effect for the non-stationary strong-noise wire rope break electromagnetic signal and the SNR is bigger.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **SNR (dB)** & **RSME** \\ \hline Before denoising & \(-2.72\) & 0.36 \\ Improved threshold wavelet & 5.027 & 0.17 \\ Combined use of improved threshold wavelet and EMD & 11.63 & 0.069 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of two denoising methods for joint signal.  
Figure 9: Comparison of two noise reduction methods for wire rope break electromagnetic signal: (**a**) Electromagnetic signal with noise of wire rope break; (**b**) Normalized spectrum diagram of wire rope break; (**c**) Denoised result by the improved threshold wavelet; (**d**) Denoised result by combined use of improved wavelet and EMD.
## 6 Discussion  
This section describes the discussion of handover decision, whichis divided into two parts: (A) The mobility forecast part, in which usually the articles consulted use techniques for estimating the position of a SL, and (B) The part of classification techniques to determine which characteristics belong to a class.  
With regard to mobility prognosis, the techniques used in this thesis are Kalman filter (KF), particle filter (PF), and artificial neural networks (ANN).  
The specialized literature usually uses different filters to estimate the position of an NM as in (Yan, 2019), which proposes a CSI scheme (Canal State Information) contrasting its results with a Kalman filter of (S  
Figure 12 diagonally shows the distribution of the eight variables of our data set. In other cells of the plot matrix, we have correlation plots of each variable combination of our data frame. In the first row and second row we can see latitude on ordinate and longitude on abscissa, and the right graph shows the correlation between latitude on ordinate and altitude on abscissa and then each combination to other variables.  
Support-vector machines work very well with a lot of data because they can have more support-vectors; however, in two case studies the classifier with similar behavior in percentage accuracy is the naive bayes classifier with an approximate accuracy of 80%. For this reason we recommend the naive bayes classifier as the best option because the other classifiers vary depending on the amount of data, data distribution, or imbalanced data.  
Figure 13 shows the confusion matrix results.  
Figure 12: Estimated feature comparison chart.  
The confusion matrices (B), (D), and(E) on Figure 13 have similar behaviors, which indicates that the NB, KNN, and DT models fit better than LR and SVM to the data set.  
The time to train and test models is presented in Table 4. NB, KNN, and DT have similar behavior, while SVM and LR take longer time to fit the model to the data set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Training Time (ms)** & **Testing Time (ms)** \\ \hline LR & 33.9 & 1.0 \\ NB & 1.0 & 1.0 \\ SVM & 6.0 & 3.0 \\ KNN & 1.0 & 9.0 \\ DT & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of training and testing time.  
Figure 13: Confusion matrix of the model’s comparison chart. (**A**) Confusion matrix of logistic regression. (**B**) Confusion matrix of naive bayes. (**C**) Confusion matrix of SVM. (**D**) Confusion matrix of KNN. (**E**) Confusion matrix of DT.  
Other proposals in the literature have been developed with similar results as shown in Table 5, in similar scenarios, but this approach is part of an application for mobile devices that will be added in the future with geo-fences to delimit the behavior of the mobile node.
## 2 Modeling of Photovoltaic Systems
### Explicit Equations/Models as Alternative to the 1-D/2-R Equivalent Circuit Model  
The difficulties associated with solving the 1-D/2-R model implicit equation drove researchers to develop explicit equations for modeling the performance of solar cells/ panels [58; 59; 76; 77; 78; 79; 80; 81; 82]. These photovoltaic models give the output current as a function of the output voltage by using simple mathematical models or equations that can be easily solved without the need for advanced mathematical tools. Of these, three models appear to be fairly accurate in relation to the \(I\)-\(V\) curve. The model proposed by [PERSON] and [PERSON] [58; 59] is:  
\[\frac{I}{I_{sc}}=1-(1-\gamma)\frac{V}{V_{oc}}-\gamma\bigg{(}\frac{V}{V_{oc}} \bigg{)}^{m}, \tag{14}\]  
where [56]:  
\[\gamma=\frac{2\Big{(}\frac{I_{mp}}{I_{sc}}\Big{)}-1}{(m-1)\Big{(}\frac{V_{mp }}{V_{oc}}\Big{)}^{m}}, \tag{15}\]  
\[m=\frac{W_{-1}\bigg{(}-\Big{(}\frac{V_{oc}}{V_{mp}}\Big{)}^{\frac{1}{k}} \Big{(}\frac{1}{k}\Big{)}\ln\Big{(}\frac{V_{mp}}{V_{oc}}\Big{)}\bigg{)}}{\ln \Big{(}\frac{V_{mp}}{V_{oc}}\Big{)}}+\frac{1}{K}+1, \tag{16}\]  
and:  
\[K=\frac{1-\Big{(}\frac{I_{mp}}{I_{sc}}\Big{)}-\Big{(}\frac{V_{mp}}{V_{oc}} \Big{)}}{2\Big{(}\frac{I_{mp}}{I_{sc}}\Big{)}-1}, \tag{17}\]  
the model proposed by [PERSON] [60] is:  
\[\frac{I}{I_{sc}}=\frac{1-\Big{(}\frac{V}{V_{oc}}\Big{)}^{k}}{1+h\Big{(}\frac{ V}{V_{oc}}\Big{)}}, \tag{18}\]  
where [56]:  
\[k=\frac{W_{-1}\Big{(}\Big{(}\frac{I_{mp}}{I_{sc}}\Big{)}\ln\Big{(}\frac{V_{mp }}{V_{oc}}\Big{)}\Big{)}}{\ln\Big{(}\frac{V_{mp}}{V_{oc}}\Big{)}}, \tag{19}\]  
\[h=\bigg{(}\frac{V_{oc}}{V_{mp}}\bigg{)}\bigg{(}\frac{I_{sc}}{I_{mp}}-\frac{1}{ k}-1\bigg{)}, \tag{20}\]while the model of [PERSON] and [PERSON] [56; 80] is:  
\[I=\left\{\begin{array}{c}I_{sc}\left(1-\left(1-\frac{I_{up}}{I_{sc}}\right) \left(\frac{V}{V_{up}}\right)^{\frac{Imp}{I_{sc}-Imp}}\right)\ ;\ V\leq V_{mp}\\ I_{mp}\left(\frac{V_{up}}{V}\right)\left(1-\left(\frac{V-V_{mp}}{V_{oc}-V_{mp} }\right)^{\phi}\right)\ ;\ V\geq V_{mp}\end{array}\right., \tag{21}\]  
where:  
\[\phi=\left(\frac{I_{sc}}{I_{mp}}\right)\left(\frac{I_{sc}}{I_{sc}-I_{mp}} \right)\left(\frac{V_{oc}-V_{mp}}{V_{oc}}\right). \tag{22}\]  
In the graph in Figure 2, these models are fitted to the experimental \(I\)-\(V\) curve for an RTC solar cell, taken from the well-known work by [PERSON] et al. [83]. The 1-D/2-R equivalent circuit model is also fitted to these data, and it can be seen that the accuracy of the explicit models is quite high, as stated in [56; 81]. The problem with using some of these models again lies in the need to solve the Lambert W-function, which requires some mathematical ability.
## 2 Overview of [PERSON]'s Theories for Non-Uniform Sediment Transport  
### 2.1 Incipient Probability of Non-Uniform Sediment  
The probability of incipient motion is one of the key parameters in the mechanics of sediment transport, and most of the previous research [33] has focused on non-cohesive uniform sediment. The probability of fractional incipient motion for non-cohesive non-uniform sediment was deduced by [PERSON] [27] after analyzing the random properties of the acting forces including drag force \(FDk\), lift force \(FLk\), and gravity \(Gk\), as well as their moment arms \(LDk\), \(LLk\), and \(LGk\). Further introducing the cohesive force \(FC\) and its moment arm \(LCk\), the incipient probability for cohesive non-uniform sediment was derived as follows [30]:\[a_{k}=P_{r}(L_{\text{D}k}F_{\text{D}k}+L_{Lk}F_{Lk}\geq L_{\text{G}k}G_{k}+L_{ \text{C}k}F_{\text{C}k})=1-\frac{1}{\sqrt{2\pi}}\int_{-2\gamma(\sqrt{0.0822/\tau _{k}+1})}^{2\gamma(\sqrt{0.0822/\tau_{k}+1}-1)}e^{-0.5x^{2}}dx \tag{1}\]  
where the subscript \(k\) is the \(k\)-th fraction of non-uniform sediment, and \(\tau_{sk}\) is the dimensionless shear stress. For cohesive non-uniform sediment, the following holds:  
\[\tau_{sk}=\frac{\tau_{0}^{\prime}}{\xi_{k}(\rho_{s}-\rho)gD_{k}}\left[1+1.5C_{0 }\left(\frac{\rho_{s}^{\prime}}{\rho_{s*}^{\prime}}\right)^{l}\frac{\rho}{ \rho_{s}-\rho}\sqrt{\frac{D_{m}}{D_{k}}\left(H+h_{0}\right)\delta}\right]^{-1} \tag{2}\]  
in which \(\rho\) is the density of water; \(\rho_{s}\) is the density of sediment particles; \(\tau_{0}^{\prime}\) is the bed-shear stress with respect to grain roughness; \(D_{k}\) is the average diameter for the \(k\)-th size fraction; \(\xi_{k}\) is the coefficient of hiding and exposure effects; and \(\xi_{k}=\left(D_{k}/D_{m}\right)^{0.5}\rho_{s}^{0.25}\), where \(\sigma_{g}\) and \(D_{m}\) are the geometric deviation and mean diameter of bed material. \(C_{0}\) is a coefficient, and \(l\) is an exponent, and \(C_{0}\) is approximately 0.032-0.046, and \(l\) ranges from 2.2 to 3.1 in experimental studies. Both quantities can be calibrated in the application. \(\rho_{s}^{\prime}\) is the dry density of sediment; \(\rho_{s*}^{\prime}\) is the stable dry density of sediment, taken as 1.6 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\); \(H\) is the water depth; \(h_{0}\) is the additional head pressure caused by molecular attraction; in general, \(h_{0}\) = 3.5 m; \(\delta\) is the thickness of the bonded water at the particle surface; and \(\delta=2.0\times 10^{-7}\)m.  
Formula (1) results in a relatively comprehensive description of the forces acting on the particles. The incipient velocity and critical bed-shear stress for erosion of non-uniform sediment can be deduced from Formula (1).
# Green Scalar Function Method for Analyzing Dielectric Media
## 2 Theoretical Background  
The Green's function applied to the Helmholtz equation is the solution of the wave equation for a point source. Thus, if the solution for this point source is known, then the solution for a general source can be obtained by the superposition principle. This result follows from the linearity of the wave equation itself, since any source can be described by the linear superposition of point sources. For our case, we will consider a scalar field \(E(\mathbf{r})\), which has a source \(s(\mathbf{r})\). The Helmholtz equation associated with this system would be  
\[(\
abla^{2}+k^{2})E(\mathbf{r})=s(\mathbf{r}) \tag{1}\]  
and using the definition of [PERSON]'s scalar function, now renamed \(g(\mathbf{r},\mathbf{r^{\prime}})\), we have that  
\[\Big{(}\
abla^{2}+k^{2}\Big{)}g(\mathbf{r},\mathbf{r^{\prime}})=-\delta(\mathbf{r}-\mathbf{r^{ \prime}}) \tag{2}\]  
Thus, we can establish a relationship between (1) and (2) as  
\[E(\mathbf{r})=-\int_{\mathcal{V}}g(\mathbf{r},\mathbf{r^{\prime}})s(\mathbf{r^{\prime}})d\mathbf{ r^{\prime}} \tag{3}\]  
This solution will only depend on \(R=|\mathbf{r}-\mathbf{r^{\prime}}|\) and in general could be written as  
\[g_{\text{3D}}(\mathbf{r},\mathbf{r^{\prime}})=g(\mathbf{r}-\mathbf{r^{\prime}})=\frac{e^{k| \mathbf{r}-\mathbf{r^{\prime}}|}}{4\pi|\mathbf{r}-\mathbf{r^{\prime}}|}=\frac{e^{ikR}}{4\pi R} \tag{4}\]  
Given the Equations (3) and (4), we can write the scalar field associated with such a source as  
\[E(\mathbf{r})=-\int_{\mathcal{V}}\frac{e^{ik|\mathbf{r}-\mathbf{r^{\prime}}|}}{4\pi|\mathbf{r }-\mathbf{r^{\prime}}|}s(\mathbf{r^{\prime}})d\mathbf{r^{\prime}} \tag{5}\]