esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evaluation of Healthy Eating Scale for Patients with Pre-Diabetes Using Rasch Analysis
## 3 Results
### Differential Item Functioning (DIF)  
DIF was examined for ethnicity (Chinese/Non-Chinese), gender, and age groups by a two-way ANOVA on the residuals for each item across subgroups and across class intervals.  
As seen in Table 3, the scale showed no evidence of DIF by age and gender, but there was evidence of DIF between non-Chinese and Chinese, as shown with the effect size. The mean (95% CI) logit for non-Chinese was higher at 0.35 (0.09, 0.61), \(p\) = 0.008 compared to Chinese with [PERSON]'s d =0.66. [PERSON]'s d is commonly used to refer to effect sizes as small (d = 0.2), medium (d = 0.5), and large (d = 0.8) based on benchmarks suggested by [PERSON] (1988) [62], which implies that ethnicity had more than a medium effect size. However, gender and age group did not show significant effect size (Table 3).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{4}{c}{**Gender**} \\ \cline{2-5}  & **Total, n = 119** & **Male, n = 41** & **Female, n = 78** \\ \hline Age group (in years) & & & & \\ \textless{}60 & 35 (29.4\%) & 8 (19.5\%) & 27 (34.6\%) \\ 60–69 & 54 (45.4\%) & 19 (46.3\%) & 35 (44.9\%) \\ 70+ & 30 (25.2\%) & 14 (34.1\%) & 16 (20.5\%) \\ \hline Ethnicity & & & & \\ Non-Chinese & 99 (83.2\%) & 35 (85.4\%) & 64 (82.1\%) \\ Chinese & 20 (16.8\%) & 6 (14.6\%) & 14 (17.9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of participants.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Fit Residuals Mean (SD)**} & \multicolumn{2}{c}{**Item-Trait Interaction**} & \multicolumn{2}{c}{**Reliability**} \\ \cline{2-7}
**Scale Analysis** & **Item** & **Person** & \(\chi^{2}\) **(df)** & \(p\)** & **PSI** & **Power of Fit** \\ \hline
**Initial analysis** & & & & & & \\
1. Full sample (no refinements or alteration) & 0.12 (0.91) & \(-\)0.22 (0.98) & 29.29 (30) & 0.500 & 0.57 & Reasonable \\
2. Chinese only (full sample, no alteration) & 0.41 (0.59) & 0.16 (0.80) & 38.82 (30) & 0.130 & 0.457 & Low \\
3. Non Chinese only (full sample, no alteration) & 0.10 (0.95) & \(-\)0.24 (0.96) & 23.90 (30) & 0.129 & 0.585 & Reasonable \\
**Analysis after reducing to three scoring categories** & & & & & \\
1. Scoring structure 0112 for all items & \(-\)0.14 (0.70) & \(-\)0.33 (1.03) & 37.36 (30) & 0.166 & 0.55 & Reasonable \\
**Analysis after removing items and persons (last resort)** & & & & & \\
1. Scoring Structure 0112 delete item 1 only (disordered) & \(-\)0.10 (0.61) & \(-\)0.33 (1.10) & 37.91 (28) & 0.100 & 0.511 & Reasonable \\
2. Scoring structure 0112 delete item 10 and ID 193 161 & \(-\)0.17 (0.52) & \(-\)0.32 (0.97) & 32.31 (28) & 0.262 & 0.559 & Reasonable \\
3. Scoring structure 0112 delete item 1 \&10 and ID 193 161 & \(-\)0.12 (0.40) & \(-\)0.31 (1.05) & 27.49 (26) & 0.384 & 0.525 & Reasonable \\ \hline _Optimal values_ & _0 (1.00)_ & _0 (1.00)_ & & _>0.05_ & _>0.70_ & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fit residuals, item–trait interaction, and reliability for different modifications of the scale.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Opportunistic Routing Protocols
#### 5.2.4 Mor  
In 2017, [PERSON] et al. proposed Multichannel Opportunistic Routing (MOR) [14], a multi-channel opportunistic routing protocol for low-power asynchronous duty-cycled WSNs. In this protocol, the authors applied the concept of the opportunistic routing mechanism in the frequency domain, in order to improve the robustness of the network and the handling interference. The main concept of this protocol is as follows: The first node to wake up in the currently-transmitting channel will successfully receive the packet and give a routing progress update using the ACK packet. Later, this node will work as a potential forwarder for passing the packet to the next hop towards the sink. MOR has the advantages of using spatial and frequency diversities; if the suitable neighbor node for data forwarding is not available on a specific channel, then this protocol either uses a different channel or different available neighbor node to pass the packet, rather than wait longer. This protocol was practically applied on a real testbed to evaluate its performance. By increasing energy usage by only a small amount, MOR increased E2E reliability and decreased E2E latency, regardless of the channel interference. MOR uses two types of channel hopping: (i) sender-initiated slow hopping, and (ii) sender-initiated and receiver-initiated fast hopping. For the slow hopping scheme, MOR uses the MiC-MAC protocol, whereas in fast hopping both sender and receiver switch channels frequently within the same duty cycle, without waiting for the whole duty cycle (Figure 16). Usually, in other protocols, nodes re-transmit packets continuously on the same channel, but in MOR nodes re-transmit each time on different channels. However, in MOR, senders and receivers both use fast channel hopping, which is most of the time unnecessary and has a high energy cost.
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 4 Discussion
### Comparison of PA Imaging with Other Imaging Modalities
#### 4.1.2 PA Imaging Versus MRI  
When X-ray mammography produces conflicting inference with that of the US image, MR imaging was performed before referring the patient for biopsy procedure [10]. MRI provides better sensitivity than X-ray mammogram or US imaging for breast cancer detection [15]. [PERSON] et al. [25], [PERSON] et al. [108], [PERSON] et al. [39], and [PERSON] et al. [110] compared the performance of the PA imaging with that of the MR images acquired of the same patients. For a total of 11 patients, [PERSON] et al. [25] observed the excellent correspondence between PA images and MR images in terms of lesion localization, lesion shape, and appearance. They observed [35] that the average size deviation of the lesion detected in PA imaging with respect to the lesions detected in histopathology was 28% whereas, in MRI, it was 65%. [PERSON] et al. [108] acquired in vivo PA images of 27 breast cancer lesions and concluded that PA images of the tumors were comparable to the corresponding MR images at relevant depths. [PERSON] et al. [39] acquired high-resolution in vivo 3D PA images of breast cancer lesions, and they observed that the blood vessel structures and morphological characteristics of these images were more detailed than those observed in the corresponding MR images. These study results suggest that the contrast was better, as the small blood vessels and their branching structures were more clearly visible in PA images than MR images. [PERSON] et al. [110] detected six breast lesions using functional PA imaging and matched the results with that of the corresponding MR images. A retrospective study performed by [PERSON] et al. [121] on 650 high-risk women undergone 806 MRI and X-ray mammography screening rounds reported that breast MRI was more sensitive than X-ray mammograms (92.3% vs. 30.8%) while the specificity provided by breast MRI was less than the X-ray mammograms (85.9% vs. 96.8%). High sensitivity of breast MRI results in more false positive findings than X-ray mammograms. A study performed on largest number of patients [37] reported that the dual mode PA-US imaging has the potential in reducing the number of false positive examinations as compared to US alone.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 4 Discussion  
### Main Variables Affecting Carbon Fluxes  
Through correlation analysis, major factors affecting carbon fluxes could be determined among the existing predictors. Figure S1 shows the correlation coefficient matrix, which calculates all correlation coefficients among the 11 predictors and 3 target variables in this study. As can be seen from Figure S1, FAPAR, LAI, LE, and TA had the greatest influence on the three carbon flux variables, where the absolute values of correlation coefficients between these predictors and GPP (or RECO) were all above 0.5. The high correlation of remote sensing predictors and carbon fluxes is consistent with previous studies [51; 52; 53].  
In order to further analyse the influence of predictors on the carbon flux variables, the feature importance of random forest was used to determine and rank the important variables. Figure 9 shows the importance of each predictor by RF in Experiment 2, in which the sum of the importance values of all predictors is one. The result shows that LAI, FAPAR, and LE had larger feature importance than other variables when predicting GPP and RECO, while the five remote sensing predictors were the most important in predicting NEE, which is consistent with the result of correlation coefficients. However, air temperature was not important in the RF models compared to the correlation analysis result. This may be due to that the RF importance can reflect a nonlinear relationship, while the correlation coefficient measures only linear relationship between parameters.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.3 High-Temperature Condition Analysis (August 2024)  
August 2024 presented significant forecasting challenges due to sustained high temperatures across the Eastern China grid region. Figure 9 renders visible that these extreme weather conditions substantially amplified forecasting errors, particularly for direct aggregation, which consistently underestimated peak loads by 15-20%. This underestimation is especially pronounced during the first half of August (1-15 August), where daily peak loads frequently exceeded 14 million MW.  
Figure 8: June 2024 scatter comparison.  
Figure 7: June 2024 hourly error (The yellow shadow indicates the peak area).  
The weighted fusion methodology maintained significantly better performance under these extreme conditions, with peak-hour MAPE values averaging 6.1% when juxtaposed with 16.3% for direct aggregation in Figure 10. This represents a 10.2 percentage point improvement during critical high-demand periods. The scatter plot analysis in Figure 11 reveals that direct aggregation actually produced negative \(R^{2}\) values (\(-\)0.0197) for peak hours, indicating complete forecasting failure, while weighted fusion maintained a strong \(R^{2}\) of 0.8636.  
Figure 10: August 2024 hourly error (The yellow shadow indicates the peak area).  
Figure 9: August 2024 forecast comparison (The yellow shadow indicates the peak area).  
These results validate that our weighted fusion approach is particularly valuable during extreme weather conditions when forecasting accuracy is most critical for grid stability and economic operation.
## 3 Results
### Full Diagnostic Pipeline by Source  
Figure 6 compares the extent to which reviewed articles from each source addressed the complete fault management pipeline. Elsevier had the highest proportion, followed by MDPI, while Google Scholar had the lowest.  
In addition to the type of algorithm used, an important factor in evaluating the completeness of a diagnostic system is whether it covers the entire fault management pipeline: detection, localization, and resolution. Figure 6 provides a comparative overview of diagnostic pipeline by source.  
Out of 185 total articles, only 56 (30.3%) presented a complete workflow encompassing all three stages. The rest--nearly 70%--focused exclusively on fault detection or partial diagnosis, often without proposing a solution or identifying the fault's root cause.  
When comparing by source  
* Elsevier had the highest share of complete-process articles, with many studies incorporating both detection and intelligent control solutions.
* MDPI displayed a more balanced distribution, with several review articles and original studies addressing detection and partial localization.
* Google Scholar had the lowest percentage of full-process articles, often containing conceptual frameworks or limited empirical validation.  
This disparity may reflect differences in editorial focus, peer-review standards, or access to real-world industrial data across platforms. Full pipeline articles often required not only algorithmic modeling but also access to plant infrastructure or detailed simulations--resources not uniformly available to all authors.  
The findings suggest a need for greater emphasis on end-to-end diagnostic systems, especially in complex, high-stakes environments like power generation. Moving forward,  
Figure 6: Full diagnostic pipeline by source.  
integrating all stages of the fault management process will be essential for practical deployment, regulatory compliance, and ensuring plant safety and efficiency.
## 5 Discussion  
While the Feedforward Neural Network (FNN) used in this study demonstrated high precision in the training and validation phases, the inherent randomness in neural network algorithms may still introduce some uncertainty. Cross-validation can help manage this uncertainty and provide a more robust estimation of model performance. Cross-validation involves:  
* Partitioning the dataset into several subsets or folds;
* Training the model on most of these folds;
* Testing the model's performance on the remaining fold.  
This process is repeated with different folds serving as the test set each time, ensuring that the performance of the model is assessed on various subsets of data.  
For instance, one cross-validation method involves splitting the dataset into ten parts. The model is trained on nine parts; the remaining part is used for validation. This process is repeated ten times, each part being used for training and validation. The results are then averaged to provide a more accurate estimate of the model's performance. Cross-validation can also help determine if the model is overfitting the training data, which is important when using complex models like FNNs. This method provides a better understanding of the model's ability to classify player skill levels and reliability.  
In terms of development challenges, we can highlight the following:  
* **Data quality and availability**: In the context of our project, the performance of the FNN relies heavily on the amount and diversity of the gameplay data it is trained on. Accumulating relevant, high-quality data representing diverse gameplay styles, skill levels, and game situations is a substantial challenge.
* **Hyperparameter tuning**: The performance of our model depends significantly on the precise tuning of various hyperparameters, including the number of hidden layers, neurons in these layers, and the learning rate. Determining the optimal values of these parameters requires multiple trial-and-error runs.
## 5 Conclusions  
Underwater rock-plug blasting experiments were conducted with small explosive charges in this research. A high-speed camera was used to observe and study the motion of block particles under conditions involving different heads of water and different opening angles for the rock plug. Numerical simulations were also conducted in the experiment using the Fluent-EDEM fluid-structure interaction method, and the method was verified to be feasible and accurate. The key conclusions are as follows:  
1. The motion of the block particles in the underwater rock-plug blasting experiments can be divided into two stages: in the first stage, the block particles were ejected from the two ends of the rock plug along its axis by the blast load; in the second stage, block particles flowed to the downstream water tank under the effect of upstream water scouring and gravity;
2. The penetration mechanism in the rock-plug blasting was obtained by combining experiments and numerical simulation. A blasting crater was formed on the downstream  
Figure 18: Streamline distribution diagram (\(\beta=30^{\circ}\), h = 305 mm).  
end face of the rock plug under the effects of the blast load due to the influences of the free face, while blocks on the upstream end face were loosened or weakly ejected under the influence of the water pressure. Finally, the blasted blocks flowed to the block pit under the effects of the scouring of the fluid flow and gravity, forming a through-going channel. With the increase in the head of water, a better penetration effect for the rock-plug sample was determined;
* The numerical calculation results based on the Fluent-EDEM coupling method were consistent with the experimental results in terms of the blasting effect and the velocity curve of the block particles. The change in the numerically calculated bonding force followed a similar trend as the theoretical drag force, indicating that the coupling method is feasible and accurate for the simulation of the interaction between particles and water during underwater rock-plug blasting.  
Conceptualization, methodology, funding acquisition, project administration, formal analysis, L.W.; conceptualization, data curation, formal analysis, investigation, writing--original draft, Z.L.; funding acquisition, project administration, M.C.; investigation, writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chinese National Natural Science Foundation, grant numbers 51909196 and 51979205.  
Not applicable.  
All data generated or analyzed during this study are included in the published article.  
The authors wish to express their thanks to all supporters and especially to the laboratory \"Engineering Technology Research Center in Intelligent Blasting of Hubei Province\" for providing experimental equipment.  
The authors declare no conflict of interest.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods
### Empirical Methods  
In the early stages of a project, empirical methods are often used to evaluate the quality of the rock mass and assess the likelihood of rockbursts in underground apertures for feasibility and preliminary design purposes. [PERSON] [159] noted that classification is a valuable tool in understanding rock engineering problems when object label information is provided. This allows for the application of techniques such as decision theory to select the best classification method for a specific rock engineering issue. Classification systems are commonly used in mining and geotechnical engineering to characterize the rock mass and provide empirical guidance for design and support [159,160]; rockburst prediction is an example of a classification problem. Lithology is the primary cause of rockbursts and both surrounding rock stress and lithology play a role in rockburst occurrence [161]. Empirical methods, such as \(BPI\), have been applied to estimate rockburst tendency. \(BPI\) is defined as the ratio of the strain energy retained (\(E_{R}\)), to the permanent strain energy (\(E_{D}\)) [162]. \(BPI\) is written in Equation (5), as follows:  
\[BPI=\frac{E_{R}}{E_{D}} \tag{5}\]  
Despite the advantage of the \(BPI\) criterion, obtaining 80-90% of the rock strength with \(BPI\) is difficult for two reasons: first, the strength can only be known probabilistically; second, the value of the index is determined by the relative value of the load attained. Furthermore, [PERSON] [8] proposed the excavation vulnerability potential (EVP) as an empirical assessment tool for seismically active underground mines based on case histories and incidents of rockburst damage. Empirical models are often used in rock engineering to make predictions; however, they have limitations. They are obtained through curve interpolation and lack physical significance, which raises questions about their validity. Despite this, their ease of use in engineering applications is an advantage. However, a single evaluation indicator is not enough to fully consider all the factors affecting rockburst occurrence, making it challenging to accurately predict rockburst events. As rockbursts are complex and unpredictable, it is difficult to establish a universal criterion for rockburst prediction.
## 6 Experiment and Results
Standard error is \(\frac{\varphi}{\sqrt{100}}\) where \(\hat{\varphi}\) is the sample variance of the 100 \(\delta\)s. (**a**) Stationary Base Model and Heterogeneous Effect Size; (**b**) Nonstationary Base Model and Heterogeneous Effect Size; (**c**) Stationary Base Model and Population Effect Size; (**d**) Nonstationary Base Model and Population Effect Size. faster by leveraging the data of multiple users to learn. Even though the algorithms with larger cluster sizes are less able to model and learn the heterogeneity across users, this is outweighed by the immense benefit of sharing data across users to learn faster and reduce noise. There are some limitations to these experiments. Fixed Reward Noise Variance for BLR: The BLR algorithm includes a noise variance hyperparameter (\(\eta^{2}\) in Equation (A3)). In our experiments, we set \(\eta^{2}\) to the reward variance observed in the ROBAS 2 data set. Assuming that \(\eta^{2}\) is known is unrealistic; in the future, we plan to learn \(\eta^{2}\) along with other BLR algorithm parameters in the real study. The known value of \(\eta^{2}\) could be a reason that BLR performed comparably to ZIP. More Distinct and Complex Simulation Environments: We may not be looking widely enough across environment variants to find settings where these algorithms perform differently. With sufficient data per user in a highly heterogeneous user environment, we expect cluster size \(k=1\) to do the best. In future work, we aim to add simulation environments with greater heterogeneity and less noise to see if large cluster sizes still perform well, and we aim to create more complex simulation environment variants that are more distinct (e.g., environments where users may differ by heterogeneous demographic features like age and gender). Additionally, we want to impute state features of interest in the real study that were not present in the data set, such as phone engagement. Additional RL Algorithm Candidate Considerations. We also aim to consider other axes for algorithm candidates such as algorithms with other update cadences (e.g., every night or biweekly) and algorithms with an informative prior. In initial simulations using algorithms with informative priors, we found that since the same (limited amount) of ROBAS 2 data was used to build both the simulation environment and the prior, the algorithms did not need to learn much to perform well. An open question is how to develop both simulation environments and informative priors in a realistic way using a limited amount of data. Finally, we will also explore additional design decisions such as how to carefully design the feature space for the RL algorithm. These investigations will determine the final algorithm that goes into the actual study.