esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multicolumn{3}{c}{**Technologies Considered**} & \multicolumn{3}{c}{**ITS Services Considered**} & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **Criteria** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Selection** \\ **Criteria** \\ \end{tabular} } \\ \cline{2-2} \cline{6-10}  & **PC5** &
\begin{tabular}{c} **DSRC/** \\ **ITS-G5** \\ \end{tabular} & & & & **Experiment** & **CAM** & **DENM** & **Joint** & **Adaptation** & **KPIs** & **Sclection** \\ \hline
[5] & mode 4 & ✓ & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & Latency, range &
\begin{tabular}{c} ITS-G5 channel \\ capacity \\ \end{tabular} \\ \hline
[6] & mode 3 & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Reliability, PRR & SNIR, latency \\ \hline
[7] & mode 3 & ✓ & \(\times\) & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \
\begin{tabular}{c} CBR, PRR, \\ throughput \\ \end{tabular} &
\begin{tabular}{c} SNIR, channel \\ load, latency \\ \end{tabular} \\ \hline
[8] & mode 3 & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \
\begin{tabular}{c} Throughput, \\ PDR, network \\ satisfaction \\ \end{tabular} &
\begin{tabular}{c} Queuing delay, \\ capacity, \\ connectivity \\ time, cost. \\ \end{tabular} \\ \hline
[9] & mode 4 & ✓ & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) &
\begin{tabular}{c} Packet size, \\ distance \\ \end{tabular} \\ \hline
[10] & \(\times\) & ✓ & ✓(4G) & \(\times\) & ✓ & ✓ & \(\times\) & ✓ & \
\begin{tabular}{c} Data rate, \\ latency, PDR, \\ handovers \\ \end{tabular} &
\begin{tabular}{c} LTE LLI, DSRC \\ Channel \\ Occupancy \\ \end{tabular} \\ \hline
[11] & mode 4 & ✓ & \(\times\) & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ & PDR &
\begin{tabular}{c} SPS period \\ control \\ \end{tabular} \\ \hline
[12] & \(\times\) & ✓ & ✓(4G) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & PDR, FDR & DSRC PLR \\ \hline
[13] & \(\times\) & ✓ & ✓(4G) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \
\begin{tabular}{c} Load \\ balancing, data \\ rate, vehicle \\ fairness \\ \end{tabular} &
\begin{tabular}{c} RSS (of RSUs \\ and BSs) \\ \end{tabular} \\ \hline
[14] & \(\times\) & ✓ & ✓(4G) & \(\times\) & ✓ & \(\times\) & \(\times\) &
\begin{tabular}{c} Delay \\ 802.11p load \\ \end{tabular} \\ \hline
[15] & \(\times\) & \(\times\) & ✓(4G) & ✓ & \(\times\) & \(\times\) & \(\times\) & \begin{tabular}{c} RTT \\ \end{tabular} & \begin{tabular}{c} WiFi signal \\ strength \\ \end{tabular} \\ \hline This work & mode 4 & ✓ & ✓(5G) & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & \
\begin{tabular}{c} Latency, PDR, \\ Reliability, \\ CAM/DENM \\ \end{tabular} &
\begin{tabular}{c} distance, \\ traffic density, \\ C-ITS message \\ type, speed \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work in multi-technology enabled vehicular communication and RAT selection algorithms.
# Revamping Fluid Catalytic Cracking Unit, and Optimizing Catalyst to Process Heavier Feeds
## 3 Results
### Performance of the FCC Unit before and after the Revamp
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **10 September–11** & **11 September–13** & \\
**Date** & **September 2018** & **September 2022** & **2 October–3 October 2022** \\  & **before Revamp** & **after Revamp** & **after Revamp** \\ \hline H-Oil VGO in FCC feed & & & \\ (coming from FCCPT and directly in FCCU), \% & 27.5 & 31.2 & 18.8 \\ FCC led density at 15 °C, g/cm\({}^{3}\) & 0.9105 & 0.9157 & 0.9119 \\
5\%, \({}^{\circ}\)C & 368 & 337 & 348 \\
10\%, \({}^{\circ}\)C & 388 & 359 & 368 \\
50\%, \({}^{\circ}\)C & 442 & 434 & 442 \\
90\%, \({}^{\circ}\)C & 501 & 514 & 519 \\
95\%, \({}^{\circ}\)C & 519 & 533 & 537 \\
**Kw** & **11.95** & **11.84** & **11.93** \\ Sulphur, \% & 0.254 & 0.281 & 0.19 \\ Concarbon, \% m/m & 0.14 & 0.14 & 0.26 \\ Reactor temperature, \({}^{\circ}\)C & **535** & **550** & **543** \\ Combined feed temperature, \({}^{\circ}\)C & 308 & 307 & 323 \\ Regenerator dense bed temperature, \({}^{\circ}\)C & 694 & 704 & 710 \\ Regenerator dilute phase temperature, \({}^{\circ}\)C & 708 & 710 & 718 \\ Air, kNm\({}^{3}\)/h & 124,480 & 136,745 & 138,034 \\ CTO wt/wt, & 6.60 & 8.5 & 7.3 \\ Heat of reaction kJ/kg feed & 309 & 412.2 & 470 \\ Hydrogen in coke, \% & 8.5 & 6.3 & 7.2 \\ \(\Delta\)**Coke, \%** & **0.63** & **0.59** & **0.65** \\ TOS, s & 2.2 & 2.1 & 2.1 \\
**Yields on fresh feed basis, wt\%** & & & \\ C2 & 3.62 & 6.4 & 5.42 \\ C3 & 6.61 & 6.39 & 7.1 \\ C4 & 10.8 & 10.89 & 11.85 \\ C5+ Gasoline (corrected to T\({}_{90\%}\) = 170 \({}^{\circ}\)C) & 47.2 & 45.1 & 47.1 \\ LCO & 10.13 & 7.6 & 8.54 \\ HCO & 10.4 & 9.0 & 8.92 \\ Slurry & 6.34 & 8.13 & 6.85 \\ Coke & 4.18 & 4.96 & 4.72 \\ Conversion & 73.2 & 73.1 & 75.7 \\ C3 = in PPF, vol, \% & 81.1 & 81.9 & 81.5 \\ iC4 in BBF, vol, \% & 31.5 & 30.0 & 31.9 \\ iC4 = in BBF, vol, \% & 16.2 & 17.1 & 15.6 \\ C4 = in BBF, vol, \% & 59.4 & 61.8 & 59.1 \\
**FCC gasoline properties** & & & \\ Density at 15 \({}^{\circ}\)C, kg/m\({}^{3}\) & 739.5 & 738.4 & 738.1 \\ IBP, \({}^{\circ}\)C & 36 & 34.3 & 34.9 \\
10\%, \({}^{\circ}\)C & 52 & 50 & 51.8 \\
50\%, \({}^{\circ}\)C & 92 & 86.9 & 88.9 \\
90\%, \({}^{\circ}\)C & 167 & 163.5 & 167.1 \\ FBP, \({}^{\circ}\)C & 199 & 200.3 & 204.2 \\ RON & 93.6 & 94.4 & 94.2 \\ RVP, kPa & & 58.9 & 54.9 \\ Sulphur content, ppm & 144 & 134 & 94 \\ MON & 81.8 & 82.5 & 82.6 \\
**LCO Properties** & & & \\ Density at 15 \({}^{\circ}\)C, kg/m\({}^{3}\) & 928.4 & 934.1 & 926.9 \\ IBP, \({}^{\circ}\)C & 209 & 186 & 183 \\
10\%, \({}^{\circ}\)C & 213 & 204 & 204 \\
50\%, \({}^{\circ}\)C & 237 & 241 & 239 \\
90\%, \({}^{\circ}\)C & 272 & 273 & 267 \\ FBP, \({}^{\circ}\)C & 304 & 299 & 306 \\ Kw & 10.56 & 10.57 & 10.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._Figure 4: Variation in average particle size (APS) of E-Cat before and after the revamp. Figure 3: Variation in catalyst losses before and after the revamp of the LNB FCCU.
## 4 Derive Assessment Templates
### Allocation Requirements Based on Technology Reliance
#### 4.2.1 V2X Communication  
Communication and associated requirements can be crucial in ADSs. Vehicle-to-everything (V2X) communication technologies enable vehicles to wirelessly communicate with various entities that can impact their operation, including vehicle-to-infrastructure (V2I), vehicle-to-network (V2N), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-device (V2D), vehicle-to-grid (V2G), and Tele-operated Driving (ToD). This communication capability facilitates cooperative driving, optimising collective behaviour regarding throughput, fuel consumption, emissions, and safety [25]. In the automotive industry, there are two main types of V2X communication technologies: WLAN-based, which utilises IEEE 802.11p and is used in standards such as ETSI ITS G5 and DSRC, and cellular-based, which is defined by 3 GPP and includes short-distance communication using PC5 sidelink and traditional cellular interfaces through 3G/4G/LTE/5G networks. The testing of V2X communication involves various organisations such as 3 GPP, 5 GAA, ETSI, GCF, IEEE, OmniAir, SAE, C-ITS, C-SAE, and NTCATS. Test-equipment vendors are actively developing instruments designed explicitly for V2X testing, with many of them also incorporating Global Navigation Satellite System (GNSS) testing capabilities. A 5G-based predictability system was evaluated to forecast the service quality [26], proposing an enhanced scheme for vehicle-to-network communication optimisation. This method effectively alleviates base station congestion while preserving key quality attributes like delay time and throughput, showcasing efficient network management even in high-demand scenarios.
## 4 Case Study of an IoT Enabled Bridge  
An illustrative and realistic case study of an IoT enabled bridge with wireless structural monitoring capabilities is presented in this section, as shown in Figure 2. The IoT enabled bridge is deployed into three layers. The sensing layer includes the IoT based WSN, which comprises IoT devices responsible for collecting and transmitting sensed data. Zigbee is a wireless technology used as a communication protocol among the IoT devices. It is based on the based on IEEE 802.15.4 standard [52; 53] and has been demonstrated to be a reliable technology for monitoring purposes in civil engineering infrastructure [70]. Indeed, previous experimental studies [43; 44; 71; 72], have successfully instrumented a wireless monitoring system for bridges, demonstrating the capabilities of ZigBee technology. ZigBee protocol stack includes the following layers: application (i.e., data transmission and security services); network (i.e., routing, security, and configuration of new devices); MAC (i.e., interface between physical and network layer); and physical (i.e., functions related to ZigBee hardware). The ZigBee mesh topology is based on a coordinator that governs the network, routers that establish connection from the coordinator to other routers or from a router to end devices, and end devices that collect information. In this case study we assume that the IoT enabled bridge is the target of an energy depletion attack, which is a cyber-physical attack targeting the IoT based WSN.
Sensors_**2015**, _15_, 26583-26605; doi:10.3390/s151026583  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Balancing Energy Consumption with Hybrid Clustering and Routing Strategy in Wireless Sensor Networks \({}^{\dagger}\)  
**[PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{3}\)**  
\({}^{1}\) School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350000, China;  
E-Mails: chenli7 [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (T.L.);  
[EMAIL_ADDRESS] (L.C.)  
\({}^{2}\) Fujian Key Laboratory of Industrial Control and Information Security Technology,  
Science and Technology Department of Fujian Province, Fuzhou 350000, China  
\({}^{3}\) School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University,  
Shanghai 200240, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) This paper is an extended version of our paper published in [PERSON]; _et al._ A Dynamic Clustering and Routing Protocol for Multi-hop Data Collection in Wireless Sensor Networks. In Proceedings of the 34 th Chinese Control Conference (CCC), Hangzhou, China, 28-30 July 2015.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-180-6050-7138.  
Academic Editor: [PERSON]  
_Received: 5 September 2015 / Accepted: 13 October 2015 / Published: 20 October 2015_  
**Abstract:** Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient \(k=1\), _i.e._, the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in gradient \(k=1\), the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.  
keywords: energy balance; hybrid clustering and routing; energy efficiency; dynamic clustering; data collection; wireless sensor networks +
Footnote †: journal: Energy Communications
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 4 The Proposed MAC Address Randomization Covert Channel
### Simulation Environment  
We utilized the network simulator version 3 (NS-3) release 35 as our simulation environment. This simulator allows researchers to model and assess the performance of various networking protocols and scenarios. NS-3 [45] is an open-source tool widely recognized for its extensive use in network technology research and development, as it supports a variety of networks. NS-3 is implemented in C++ and is currently being extended to support Python, enabling researchers to incorporate new models and protocols as required. We modified the NS-3 source code to align with our specific goals. These adjustments include generating random MAC addresses, scheduling transmission events, checking for acknowledgment, and more. Algorithm 1 presents a comprehensive overview of our introduced functionalities.  
Two configurations were created for the experiments as depicted in Figure 5. The first configuration consisted of a single STA and an AP, enabling an analysis of covert channel performance without external interference. In the second scenario, the network expanded gradually by adding more stations that generated UDP traffic at a constant rate. This setup allowed us to evaluate the behavior of the covert channel as external network traffic increased and identify factors that could impact covert channel performance. It is important to note that the covert STA did not associate with the network during these transmissions. Table 1 presents the main parameters employed during the simulation. In all figures, the error of each simulation point for the 95% confidence interval does not exceed \(\pm\)2%.  
Figure 5: The two configurations used in the simulation.
# Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities
## 3 Problem Formulation
### Wireless Sensor Network Routing
#### 3.2.1 Routing Based on Graph Theory  
A set of sensors is defined and connected using one-way wireless communication links. Then, if a link exists between sensor A and sensor B, it is denoted as \(e_{A,B}\); in this way, A can send data directly to B. The link \(e_{A,B}\) has a weight or distance (Haversinge for geo-referencing) that is associated and denoted as \(d_{A,B}\).  
Here, the concept of graph theory becomes essential, and we define \(V\) as the set of sensors and \(E\) as the set of existing links (partial or complete mesh topology). Therefore, graph theory describes \(G=(V,E)\) as a directed graph that represents a network topology. Below, a data stream must be transmitted from a source sensor named \(S\) to a destination sensor called \(T\). This flow is transmitted through the intermediate sensors using existing links. The flow between a pair of sensors \((s,t)\) belonging to \(V\) represents the information's source and destination.  
Then, if we define a path \(P\) of the set of sensors, we will have that \(P=\{P_{1},P_{2},\ldots,P_{M}\}\) such that the links \(e_{P_{k},P_{k}+1}\in E\). Thus, we define the path length \(P\) as \(d_{P}\), which is given by \(d_{P}=\sum_{k=1}^{M-1}d_{P_{k},P_{k}+1}\), and define a path for the flow \((s,t)\) as a path \(P_{s,t}\) such that \(P_{1}=s\) and \(P_{M}=t\).  
In addition, the path with the minimum distance is defined as the path \(P_{s,t^{*}}\) such that \(d_{P_{s},t^{*}}<=d_{P_{s},t}\) for any other possible path \(P_{s,t}\). The optimization problem posed in this paper then requires one to find the path with the minimum distance for the flow \((s,t)\).  
To write this optimization problem, it is required to define the variable \(X_{i,j}\), where the link \(e_{i,j}\) is assumed to exist and \(X_{i,j}\) has a value of 1 if the link \((i,j)\) belongs to the path \(P_{s,t^{*}}\); otherwise, the value is 0. Similarly, for a sensor \(i\in V\), we define \(E_{i,out}\) as the set of outgoing links of \(i\). We define \(E_{i,in}\) as the set of incoming links of \(i\).  
Objective function:  
\[\min\ \ \ \sum_{(i,j)\in E}d_{i,j}X_{i,j}, \tag{6}\]  
which is subject to:  
\[\sum_{j|e_{ij}\in E_{i,out}}X_{i,j}-\sum_{j|e_{ij}\in E_{i,in}}X_{i,j}=\alpha_ {i};\ \ \ \ \forall\ i\in V; \tag{7}\]\[\alpha_{i}=\left\{\begin{array}{ll}1,&\text{s i }i=s\\ -1,&\text{s i }i=t\\ 0,&\text{s i }i\
eq s,i\
eq t\end{array}\right. \tag{8}\]
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
## 4 Datasets and Experimental Setup
### Hidden Features Visualization and Comparison  
We further investigate the hidden features learned by different DL models by reducing the feature dimensions and visualizing these features. We choose four DL models (2, 4, 6, and 7 in Table 3, represent CNN, LSTM, and the two hybrid-based DL models, respectively) for the visualization.  
We first split the dataset into two parts randomly, as follows: training set 90% and testing set 10%. Using the same training set, we train the DL models separately. Then, we create a truncated model for each DL model. The truncated model keep the same network layers until the last hidden feature layer. Then, we set the weights for it from the trained model. The truncated model is used to predict the features for the testing data. After that, we apply principal component analysis (PCA) [43] to reduce the features predicted by the truncated model to a lower dimension. The PCA variance is 1.00, which implies that the reduced dimensions do represent the hidden features well (scale 0 to 1). Then, t-Distributed Stochastic Neighbor Embedding (t-SNE) [44] is applied for the visualization.  
Figure 5 illustrates the scatter plots of the hidden features on the last layer of the DL models (CNN, LSTM, CNN+bidirectional LSTM, and Bidirectional LSTM+CNN) after dimensionality reduction.  
In Figure 5, the comparison of the four DL models (CNN, LSTM, CNN+bidirectional LSTM, and bidirectional LSTM+CNN) on both datasets shows that bidirectional LSTM+CNN has a much better classification performance than the other models.  
Figure 6 illustrates the F1-score increasing trends of the four DL models as the number of epochs increase. The datasets are split into two parts randomly, as follows: training set 90% and testing set 10%.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline  & **Approach** & **BioInfer** & **Aimed** \\ \hline \multirow{4}{*}{(1)} & \multirow{4}{*}{CNN+word embedding} & \multirow{4}{*}{70.4} & \multirow{4}{*}{68.8} \\  &  
Figure 5: The scatter plots of the hidden features in the last layer of DL models (CNN, LSTM, CNN+bidirectional LSTM, and bidirectional LSTM+CNN) after dimensionality reduction in the Bioinfer and Aimed datasets. (**a**) CNN on Bioinfer; (**b**) LSTM on Bioinfer; (**c**) CNN+bidirectional LSTM on Bioinfer; (**d**) Bidirectional LSTM+CNN on Bioinfer; (**e**) CNN on Aimed; (**g**) LSTM on Aimed; (**g**) CNN+bidirectional LSTM on Aimed; (**h**) Bidirectional LSTM+CNN on Aimed  
As can be seen from Figure 6, bidirectional LSTM+CNN produces a high F1-score with less epochs than the other models. It also keeps a high classification performance as the number of epochs increases. We also found that LSTM also could obtain a high F1-score on some points, but it needs more training than the proposed bidirectional LSTM+CNN model.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 3 Theoretical Considerations on Phased Antenna Arrays for Leo Satellite Communications
### Phased Antenna Arrays for LEO Satellite Communications
\[\Delta\mathrm{s}=(m-1)d_{x}\ \sin(\theta)\cos(\varphi)+(n-1)d_{y}\ \sin(\theta) \sin(\varphi) \tag{1}\]  
\[\alpha_{mn} =k_{0}\Delta\mathrm{s}=k_{0}(m-1)d_{x}\ \sin(\theta)\cos(\varphi)+k_{0}(n-1 )d_{y}\ \sin(\theta)\sin(\varphi) \tag{2}\]  
\[E_{tot}(\theta,\varphi)=E_{el}(\theta,\ \varphi)\sum_{m=1}^{M}\sum_{n=1}^{N}a_{mn }e^{i\Delta_{mn}} \tag{3}\]  
\[E_{tot}(\theta,\varphi)=E_{el}(\theta,\ \varphi)\sum_{m=1}^{M}\sum_{n=1}^{N}a_{ nm}e^{i[k_{0}(m-1)d_{x}sin(\theta)cos(\varphi)+k_{0}(n-1)d_{y}sin(\theta)sin( \varphi)]} \tag{4}\]  
\[E_{tot}(\theta,\varphi)=E_{el}(\theta,\ \varphi)\sum_{m=1}^{M}\sum_{n=1}^{N}e^{ i[k_{0}(m-1)d_{x}\sin(\theta)\cos(\varphi)+k_{0}(n-1)d_{y}\sin(\theta)\sin( \varphi)]} \tag{5}\]  
\[E_{tot}(\theta,\varphi)=E_{el}(\theta,\ \varphi)\ F_{A}(\theta,\ \varphi) \tag{6}\]  
\[\beta_{mn}= -k_{0}(m-1)d\ \sin(\theta_{0})\cos(\varphi_{0})-k_{0}(n-1)d\ \sin(\theta_{0})\sin(\varphi_{0}) \tag{7}\]  
Figure 1.0  
Figure 1.1  
For a four-element square-phased antenna array, the distances \(d_{x}\) and \(d_{y}\) are equal and will be denoted by \(d\). Thus, the equation of the phase taper is given by (7) so that the array factor in (3) becomes as in Equation (8). As shown in (2) and (7), \(\alpha_{mn}\) parameter depends on the plane wave's natural orientation, \((\theta,\varphi)\) without a phase taper and \(\beta_{mn}\) is a function of the desired orientation \((\theta_{0},\ \varphi_{0})\) and represents the phase taper to be applied. The maximum for the rectangular planar antenna array radiation pattern will occur for \((\theta,\varphi)=(\theta_{0},\ \varphi_{0})\)[29]. In conclusion, from a theoretical point of view, by adjusting the progressive phase shift to each element, the direction of the maximum beam (main lobe) radiated by the phased antenna array system can be controlled. The concept of phase taper refers to gradually changing the phase shift (applying progressive phase shift) between elements of the array to direct the maximum beam in a specific direction.