esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 1 Introduction
This paper explores the improvement in LEO satellite communications by defining and implementing a phased antenna array system based on an original concept that can significantly increase the performance level of the radio communication link. The UHF band from 435 MHz to 438 MHz offers a distinct set of features and advantages that make it suitable for certain applications and services [7; 8]. Among those, communications with CubeSat satellites [9] (specific size and construction standard for small satellites), picosatellites [10], and other small satellites allow receiving images, environmental monitoring, reception of data collected from sensors located on the satellite (data from space or from the environments through which the satellite passes during its orbit) or data collected using sensors located on Earth (e.g., IoT applications). The 435-438 MHz frequency band is used for data collection in terrestrial, atmospheric, or aquatic environments where the IoT sensors located on the ground transmit the data to the satellites. In this paper, we will focus on a phased antenna array system designed for the 435-438 MHz frequency band used by LEO satellite communications. This article brings a new concept in the field of phase antenna arrays via the type and number of elements used and their implementation in a compact system, using a consistent ground plane that integrates all the hardware components necessary for correct operation. The potential applications and significance of a four-element square-phased antenna array system that can enhance the performance level of LEO satellite communications are explored. Following the introduction, Section 2 provides a concise overview of common satellite communication antennas, the current landscape of phased antenna arrays, and their underlying technologies for LEO satellite communications. Additionally, it provides a brief overview of the proposed phased antenna array system and its conceptual originality. Section 3 has the purpose of clarifying certain theoretical considerations on the rectangular planar antenna array radiation pattern and the derivation of the phase shifts required for the elements' feeding signals in the case of the proposed four-element square-phased antenna array. Section 4 shows the general operation principle of controlled reception of a certain LEO satellite using the proposed phased antenna array system and illustrates the potential of the proposed system to be implemented in such a specific application. Section 5 furnishes the design and implementation details of the proposed system, while Section 6 outlines the experimental methods and procedures employed in acquiring the array's radiation pattern and offers a comparative analysis alongside the limitations of the proposed system. Section 7 emphasizes conclusions and discusses future work using the proposed system in LEO satellite communications.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Password Change Phase  
When a client wants to make a password change, then the following steps must be performed without any help from a remote server.  
**Step 1:**: The client inserts the memory device and inputs \(ID_{i}\) and \(PW_{i}\). The client is authenticated before executing Step 2.
**Step 2:**: The client inputs a new \(PW_{i}^{new}\) and computes \(HPW_{i}^{new}\)\(=h(PW_{i}^{new}||b)\), \(B_{i}^{new}=h(h(ID_{i}||HPW_{i}^{new})mod~{}m)\), and \(C_{i}^{new}=C_{i}\oplus HPW_{i}\oplus B_{i}\oplus HPW_{i}^{new}\oplus B_{i}^{new}\).
**Step 3:**: The client uses \(B_{i}^{new}\) and \(C_{i}^{new}\) to replace \(B_{i}\) and \(C_{i}\) in the memory device.  
**5. Security Analysis**  
This section presents the security performance of our proposed scheme, and reveals that our scheme is resistant to several kinds of attacks, such as an offline password guessing attack, a stolen memory device attack, a privileged insider attack, etc.
## 7 Conclusions and Future Work  
In this study, a new positioning solution for indoor environments was proposed; two deep learning-based algorithms were developed using WiFi RSSI data. The first algorithm uses images generated from received signal strength indicators' (RSSIs) continuous wavelet transforms with white Gaussian noise, and then trains a convolutional neural network. The second algorithm uses power spectral density data extracted from the CWT images, which is processed and then used to train an artificial neural network (ANN) to determine the position of an object. Position accuracies of 97.3% and 70.6% for CNN and ANN for room identification, and 94.93% and 60.6% for specific reference points were obtained. Due to the fact that CNN's performance surpasses that of ANN, it is considered as the best algorithm to use in indoor location with Wi-Fi fingerprints in this study, because it achieved better results than the other models did.  
In future, we look forward to applying other data augmentation techniques to test whether they can significantly improve on the Morlet-based feature sets when ANN is applied. We also intend to further experiments the Morse wavelet abilities by using various dilating values to generate feature sets for indoor fingerprint data. Additionally, we intend to experiment with the proposed approach on inertial sensor motion (IMU) data for human subject tracking in indoor environments.  
Writing--original draft preparation, P.S. and O.S.E.; writing--review and editing, O.S.E., T.J.O., and D.S.H. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Mak-Sida Project 381 at Makerere University, Uganda. This research received no other external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Novel RFID Authentication Protocol Based on Reconfigurable RRAM PUF  
[PERSON]  
1 Key Laboratory of Microelectronics Device and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (Q.R.); [EMAIL_ADDRESS] (X.F.); [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (G.X.); [EMAIL_ADDRESS] (G.X.)  
[PERSON]  
1 Key Laboratory of Microelectronics Device and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (Q.R.); [EMAIL_ADDRESS] (X.F.); [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (G.X.);  
Recently, emerging non-volatile memory (NVM) based PUFs have been proposed [4], including phase change memory (PCM) PUFs [5], spin torque transfer magnetic random access memory (STT-MRAM) PUFs [6] and resistive random access memory(RRAM, or memristor) PUFs [7; 8; 9; 10; 11; 12; 13], such as Cross Bar PUF, RRAM PUF, and EPUF. However, most still suffer from some issues, mainly reliability, reconfigurability, and extensive integration cost. It should be noted that the NVM PUF may not follow the canonical definition of PUF, but it is more similar to a true random number generator with a (secure) NVM. Most of these prior works are based on simulations [13] or a single device measurement, which usually could not accurately reflect the statistics of variability and reliability in the memory arrays. To date, there are limited experimental data available in the literature about NVM based PUFs' characteristics at the array-level. In [5], PCM PUFs were experimentally evaluated on 1 Mb arrays, showing that the mean value of inter-Hamming distance (HD) is around 30% without hash and intra-HD can be up to 10%. Many of the prior works [7; 8] use the probabilistic switching of RRAM as the entropy source. However, the cycling endurance degradation will eventually limit its lifetime, and the active switching in each cycle consumes much more energy than the static read-out of resistance. In [12], although using RRAM resistance variability can achieve a high reliability, the weak PUF may not perform particularly well in terms of security.  
On the basis of the above research, this paper proposes a new type of RRAM PUF circuit structure and a lightweight RFID authentication protocol based on this structure. The circuit improves the computational efficiency of the PUF circuit and expand the challenge response space. This protocol can be applied not only to RFID systems that include RRAM PUF, but also to other RFID systems that include reliable PUF models, which are expected to provide more comprehensive protection for RFID.  
The remainder of this paper is arranged as follows: In Section 2, the works related to PUF-based security protocols are introduced. In Section 3, we describe the specific implementation method of the RRAM PUF circuit. In Section 4, we introduce the details of the proposed authentication protocol. In Section 5, the experimental results of the RRAM PUF circuit and the protocol characteristics' analysis are presented. Finally, the paper is summarized in Section 6.
## 3 Coupled Rail Vehicle-Track Dynamic Model
### Vehicle Equations of Motion
#### 3.2.1 Car-Body EoMs  
Equations (7) and (8) represent the NDEoMs of the car-body in vertical and pitch motions  
\[M_{c}\ddot{z}_{c}=-\ K_{sz}\Big{(}2z_{c}\ -\ z_{fb}\ -\ z_{rb}\Big{)}\ -\ C_{sz}\Big{(}2\dot{z}_{c}\ -\ \dot{z}_{fb}\ -\ \dot{z}_{rb}\Big{)}\ -\ \sum_{j=1}^{NN}K_{cz}\Big{(}2\dot{z}_{c}\  
\[ \begin{array}{ll}J_{b}\ddot{\beta}_{fb}\;=\;\Big{\{}K_{pz}\Big{(}z_{fb}&-\;L_{t} \beta_{fb}\;-\;z_{w1}\Big{)}\,+\;C_{pz}\Big{(}\dot{z}_{fb}\,-\,L_{t}\dot{\beta}_ {fb}\,-\,\dot{z}_{w1}\Big{)}\Big{\}}L_{t}\\ &+\;K_{m2}\Big{[}z_{fb}\,-\,(L_{b}\,-\,L_{1}\,-\,L_{2})\beta_{fb}\,-\;z_{m1}\, -\,L_{2}\beta_{m1}\Big{]}(L_{b}\,-\,L_{1}\,-\,L_{2})\\ &+\;\Big{\{}C_{m2}\Big{[}\dot{z}_{fb}\,-\,(L_{b}\,-\,L_{1}\,-\,L_{2})\hat{\beta }_{fb}\,-\,\dot{z}_{m1}\,-\,L_{2}\hat{\beta}_{m1}\Big{]}\\ &-\;K_{m2}\Big{[}z_{fb}\,+\,(L_{b}\,-\,L_{1}\,-\,L_{2})\hat{\beta}_{fb}\,-\, \dot{z}_{m2}\,-\,L_{2}\hat{\beta}_{m2}\Big{]}\Big{\}}(L_{b}\,-\,L_{1}\,-\,L_{2} )\\ &-\;C_{m2}\Big{[}\dot{z}_{fb}\,+\,(L_{b}\,-\,L_{1}\,-\,L_{2})\hat{\beta}_{fb} \,-\,\dot{z}_{m2}\,+\,L_{2}\hat{\beta}_{m2}\Big{]}(L_{b}\,-\,L_{1}\,-\,L_{2}) \\ &-\;C_{m1}\Big{[}\dot{z}_{fb}\,+\,L_{b}\hat{\beta}_{fb}\,-\,\dot{z}_{m2}\,-\, L_{1}\hat{\beta}_{m2}\Big{]}L_{b}\,+\;K_{m1}\Big{(}z_{fb}\,-\,L_{b}\beta_{fb}\,-\,z_{m1}\,+\,L_{1} \beta_{m1}\Big{)}L_{b}\\ &+\;C_{m1}\Big{(}\dot{z}_{fb}\,-\,L_{b}\hat{\beta}_{fb}\,-\,\dot{z}_{m1}\,+\, L_{1}\beta_{m1}\Big{)}L_{b}\,-\,K_{m1}\Big{(}z_{fb}\,+\,L_{b}\beta_{fb}\,-\,z_{m2}\,-\,L_{1} \beta_{m2}\Big{)}L_{b}\\ &-\;K_{pz}\Big{(}\dot{z}_{fb}\,+\,L_{t}\beta_{fb}\,-\,z_{w2}\Big{)}L_{t}\,- \,C_{pz}\Big{(}\dot{z}_{fb}\,+\,L_{t}\hat{\beta}_{fb}\,-\,\dot{z}_{w2}\Big{)} L_{t}\\ \end{array}  \tag{10}\]  
\[ \begin{array}{ll}M_{b}\ddot{z}_{fb}=\;-\;K_{pz}(\;\;z_{fb}\,-\,L_{t}\beta_{ fb}\,-\,z_{w3})\,-\,C_{pz}\Big{(}\dot{z}_{fb}\,-\,L_{t}\dot{\beta}_{fb}\,-\,\dot{z}_{ w3})\,-\,K_{m1}(z_{fb}\,-\,L_{b}\beta_{fb}\,-\,\dot{z}_{w3}\,+\,L_{1}\beta_{m 3})\\ &-\;K_{pz}(z_{rb}\,+\,L_{t}\beta_{rb}\,-\,z_{w4})\,-\,C_{m1}\Big{(}\dot{z}_{ pi}\,-\,L_{b}\beta_{fb}\,-\,\dot{z}_{w3}\,+\,L_{1}\beta_{m3}\Big{)}\,-\,C_{pz} \Big{(}\dot{z}_{rb}\,+\,L_{t}\dot{\beta}_{rb}\,-\,\dot{z}_{w4}\Big{)}\\ &-\;K_{m2}[z_{rb}\,-\,(L_{b}\,-\,L_{1}\,-\,L_{2})\beta_{fb}\,-\,z_{w3}\,-\,L_ {2}\beta_{m3}]-C_{m2}\Big{[}\dot{z}_{rb}\,-\,(L_{b}\,-\,L_{1}\,-\,L_{2})\hat{ \beta}_{rb}\,-\,\dot{z}_{w3}\,-\,L_{2}\beta_{m3}\Big{]}\\ &-\;K_{m2}[z_{rb}\,+\,(L_{b}\,-\,L_{1}\,-\,L_{2})\beta_{fb}\,-\,z_{m4}\,+\,L_ {2}\beta_{m4}]-C_{m2}\Big{[}\dot{z}_{rb}\,+\,(L_{b}\,-\,L_{1}\,-\,L_{2})\hat{ \beta}_{rb}\,-\,z_{m4}\,+\,L_{2}\hat{\beta}_{m4}\Big{]}\\ &-\;K_{m1}(z_{rb}\,+\,L_{b}\beta_{rb}\,-\,z_{m4}\,-\,L_{1}\beta_{m4})-K_{m2}(z _{rb}\,-\,z_{c}\,-\,L_{t}\beta_{c})\,-\,C_{m1}\Big{[}\dot{z}_{rb}\,+\,L_{b}\dot {\beta}_{rb}\,-\,\dot{z}_{m4}\,-\,L_{1}\hat{\beta}_{m4}\Big{]}\\ &-\;C_{a2}\Big{(}\dot{z}_{rb}\,-\,\dot{z}_{c}\,-\,L_{t}\beta_{c}\Big{)}\\ \end{array}  \tag{11}\]  
\[
## 5 Experiments and Results  
The experiments were conducted using Python 3 in a Google Colab environment with GPU support. Several libraries and tools were used to support various workflowsteps, including feature selection, interpretability, data preparation, model training, and visualization. For data manipulation and management, we utilized Pandas (version 2.2.2) and NumPy (version 1.26.4), while Scikit-learn (version 1.6.1) provided machine learning algorithms and preprocessing functions.  
To develop advanced learning models, TensorFlow (version 2.17.1) and Keras (version 3.5.0) were used, offering robust frameworks for building and training neural networks. For visualizing the results, Matplotlib (version 3.10.0) and Seabborn (version 0.13.2) were employed to create clear and informative graphical representations. Additionally, SHAP and LIME were used to explain and interpret model predictions, ensuring trust and security in advanced networks like 6G, which is expected to rely on autonomous and intelligent decision-making capabilities, crucial for safeguarding critical systems and maintaining system integrity.  
This integrated toolset ensures efficient data processing, reliable model development, and insightful visualizations and explanations. With the increasing complexity and demands of future 6G networks, this framework provides a scalable approach to building and optimizing security solutions.
## 2 Materials and Methods
### Offline Analysis  
In our previous study, we concentrated only on full insertion prediction and partial insertion prediction using multiple EPs data only in the offline setting. In this study, machine learning algorithms, namely, shallow neural network (SNN) [34], support vector machine (SVM) [35], k-nearest neighbour (kNN) [36], and random forest (RF) [37], were used for offline analysis of trajectory and position prediction. Offline prediction entails recording of the data and then analysing it for prediction accuracy. In online (real-time when there are limits to latency) prediction, the trained model is tested in real time and its prediction accuracy is validated. Real-time prediction of trajectory and position of electrode array was carried out using only the most apical EP data recordings. An ensemble of the three best performing classifiers (SNN, kNN, and RF) with majority voting mechanism was chosen as our final predictor, as shown in Figure 5.
# Strengthening Design of RC Columns with Direct Fastening Steel Jackets
## 5 Worked Example  
The prototype of the RC column is shown in Figure 10. The height of the column is 3000 mm. The cross-sectional dimension is 500 mm \(\times\) 500 mm. The reinforcement cage is formed by 12T20 and R10@150. At the ultimate limit state, the factored axial load (\(N_{0}\)) and factored lateral load (\(V\)) are 1400 kN and 300 kN, respectively. The elastic modulus of the rebar is 200 GPa. The yield strength of the stirrup and the longitudinal rebar is 400 MPa and 500 MPa, respectively. The cylinder strength (\(f_{c}\)), cube strength (\(f_{cu}\)), and elastic modulus (\(E_{c}\)) of the concrete are 30 MPa, 40 MPa, and 25 GPa, respectively. The cover thickness (\(c\)) is 20 mm. It is postulated that the column experienced a fire event up to 570 \({}^{\circ}\)C. The strength and stiffness of the RC column can be greatly impaired after exposure to high temperature. The proposed strengthening method of directly fastening steel jackets can be used to restore fire-damaged RC columns. The elastic modulus, yield strength and ultimate strength of the steel plate are 200 GPa, 300 MPa and 400 MPa, respectively. The steel angle bracket used to connect the two adjacent steel plates measures 75 mm \(\times\) 75 mm \(\times\) 5 mm with a length of 50 mm. The 4 mm diameter high strength knurled fastener is used in this strengthening system. The partial safety factor of steel and concrete is taken as 1.2 and 1.5.
## 3 Experimental Analysis
### Mixing Ratio Optimization Setup  
In addition to the above experiments, we performed experiments to derive the optimum mixing ratio for satisfying the mechanical properties and vulcanization characteristics based on the experimental results obtained by applying the mixture design strategy. Table 2 lists the target values, upper and lower limits, and weights. These were employed for manufacturing a car grommet. The hardness and tensile strength of the grommet are the factors influencing the insertion force. TC 10 is an important value for scorch management and TC 90 is used for predicting the final curing time and setting the target values given in the table.  
Figure 3 illustrates the optimization plot representing the mixing ratio of each component required to reach the target value, obtained using the response optimization tool Minitab. The plot indicates how different experimental settings affect the predicted responses for a stored model. The predicted values of hardness, tensile strength, and elongation were 50.93, 13.83, and 725.2, respectively, and the T MAX, TC 10, and TC 90 values were 1.89, 70.0, and 180.0, respectively, for the following mixing ratio: CMB 988.09, DM 2.49, TRA 1.15, DPG 1.90, BZ 4.24, S 1.40, and MBT 0.73. The overall desirability of the composite was 79.9%, hardness was 90.6%, tensile stress was 83.2%, and elongation was 98.8%. The desirabilities of T MAX, TC 10, and TC 90 were 73.4%, 99.9%, and 71.3%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Division** & **Lower** & **Target** & **Upper** & **Weight** \\ \hline \multirow{3}{*}{Mechanical characteristics} & Hardness (Shore A) & 40 & 50 & 60 & 2 \\ \cline{2-6}  & Tensile strength (MPa) & 11 & 12 & 13 & 1.5 \\ \cline{2-6}  & Elongation (\%) & 700 & 725 & 750 & 2.5 \\ \hline \multirow{3}{*}{Vulcanization characteristics} & T MAX (Nm) & 1.35 & 1.80 & 2.26 & 1.5 \\ \cline{2-6}  & TC 10 (s) & 65 & 70 & 75 & 1.5 \\ \cline{2-6}  & TC 90 (s) & 150 & 175 & 200 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of parameters for optimization experiments.  
Figure 3: Optimization plot representing the mixing ratio of each component.  
To test the validity of the derived mixing ratio and conditions, we performed additional experiments. For the validation experiments, we mixed the sample at the set mixing conditions and measured the hardness, tensile strength, and elongation, employing the same experimental methods as above; we also analyzed the vulcanization characteristics. Each validation experiment was repeated four times. The analysis of the results yielded a maximum error of 8.9% relative to the predicted values, as indicated in Table 3.
## 3 Materials and Methods
### Software Features and Algorithm
#### 3.1.3 Object Detection  
After preprocessing, manual and automatic image thresholds are applied to detect individuals (Figure 1; step 1.3). In manual thresholding, the classification of pixel \((x,y)\) is performed according to a global threshold defined by the user (\(g\)):  
\[f(x,y)=\left\{\begin{array}{ll}1\;if(x,y)>g\\ 0\;if(x,y)\leq g\end{array}\right\}. \tag{1}\]  
One of the automatic thresholding options is based on [PERSON]'s method [22], wherein the optimal threshold minimizes the within-class variance. This algorithm attempts to find  
Figure 1: Flowchart of the Ethoflow algorithm. The numbers on the right side of the rectangles indicate the steps in the algorithm process. These steps are described in the subsequent sections from Sections 3.1.1–3.1.9. Diamond symbols indicate the option of using the deep learning algorithms (for analysis in a heterogeneous environment or measurement of complex behaviors) according to the need. Thus, the Ethoflow performs kinematic measurements on an ordinary computer and assesses more complex behavior with a graphics processing unit hardware.  
a threshold value (\(k\)) that minimizes the within-class variances \(c_{0}\) and \(c_{1}\) (background and objects, respectively). If the set of gray levels of an image \(L=\{1,\,2,\cdots,l\}\) and the total number of pixels \(N=\{n_{1},n_{2},\cdots,n_{l}\}\), then the probability of occurrence of a gray level \((p_{i})\) is given by  
\[p_{i}=\frac{n_{l}}{N}. \tag{2}\]  
As the method is based on the normalized histogram,  
\[\sum_{i=1}^{L}p_{i}=1. \tag{3}\]  
Thus, the probability of occurrence \((\omega_{i})\), means \((\mu_{i})\), and variances \((\sigma_{i})\) of each class, are given by  
\[\omega_{0}=\sum_{i=1}^{k}p_{i}\text{ and }\omega_{1}=\sum_{i=k+1}^{L}p_{i}, \tag{4}\]  
\[\mu_{0}=\frac{\sum_{i=1}^{k}i*p_{i}}{\omega_{0}}\text{ and }\mu_{1}=\frac{ \sum_{i=k+1}^{L}i*p_{i}}{\omega_{1}}, \tag{5}\]  
\[\sigma_{0}^{2}=\frac{\sum_{i=1}^{k}(i-\mu_{0})^{2}p_{i}}{\omega_{0}}\text{ and }\sigma_{1}^{2}=\frac{\sum_{i=k+1}^{L}(i-\mu_{1})^{2}p_{i}}{\omega_{1}}. \tag{6}\]  
The within-class \((\sigma_{w})\) and between-class \((\sigma_{b})\) variances are  
\[\sigma_{w}^{2}=\omega_{0}\sigma_{0}^{2}+\omega_{1}\sigma_{1}^{2}, \tag{7}\]  
\[\sigma_{b}^{2}=\omega_{0}\omega_{1}(\mu_{1}-\mu_{0})^{2}. \tag{8}\]  
The total variance is \(\sigma_{t}^{2}=\sigma_{w}^{2}+\sigma_{b}^{2}\), and calculating the between-class variance improves the computational time because the variance between classes is based on first-order statistics (class means) [22].
## 2 System Introduction
### Hardware Design  
The automated stimulus module is based on a stepper motor connected to the controller, a control circuit, which makes the clinical cover test automated. The stepper motor used in the proposed system is iHSV57-30-10-36, produced by JUST MOTION CONTROL (Shenzhen, China). The occluder is hand-made cardboard, 65 mm wide, 300 mm high, and 5 mm thick. The subject's sight is completely blocked when the occluder occludes the eye so that our method can properly simulate the clinical cover test. XC7Z020, a Field Programmable Gate Array (FPGA), is the core of the control circuit. The communication between the upper computer and the FPGA is via a Universal Serial Bus (USB). The motor rotates at a particular speed in a particular direction, clockwise or counterclockwise, to drive the left and right movement of the occluder on the slider, once the servo system receives the control signals from the FPGA.  
As for the motion-capture module, the whole process of the test is acquired by the high-speed camera RER-USBFHD05 MT with a \(1280\times 720\) resolution at 60 fps. A near-infrared led array with a spectrum of 850 nm and a near-infrared lens were selected to compensate for the infrared light illumination and separately reduce the noise from the visible light. AMCap is used to perform the control of the camera, such as the configuration of the frame rate and resolution, exposure time, the start and end of a recording, and so on.  
Being ready to execute the strabismus evaluation, the subject is told to sit in front of the workbench with chin on the chin rest and fixate on the given target. The target is a cartoon character displayed on a MATLAB GUI on Monitor 2, for the purpose of attracting attention, especially for children. The experimenter sends a code \"0h07\" (the specific code for automatic cover test) to the system, and the stimulus module reacts to begin the process of the cover test. Meanwhile, the video acquisition application AMCap automatically starts recording. When the cover test ends, AMCap stops recording and saves the video in a predefined directory. Then the video is fed into the proposed intelligent algorithm performing the strabismus evaluation. Finally, a report is generated automatically which contains the presence, type, and degree of strabismus.