esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Community Detection Using a Small World Model
## 3 Preliminaries
### Clustering Coefficient
#### 3.4.1 Global Clustering Coefficient (GCC)  
Small world network is often observed to have high global clustering coefficient. GCC of the network is defined as the ratio of a number of triangles and the possible number of connected triplets in the network. A triplet is the set of three connected nodes in the network. Each triangle in a network contributes to three triplets. GCC of a network may be framed as below:  
\[GCC=\frac{3\times Number\ of\ triangle\ in\ the\ network}{Number\ of\ triplets\ in\ the\ network}. \tag{4}\]
## 3 MMW Fuze Echo Signal Model under Rainfall Conditions
\begin{array}{l}P_{r}=\sum_{i=1}^{M}\frac{P_{t}G^{2}\lambda^{2}}{\left(4\pi \right)^{3}R^{4}}P_{zi}F(\varphi_{i})e^{-2\gamma_{t}R}+\sum_{m=1}^{\infty}\sum _{1}^{f(n)}\frac{P_{t}G^{2}\lambda^{2}\sum_{i=1}^{m}\sigma_{si}P(\theta_{i})}{ \left(4\pi\right)^{3}\prod_{i=1}^{n+1}R_{mi}^{2}e^{\gamma_{t}R_{mi}}}+\\ \sum_{n=1}^{\infty}\sum_{p=1}^{\infty}\sum_{1}^{g(n,p)}\frac{P_{t}G^{2}\lambda ^{2}\sum_{i=1}^{m}\sigma_{si}P(\theta_{i})}{\left(4\pi\right)^{3}\prod_{i=1}^{ n+1}R_{mi}^{2}e^{\gamma_{t}R_{mi}}}\prod_{q=1}^{p}\frac{P_{qq}e^{\gamma_{t} (\theta_{q})}}{\left(4\pi\right)^{2}R_{q}^{2}e^{\gamma_{t}R_{q}}}.\end{array}  \tag{21}\]  
The echo signal of the MMW fuze in the frequency up-chirp segment can be expressed as follows:  
\[S_{r}(t)= \begin{cases}\sqrt{P_{rt}}\cos 2\pi(f_{st}(t-\tau_{t})+\beta(t-\tau_{t} )^{2})+\\ \sqrt{P_{rt}}\cos 2\pi(f_{st}(t-\tau_{a})+\beta(t-\tau_{a})^{2})+\\ \sqrt{P_{rat}}\cos 2\pi(f_{st}(t-\tau_{at})+\beta(t-\tau_{at})^{2})\end{cases} , \tag{22}\]  
where \(\tau_{t}\), \(\tau_{a}\), and \(\tau_{at}\) correspond to the echo delays of the target, raindrop scattering, and mixed-target raindrop scattering signals, respectively. The echo signal of the MMW fuze in the frequency down-chirp segment is similar and will not be repeated here. Therefore, the beat signal after low-pass filtering can be expressed as below:  
\[S_{b}(t)= \begin{cases}\sqrt{P_{rt}}\cos 2\pi(2\beta\tau_{t}t+\varphi_{t})+\\ \sqrt{P_{ra}}\cos 2\pi(2\beta\tau_{a}t+\varphi_{a})+\\ \sqrt{P_{rat}}\cos 2\pi(2\beta\tau_{a}t+\varphi_{at})\end{cases} . \tag{23}\]  
The target distance can be calculated from the frequency of the beat signal as follows:  
\[R=c\tau_{t}/2=Tc/(4\Delta F)\cdot 2\beta\tau_{t}. \tag{24}\]
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 3 Experimental Procedure
### Data Processing and Feature Extraction  
For each load condition, the vibration signals were segmented into two types according to the data length: 2400-point segments and 12,000-point segments. These segments were subjected to two processing approaches, as follows:  
1. Raw Signal Processing: Features were directly extracted from the raw vibration signals;
2. TKEO Processing: The Teager-Kaiser energy operator was applied to highlight transient energy changes in the signal, enhancing fault-related features. From each segment, two feature sets were extracted, as follows:  
1. Six-Parameter Set: This set included Max, Min, Mean, Standard Deviation, RMS, and Kurtosis;
2. Four-Parameter Set: This reduced feature set comprised Mean, Standard Deviation, RMS, and Kurtosis, aiming to evaluate model performance within a lower-dimensional feature space.
## 3 Methodology
### Hybrid Prediction Model for Fault Detection
The Random Forest algorithm is constructed by combining multiple decision trees for more accurate and stable prediction [77]. Every tree inside a Random Forest is independently constructed by selecting a random subset of features and bootstrap sampling of the dataset. Next, the tree is grown to the largest possible level. Each decision tree model inside the Random Forest will generate a prediction output and a majority vote is applied to obtain the final prediction output. Majority vote is a well-known method to obtain a better final prediction output [77]. Previous studies have utilized Random Forest because of its robustness when dealing with numerical data and solving real-world problems [74; 75; 76]. Recently, Random Forest was utilized for predicting the crash of stopping maneuvering [76]. The results showed that Random Forest successfully detected the crash of stopping maneuvering and forecast the safety properties of the ship before production. In our study, DBSCAN-based outlier detection was utilized to remove outlier data from the dataset and Random Forest was utilized to learn from the training set. Finally, the results of prediction were compared with the testing set to determine the model accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# Instance (Original)** & _MinPts_ & _eps_ & **\# Outlier Data** & **\# Normal Data** \\ \hline
342 & 5 & 7 & 4 & 338 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The result of DBSCAN-based outlier detection. Based on a confusion matrix [78], the prediction output can have four possible outcomes, as can be seen in Table 6. True positive (TP) and true negative (TN) results are defined as the number of correctly classified points. False positive (FP) and false negative (FN) results are defined as the number of points incorrectly classified as \"yes\" (positive) when they are actually \"no\" (negative) and incorrectly classified as no (negative) when they are actually yes (positive), respectively. In our dataset, abnormal events during the manufacturing process were defined as \"Yes\" and normal events were defined as \"No\". For training and testing the dataset, 10-fold cross-validation was applied for all classification models. The final performance measure was obtained by averaging the test performance for all folds. Weka Software 3.6.15 was utilized to run the classification models for the dataset [73]. Table 7 shows the measured performance metrics for the classification model based on precision, recall/sensitivity, and accuracy.
## 3 Results  
### User Interaction with the CAPT Tool  
Table 1 displays the results related to the user interaction with the CAPT system (experimental group, 18 participants). Columns \(\overline{n}\), \(m\), and \(M\) are the mean, minimum, and maximum values, respectively. _Time (min)_ row stands for the time spent (minutes) per learner in each training mode in the three sessions of the experiment. #_Tries_ represents the number of times a mode was executed by each user. The symbol -- stands for 'not applicable'. _Mand_ and _Req._ mean mandatory and requested listenings (see Section 2.3). The TTS system was used in both listening types, whereas the ASR was only used in the #_Productions_ row. Table 1 shows that there are important differences in the level of use of the tool depending on the user. For instance, the fastest learner performing pronunciation activities spent 22.43 min, whereas the slowest one took 72.85 min. This contrast can also be observed in the time spent on the rest of the training modes and in the number of times that the learners practiced each one of them (row #_Tries_). Overall, 85.25% of the time was consumed by carrying out interactive training modes (Exposure, Discrimination, Pronunciation, and Mixed Modes). The inter-user differences affected both the number of times the users made use of the ASR (154 minimum vs. 537 maximum) and the number of times they requested the use of TTS (59 vs. 497 times), reaching a rate of 9.0 uses of the speech technologies per minute. Tables 2 and 3 show the confusion matrices between the sounds of the minimal pairs in perception and production events, since the sounds were presented in pairs in each lesson. In both tables, the rows are the phonemes expected by the tool and the columns are the phonemes selected (discrimination training mode) or produced (production training mod) by the user. These produced phonemes are derived from the word recognized by the gASR, not because we look directly at the phoneme recognized, since gASR does not provide us with phoneme-level segmentation. _TPR_ is the true positive rate or recall. The symbol--stands for 'not applicable'. #_Li_ is the number of requested (e.g., non-mandatory) listenings of the word in the minimal pair including the sound of the phoneme in each row. As shown in Tables 2 and 3, the most confused pairs in discrimination tasks were [I]-[r], both individually (56 and 71, 127 times) and preceded by the sound [f] (69 and 64, 133 times). Furthermore, the number of requested listenings related to these sounds was the highest one (65 and 139, 204 times for [I] and 167 (52 and 115) for [r]). The least confused pair in discrimination was [O]-[f] (11 and 4, 15 times). The sounds with the lowest discrimination _TPR_ rate were [f] and [fr] (both \(<\) 66.0%), and those with the highest discrimination _TPR_ rate were [O] and [f] (both \(>\) 89%), corresponding also to the lowest number of requested listenings (45 and 16, respectively).
# The Effect of Situational Variables on Women's Rink Hockey Match Outcomes
## 2 Materials and Methods
### Statistical Analysis  
A descriptive analysis (involving absolute and relative frequencies) and an inferential analysis (in which confidence intervals for proportions were calculated using the Wilson method) were carried out on the categorical variables. The method of all possible regressions was employed to determine the optimal predictive logistic model for the match outcome [21; 22]. In the original full model, there were five situational predictors (MatLoc, TeaLev, OppLev, ScoFir, and MatStaHal), alongside one binary outcome (MatOut). However, the multiplicative term TeaLev \(\times\) OppLev was omitted due to the emergence of collinearity issues, which resulted in a substantial rise in the mean-variance inflation factor (mean VIF) from 1.79 to 4.04 when this term was included. The criteria employed for selecting the best predictive logistic model included the following aspects: (a) the parsimony principle [23]; (b) a large value of the area under the receiver operating characteristic (ROC) curve (AUC); (c) a small value of Akaike information criterion (AIC) [24]; (d) a good fit in the Hosmer-Lemeshow test (\(p>0.10\)) [25]; (e) balanced sensitivity (Se) and specificity (Sp) for a cut-off point of 0.5; and (f) a non-significant difference between the ROC curve of the full model and the candidate sub-model (\(p>0.05\)). After selecting the best predictive model, its reliability was assessed through cross-validation. Subsequently, an examination was conducted to ensure that this model met several key assumptions, with the respective statistical criteria indicated in parentheses: (a) absence of influential observations (2.0 Delta chi-squared influence statistic, \(\Delta\chi^{2}>3.84\); and Delta-D influence statistic, \(\Delta\)Dev \(>3.84\)) [26]; (b) absence of collinearity (variance inflation factor, VIF \(<5\)); and (c) presence of equi-dispersion (residual mean deviance, RMD \(\approx 1\)). It is worth noting that the assumption of linearity between the predictors and the logit was not assessed since all the predictors in this study were categorical in nature. Following the verification of the selected model's diagnostics, its parameters were estimated, and an evaluation of its overall goodness-of-fit was conducted. This assessment included a likelihood ratio test and the calculation of several pseudo-\(R^{2}\) indices, including Cox-Snell, Nagelkerke, and adjusted McFadden. Subsequently, the model equation was employed to predict the probability of winning a match under 18 common scenarios, which were determined through a two-step cluster analysis. In this clustering analysis, two fixed clusters were defined, employing a log-likelihood distance measure and [PERSON]'s Bayesian criterion as the clustering criterion. Finally, the optimal cut-off point, based on the ROC
# Augmented Reality in Surgery: A Scoping Review
## 4 Results
### Neurosurgery  
The use of AR is quite frequent also in neurosurgery. Its application in this area has already been tackled in oncology, but it finds its maximum utility in neuronavigation [73]. It can help surgeons in reducing the consequences of the treatment, improving the quality of the surgery and reducing the operation time [74; 75; 76; 77]. The first neuronavigation system (NNS) dates back to 1986. The advantage offered by AR associated to NSS consists in the mapping of the preoperative images directly onto the patient's visible surface, thus showing its anatomy on it [73; 78].
[28] recorded the changing biomass features of vegetation during the 2009/2010 droughts over the North China Plain, based on SIF. Among existing studies on crop yields, [PERSON] et al. [7] employed NDVI to forecast early crop yields in Central Europe, and [PERSON] et al. [8] used NDVI to analyze the response times of the yields of different crops (barley, winter wheat, soybean, corn, and cotton) to drought in the USA. [PERSON] et al. [28] analyzed the relations between SIF, crop yield, and the gross primary productivity (GPP) of vegetation during the growth periods of summer maize over the North China Plain. [PERSON] et al. [21] used both NDVI and SIF to identify seasonal changes in corn and soybean croplands in New York, USA. Although NDVI and SIF have already been applied in vegetation research, estimating GPP and crop yield by using SIF data combined with statistical methods is still not a common practice, mainly due to the low spatial resolution of the available SIF data. [PERSON] and [PERSON] [14] developed a global SIF dataset (GOSIF) with high spatial and temporal resolutions (0.05\({}^{\circ}\) and 8 days, respectively) representing the 2000-2020 period; this dataset could broaden the use of SIF studies over longer time periods and at higher resolutions. Although previous studies on NDVI and SIF have been conducted in research fields such as the response of vegetation to drought [32], the phenology of vegetation in high-latitude forests [33], and the relationship between GPP and SIF [4], NDVI and SIF have not been thoroughly compared due to limitations in existing research, such as a small study area [34], the analysis of only a single vegetation type [33], or the lack of measured crop-yield data [32]. Therefore, this study aims to examine the performances of NDVI and SIF from 2001-2019 in a large study area (China) for various land-cover types based on measured grain-yield data. The primary objectives of this study are (1) to explore the response relationship between NDVI and SIF; (2) to check whether SIF has an advantage over NDVI in GPP monitoring applications; and (3) to compare the application of SIF and NDVI in grain-yield monitoring. The obtained results represent systematic performance differences between NDVI and SIF and provide a reference for selecting suitable vegetation-monitoring indicators in future vegetation studies.
## 3 Results and Discussion
### Effects of Resin Type  
#### 3.3.1 Flexural (Static Bending)  
Figure 11 illustrates the MOR and MOE as a function of the adhesive type. The bending properties of the particleboard strongly depend on the type of adhesive used. In general, it can be seen that the utilization of adhesive materials resulted in significant improvement of the particleboard's bending properties. The particleboards blended with urea formaldehyde generate a higher number of contact points so that an inter-bonding between fibers results. This improves the bending strength of the board. For particleboards blended with cornstarch and binder less particleboards, the MOR values reached were about 0.3 and 0.46 MPa, respectively, and the corresponding MOE values reached 5.73 and 23.1 MPa. The produced particleboard blended with urea formaldehyde lead to improve the bending properties, so that MOR increases to 1 MPa and the corresponding MOE value reached approximately 181.9 MPa.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 3 Methods
### Preprocessing  
For both the spring and summer flights, a DSM with a GSD of 10 cm was derived for its usefulness in further classification stages since trees, shrubs, and grass have very different heights. For every informative layer used in the next stage of classification, and therefore for every input layer, a linear band stretching (rescale) to a range at 8 bits, from 0 (minimum) to 255 (maximum), was performed. The aim of this process, as described in [PERSON] et al. [(41)], was to normalize each band to a common range of values in order to reduce the effect of potential outliers on the segmentation. Subsequently, for both raster files (spring and summer) containing the R-G-NIR bands, a layer stacking process was performed by merging NDVI and DSM data to obtain two final five-band orthomosaics. This step was necessary because OTB segmentation procedure requires a single raster image as input data. Successively, a window of the original scene was made so that only a smaller area of interest, corresponding to the footprint of the flux tower, was classified (approximately 16.85 ha).
## 3 Materials and Methods
### Data Refinement
#### 3.2.1 Selection  
In the first step, the selection is applied to full-precision data using one of the rules:  
1. **Keep energies above the threshold.** Here, a fixed threshold value of 0.1, 0.5, 1, 2 GeV is applied, all pt values greater than the threshold are kept, and others are set to 0. The smallest energy values are assumed to have minimal impact on determining if a cluster represents a signal or background. The reason behind the small values proposed for the thresholds is that TC energies are much smaller than cluster pt.
2. **Keep layer maximum (single sample per layer).** With this approach, just the maximum value in each layer is kept, while other values are set to 0. This approach assumes that the highest pt deposits have a decisive role in shower classification.
3. **Capping.** Data capping is a method in which the maximum value of a feature is set to a specific value. It is a standard technique to treat outliers and is often a necessary step in executing the model in FPGA. A preliminary test with models implemented in FPGA using hls4 ml has shown a drop in the accuracy of even 10% in dealing with non-capped data (compared with QKeras model accuracy).
4. **Reduction in longitudinal profile.** Similar to the approach presented in [49] where detector images consist of three sub-detector channels: one each for CE-E, CE-H, and one for the reconstructed tracks, the following data refinement method is based on summing pt/pixels from particular detector layers. By summing the values of individual layers, it is possible to considerably reduce the image (channels) size, drastically decreasing the NN model's size. Two different solutions are examined:  
* Considering the fact that EM shower leaves pt deposits mainly in the CE-E part of the HGCAL, all CE-H layers are projected in one plane, reducing the number of CE-H layers from 22 to 1. This approach is named E+Hf, and the same logic with the CE-H part is also applied in the next solution.
* Here, the fact that EM starts showering in the earlier detector layers and reaches the peak between layers 10 and 15 is used. The CE-E is divided into three parts: layers 1-5, where the EM shower starts; layers 6-15, where the most pt is deposited; and layers 16-28, shown in Figure 8a. Each part is summed (Figure 8b), resulting in 3 5 \(\times\) 5 CE-E images, and the CE-H part is reduced, creating a 5 \(\times\) 5 CE-H image. This approach is referred to as 3 Ef+Hf.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Construction and Analysis of Multi-Dimensional Correlation Network of Tourist Attractions
#### 4.2.2 Resonance Correlation Network  
Figure 10b depicts the resonance correlation network, illustrating the semantic co-occurrence relations and tourists' subjective cognitive associations among attractions, thereby revealing tourists' mental perceptions of these sites. Table 6 highlights the top 10 attractions by composite influence within this network. Hankou Riverside tops the list with a score of 0.6993, establishing itself as a pivotal node in the resonance correlation network. As a critical component of Wuhan's riverside scenic belt, it serves both as a favored recreational area for residents and the primary choice for tourists seeking to experience the city's urban charm. East Lake, scoring 0.5984, ranks second, renowned for its expansive lake and picturesque landscapes, while also being steeped in cultural heritage, including landmarks like the Qu Yuan Shrine and Chu Tian Terrace, underscoring its significance in tourist experiences. Jianghan Road Pedestrian Street, positioned third, emphasizes its role as the heart of Wuhan's commercial center.  
The resonance coherence network exhibits high modularity but low global clustering coefficients, suggesting pronounced community structures with weak internal connectivity and minimal inter-community links, manifesting a notable \"semantic isolation\". This indicates that attractions with resonance and coherence connections form distinct semantic clusters. Extremely low average degree centrality and long average shortest path lengths signify a loosely connected network with inefficient information dissemination, severely limiting its utility for broad recommendation systems or path planning tasks.
## 2 PFC Converters Power Components Design Analysis and Selection
### The Boost Inductor Design and Manufacturing
#### 2.1.2 Interleaved PFC Boost Inductor
The copper loss of the winding wire resistance (DCR) can be calculated on \(\text{I}_{\text{in\_rms}}\) as given  
\[\text{P}_{\text{L1\_Loss}}=\text{P}_{\text{L2\_Loss}}=\left(\frac{\text{I}_{ \text{in\_rms}}}{2}\right)^{2}\times\text{DCR} \tag{15}\]  
where DCR is the direct current resistance of the inductors \(\text{L}_{1}\) and \(\text{L}_{2}\). In this work the interleaved CCM PFC inductors in each channel are manufactured with 65 turns of the copper wire with 0.75 mm diameter size around the single Kool Mu 7735A7 toroids core. The manufactured inductor DCR value was experimentally measured, and it was about 0.055 \(\Omega\). Table 2 shows the comparison between the boost inductor specifications designed for the conventional and the interleaved CCM PFC converters. The view of the manufactured boost inductors for both topologies is shown in Figure 7. From the manufactured boost inductors specifications and the view for both topologies, the reduction in the boost inductor weight, size, and cost can be observed in case of the interleaved topology as compared with the conventional topology. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conventional CCM PFC** & **Interleaved CCM PFC** \\ \hline Inductor No. of units & 1 & 2 \\ Core type & Kool Mu (77083A7) & Kool Mu (77354A7) \\ Inductive factor (\(\text{A}_{\text{L}}\)) & 81 \(\pm\) 8\% (\text{nH}/\text{T}^{2}\)) & 76 \(\pm\) 8\% (\text{nH}/\text{T}^{2}\)) \\ No. of Turns (N) & 60 & 65 \\ Winding wire & \multicolumn{2}{c}{Upper 1.15 mm} & \multicolumn{1}{c}{Upper 0.75 mm} \\ DC resistance (DCR) & 0.078 \(\Omega\) & 0.055 \(\Omega\) \\ Total Weight & 175 g & 95 g \\ Total cost & 17.00 USD & 11.00 USD \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Boost inductor specifications for the PFC (power factor correction) topologies. CCM (continues-current-conduction mode). Figure 7: The manufactured PFC boost inductors.