esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 3 Results  
This study employs _RMSE_ (root mean square error) and _MAD_ (mean absolute deviation) to examine the bias of the EDG-DNN model since the validation of predictive models necessitates assessment metrics to precisely measure the reliability and validity of the models. These are their mathematical expressions:  
\[RMSE=\sqrt{\frac{1}{n}\times\sum_{i=1}^{n}(F_{i}-O_{i})^{2}} \tag{1}\]  
\[MAD=\frac{1}{n}\sum_{i=1}^{n}|F_{i}-O_{i}| \tag{2}\]  
where \(F_{i}\) represents the predicted electron density, which is produced by models, \(O_{i}\) represents the COSMIC electron density.  
Figure 4: Loss changes in the training and validation sets during training. The blue curve is the loss change in the training set and the red curve is the loss change in the validation set.  
Figure 5: The flowchart of the EDG-DNN model shows the ionospheric tomography on 20 March 2012: the inputs are on the left, the main structure of the DNN is in the middle, and the output of the physical gridding is on the right.
## 3 Results  
The UNet, LSTM-UNet, and UNet-LSTM-UNet were each trained with the same 109 training and 34 testing AO-OCT volumes. We evaluated each model's performance on the testing set with an average Dice coefficient, area under receiver operating characteristic curve (AUC), precision, recall, and accuracy and report their relative performance in Figure 4 and Table 2. Of the three models, the LSTM-UNet had the greatest average Dice coefficient (0.69), recall (0.80), and AUC (0.88), while the UNet-LSTM-UNet had the best precision (0.65). The LSTM-UNet and UNet-LSTM-UNet had similar average pixel-wise classification accuracy (0.92). Both the LSTM-UNet and UNet-LSTM-UNet had significantly better AUC (\(p\) value < 0.001 for both) and recall (\(p\) value < 0.001 and = 0.001, respectively) performance on the testing set than UNet alone.  
We show representative examples of all three models' qualitative performance in Figure 5. Visually we observe that all three models were affected by shadowing artifacts in deeper layers (ICP and DCP), but generally the LSTM-UNet and UNet-LSTM-UNet were less affected than the UNet model.  
Table 3 shows the number of learnable parameters and the evaluation time on a sample 30 slice volume for each architecture, demonstrating that the size of the model was correlated with evaluation time. However, the relationship between number of parameters and time to evaluation was not necessarily linear, with the LSTM-UNet and UNet-LSTM-UNet segmenting the sample volume in 184.8 and 241.7 s respectively, while the UNet alone, which had 20-25% of the number of parameters of the LSTM-based models, segmented a single 2D projection of the 30 slices in 1.6 s, including image loading speed.
## 4 Methods
### Clinical Data Collection  
Table 1 lists the clinical data (two physical tests and three questionnaires) that were collected through biweekly video calls using the Microsoft Teams application. The video calls were recorded and conducted biweekly, and the interviews were audio recorded during the initial sensor installation and final sensor takedown.  
The biweekly assessments were scheduled based on participant's availability, resulting in a mix of morning and afternoon sessions. Research indicates that circadian rhythms can indeed influence physical performance and cognitive function, for example, [33]. Similarly, cognitive performance and alertness can vary throughout the day [34]. To mitigate these circadian effects, we ensured that assessments were consistently scheduled at similar times for each participant throughout the study. This approach aimed to reduce intra-individual variability related to time-of-day effects.  
Further, demographic data (e.g., age, gender, type of fracture, employment status, etc.) were collected at baseline during sensor installation.  
The clinical assessments conducted in this study are part of standard care in lower limb fracture rehabilitation. The questionnaires include SIS [19], Oxford Hip Score [35], and Oxford Knee Score [36]. The physical tests are Timed Up and Go Test (TUG) [37] and the 30 s chair stand [38]. The Oxford Hip Score, the Oxford Knee Score, and the physical tests were chosen to evaluate functional mobility in OAs. The results from these assessments were compared to the sensor data collected to determine correlations between the functional recovery of the patients and their SI as they recovered from their lower limb fractures.
# Crossover Rate Sorting in Adaptive Differential Evolution
## 2 Materials and Methods
### Proposed Approach  
The idea of sorting a pool of generated crossover rates before applying them in creating offspring is relatively straightforward, and can be applied to most JADE and SHADE-based algorithms. Nevertheless, it is not widely applied in most well-performing DE algorithms, judging by the top algorithms in the CEC 2021 and CEC 2022 competitions. Here, in addition to the NL-SHADE-RSP and NL-SHADE-LBC algorithms, L-SHADE-RSP and L-NTADE will be considered, for which the crossover rate sorting was not applied before. For L-SHADE-RSP [13], the modification is quite simple; however, the L-NTADE algorithm [16], which has a different algorithmic structure, requires a more thorough discussion. L-NTADE maintains two populations instead of one on the classical DE scheme. The first population is the continuously updated population of newest solutions \(x^{new}\), while the second one contains the best individuals from the whole search, and is called the top population \(x^{top}\). The idea of dividing the population in two parts was inspired by the unbounded differential evolution approach, proposed in [35]. The parameter adaptation applied in L-NTADE is the same success-history adaptation, as in L-SHADE; and thus, a pool of crossover rates \(Cr_{i}\), \(i=1\ldots N\) can be generated at the beginning of the generation, before creating new solutions with mutation and crossover. However, in L-NTADE, there are several different mutation strategies applied, which combine individuals from both populations in a way similar to the current-to-pbest strategy. If the target vector is chosen from the newest population, the index \(r1\) of the target vector to be used is generated randomly. In this study, the r-new-to-ptop/n/t is used, described as follows: \(v_{i,j}=x^{new}_{r1,j}+F\times(x^{top}_{pbest,j}-x^{new}_{r,j})+F\times(x^{new }_{r2,j}-x^{top}_{r3,j})\). Random sampling of the \(r1\) index from the newest population means that if there are \(N\) crossover rates, \(Cr_{i}\) generated and sorted before the crossover, then some of them can be used twice within the same generation. However, this should not be a major problem, as the \(Cr\) values are used for the random choice of donor vector components to be taken into the offspring solution. The selection step in L-NTADE updates an individual in the newest population with index \(nc\), iterated from 1 to current population size \(N\), but the trial vector's fitness is compared to the fitness of the \(r1\)-th individual:  
\[x_{nc}= \begin{cases}u_{i},&\text{if }f(u_{i})\leq f(x^{n}_{r1})\\ x_{nc},&\text{if }f(u_{i})>f(x^{n}_{r1}).\end{cases}  \tag{8}\]  
This means that it is possible in L-NTADE_s, with the crossover rate sorting, to generate two new successful solutions with the same \(Cr\) value; hence, the same value is used in updating memory cells in the success-history adaptation mechanism. The pseudocode of the L-NTADE_s is shown in Algorithm 1.
# Emotion Recognition Method for Call/Contact Centre Systems
## 3 Research Methodology
Therefore, in the next step, the feasibility of using appropriate data balancing techniques was tested and their impact on the obtained classification results was investigated, as detailed in Section 4.1. In order to verify the prepared models, a 5-fold cross validation procedure was performed, for which the prepared datasets (text and voice) were randomly divided into five parts with similar numbers. Then, in five consecutive steps, one of the sets was selected as the testing set (20% of the data), while the other four were the training set (80% of the data). The same research methodology applied to text data and voice data. Precision and F1-score metrics were used to evaluate the classification models proposed in this paper. These metrics can be referred to in three ways depending on the problem, namely, macro-averaged, micro-averaged or weighted-averaged. From the point of view of the target applications of the method proposed in this paper, as well as due to the high degree of variation in the amount of data in each class, it was decided to use weighted-averaged metrics. In the subsequent part of the paper, they are labelled as: WAP (Weighted Average Precision), WAF1 (Weighted Average F1-score). This ensures that the results obtained for each class also take into account its size in the actual data sample. This, in turn, influences a much more reliable evaluation of the models prepared for the classification problem in question. Additionally, the Accuracy metric labelled ACC was also examined. It shows the accuracy, which is defined as the averaged percentage of correctly classified cases (sum of diagonal values in the confusion matrix) over all cases. For weighted-averaged metrics, ACC values converge to weighted-averaged Recall values, therefore the Recall metric is not separately analysed in this paper. The metrics used in the verification process of the proposed method are integrated in the scikitlearn library [55,56]. The above metrics applied separately do not offer a complete picture of the classifier performance. Moreover, for unbalanced sets, some may even give an impression of misclassification. Therefore, it is important to analyse all of the metrics listed above for the purpose of evaluating the proposed solution. This will allow for avoidance of potential controversy and provision of a complete picture of classification performance. DBT2 and DBV2/DBVT2 databases were used for the final verification of the developed method. These data were not used in the training process, they were only used to validate the method. For this purpose, emotional states were recognized by the classifier in the first phase, while in the second phase the results returned by the system were evaluated by the judges. The effectiveness of the method was evaluated according to the procedure described above for evaluating the data by the judges using the proposed metrics.
## 3 Proposed Algorithmic Approach
### Uplink and Downlink  
For algorithm execution, a wireless network is configured with nodes capable of receiving and transmitting frames from nearby nodes, emulating a multi-hop tree topology. This involves defining uplinks and downlinks: the uplink facilitates the flow of frames from nodes to the controller node, while the downlink enables frames from the controller node to reach each networked node. Initially, the downlink allows for the neighbor tables of each node to be populated. After executing the algorithms, this link will allow you to reconfigure a new default route. The uplink makes it possible to send the neighbor tables, with the information necessary for executing the Kruskal and Dijkstra algorithms, to the controller node and then to the route-generating application. Subsequently, it allows for verifying the operation of the algorithms. This link depends on the default route configured on the nodes.
## 3 Experiment and Results
### Experiment Results
From Equation (13), the depth of damages on the tube (\(\Circle\)), membrane (\(\square\)), and weld (\(\Delta\)) were estimated, as shown in Figure 16. The standard deviations of the depth estimation are 0.329, 0.269, and 0.523 mm for the damages on the tube, membrane and weld, respectively. The best estimation result is for damages on the membrane because the surface specimen is flat. The worst case for the damages on weld were due to the roughness of the weld surface, the sensor lift-off variation due to welding bead, and the edge effect at the terminal of the magnetizer. Figure 17 shows the B-scan result measured by the FUP after filling the acoustic medium and wrapping it with PET to the damage of Tube-1 (t11-t14). The horizontal axis represents time (\(T_{i}\)), and the vertical axis represents the moving distance of the FUP. The position of \(T_{1}\) for each movement distance was about 20 \(\upmu\)s before the start of the scan, but after 550 mm, it appeared at 17-18 \(\upmu\)s. This is because of the variation of the inclined angle of the FUP and deformation of the membrane due to variation of the FUP lift-off. Therefore, it is necessary to shift the flying time on specimen surface (\(T_{1}\)) for each scan position, as expressed in Equation (14). Furthermore, it can be seen that in the vicinity of 100, 300, 525, and 725 mm, the delay of the FUP signal is longer than that of other locations, and near-side damage occurs in the corresponding region. It can also be determined from the delay of the signal that the shape of the damage is inclined to one side, and the depth can be estimated. \[[\overrightarrow{T_{1}}]=[\overrightarrow{0}] \tag{14}\]  
Figure 18 shows the B-scan results of the FUP measured from the back surface of the damages (m31-33) of Membrane-3 and (w21, w22) of Weld-2 using FUP. Similar to the previous experimental results. The position of T\({}_{1}\) for each movement distance was about 22 \(\upmu\)s before the start of the scan, but after 450 mm past m32, it is back to 21 \(\upmu\)s. Unlike the case of the near-side damage in Figure 17, it is possible to recognize that there is no near-side damage because the FUP signal appears continuously. On the other hand, it is observed that m31, m32, and m33 damages occur around 130 mm, 460 mm, and 750 mm, respectively. In addition, signal attenuation appears in the range of 625-700 mm. This is because the ultrasonic wave attenuates at the edge of the weld defect w42 located in Membrane-3. A similar phenomenon occurred near the weld defect (w41) at 325-380 mm. The depth of the damages was estimated, as shown in Figure 11:0 The standard deviation of the depth estimation is about 0.089 mm, which is much more accurate than using the magnetic flux leakage testing method. Figure 11:1 Estimation of depth of damages on the tube, membrane, and weld. Figure 11:2 B-Scan results with FUP on the near-side damages of Tube-1 (**a**) before and (**b**) after \(T_{1}\) adjustment. Figure 11:3 B-Scan results with FUP on the far-side damages of Membrane-3 and Weld-2 (**a**) before and (**b**) after \(T_{1}\) adjustment.
## 4 Foundations of Recommendation Systems and LLM Integration
### Large Language Models: Capabilities and Evolution
#### 4.2.11. ERNIE (Enhanced Representation Through Knowledge Integration)  
By incorporating external knowledge, ERNIE improves natural language processing (NLP) task performance. Based on BERT, ERNIE introduces innovative knowledge masking techniques, such as entity-level and phrase-level masking, to enrich language understanding. ERNIE employs entity-level masking to mask entire entities, often multi-wordconstructs. This encourages the model to develop representations that encapsulate the semantics of these entities, thus enhancing its proficiency in tasks like named entity recognition and question answering [58]. Complementing this, phrase-level masking conceals entire phrases that function as conceptual units. This enables ERNIE to interpret better sentence relationships, improving its performance in semantic similarity and sentiment analysis tasks. ERNIE's efficacy is demonstrated through superior results across various Chinese NLP tasks, such as natural language inference, semantic similarity, named entity recognition, sentiment analysis, and question answering. This underscores the effectiveness of integrating structured knowledge to achieve advanced comprehension and inference. Additionally, ERNIE's knowledge inference capacity is particularly pronounced in cloze tests, predicting missing words by leveraging contextual and external knowledge. Comparative studies with other knowledge-enhanced models reveal diverse approaches to knowledge integration. While ERNIE and K-Adapter incorporate distinct types of knowledge, the degree and method of integration vary, suggesting that model performance can be significantly influenced by the choice of knowledge sources and integration strategies [59].
# Thermal Exchange and Skid Resistance of Chip Seal with Various Aggregate Types and Morphologies
## 3 Results and Discussion
### Properties of Aggregates
#### 3.1.2 Chemical and Phase Compositions  
The chemical compositions of three kinds of aggregates were tested by X-ray fluorescence and the results are presented in Table 2. It can be found from the table that the main chemical composition of basalt is SiO\({}_{2}\), which takes up half of all the components. BOF comprises elements such as SiO\({}_{2}\) and CaO, and they constitute more than 70% of the chemical component. Fe\({}_{2}\)O\({}_{3}\) was naturally found in BOF. Furthermore, three main chemical elements were captured in FER. They are SiO\({}_{2}\), CaO and Fe\({}_{2}\)O\({}_{3}\).  
The phases of two kinds of slag were also determined by XRD, and the results are given in Figure 5. The diffraction pattern of BOF shows that the strongest peak is located on 20 at 32-33\({}^{\circ}\), which belongs to the calcium silicate mineral. Combined with the XRF results of BOF, it can be inferred that dicalcium silicate (C\({}_{2}\)S) and tricalcium silicate (C\({}_{3}\)S) are the main phases of BOF. In addition, there are some iron oxide and RO phases (FeO-MgO-CaO-P\({}_{2}\)O\({}_{5}\) solid solution). In the diffraction pattern of FER, although the type of mineral phase is basically the same as that of BOF, the substance corresponding to the strongest diffraction peak is iron oxide, while C\({}_{3}\)S and C\({}_{2}\)S correspond to the second strongest peak. This is consistent with the result of XRF suggesting that iron is one of the main chemical elements in FER, indicating that the main mineral phases of FER are iron oxide, C\({}_{3}\)S and C\({}_{2}\)S.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Materials** & **MgO/\%** & **Al\({}_{2}\)O\({}_{3}\)/\%** & **SiO\({}_{2}\)/\%** & **CaO/\%** & **Fe\({}_{2}\)O\({}_{3}\)/\%** & **Others/\%** \\ \hline BS-1 & 7.1 & 15.4 & 50.0 & 8.6 & 14.5 & 4.4 \\ BOF-1 & 5.1 & 1.8 & 25.5 & 47.4 & 13.6 & 6.6 \\ FER-1 & 6.1 & 3.1 & 16.0 & 36.6 & 27.6 & 10.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical compositions of three kinds of aggregates.  
Figure 4: Cooling curves of different aggregate.  
Based on the results of XRD and XRF, the reason for the difference in the thermal conductivity of the aggregates can be analyzed to a certain extent. BOF has the lowest thermal conductivity since the calcium silicate mineral phase is its main component, and the thermal conductivity of calcium silicate is only 0.042-0.055 W/(m-K). The thermal conductivity of FER slag is larger than that of basalt since the iron oxide content of FER is higher, the thermal conductivity of which is about 10 W/(m-K) [27], while albite and diopside, which account for the main components of basalt, have thermal conductivities between 2 and 4 W/(m-K).
## 7 Discussion
### System and Technical Challenges
#### 7.1.3 Model Accuracy and Updating  
Model accuracy is a cornerstone of the predictive capabilities of DTs across the entire lifecycle of transportation infrastructure. The simulation and prediction models within a DT--whether for forecasting traffic patterns or assessing infrastructure health--are highly sensitive to the quality and completeness of input data. Inaccurate or incomplete data can lead to flawed predictions, resulting in poor decision-making [125].  
During the planning and design phase, accurate models are essential for simulating different scenarios and optimizing designs. In the construction phase, models must be updated in real time to reflect progress and ensure quality control. During the operation and maintenance phase, accurate models enable predictive maintenance and optimize system performance. Building accurate models requires extensive and diverse datasets that capture the complexities of transportation systems. For instance, a DT for an urban road network may need years of historical traffic data, detailed weather records, and vehicle usage patterns to reliably predict future traffic conditions. However, collecting such comprehensive datasets can be challenging, especially in regions with limited data collection infrastructure [50].  
Maintaining model accuracy over time is another significant challenge. Transportation systems are dynamic, and DTs must continuously update their models to reflect current conditions. For example, a DT used for bridge health monitoring must constantly process data from sensors measuring structural stress and vibrations. This requires advanced machine learning algorithms capable of adapting to new data and evolving conditions [47]. However, frequent updates can introduce errors or inconsistencies, underscoring the need for rigorous validation and quality assurance processes.  
An increasingly vital factor in ensuring accurate updates for structural components is the integration of advanced Structural Health Monitoring (SHM) and non-destructive testing (NDT) data--particularly from UAV-based inspections, robotic crawlers, and emerging sensor technologies. These high-resolution data streams enable the precise identification of localized defects (e.g., cracks, corrosion) in real time, providing granular updates to the DT model. By integrating NDT-derived insights with geometric information from BIM and sensor data within the DT framework, engineers obtain a robust foundation for structural performance analysis, including assessments of load-bearing capacity and fatigue predictions. This dynamic incorporation of NDT data enhances the fidelity of digital replicas and facilitates proactive, precision-targeted interventions, thereby mitigating catastrophic failure risks and optimizing maintenance schedules.