esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 4 Experiments and Results Analysis
### Experimental Design
#### 4.1.1 Dataset Introduction  
This experiment utilizes the MOOCCubeX dataset [27] provided by Tsinghua University's Knowledge Engineering Laboratory. Supported by \"XuetangX\", one of China's largest MOOC platforms, MOOCCubeX is one of the most comprehensive open education datasets in terms of scale and coverage. It encompasses multiple modalities of course resources and student behavior data, providing extensive research material and analytical bases.  
The MOOCCubeX dataset mainly comprises two parts:  
1. Course Resource Data  
This includes course information, videos, exercises, and course concepts. The course information section covers 4216 courses, video data contains metadata for 230,263 videos, and exercise data includes 358,265 exercises with over 1.2 million related questions. The course concepts section includes 637,572 fine-grained course concepts, providing links between these concepts and the corresponding videos and exercises.
2. Student Behavior Data  
This part consists of user profiles for 3,330,294 students, over 296 million video viewing records, and 21 GB of exercise responses. These data provide a robust foundation for modeling and analyzing learner behavior.  
Figure 5 illustrates the structure and processing flow of the MOOCCubeX dataset, encompassing the handling of course resources and student behavior data, concept extraction, and knowledge graph construction. Course resources cover videos, exercises, and course concepts, while student behavior data records user learning activities. Througha concept acquisition module, the system associates fine-grained course concepts with learning resources to build a concept graph, which, in conjunction with external resources (e.g., academic papers, technical Q&A), expands the knowledge coverage.
# Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
## 1 Introduction  
Artificial reverberation algorithms that can simulate a natural reverberation effect have continuously advanced since [PERSON] first added a simulated room acoustic effect to a sound more than 60 years ago [1]. Such algorithms are applied in the field of virtual environments to simulate room acoustics and create convincing immersive experiences [2], for example, in augmented reality (AR) scenarios. AR is a technology that supplements the real world with virtual objects or scenes to make them appear to coexist in the same place as the real world [3]. If artificial reverberation is to be used in AR scenarios, it needs to meet the three properties of AR [3]:  
* Combining real and virtual objects in a real environment;
* Running interactively and in real time;
* Registering (aligning) real and virtual objects with each other.  
These requirements emphasise the coexistence of virtual and real in the same space, the interactive alignment and mutual registration of virtual sources with physical reality and the real-time embeddedness (i.e., deviation from virtual reality) of AR, and its interactive nature [4]. Therefore, an artificial reverberation suitable for AR environments needs to be perceived as similar or the same as real reverberation, and react dynamically and in real-time.  
Beginning in the 1960s, many artificial reverberation algorithms have been presented, the earliest and most famous being the reverberation algorithm presented by [PERSON] [5]. Five popular reverberation algorithms presented by [PERSON] [6], [PERSON] [7],[PERSON] [8], [PERSON] [9], and [PERSON] [10] are considered in this paper. A set of new binaural reverberation algorithms adapted from the [PERSON] and [PERSON] reverberation algorithms are presented as well, with a linear structure that is simpler than the topology of [PERSON] [9] and the feedback matrix structure of Alary [10]. The input audio enters through an FIR filter that simulates early reflections, and is output through a final all-pass filter without passing through a feedback loop [11].  
In this paper, the seven aforementioned reverberation algorithms were implemented and the similarities between their perceived reverberation effects and measured binaural room reverberation were compared. A succinct review of reverberation algorithms applied to AR scenarios is presented before discussing each of the algorithms in depth. Section 2 presents the experimental materials, and methods and the results are presented in Section 3. Section 4 discusses the results, and Section 5 presents the conclusions of the study.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### Passwords and Their Vulnerabilities  
Passwords as an authentication method have become vulnerable to numerous attacks [1]. The most famous is phishing, where an attacker misleads the victim to reveal their credentials. However, phishing is not the only existing threat that passwords are subject to. Keyloggers are hardware or software tools that attackers use to log the user input into a system. Some hardware keyloggers can be physically placed between the keyboard and the computer to log keystrokes that are directly sent to the attacker. Moreover, it is a reality that information leaks often appear in the news. These leaks can threaten passwords if they contain password hashes used by authentication servers [1]. Although an attacker is not able to directly use password hashes in the authentication portal, the password can be obtained from them with different techniques. Password-cracking techniques are based on automated trial and error methods for finding the original password.  
In conclusion, attacks like phishing, password stealing, and cracking attacks entail a security risk for password-based authentication methods that leave the user unprotected and without control over their credentials. This makes the attacker able to access these systems and, therefore, to steal the information and data they hold.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 3 Methodology  
In this section, we provide details on our method, starting with FreANet's overall architecture and then introducing its core modules: the MDFE and MPA.  
Before delving into the details, we define the notations and Fourier transform used throughout the paper. Let \(x\in\mathbb{R}^{H\times\text{W}\times\mathbb{C}}\) denote an input feature in the spatial domain.  
The Fourier transform \(\mathcal{F}\) converts the input feature into the frequency domain, representing it as its corresponding complex component \(X=X_{\text{re}}+jX_{\text{im}}\in\mathbb{C}^{H\times W\times C\times 2}\). Here, the last dimension, '2', refers to the real \(X_{\text{re}}\) and imaginary \(X_{\text{im}}\) components. The Fourier transform is expressed as  
\[\mathcal{F}(x)(u,v)=\frac{1}{\sqrt{HW}}\sum_{h=0}^{H-1}\sum_{v=0}^{W-1}x(h,w)e^ {-j2\pi(\frac{h}{H}u+\frac{w}{W}v)}, \tag{1}\]  
where \(u\) and \(v\) are the corresponding coordinates in the frequency domain. The inverse Fourier transform is denoted by \(\mathcal{F}^{-1}(X)\). We employ the FFT and IFFT algorithms [34] for the Fourier transform and its inverse process.
# Provision of Energy- and Wavelength-Efficient Traffic
## 3 Traffic Grooming Algorithm Design  
The parameters defined in the last section are included in constraints and objective functions, and once the values of these parameters are determined, the objective functions are only influenced by the defined decision variables. Consequently, we can resort to heuristic algorithms to solve the energy- and wavelength- minimized traffic grooming problem. To formulate the traffic grooming, we introduce the sub-wavelength as the intermediate granularity between the basic requests and wavelengths. Consequently, the traffic grooming problem can be formulated as a two-phase ILP problem, i.e., the first phase for TAASA and the second phase for SG. For analytical derivation, we regard the two-phase traffic grooming as a multivariate matching process. Specifically, the connection requests, sub-wavelengths, and wavelengths are three sets of players to be matched with each other to minimize the energy consumption and the used wavelengths, while the interdependencies exist among the requests due to the flow aggregation mechanism. It enables us to solve the two-phase ILP problem by utilizing the matching algorithm.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.4 Heat-Exchange Devices (HX)  
The heat-exchange (HX) devices convert the steam heat of the waste heat boiler into the heat energy required by the user, and its mathematical model is as follows:  
\[\begin{array}{l}H_{HX}(t)=H_{h}(t)\eta_{HX}\\ 0\leq H_{h}(t)\leq H_{WH}(t)\\ H_{HX}^{\min}\leq H_{HX}(t)\leq H_{HX}^{\max}\end{array} \tag{7}\]  
where \(H_{HX}(t)\) is the output heat of HX at time slot \(t\). \(H_{h}(t)\) is the heat gained from WH by HX. \(H_{HX}^{\min}\), \(H_{HX}^{\max}\) are minimal and maximal output heating power of HX, respectively.
## 4 Prototype Implementation and Experiment  
### Prototype Design  
The prototype of RHop is shown in Figure 12. The main structural parts are manufactured by 3D printing, and the axles are made of carbon fiber rods to reduce the overall weight of the RHop. The driving motor adopts a servo that can rotate around the whole circumference, with an output driving torque of 0.22 N\(\cdot\)m. The prototype size is 252 mm \(\times\) 155 mm \(\times\) 85 mm, and its weight is about 560 g (including batteries, motor, etc.).
## 4 Discussion  
In the previous two chapters, we discussed the system model, the controller, and the operation results. In the design of the Y-direction controller, we used method 4 to reduce the lagging effect. In this chapter, we show the performance of this method in this system.  
We simplified the structure of the system, keeping only the actuator y\({}_{\text{b}}\), and the movement of the point was limited to the vertical direction. The performance is shown in Figure 13 in contrast.  
Figure 12: Speed inputs of the four actuators and the curves of the three outputs of the real system. (**a**) Speed input, v\({}_{\text{ys}}\) in green, v\({}_{\text{yb}}\) in dark red, and v\({}_{\text{y}}\) in purple; (**b**) speed input, v\({}_{\text{x1}}\) in yellow and v\({}_{\text{z1}}\) in green; (**c**) output F in red; (**d**) output \(\alpha\) in yellow and \(\gamma\) in green.  
Figure 13: Performance. (**a**) Inputs v\({}_{\text{yb}}\) in blue and v\({}_{\text{ybmes}}\) in purple without method 4. (**b**) Inputs v\({}_{\text{yb}}\) in blue and v\({}_{\text{ybmes}}\) in purple with method 4. (**c**) Output F in red without method 4. (**d**) Output F in red with method 4.  
It can be seen from the curve that when method 4 was used, the fluctuation amplitude of the system input and the output curve decreased significantly. Detailed results for 200 ms are shown in Figure 14.  
It can be seen that there was an obvious and stable lagging relationship between the system input speed and the measured speed, and the system output F fluctuated by approximately \(\pm\)10 N before method 4 was applied. When method 4 was applied, the input speed of the system was almost consistent with the measured speed without obvious lagging, and the fluctuation of F was also suppressed to approximately \(\pm\)2 N.  
The lag compensation method proposed in this paper can compensate for the execution lag in this system. The particularity of this compensation method lies in directly processing the deviation between the calculated input and the measured input of the controller, making the calculated input closer to the measured input, thereby reducing the oscillation effect caused by the lag.
## 4 Results
### Economic Study  
To carry out an economic study, a general methodology is developed to characterize the savings provided by the proposed strategy in different electricity markets, for different batteries and PV power installed. The most important parameter is related to the market and is based on the average price differential between the PT and OT for battery savings and the energy prices during the hours of PV production.  
To make the specific calculation, the Copernicus Atmosphere Monitoring Service [58] is used to calculate the power and the moments when the PV energy is produced at the location of the BTS. By performing these calculations for the last year, savings of 153.93 \(\upxi\)/kW of installed PV power are obtained; the total savings are 461.78 \(\upxi\) for the 3 kW PV installed. Considering an installation price of 1000 \(\upxi\)/kW installed, the PV installation return on investment occurs after 6.5 years.  
In the case of the BESS, the calculations are based on the datasheet provided by the manufacturer, which sets a battery discharge efficiency of 85%, an optimum Depth of Discharge (DoD) of 60% and 1500 cycle life for the battery used in the BTS. In addition, it is assumed that these batteries are normally changed every eight years (2920 days); since a cycle is carried out every day, a factor of 0.513 (1500/2920) is applied. This way, the number of days per year that the strategy can be applied can be computed, to match the end of BESS life with the time when the BESS would be replaced. Taking into account that the Spanish average daily price differential between the charging and discharging hours of a whole year is approximately 0.09 \(\upxi\)/kWh, the annual savings obtained with a BESS with the described characteristics is 16.8 \(\upxi\)/kWh of installed capacity. If the correction factor for the batteries to last eight years is considered, the savings obtained is 8.63 \(\upxi\)/kWh for each of the eight years. The total annual savings for the installed battery capacity, 9.12 kWh, is 153.26 \(\upxi\)/year without the correction and 79.73 \(\upxi\)/year after applying the factor.  
Finally, Table 2 provides a summary of the obtained results for a BTS with 2 kW of critical loads and 1 kW of non-critical load with energy supplied by the Spanish energy market, considering a daily price differential between the peak and off-peak hours of approximately 0.09 \(\upxi\)/kWh.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Characteristics** & **Annual Savings** & **Total Annual Savings** \\ \hline PV & Peak power = 3 kW & 153.93 \(\upxi\)/kW & 461.78 \(\upxi\) \\ \hline \multirow{4}{*}{BESS} & Capacity = 9.12 kWh & & \\ \cline{2-4}  & DoD = 60\% & & \\ \cline{1-1} \cline{2-4}  & Efficiency = 85\% & & \\ \cline{1-1} \cline{2-4}  & Cycle life = 1500 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results summary.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 1 Introduction
For instance, parallel processing technologies, like GPUs or FPGAs, can be used to increase computation speed, or the algorithm itself can be optimized to reduce computational complexity. Therefore, this study proposes an efficient architecture leveraging GPU parallel processing technology to meet the constraints of limited computational resources and real-time inference in LEO satellites while effectively handling large-scale data processing and complex computations required for advanced routing algorithms. This approach not only addresses the technical limitations but also has the potential to revolutionize the range and quality of services that LEO satellite networks can provide in the future. Furthermore, the feasibility of this architecture is directly validated onboard, offering a practical and innovative solution for real-world applications. Our main contributions are as follows:  
* Implementing a DQN-based inference process considering disconnection between LEO satellites in dynamic networks. * Proposing a GPU-based parallelization method to accelerate the DQN-based inference process for real-time systems. * Verifying the improved performance of the proposed method in real onboard systems with limited resources. Section 2 introduces related work. In Section 3, we discuss grid-based MDPs (Markov decision processes), Dueling DQN, and the challenges of onboard real-time CNN (convolutional neural network) inference. Section 4 introduces GPU-based CNN acceleration techniques in detail, along with the proposed approach for onboard computation. In Section 5, we analyze routing simulation results and evaluate the acceleration performance of the proposed method.
# Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening
## 4 Multi-Objective ACC Strategy with Constraint Softening
### Objective Functions  
Considering the change rate of the desired acceleration and making the control signal change smoothly, this paper takes the variation of the desired acceleration as the first control objective. The first control objective function can be expressed in the form of the least squares norm as  
\[\begin{array}{rl}I_{1}&=\frac{1}{2}\overset{c-1}{\underset{i=0}{\sum}}\left\| \Delta u(k+i)\right\|_{\text{s}}^{2}\\ &=\frac{1}{2}\overset{c-1}{\underset{i=0}{\sum}}\left\|u(k+i)-u(k+i-1) \right\|_{\text{s}}^{2}\\ &=\frac{1}{2}\left[\begin{array}{ccccc}u(k)\\ u(k+1)\\ \vdots\\ u(k+c-1)\end{array}\right]^{\text{T}}\left[\begin{array}{ccccc}2s&-s&&\\ -s&2s&-s&\\ &\ddots&\ddots&\ddots&\\ &&-s&2s&-s\\ &&&-s&s\end{array}\right]\left[\begin{array}{c}u(k)\\ u(k+1)\\ \vdots\\ u(k+c-1)\end{array}\right]\\ &+\left[\underbrace{-\left[\begin{array}{c}s\\ 0\\ \vdots\\ 0\end{array}\right]}_{M_{2}}u(k-1)\right]^{\text{T}}\left[\begin{array}{c}u (k)\\ u(k+1)\\ \vdots\\ u(k+c-1)\end{array}\right]+\underbrace{\frac{1}{2}u^{\text{T}}(k-1)su(k-1)}_{p_{1}} \\ &=\frac{1}{2}U_{c}^{T}H_{1}U_{c}+g_{1}^{\text{T}}U_{c}+\rho_{1}\end{array} \tag{23}\]  
where \(s\) is the weight matrix of the desired acceleration variation.  
According to the MPC algorithm, the aim of the controller is to minimize the error between the predicted output value and the reference output value. Therefore, the second objective function can be expressed in the form of the least squares norm as \[\begin{array}{ll}J_{2}&=\frac{1}{2}\|\widehat{Y}_{p}-Y_{r}\|_{Q}^{2}=\frac{1}{2} \|\overline{D}U_{c}+\Phi_{2}-Y_{r}\|_{Q}^{2}\\ &=\frac{1}{2}\|\overline{D}U_{c}-\underbrace{\left(Y_{r}-\Phi_{2}\right)}_{b_{1 }}\|^{2}_{Q}=\frac{1}{2}\|\overline{D}U_{c}-b_{1}\|_{Q}^{2}\\ &=\frac{1}{2}U_{c}^{T}\overline{D}^{T}Q\overline{D}U_{c}+\left(\underbrace{- \overline{D}^{T}Qb_{1}}_{g_{2}}\right)^{T}U_{c}+\underbrace{\frac{1}{2}{b_{1}} ^{T}Qb_{1}}_{\rho_{2}}\\ &=\frac{1}{2}U_{c}^{T}H_{2}U_{c}+g_{2}^{T}U_{c}+\rho_{2}\\ &\text{with }Y_{r}=\underbrace{\left[\begin{array}{c}fa\\ fa^{2}\\ \vdots\\ fa^{p}\end{array}\right]}_{FA}y(k)\text{ and }Q=\left[\begin{array}{ccc}q\\ &q\\ &&\ddots\\ &&&q\end{array}\right]\end{array} \tag{24}\]  
where \(Y_{r}\) is the reference output matrix and \(Q\) is the output weight matrix at each step of the predict horizon; \(\textit{fa}=\text{diag}[\varphi_{1},\,\varphi_{2},\,\varphi_{3},\,\varphi_{4}]\) is the reference output matrix of the first step; \(q=\text{diag}[q_{1},\,q_{2},\,q_{3},\,q_{4}]\) is the output weight vector at each step; and \(q_{1},\,q_{2},\,q_{3},\,\) and \(q_{4}\) are the weight coefficients.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 3 JPS Path Optimization Based on Angle-Propagation Theta* Algorithm
### Angle-Propagation Theta* Algorithm  
In this paper, based on the LOS reachability detection between two nodes proposed in the above optimization process, the Angle-Propagation Theta* algorithm is introduced to determine whether there is LOS reachability between two nodes by calculating the viewable range of the nodes in the section to be optimized. As shown in Figure 6, AP Theta* calculates the viewable angle range when expanding outward from a grid and determines whether the new node is reachable based on whether the angle at which it is located is included in the viewable angle range. Because the time to compute the angle is essentially constant, the consumption of AP Theta* in scaling is not linearly related to the number of grids, and the optimization efficiency of the JPS shortest path is further improved.
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 2 Related Work
In particular, many factors are not taken into account, from taxi driving rules to passenger behavior in the city according to different timelines [23]. Furthermore, urban taxi drivers are rarely affected by dispatch centers, especially in China. In recent years, car-hailing apps appear as valuable solutions through which a passenger can directly order a taxi online. Taxi dispatching algorithm used in car-hailing apps have a great impact on the taxi running rules, which effectively reduced waiting times to a certain extent. The mode of demand-response (DR) taxi, such as Didi in China, UBER in the USA is able to show the waiting times for users, while this is the possible waiting time taken to drive from the location of the taxi whose driver is willing to go to the user's place. The length of this waiting time in DR mode is decided by the distance between the responding taxi and user. Users always prefer to know the possible arrival time of the 1 st empty taxi that is based on historic experiences. Learning taxi running rules from a large amount of historic taxi trajectory is a promising method for the waiting time prediction. This paper introduces a spatio-temporal schedule-based neural network model for urban taxi waiting time prediction. Based on the implicit influence of time, location, weather and urban residents' daily schedules, the neural network was designed to learn taxi running rules for deriving the most accurate prediction of taxi waiting times.
## 4 Proposed Solution
### Trajectory Optimization Algorithm Based on DDPG
#### 4.3.6 Complexity Analysis  
After sufficient training, the model proposed in this paper, including the networks, was used for testing. In each time slot, all UAV actions were generated by the actor network instead of being selected from random actions. In this article, the time complexity for the UAV to select actions from the actor network is \(O\left(\sum_{g=1}^{G}n_{g}n_{g-1}\right)\). Where \(G\) is thenumber of network layers in the deep learning network, and \(g\) is the number of neurons in layer \(G\). The time complexity of determining whether the UAV has collided and is within a reasonable communication distance is \(O\big{(}M^{2}\big{)}\). The time complexity of updating user coverage in observation space \(\mathcal{O}\) is \(O(KM)\). Therefore, our algorithm's time complexity is \(O\big{(}M+PN\Big{(}M\sum_{g=1}^{G}n_{g}n_{g-1}+M^{2}+KM\Big{)}\big{)}\).