esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Location of Tweets Using Machine Learning Approaches
## 2 Related Works
### The Purpose of Tweets' Location Prediction  
Geographical information can be attached to tweets in two ways [23]: (i) through accurate longitudinal and latitudinal coordinates when the user's device location is enabled and (ii) through a suggested location from a list that can be interpreted as longitudinal and latitudinal coordinates. Despite this, less than 1% of tweets contain geographical information [13]. This highlights the significance of research on location prediction in social media, as geographical data opens up new opportunities for a wide range of real-time applications.  
Twitter is a highly accessible platform for users to share their unexpected encounters with their online followers, causing it to be useful for real-time event detection [3; 4; 5]. Hashtags can be used in scenarios where people are tweeting about the same event using the same hashtag. Additionally, the precise location of a tweet is crucial in emergency management scenarios [10; 12] as it enables safety enforcement personnel to perform prompt action. Inferring the ground truth of Twitter users is another area of interest [9; 22; 24; 25; 26].
## 4 System Design
### Design of Q(s)  
In the design of \(Q(s)\), the weighting function \(W_{3}(s)\) represents the upper limitation of the system uncertainty, and it can be designed according to the model uncertainty \(\Delta_{\rm L}\). In Figure 11, the black lines represent the possible multiplicative perturbation \(\Delta_{\rm L}^{(i)}\) of the models in \(P_{0}(s)\) against the nominal model \(P_{\rm n}(s)\). In order to cover all the possible uncertainties \(\Delta_{\rm L}^{(i)}\), \(W_{3}(s)\), the blue line in Figure 11, is obtained as  
\[W_{3}(s)=1.914\times 10^{-10}(s+1320)^{3} \tag{26}\]  
Figure 11: The defined system uncertainties and the \(W_{3}(s)\).  
The weighting \(W_{1}(s)\) is the spectrum characteristics of the interference. The weighting reciprocal \(W_{1}^{-1}(s)\) determines the ability of disturbance suppression. In order to suppress 95% of the equivalent disturbance below 3 Hz, \(W_{1}(s)\) can be chosen as  
\[W_{1}(s)=\frac{0.64(s+262.5)^{2}}{(s+15)^{2}} \tag{27}\]  
By solving the mixed sensitivity problem described in Equation (23) with MATLAB Control System Toolbox (MathWork, New York, NY, USA, 2010) and Robust Control Toolbox (MathWork, New York, NY, USA, 2010), \(K(s)\) is obtained. After being computed by Equation (21), \(Q(s)\) can be determined as  
\[Q(s)=\frac{1.752\times 10^{15}s+2.137\times 10^{17}}{s^{5}+485255s^{4}+2.293 \times 10^{9}s^{3}+4.153\times 10^{12}s^{2}+1.875\times 10^{15}s+2.147\times 10^{17}} \tag{28}\]  
Figure 12a,b display the magnitude frequency response curves of \(Q(s)\), \(W_{1}^{-1}(s)\), \(1-Q(s)\) and \(W_{3}^{-1}(s)\), respectively. Obviously, the robust stability condition and the interference suppression requirement of the DOB system are both satisfied.  
As seen from Figure 12a, the maximum frequency bandwidth of \(Q(s)\) is directly limited by the weighting function \(W_{3}(s)\), and thus it is very important to determine one appropriate \(W_{3}(s)\).  
Meanwhile, \(1-Q(s)\) determines the ability of interference suppression. From Figure 12b, the remaining extraneous force will be attenuated to \(-42\) dB (decreased by 98.7%) at least; that is, combined with the constant velocity compensator, the extraneous force will be eliminated almost completely. Other low-frequency disturbance will also be suppressed effectively. Then, the disturbance will have little effect on the force-tracking error.
## 4 Methodology
### Case Study  
In order to verify the prediction performance of the proposed PCA-PANN model in practical application, this model is applied to a slope project in Heihe City, Heilongjiang Province, China. The stability of this slope directly affects the stability of oil and gas transportation. The AA001-AA004 piles of the China-Russia East Line Natural Gas Pipeline are the control works of the transit section. The positions of AA001-AA004 piles and the monitored slope are shown in Figure 9a. The slope angle is about 40\({}^{\circ}\), the horizontal distance of the slope is 477 m from the total length, and the slope height is 120 m. According to the field hydrogeological data (see Table 5 for the specific characteristics), the stability of the slope was evaluated by the model established by the numerical simulation method in Ref. [83] as shown in Figure 9b, and the FoS of the slope was calculated.  
Table 6 gives the calculation of the FoS of the slope based on the ANN model established in Ref. [83] and the proposed PCA-PANN model. It can be seen that compared with Ref. [83], the proposed model is closer to the numerical results. Interestingly, with the increase in moisture content, the FoS predicted by this model shows a trend of first strengthening and then weakening, which is consistent with the trend of slope stability obtained by the numerical simulation method.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Dataset** & \(\mathbf{R^{2}}\) & _RMSE_ & _MAE_ \\ \hline \multirow{2}{*}{PANN} & Training set & 0.951 & 0.16 & 0.092 \\  & Test set & 0.90 & 0.24 & 0.139 \\ \hline \multirow{2}{*}{PCA-PANN} & Training set & 0.988 & 0.08 & 0.044 \\  & Test set & 0.971 & 0.13 & 0.125 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of prediction effects of each model.  
Figure 8: Comparisons between the measured and predicted values. (**a**) PANN model; (**b**) PCA-PANN model.  
These results strongly prove the feasibility and effectiveness of the proposed model. In other words, machine learning simply offers a promising alternative to solving the challenge of slope stability modeling. It is worth noting that this method cannot completely replace the numerical analysis methods at present. Last but the most important, all methods should corroborate each other and jointly provide a reference for solving slope stability problems.
## 2 Results and Discussion
### Predicted Pharmacokinetics and Pharmacodynamics Parameters  
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction for drug candidates is mandatory in the drug design process, as these parameters contribute to determining the failure of approximately 60% of all drugs in the development and approval phases. It is well-known that ADMET prediction is performed at the last stage of the drug development process with high cost and effort. At present, ADMET is determined at the beginning of drug discovery stages in order to eliminate molecules with poor ADMET properties from the drug discovery pipeline with the aim to save research costs. In this regard, computational tools were used to predict ADMET properties in this study [14].  
The Caco-2 cell, percentage of human intestinal absorption (HIA), and skin permeability models have all been suggested as reliable in vitro models to estimate oral drug absorption and transdermal delivery [15]. Drug penetration to the blood brain barrier (BBB) provides insight into drugs that act on the central nervous system and on plasma protein binding (PPB). Compared to the other compounds, **5g** showed the lowest BBB penetration value (0.017) and a low value in the Caco-2 cell model (18.80). All compounds showed high PPB and HIA values, as well as very low skin permeability values in the range of \(-\)1.80 to \(-\)2.79 (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Lipinski’s Rule**} & \multicolumn{5}{c}{**PreADMET [16]**} & **Prediction** \\ \cline{2-11} \# & \multirow{2}{*}{**MW**} & **LogP** [17]** & \multirow{2}{*}{**HBD**} & \multirow{2}{*}{**HBA**} & \multirow{2}{*}{**BBB**} & \multirow{2}{*}{**PPB**} & \multirow{2}{*}{**HIA**} & **Caco-2** & \multirow{2}{*}{**Skin**} & **Drug-Likeness** \\  & & & [17] & & & & & & & & **Model Score** \\ \hline
5a & 607.98 & 5.02 & 1 & 4 & 0.39 & 100.00 & 97.99 & 39.51 & \(-\)2.45 & 0.36 \\ \hline
5b & 636.01 & 5.82 & 1 & 4 & 0.95 & 94.44 & 98.03 & 41.48 & \(-\)2.28 & 0.60 \\ \hline
5c & 679.25 & 6.44 & 1 & 4 & 1.64 & 100.00 & 98.04 & 44.04 & \(-\)2.49 & 0.83 \\ \hline
5e & 646.34 & 5.56 & 1 & 4 & 0.51 & 100.00 & 97.99 & 40.35 & \(-\)2.75 & 0.63 \\ \hline
5f & 768.15 & 6.72 & 1 & 4 & 1.96 & 100.00 & 97.96 & 46.69 & \(-\)2.15 & 0.71 \\ \hline
5g & 700.35 & 4.47 & 1 & 8 & 0.017 & 100.00 & 98.94 & 18.80 & \(-\)2.46 & 0.44 \\ \hline
5l & 768.15 & 6.72 & 1 & 4 & 1.69 & 100.00 & 97.96 & 46.60 & \(-\)2.15 & 0.63 \\ \hline
5m & 646.34 & 5.56 & 1 & 4 & 0.49 & 100.00 & 97.99 & 40.35 & \(-\)2.79 & 0.72 \\ \hline
5n & 746.34 & 7.49 & 1 & 4 & 3.33 & 100.00 & 98.03 & 48.45 & \(-\)1.80 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predicted pharmacokinetic and pharmacodynamic parameters of the most active compounds.  
Figure 2: Shape similarity of **5g** with **BI-0252** as analyzed by Rapid Overlay Shape Chemical Structure (ROCS) and visualized by VIDA application.
## 1 Introduction
By addressing the challenges highlighted by [25] in optimizing bandwidth utilization for multimedia transmission, a more effective and diverse vehicular communication system can be achieved. Further research and development efforts are necessary to overcome these challenges and improve the efficiency of multimedia content dissemination within vehicular networks. While advancements have been made, ongoing research and development efforts, along with international collaborations, are crucial to further enhance the capabilities of vehicular communication systems, address the remaining challenges, and unlock the full potential of multimedia transmission with the DSRC VANETs. This paper is focused on a comprehensive review of DSRC in the context of multimedia transmission. The subsequent sections of the paper are organized as follows: Section 2 provides background on VANET/DSRC, DSRC standards, DSRC channelization, and multimedia in VANET/DSRC. In Section 3, a literature review on multimedia in VANETs is presented. Section 4 introduces the challenges and metrics of the study. Section 5 identifies opportunities and open research areas, discusses technical challenges, and presents potential research approaches. Finally, Section 5 presents the conclusions of the paper.
## 2 Methodology
### Multi-GNSS Combined PPP Estimation  
Assuming that the receiver tracks n satellites, where the number of GPS, GLONASS, BDS, and Galileo satellites is \(n_{G}\), \(n_{R}\), \(n_{C}\), and \(n_{E}\), respectively, and the matrix \(P\) is the weight matrix of the observations, the observation equation for the quad-system combined PPP can be written as  
\[L=AX+e,P \tag{7}\]  
where \(L\) is the OMC vector, \(A\) is the design matrix, \(X\) is the estimated parameter vector, and \(e\) is the measurement noise vector; for the elements of matrices, see the reference [30].  
Using the sequential least squares adjustment, the solution of multi-GNSS PPP estimation is as follows:  
\[\begin{array}{l}\hat{X}=\left(A^{T}PA+\overline{N}\right)^{-1}\left(A^{T}PL +\overline{W}\right)=N^{-1}W\\ V=A\hat{X}-L\\ \hat{\sigma}_{STD}=\sqrt{\frac{VTPV}{n}}\end{array} \tag{8}\]  
where \(\overline{N}\) and \(\overline{W}\) are the existing normal equation and will be replaced by \(N\) and \(W\), which are the normal equation after contributing the observations at this epoch. \(V\) is the residual vector of dimension \(n\), \(\hat{X}\) is the parameter vector, and \(\hat{\sigma}_{STD}\) is the unit weight STD.
## 1 Introduction
### Lateral-Torsional Buckling  
Another critical failure mode that must be considered in the design of large I-girders is the lateral-torsional buckling. Most methods developed for the prediction of the lateral-torsional buckling capacity are only applicable in the case of a uniform bending moment distribution and require a moment gradient factor for the adjustment of the predicted capacities to the case of non-uniform moment distributions [20].  
Figure 3: Schematic of [PERSON]’s tension field theory model [5].  
The moment gradient factor \(C_{b}\) incorporates the variation of the bending moment along unbraced sections of the I-girder into the buckling load capacity prediction. According to [PERSON] and [PERSON] [21], \(C_{b}\) can be calculated for any non-uniform moment distribution along an unbraced span as follows:  
\[C_{b}=\frac{4M_{max}}{\sqrt{M_{max}^{2}+4M_{A}^{2}+7M_{B}^{2}+4M_{C}^{2}}}\leq 2.5. \tag{4}\]  
where \(M_{max}\) is the absolute value of the maximum bending moment, and \(M_{A},M_{B}\), and \(M_{C}\) are the absolute values of the bending moments at lengths of \(a/4\), \(a/2\), and \(3a/4\) along the unbraced span of the I-girder, respectively, where \(a\) denotes the total length of the unbraced span. In this study, the moment distribution between two stiffeners is assumed to be linear, corresponding to the unbraced span of the girder beam shown in Figure 2. Once \(C_{b}\) is known, the critical bending moment for the lateral-torsional buckling can be calculated as follows:  
\[M_{cr}=C_{b}M_{0 cr}, \tag{5}\]  
where \(M_{0 cr}\) is the critical bending moment of an unbraced span under uniform bending moment, which can be expressed as [22]:  
\[M_{0 cr}=\frac{\pi}{a}\sqrt{El_{y}\bigg{(}GJ+\frac{\pi^{2}EC_{w}}{a^{2}}\bigg{)}}. \tag{6}\]  
where \(G\) is the shear modulus, \(E\) is the modulus of elasticity, \(J\) is the St. Venant torsion constant, \(I_{y}\) is the moment of inertia with respect to the minor axis of the I-section, and \(C_{w}\) is the warping constant. The equations for the calculation of \(J\) and \(C_{w}\) are given in the Appendix A.
# Two-Dimensional Constellation Shaping in Fiber-Optic Communications
## 2 Typical Constellation Shaping Schemes
### Hybrid Probabilistic-Geometric Shaping
As a result, the well-known PAS scheme cannot be used for the PS purpose. Figure 6 shows the modified probabilistic fold shaping and universal probabilistic shaping schemes [60]. Probabilistic fold shaping can be used for shaping any \(F\)-fold rotationally symmetric constellation, like regular-32 QAM, as shown in Figure 6a. In such kind of constellations, the bits determining the fold index yield uniform distribution, which can be used to carry the parity-check bits. There is a major difference between PAS and probabilistic fold shaping. PAS is a 1D shaping scheme, which uses the single bit determining the positive or negative amplitude to carry the parity-check bit  
Figure 5: Constellation formats for (**a**) hybrid probabilistic-geometric shaping (HPGS)-32 QAM, (**b**) regular-32 QAM, (**c**) shallowly shaped 32 QAM, and (**d**) deeply shaped 32 QAM. and shapes the 1D-PAM with a 1D M-B distribution. On the contrary, probabilistic fold shaping is a 2D shaping scheme, which uses the \(\log_{2}(F)\) bits determining the fold indexes to carry the parity-check bits, and shapes the 2D constellation points in one fold with a 2D M-B distribution. As an illustrative example, the 32 QAM shown in Figure 6a is featured with four-fold rotational symmetry. The 8-ary constellation points in one fold are firstly shaped with a 2D M-B distribution, where different colors indicate different probabilities. The bit sets {11,01,00,10} determining the fold indexes will carry the parity-check bit after FEC encoding and rotate the 8-ary constellation by \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\), respectively. Therefore, 2D M-B distribution can be applied to the constellation points in one fold, and the selection of the fold-index can be performed by the parity-check bits. The target distribution will not be changed after the binary FEC coding. The universal probabilistic shaping scheme shown in Figure 6b can be applied to shape any QAM format. The MQAM symbols generated from the CCDM may not yield a M-B distribution. The binary bits generated after the bit labeling block are used to carry the uniformly distributed parity-check bits. During the process of B2S mapping, the parity-check bits will be uniformly mapped to partial MQAM symbols, i.e., \(N\)-ary QAM (NQAM) symbols, where \(N\) is the largest power of 2 to contain the MQAM constellation points with the desirable probabilities of \(>\)\((1-R)/M\). Assuming that the desirable probability distribution of the MQAM symbols is \(P_{M}(x)\), the probability distribution of the MQAM symbols after the CCDM is \(P_{D}(x)\), and probability distribution of the NQAM symbols is given by \(P_{N}(y)=1/N\). The final relationship can be written as \(P_{M}(x)=\left[RP_{D}(x)/\log_{2}(M)+(1-R)P_{N}(y)/\log_{2}(N)\right]/\left[R /\log_{2}(M)+\left(1-R\right)/\log_{2}(N)\right]\). In such a way, any desirable distribution of the HPGS-MQAM symbols can be obtained.
## 2 Materials and Methods
### Observation Matching  
Due to the different sampling rates of RTS and UWB observations, the moments at which RTS and UWB record their coordinates are usually different. To obtain the benchmark value for the UWB's location at a moment T1, the RTS positioning coordinates at the two adjacent moments t1 and t2 are interpolated linearly to the moment T1 (see also Figure 4) as follows:  
\[L(T1)=\frac{\triangle t1}{\triangle t}L(t2)+\frac{\triangle t2}{\triangle t}L(t 1) \tag{4}\]  
where \(L\) represents any component of the RTS coordinates (\(x\), \(y\), or \(z\)), the variable in the bracket denotes its moment, and \(\triangle t\) is \(t2-t1\), \(\triangle t1\) is \(T1-t1\), and \(\triangle t2\) is \(t2-T1\).  
Figure 3: The structure diagram of the moving target, whose coordinates can be observed by the UWB and the RTS synchronously. The vertical offset between the UWB antenna and the center of the prism is 4.7 cm, which is used to convert the \(z\) coordinate observed by the RTS from the prism center to the UWB tag.  
Figure 2: Schematic diagram of the hardware and software system.  
To ensure the accuracy of the interpolation, both the time threshold for \(\triangle t1\) and \(\triangle t2\) and the displacement threshold for \(L(t1)\)-\(L(t2)\) are set. Interpolation is only implemented when \(|\triangle t1|<0.5\) s and \(|\triangle t2|<0.5\) s and \(|L(t1)-L(t2)|<0.1\) m.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 3 Methodology
### Training a Fuzzy Wavelet Network with Backpropagation  
After the initialization phase, the network is further trained in order to find the weights that minimize the cost function. In our implementation, backpropagation (BP) was used to adjust the free parameters of the FWNN models. This technique has been used by several authors, and it is the most frequently used technique to train FWNNs [30; 32; 33; 34; 35; 36]. The BP is less fast but also less prone to sensitivity to initial conditions than higher-order alternatives. However, the use of wavelet functions in neural network structures reduces the inconvenience that it may get stuck on a local minimum of the error surface and that the training convergence rate is generally slow [32; 37]. Typically, mean squared error (MSE) is used as the cost function with the BP algorithm [14]. Other technique includes the use of the maximum correntropy criterion (MCC) and the extended Kalman filter (EKF) algorithm [15; 32].  
Explicit formulas for the partial derivatives of the output of the FWNN with respect to each component of the parameter vector are listed as follows:  
\[\frac{\partial\xi}{\partial w_{nk}}=\frac{\left(Y(n)-Y^{d}(n)\right)}{\sum\limits \limits_{k=1}^{c}y_{k}}\cdot y_{k} \tag{17}\]  
\[\frac{\partial\xi}{\partial w_{j}}=\left(Y(t)-Y^{d}(t)\right)\bullet w_{nk} \bullet\mu_{j}(x)\bullet\varphi_{j}(z)\bigg{/}\sum\limits_{j=1}^{N_{t}}\mu_{j }(x) \tag{18}\]  
\[\begin{array}{l}\frac{\partial\xi}{\partial d_{ij}}=\left(Y(t)-Y^{d}(t) \right)\bullet w_{nk}\bullet\mu_{j}(x)\bullet w_{j}\bigg{/}\sum\limits_{k=1}^{N _{j}}\mu_{k}\\ \bullet(3.5z_{ij}^{2}-z_{ij}^{4}-0.5)e^{-\frac{z_{ij}}{2}/2}\Bigg{/}(\sqrt{a_ {ij}^{3}})\\ \\ \frac{\partial\xi}{\partial t_{ij}}=\left(Y(t)-Y^{d}(t)\right)\bullet w_{nk} \bullet\mu_{j}(x)\bullet w_{j}\bigg{/}\sum\limits_{k=1}^{N_{j}}\mu_{k}\\ \\ \bullet(3z_{ij}-z_{ij}^{3})e^{-\frac{z_{ij}}{2}/2}\Bigg{/}(\sqrt{a_{ij}^{3}}) \\ \\ \frac{\partial\xi}{\partial p_{ij}^{1}}=\left(Y(t)-Y^{d}(t)\right)\bullet w_{ nk}\bullet\left(v_{j}-y(k)\right)\bigg{/}\sum\limits_{j=1}^{N_{t}}\mu_{j}(x) \\ \\ \bullet\mu_{j}(x_{i})p_{ij}^{3}\frac{\bigg{|}x_{j}-p_{ij}^{1}/p_{ij}^{2}\bigg{|} ^{p_{ij}^{3}}}{\left(x_{j}-p_{ij}^{1}\right)}\\ \\ \frac{\partial\xi}{\partial p_{ij}^{3}}=\left(Y(t)-Y^{d}(t)\right)\bullet w_{ nk}\bullet\left(v_{j}-y(k)\right)\bigg{/}\sum\limits_{j=1}^{N_{t}}\mu_{j}(x) \\ \\ \bullet-\frac{\mu_{j}(x_{i})}{2}\Bigg{/}\frac{x_{j}-p_{ij}^{1}}{p_{ij}^{2}} \bigg{|}^{p_{ij}^{3}}ln\left(\frac{x_{j}-p_{ij}^{1}}{p_{ij}^{2}}\right)^{2} \end{array} \tag{22}\]  
where \(i=1,2,\ldots\ldots,c\) is the index of fuzzy rules, \(j=1,2,\ldots\ldots,q\) the index of input variables, and \(k=1,2,\ldots\ldots,S\) the index of wavelets. The logistic layer can be extended into several layers to increase accuracy in the logistic prediction phase, and the gradient descent will follow the general computation of the partial derivative at each added layer, which will involve the calculation of the sigmoid function's gradient.