esa-sceva/satcom-chunk-collection huggingface dataset:
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 6 Result and Analysis  
### Application of ChatGPT in Education  
In response to research question 1, the findings indicate that ChatGPT has been widely used for personalized learning, interactive tutoring, and content creation. Future research should explore longitudinal studies to assess the sustained impact of AI on student performance and engagement. Table 4 provides a summary of key studies, highlighting their objectives, findings, strengths, and limitations to offer a holistic understanding of ChatGPT's role in education.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Model** & **Dataset** & **Approach** & **Performance Metrics** & **Objectives** & **Main Findings** & **Strengths** & **Limitations** \\ \hline
[21] & 2023 & AI Ethics & Mixed academic data & Policy analysis & Regulatory compliance & Examine governance challenges & Identified gaps in AI policy enforcement & Strong theoretical framework & No empirical testing \\ \hline
[46] & 2024 & AI Governance & AI-driven education & Ethical AI implementation & Fairness, transparency & Propose governance strategies & User economics & Clear recommedations & Lacks implementation \\ \hline
[49] & 2023 & ChatGPT & Educational datasets & AI-driven tutoring & Student engagement, accuracy & Assess ChatGPT’s role in tutoring & Footnote & Strong empirical design & Lacks real-world design & case studies \\ \hline
[51] & 2023 & ChatGPT & University case studies & Personalized learning model & Learning outcomes & Evaluate AI’s impact on adaptive learning & Showed promise in individual learning & Effective in low-resource settings & Bias in dataset selection \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of related works on the applications of ChatGPT in education.  
Similar to prior studies [7; 52; 53], this review confirms that AI can provide individualized support for students. However, this study also reveals gaps in empirical validation; many studies focus on potential applications rather than real-world case studies demonstrating ChatGPT's effectiveness. Future research should explore longitudinal studies to assess the sustained impact of AI on student performance and engagement.
# Analysis and Design of Rateless Two-way Relay Networks Based on a Multiply-and-Forward Scheme
## 4 Fountain-coded MF-TWRN  
Based on the analysis of three-stage TWRN with different relaying schemes, the performance of AF and MF has been compared by SER derivations in an uncoded system. In this section, asymptotic analysis of rateless coded MF-TWRN is further studied when SLT codes are employed at source nodes and then the corresponding optimization model is proposed to improve the performance of SLT-coded MF-TWRN.
## 3 Results and Discussion  
### Dataset and Experimental Setup  
The dataset utilized in this study comprises 34 imaging logging images, each with dimensions of \(280\times 280\). These images were annotated using Labelme, with two target classes: cracks and background. Corresponding JSON label files were generated and subsequently converted into the PNG format required for UNet training. Offline data augmentation techniques, such as rotation and scaling, were employed to augment the dataset size by a factor of 10, resulting in a total of 340 images. The final images were resized to dimensions of \(256\times 256\) and underwent online data augmentation before being used for training.  
The experimental hardware setup comprises a Windows 10 system equipped with an Intel(R) Xeon(R) CPU @ 20.20 GHz (6 cores) and an NVIDIA GeForce RTX 3060 GPU boasting 8 GB of memory. The software environment encompasses Python 3.10, PyTorch 2.0.1, and CUDA 11.8.  
Considering the relatively limited size of the dataset utilized in this paper, it is crucial to obtain a more robust and reliable evaluation of model performance. Therefore, we have employed k-fold cross-validation with \(k\) set to 7. The specific steps are as follows: Firstly, the entire dataset is divided into seven equally sized subsets. Subsequently, seven rounds of training and validation are conducted. In each round, the i-th subset serves as the validation set, while the remaining six subsets are combined to form the training set. The model undergoes training on this set and is then tested on the validation set, with performance metrics being recorded accordingly. Finally, an average of these performance metric results from all seven rounds \((S1\sim S7)\) is calculated to derive a comprehensive evaluation of the model's performance [30].
## 4 Performance Evaluation
### Simulation Results
the packet delivery ratio in the dynamic ship scenario is 46.94% and 2.31% higher than in the static ship scenario when the RI is set to 2 and 6, respectively. Furthermore, in the dynamic ship scenario, SOCA achieves a 11.82% higher packet delivery ratio than that of the existing VDES.
## 2 Materials and Methods  
### General Architecture  
The general architecture of the examined solution, which includes subsystems for the observation of both the cloud coverage/heights and the prevailing visibility, can be characterized as follows. A visual camera attached to a rotator with accessories is installed on a high point at the airport with an obstacle-free view. The camera sends images of the sky/horizon to the central server every 5 min. Note that the frequency of shooting is customizable: one can also get to a 1 min periodicity if needed.  
In the case of cloud observations, the algorithm of stitching the individual photos of the sky together and its subsequent computer processing is performed, after which the whole sky image is presented to the remote observer (described in detail in Section 2.2). In the case of the visibility observations, pre-defined distance markers are added to the images. With the help of these markers, the remote observer determines the prevailing visibility and saves the results to the server (more detail in Section 2.3).  
Additionally, a complete METAR (METREPORT/SPECI/SPECIAL) (METAR--Meteorological Aerodrome Report, or Meteorological Terminal Air Report, or Meteorological Terminal Aviation Routine Weather Report) message can be generated. The original and the processed images are stored in a database and are also available through a web-based human-machine interface (HMI) to a remote human observer, who can be located anywhere with Internet access. The database allows for any posterior verification of the decision procedure. The output information on the meteorological variables is then sent to the air traffic control (ATC) tower or any institution directed.  
The basic workflow of the system and its components at the Poprad-Tatry Airport (ICAO: LZTT; IATA: TAT) is visualized in Figure 3. The red rectangle denotes the automated workflow, whereas the green one indicates the intervention by a (remote) human observer.
## 2 Methods
### The Principle of Improved VMD Algorithm  
#### 2.2.1 Construction and Solution of Variational Problem  
VMD [23] is a new method that decomposes an input signal into an ensemble of band-limited intrinsic mode functions with the minimal sum of estimated bandwidths for each mode. Based on the assumption that each mode that make up the complex signal has a finite bandwidth around its central frequency, the variational problem can be described as: Under the constraint that the sum of all modes is equal to the original signal \(f\), \(k\) intrinsic modes \(u_{k}(t)\) are sought, and the sum of the estimated bandwidth of these modes is the minimum.  
Firstly, computer the corresponding analytical signal and unilateral frequency spectrum of each intrinsic mode by the Hilbert transform:  
\[\left[\delta(t)+\frac{j}{\pi t}\right]*u_{k}(t) \tag{4}\]  
Second, in order to modulate the spectrum of each intrinsic mode to the corresponding baseband, each mode function is shifted to an estimated central frequency by mixing with an exponential term:  
\[\left[(\delta(t)+\frac{j}{\pi t})*u_{k}(t)\right]*e^{-j\omega_{k}t} \tag{5}\]  
Finally, by calculating the squared norm of the demodulated signal gradient and estimating the bandwidth of each intrinsic mode, the variational problem can finally be expressed as:  
\[\begin{array}{ll}\underset{\{u_{k},\omega_{k}\}}{\min}&\left\{\sum\limits_{ k}\left\|\partial_{t}\left[(\delta(t)+\frac{j}{\pi t})*u_{k}(t)\right]*e^{-j\omega_{k}t} \right\|_{2}^{2}\right\}\\ \text{s.t.}&\sum\limits_{k}u_{k}=f\end{array} \tag{6}\]  
where \(u_{k}\) is the \(k\)-th intrinsic mode, \(\omega_{k}\) is the central frequency of \(u_{k}\), \(\delta\) represent the Dirac distribution, and \(*\) is the convolution operator.  
In order to transform the constraint problem into unconstraint one to search for the optimal solution of \(u_{k}\), penalty factor \(\beta\) and Lagrange multiplication operator \(\lambda(t)\) are introduced. The above Equation (6) constrained optimization problem can be expressed as  
\[\begin{array}{ll}L(\{u_{k}\},\{\omega_{k}\},\lambda)=&\beta\sum\limits_{k} \left\|\partial_{t}\left[(\delta(t)+\frac{j}{\pi t})*u_{k}(t)\right]*e^{-j\
u_{ k}t}\right\|_{2}^{2}+\left\|f(t)-\sum\limits_{k}u_{k}(t)\right\|_{2}^{2}\\ &+\left\{\lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\right\}\end{array} \tag{7}\]  
Step 5: Calculate the correlation coefficient \(\rho_{k}\) between \(u_{k}^{n+1}\) and \(f\), if \(\rho_{k}<a\), then \(m=m+1\); Step 6: \(k=k+1\), repeat steps 2-3 until \(k=N\); Step 7: Determine the optimal value of \(K\) according to (9); Step 8: Initialize \(\left\{u_{k}^{1}\right\}\), \(\left\{\omega_{k}^{1}\right\}\), \(\lambda^{1}\) and \(n=0\); Step 9: Repeat steps 2-4, \(k=k+1\) until \(k=K+1\).
## 3 UAN Generic Architecture
### Backhaul  
In the UAN context, a backhaul is a wireless networking infrastructure which offers connectivity and data transport between sensor nodes and a gateway. UAN backhauls range from single-hop to multihop approaches. In the first case, the link between the sensor node and the gateway constitutes the backhaul.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Event-Based**} & \multicolumn{2}{c}{**Notification**} & \multirow{2}{*}{**Actuators Involved**} & \multicolumn{2}{c}{**Sensor Node**} \\  & **Alerts** & **Periodicity** & & **Location Accuracy** \\ \hline
**Garbage collection** & No & 1 h–24 h & No & High \\ \hline \multirow{3}{*}{**Lighting control**} & Yes (if presence & & \\  & sensors are & 30 min & Yes & Medium \\  & used) & & & \\ \hline
**Green zone** & No & 1 h–24 h & Yes & Medium \\
**management** & No & 1 h & No & Low \\ \hline
**Environmental control** & No & 5 min & No & High \\ \hline
**Parking availability** & Yes & 5 min & Yes (traffic light control and info. panels) & High/Medium \\ \hline
**Street traffic** & No & 5 min &
\begin{tabular}{c} Yes (traffic light control and info. panels) \\ \end{tabular} & High/Medium \\ \hline
**Utility infrastructure** & Yes & 12 h & No & High \\ \hline
**Security** & Yes & 5 min &
\begin{tabular}{c} Yes (for alarm \\ activation) \\ \end{tabular} & High/Medium \\ \hline \end{tabular}
\end{table}
Table 1: Requirements of Smart City applications enabled by UANs. The values shown in this table have been obtained considering Smart City application descriptions found in the literature [5, 6, 11], as well as our own experience in the design and deployment of Smart City pilots [8, 15, 16].  
In the second one, the backhaul is composed of a set of backhaul nodes (with their corresponding links), which are in charge of relaying the data collected by or sent to sensor nodes. In order to enable long sensor node lifetime, most communication models assume that backhaul nodes are always prepared to receive messages from sensor nodes (_i.e._, their radios are in receive mode by default), and thus backhaul nodes must not suffer energy consumption limitations.
## 3 Mathematical Model
### Six-Degree-of-Freedom Motion Equations
\begin{array}{ll}&I_{y}\dot{q}+\big{(}I_{x}-I_{z}\big{)}pr+\big{(} p^{2}-r^{2}\big{)}I_{xz}-m\big{[}x_{G}\big{(}\dot{w}-uq+vp\big{)}-z_{G}\big{(} \dot{u}-vr+wq\big{)}\big{]}\\ &=\frac{\varrho}{2}L^{5}\Big{[}M^{\prime}_{\dot{q}}\dot{q}+M^{\prime}_{rr}r^{2} +M^{\prime}_{rp}rp\Big{]}\\ &+\frac{\varrho}{2}L^{4}\Big{[}M^{\prime}_{q}uq+M^{\prime}_{|w|}\Big{|}\big{(} v^{2}+w^{2}\big{)}^{\frac{1}{2}}\Big{|}q+M^{\prime}_{\dot{w}}\dot{w}+M^{\prime}_{vr}vr+M^{ \prime}_{vp}rp\Big{]}\\ &+\frac{\varrho}{2}L^{3}\Bigg{[}M^{\prime}_{uu}u^{2}+M^{\prime}_{w}uw+M^{ \prime}_{w|w|}w\Big{[}v^{2}+w^{2}\big{)}^{\frac{1}{2}}\Big{|}\Bigg{]}\\ &+\frac{\varrho}{2}L^{3}\Bigg{[}M^{\prime}_{|w|}u|w|+M^{\prime}_{ww}\Big{|}w \big{(}v^{2}+w^{2}\big{)}^{\frac{1}{2}}\Big{|}+M^{\prime}_{vv}v^{2}\Big{]}+ \frac{\varrho}{2}L^{3}u^{2}\Big{[}M^{\prime}_{\delta_{r}}\delta_{s}+M^{\prime }_{\delta_{b}}\delta_{b}\Big{]}\\ &-(Wx_{G}-Bx_{B})\cos\theta\cos\phi-(Wz_{G}-Bz_{B})\sin\theta+WF_{P}\end{array} \] (19) Yaw \[ \begin{array}{ll}&I_{z}\dot{r}+\big{(}I_{y}-I_{x}\big{)}pq+\big{(}rq- \dot{p}\big{)}I_{xz}+m\big{[}x_{G}\big{(}\dot{v}+ur-wp\big{)}-y_{G}\big{(} \dot{u}-vr+wq\big{)}\big{]}\\ &=\frac{\varrho}{2}L^{5}\Big{[}N^{\prime}_{r}\dot{r}+N^{\prime}_{pq}pq+N^{ \prime}_{\dot{p}}p\Big{]}\\ &+\frac{\varrho}{2}L^{4}\Big{[}N^{\prime}_{r}ur+N^{\prime}_{|v|r}\Big{|}\big{(} v^{2}+w^{2}\big{)}^{\frac{1}{2}}\Big{|}r+N^{\prime}_{p}up+N^{\prime}_{\dot{v}}\dot{v}+N^{ \prime}_{wp}wp\Big{]}\\ &+\frac{\varrho}{2}L^{3}\Big{[}N^{\prime}_{uu}u^{2}+N^{\prime}_{p}uv+N^{\prime}_{v| r|}\Big{[}(v^{2}+w^{2})^{\frac{1}{2}}\Big{|}+N^{\prime}_{wp}ww\Big{]}+\frac{ \varrho}{2}L^{3}u^{2}N^{\prime}_{\delta_{r}}\delta_{r}\\ &+(Wx_{G}-Bx_{B})\cos\theta\cos\phi+(Wy_{G}-By_{B})\sin\theta+WF_{Y}\end{array} \] (20)  
where \(I_{x}\), \(I_{y}\), and \(I_{z}\) are the inertia moments about the axes of the body-fixed coordinate system, and \(I_{xz}\) is the cross-sectional area on plane \(xz\). \(X_{P}\) and \(X_{R}\) are defined as the propeller thrust and the resistance, respectively. \(WF_{S}\), \(WF_{H}\), \(WF_{R}\), \(WF_{P}\), and \(WF_{Y}\) are the exciting forces of waves that act on each DOF motion. Since the manoeuvre of the SFRM was given in the condition of neutral buoyancy, the effects of wave exciting forces have been disregarded.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Radar Sensors
#### 2.6.4 Polarimetry SAR Interferometry Technique  
The polarimetry SAR interferometry technique estimates crop modeling using vegetation water content, which is estimated from the collected imagery [124]. Polarimetric SARdata have benefits in identifying and separating the scattering methods of a natural filter, while SAR interferometry data are sensitive to vertically spatially dispersed parameters that allow for data collection with high precision. The polarimetry SAR interferometry method uses both polarimetry and interferometry techniques to deliver the sensitivity scale to the source target. The polarimetry SAR interferometry technique significantly develops upon the application capacity of SAR analysis [125].
## 5 System Model and Proposed Technique
### Calculation of Direct Distance Traveled by the Ray  
For accuracy, we will take the layer at a distance of 1 m that is why Pythagoras theorem is used for calculating the direct distance traveled by the ray \(d_{i}\) from anchor to the sensor as,  
\[d_{s_{i}}\ =\ \sqrt{1^{2}\ +\ r_{i}^{2}} \tag{18}\]  
After getting the value of \(d_{s_{i}}\) from (18) we can be able to calculate the travel time by using Equation (10) as mentioned before since we are taking the depth of 1 m, therefore rather usingEquation (11). In modified form (10) will give the time by using \(d_{s_{i}}\) for each layer with the help of Equation (19),  
\[t_{i}\ =\ \frac{d_{s_{i}}}{v_{i-1}} \tag{19}\]  
if the depth of each layer is greater than 1 than the travel time can be calculated by using Equations (14) and (15) in the form of,  
\[t_{i\ +\ 1}-t_{i}\ =\ \frac{1}{\left|g_{i}\right|}ln\Bigg{[}\frac{c_{i\ +\ 1}}{c_{i}}\frac{1\ +\ \sqrt{1-\varepsilon^{2}c^{2}(z_{i})}}{1\ +\ \sqrt{1- \varepsilon^{2}c^{2}(z_{i\ +\ 1})}}\Bigg{]} \tag{20}\]  
which is equals to  
\[t_{1}-t_{0}\ =\ \frac{1}{\left|g_{0}\right|}ln\Bigg{[}\frac{c_{1}}{c_{0}} \frac{1\ +\ \sqrt{1-cos{\theta_{0}}^{2}}}{1\ +\ \sqrt{1-cos^{2}{\theta_{0}}\frac{c_{1}^{2}}{c_{ \sigma}^{2}}}}\Bigg{]} \tag{21}\]  
Then, each travel time will be added and divided by the corresponding summation of horizontal distance to get the accurate velocity, from anchor to sensor by using Equation (22),  
\[v_{equ}\ =\ \frac{d_{tot}}{t_{tot}} \tag{22}\]  
As proposed, the study jointly estimating the energy-efficient time synchronization and location of underwater wireless sensors which have special importance and requirement in underwater wireless sensor networks (UWSNs). According to our methodology, this whole process will work within 5 phases which are as follows: phase I is the message exchange procedure, phase II is the calculation of sound velocity, phase III is the calculation of clock skew, phase IV is the calculation of position, phase V is the estimation of offset. According to the procedure the message exchange procedure in phase I, is a one-way from anchor to a sensor which performs a pair-wise synchronization mechanism for estimating the clock skew and offset. Due to the one-way broadcast of message packets, the proposed algorithm is energy efficient for synchronization and localization as the target sensors are silent and only receive signals from the anchor.
## 3 Results
### Mechanisms of BFRT
#### 3.2.5 Muscle Damage  
Exercise-induced muscle damage (EIMD) is a common consequence of strength training, which, from a mechanical perspective, is thought to be induced by overstretching the sarcomere with subsequent extracellular matrix degradation as well as disruption of the cytoskeletal matrix and z-disk streaming [43]. However, as BFRT is performed with low levels of mechanical stress, other mechanisms such as ischemia and subsequent reperfusion are likely responsible for muscle damage [44]. Furthermore, activation of stretch-activated calcium channels or transient receptor potential channels may also contribute to muscle damage by increasing intracellular calcium levels, which ultimately lead to muscle damage and necrosis [45; 46].  
Although EIMD is often associated with high levels of discomfort, it simultaneously triggers positive muscle adaptations, as EIMD has been claimed to be an important regulator of satellite cell (SC) proliferation. Following EIMD, satellite cells located under the basal lamina rapidly proliferate and subsequently contribute to muscle remodeling and muscle growth [25]. Indeed, several studies with HLRT have already shown that it produces significant levels of muscle damage due to the high loads and concomitant high mechanical stress placed upon muscles, compared with LLRT, to which KOA patients are often referred.  
While the evidence for the effects of HLRT and LLRT on muscle damage is well established, the impact of BFRT on muscle damage appears to be more enigmatic. Although some studies found BFRT to induce only minimal levels of muscle damage [47; 48], expressed in terms of delayed onset muscle soreness (DOMS) [47], creatine kinase [49], or interleukin (IL)-6 after [36] 24 h, other studies reported a larger degree of EIMD following BFRT, up to 48 h after exercise [50; 51]. However, elevated levels of muscle soreness, CK, and IL-6 levels as a result of BFRT have especially been reported in individuals who are not accustomed to the high metabolic stress associated with BFR, particularly when performing BFRT until volitional failure [52].  
Despite the inconclusive evidence on the extent of EIMD caused by BFRT, literature suggests that muscle degeneration and regeneration occur in response to exercise [53], resulting in an increased proliferation of satellite cells in both acute and chronic BFRT [54; 55]. This proliferation could be explained by an increase in stretch-, hypoxia-, and/or contraction-induced nitric oxide (NO) secretion [55]. Furthermore, other mechanisms, such as increased levels of vascular endothelial growth factor (VEGF) and Hepatocyte Growth Factor (HGF) are associated with increased satellite cell proliferation and a concomitant increase in MPS and muscle hypertrophy [53; 56].
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
###### Abstract  
The article presents an innovative system for non-contact high voltage (HV) measurement, which extends the measurement capabilities of a portable partial discharges (PD) monitoring system intended for diagnostics of power transformers. The proposed method and the developed measuring system are based on the use of a capacitive probe, thanks to which the high voltage measurement is safe (galvanic separation from the objects at ahigh potential). It is also flexible because the voltage ratio of this system can be configured in a wide range by changing the probe's position. The proposed solution makes the portable PD monitoring system fully autonomous and independent of the substation systems and devices. The article presents both the concept of the non-contact HV measurement system and its practical implementation. The procedure for determining the voltage ratio and measurement uncertainty, which is at an acceptable level of 1-5% in laboratory conditions, was discussed in detail. In addition, the article discusses the digital filtering and wavelet de-noising methods implemented in the software of the monitoring system, which makes it possible to measure the voltage in the presence of strong electromagnetic disturbances occurring at the substation. Finally, the results of field tests carried out on a 250 MVA power transformer are presented, which confirmed the high accuracy of the HV measurement using a capacitive probe and the advantages of this technique.  
Keywords:partial discharge (PD); power transformer monitoring; capacitive probe; high voltage measurement; non-contact measurement +
Footnote †: journal: _Energies_
## 2 Materials and Methods
### EC and Climate Measurements  
The EC technique has been used to measure continuous half-hourly CO\({}_{2}\) and water vapor fluxes since 2000, 2001 and 1997 at DF49, HDF88 and HDF00, respectively. Climate variables that were also measured included precipitation, net radiation (\(R_{n}\)), photosynthetically active radiation (PAR), wind speed, air temperature (\(T_{a}\)) and humidity, soil temperature (\(T_{s}\)) and volumetric soil moisture content (\(\theta\)). Details on EC instrument characteristics, measurements and calculation procedures for these stands are described in [PERSON] _et al._[54], [PERSON] _et al._[55] and [PERSON] _et al._[57]. Half-hourlyEC-measured CO\({}_{2}\) fluxes above the stand were corrected by adding the estimated rate of change in CO\({}_{2}\) storage in the air column below the EC sensor height to obtain the net ecosystem exchange (NEE). NEP was obtained as NEP = \(-\)NEE [54].  
The methodology of gap-filling for EC-measured NEP and its separation into GPP and \(R\) was adopted from the Fluxnet-Canada Research Network protocol [55, 58]. Missing nighttime values of \(R\) were obtained using an exponential relationship between NEE and \(T_{s}\) (\(Q_{10}\) model) using measured NEE values corresponding to friction velocity values greater than a threshold value. This model was also used to obtain daytime values of \(R\). GPP during the growing season was obtained as the sum of daytime-measured NEP and daytime \(R\) and was set to zero during the cold season. An empirical light-response relationship was used to fill gaps in GPP. The details on NEP gap-filling and its separation into GPP and \(R\) as well as the uncertainties in the annual sums of NEP, GPP and \(R\) can be found in [PERSON] _et al_. [59].