esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The key contribution of this effort is threefold: (1) A modular framework is presented to extract crowd congestion information from raw surveillance footage using deep learning-enabled detection and tracking algorithms. (2) The crowd mobility graph (CMGraph) data structure is proposed to store dynamic, macroscopic crowd flow data. The CMGraph formulation is spatiotemporal: the graph topology contains information on the spatial connectivity of the egresses, and the node feature stores temporal crowd flow data. (3) Leveraging the generated spatiotemporal crowd data, we develop real-time congestion alerts and future-time prediction visualizations to assist with manual crowd congestion monitoring. Furthermore, to quantify the benefits of our proposed framework, we validate our implementation with a fully annotated publicly available dataset from New York's Grand Central Station, a busy public urban location [9]. In order to demonstrate the generalizability and capacity in providing qualitative analysis, we also collect an unannotated video from a stadium during a crowded football game. While these datasets have varying crowd density, locations, and public space use cases, the framework can potentially be extended to both videos and likely to other crowd congestion applications. This paper is organized as follows: In Section 2, we review existing crowd-monitoring methods that leverage deep learning and computer vision technologies. In Section 3, we present the overall workflow framework for crowd congestion analysis and detail the three main components of the proposed framework: (1) Dataset Preparation, (2) Trajectory Generation, and (3) CMGraph for Efficient Congestion Monitoring. Section 4 reports qualitative and quantitative results obtained from experiments on real CCTV footage. Section 5 discusses conclusive remarks.
## 2 Fundamental Theory  
### Second-Order Coupled Stochastic Resonance System Model  
The SCSSR model composed of two second-order bistable SR system models is as follows:  
\[\begin{array}{l}\overset{\bullet\bullet}{x}=-kx-\frac{dV_{1}(x)}{dx}+r(y-x)+ s(t)+n(t)\\ \overset{\bullet\bullet}{y}=-ky-\frac{dV_{2}(y)}{dy}+r(y-x)\end{array} \tag{1}\]  
In the above formula:  
\[\begin{array}{l}V_{1}(x)=-\frac{1}{2}a_{0}x^{2}+\frac{1}{4}b_{0}x^{4}\\ V_{2}(y)=-\frac{1}{2}ay^{2}+\frac{1}{4}by^{4}\end{array} \tag{2}\]  
where \(r\) is the coupling coefficient, \(k\) is the damping coefficient, and \(a_{0}\) and \(b_{0}\) are fixed values. In this paper, \(a_{0}=1\), \(b_{0}=1\), \(a\) and \(b\) are parameter variables, and the input signal is \(s(t)+n(t)\). The coupled bistable system consists of control system \(y\) and controlled system \(x\); \(x(t)\) and \(y(t)\) are the output variables of two second-order SR systems. Because the noise and signal act on the controlled system,\(x(t)\) is the final output for the coupled SR system. Formula (1) can be solved with the fourth-order Runge-Kutta equation [54].  
When \(r=0\), it can be seen from Formula (1) that the coupled SR system is two independent second-order SR systems, its output is still \(x(t)\), and the final SR system is changed to an USSSR system.  
When the coupling system interacts with the signal and noise to produce an SR phenomenon, it can be expressed as the following equation:  
\[U(x,y)=-\frac{1}{2}a_{0}x^{2}+\frac{1}{4}b_{0}x^{4}-\frac{1}{2}ay^{2}+\frac{1} {4}by^{4}-\frac{r}{2}(y^{2}-x^{2}) \tag{3}\]  
Take \(a_{0}=1\), \(b_{0}=1\), \(a=1\), \(b=1\), \(r=-0.06\); Figure 1 shows the coupling system potential function \(U(x,y)\). This potential well function is more complex and comprehensive than the traditional bistable potential well function, the dynamic performance of the potential function is better, the particle motion is accelerated, and the SR state is better.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 3 Experimental Results and Analysis
### Resource Utilization  
In this section, the resource utilization of the proposed algorithm is analyzed since the system must utilize less hardware for real-time requirements. Table 2 illustrates the resource utilization comparison of the proposed Canny edge detection algorithm with other Canny edge detection algorithms. Observations show that compared with the implementation [6], the proposed architecture uses less hardware due to the approximation of complex operations, but more storage resources are used because the implementation [6] uses a fixed threshold which degrades the performance of edge detection while our architecture uses an adaptive threshold that requires caching of the entire image. For the implementation [7; 8], they divide an image into several blocks and then use the Canny edge detection algorithm for each block in parallel, which greatly reduces latency and has an absolute advantage in terms of processing speed. However, at the same time, the consumption of hardware resources also increases sharply.  
Figure 20: Edge map: Left: original image; Middle: Edge map without using the logarithm approximation; Right: Edge map with using the logarithm approximation.
# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 3 Timeline Modeling
### Traffic Sensor Data
#### 3.1.3 Seattle Traffic Sensor Data  
Seattle traffic data were provided by Washington State Transportation Center (TRAC) [18]. In contrast with the Busan ITS data, the Seattle traffic data contain volumes and lane occupancies of road parts. Figure 4 illustrates an ERD of Seattle data with three entities and two relationships. Cabinet implies a single location point where several loop detectors are installed. Every loop detector records traffic conditions in each lane. Thus, a CONTAIN relationship is located between LOOP and CABINET entities, and a GENERATE relationship is located between LOOP and LOOP DATA entities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LinkId** & **Date, Time** & **Speed (km/h)** \\ \hline \(\cdots\) & \(\cdots\) & \(\cdots\) \\
1410046200 & 2016-02-14 06:00 & 57 \\
1410046200 & 2016-02-14 06:10 & 45 \\
1410046200 & 2016-02-14 06:15 & 48 \\
1410046200 & 2016-02-14 06:20 & 51 \\
1410046200 & 2016-02-14 00:00 & 58 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traffic log data.  
Figure 3: Entity-relationship (ER) diagram of Busan traffic data.  
To conform the Seattle dataset to the formal definition of road network 2, we convert the road network information. Figure 5a depicts an illustration of cabinets and loop positions. The cabinets are denoted by \(cab_{1},cab_{2},cab_{3},cab_{4}\), where each cabinet contains several loop detectors. For example, \(cab_{1}\) contains loop detectors \(L_{1},L_{5},L_{9},L_{13}\) in different road lanes. The conversion was conducted by transforming a loop detector into a road link and setting LinkID to be the same as the LoopID. The last step is to create a virtual road segment that connects two road links. Figure 5b shows the converted road network of Seattle traffic data, which are consistent with road network Definition 2.  
To conform LOOP DATA with the formal definition of Traffic Data 2, we use only _Date_, _Time_, \(LoopID\), \(Volume\), and \(OccupancyX10\) attributes. _Date_ and _Time_ attributes represent a time point \(t\) on a certain day \(d\). A LoopID denotes a road segment ID with the value of \(L_{i}d\). Finally, we need to calculate speed value \(v\) from \(OccupancyX10\) and \(Volume\) attributes by implementing the equation explained in [18].
# 3D and 4D Printing of Multistable Structures
## 2 3D Printing of Mechanical Multistable Structures
### Compliant Mechanism  
A compliant mechanism is another possible method to realize mechanical multistable structures via 3D printing. A linear deformation of rigid materials can induce beam deflection in compliant mechanisms, and this can be used to induce multistable structures [47; 48; 49; 50; 51; 52]. Beam deflection can occur in 3D-printed structures if a beam is thin enough; thus, 3D-printed compliant mechanisms can be used to create various multistable structures. For example, [PERSON] et al. fabricated global bistable structures via polyjet 3D printing [53]. By 3D printing ball and pin joints, they could realize twisting and rotating bistable structures without post assembly. Figure 6a shows the two stable states of the fabricated twisting bistable structure. Because two stable states have the same shape, the overall energy diagram is also symmetric (Figure 6b). Using pin joints, they also fabricated rotational bistable structures with two different boundary conditions: fixed-pinned and pinned-pinned boundaries (Figure 6c). The fixed-pinned boundary causes the beams in the stable state B to remain deformed. Because the deformed beams retain higher elastic energy, the overall energy diagram becomes asymmetric, as shown in Figure 6d (blue line). On the other hand, the pinned-pinned boundary allows the stable states A and B to have identical shapes. Therefore, the overall energy diagram remains symmetric in this case, as shown in Figure 6d (red line).  
Figure 6: (**a**) Two stable states of a 3D-printed twisting bistable structure with ball joints. The black small dot is marked for eye tracing; (**b**) Simulated energy diagram of the twisting bistable structure. Due to the same shape of beams between two stable states, the energy diagram is also symmetric; (**c**) Two stable states of a 3D-printed rotational bistable structure with pin joints. When the inner cross is rotated clockwise while the outer ring is held fixed, the structure can transform from stable state A to another stable state B; (**d**) Energy diagram of the rotational bistable structure with different boundary conditions (Adapted from [53]. Copyright (2019) Nature Publishing Group).  
Therefore, it is possible to adjust the overall energy diagram of the bistable structure and this can be used to tailor the mechanical response of printed structures. By adjusting the structural parameters or printing materials, it is possible to control the barrier height (i.e., the threshold energy for a shape change), the slope of the barrier (the force required for a shape change), and the amount of initial displacement to trigger a shape change. By connecting bistable structures, it is also possible to create multistable components. Therefore, this work demonstrates that 3D-printed multistable structures can be employed to realize highly controlled reconfiguration.
## 3 Materials and Methods
### Use Case Description  
The chosen use case describes a real-time AF detection and classification system starting with raw ECG data collected from a sensor and ending with a classification result. A visualization of the system is shown in Figure 2. The whole pipeline consists of four processes as follows:  
* Continuously collecting raw ECG data from an ECG holder that is worn by the patient for at least 24 h and forwarding the data to a mobile phone via Bluetooth.
* A mobile application receives the raw ECG data and, after a given time window, data is sent to the algorithm running in the cloud.
* The AF detection algorithm processes the raw ECG data and forwards the results to a second algorithm for further processing.
* Based on the results of the AF detection, the AF classifier predicts the category of AF and sends back the result to the clients.  
Figure 1: Schematic diagram showing the architecture of the framework including the steps of the main phases.  
In the presented use case, AF detection and classification do not require personal data, only the ECG signals that are handled anonymously, so security measures are not required.
# An Adaptive Control Based on Improved Gray Wolf Algorithm for Mobile Robots
## 2 Problem Formulation
To minimize (20) and achieve optimal control, it is sufficient to set a function \(V\Big{(}\widetilde{z}(t),t\Big{)}\) that is fulfilled by the Hamilton-Jacobi-Bellman equation  
\[\frac{\partial V\Big{(}\widetilde{z}(t),t\Big{)}}{\partial t}+\min_{u}H\Bigg{[} \widetilde{z},u,\frac{\partial V\Big{(}\widetilde{z}(t),t\Big{)}}{\partial t }\Bigg{]}=0 \tag{22}\]  
\[H\Bigg{(}\widetilde{z},u,\frac{\partial V\Big{(}\widetilde{z}(t),t\Big{)}}{ \partial t}\Bigg{)}=\frac{\partial V\Big{(}\widetilde{z}(t),t\Big{)}}{ \partial\widetilde{z}}\dot{\widetilde{z}}(t)+L\Big{(}\widetilde{z},u\Big{)} \tag{23}\]  
Furthermore, the \(V\Big{(}\widetilde{z}(t),t\Big{)}\) obeys the following differential equation  
\[-\frac{\partial V\Big{(}\widetilde{z}(t),t\Big{)}}{\partial t}=\frac{ \partial V\Big{(}\widetilde{z}(t),t\Big{)}}{\partial\widetilde{z}}\dot{ \widetilde{z}}(t)+L\Big{(}\widetilde{z},u_{o}\Big{)} \tag{24}\]  
Therefore, the minimum term \(\min_{u}H\) is attained when \(u(t)=u_{o}(t)=-R^{-1}B^{T}\widetilde{Pz}(t)\) such that  
\[H^{*}=\min_{u}H=\min\Bigg{(}\frac{\partial V\Big{(}\widetilde{z}(t),t\Big{)}}{ \partial\widetilde{z}}\dot{\widetilde{z}}(t)+L\Big{(}\widetilde{z},u\Big{)} \Bigg{)} \tag{25}\]  
On the other hand, in accordance with the universal approximation theorem, a neural network is used to achieve the approximation of continuous nonlinear functions in the dynamic system. The relationship between the inputs and outputs can be clearly represented by the following matrix equation  
\[j(q)=W^{T}\varphi(q)+\varepsilon \tag{26}\]  
\[\hat{j}(q)=\hat{W}^{T}\varphi(q) \tag{27}\]  
where, \(q\in\Omega\in R\) is the input vector, \(W\in R^{l}\) is the optimal weight vector, with \(l>1\), \(\hat{W}\) is the neural network estimated weights, \(\hat{j}\) is an estimated value, \(\varepsilon\) is an inherent approximation error, satisfying \(|\varepsilon|\leq\varepsilon_{m}\), \(\varepsilon_{m}>0\) is the upper bound of the neural network approximation error, \(\varphi(q)=\left[\varphi_{1}(q),\varphi_{2}(q),\cdots,\varphi_{m}(q)\right]^{T}\) is the smoothing vector. The Gaussian function is usually chosen as the radial basis function, expressed as  
\[\varphi_{k}(q)=\frac{1}{\sqrt{2\pi\widetilde{\varphi}_{k}}}\text{exp}\left(- \frac{(q-v_{lk})^{T}(q-v_{lk})}{2\widetilde{\varphi}_{k}^{2}}\right) \tag{28}\]  
where \(k=1,\cdots,n\), \(v_{lk}=\left[v_{l1},v_{l2},\cdots,v_{ln}\right]^{T}\) is the center of the attraction domain, \(\widetilde{\varphi}_{k}\) is the standard deviation of the Gaussian function.
## 4 Results
### Performance Comparison  
In this research, a multi-objective experience replay algorithm based on multiple indicators is proposed to address the problem of the slow convergence of the multi-objective deep reinforcement learning algorithm. We compare the multi-indicator experience replay with the random replay (Random_Replay) [36] under the constraint of Scenario 1 to illustrate the effectiveness of the multi-indicator experience replay applied to multiple objectives. The error between the current value and the predicted value needs to be extracted when adjusting the weights for various objectives of the DQN network, called Loss. Due to the fact that this article has two optimization targets, it will also produce two Losses. We can observe the changing trend of the losses throughout the experiment, and so we are able to roughly predict whether the method is close to convergence and the speed of convergence. We define the Loss of DQN for tumor cells as Loss1, and the loss of DQN for effector-immune cells as Loss2. The changes in Loss1 and Loss2 are shown in Figure 10(a). It is clear that the MIER-MO-DQN approach exhibits greater fluctuations in the early iterations. This is because the size of the loss is mostly driven by the TD error, and the larger the TD error, the larger the loss. In fact, the multi-indicator experience replay mainly learns from the experience with the larger TD error and the information entropy of Q in the early iterations in order to speed up model convergence. The loss of the MIER-MO-DQN method is therefore initially much larger than the loss of the random method, but as the number of iterations rises, the loss of the MIER-MO-DQN method begins to become significantly lower than the loss of the random replay, and the loss1 and loss2 of the MIER-MO-DQN method always fluctuates within a particular range after 200 iterations without any greater change. Therefore, MIER-MO-DQN's multi-indicator experience replay is more effective in this problem.  
Another crucial indices of the deep reinforcement learning algorithm's performance is the average reward. The average reward of the reward function \(r_{1}\) during each iteration is shown in Figure 11(a), and the average reward for \(r_{2}\) throughout each iteration is also shown in Figure 11(b). Because the effects of random strategy and action decision mechanisms are almost identical in the initial time, the average reward of \(r_{1}\) is higher and the average reward of \(r_{2}\) is lower in the initial time. The time of treatment gradually increases as the iteration rises, and as the action-value function becomes closer to the true value, the average reward of \(r_{1}\) starts to decline while the average reward of \(r_{2}\) exhibits a general increasing trend. After about 4600 iterations, the average reward of both approaches converges near zero. The MIER-MO-DQN algorithm has strong convergence in personalized dosing on the basis of the aforementioned experimental data.  
Figure 11: Change in (**a**) Loss1, (**b**) Loss2.
# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 4 Experimental Results
### Quantitative Evaluations
#### 4.1.3 Influence of User Parameters on the Number of Transitions  
Transitions in StageMix are generated using FBT, INTER-C, and INTRA-C. To control their frequency, two user-defined parameters were introduced to specify the minimum time interval between different types of transitions. The first parameter, \(t_{INT}\), defines the minimum number of frames that must elapse between an INTER-C or INTRA-C and the subsequent transition. The second parameter \(t_{FACE}\), sets the minimum number of frames between an FBT and the subsequent transition. For example, setting \(t_{INT}=90\) ensures that, after an INTER-C or INTRA-C, the _None_ type will last for at least 90 frames within the same stage footage. In our implementation, \(t_{FACE}\) was set to 45, ensuring that after an FBT, the same stage is maintained for at least 45 frames before the next transition occurs.  
Table 5 presents the number of transitions in the three \"Darl+ing\" [28] videos by Seventeen, using different values of the user-defined parameter \(t_{INT}\). As \(t_{INT}\) increases, the frequency of INTER-C and INTRA-C decreases, while the frequency of FBT increases. Additionally, a higher \(t_{INT}\) value results in a reduced total number of transitions and longer average time intervals between scene changes. This enables users to adjust \(t_{INT}\) to control the transition frequency and the number of FBT in the StageMix according to their preferences.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(t_{INT}\) (Frame) & FBT & INTER-C & INTRA-C & \
\begin{tabular}{c} **Total Number of** \\ **Transitions** \\ \end{tabular} &
\begin{tabular}{c} **Average Time Interval Between** \\ **Transitions (s)** \\ \end{tabular} \\ \hline
45 & 16 & 24 & 13 & 53 & 2.19 \\ \hline
90 & 17 & 12 & 7 & 36 & 3.23 \\ \hline
150 & 20 & 9 & 4 & 33 & 3.52 \\ \hline
240 & 21 & 5 & 1 & 27 & 4.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of Transitions Based on Different \(t_{INT}\) Values.
## 2 Existing Studies and Challenges in Underwater Wireless Sensor Networks (UWSNs)
The major contributions of our proposed protocols are as follows:  
* We propose an underwater acoustic channel detection mechanism to find the vacant channels with a high probability of detection and low probability of missed detection and false alarms. * An underwater acoustic channel assignment mechanism is proposed to assign high data rates channels to acoustic sensor nodes with longer idle probability in a robust manner. * A hybrid underwater routing mechanism is proposed to convey collected data to the sink. The proposed mechanism while conveying information avoids congestion, data path loops and balances the energy consumption load of UWSNs. * The performance of the QoSRP protocol against existing routing schemes is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in the UWSNs. The later sections explain the working mechanism of our proposed routing protocol in UWSNs.
# Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence
## 2 Literature Review
### Failure Mode Ranking
As a matter of fact, FMEA risk assessment is a group decision behavior that relates to the agreement degree of FMEA team experts and the interdependent relationship between the experts' preferences. However, the existing methods fail to consider these two situations. This study proposes a hybrid risk priority model based on the extended geometric Bonferroni mean, a consensus-reaching process, and an improved MABAC method, where the ranking of failure modes can be determined. In this model, on the one hand we define an extended GBM operator to integrate the experts' preferences, and on the other hand we construct a consensus-reaching process to measure the agreement degree of team experts. In addition, linguistic terms expressed in interval-valued Pythagorean fuzzy numbers are used to depict the experts' preferences, and the improved MABAC method is employed to determine the risk priority of failure modes. The contribution of this paper is that the extended GBM operator is utilized to fuse the experts' preferences, which can reflect the interdependence of experts' preferences, and the consensus-reaching process is introduced into the FMEA to improve the experts' acceptance of the results.
## 2 Materials and Methods
### Data Analysis  
According to best praxis for in situ measurements, there should only be a short time between background \(BG2(t_{BG2})\) and system \(M_{sys}(t_{sys})\) measurements, to calculate system emissions \(Sys\big{(}t_{sys}\big{)}\) according to Equation (1).  
\[\begin{split} Sys\big{(}t_{sys}\big{)}&=M_{sys} \big{(}t_{sys}\big{)}-BG2_{sys}\big{(}t_{sys}\big{)}\\ &\approx M_{sys}\big{(}t_{sys}\big{)}-BG2(t_{BG2})\end{split} \tag{1}\]  
Because of the short time assumed in standard testing, differences are neglected between background measurement \(BG2(t_{BG2})\) and the implicit system background \(BG2_{sys}(t_{sys})\). This means that \(BG2(t_{BG2})\) and \(BG2_{sys}\big{(}t_{sys}\big{)}\) are treated as equal \(BG2(t_{BG2})\approx BG2_{sys}(t_{sys})\).  
This short time assumption cannot be made for in situ measurement of PV systems.  
Whereas energizing and starting up a PV system take several minutes, as do de-energizing and disconnecting optimizers, this time delay causes problems in distinguishing background \(BG2_{sys}\big{(}t_{sys}\big{)}\) from system emissions \(Sys\big{(}t_{sys}\big{)}\).