esa-sceva/satcom-chunk-collection huggingface dataset:
# ChatGPT for Education and Research: Opportunities, Threats, and Strategies
## 3 Opportunities with ChatGPT
### Opportunities for Researchers  
The ChatGPT model offers many advantages to researchers. First, it can effectively support the writing process of research. At its most basic, it can improve writing by finding and correcting typographical errors, improving grammatical inconsistencies, providing advanced vocabulary, and recommending improvement strategies. This allows researchers to devote more time to experimentation and implementation. The model can also summarize published work on a particular topic, which helps researchers understand the work. It can also provide clues and research ideas by analyzing a specific topic. For example, we asked ChatGPT to provide an unexplored research idea on \"**how to reduce errors related to resource constraints (time and memory limit exceeded) in code for programmers**\". Based on this query, ChatGPT provided some interesting and promising research ideas, as illustrated in Figure 6.  
Figure 5: ChatGPT generates quizzes with different difficulty levels on the same topic.  
Provide an unexplored research idea on how to reduce resource limitation related errors (time limit exceeded, memory limit exceeded) in code for programmers  
One unexplored research idea to reduce resource limitation related errors in code for programmers is to develop a hybrid approach that combines static and dynamic analysis techniques.  
Static analysis involves analyzing code without executing it, while dynamic analysis involves analyzing code as it executes. By combining these techniques, it may be possible to identify potential resource limitation issues before the code is executed and also monitor the code as it runs to detect any issues that may arise.  
The hybrid approach could involve using static analysis tools to analyze code and identify potential issues, such as memory leaks or infinite loops. These tools could also provide suggestions for optimizing the code, such as reducing the number of operations or using more efficient data structures.  
At runtime, the code could be monitored using dynamic analysis tools to detect any resource limitation errors that may occur. This could involve tracking memory usage, execution time, and other metrics to identify potential issues in real-time. The dynamic analysis tools could also provide suggestions for optimizing the code or adjusting resource allocation to reduce the risk of errors.  
By combining these two approaches, programmers could potentially reduce the risk of resource limitation errors in their code and optimize its performance. However, this approach would require significant development of both static and dynamic analysis tools and integration between them, making it a challenging but promising area for future research.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### Resonant Constellation Configuration Analysis for Distinct Regions
#### 4.1.3 Combined Region  
The combined region combines both the near-Earth and lunar regions as targets, with discretization parameters consistent with those defined in Sections 4.1.1 and 4.1.2. Following the same methodology, five constellations with optimal positioning performance for this region were identified. The optimal configurations and their corresponding spatiotemporal average DOP values and standard deviations are listed in Table 4. A schematic of the best-performing resonant constellation and the temporal average DOP values across grid points in the integrated target region is illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Resonant Configuration** & \begin{tabular}{c} **Baseline** \\ **Period** \\ \end{tabular} & \begin{tabular}{c} **Resonance** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ **DOP** \\ \end{tabular} &
\begin{tabular}{c} **DOP** \\ \end{tabular} \\ \hline
1 & L1V-L2 NH-L2 SH-L4V & 3.1417 & 1:1:1:2 & 5.89 & 2.07 \\
2 & L1V-L2 NH-L2 SH-L5V & 3.1417 & 1:1:1:2 & 5.89 & 2.66 \\
3 & L1V-L2 NH-L2 SH-DRO & 2.7695 & 1:1:1:1 & 10.65 & 4.57 \\
4 & L1V-L2 NH-L1 NH-L1 SH & 2.7695 & 1:1:1:1 & 8.46 & 13.80 \\
5 & L1L-L2L-L4P-L5P & 3.2693 & 2:3:4:4 & 15.38 & 2.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dominant configurations and spatiotemporal average DOP values in the lunar region.  
Figure 6: Schematic diagram of optimal resonant constellation configurations and time-averaged DOP distribution in the lunar region.  
The charts reveal that the Rank 1 and Rank 2 configurations for the combined region--composed of L2 southern/northern NRHO orbits and L4/L5 planar/vertical orbits--also rank as optimal configurations for both the near-Earth and lunar regions. The remaining three configurations (Rank 3-5) primarily consist of one L1 or L2 Halo orbit combined with L4/L5 orbits of the same type. Among them, Rank4 and Rank5 differ only by swapping the north-south family Halo orbit types and can thus be regarded as equivalent. An analysis of the DOP distribution indicates that the optimal configurations exhibit superior performance in the lunar vicinity.
## 3 System Model and Data Preprocessing
### Problem Description
#### 3.3.2 Semi-Supervised AMC Problem  
In the semi-supervised AMC problem, the following settings were used. The training dataset is \(D_{l}=\{(x_{1},y_{1}),\cdots,(x_{L},y_{L}),x_{L+1},\cdots,x_{N}\}\), where \(L\) is the number of labeled training samples and \(N-L\) is the number of unlabeled training samples. For ease of illustration, we use \(D_{l}=\{(x_{l}^{i},y_{l}^{i})|i=1,\cdots,L\}\) to denote the labeled dataset and \(D_{ul}=\{x_{ul}^{j}|j=1,\cdots,N-L\}\) to denote the unlabeled dataset. The correlation between \(D_{t}\), \(D_{l}\) and \(D_{ul}\) is \(D_{t}=D_{l}\cup D_{ul}\), where \(N-L\gg L\).  
The core idea of the semi-supervised AMC problem is to find a mapping function that minimizes the expected error. In contrast to the general machine-learning-based AMC problem, due to the sparse number of labeled signal samples, we cannot compute the expected error directly. This is a frequent concern encountered in semi-supervised tasks. Information about data distribution can be obtained from unlabeled samples. Thus, in the semi-supervised AMC task, the expected error can be approximated as:  
\[\min_{f\in F}\epsilon_{em}=\min_{f\in F}E_{(x,y)\sim D_{l}}L(f(x),y)+\lambda E _{(x)\sim D_{ul}}L_{ul}(\cdot) \tag{7}\]  
where \(L_{ul}(\cdot)\) represents the loss obtained by training on unlabeled signal samples. The remaining portion remains identical to prior usage. Both losses utilize the cross-entropy loss function, which is a frequently utilized loss function in image classification tasks. \(\lambda\) denotes the weight value of the unsupervised loss, which impacts the classification proficiency of the model to an extent. However, the aforementioned must adhere to a significant assumption that the data distribution in the unlabeled samples is useful for the AMC problem. Specifically, labeled and unlabeled data share the same label space.
## 3 Methodology
### Bias Correction Methods
#### 3.1.2 Ratio Bias Correction  
Compared to SMBC, the RBC technique considers the distribution of time and space, which can correct the NRT SPPs more reasonably. Moreover, it is simple and easy to implement applicable. It has a wide range of applications in the field of SPPs correction [32; 33] and the Inverse Distance Weighted (IDW) was selected as the interpolation method since it has the advantages of simplicity, computational speed, and good performance [47; 48]. The main calculation steps are presented below:  
(1) Interpolation of gauge rainfall at each station \(i\) with Inverse Distance Weighted (IDW) technique to create the grid-based rainfall same as SPPs:  
\[D_{i}=\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2}} \tag{3}\]  
\[P_{j}=\sum_{i=1}^{N}\frac{1}{\left(D_{i}\right)^{p}}P_{i}/\sum_{i=1}^{N}\frac {1}{\left(D_{i}\right)^{p}} \tag{4}\]  
where \(D_{i}\) is the distance between station \(i\) and the center of grid \(j\), \(P_{i}\) and \(P_{j}\) are rainfall at station \(i\) and grid \(j\), respectively. \(p\) is the power of distance, which usually takes the value of 2. \(N\) is the number of stations, and we took 3 in this study to avoid possible new biases since the study case has sufficient data.  
(2) Calculating the bias factor \(f_{j,T}\) for each grid and time period \(T\) by dividing the gauge with SPPs:  
\[f_{j,T}=P^{S}_{j,T}/P^{S}_{j,T} \tag{5}\]  
(3) Finally, the bias-corrected rainfall of SPPs equals the multiplication of bias factor and SPPs at hourly and daily steps within different \(T\) (monthly and 10-day):  
\[P^{RBC}_{j,t}=f_{j,T}*P^{S}_{j,t} \tag{6}\]
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 4 Experimental Results
### Objective Assessments
Figure 10: Experimental results of haze removal using the proposed and existing methods: (**a**) an input haze image [4], reproduced with permission from [4], IEEE, 2014, (**b**) [PERSON]’s method [1], (**c**) [PERSON]’s method [9], (**d**) [PERSON]’s method [8], (**e**) [PERSON]’s method [2], (**f**) [PERSON]’s method [6], and (**g**) the proposed method. method was able to provide similar MSE values with a smaller number of decision trees for the random forest, as summarized in Table 3.  
Table 3 shows the comparative results for the mean squared error (MSE) of the estimation of transmission using synthesized outdoor haze images. Figure 12 is a set of original haze-free images that were used to synthesize haze patches to estimate MSE values in reference [23]. As summarized in Table 3, the proposed method provided lower MSE scores than [PERSON]'s method [4]. This means that the proposed method can estimate more accurate atmospheric light and transmission with a significantly reduced computational load regardless of the number of trees.
# A Review on Flavonoids as Anti-_Helicobacter pylori_ Agents
## 3 Results
### Medicinal Plants and Their Potential Application
Plants from the family Euphorbiaceae, which is considered a family rich in bioactive molecules (terpenes, alkaloids, phenolic compounds, flavonoids, and others), may have bioactive compounds that can inhibit the \(H\). _pylori_ growth, and the mechanism from some extracts was by the interaction with urease protein subunit A or subunit B. Some genera, e.g., _Euphorbia, Croton_, and _Plukenstein_, had a further characterisation of the bioactive molecules [79; 80; 81; 82]. Flavonoids such as myricetin, quercetin, luteolin, naringenin, hesperidin, kaempfer, apigenin, isorhammetin, astragalin, and their derivatives, and some phenolic acids such as gallic acid, chlorogenic acid, vanillic acid, caffe acid, and \(p\)-coumaric acid are among the bioactive molecules found in this genus [83; 84; 85; 86; 87]. The aqueous extract obtained from _P. volubilis showed promising anti-H. pylori activity_. Astragalin, a flavonoid isolated from this extract, can inhibit \(H\). _pylori_ growth in vitro, affecting sensitive and resistant strains. Furthermore, in silico analysis demonstrated its capacity to interact with specific genes/proteins (Table 1), likely interfering with their function [88]. The results presented herein show that flavonoids can be an excellent alternative source for treatment or combined with antibiotics or PPIs.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 6 Experimental Setup  
To validate the numerical results of the proposed discrete map based jerk chaotic system, an electrical circuit was made. Op-Amps were used to implement the system of ODEs given by Equations (7), (8) and (10) [38]. Two analog multipliers ICs (AD633 JR) were required in series to get the cubic term in Equation (10). It has four inputs \((X_{1},X_{2},Y_{1}\ \&\ Y_{2})\) and two outputs (\(W\ \&\ Z\)). The output is given as \(W=(X_{1}-X_{2})*(Y_{1}-Y_{2})/10+Z\). It  
Figure 8: Graphical representation of calculating stroboscopic distribution by finding the points of intersection of the phase trajectories with the Poincare Section.  
can be shown that the Equations (7), (8) and (10) [38] can be easily transformed into the following set of Equations (17)-(19).  
\[\frac{dX_{1}}{dt}=\frac{X_{2}}{RC} \tag{17}\]  
\[\frac{dX_{2}}{dt}=\frac{X_{3}}{RC} \tag{18}\]  
\[\frac{dX_{3}}{dt}=\frac{X_{1}}{R_{1}C}-\frac{X_{2}}{R_{2}C}-\frac{X_{3}}{R_{3} C}-\frac{X_{1}^{3}}{600R_{4}C} \tag{19}\]  
Using a time constant of \(1\times 10^{-4}\), the control parameters in terms of resistance and capacitor can be calculated from the following relations while maintaining the critical relation of \(R_{4}=R/100\)  
\[\alpha=\frac{1\times 10^{-4}}{R_{1}C}=1,\quad\beta=\frac{1\times 10^{-4}}{R_{2}C}= Variable, \tag{20}\]  
\[\sigma=\frac{1\times 10^{-4}}{R_{3}C}=6,\quad\gamma=\frac{1\times 10^{-4}}{600R _{4}C}=6 \tag{21}\]  
NI Multisim was used to simulate the electrical circuit before implementing it on the breadboard. The schematic of the proposed circuit is shown in Figure 9.  
Using appropriate time scaling, the output voltage of \(U_{1}A\) (\(x_{3}\)) and \(U_{3}A\) (\(x_{2}\)) are directly monitored using a two-channel digital oscilloscope shown in Figure 10.  
Figure 9: The circuit schematic for the proposed discrete–time map based jerk chaotic system.  
Figure 12 shows a comparison between the numerical (left) and hardware (right) results. Period 1 (limit cycle) attractor was obtained by setting the control resistor R6 to 1112 \(\Omega\), similarly increasing the value of R6 to 1667 \(\Omega\) gives the period 2 attractor, R6 = 1715 \(\Omega\) corresponds to period 4 attractor finally, R6 = 1807 \(\Omega\) gives the chaotic attractor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No.** & **Value of R6** & **Attractor Type** \\ \hline
1 & 1112 \(\Omega\) & Period 1 \\
2 & 1667 \(\Omega\) & Period 2 \\
3 & 1715 \(\Omega\) & Period 4 \\
4 & 1807 \(\Omega\) & Chaotic Attractor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Different Attractor with the variation of Control Resistance R6.  
Figure 12: _Cont._
## 2 Materials and Methods
### GIS-Based Analysis Method  
This study applied GIS spatial analysis techniques to investigate the spatiotemporal distribution of archaeological sites in the middle and lower reaches of the Yuanjiang River, spanning from the Paleolithic to the Shang and Zhou dynasties. ArcGIS 10.7 software was used to process and vectorize 90-m resolution SRTM DEM data, generating elevation maps for the study area. The DEM data were first cleaned to remove noise and then cropped to align with the study area's boundaries, ensuring data accuracy. Additionally, vector data for water systems and administrative divisions in China and Hunan Province were obtained from the Geoinformation Network forum. After registration and precise cropping, relevant data for the study area were extracted, forming the basis for further spatial and environmental analysis.  
The analysis focused on three key dimensions: temporal distribution, planar spatial distribution, and vertical spatial distribution. Although precise age data were unavailable for many sites, estimates were made based on the terraces on which the sites were located and published chronological data from the region and surrounding areas [35; 40; 45; 46; 47]. Paleolithic sites were estimated to date from 300,000 to 10,000 years ago, Neolithic sites were estimated to date from 10,000 to 3600 years ago, and sites from the Shang and Zhou dynasties were estimated to date from 3600 to 2300 years ago. These timeframes allowed for the classification of the sites into three periods: Paleolithic, Neolithic, and Shang-Zhou.  
By analyzing artifacts and site characteristics from 804 locations, the cultural evolution of the region was divided into sequential stages. Detailed data from these sites were compiled into a comprehensive dataset and mapped using ArcGIS to generate vector layers for each period. These data were integrated with DEM maps, enabling statistical analysis of various factors, such as elevation and proximity to riverine water sources. This approach provided valuable insights into the spatial distribution of ancient cultural sites and demonstrated how natural environmental factors influenced settlement patterns and cultural development throughout history.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 3 Proposed Methodology
### Nodule Detection Model  
Among all two-stage object detection models, the single-shot detector (SSD) is the fastest detection model, but it is not the most accurate one [27]. In our framework, we aimed at developing an accurate segmentation model; Faster RCNN is considered one of the most accurate detection models in inferring the locations of the target in the input image [28]. Thus, we preferred to choose the Faster RCNN model rather than SSD for localizing the nodules in CT images. In this stage, we attempted to fine-tune the important parameters of the Faster RCNN detection model. We focused on finding the best combination of the learning rate, step size, factor of dropped learning rate \(\gamma\), and drop-out ratio to make Faster RCNN more appropriate for lung nodule detection.  
As shown in Figure 2, the Faster-RCNN detection model is a two-stage detection network containing three main blocks: a backbone network, a region proposal network (RPN), and a box head. We used ResNet50 [23] as a backbone network to extract feature maps from the input image. The feature map is then fed into the RPN to perform boundary regression and classification analysis, and the output is a set of ROI candidates. The classification principle is based on whether a candidate ROI is either related to background or to the object (i.e., in our case, tumour nodules). The position and score of the candidate ROI are forwarded to the box head, where the final regression and classification of the object is performed. Finally, the bounding box of the target (nodule) with the classification score is returned from the detection model.  
Figure 1: The step-by-step workflow of the proposed method. (**a**) The converted input image is extracted from the original CT slice. (**b**) The pre-processing step for selecting ROI of lung. (**c**) The detection of lung nodules using Optimized Faster R-CNN. (**d**) The segmentation of lung nodules using the proposed AWEU-Net model.  
Figure 2: The detailed architecture of Optimized Faster R-CNN.
# Review Rating Prediction Based on User Context and Product Context
## 3 RRP Based on User Context and Product Context
### RRP Based on User Context and Product Context  
In Section 3.1, we present our research goals through a toy example. In Section 3.2, we present a RRP model based on the review content and user context. In Section 3.3, we present a RRP model that is based on the review content and product context. In this part, we propose a RRP method based on user context and product context by integrating the user context information and the product context information.  
\[\hat{v}_{ui}=\left(\mathbf{w}+\mathbf{w}_{u}+\mathbf{w}_{i}\right)^{\mathrm{T }}\mathbf{r}_{ui} \tag{10}\]  
Here, \(\hat{v}_{ui}\) is the predicted rating of user \(u\) for item \(i\); \(\mathbf{w}\), \(\mathbf{w}_{u}\), \(\mathbf{w}_{i}\) are the common, user-specific and product-specific parameters in RRP model; and, \(\mathbf{r}_{ui}\) is the vector representation of review content.  
In order to get optimum parameters \(\mathbf{w}\), \(\mathbf{w}_{u}\), \(\mathbf{w}_{i}\), given training datasets available, we use the least square error loss principle to minimize the objective function.  
\[\underset{\mathbf{w},\mathbf{w}_{u},\mathbf{w}_{i}}{\text{min}}\underset{ \text{transsets}}{\sum}\left(v_{ui}-\left(\mathbf{w}+\mathbf{w}_{u}+\mathbf{w }_{i}\right)^{\mathrm{T}}\mathbf{r}_{ui}\right)^{2}+\lambda\Big{(}\|\mathbf{ w}\|^{2}+\|\mathbf{w}_{u}\|^{2}+\|\mathbf{w}_{i}\|^{2}\Big{)} \tag{11}\]  
Here, \(\lambda\) is the regular coefficient, \(|\ |\ \mathbf{w}\ |\ |\), \(|\ |\ \mathbf{w}_{u}\ |\ |\), and \(|\ |\ \mathbf{w}_{i}\ |\ |\) are the regular term of the parameter. To estimate the parameter \(\mathbf{w}\), \(\mathbf{w}_{u}\), and \(\mathbf{w}_{i}\), we use a stochastic gradient descent algorithm to solve this optimization problem. We use the following update rules to learn the parameters \(\mathbf{w}\), \(\mathbf{w}_{u}\), and \(\mathbf{w}_{i}\).  
\[\mathbf{w}\leftarrow\mathbf{w}+\eta\left(\varepsilon_{ui}\mathbf{r}_{ui}- \lambda\mathbf{w}\right) \tag{12}\]  
\[\mathbf{w}_{u}\leftarrow\mathbf{w}_{u}+\eta\left(\varepsilon_{ui}\mathbf{r}_{ui }-\lambda\mathbf{w}_{u}\right) \tag{13}\]  
\[\mathbf{w}_{i}\leftarrow\mathbf{w}_{i}+\eta\left(\varepsilon_{ui}\mathbf{r}_{ui }-\lambda\mathbf{w}_{i}\right) \tag{14}\]  
Here, \(\varepsilon_{ui}=v_{ui}-\left(\mathbf{w}+\mathbf{w}_{u}+\mathbf{w}_{i}\right) ^{\mathrm{T}}\mathbf{r}_{ui}\), \(\eta\) is learning rate. After getting \(\mathbf{w}\), \(\mathbf{w}_{u}\), and \(\mathbf{w}_{i}\), given reviews in test datasets, we can use \(\hat{v}_{ui}=\left(\mathbf{w}+\mathbf{w}_{u}+\mathbf{w}_{i}\right)^{\mathrm{T }}\mathbf{r}_{ui}\) to predict the review rating.
## 4 Methodology
### Model Training
#### 4.2.3 Training Stability  
Training stability is a critical concern in actor-critic RL methods due to the close coupling between learning targets and policy updates. In this work, the Maximum a Posteriori Policy Optimization (MPO) algorithm [38] provides a robust foundation for stable learning by enforcing a hard Kullback-Leibler (KL) divergence constraint during the E-step of the policy update. This constraint limits deviations from the previous policy, thereby ensuring smoother transitions between successive policies. The corresponding constrained optimization objective is defined as follows:  
\[\arg\max_{q} \mathbb{E}_{\mu(s)}\big{[}\mathbb{E}_{q(a|s)}[Q^{\pi_{i}}(s,a)] \big{]}\] \[\text{subject to} \mathbb{E}_{\mu(s)}[\text{KL}(q(a\mid s)\parallel\pi(a\mid s; \theta_{i}))]<\epsilon \tag{9}\]  
This formulation seeks to maximize the expected Q-value under a new action distribution \(q(a\mid s)\), while ensuring that it remains close to the current policy \(\pi(a\mid s;\theta_{i})\) within a KL divergence bound \(\epsilon\). To further enhance training robustness, several stabilization techniques were integrated into the training process. A soft target update mechanism [39] was applied to the critic network, with a smoothing coefficient of \(\tau=0.005\), to reduce variance in bootstrapped target estimates. Gradient clipping with a global norm threshold of 1.0 was implemented to prevent instability in weight updates, particularly during the early phases of training [40]. Entropy regularization [41] was maintained during the E-step to encourage exploration and mitigate premature policy convergence.  
Separate Adam optimizers [42] were employed for the actor and critic networks, with learning rates set to \(10^{-4}\) and \(10^{-3}\), respectively. Additionally, a short warm-up phase for the learning rates was introduced to smooth the gradient dynamics at the start of training. All experiments were conducted with fixed random seeds to ensure reproducibility. The MPO algorithm inherently incorporates strong regularization mechanisms that contribute to stable learning behavior. As a result, minimal hyperparameter tuning or manual intervention was required. When combined with the aforementioned stabilization strategies, the algorithm consistently demonstrated reliable and robust training performance across all evaluated environments.