esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Current Research Gaps and Our Contributions  
Despite considerable progress across these areas, significant gaps remain. Most existing research addresses individual components--such as question generation, grading, or feedback--in isolation, without offering a comprehensive framework that integrates these elements under a unified evaluative rubric. Moreover, there is a notable lack of systematic guidelines that consider both the technical quality and the pedagogical alignment of AI-generated exam questions, especially in the context of higher education where even minor errors can have severe consequences.  
To overcome these gaps, our study introduces a multi-purpose AI framework that:  
* Proposes a three-task framework (Alignment Evaluation, Question Improvement, and New Question Generation) that reflects real-world academic assessment needs.
* Incorporates a human-supervised meta-evaluator to provide both numerical scores and detailed explanations.
* Utilizes mixed-effects modeling to rigorously quantify agent performance and isolate sources of bias or misalignment.
* Offers a set of best practices and actionable guidelines for educators and developers seeking to integrate LLMs into higher-education assessments.  
While the previous work has provided an important groundwork in both automated question generation and AI in education, our study bases its uniqueness in its meticulous, multi-dimensional evaluation of LLM-based agents, to offer practical, actionable guidelines for educators seeking to implement responsible, context-aware AI in their assessment processes. By addressing the specific needs of higher education, where even minor misalignment can have severe impacts, we provide a framework that is not only scientifically rigorous but also practically valuable for safeguarding academic integrity and enhancing learning outcomes.
## 4 Conclusions  
We have demonstrated compact, low-threshold and high power widely tunable III-V/Si lasers co-integrated with semiconductor optical amplifiers. The device presents a laser current threshold of 10 mA, a fiber-coupled output power of 10 mW, a wavelength tuning range of 50 nm, and a total power consumption below 0.5 W. The spectral purity has been evaluated, obtaining a side mode suppression ratio over 50 dB for the entire wavelength range and a laser linewidth of 259 KHz when delivering its maximum output power.  
The demonstrated co-integrated, widely tunable laser-SOA booster device can have a key role in applications where compact, low-threshold, high power, and narrow linewidth lasers are required, such as in coherent communications, LiDAR, coherent sensing, spectroscopy, or frequency synthesis. Future work on the co-integration of compact III-V/Si devices will focus on the inclusion of a high-speed electro-absorption modulator and on the improvement of the fiber-coupled optical power by using optimized edge couplers.  
[PERSON] contributed to the wafer level and chip-on-carrier characterization, methodology, formal analysis, data curation, simulation, and writing/original draft preparation. [PERSON] contributed to review and editing. [PERSON]-[PERSON] contributed to methodology, chip-on-carrier characterization, review, and editing. [PERSON] contributed to SOI fabrication. [PERSON] contributed to III-V fabrication, simulation, review, and editing. [PERSON] contributed to III-V fabrication. [PERSON], [PERSON], and [PERSON] contributed to III-V epitaxy, review, and editing. [PERSON] contributed to wafer level characterization, review, and editing. [PERSON] contributed to simulations, formal analysis, review, and editing. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge funding from the European program H2020 (Medphab) N\({}^{\circ}\) 871345 and from the french economic and financial department through the program Nano 2022-IPCEI.
## 4 Data Analyses and Results  
### Descriptive Statistics  
A total of 516 surveys were collected online and 132 surveys were collected in person (N = 648). All responses were entered into the same dataset. Of these surveys, 86 surveys (_i.e._, 78 online surveys and 8 in-person surveys) were discarded due to missing data. As a result, 86.7% of the original sample was used (N = 562). The majority of student respondents in the study were White (70.3%), female (70.4%), undergraduates (81.5%), and resided in the United States (84.8%). The age of the participants ranged from 18 to 58 years of age with a mean of 23.2 years (SD = 5.7 years). When examining Polish and American students, descriptive statistics revealed that the groups contained similar characteristics. For instance, the majority of student respondents in the American sample were White (65.5%), female (71.5%), and undergraduates (83.8%) with a mean age of 23.4 (SD = 6.1) while the majority of student respondents in the Polish sample were also White (97.6%), female (63.5%), and undergraduates (68.2%) with a mean age of 22.1 (SD = 3.4). All respondents reported having recently used mobile cellular phones and/or social networking sites (100.0%). On average, respondents reported sending and/or receiving 73.4 text messages per day (SD = 139.6) and spending about 2.5 hours per day on social networking (SD = 2.6).  
Results revealed that socially interactive intimate violence is not uncommon. The majority of the sample (60.5%) indicated that their partners had sent a text and/or made a post designed to upset, anger, or hurt them at some point in time. When looking at the ways by which partners aggressed, results showed that most psychological aggression involved both text messaging and social network posting (32.1%), followed by text messaging only (23.1%), followed by social network posting only (5.3%). Results also indicated that over three-quarters of respondents (76.3%) felt that punishment should be given at times when hurtful, harmful, or wrongful acts committed through socially interactive technology. Of the respondents feeling punishment is warranted, nearly half the sample felt there were multiple acts for which one should be punished (46.2%). Among the acts identified were combinations of threats, harassment, bullying, libel, slander, and defamation of character. Others felt punishment should only be implemented when there were serious direct threatsto life (21.3%), followed by only when physical injury resulted (9.6%), followed by when harassment, stalking or bullying occurs (8.2%), followed by libel, slander, and/or defamation (5.8%).
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 4 Evaluation
### Phase Offset of Modified ZigBee Signal in WiFi Preamble Detection  
In this experiment, we set the number of modified chips to 6 and the results are recorded in Figure 12. In this figure, phase offset of the extended sinusoidal waveform is around \(-\frac{2\pi}{5}\) with standard variance of 0.07. These fluctuations are caused by noise and multipath fading.
## 3 Results
### Assessing PlanetScope NDVI for Capturing Saltmarsh Spectral Response  
There was a strong correlation (r = 0.78; \(p\) < 0.001; Figure 6) between the aboveground carbon content extracted from the resampled 3 m map created using the ArborCam image and the NDVI extracted from the 3 m PlanetScope image captured 8 days later for the 47 quadrat locations. However, the extracted NDVI values were not directly comparable. The dynamic range of the extracted PlanetScope NDVI values (0.37-0.70) was reduced compared to the extracted ArborCam NDVI values (0.10-0.68). The large positive offset (0.401) in the linear model indicates a significant positive bias in the PlanetScope NDVI values compared to the ArborCam NDVI (see also Figure 3).
## 6 Conclusions  
We presented the DRL-JCBRTP framework, a novel deep reinforcement learning approach for beaconing that dynamically adjusts BR and TP to enhance cooperative vehicle awareness. By interpreting awareness as a multi-faceted concept involving vehicle detection, tracking, and safety distances, our DRL framework introduces innovative reward functions to increase the minimum awareness failure distance, balancing both the vehicle's internal state and external influences. Our SLMLab-Gym-VEINS simulations demonstrate that the DRL-JCBRTP scheme outperforms existing beaconing schemes by minimizing awareness failure probability and maximizing awareness distance. These results highlight its capability to improve cooperative awareness in vehicular environments, consequently enhancing driving safety.  
While the DRL-JCBRTP framework has demonstrated significant improvements in cooperative vehicle awareness across a range of traffic densities and inter-vehicle distances, its robustness in handling a wider variety of real-world scenarios--such as different terrains, weather conditions, and other environmental factors--remains an open question. Future research will focus on extending the versatility of the DRL-JCBRTP scheme by training it on more diverse datasets that encompass these variables. This effort will aim to further enhance the model's adaptability and effectiveness in real-world applications, ensuring that it can maintain high levels of performance even in conditions that were not part of the initial training environment. By addressing these challenges, we hope to develop a more comprehensive solution capable of reliably supporting V2X communications in a broad array of driving scenarios.  
Conceptualization, methodology, formal analysis, A.K.G.L. and H.-Y.J.; software, validation, investigation, visualization, resources, data curation, A.K.G.L. and H.-H.N.; writing--original draft preparation, writing--review and project administration, editing, A.K.G.L., D.G.G., J.Y. and H.-Y.J.; funding acquisition, supervision, H.-Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea under Grant NRF2021R1A5A1032937 and under Grant 2019R11A3A01060890.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 3 Related Work
### Statistical Method  
Statistical or empirical forecasting methods have a rich history in meteorology, serving as the initial approach to weather prediction before the advent of computational models. Statistical prediction methodologies serve as the linchpin for data-driven approaches in meteorological forecasting, focusing on both short-term weather patterns and long-term climatic changes. These methods typically harness powerful statistical algorithms, among which Geographically Weighted Regression (GWR) and Spatio-Temporal Kriging (ST-Kriging) stand out as particularly effective [32; 33].  
GWR is instrumental in adjusting for spatial heterogeneity, allowing meteorological variables to exhibit different relationships depending on their geographical context. ST-Kriging extends this spatial consideration to include the temporal domain, thereby capturing variations in weather and climate that are both location-specific and time-sensitive. Such spatio-temporal modeling is especially pertinent in a rapidly changing environment, where traditional stationary models often fail to capture the dynamism inherent in meteorological systems.  
Forecasting using inter-annual increments is now a statistically based forecasting method with better results. The interannual increment of a variable such as precipitation is calculated as:  
\[\text{Interannual Increment}=\text{Value}_{\text{year}}-\text{Value}_{\text{ year}-1}\]  
Through meticulous analysis of variables correlating with the inter-annual growth rate of the predictive variable, five key predictive factors have been identified. A multivariate linear regression model was developed, employing these selected key predictive factors to estimate the inter-annual increment for future time units. The estimated inter-annual increment is subsequently aggregated with the actual variable value from the preceding year to generate a precise prediction of the total quantity for the current time frame.  
However, these statistical models operate on a critical assumption cited in literature [34; 35], which posits that the governing laws influencing past meteorological events are consistent and thus applicable to future events as well. While this assumption generally holds for many meteorological phenomena, it confronts limitations when dealing with intrinsically chaotic systems. The Butterfly Effect serves as a prime example of such chaotic behavior, where minuscule perturbations in initial conditions can yield dramatically divergent outcomes. This implies that the reliability of statistical models could be compromised when predicting phenomena susceptible to such chaotic influences.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 1 Introduction
This paper aims to evaluate the performance of a location estimation system using the HAPS for illegal wireless stations and mobile terminals during disasters that occur in various environments and distances through numerical simulations.
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods
### RF Algorithm  
RF is a machine learning algorithm developed by [PERSON] that combines the results of multiple decision trees to reach a single conclusion [46]. RF is an extension of the bagging method and employs both bagging and feature randomness to create a forest of uncorrelated decision trees. The process for constructing the RF model was as follows. First, samples were randomly selected from the training set using the bootstrap method until the number of samples in the sample subset equaled the number of training samples. Some samples may be selected more than once, and others may not be selected at all. Typically, approximately 36.8% of the training samples are never selected and are referred to as out-of-bag samples, which form an unbiased internal estimate of the generalization error [47]. Second, a number of trees were constructed using the data in the subset of samples. Several variables were chosen randomly. The best split of the node was determined based on the Gini impurity of all selected variables. The variable that produced the best (lowest) Gini impurity and the corresponding threshold were selected as the criteria for node splitting. Finally, the results of all trees were combined, and their average was used to determine the final output. The RF algorithm is straightforward to implement and typically requiresonly two hyperparameters, namely, ntree and mtry. The ntree hyperparameter specifies the number of trees, whereas the mtry hyperparameter indicates the number of variables used in each node segmentation.
## 6 Decision Engine for Multi-User Systems
### Single Link  
This subsection simulates the method described in Section 4. As a reminder, the goal was to optimize the number and type of IR bits to be sent when there is a direct link between the transmitter and the receiver. Value iteration was applied to Equations (21) and (22) to yield a policy \(A(f,\mathcal{SNR},R,\mathcal{SNR}_{IR})\) specifying the number of extension bits (\(\alpha\)) and bundle parity bits (\(\beta\)) to be requested as a function of the number of failed codewords remaining in the bundle \(f\) and their effective \(\mathcal{SNR}\). We restrict the range of \(\alpha\) and \(\beta\) to be \([0,216]\) and \([0,648]\) respectively, so that the set of actions is finite. Figure 5 shows a slice of the policy for code rate \(R=2/3\) and the IR having the same SNR as the original bundle, \((\mathcal{SNR}_{IR}=\mathcal{SNR})\). It can be seen that the sum of \(\alpha\) and \(\beta\) increases as the SNR decreases. This is because more IR bits are required to recover a highly corrupted bundle. In addition, our policy suggests that bundle parity bits are preferred over extension bits when there is a small number of failed codewords. This is worth noticing, since bundle parity is equivalent to Chase Combining when there is a single failure and extension bits generally offer better performance than Chase Combining [46]. However, the feedback limitations in our system prevent the receiver from conveying to the transmitter the specific codewords that failed; if extension bits were requested, the transmitter would have to send them for every codeword in the bundle, even for those that have already been successfully decoded. The policy illustrated in Figure 5 has less than 16 possible combinations of \((\alpha,\beta)\), so it suffices to use 4 feedback bits to specify the request. This translates to only 1 bit of feedback per 2 codewords, which is half as much feedback as traditional fixed-length IR schemes with individual acknowledgements.  
Figure 6 compares the number of information bits delivered per unit cost for different IR schemes. Each point in the plot is the result of averaging Monte Carlo simulations for 1000 bundles and unlimited rounds of IR until success. If we interpret the cost as delay, then the number of information bits delivered divided by the cost will be the throughput. It can be observed that our HARQ policy provides modest gains over those with a fixed IR length, regardless of what this fixed number is and the SNR of the channel. These gains would be even larger in a scenario with variable SNR where, unlike fixed IR schemes, the proposed HARQ protocol would be able to adapt the IR length to each individual bundle.
## 2 Observations and Method for Data Analysis
### SABER Temperature Data
#### 2.1.1 Method for Extracting Waves from the SABER Temperatures  
The data analysis method for calculating the climatological tidal characteristics is well-known from all our studies based on the SABER/TIMED data. It has been described in detail by [PERSON] et al., 2009 ([PERSON] et al., 2009), and will be only briefly considered here; additionally, the method is the same as that reported by [PERSON] and [PERSON], 2023 ([PERSON] and [PERSON], 2023), in studying the climatology and interannual variability of the quarterdiurnal migrating tide (QW4). It is carried out in two steps: first, the daily characteristics of the waves for each year over the period of 2002-2022 are derived; and second, the monthly mean wave characteristics are calculated by vector averaging of the daily wave parameters for each calendar month. The climatology of the wave characteristics during the entire 21-year period is estimated again by vector averaging of the monthly mean wave characteristics for each month of the year.  
The method for deriving the daily characteristics of the atmospheric waves from the SABER temperature data is described in detail by [PERSON] et al., 2009 ([PERSON] et al., 2009). It is based on the two-dimensional (2D) least-squares fitting approach (to avoid the constraint of sampling uniformity) where all tidal (24-, 12-, 8- and 6-h) components and stationary planetary waves (SPWs) with zonal wavenumbers up to 4 are extracted simultaneously from the data. In this way we minimize both some aliasing effects and a possible distortion of the weaker waves by the stronger ones. We underline that, in this study, we do not include the traveling planetary waves (PWs) because, due to very large statistics used here, only the climatology of coherent waves with regularly repeated features could be successfully separated. However, it is known that the traveling PWs have no coherent phase structure.  
The length of the sliding window for producing the wave characteristics is 60 days because it takes about 60 days of combined ascending and descending SABER data to sample 24 h in local time.  
Then, the 60-day window is moved through the time series with steps of 1 day in order to derive the daily values of the wave characteristics. To remove some patchy structures with small amplitudes that may not be related to the tidal waves, an additional smoothing of the daily characteristics is done using a 5-day sliding time segment. The smoothing very slightly suppresses the wave amplitudes, but gives a clearer view of the overall tidal picture.  
A very important problem in the tidal assessment is how to determine the reference noise level due to the satellite data sampling pattern and the variability of the SPWs during the 60-day time windows used for extracting the tides. This problem has been thoroughly investigated by [PERSON] et al., 2009 [6]. The analysis revealed that all tidal waves extracted from a given 60-day time window with amplitudes larger than 1 K are statistically significant, and only these tides are considered in this analysis.
## 4 Simulation Results and Discussion
### Discussion  
All the simulation results show a better performance of CEDOT because of its comprehensive mathematical model for cost function. In the cost function, we consider all the important and realistic parameters to calculate time delay and energy consumption for the completion of the task. Most of the related works have not considered the task-division-cost in the mathematical models and assumed random values for each component of a task. However, there must be some energy consumption and time delay due to a task division process which we have considered in our proposed work. For the sake of better comparison we also include the cost of division process in all the benchmark techniques. The comprehensive mathematical model for cost function gives the more authentic training dataset for DNN. Therefore, the other techniques select the offloading policy with higher energy consumption and time delay as compared to the CEDOT because, their cost function and mathematical model are not that realistic and comprehensive. By considering more and realistic parameters in the cost function, it increases the accuracy of DNN because the relation and linkages between input data and labels become stronger and clearer. The proposed technique selects the offloading policy and partitioning with minimum cost which means that the optimal offloading policy selected by CEDOT will consume minimum energy with faster execution of a task.  
We can observe that the accuracy of the deep learning approaches increases by using a larger dataset, while the accuracy of the TOT and ROT is not affected by the size of data since there is no learning mechanism involved. The offloading decisions of TOT are constant and ROT has a random nature of selection without any mechanism. The accuracy of all the techniques decreases as the number of components per task increases. The decline in accuracy of TOT and ROT, with a number of components per task, is due to the increase in the number of offloading policies and therefore, the probability of selecting policy with minimum cost is decreased. The decline in the accuracy of deep learning-based approaches is because of the decision boundaries becoming more complex with an increase in the number of components per task. The accuracy of only TOT is affected by distance, the rest of the techniques have constant accuracy during the mobility of UEs. Since, at lower distances the cost of offloading all components become smaller, therefore, TOT has higher accuracy at lower distances.  
A limitation of our proposed model is that it cannot find the optimal number of components per task. It is necessary to know the number of components per task before the execution of the task. Similarly, the proposed technique works only for the applications having sequential execution of components due to dependencies of components. For example, if the application has callbacks or loops through previous components, then the proposed technique cannot handle that scenario.  
Figure 8: Comparison of the DNN accuracy with respect to distance.