esa-sceva/satcom-chunk-collection huggingface dataset:
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 5 Challenges of Using ChatGPT in Education
### Academic Dishonesty  
Any behavior that provides an unfair academic advantage or falsifies one's own work, knowledge, or abilities in a learning environment is considered academic dishonesty. This includes behaviors that lead to plagiarism, cheating, and lying about one's work or accomplishments. Because of inaccuracies and a tendency to rely only on artificial intelligence, ChatGPT in education poses risks to academic integrity and critical thinking [49]. Finally, the ethical framework that is used is based on striking a balance between the usage of new technology and societal welfare. However, questions of dependency, ethical use, and even abuse arise, emphasizing the importance of carefully considering its consequences on academic dishonesty [50].  
Therefore, the integration of ChatGPT in education presents significant challenges to academic integrity, as it facilitates plagiarism and unauthorized aid, necessitating robust detection tools and AI literacy programs [44; 45; 46]. While it promises to reduce teacher workload through automation, it paradoxically increases responsibilities by requiring educators to verify AI-generated content and guide ethical usage [22; 47]. Ethical concerns are further compounded by bias in AI outputs and data privacy risks, demanding stringent governance policies to ensure fairness and security [20; 44; 45]. Additionally, ChatGPT's limitations--such as its inability to produce self-reflective or non-textual responses (e.g., STEM visualizations) and its avoidance of future predictions--restrict its applicability in higher-order cognitive tasks and complex disciplines [21; 23; 48]. These shortcomings, combined with risks of academic dishonesty and over-reliance, underscore the need for balance policies that leverage ChatGPT's benefits while safeguarding educational integrity and fostering critical thinking [49; 50]. A holistic approach, combining teacher training, ethical guidelines, and adaptive assessments, is essential to mitigate these challenges and ensure responsible AI integration in academia.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
###### Abstract  
With the advancement in networking, information and communication technologies, wireless body area networks (WBANs) are becoming more popular in the field of medical and non-medical applications. Real-time patient monitoring applications generate periodic data in a short time period. In the case of life-critical applications, the data may be bursty. Hence the system needs a reliable energy efficient communication technique which has a limited delay. In such cases the fixed time slot assignment in medium access control standards results in low system performance. This paper deals with a dynamic time slot allocation scheme in a fog-assisted network for a real-time remote patient monitoring system. Fog computing is an extended version of the cloud computing paradigm, which is suitable for reliable, delay-sensitive life-critical applications. In addition, to enhance the performance of the network, an energy-efficient minimum cost parent selection algorithm has been proposed for routing data packets. The dynamic time slot allocation uses fuzzy logic with input variables as energy ratio, buffer ratio, and packet arrival rate. Dynamic slot allocation eliminates the time slot wastage, excess delay in the network and attributes a high level of reliability to the network with maximum channel utilization. The efficacy of the proposed scheme is proved in terms of packet delivery ratio, average end to end delay, and average energy consumption when compared with the conventional IEEE 802.15.4 standard and the tele-medicine protocol.  
Keywords:wireless body area networks; slot allocation; fuzzy; fog computing; healthcare; medium access +
Footnote †: journal: Sensors
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
## 6 Conclusions  
In this paper, we exploit the percolation theory to achieve the throughput capacity of three-dimensional heterogeneous networks in which the nodes are distributed according to the SNCP. We focus on the cluster-sparse network. In order to get the lower bound of the throughput  
Figure 6: The dashed lines are the “information pipes”. Each cluster is connected by the “information pipes”.  
capacity, the transmission is divided into two parts: the intra-cluster transmission and the inter-cluster transmission. For the first part, we novelly establish a spheroidal percolation model in which the information is transmitted through five phases. The bottleneck of throughput capacity is due to the information carried through the RH. After the derivation, we get the achievable per-node rate of \(\Omega\left(1/(n^{1-v}((1-v)\text{log}\,n\,\,)^{2})^{\frac{1}{3}}\right)\) for the intra-cluster transmission. For the inter-cluster transmission, we employ the idea of \"information pipes\" to complete the transmission and then obtain a per-node rate of \(\Omega\left(n^{\frac{2v}{3}-1}\underline{\Phi^{3}}\right)\). Finally, taking the whole network traffic into account, the achievable per-node throughput capacity for three-dimensional heterogeneous wireless networks is \(\Omega\left(n^{\frac{2v}{3}-1}\underline{\Phi^{\frac{2}{3}}}\right)\).  
Acknowledgments: This work was supported in part by the Foundation of Jiangxi Educational Committee (Grant Nos. GJJ160626 and GJJ150695).  
**Author Contributions:** [PERSON] proposed the idea, derived the results and wrote the paper. [PERSON] reviewed the article in initial and revised versions. [PERSON] assisted in revising the paper. All authors have read and approved the final manuscript.
## 3 Data Analysis
### Statistical Analysis
To obtain smoother transitions, two parameters can be adjusted: the frame resolution \(K_{\theta}\times K_{\varphi}\) and the step \(\Delta s\). Therefore, for the _SeeTurn_ scenario, we consider three trajectories with \(\Delta s\) set to 1, 10, and 30 cm. The trajectories are depicted in Figure 2 and the corresponding distance matrices in Figures 5 and 6. It can be observed that by reducing the step \(\Delta s\), not only do we obtain larger diagonal blocks, but also a smoother transition between them. Figure 6: _Cont._
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Embedding Layer
#### 3.4.1 BERT Embedding  
BERT embedding is a technique used to convert tokens into dense, low-dimensional vector representations that capture the semantic meaning and contextual information. By encoding the tokenized URLs into BERT embedding, the model can benefit from the pre-trained knowledge of BERT, which captures the relationships between tokens and the context in which they appear. To represent tokenized URL input data, BERT relies on three distinct types of embedding: token embedding, position embedding, and token-type embedding [21]. In the model construction phase, the data are passed through embedding layers that feed into two distinct encoder components: the BERT encoder layer, which captures contextual and semantic relationships, and the Char2B encoder layer, which focuses on extracting character-level features. The BERT and Char2B models, respectively, then utilize these encoded representations. The final outputs are directed to a classification layer that predicts the target labels and performs evaluation. This architecture enables comprehensive feature extraction and enhances the model's ability to distinguish accurately between legitimate and malicious URLs.
## 1 Introduction
Outer race bearing failures are some of the most common causes of bearing failures since the development of variable frequency drives [16]. This paper presents a robust health prognostics technique that identifies outer race bearing failures in electric motors and predicts the RUL of the bearings of electric motors. Amplitudes of outer race bearing vibration frequencies (2X, 3X, and 4X BPFO) are used as input features and fused by means of a stacked autoencoder (SAE). Then, RUL is predicted with a bidirectional long short-term memory (BiLSTM) model. This model is trained with a dataset from the intelligent maintenance system (IMS) bearing dataset [17], tested with another dataset of IMS dataset, and validated with the FEMTO-ST Institute (FEMTO) and Xi'an Jiaotong University with Changxing Sumyoung Technology Co., Ltd. (XJTU-SY)-bearing datasets [18; 19] with no retraining. This way, the proposed model is robust between different motor conditions and bearing types. The results obtained with this model are more accurate than those of similar works. The rest of the paper is organized as follows. Previous research works are outlined in Section 2. The experimental set-up of the model is shown in Section 3. Section 4 shows the results obtained by the proposed model for training and testing and compares them with other works. Finally, Section 5 presents the concluding remarks and outlines future work.
## 3 System Design and Platform Subsystems
### Command and Data Handling System (CDHS)  
The CDHS is responsible for running the flight software and with handling all data on-board the satellite. The central component of the CDHS is the on-board computer (OBC) which is based on a 400 MHz ARM9 processor with 64 MB RAM, 1 MB NOR flash for code storage, 256 KB FRAM for critical storage and two 2 GB microSD cards. I2C is used as the primary bus for the spacecraft bus, and UART is used for data handling between the OBC and the payloads.
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 3 Background and Notation
### The Tor Network  
The Tor network is an overlay network, based on TCP/TLS connections, consisting of multiple relay routers called _Onion routers_ (OR). Each client runs locally an _Onion proxy_ (OP) which establishes a virtual circuit of ORs to communicate anonymously with the destination. To build a circuit, the OP contacts periodically a trusted server called _Directory Server_ (DS) that keeps information about the state of the network and provides the OP with _router descriptors_ of the ORs. These router descriptors contain the IP addresses and the public keys of the ORs, along with their network information, such as the bandwidth. Then, the OP selects, according to some strategies, a number \(n\) of OR relays that form the virtual circuit. By default, \(n=3\). The first OR is called the _entry router_, the second the _middle router_ and the last the _exit router_. Once the three ORs have been selected, the OP starts a set-up phase to build the virtual circuit. This phase is performed in such a way that each OR only knows the previous and the next node of the path. Moreover, in this phase, the OP exchanges some messages with the ORs, which include some Diffie-Hellman (DH) parameters, to share a secret key. These messages are encapsulated into _control cells_ of a fixed size of 512 bytes. Since the OP has to be sure about the authenticity of the ORs, the DH parameters are encrypted by using the public keys of the ORs. At the end of this set-up phase, the OP shares a secret key with each OR. These keys are used by the OP to encrypt (symmetrically) in Onion fashion the messages intended for the destination. Once the circuit is established, the OP sends the messages to the destination encapsulated into _relay cells_ of size of 512 bytes. These relay cells include a header of 3 bytes in plaintext plus 11 bytes encrypted for the exit router. Therefore, the effective payload is 498 bytes.  
Through this paper, for both symmetric and public-key encryption, we denote by \(E_{k}(M)\) the encryption of a message \(M\) with key \(k\). Similarly, we denote by \(D_{k}(C)\) the decryption of the ciphertext \(C\) with (symmetric or public) key \(k\). Even though we do not explicitly highlight this aspect, the encryption we consider in this paper is only probabilistic, in such a way that, for an eavesdropper, two different encryptions of the same message are unlinkable.
## 3 Results and Discussion  
### Slurry Test Model Validation Result  
The SRT dataset for the slurry test was divided into four sets, as shown Table 2, where each set was fitted separately by the output variables. Therefore, the prediction of  
The model validation results of the slurry test presented above showed that the hyperparameter configuration in model 5 gave the lowest average errors and the highest R\({}^{2}\) in predicting sand production and retained permeability. However, the classification model for plugging sign gave the best result using a combination of hyperparameters in model 2, 5, and 6. Therefore, model 5 is selected as the best model for predicting sand production and retained permeability of the slurry test, but either model 2, 5, or 6 can be chosen as the final model for the plugging sign. The line plot of model 5 was used for the plugging sign to visualize the trend between actual and predicted data. The actual versus predicted plots of model 5 for each slurry test set are shown in Figure 6.  
Figure 6: _Cont_.  
In Figure 6, the trend of actual data is shown by the red line, while the blue line shows the predicted data for all slurry test sets. The line plot of the plugging sign in Figure 6a represents the classification of 49 observations of the actual and predicted data. The observations that fall under the 0 class refer to the observations with no sign of screen plugging, while the observations that were categorized as 1 refer to a sign of screen plugging. Two peaks at the red line represent actual data with a sign of screen plugging, but the blue line does not follow the peaks that portray the false classification of two observations, as shown in Table 5. The line plot of sand production in Figure 6b represents 111 predicted values along with the actual data. The predicted amount of sand produced in grams approximates the actual trend with 77% accuracy.  
Furthermore, the line plot of retained permeability in Figure 6c represents 20 observations of screen-retained permeability. The prediction line closely follows the actual  
Figure 6: The actual versus predicted plot of model 5 for the slurry test: (**a**) The plot for plugging sign; (**b**) the plot for the amount of sand produced in grams; (**c**) the plot for retained permeability; (**d**) the plot for the amount of sand produced per unit area.  
percentage of retained permeability with 99% accuracy. Lastly, Figure 6d shows a line plot of sand produced per unit area with 103 observations and 77% accuracy. Most of the prediction points are almost identical to the actual points.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.2 The Message Queuing Telemetry Transport  
MQTT is a lightweight protocol designed and used in a plethora of IoT applications and M2M communications [53], utilising the publish/subscribe architecture, which is well-suitable for remote connections with limited network bandwidth and system resources. This model facilitates efficient data dissemination techniques. Its ability to process massive volumes of data concurrently while consuming little bandwidth becomes an asset in V2I communications, where real-time, widespread information sharing is critical. Furthermore, MQTT's QoS levels ensure message delivery, which is critical in the context of traffic safety and infrastructure coordination. The protocol supports scalability, which aligns well with the requirements of the V2I environment that necessitates efficient and dynamic connectivity between a multitude of clients and a central broker. The broker plays an integral role in the MQTT network, effectively managing a catalogue of topics, e.g., traffic information and weather conditions, that function akin to communication channels. These topics facilitate the publishing and subscription of messages, thereby orchestrating the flow of information within the V2I domain. Specifically, when a client publishes a message on a given topic, the broker ensures this message reaches all clients subscribed to that specific topic. The multi-topic subscription capability of MQTT [54] provides clients with the advantage of receiving messages from diverse sources, enhancing the scope and versatility of information dissemination within the context of V2I. Refer to Figure 4 for MQTT architecture.  
Figure 4: MQTT architecture.  
Figure 3: CoAP architecture.
# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 1 Introduction
### Contribution of This Paper  
The major contribution of this paper was to develop a complete algorithmic process that addresses appropriate processing methods for an automatic system of monitoring student behavior. The system acts as a data collection and aggregation tool for decision making. There are many different types of behaviors of students in the classroom, as mentioned in the previous section; in this research, we focused on determining where the learner was observing across time. Our system was designed to surpass all the existing student behavior monitoring systems by evaluating and applying several computer vision techniques, such as face detection, facial landmark detection, face embedding, face classification, and gaze estimation. We implemented the algorithm for 3D position estimation. The combination of estimated locations and eye-gaze is a reliable assessment of the estimated user attention. It allows us to be able to respond in the real environment, where the layout of classrooms is different (special layout, large area, etc.) by using a combination of several cameras. Together with the combination algorithm, we used the inference ability based on the statistics of previous observations to enhance the accuracy of computer vision techniques. We also proposed a data summarization algorithm to combine and enhance the performance of these techniques.  
As an additional contribution, a web application that supports the lecturers and academic staff has been developed. The web application can take part in the academic portal as part of the business intelligence module. Videos recorded during a student session will be processed, and then the system performs several computer vision techniques automatically. We visualized the analyzed data in the form of charts and slideshows on the web. Not only are the aggregation results included in the report, but the detail level of data can be accessed. Through this application, faculties not only assess the general situation of all students in the class but also grasp the details of the situation of each specific student to propose strategies to improve the quality of learning.  
Finally, we evaluated our proposed algorithmic process to verify its performance. We conducted thorough scrutiny of every part of our system from facial recognition, to estimating the position of the learner in the classroom, and finally, the result of classifying the behavior of each student. This study also includes comparisons with other researches. Although the implementation conditions for each method are different, we tried to describe our systems as being adaptable to the actual environment. The remainder of the paper is organized as follows. Section 2 describes system design and the implementation of the algorithms and its results are analyzed in Section 3. Brief conclusions are finally discussed in Section 4.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 3 Data Analysis
### Global Positioning System
The second characteristic is the fact that the GPS satellite navigation system is radio-based. Therefore, solid obstacles, such as mountains and tall buildings, can weaken and even block GPS signals. We used this characteristic to identify if the sensor is in an indoor or outdoor environment.
## 4 Methodology
### Local Oscillator Frequency Drift  
The Local Oscillator (LO) is an essential part of a transponder as it creates the reference frequency needed for transmitting and receiving signals. The LO generates a quasi-stable reference frequency used to generate the carrier frequency, called the Local Oscillator Frequency (LOF), transmitted by the transponder. The LOF is subject to Doppler shifting of the signal frequency during transmission. The Doppler shift results from relative motion between the user satellite and the TDRS satellite, causing a change in the signal's frequency as it is transmitted. However, the LO's stability is usually limited to short durations and may experience drift caused by temperature variations, electronic component aging, and other factors.  
The FDF operationally monitors and measures the drifts of numerous satellites through the Tracking Data Evaluation (TDE) process. This process involves predicting one-way Doppler measurements using definitive ephemerides of both the user and the TDRS satellites and comparing them to actual measurements. The difference between predicted and actual measurements is then analyzed to compute the mean and standard deviation of the frequency offset across all passes.  
The study utilized the standard FDF procedure to assess the LOF offset in the user spacecraft transponder. The mean and standard deviation of the LOF offset were calculated for each set of TDRS and user spacecraft definitive ephemerides. The findings and their implications on the estimated values of these offsets due to uncertainties in the TDRS and user spacecraft ephemerides introduced by TT&C-only measurements are presented in Section 5.2.2.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Image Blending  
To eliminate mosaic beams and achieve a smooth transition after projection, image blending, which can process the pixel values of overlapping areas, becomes indispensable. The weighted-average algorithm is a simple and effective image blending method that sums the pixel values after assigning them a particular weight [38]. In the case of the transverse mosaic, the pixel values of overlapping areas after distance weighting can be expressed as:  
\[I(i,j)=wl_{\alpha}(i,j)+(1-w)I_{\beta}(i,j) \tag{3}\]  
where \(I_{\alpha}\) and \(I_{\beta}\) stand for the overlapping areas of the reference image and the image to be stitched, respectively, before blending, and \(w=\frac{d_{\alpha}}{d_{\alpha}+d_{\beta}}\) represents the weight, where \(d_{\alpha}\) or \(d_{\beta}\) are the distances from any pixel location to the left or right boundary, respectively, of the overlapping area. After image blending, the final mosaic result was obtained.  
The Proposed Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation  
To eliminate intensity inconsistencies, reduce redundant computations and improve efficiency under the premise of ensuring the images' high resolution, we optimize several parts of the traditional process.