esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 3 Model Design
### Signal Model  
In the last section, we gave a more detailed description of the signal generation system. Below, we mathematically describe the CE-OFDM-PM signal scheme. The CE-OFDM-PM signal transmission model is shown in Figure 4.  
The time domain mathematical model of the bandpass is given by:  
\[s(t)=A\cdot Re\{\exp[j\phi(t)]\} \tag{1}\]  
where \(A\) is the signal amplitude, \(Re\{\cdot\}\) is the real operator, and \(\phi(t)\) is the time-varying phase function. This can be expressed:  
\[\begin{array}{c}\phi(t)=\theta_{i}+2\pi hC_{N}\sum\limits_{k=1}^{N}I_{i,k}q_ {k}(t-iT_{c}),\\ iT_{c}\leq t\leq(i+1)T_{c}\end{array} \tag{2}\]  
In Equation (2), \(\theta_{i}\) is the phase of the \(i\)-th symbol with the memory term, \(h\) is the phase modulation index, \(C_{N}\) is the normalized constant, and \(C_{N}=\sqrt{2/N}\). \(I_{i,k}\) denotes the data symbol of the \(i\)-th symbol, the \(k\)-th sub-carrier is \(I_{i,k}\in\{\pm 1\}\), \(T_{c}\) denotes the symbol period, and the \(k\)-th sub-carrier shape function \(\{q_{k}(t)\}\) is defined as:  
\[q_{k}(t)=\left\{\begin{array}{c}\cos 2k\pi t/T_{c},0\leq t<T_{c}\\ \sin 2\pi(k-N/2)t/T_{c}0\leq t<T_{c}\\ 0\end{array}\right. \tag{3}\]  
Furthermore, the parallel data symbol can be expressed by:  
\[I_{i,k}=a_{i,k}\cdot d_{i,k} \tag{4}\]  
where \(a_{i,k}\) is the binary PRN with {0,1} and \(d_{i,k}\) is the navigation data with binary {0,1}.  
The subcarrier orthogonality condition holds:  
\[\int_{iT_{c}}^{(i+1)T_{c}}q_{k_{1}}(t-iT_{c})\ \cdot\ q_{k_{2}}(t-iT_{c})dt= \left\{\begin{array}{c}T_{c}/2,\quad k_{1}=k_{2}\\ 0,\quad k_{1}\
eq k_{2}\end{array}\right. \tag{5}\]  
Figure 4: Inner structure of a model of a CE-OFDM-PM signal generator. The navigation data \(d_{i}\) and \({}_{k}\) and communication data \(a_{i}\) and \({}_{k}\) are operated bitwise. Then, \(I_{i}\) and \({}_{k}\) undergo serial-to-parallel conversion, and CPFSK modulation is performed on each parallel bit to obtain the unique phase of the binary data. After Fourier transform is carried out, the sum of each parallel signal is used to find the final transmitted time domain signal.  
Assume that the phases between different symbols are relatively independent--that is, the memory term \(\theta_{i}=0\). According to the Maclaurin series, Equation (1) can be expressed as:  
\[s(t)=Ae^{i\varphi_{\phi}p(t)}=A\sum_{n=0}^{\infty}\frac{\left(j\varphi_{\phi} \right)^{n}}{n!}p^{n}(t) \tag{6}\]  
where \(\sigma_{\phi}^{2}\) denotes the phase signal variance; \(\sigma_{\phi}^{2}=\left(2\pi h\right)^{2}\); and \(p(t)\) is the normalized OFDM signal, which is given by:  
\[p(t)=C_{N}\!\sum_{i}\sum_{k=1}^{N}I_{i,k}q_{k}(t-iT_{c}) \tag{7}\]
## 3 Performance Analysis for Single SWIPT Relaying System
### Average SNR Derivation for the Indirect Link  
The joint probability density function (PDF) of \(\gamma_{\rm sr}\) and \(\beta_{\rm rd}\) can be written as  
\[f_{\gamma_{\rm sr},\beta_{\rm rd}}(x,y)=\frac{1}{\tilde{\gamma}_{\rm sr}} \exp\biggl{(}-\frac{x}{\tilde{\gamma}_{\rm sr}}\biggr{)}\frac{1}{\beta_{\rm rd }}\exp\biggl{(}-\frac{y}{\tilde{\beta}_{\rm rd}}\biggr{)}u(x)u(y) \tag{12}\]  
where \(u(\cdot)\) denotes the unit step function [40]. By employing (9) together with (12), the average SNR of the indirect link, \(\tilde{\gamma}_{\rm id}\), can be obtained after some manipulations:  
\[\tilde{\gamma}_{\rm id}=E\biggl{[}\frac{\gamma_{\rm sr}\beta_{\rm rd}}{ \beta_{\rm rd}+1}\biggr{]}=\tilde{\gamma}_{\rm sr}-\tilde{\gamma}_{\rm sr} \frac{\exp\bigl{(}1/\tilde{\beta}_{\rm rd}\bigr{)}}{\beta_{\rm rd}}E_{1} \biggl{(}\frac{1}{\beta_{\rm rd}}\biggr{)} \tag{13}\]where the exponential integral function is defined as \(E_{1}\left(\frac{1}{\tau}\right)=\int_{1/\tau}^{\infty}\frac{c^{-\gamma}dy}{y}dy\)[41; 42].
###### Abstract  
Improving the microwave absorption performance of Co-MOF-derived Co@N-C composite by constructing the morphology and spatial structure is a known challenge. In this work, under the action of the binder polyvinylpyrrolidone, 3D-graphene particles can be well decorated on the surface of the Co@N-C composite after high-temperature pyrolysis. In addition, due to the structural characteristics of MOFs, Co particles can be well covered by a carbon layer, which effectively solves the problem that magnetic metal particles are prone to corrosion and oxidation. The microwave absorption performances of the composite can be well adjusted by changing the average dotted density of the 3D-graphene on the Co@N-C composite. It is worth noting that the maximum reflection loss can reach \(-\)58.72 dB at the thickness of 1.64 mm, and the maximum effective absorption bandwidth can achieve 5.74 GHz at the 1.79 mm thickness, which almost covers the whole Ku band. Importantly, these results demonstrate that 3D-graphene@C/Co@N-C composites have great potential as high-efficiency microwave absorption materials.  
3D-graphene@C/Co@N-C; microwave absorption; double-loss mechanism; impedance matching
## 3 Results
Cell viability was evaluated using the MTT assay and the concentration required to reduce cell viability by 50% (IC50) for each condition was calculated using GraphPad Prism. Each data point represents the mean of at least three independent experiments, with vertical bars indicating the standard deviation. Figure 3: Effect of doxorubicin–MET70 combined treatment on mRNA and protein expression of redox balance regulators in HepG2 cells. Cells were treated with doxorubicin (0.5 uM) and MET70 (100 ug/mL), either alone or in combination, for 72 h. DMSO (0.02%) was used as a vehicle control. Figure 2: Effect of doxorubicin and doxorubicin combined with MET70 on intracellular ROS levels in HepG2 cells. Cells were treated for 72 h with doxorubicin (0.5 uM), MET70 (100 ug/mL), or their combination. Data are presented as mean fluorescence \(\pm\) SEM from three independent experiments, each performed in triplicate. Statistical significance was assessed using one-way ANOVA followed by Holm–Sidak correction for multiple comparisons (* \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001). **(a,b)** Relative mRNA expression levels of SOD2, Catalase, GPX-1, Nrf2, NQO1 and UCP2, analyzed by qRT-PCR were calculated using the \(2^{-\Delta\text{CI}}\) method, with \(\beta\)-actin as the endogenous reference gene. Data are presented as mean \(\pm\) SEM from three independent experiments. (**c,d**) Protein expression levels of GPX-1 and UCP2, assessed by Western blot analysis. Representative blots and densitometric quantifications are shown, with protein levels normalized to \(\beta\)-actin and expressed relative to control (set to 100%). Data are presented as mean \(\pm\) SEM from at least three independent experiments. Statistical significance was determined by one-way ANOVA followed by a Holm-Sidak correction for multiple comparisons (\({}^{*}p<0.05;^{**}p<0.01;^{***}p<0.001\)). The expression level of tumor necrosis factor alpha (TNF-\(\alpha\)) increased significantly in the presence of doxorubicin. However, the effect was reduced when doxorubicin was combined with MET70. Doxorubicin in combination with MET70 induced an increase in the expression of interleukin 6 (IL-6) (Figure 4).
## 2 Related Work
### Anomaly Detection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Normal Data** & **Abnormal Data** \\ \hline Supervised Anomaly Detection & Yes & Yes \\ Semi-supervised Anomaly Detection & Yes & No \\ Unsupervised Anomaly Detection & No Labels Required \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types of anomaly detection based on the presence of normal and abnormal data labels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Method** & **Strengths** & **Limitations** \\ \hline \multirow{4}{*}{Statistical} & Z-score [32] & Fast, simple, interpretable & Needs normality assumption, \\  & ARIMA [33] & Good for trend-based anomalies & Sensitive to noise \\  & Entropy-based [34] & Captures data randomness & Weak for nonlinear data \\ \hline \multirow{4}{*}{Machine Learning} & KNN [35] & Works without assumptions & Poor for high-dim data \\  & SVM [36] & Effective on small data & High cost for large data \\  & PCA [37] & Reduces complexity & Limited to linear data \\ \hline \multirow{4}{*}{Deep Learning} & LSTM [38] & Captures long-term dependencies & High training cost \\  & Autoencoder [39] & Learns normal patterns & Fails if abnormal data are \\ \cline{1-1}  & GAN [40] & Learns complex patterns & Unstable training \\ \cline{1-1}  & Transformer [41] & Strong for sequence data & Needs large dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Time Series Anomaly Detection Methods.
## 2 Materials and Methods
### Biophysical RBE Models
#### 2.6.2 Microdosimetric-Kinetic Model (MKM)  
A more advanced biophysical model, which is based on the TDRA, is the microdosimetric-kinetic model (MKM) developed by [PERSON] [68; 69; 70]. Contrary to the TDRA, the MKM takes into account the cellular repair, as well as the mechanisms that are responsible for the conversion of a single sub-lesion or a pair of sub-lesions into a lethal lesion. More specifically, in the MKM it is assumed that the radiation effects can be caused by two different types of lesions, type I lesions and type II lesions, each of which is produced with a rate proportional to the specific energy (\(z\)) in a subcellular volume of interest, called the 'domain'. Type I lesions are lethal (and unrepairable) while type II lesions are potentially lethal if left unrepaired. It can be shown that the average number of lethal lesions per cell is a linear-quadratic function of the absorbed dose (\(D\)), so the MKM uses the LQ formalism Equation (31). The linear and quadratic parameters of the LQ model are expressed, according to the MKM, as \(\alpha=\alpha_{0}+z_{D}\)\(\beta_{0}\) and \(\beta=\beta_{0}\), where \(\alpha_{0}\) and \(\beta_{0}\) are the LQ model coefficients in the limit of zero LET. Then, the \(\text{RBE}_{\text{LD}}\) and \(\text{RBE}_{\text{HD}}\) of the MKM are given by the following expressions:  
\[\text{RBE}_{\text{LD, MKM}}=\frac{a_{P}}{a_{R}}=1+\frac{0.204}{4r^{2}}\frac{( y_{D,P}-y_{D,R})}{(\alpha/\beta)_{R}}\, \tag{35}\]  
\[\text{RBE}_{\text{HD, MKM}}=\sqrt{\frac{\beta_{P}}{\beta_{R}}}=1. \tag{36}\]
## 4 Common Latitude Power Series Expansions with Geocentric Latitude as Variable
### Power Series Expansion of Conformal Latitude with Geocentric Latitude as Variable  
According to the map projection theory, the formula of the isometric latitude \(q(B)\) with geocentric latitude \(\phi\) as the variable is  
\[q=\int_{0}^{\phi}\frac{M(\phi)}{r}d\phi=\int_{0}^{\phi}\frac{\sqrt{1-(2-e^{2} )e^{2}\cos^{2}\phi}}{(1-e^{2}\cos^{2}\phi)\cos\phi}d\phi. \tag{25}\]  
If the earth is regarded as a sphere, then \(e=0\) and \(\phi\) becomes the conformal latitude \(\varphi\):  
\[\varphi=2\arctan(e^{q})-\frac{\pi}{2}. \tag{26}\]  
Using Equations (25) and (26), the power series expansion of the conformal latitude with geocentric latitude as the variable is  
\[\varphi(\phi)=\phi+c_{2}\sin 2\phi+c_{4}\sin 4\phi+c_{6}\sin 6\phi+c_{8}\sin 8 \phi+c_{10}\sin 10\phi. \tag{27}\]  
The coefficients in Equation (27) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Expand to \(e^{10}\) Based on the First**} & \multicolumn{1}{c}{**Expand to \(n^{5}\) Based on the Third**} \\ \multicolumn{1}{c}{**Eccentricity \(e\)**} & \multicolumn{1}{c}{**Flattening \(n\)**} \\ \hline \(\left\{\begin{array}{l}a_{2}=\frac{1}{8}e^{2}+\frac{1}{16}e^{4}+\frac{53}{10 24}e^{6}+\frac{95}{2048}e^{8}+\frac{1359}{32768}e^{10}\\ a_{4}=-\frac{1}{25}e^{4}-\frac{1}{256}e^{6}+\frac{5}{81972}e^{8}+\frac{21}{20 96}e^{10}\\ a_{6}=-\frac{1}{50}e^{6}-\frac{1}{2048}e^{8}-\frac{1811}{150}e^{10}\\ a_{8}=-\frac{1}{15024}e^{8}-\frac{261}{150356}e^{10}\\ a_{10}=-\frac{1}{1280}n^{4}e^{10}\end{array}\right.\) & \(\left\{\begin{array}{l}a_{2}=\frac{1}{2}n+\frac{13}{16}n^{3}-\frac{15}{32}n^{ 5}\\ a_{4}=-\frac{1}{16}n^{2}+\frac{33}{23}n^{4}\\ a_{6}=-\frac{1}{50}n^{3}+\frac{349}{256}n^{5}\\ a_{8}=-\frac{15}{502}n^{4}-\frac{16}{105}n^{5}\\ a_{10}=-\frac{1}{1280}n^{5}\end{array}\right.\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Coefficients in Equation (24).  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Expand to \(e^{10}\) Based on the First**} & \multicolumn{1}{c}{**Expand to \(n^{5}\) Based on the Third**} \\ \multicolumn{1}{c}{**Eccentricity \(e\)**} & \multicolumn{1}{c}{**Flattening \(n\)**} \\ \hline \(\left\{\begin{array}{l}a_{2}=\frac{1}{8}e^{2}+\frac{1}{16}e^{4}+\frac{53}{10 24}e^{6}+\frac{95}{2048}e^{8}+\frac{1359}{32768}e^{10}\\ a_{4}=-\frac{1}{25}e^{4}-\frac{1}{256}e^{6}+\frac{5}{81972}e^{8}+\frac{21}{20 96}e^{10}\\ a_{6}=-\frac{1}{5024}e^{6}-\frac{1}{150}n^{5}-\frac{1}{2048}e^{8}-\frac{1811}{ 2048}e^{10}\\ a_{8}=-\frac{1}{1024}n^{4}-\frac{1}{15024}n^{5}\\ a_{10}=-\frac{1}{1280}n^{5}\end{array}\right.\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Coefficients in Equation (27).
# Broadband Millimeter-Wave 5G Power Amplifier Design in 22 nm CMOS FD-SOI and 40 nm GaN HEMT
###### Abstract
This work will also use Global Foundries' 22 FDX technology, which is a 22 nm FD-SOI (fully depleted SOI) CMOS process to design a differential cascode mm-Wave broadband PA. Devices in this technology achieve lower off-state leakage current due to the buried oxide layer and a fully depleted channel [5]. Our design uses SLVTNFET (i.e., super low threshold voltage NFET), which can achieve a peak \(f_{T}\) of \(\sim\)350 GHz and a peak \(f_{MAX}\) of \(\sim\)370 GHz in the smallest device. The 22 FDX technology also enables back-gate biasing, allowing us to change the NFET threshold voltage (\(V_{T}\)) by back-gate controls to possibly adjust for \(V_{T}\) variation (from poly CD and short-channel effects), and can thus improve the yield for nm-CMOS and possibly improve linearity as well [6]. Figure 1 shows the simulated \(f_{T}\) and \(f_{MAX}\) of the 40 nm GaN HEMT and 22 nm CMOS FD-SOI devices used in our mm-Wave PA designs. Although the 22 FDX PDK (process design kit) offers layouts up to mid-level metal layers, the parasitic up to the top metal layer is extracted using Calibre xACT R + C + CC extraction [7]. Here \(f_{T}\) and \(f_{MAX}\) are extrapolated when \(H_{21}\) (small-signal current gain) and the value of \(G_{MAX}\) (maximum transducer power gain) are equal to unity, respectively (note: \(G_{max}=\left|\frac{S_{21}}{S_{21}}\right|(K-\sqrt{K^{2}-1})\)). For a fixed current density, it is clear from Figure 1b that \(f_{MAX}\) of the GaN device is much higher than those of the CMOS (by \(\sim\times 2\)), suggesting it may be more suitable for low-power operations, even though Figure 1a shows CMOS can reach about the same \(f_{MAX}\) as the GaN device at a higher bias current (i.e., \(>\)10 mA). In each of the two technologies, we have designed a differential PA, using neutralization capacitors to increase the maximum stable power gain (MSG) and reverse isolation. This technique has been explored in many previous works [8; 9] and thus will not be discussed in detail here but we will instead just provide a brief review. From the MSG equation and the small-signal model of a MOSFET, for a differential pair with a neutralization capacitor \(C_{x}\), Equation (1) can be derived where the parasitic gate to drain capacitance, \(C_{gd}\), can be reduced by adding the neutralization capacitor \(C_{x}\), and thus can increase the MSG of a differential amplifier [8; 9]. \[G_{S}=\frac{|Y_{21}|}{|Y_{12}|}=\sqrt{\frac{g{m^{2}}}{\omega^{2}(C_{gd}\ -\ C_{x})^{2}}\ +\ 1} \tag{1}\]
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Monitoring and Control through Smart Metering
#### 4.2.4 Remarks on projects for Grid Monitoring and Control through Smart Metering  
In these three projects, smart metering has been used for grid monitoring purposes. Wireless solutions are used for different telecommunication purposes in all of them. In the project E2 SG the smart meters worked with a low power wireless transceiver, in the project NOBEL the smart  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Project** & **Aim** & **Characteristics** & **SM Technologies Used** \\ \hline \multirow{2}{*}{InvoGrid} & Control and Management & Provide with equipment for & PLC DCSK, PLC \\  & of the grid & grid control & PRIME, GPRS \\ \hline \multirow{2}{*}{MeRegio} & \multirow{2}{*}{Smart Metering installation} & Sending SM data through & \\  & & customer’s Internet & \\ \hline \multirow{3}{*}{Hook Norton} & Examine energy consumption & Examine Customer & \\  & and monitor the distribution & involvement to energy & NB-PLC (first link) \\ \cline{1-1}  & substations & reduction & Wireless (second link) \\ \hline \multirow{2}{*}{EDRP} & Impact of customer feedback on & Smart Meter and In-home & GSM (both links) PLC at \\ \cline{1-1}  & reduction of consumptions & display installation & small extent (first link) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Smart Metering Application projects and their main characteristics.  
meters form a wireless network, whereas in the project Mirubee, WiFi technology has been used for transmitting device consumption to a cloud server. Table 4 summarizes the characteristics of the projects mentioned in this Subsection.
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 4 Experiment and Discussion
### Performance Comparison of Optimization Algorithm  
The SGD algorithm is a common optimization algorithm, which is often used to optimize the weight and bias of neural network in the training stage. However, the performance of its sub algorithm and variant is different. For this reason, in order to obtain the best training results, we compared several popular optimization algorithms, namely SGDM, RMSProp, and Adam [50]. Different optimization algorithms are used to train the Bi-LSTM. In this experiment, the training data set generated in Section 4.1 is used to complete the training. The initial learning rate is 0.05, and the batch size is 100. They were trained for 20 epochs and collected the corresponding training losses. The results are shown in Figure 6.  
It can be seen from Figure 6 that the SGDM algorithm has a slow convergence speed, and has a large training loss. The convergence speed of the RMSProp algorithm is fast, but the training process fluctuates greatly, and the training loss is not ideal. The Adam algorithm has the fastest convergence speed, the smallest loss, and the process is stable. Therefore, this optimization algorithm with the best performance is applied in the following experiments.  
SGDM algorithm uses a single learning rate in the whole training process. Other optimization algorithms use different learning rates to improve the network training, and can automatically adapt to the loss function being optimized. This is how the RMSProp algorithm works. Adam updates with parameters similar to RMSProp, but adds a momentum term to that [50]. Therefore, the neural network can obtain fast and stable training effect.
## 5 Performance Evaluation
Figure 5: Effect of the number of targets on the target coverage rate, active sensor rate, and fitness values under the greedy, GA, DPSO, and DPSO_GA algorithms. (**a**) Target coverage rate, (**b**) active sensor rate, (**c**) fitness values, and (**d**) fitness values versus number of generations for the GA, DPSO, and DPSO_GA algorithms. Figure 6: Effect of the field of view on the target coverage rate, active sensor rate, and fitness values in greedy, GA, DPSO, and DPSO_GA algorithms. (**a**) Target coverage rate, (**b**) active sensor rate, (**c**) fitness values, and (**d**) fitness values versus number of generations for the GA, DPSO, and DPSO_GA algorithms. Figure 7 presents the effect of the sensing radius on the target coverage rate, active sensor rate, and fitness values for the GA, DPSO, and DPSO_GA algorithms compared with the solutions of the greedy algorithm. In this experiment, \(N=200\) targets and \(M=100\) sensors were randomly generated in the specified region. The field of view \(\theta=60^{\circ}\) and the weight of fitness function \(w=0.5\). The sensing radius varied from 60 to 120 m in increments of 20 m. Figure 7a reveals that the number of covered targets increases as the sensing radius increases because the cover sector area increases. Again, Figure 7a,b reveals that the greedy algorithm achieved a 23-35% higher target coverage rate but required 19-51% more activated sensors than did the evolutionary algorithms and thus was outperformed by them by 1.2-15% (Figure 7c). Notably, the greedy algorithm outperforms the GA and DPSO algorithms if the sensing radius is 120 m because both the GA and DPSO algorithms become trapped in local optima. However, the proposed DPSO_GA operators' algorithm still outperforms the greedy algorithm. Figure 7d plots the fitness values versus the number of generations for GA, DPSO, and DPSO_GA with \(N=100\), \(M=200\), \(R=100\), \(\theta=60^{\circ}\), \(w=0.5\), and \(lc=1000\). DPSO_GA and DPSO had higher fitness than did the GA approach after five generations, and DPSO_GA, ultimately, achieved solutions 1.1% and 3.3% better than those of DPSO and GA, respectively. As a result, the hybrid scheme was more likely to achieve the global optimum solutions for the MCMRS problem in terms of the sensing radius.
## 3 Methodologies
### CMGraph for Efficient Congestion Monitoring
#### 3.3.2 Real-Time Congestion Visualization  
While the preceding sections describe the methodology for acquiring data on crowd mobility, we provide two data visualization interfaces so that various stakeholders, including security professionals, infrastructure operators, and crowd managers, can easily interpret the data generated by our framework for congestion monitoring.  
1. **Crowd count and individual monitoring** The objective of the first data visualization tool is to reduce the amount of human effort required for surveillance video observation. We add a crowd count and assign a unique identifier to each pedestrian so that surveillance operators may simply determine the general crowd size and identify individuals who may require particular attention. This is especially beneficial in crowded areas when it is difficult to quantify the number of people and to locate an individual. Figure 5a depicts the visualization that was implemented.
2. **Real-time congestion alert** The second tool is designed to notify the operator of probable congestion hotspots. To do this, a density heatmap is constructed to indicate busy areas from a bird's-eye view of the physical space. The color automatically alters dependent on a region's congestion level. Figure 5b illustrates a congested egress in the Stanford Stadium. In order to be alerted about overcrowding and to prevent hazards such as stampedes from occurring, operators can continuously use the heatmap to monitor crowded areas.  
Figure 4: Egress region division and CMGraph formulation of the Grand Central Station. Nine egress regions were manually identified. Grand Central Station floorplan adopted from [19].