esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Results  
### MSP-Derived Models  
The M5P algorithm is a method that segments data spaces into numerous smaller regions, known as subspaces. Within each subspace, a local multivariate linear regression (MLR) model is constructed. However, relying solely on local MLR models for predicting outcomes is a shortcoming of the M5P algorithm. To address this issue, we convert all input output and input variables into a logarithmic form. Subsequently, the M5P algorithm is refined within this logarithmic space. Following this transformation, the local MLR models in each individual subspace are restructured to enhance their predictive capabilities and overall accuracy as follows:  
\[V_{u}=a^{\prime}(b)^{y^{\prime}}(d)^{c^{\prime}}(f_{c})^{d^{\prime}}\big{(}f_{ y}\big{)}^{e^{\prime}}(\rho)^{f^{\prime}}(\lambda)^{g^{\prime}} \tag{3}\]  
where \(a^{\prime}\), \(b^{\prime}\), \(c^{\prime}\), \(e^{\prime}\), \(f^{\prime}\), and \(g^{\prime}\) are constants. The M5P algorithm is a tree-based model that generates regression rules for predicting the target variable. These rules are quite effective in capturing the underlying relationship between the input parameters and the structural strength. The M5P algorithm is illustrated in Figure 4. The developed rules are as follows:  
\[LM1:\;V_{u}=0.0127(b)^{0.5974}\;(d)^{1.4115}\;(f_{c})^{0.5007}\;(\rho)^{0.1877} (\lambda)^{-0.1634} \tag{4}\]  
\[LM2:\;V_{u}=1.4699(b)^{0.3491}(d)^{0.8836}\;(f_{c})^{0.3137}(f_{y})^{0.009}\;( \rho)^{0.314}\;(\lambda)^{-0.1889} \tag{5}\]  
\[LM3:\;V_{u}=0.0855(b)^{0.335}\;(d)^{1.2632}(f_{c})^{0.3025}\;(f_{y})^{0.2581} (\rho)^{0.3736}(\lambda)^{-0.1823} \tag{6}\]  
In the analysis of the results, it was observed that \(d\) (slab's depth) was identified as the primary factor for classification purposes. The equations were differentiated based on the values of \(d=82.79\) mm and \(133.66\) mm. Given these selected splitting parameters, it is possible to deduce that the RC flat slab depth plays a crucial role in predicting the maximum punching shear strength. This observation aligns with previous subsection results, demonstrating that the correlation coefficient (R) between \(d\) and \(V_{u}\) stands out as significantly higher when compared to the majority of the other parameters evaluated, further validating the relationship as depicted in Figure 4. It is crucial to acknowledge that the chosen splitting value may not always possess a distinct physical interpretation, as it is determined with the objective of minimizing the prediction error, as per reference [25]. Nevertheless, the majority of the physical interpretations that are derived from these equations align with the principles of structural engineering.  
Figure 4: Data splitting diagrams for the M5P model.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 4 Evaluation Methods
### Quantitative Evaluation  
For the quantitative evaluation, we use both the abstract-only dataset and the RTA dataset to perform the following evaluations:  
1. **Clustering Evaluation:** We performed clustering experiments on the abstract-only dataset using various clustering algorithms such as K-means, GMM, DBSCAN, agglomerative clustering, and spectral clustering. To evaluate the clustering performance, we used standard clustering evaluation metrics, such as the adjusted rand index (ARI) [48], silhouette score [49], and Calinski-Harabasz index (CHI) [50]. We computed these metrics for each clustering algorithm and compared their performance.
2. **Dimensionality Reduction Evaluation:** To evaluate the impact of dimensionality reduction on clustering performance, we compared the clustering results obtained using the original high-dimensional word embeddings with those obtained using the reduced-dimensional embeddings. We used the same clustering evaluation metrics mentioned above (ARI, Silhouette Score, and CHI) for this comparison.
3. **Keyphrase Generation Evaluation:** To assess the comparative performance of KeyBARTadapter and KeyBART, we initially trained both models on the training set of the RTA dataset, and subsequently fine-tuned their hyperparameters using the validation set. In the case of KeyBART-adapter, we fine-tuned the hidden dimension of adapter layers over a set of five logarithmically-spaced values, ranging from 32 to 512. Additionally, the learning rate was fine-tuned over logarithmically-spaced ranges, i.e., \(1\times 10^{-6}\) to \(1\times 10^{-4}\) with 20 equally-spaced steps, resulting in 100 configurations for the KeyBART-adapter. For KeyBART, we solely fine-tuned the learning rate within the same range as the KeyBART-adapter, resulting in a total of 20 configurations. Eventually, we selected the best configuration for KeyBART-adapter and KeyBART on the validation set and evaluated them on the test set.  
We adopt the same evaluation metric as used in the original KeyBART paper [4] to select the best configuration on the validation set and for evaluation on the test set. The chosen metric is the F-score@M, which is a harmonic mean of precision and recall, considering the top-M generated keywords. By employing the F-score@M, we can compare our KeyBART-adapter with the KeyBART, ensuring a fair evaluation and allowing us to identify potential improvements or drawbacks in our approach.
## 2 Material and Methods
#### 2.2.3 Random Forest Algorithm
For large-scale data, the computational complexity of the K-means algorithm may be very high, resulting in low analysis efficiency. The Wide Applicability of the Model  
Although the model has some limitations, it also has a wide range of applicability. For data sets whose data distribution is not obvious or irregular, the K-means algorithm can effectively perform clustering, thus improving the accuracy of factor importance analysis. The BPNN model can model nonlinear data to improve the accuracy of factor importance analysis. The random forest algorithm can effectively avoid overfitting and improve the accuracy of factor importance analysis.
## 5 Discussion
### Comparison between Stability Indices and CP
#### 5.2.2 CP with Downward Force of Body  
In particular, T implies the shifting of force from the heel to the toe. The transfer of body balance resulting from the force causes the body to shift from backward to forward in the direction of walking. At this point, the value of CP can be used to help estimate when the T state will be reached, which is the moment at which the acting point of force changes.  
Figure 10 displays the force on the hip and knee joints depending on the speed of walking [32]. One step is composed of the SS state (0-60%) and the DS state (60-100%), as presented in Figure 9. In the SS state, the heel touch state is the period from 0% to 20% and the toe touch state is the period from 40% to 60%. In addition, during the period 20-40%, the T moves from the HO state to the TS state in our approach. Force is largely at work on the hip and the knee joint during 0-20% and 40-60%, independent of walking speed. The reason the force is the greatest during these periods is that the body maintains its balance using only toe or heel. Thus, a greater amount of torque is generated at the hip and the knee to maintain body balance. That is to say, the T state is significant for identifying when the body segment will require additional force and when foot pressure will move from the heel to the toe.  
Similarly, the authors in [33] studied the characteristics of plantar stress and its effect on the change of velocity during flat walking. The results indicated that the stress of the walking body on the ground was concentrated at the toe and the heel, and the stress of the toe increased significantly with increasing walking speed. Specifically, the role of the heel is to step on the ground first in each step and to absorb the impact from the ground. Additionally, the area of the toe connects  
Figure 10: The magnitude of the results during walking at slow, normal and fast speeds (0–20% of cycle time: heel strike, 40–60% of cycle time: toe off, 80–100% of cycle time: other heel strike) [32]: (**a**) Average variation during walking cycle of the resulting hip joint force; (**b**) average variation during walking cycle of the force transmitted to the tibo-femoral part of the knee joint.  
Figure 9: Walking state in two steps: one step is indicated as being from 0 to 100%.  
smoothly and provides the impetus for the transition. Therefore, it is important to be aware of this moment in every step in order to understand the transfer of force in the foot. From this perspective, the CP is a more economical method than that used in in [33], which employed a PVDF thin film pressure acquisition system consisting of two sets of 16 piezoelectric films. With the T from the CP estimation representing the momentum, it is possible to catch when the overall arrangement of forces in the body change.
## 1 Introduction  
During fixed orthodontic treatment, patients perceive pain, which is a concern for patients as well as orthodontists. The perception of pain is influenced by different patient characteristics (e.g., gender, age, etc.) [1; 2; 3]. Pain during orthodontic treatment peaks between the first and second day of treatment. After seven days the pain described is almost nil [3; 4; 5]. Pain is a side effect of orthodontic treatment [6]. Pain indicated by patients can influence patient follow-up during orthodontic treatment [7].  
Orthodontics aims to improve oral health-related quality of life (OHRQoL). Improving facial appearance and occlusion are the main objectives of the treatment. Patients' occlusion problems have a negative impact on their OHRQoL. The current scientific literature concludes that the impact of orthodontics on patients has an important psychosocial component and therefore should be measured to assess OHRQoL [8; 9]. In the advanced stages of orthodontic treatment, the negative impact on HRQL decreases [9].  
Physical pain is the domain of OHRQoL that has the greatest impact on subjects during treatment. This domain presents the worst impact between the first seven days and the first four weeks after starting treatment. From this point on, OHRQoL gradually decreases until reaching baseline values six months after starting treatment. At the end of orthodontic treatment, OHRQoL improves considerably compared to the initial level and during treatment [10].  
There is a lack of scientific studies addressing the influence of bracket slot size on pain and OHRQoL during orthodontic treatment. OHRQoL and pain in subjects in treatment with conventional orthodontics and low-friction have not been previously analyzed. Analyzing the data from this research can give us specific information on how the treatment affects the physical, social, and psychological well-being of patients wearing fixed multibrackets. It would also improve the patient's understanding of the potential benefits and decrease the risk factors of orthodontic treatment. Healthcare professionals must know the objectives that patients expect from their orthodontic treatment to provide not only improved oral functioning and health but also enhancement of esthetics, self-esteem, and social life. Thus, this study has evaluated the effect of bracket systems (conventional versus low-friction brackets) and bracket slot size (0.018\" versus 0.022\") on the perception of pain and on OHRQoL during the first days of orthodontic treatment. The null hypothesis was that intensity of pain and the impact on OHRQoL would not differ in subjects.
## 3 Results  
The number of characters per definition sentence was 20.1 characters, and the difference in the number of characters between the definition sentence pairs was 7.1 characters. The number of words per definition sentence was 11.8 words, and the difference in the number of words was 4.3 words. The sensitivities, specificities, and AUC were obtained from the ROC analysis (Table 1). In the sensitivity, the values in all algorithms tend to be low. The value of the Jaro-Winkler Distance was 0.780, and this value was the highest among the sensitivities. In addition, only the sensitivity of the Jaro-Winkler Distance was higher than the specificity. In specificity, the highest was 0.880 in Doc2 vec with DM. The second-best methods were the Levenshtein distance, fastText with skip-gram, and Doc2 vec with DBOW. In AUC, the AUC values in the edit distance algorithms tended to be better compared to those in the distributed representation. In particular, the AUC in the Levenshtein distance was the highest. The distributed representation that has the highest AUC was Doc2 vec with DBOW. Table 2 shows examples of our results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Sensitivity** & **Specificity** & **AUC** \\ \hline Levenshtein Distance & 0.680 & 0.840 & **0.814** \\ Jaro–Winkler Distance & **0.780** & 0.680 & 0.737 \\ Word2 vec with CBOW & 0.600 & 0.733 & 0.707 \\ Word2 vec with skip-gram & 0.560 & 0.787 & 0.723 \\ fastText with CBOW & 0.600 & 0.787 & 0.744 \\ fastText with skip-gram & 0.560 & 0.840 & 0.745 \\ Doc2 vec with DBOW & 0.640 & 0.853 & 0.768 \\ Doc2 vec with DM & 0.480 & **0.880** & 0.681 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of the algorithms. Underlined numbers are the highest value of each item.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
## 4 Results  
### Phase Variation Only  
The first case has no variation in the pulse separation. The delay between pulses is set to 720 fs and the relative phase between pulses is allowed to vary freely. The averaged autocorrelation over the whole set of pulses is identical to the autocorrelation of one double pulse (see Figure 3a). The pulse separation as a function of phase is simply a circle (see Figure 3c). The variations in the relative phase between pulses cause the average spectrum to be almost Gaussian (Figure 3b). The average SPIDER trace (Figure 3d) has very little background. The background is expected to be low for this case because the chosen pulse separation is small compared to the temporal support of the measurement. The fringes in the SPIDER trace produce a flat spectral phase (Figure 3h). As a result, the second pulse is invisible (Figure 3g) and the measurement result is a single pulse. The measured FROG trace (Figure 3e) follows the predicted appearance. The retrieved trace (Figure 3f) is visually distinct from the measured trace. Specifically, the retrieved trace has less intense side lobes than the measured trace and has spectral fringes in its center lobe. The G error between measured and retrieved traces is 0.014, which is too large to be acceptable. The retrieved pulse (Figure 3i) has a satellite with the correct temporal separation, but the satellite is much less intense than the satellites in the real pulse train. The retrieved satellite has the same temporal width as the main pulse and a relative energy and intensity of 0.34. The relative pulse energy calculated from the energy of lobes in the measured trace is 1.00, in excellent agreement with the actual relative energy of the pulses.
## 3 Results  
### Results from MASH Phantom  
Figure 3 shows the region inside the blue box that was magnified to clearly illustrate the performance of each noise reduction algorithm. The original (top left, Figure 3), noisy (top right, Figure 3), Gaussian (center left, Figure 3), median (center right, Figure 3), Wiener (bottom left, Figure 3), and FNLM (bottom right, Figure 3) images are depicted. We performed qualitative visual evaluation of the images obtained by applying the conventional denoising filters and FNLM denoising algorithm. Subsequently, the performance of the FNLM denoising algorithm was evaluated in terms of various quantitative evaluation factors.  
Figure 4 shows the COV and CNR results for evaluating the noise level of the reconstructed images. In the noise level evaluation, the FNLM denoising algorithm achieved the best performance as compared with the conventional denoising filters. The COV values of images applied the noisy, Gaussian, median, Wiener, and proposed FNLM denoising algorithm were 0.237, 0.154, 0.087, 0.046, and 0.023, respectively. The COV of the image obtained with the FNLM denoising algorithm was approximately 10.3\(\times\), 6.7\(\times\), 3.8\(\times\), and 2.02\(\times\) higher than that of the noisy, Gaussian, median, and Wiener images, respectively. Moreover, the CNRs of the noisy, Gaussian, median, Wiener, and proposed FNLM denoising algorithm were 4.908, 7.600, 11.947, 25.511, and 55.627, respectively. The CNR of the image obtained with the FNLM denoising algorithm was approximately 11.33\(\times\), 7.32\(\times\), 4.66\(\times\), and 2.18\(\times\) higher than that of the noisy, Gaussian, median, and Wiener images, respectively.  
Figure 5 shows the PSNR and CC results for evaluating the similarity between the original and reconstructed images. In the similarity evaluation, the FNLM denoising algorithm again showed the most efficient value as compared with the conventional denoising filters. The PSNRs of the noisy, Gaussian, median, Wiener, and proposed FNLM denoising algorithm were 77.261, 79.537, 82.094, 81.882, and 82.354, respectively. The FNLM denoising algorithm had the highest PSNR, followed by the median, Wiener, Gaussian, and noisy images. Moreover, the CC values of the noisy, Gaussian, median, Wiener, and proposed FNLM denoising algorithm were 0.958, 0.977, 0.985, 0.990, and 0.990, respectively. Unlike the PSNR results, the FNLM denoising algorithm had the highest CC value, followed by the Wiener, median, Gaussian, and noisy images.  
Figure 3: Magnified views (region inside the blue box). (**a**) Original; (**b**) Noisy; (**c**) Gaussian filtered; (**d**) Median filtered; (**e**) Wiener filtered; (**f**) Fast non-local means (FNLM) filtered images.  
Figure 4: The (**a**) coefficient of variation (COV) and (**b**) contrast-to-noise ratio (CNR) metrics computed in the set ROIs to evaluate the noise level of the reconstructed image obtained from the MASH phantom.  
Figure 5: The (**a**) peak signal-to-noise ratio (PSNR) and (**b**) correlation coefficient (CC) values used to evaluate the similarity between the reconstructed images and original image.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 3 TTP-GPR for Coal Seam Thickness Prediction
### Data Preprocessing  
Assume the coal seam thickness data along marching direction to be \(H=\{z_{1},z_{2},\ldots,z_{n}|z_{i}\in R^{+},0<i\leq n\}\), where \(z_{n}\) is the nth cutting data in the coal seam cutting process. In the time series prediction method [16], five to seven points is usually used to build the prediction model, and predicting the next point. This method predicts a fixed value without any probability description. Another problem is that in the time series forecasting process, the spatial distribution of the prediction points has not been considered, and the impact of coal seams on the prediction points, except for the marching direction, has not been considered. For example, when using  
Figure 1: Flowchart of the proposed RBF-Kriging interpolation algorithm.  
a column of data \(z_{i},z_{i+1},\ldots,z_{n-1}\) to the predicted \(z_{n}\) value, the data format of the iterative prediction in the marching direction is as follows.  
\[z_{n}=f(z_{i},z_{i+1},\ldots,z_{n-1}),0<i\leq n \tag{11}\]  
Like Figure 2a, other researchers used a few points in front of the prediction point to predict the coal seam thickness in the marching direction, but this method just considered coal seam data in one direction on the coal seam surface. In order to consider the spatial distribution of coal seams, we chose the structural elements centered on the prediction point on the coal seam surface that is shown in Figure 2b. Due to the missing edge data of structural elements when the center of the structural elements is at the boundary of the known coal seam data, the RBF-Kriging algorithm is used to interpolate the missing data. Like in Figure 3, when the center of the \(3\times 3\) structural element is the point A, the blue points need to interpolate with the RBF-Kriging algorithm.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
###### Abstract  
Recently, mobility support has become an important requirement in various Wireless Sensor Networks (WSNs). Low-power and Lossy Networks (LLNs) are a special type of WSNs that tolerate a certain degree of packet loss. However, due to the strict resource constraints in the computation, energy, and memory of LLNs, most routing protocols only support static network topologies. Data collection and data dissemination are two basic traffic modes in LLNs. Unlike data collection, data dissemination is less investigated in LLNs. There are two sorts of data-dissemination methods: point-to-multipoint and point-to-point. In this paper, we focus on the point-to-point method, which requires the source node to build routes to reach the destination node. We propose an adaptive routing protocol that integrates together point-to-point traffic and data-collection traffic, and supports highly mobile scenarios. This protocol quickly reacts to the movement of nodes to make faster decisions for the next-hop selection in data collection and dynamically build routes for point-to-point traffic. Results obtained through simulation show that our work outperforms two generic ad hoc routing protocols AODV and flooding on different performance metrics. Results also show the efficiency of our work in highly mobile scenarios with multiple traffic patterns.  
L 2019, 11, 18; doi:10.3390/611010018
## 5 Discussion
### GPT-3.5 Classification Performance  
The GPT-3.5 model achieved an overall accuracy of 98.75% across 6400 crash records, indicating strong agreement with the original dataset labels. It correctly identified 6307 non-construction zone (czone = 0) cases and 13 construction zone (czone = 1) cases, with only 2 false positives and 78 false negatives. However, a closer inspection reveals a significant class imbalance in performance:  
For non-construction zone cases, the performance was as follows:  
* Precision: 98.78%;
* Recall: 99.97%;
* F1 score: 99.37%.  
For construction zone cases, the performance was as follows:  
* Precision: 86.67%;
* Recall: 14.29%;
* F1 score: 24.53%  
While the model performs exceptionally well in avoiding false alarms, its low recall for construction zone cases reflects a conservative bias--erring on the side of caution and underpredicting positives. This trade-off may be acceptable in operational contexts, where overprediction leads to high review costs, but it limits the model's utility in safety-critical settings, where detecting all true cases is essential.  
The confusion matrix (Figure 5) further illustrates this imbalance, highlighting the model's tendency to favor the majority class (no WZ) and under-detect the minority class (WZ), despite some correct positive identifications.