esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis
### SBTS Identification
Figure 4 shows that the \(Ang_{t}\) and average-speed plots on the 12 March 2016 when the two peak congestions in the morning and evening typically occurred. As can be seen, the overall patterns of the two plots were similar during the period of changing SBTSs. In the second congestion, however, the identified points in time that the congestion section state changed into the free flow section state and were different among the two indices. The congestion recovery time using the average-speed showed a later time than that of using the \(Ang_{t}\). In order to compare the classification performances with respect to the theoretical congestion, Figure 5 shows the speed-flow plot and occupancy-flow plot at the VDS 4 which was located at the front of the congestion queue. To clarify the difference of the average speed and \(Ang_{t}\), the time period of Figure 5 was set from 13:30 p.m. to 24:00 p.m. in the figure. The black dots indicate that the SBTS of VDS 4 was under a congestion section state. Figure 5a shows the classification using the \(Ang_{t}\) and Figure 5b shows the classification using the average speed. The congestion section states identified by \(Ang_{t}\) and the distribution of black dots in the plots of Figure 5a are mostly matched with the theoretical congestion section state described in Highway Capacity Manual [28]. As described by [PERSON] [30] and other many related studies, the upper regime of the speed-flow plot is described as the free-flow section state and the lower regime is referred to as the congestion section state. Under the free-flow section state, the speed decreases as the flow increase up to the maximum flow. When the flow reached the maximum flow (capacity), which occurred mostly at the inflection point, further speed reduction, coupled with flow-reductions, began. The lower regime of the plots mostly depicts the congestion  
Figure 3: Speed contour and \(Ang_{t}\) plots for empirical data on 3 March 2016.  
section state. The congestion section state identified by the average speed, and the distribution of black dots in the plots of Figure 5b, however, are not matched well for theoretical congestion or for a free-flow section state. From these empirical results, it is shown that using \(Ang_{t}\) for classifying SBTS based on the traffic flow theory is better than the average-speed approach.
###### Abstract  
To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5 MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous-discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.  
wind turbine; wind speed estimation; feed-forward control; available power +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Unrestrained Concentric Dent-Crack Defects
#### 3.1.4 Effect of Denting Pressure  
Figure 9 shows the variation of the burst pressure with denting pressure for pipes having different dent depths and crack dimensions. The results show that dent-crack defects formed when the pipe was under pressure were more severe on burst strength. For a given crack length and dent depth, burst pressure dropped with increase in denting pressure. Specimens having crack length \(\geq\) 100 mm in dents of depth \(\geq\) 4% OD formed at 0.3 P\({}_{\rm y}\) were likely to burst at pressures lower than MAOP of the pipe. All specimens with dents formed at 0.5 P\({}_{\rm y}\) and having crack length \(\geq\) 100 mm burst at pressures lower than MAOP. It is thus valid to say that dents augmented the effect of cracks on burst pressure, and denting pressure magnified the effect of the dent depth. Dent-crack defects formed at 0.3 P\({}_{\rm y}\) or lower denting pressures sustained the MAOP if the dent depths are less than 4%OD and cracks inside the dents were less than 100 mm long and 0.5 t deep. Defects formed at 0.5 P\({}_{\rm y}\) only sustained the MAOP if the crack lengths were less than 100 mm. As concluded by [PERSON] et al. [2], dent-crack defects having 4% OD deep dents formed at 0.3 P\({}_{\rm y}\) are likely to sustain the MAOP if the crack length is \(<\)75 mm and crack depth is 0.5t.  
Figure 8: Variation of burst pressure with dent depth, for pipes having 0.5 t deep cracks of various lengths inside dents formed at: **(a)** Denting pressure = 0, **(b)** Denting pressure = 0.3 P\({}_{\rm y}\); **(c)** Denting pressure = 0.5 P\({}_{\rm y}\).
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 3 Theoretical Framework
### Kaiser Effect and Felicity Ratio  
In the field of Acoustic Emission (AE) monitoring, the Kaiser effect is a well-documented phenomenon, whereby a material subjected to cyclic loading does not exhibit significant AE activity until the previously applied maximum load level is exceeded. The Kaiser effect is graphically represented by the absence of AE activity until the previously applied maximum load is exceeded (Figure 1a). This load is referred to as Lmax [26]. This effect is indicative of an undamaged or non-degraded state, as the material \"remembers\" the past maximum stress and remains silent under repeated lower loads. It is particularly useful in distinguishing between new and existing damage within a structure and in assessing whether ongoing loading conditions are inducing further deterioration.  
However, deviations from the Kaiser effect may occur in materials that have already experienced damage. In such cases, AE activity is detected even before reaching the previous maximum load. This behaviour is quantified by the Felicity ratio, defined as the ratio between the load at which emissions resume and the maximum load previously applied (Figure 1b). The Felicity ratio is calculated by dividing the load at which AE activity next occurs (Lnex ae) by the previous maximum load (Lmax): Felicity Ratio = Lnex AE/Lmax. A Felicity ratio close to 1 indicates minimal damage accumulation; values below 0.95 suggest internal deterioration. Specifically, a Felicity ratio close to 1 suggests that the material retains its structural integrity, whereas lower values (typically below 0.95) may indicate progressive damage and a reduced ability of the material to sustain repeated loading.  
The combined use of these parameters provides a valuable tool for interpreting the mechanical response of materials, especially in the case of historic masonry, where microcrack propagation under sustained or cyclic loads may not be visible externally. When applied to load test data, the Kaiser effect and Felicity ratio allow for the detection of early warning signs of structural degradation. Their effectiveness is enhanced when interpreted alongside the AE hit rate, cumulative energy, and source localisation methods, contributing to a more comprehensive understanding of the internal state of the structure.  
However, as will be discussed in Section 5, the experimental conditions did not allow for a quantitative evaluation of the Kaiser effect and Felicity ratio due to the absence of a prior load history and the limitations inherent to in situ testing on restored heritage structures.
## 5 Performance Metrics
### Visibility  
Throughout our analysis, we realized that the Android API provides an event at a regular sampling rate (i.e., 1 Hz), while the measurements per satellite are not regular. Gaps of several seconds (1-5 s) can be seen for certain satellites, leading to the idea that the device performs some kind of sequential tracking. The signal stops being tracked by the receiver for a certain amount of time to concentrate on the tracking of another signal. The receiver then comes back to the first signal to converge again, and avoids the signal falling out of the tracking loop. The most probable reason is to limit the energy consumption, and possibly to enable more signal tracking than supported by the number of channels available in hardware. This phenomenon has been seen on every device, even with a different number of total visible signals per epoch.  
Today, the GNSS tracking is often limited to the four major constellations (GPS, GLONASS, Galileo, BeiDou), with dual-frequency (L1/L5) for the most advanced ones. Some Android devices do support some other satellite-based systems such as IRNSS and QZSS. However, none of the devices reviewed showed the reception of SBAS satellites. Consequently, while the geodesic-grade receivers used as a reference allowed the coverage of almost all constellations and frequency bands, we limited our comparative analysis only to the signal available in the Android devices, for a fairer comparison.  
Constellations in the results are named based on their RINEX code, as defined in [47]: G (GPS), R (GLONASS), E (Galileo), C (BeiDou), I (IRNSS) and J (QZSS). Similarly, frequencies and signals are simplified to L1 (\(\sim\)1575.42 MHz) and L5 (\(\sim\)1176.45 MHz). For comparison with the reference receiver, we extracted the following signals based on RINEX definition: L1 \(\Rightarrow\) (L1C, L1L), L5 \(\Rightarrow\) (L5Q, L2I, L5P, L5A).  
To best assess the satellite's visibility of the devices, we provide two types of graphs: the number of signals seen at each epoch w.r.t time, and bar graphs with the total amount of unique signals seen during the whole scenario. For the latter, we also add the total amount of signals seen by the reference receiver (the left bar) to compare against each device (the right bar). The bar graphs come in two variants: one per constellation and one per frequency. We also provide a summary table comparing the percentage of visible signals compared to the reference receiver.
## 2 Theoretical Background
[39] focused on an identification of the principal factors required to enhance air cargo operations at Paro International Airport in Bhutan, and to assess the importance of each factor to establish solutions for efficient airport development. The fuzzy analytic hierarchy process (AHP) was used. Following analysis of the collected data via the AHP survey, the cargo handling infrastructure has been determined as the most important segment requiring improvement, followed by airport charges and the cargo security service. The results of the study point out that the airport management should focus on financing planning, personnel utilization, and expanding the supply of flight services. [PERSON] et al. [40], in 2021, analyzed the airport ground handling services equipment supplier provider and their need to avoid aircraft damage and airline delays and ensure the provision of reliable and high-quality services. The research proposed new purchasing decision model based on the analytic hierarchy process (AHP), AHP-fuzzy linear programming (FLP), and AHP-Taguchi loss function (TLF) multi-choice goal programming (MCGP) to assist the managers in selecting the optimal equipment supplier provider. The results obtained were analyzed and compared with the conclusion that the proposed model provides useful information and has practical value for airport ground handling services providers. In 2022, [PERSON] et al. [41] proposed a new hybrid methodology for airport efficiency assessment, combining the spherical fuzzy sets analytic hierarchy process (SFS-AHP) and data envelopment analysis (DEA). This study investigated the operational efficiency of 46 Turkish civil airports between 2015 and 2018. The results showed that 67.2% of Turkish airports operate below optimal efficiency levels, while airports located in popular tourist areas achieve higher levels of efficiency. In 2023, [PERSON] and [PERSON] [42] redesigned the classical boarding process using IoT technology by developing Boarding 4.0 to reduce boarding times. Compared to the classical boarding process and Boarding 4.0 with the use of the fuzzy AHP (FAHP) method, the results show that Boarding 4.0 has more benefits and a reduction in passenger movement times during boarding. The study is valuable as it improves airport resource allocation and utilization. In 2024, [PERSON] et al. [43] developed a decision framework consisting of 4 primary factors (master planning, environmental impact, techno-economic considerations, and operational aspects) and 30 subfactors. The fuzzy analytical hierarchy process (FAHP) was used to prioritize factors and subfactors, in two stages. The results show that the upmost priority are techno-economic factors, followed by operational, master planning, and environmental factors. The proposed framework can improve strategic decision-making regarding the air cargo infrastructure location which can increase airport competitiveness.
## 4 Results
### Reliable Network Connectivity  
Figure 13 presents the outage ratio, \(P_{out}\), as a function of the normalized maximum transmission power, \(P_{ratio}^{max}\). A threshold effect is evident in all curves, where a certain maximum normalized  
Figure 12: Increase in Energy per Bit for \(M=100\)_vs._\(M=0\) and varying number of antennas at the off-body access point. The straight horizontal lines depicts the mean value.  
transmission power threshold must be exceeded to ensure the routing algorithm would almost always be able to connect all nodes to AP via other relaying nodes. For example, in the architecture with on-body AP if the transmitter's maximum power output is 70 dB higher than the target RSSI then the outage ratio falls below 0.1. However, if the power output is 60 dB lower than the target RSSI then the outage ratio rises above 0.9. In another example, in the architecture with off-body AP with a single antenna, the transmitter's maximum power output is required to be 67 dB higher than the target RSSI to achieve an outage ratio below 0.1. The benefit of using multiple antennas is evident as well, as the threshold drops to 62.5 dB when four antennas are used. Recall that the experimental setup used in our experimentation and the target RSSI are such that we operated above the maximum normalized transmitted power threshold.
## 3 Results
### Flux Tower Scale Simulation
#### 3.1.2 NPP Influencing Factors Analysis  
An increase in LAI within a certain range would lead to an increase in solar radiation used in photosynthesis and the surface area of organs involved in photosynthesis, resulting in an increase in NPP. As the range of values of LAI varied from +50% to \(-\)50%, the results of NPP decreased accordingly from 767.94 g C m\({}^{-2}\) a\({}^{-1}\) to 342.81 g C m\({}^{-2}\) a\({}^{-1}\). The sensitivity of NPP to LAI was 0.722.  
The increase in temperature would promote the growth of NPP, but there was a warming threshold. Heating up simultaneously affected photosynthesis and respiration, which in turn affected NPP. As the temperature changed from an increase of 5 \({}^{\circ}\)C to a decrease of 5 \({}^{\circ}\)C, the NPP results decreased accordingly from 636.93 g C m\({}^{-2}\) a\({}^{-1}\) to 470.40 g C m\({}^{-2}\) a\({}^{-1}\).  
Radiation provided radiation energy for plant photosynthesis, serving as a signal to regulate several physiological processes of vegetation growth cycle, affecting transpiration of vegetation leaves, and thus affecting NPP. As the range of values of radiation varied  
Figure 6: Simulated NPP of the flux tower in 2020: (**a**) Daily NPP; (**b**) monthly NPP.  
from +50% to \(-50\%\), the results of NPP decreased accordingly from 624.96 g C m\({}^{-2}\) a\({}^{-1}\) to 469.63 g C m\({}^{-2}\) a\({}^{-1}\). The sensitivity of NPP to radiation was 0.233.  
Water was a necessary condition for various physiological activities of vegetation. It controlled the opening and closing of stomata and was also a means of transporting plant nutrients. Low or high precipitation would limit vegetation growth. As the range of values of precipitation varied from +50% to \(-50\%\), the results of NPP remained virtually unchanged. The sensitivity of NPP to precipitation was 0.0004. The sensitivity levels of NPP influencing factors were shown in Table 2.
## 3 Image Acquisition and Methodology
### Single-Channel Gravitational Kernel Density Clustering Algorithm  
#### 3.3.1 Automatic Identification of the Initial Value of a Cluster  
The number of clusters and the cluster centers of the clustering algorithm were automatically obtained by fitting the characteristic histogram of the R-channel. Through analyzing a large number of collected leaf image features, the results showed that the diseased leaf images usually had two composition modes: the first was that the diseased leaf image consists of diseased spots and leaves, while the second was that the diseased leaf  
Figure 3: L, a, and b changes of the diseased leaves in the lab color space.  
image is composed of three regions: the lesion, leaf, and background. Therefore, most of the diseased leaves showed a bimodal or triplet feature on the histogram. The As are shown in a1, b1, c1, and d1 in the Figure 4 diagrams of the diseased leaf red channel histogram. The corresponding diseased leaf images are shown in Figure a, b, c, and d in Figure 4. The different peak areas of the red R histogram represent the different areas of the diseased leaf image, and the peak points of the peak area represent the pixels that appeared most frequently in this area.  
Figure 4: _Cont_.  
So, Equation (5) may be written as:  
\[XA=Y. \tag{6}\]  
Then, the polynomial parameter A is calculated using the least square method.  
\[A=\Big{(}X^{T}X\Big{)}^{-1}Y. \tag{7}\]  
Find the peak point by deriving Equation (1) so that the derivative is equal to 0. The result of the derivation is as in Equation (8).  
\[\frac{dy}{dx}=a_{1}+2a_{2}x+\cdots+a_{k}x^{k-1}=0. \tag{8}\]  
After substituting all integer values into Equation (8), the x value meeting the following two conditions is the peak point \(x_{p}\):  
1. Label the polynomial derivative as 0;
2. The value of the polynomial at this point is greater than the value of the polynomial corresponding to the neighborhood point.  
The feature histogram curve of the polynomial fit is shown in a2, b2, c2, and d2 in Figure 4. After finding all the peak points of the feature histogram fitting curve, we found all the peak areas of the diseased leaf histograms and roughly found the different areas of the diseased leaf. The peak point was used as the initial clustering center to cluster the diseased leaves. Therefore, according to the peak area and peak point of the feature histogram, the cluster number and cluster center of the diseased leaves could be preliminarily determined.
## 3 Results  
In this chapter, we present the results obtained from our research, encompassing the comprehensive performance assessment of each classifier (logistic regression (LR), decision trees (DTs), random forest (RF), gradient boosting (GB), XGBoost (XGB) and CatBoost) subsequent to the application of each resampling technique (SMOTE, ADASYN, SMOTE-Tomek, SMOTE-ENN). The evaluation metrics employed in our analysis include accuracy, recall, precision, F1-score, the area under the ROC curve (AUC), alongside the elucidation of the confusion matrix. Highlighted in bold within the tables are the superior performance outcomes the models achieved, representing the best-performing model when coupled with each of the resampling techniques.  
For the sake of robustness, a stratified split of 70% for training and 30% for testing was adopted across all experiments. It is noteworthy that each resampling method was exclusively applied to the training dataset to forestall any potential data leakage from the test set. This meticulous approach not only upholds the integrity of the test dataset but also provides a more precise evaluation of the model's generalization ability.
## 2 Methods and Techniques
### Experimental Protocol and Validation  
A previously acquired set of data was used to validate the approach proposed in this paper. These data were obtained by pedaling on a cycling simulator for sessions about 50 minutes long with a pedaling cadence fixed at 70 rounds per minute (rpm). The cycling simulator was equipped with a system to control the power exerted by the participant [43,44]. This system has been validated in recent works as suitable for force and power measuring during cycling activity in real time [45]. Force data, obtained from the previous work [15], were acquired (2000 Samples/s sampling frequency, 12-bit A/D converter) by a homemade instrumented pedal mounted on the cycling simulator. With this system, it was possible to measure force components exerted on the pedal, the angular displacement of the pedal, \(\theta_{\mathrm{P}}\), and the angular displacement of the crank, \(\theta_{\mathrm{C}}\). These data were used as input for the biomechanical model described above. The experimental protocol was used for the estimation of the muscular forces using the two different techniques and its validation was:  
1. Training and validation set of NNs using data obtained previously by a deterministic optimization algorithm.
2. Plot analysis between the signals obtained by NNs and signals obtained by the optimization algorithm, and the evaluation of the RMSE and RMSE Standard Deviation.
3. Validation of the experimental protocol analyzing Bland-Altman plots extrapolating 1180 random samples from each muscle forces signals.