esa-sceva/satcom-chunk-collection huggingface dataset:
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 4 Opportunities of ChatGPT in Education
### Bridging Educational Gaps and Accessibility  
AI-powered learning tools like ChatGPT can address educational disparities by providing access to learning resources for underserved communities [38]. ChatGPT can also support learners with disabilities through speech-to-text, language translation, and accessibility-friendly interfaces [39].
## 3 Results
### Adaptability Assessment Tools and Models
#### 3.1.1 Adaptive Reuse Potential Model (ARP), 2007  
The ARP model was developed by [PERSON] et al. [41] to assist the industry in identifying and evaluating buildings potentially adequate for adaptive reuse before they reach a demolition point. By this meaning, a candidate building for ARP assessment should be at a point in its age near the end of its useful life. The tool framework suggests that a building's useful life can be determined by multi-nature obsolescence criteria that manifest as reductions in its physical life. Conducting an ARP assessment requires an estimation of the expected physical life of the building and its current age. Evaluation of the physical, economic, functional, technological, social, and legal obsolescence is also required. In this respect, the authors developed a measurement methodology for each of the obsolescence criteria and created a scale to calculate each criterion's physical life reduction percentage. These data requirements allow users to determine the useful life of the building in question using Equation (1).  
\[(L_{u})=\frac{L_{p}}{\left(1+\sum_{i=1}^{6}O_{i}\right)^{L_{p}}} \tag{1}\]  
where \(Lu\) = useful life (years); \(Lp\) = expected physical life (years); \(O_{1}\) = physical obsolescence (%); \(O_{2}\) = economic obsolescence (%); \(O_{3}\) = functional obsolescence (%); \(O_{4}\) = technical obsolescence (%); \(O_{5}\) = social obsolescence (%); and \(O_{6}\)= legal obsolescence (%).  
According to the equation, the useful life is only a fraction of the physical life reduced by the sum of obsolescence factors' rates. The ARP index is then calculated using a mathematical algorithm and expressed in a percentage. The higher the percentage, the higher the potential for adaptive reuse.  
The utilization of this method allows for prioritizing buildings with a higher potential for adaptive reuse. Although the ARP model was first developed considering Hong Kong's context, it has been tested on case studies in different cities worldwide; examples can be found in [42]. ARP has also been used to validate later adaptability assessment models such as the IconCUR [43] and AdaptSTAR [44], which will be discussed in the following sections.  
The ARP model considers that all obsolescence criteria have an identical influence on a building's useful life which contradicts other literature findings that usually give more importance to technical, physical, and economic factors.
## 2 Basic Principles  
### Radar-Based Sensing  
The underlying principle of the radar-based detection of targets, in general, is to emit and receive electromagnetic waves (_RF signals_), which contain information about the targets' properties. A common categorization of radar systems is to classify them as pulse-radar or continuous-wave systems. Both categories have individual applications with specific advantages and disadvantages with regard to distance resolution, velocity resolution, power consumption, technical equipment, waveform generation, signal processing, etc.
# Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum
## 4 Control Results and Discussion
### Simulation Results
#### 4.2.3 Path Following  
In a path-following task, the motion of a bicycle is usually a combination of running straight ahead, turning left, and turning right with various radii of curvature and at various longitudinal speeds. When following a path, the magnitude of centrifugal force on the bicycle system varies over time, so the bicycle's roll angle must be changed over time to compensate for the centrifugal force.  
Figure 11: Simulation results for case 2: (**a**) lean angle; (**b**) gimbal angles; and (**c**) gimbal torques.  
In this case, the bicycle is assumed to follow an S-shaped path which involves combinations of straight running and turning at the constant radii of curvature of 6 m as shown in Figure 12. The longitudinal speed of the bicycle during the task is maintained at 15 km/h. The control problem for the inverted pendulum in this case is tracking the trajectory of the equilibrium roll angle over the path. In order to implement a strict controller that gives small tracking errors, the MPC uses the parameters that are proposed in Section 4.2.1. This control strategy is performed on the real-time simulator using the same approach with Section 4.2.2.  
Figure 13 shows the control results of the inverted pendulum for this case. In the first 3 s, the bicycle runs straight ahead and the equilibrium of the system is the upright position. Therefore, \(\theta\), \(\alpha_{1}\), and \(\alpha_{2}\) are maintained at zero positions. Thereafter, the vehicle changes direction by turning to the left, so the equilibrium roll angle \(\theta_{r}\) is \(-16.40^{\circ}\). When the bicycle comes out of the corner, at 13 s, it returns to its upright position. At 23 s, the bicycle changes direction again by taking a right turn and the reference signal is changed to \(16.40^{\circ}\). The maximum gimbal angle during the simulation is \(6.12^{\circ}\) and the gimbal torques are relatively small. The maximum value is \(0.44\) Nm. As shown in the control results, the MPC provides good control performance, with a maximum tracking error for \(\theta\) of \(0.51^{\circ}\).  
Figure 12: Bicycle model following an S-shaped path.  
Figure 13: Simulation results for case 3: (**a**) lean angle; (**b**) gimbal angles; (**c**) gimbal torques.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 3 Performance Analysis
### Outage Probability  
To analyze the outage probability of the system, we first derive the expression of the achievable rate of the \(i^{th}\) subcarrier in the uplink in bits/second/Hz (\(\text{pps}/\text{Hz}\)) as:  
\[R_{u}(i)=(1-\tau_{0})\log_{2}(1+\gamma_{u}(i)). \tag{25}\]Then the outage probability of the \(i^{th}\) subcarrier in the uplink can be expressed as:  
\[P_{o}(i)=P(R_{u}(i)\leq R_{th}), \tag{26}\]  
where \(R_{th}\) is the threshold of the information rate.  
According to Equations (25) and (26) \(P_{0}(i)\) can be transformed as:  
\[P_{0}(i)=P(\gamma_{u}(i)\leq 2^{\frac{R_{th}}{1-\gamma_{0}}}-1)=\int_{0}^{ \frac{R_{th}}{1-\gamma_{0}}-1}f_{\gamma_{u}(i)}(z)dz. \tag{27}\]  
By substituting Equation (19) into Equation (27), \(P_{0}(i)\) can be further expressed as  
\[P_{0}(i)=\int_{0}^{2^{\frac{R_{th}}{1-\gamma_{0}}}-1}f_{\gamma_{u}(i)}(z)dz= \frac{N[f_{u}(i)]\beta}{\Gamma(\alpha)\Omega_{u}(i)}\int_{0}^{2^{\frac{R_{th}} {1-\gamma_{0}}}-1}G_{2,4}^{2}\bigg{(}\frac{N[f_{u}(i)]\beta z}{\Omega_{u}(i)} \bigg{|}_{\alpha-1,0,0,\alpha-1}\bigg{)}dz. \tag{28}\]  
With the aid of the truncated Laplace transform of the Meijer G function ([41], eq. (42)), a closed-form expression of \(P_{o}(i)\) can be derived as:  
\[P_{o}(i)=\frac{D(i)}{\Gamma(\alpha)}G_{3,5}^{2}\bigg{(}F(i)\bigg{|}_{\alpha-1,0,0,\alpha-1,-1}\bigg{)}, \tag{29}\]  
where \(F(i)=(2^{\frac{R_{th}}{1-\gamma_{0}}}-1)\frac{N[f_{u}(i)]\beta}{\Omega_{u}(i)}\).  
The system will not be in a state of outage until each of the subcarriers reaches the threshold of the information rate [45], thus the overall outage probability can be expressed as:  
\[P_{out}=1-\prod_{i=1}^{I}(1-P_{0}(i)). \tag{30}\]  
By substituting Equation (29) into Equation (30), the outage probability of the described system can be derived as:  
\[P_{out}=1-\prod_{i=1}^{I}\bigg{[}1-\frac{D(i)}{\Gamma(\alpha)}G_{3,5}^{2} \bigg{(}D(i)\bigg{|}_{\alpha-1,0,0,\alpha-1,-1}\bigg{)}\bigg{]}. \tag{31}\]
## 5 Conclusions  
In summary, we developed a CNN-LSTM architecture to automate the detection of POAG progression using longitudinal CFP data from the OHTS. Our model, trained on both structural and functional features of glaucoma, achieved the highest AUC (0.939) compared to the structural-only and functional-only models (0.894 and 0.911, respectively). Grad-CAM-generated heatmaps revealed that the models emphasize both the vasculature and the ONH for predictions, suggesting that retinal vasculature could serve as an early biomarker for the detection and progression of POAG, complementing the traditionally used optic disc features. Our study highlights the potential for AI-based deep learning models to advance POAG diagnostics and underscores the potential for integrating AI-based diagnostic tools into clinical workflows. These models could assist clinicians by providing objective assessments of disease progression, especially in settings with limited access to glaucoma specialists.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15031627/s1](https://www.mdpi.com/article/10.3390/app15031627/s1)]([https://www.mdpi.com/article/10.3390/app15031627/s1](https://www.mdpi.com/article/10.3390/app15031627/s1)), Table S1. All layers of MobileNetV2, used as the feature extractor in our CNN-LSTM model.  
Conceptualization, I.M. and Z.J.H.; methodology, I.M., Z.M. and Z.J.H.; software, I.M., Z.M. and Z.C.W.; validation, I.M.; formal analysis, I.M.; investigation, I.M.; resources, Z.C.W.; data curation, I.M., Z.C.W. and Z.J.H.; writing--original draft preparation, I.M.; writing--review and editing, I.M., Z.M., Z.C.W., V.C., D.H. and Z.J.H.; visualization, I.M.; supervision, Z.J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All Clinical Centers and Resource Centers have received local IRB approval for the OHTS.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
Code and data are available on request to the corresponding author.  
The authors declare no conflicts of interest.
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
### Why These Problems Need to Be Addressed  
The challenges outlined above highlight the need for a more flexible and intelligent approach to congestion control. The persistent issues of unfairness, inefficiency, and starvation in existing CCAs undermine the potential of modern networks to deliver high QoS and QoE, particularly as applications become more demanding and network environments more complex.  
**Ensuring Fairness and Efficiency**: Addressing the fairness and efficiency issues in CCAs is critical in providing a more equitable network experience. As users increasingly rely on real-time applications that require consistent and low-latency performance, the ability to ensure that all flows receive a fair share of network resources becomes essential [18; 21]. Without solving these issues, certain applications and users will continue to suffer from poor performance, leading to widespread dissatisfaction [14] and potentially limiting the adoption of advanced Internet services.  
**Adapting to Diverse Network Conditions**: The modern Internet is characterized by its diversity, with traffic patterns and network conditions varying widely across different environments. The static nature of existing CCAs makes them ill-suited to cope with this variability. Developing CCAs that can dynamically adapt to changing conditions in real time is crucial for maintaining optimal performance [24] and avoiding the pitfalls of traditional algorithms.  
**Leveraging Modern Technologies**: The rise of machine learning and artificial intelligence presents an opportunity to develop more intelligent CCAs that can learn from network conditions and make real-time adjustments. By leveraging Deep Reinforcement Learning (DRL), it is possible to design CCAs that are not only more adaptive but also more capable of balancing the competing demands of throughput, latency, and fairness [19].  
DRL-based approaches can predict and mitigate the risks of unfairness and starvation, leading to a more efficient and user-friendly Internet.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 4 Mathematical Models for Energy-Aware Routing Calculation
### Optimization Problems Formulation
#### 4.1.1 SEUlm Optimization Problem  
Given the above notation, we can formulate the energy-saving optimization problem with the simple model of energy usage by the network components.  
\[\min_{x_{n}\vartheta_{lm}}\left[f_{SEUlm}(x_{n},y_{lm})=\sum_{m\in M}\sum_{l \in L}y_{lm}w_{m}e+\sum_{n\in N}x_{n}r\right], \tag{1}\]  
subject to the constraints:  
\[\forall_{n\in N,m\in M} \sum_{l\in L}a_{lm}y_{lm}+s_{mn}=\sum_{l\in L}b_{ln}y_{lm}+d_{mn}, \tag{2}\] \[\forall_{n\in N,m\in M} \sum_{l\in L}a_{lm}y_{lm}\leq x_{n}k,\] (3) \[\forall_{n\in N,m\in M} \sum_{l\in L}b_{ln}y_{lm}\leq x_{n}k,\] (4) \[\forall_{l\in L} \sum_{n\in M}w_{m}y_{lm}\leq c_{l},\] (5) \[\forall_{m\in M} \sum_{l\in L}\sum_{q\in Q}a_{lq}y_{lm}\geq 1. \tag{6}\]  
where variables and constants used in the above formulas denote \(x_{n}=1\) if the node \(n\) is in the active mode (0 otherwise), \(y_{lm}=1\) if the link \(l\) is used for the flow \(m\) transmission (0 otherwise), \(s_{mn}=1\) if the server \(n\) is the source of the flow \(m\) (0 otherwise), \(d_{mn}=1\) if the server \(n\) is the destination of the flow \(m\) (0 otherwise), \(a_{ln}=1\) if the link \(l\) is incoming to the node \(n\) (0 otherwise), \(a_{lq}=1\) if the link \(l\) is incoming to the pivot \(q\) (0 otherwise), \(b_{ln}=1\) if the link \(l\) is outgoing from the node \(n\) (0 otherwise), \(r\) denotes the node energy used in the active mode, \(e\) is the energy consumed by each DCN link for data transfer, \(k\) is the number of ports of the \(n\)-th node, \(w_{m}\) the size of the \(m\)-th flow, and \(c_{l}\) the capacity of the \(l\)-th link.  
In the problem defined above, the conditions (2) assure that the sum of the flows incoming to a given node is equal to the sum of the flows outgoing from this node, and the constraints (3) and (4) determine the number of nodes used for data transmission (a disabled node cannot receive or transmit data). The constraint (5) assures that the flow will not exceed the capacity of a given link. The constraint (6) forces each calculated transmission path to contain at least one pivot node.
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 4 Evaluations and Results
### Validation of The Topology Change Awareness Method  
The experiment in this section is intended to discuss the validity of the topology change awareness method and to explain the calculation of the topological change threshold \(\mathrm{TCD_{TH}}\).  
We have selected several different forms of node motion, which are RWP, RPGM (g = 50), RPGM (g = 25), RPGM (g = 10), RPGM (g = 5) and Pursue. g represents the group number.  
The simulation region is still 2000 m \(\times\) 2000 m. The duration is 950 s. the number of nodes is 50. The \(\mathrm{TCD_{ntwork}}\) of different motion patterns is shown in Figure 11.  
From Figure 11a, it can be seen that:  
* The \(\mathrm{TCD_{ntwork}}\) value is different when nodes move in the different patterns. As the number of groups decreases, the value of \(\mathrm{TCD_{ntwork}}\) gradually increases.
* In the same motion mode, the change of node speed has little effect on the \(\mathrm{TCD_{ntwork}}\) value.  
Figure 10: Performance comparisons of different TARCS: (**a**) packet delivery ratio; (**b**) network throughput; (**c**) average end-to-end delay; (**d**) average jitter.  
The explanation for why the \(TCD_{nturk}\) value of RWP model is the smallest and that of Pursue model is the largest depends on how the nodes move in these models.  
Under the premise of the same area, the same number of nodes and the similar speed, the \(TCD_{nturk}\) of the RWP model is smaller than that of the RPGM model and of the Pursue model, which is caused by the motion pattern of the nodes in the RWP model. When moving in the RWP mode, the nodes are randomly distributed in the active area. Generally, the area of the active area is much larger than the transmission range of the nodes. In this case, the motion of the node belongs to the individual motion, so the number of neighbors is small and not fixed, thus the value of each \(TCD_{i,j}\) is small, and the values of \(TCD_{i,ntbs}\) are also small.  
When nodes move in groups, such as RPGM (g = 5), every 10 nodes are gathered into one group, and each group moves in an overall manner. The number of neighbors of a node is fixed and may increase instantaneously. Because the relative motion of the nodes inner group is weak, and the inter-group relative motion is frequent, when different groups meet and are interlaced, the number of neighbor nodes may increase instantaneously.  
When nodes move in the manner of Pursue, all nodes are gathered into one group. The number of neighbors of each node is large and fixed, and the node is usually connected with its neighbors, so the \(TCD_{i,ntbs}\) value are larger.  
Figure 11b shows the average value of each mobility model in Figure 11a. The average value of \(TCD_{nturk}\) of each mode is more likely to show this difference. A feasible threshold is calculated by averaging the \(TCD_{nturk}\) average values of the two modes of movement that need to be compared. The threshold values in the above experiments are calculated by this method.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
### Paper Structure  
This paper adopts a logical structure progressing from theory to practice and from current status to prospects, comprising seven sections: Section 1 introduces the research background and core problems; Section 2 reviews the development history and application status of ETC systems; Section 3 constructs a multi-level traffic sensing and feature extraction framework based on ETC data; Section 4 explores traffic state analysis and assessment methods in depth; Section 5 demonstrates the practical value of ETC data in urban traffic management through rich application cases; Section 6 analyzes the current technical challenges and identifies future research directions; Section 7 summarizes the main contributions and provides prospects for future development.
## 1 Introduction
In this paper, we propose a train-to-ground communication system solution dedicated to the Hyperloop, which mainly involves wireless channel, network architecture, multiplexing scheme of the physical resources, and the on-board passengers' Internet resources management. Our contributions are listed as follows:  
1. In terms of the wireless access method, the challenges of the DAS method are analyzed from the aspect of Doppler effect. Simulation reveals that a severe Doppler shift appears when crossing the trackside antenna. Thereby, the LWS is a better option since it can yield a stable frequency shift compared to DAS. Then, we discuss the radio coverage based on the DAS and LWS. 2. To reduce the handover frequency stemmed from the ultra-high speed, a cloud architecture is used to integrate several successive roadside leaky waveguides into a logical cell. Then, a novel access network architecture dedicated to the Hyperloop is proposed based on a many-to-many mapping relationship between DUs and CUs. The graph theory is adopted to alleviate the inevitable handover cost when crossing different cells and the corresponding simulation result is provided. 3. As for the coexisting mission-critical and non-critical services, we propose a PRB multiplexing scheme dedicated to the Hyperloop by considering the tolerable latency margin of each type of traffic. Simulation shows this scheme can satisfy the low-latency demand and achieve a large network throughput in the physical layer. 4. To boost the on-board users' quality of experience (QoE) as well as alleviate the midhaul link burden, two cache-based resource management strategies, i.e., the pre-fetching and post-uploadingschemes are investigated to cope with such non-critical mission services. Simulation presents that this cache-based scheme can yield an enhancement in throughput. The remaining part of this paper proceeds as follows: Section 2 presents the train-to-ground communication challenges and analyzes the communication service types in brief. Then, the possibility of two wireless access methods (the DAS and LWS) are assessed based on some simulation results in Section 3. In Section 4, a novel wireless access network architecture is illustrated in detail. As for the mission-critical traffic, the multiplexing approach is used in the physical layer and presented in Section 5. Moreover, two cache-based resource management strategies mainly served for the non-critical services are illustrated based on our proposed network architecture in Section 6. Finally, the conclusions are summarized in Section 7. Some abbreviations and the corresponding definitions are listed in Table 1 for convenience.
## 3 Proposed Protocol
### Key Revocation Mechanisms
#### 3.3.2 Other Revocation Mechanisms  
Several other revocation mechanisms can be used in UWSNs. One such mechanism is the use of blacklists. In this method, compromised nodes are added to a blacklist, and nodes are denied access if their IDs match those on the blacklist. Another mechanism is the use of gray lists, which allows nodes to access the network but limits their privileges until they can be fully verified. Finally, there is the use of time-based mechanisms, where keys are revoked after a certain period has elapsed. Overall, the selection of an appropriate revocation mechanism depends on the specific requirements of the UWSN and the level of security desired.
## 3 Results and Discussion
### MMRTG Emissivity Analysis
#### 3.3.2 Radiative Heat Transfer  
\[\dot{Q_{ar}}=\varepsilon_{amb}\sigma A_{b}(T_{b}^{4}-T_{a}^{4})\sim 1348\,\mathrm{W} \tag{6}\]  
where:  
* \(\dot{Q_{ar}}\): radiative heat transfer rate (W);
* \(\varepsilon_{amb}\): emissivity of the ambient environment (dimensionless);
* \(\sigma\): Stefan-Boltzmann constant (\(5.67\times 10^{-8}\) W/m\({}^{2}\cdot\)K\({}^{4}\));
* \(A_{b}\): surface area of the body (m\({}^{2}\));
* \(T_{b}\): temperature of the body (K);
* \(T_{a}\): ambient temperature (K).  
The materials used in the construction of the MMRTG are metal alloys known for their high resistance to heat and radiation. To simplify this study, the radiative emissivity of the block surface will be approximated to that of cast iron, \(\varepsilon_{amb}=0.44\).  
These data will help us estimate the potential influence of the MMRTG's thermal emission on the MEDA station. [PERSON] et al. (2010) [22] and [PERSON] et al. (2020) [3] show that the sensed plume perturbation was approximately \(\sim 2\,\mathrm{K}\), while the temperature of [PERSON]'s deck was about 15 K higher than that in the region without thermal disturbance. These values could have a significant impact on the measurements if they are not properly accounted for.  
In the Martian environment, the extremely low atmospheric density substantially limits the effectiveness of convective heat transfer. As a result, heat exchange between the MMRTG and the MEDA station positioned approximately 2 meters apart is dominated by radiative processes. The scarcity of atmospheric particles capable of transporting thermal energy makes convection negligible. Consequently, even though convective effects could theoretically occur at such a distance, their contribution is minimal compared with the radiative component. This observation aligns with prior studies, such as [PERSON] (2011) [13], which highlight the significant influence of the Martian atmosphere's low density on limiting convective dynamics and emphasizing radiative processes as the dominant mechanism of heat transfer. This justifies focusing the analysis and modeling efforts predominantly on radiative heat exchange.