esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 4 Methods
The fundamental concept underlying the Random Forests method is the bagging method itself. In other words, the combination of decision trees and the bagging technique constitutes the Random Forests method. The RF method is chosen for its robustness and ability to handle high-dimensional data and interactions between features with high accuracy, as demonstrated in [25; 31; 38]. \(\bullet\) XGBoost  
It was developed to improve the GBDT technique. This technique has been successful in many machine learning competitions. XGBoost has many advantages over classical methods [59]. The first is that it contains a revolutionary tree-learning technique for sparse datasets. Second, it employs a theoretically justified weighted quantile sketch approach that allows approximation tree learning to handle instance weights. Parallel and distributed computing reduces the training time, allowing researchers to conduct model explorations more quickly. Finally, using out-of-core computation enables data scientists to process massive amounts of information. As one of the widely preferred algorithms, XGBoost was included for its high performance and efficiency, especially in handling sparse datasets and complex patterns, as reported by [29; 31; 49]. \(\bullet\) LightGBM  
LightGBM is another widely used technique that has gained recognition, particularly in classification problems. The motivation for improvement was stated as the inadequacy of the available methods for datasets containing high amounts and many features [60]. Focused on development based on XGBoost, it takes a different approach to classification problems by adding and combining the two techniques of gradient-based one-side sampling and exclusive feature bundling. Thus, LightGBM was included for its speed and efficiency, especially with large datasets with many features, as evidenced by [31; 42]. \(\bullet\) CatBoost  
It is a technique especially developed for categorical features. The CatBoost method adds two new fundamental functions: a permutation-driven ordered boosting algorithm and a novel algorithmic approach specifically for processing category information [61]. It is claimed that CatBoost can also handle cases when the number of categories is too large for conventional GBDT models. It achieves this by dividing the dataset into random subsets, converting the labels to integer values, and converting the remaining categorical features to numerical values. The reasons for its selection were its superior handling of categorical data and avoidance of overfitting through ordered boosting, as demonstrated in [43]. By employing a diverse set of models, we aimed to leverage the strengths of each technique to develop a comprehensive and accurate predictive model for flight delays.
## 3 Results  
The demographic features of the participants, including sex and age, did not significantly differ among the three groups (Table 1). There were no differences in the pretreatment maxillary second molar measurements among the three groups (Table 3). However, there were significant differences in some pretreatment dental variables, such as the FH plane to functional occlusal plane (\(p<0.05\)) and FH plane to anatomical occlusal plane (\(p<0.01\)).  
Comparison within each group during the treatment period revealed significant differences before and after treatment in the pterygoid vertical (PtV) to maxillary second molar (Mx 7) (1.29 \(\pm\) 1.73 mm, \(p<0.01\); Table 4) in group 1. Groups 2 and 3 also showed significant differences in the PtV to Mx 7 (group 2, \(p<0.001\); group 3, \(p<0.01\); Table 4) and the angles between the FH and anatomical occlusal planes (group 2, \(p<0.05\); group 3, \(p<0.01\); Table 4). The distance between the PtV and Mx 7 increased by 2.01 \(\pm\) 1.46 mm and 1.12 \(\pm\) 1.43 mm in groups 2 and 3, respectively. The angle between the FH and anatomical occlusal planes increased by 1.96 \(\pm\) 2.88\({}^{\circ}\) (\(p<0.05\)) in group 2 and by 2.51 \(\pm\) 2.57\({}^{\circ}\) (\(p<0.01\)) in group 3 (Table 4). There were no other significant differences in the measured variables. Skeletal and dental measurements did not significantly differ among the three groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**T2**} & \multicolumn{2}{c}{\(\Delta\)**T1–T2**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{2-2} \cline{4-4}  & & **Group 1 (\(n=19\))** & & \\ \hline PtV to Mx 7 (mm) & 6.97 \(\pm\) 3.91 & 1.29 \(\pm\) 1.73 & 0.004 ** \\ Palatal plane to Mx 7(mm) & 15.33 \(\pm\) 2.87 & \(-\)0.56 \(\pm\) 1.29 & 0.074 \\ FH to functional occlusal plane (\({}^{\circ}\)) & 12.07 \(\pm\) 4.10 & \(-\)0.32 \(\pm\) 3.24 & 0.676 \\ FH to anatomical occlusal plane (\({}^{\circ}\)) & 11.82 \(\pm\) 3.83 & \(-\)0.56 \(\pm\) 2.77 & 0.392 \\ FH to mandibular plane (\({}^{\circ}\)) & 28.37 \(\pm\) 5.94 & 0.13 \(\pm\) 0.82 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the skeletal and dental changes during the treatment period.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Group 1 (\(n=19\))** & **Group 2 (\(n=16\))** & **Group 3 (\(n=16\))** & \(p\)**-Value** \\ \hline PtV to Mx 7 (mm) & 8.26 \(\pm\) 3.34 & 10.56 \(\pm\) 5.53 & 9.02 \(\pm\) 2.59 & 0.238 \\ Palatal plane to Mx 7 (mm) & 14.76 \(\pm\) 3.22 & 16.33 \(\pm\) 3.45 & 14.99 \(\pm\) 1.90 & 0.265 \\ FH to functional occlusal plane (\({}^{\circ}\)) & 11.76 \(\pm\) 4.29 & 8.99 \(\pm\) 4.28 & 7.75 \(\pm\) 4.56 & 0.027 * \\ FH to anatomical occlusal plane (\({}^{\circ}\)) & 11.26 \(\pm\) 4.07 & 6.71 \(\pm\) 4.12 & 6.56 \(\pm\) 3.55 & 0.001 ** \\ FH to mandibular plane (\({}^{\circ}\)) & 28.49 \(\pm\) 5.84 & 24.14 \(\pm\) 24.19 & 26.31 \(\pm\) 4.95 & 0.132 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of pre-treatment cephalometric variables between groups.
## 2 The AAL Prototype CARIMO for Home Care Service Users
### Components of CARIMO  
CARIMO consisted of two commercially available hardware components: (1) a tablet (Galaxy Tab A 2016, Samsung Electronics Co., Ltd., 129, Samsung-ro, Yeongong-gu, Suwon-si, Gyeonggi-do, Korea) and (2) a bracelet with a fitness/activity tracker (Gear Fitz2, Samsung Electronics Co., Ltd., 129, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Korea) (see Figure 1). Both components were selected according to predefined criteria. For the tablet these were: (i) display size of at least nine inches (aspect ratio of 16:10 or 16:9), (ii) Android 5 or higher, (iii) 3G or 4G internet connection, and (iv) a maximum price of EUR 320. The main criteria for the activity tracker were: (i) battery life of three to five days without using GPS, (ii) standalone operating mode (no permanent contact with a paired smartphone or tablet needed), (iii) waterproof or at least splash proof, (iv) adaptable UI and data transfer via Bluetooth or Adaptive Network Topology (ANT), and (v) maximum price of EUR 200. More details on the selection process for the fitness/activity tracker can be found in [PERSON] et al. [34].  
CARIMO consisted of a web app (for configuration purposes), an Android native app for the tablet, and a new watch face for the activity tracker. Existing systems (e.g., SimpliFlow--SimpliFlow International GmbH, Krone Platz 1, Klagenfurt, Austria--as a sports/training platform and SITOS--bit media e-solutions GmbH, Kamntner Strasse 337, Graz, Austria--as an eLearning tool for online courses) were enhanced and integrated with new components developed within the project to provide a new solution to meet the aims of the AAL Programme. On the tablet, the CARIMO app was set as the home screen displaying the menu, such that the users always came back to the menu when pressing the home button. For the fitness bracelet, a digital CARIMO watch face was implemented, indicating the time and step count, as well as a shortcut for tracking activities, such as walking, via GPS. The activity tracker was used to measure steps and physical activities lasting more than ten minutes. These parameters were considered for rating the daily amount of physical activity of the users.
# Navigation Scenario Permutation Model for Training of
## 2 Materials and Methods
### Navigation Element Distribution Analysis  
#### 2.2.1 Data Collection and Preprocessing  
In this study, Automatic Identification System (AIS) trajectory data from South Korea from August 1 to 31 in the year 2020 were collected. The AIS data include static ship data (e.g., Maritime Mobile Service Identify (MMSI) number and gross tonnage) and dynamic ship sensor data (e.g., date, time, and GPS data with latitude and longitude). The size of data was 30 million rows, and the number of ships was 5000 to 9000 per day. Data preprocessing involved sorting the data according to the designed experimental conditions, as shown in Table 1.  
The purpose of the designed experiment conditions (Table 1) was to extract the distribution of actual usage of the selected navigation elements. Thus, data for small crafts, anchored ships, drifting ships, and berthel ships were excluded. The covered area was the southern water area of South Korea, where dynamic ship movements are expected. The covered area is marked in orange in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Condition Range** & **Unit** \\ \hline Gross tonnage & 5000–300,000 & Metric tons \\ Speed & 5–25 & Knots \\ Covered area & 33–35 & Degrees \\ (Latitude) & 126–130 & Degrees \\ Covered area & 126–130 & Degrees \\ (Longitude) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions.  
Figure 2: Data coverage area (orange-colored).
## 2 Methods
### CFD Modelling of the FSI for the VI  
In the following, we describe the procedure to determine the force exerted by the fluid on the VI alone.  
Such force is calculated numerically by means of a CFD (Computational Fluid Dynamics) analysis as the one acting on the VI at rest at various AoA \(\alpha\) and different undisturbed flow velocities \(U_{\infty}\).  
The CFD model is a commercially available package [32] solving for RANS on structured, non uniform grids. The C-shaped section of the VI wings has been placed in a steady, uniform water flow with its concave side facing upstream. The numerical experiments covered a range of flow velocity \(U_{\infty}\) and AoA \(\alpha\) of \(0.567\,\mathrm{m/s}<U_{\infty}<0.693\,\mathrm{m/s}\) and \(0\,\mathrm{deg}<\alpha<20\,\mathrm{deg}\) with increments of \(\Delta U_{\infty}=0.0315\,\mathrm{m/s}\), \(\Delta\alpha=5\,\mathrm{deg}\), resulting in a total of 25 simulations. Although the model has been extensively benchmarked, a further validation was carried out: the reference drag coefficients of the two-dimensional semitubular cylinder of diameter \(D=16\) mm at null AoA, as reported in [33,34], was calculated with a maximum error of 2%.  
With reference to Figure 2, the domain extends \(4\times D\) upstream, \(7\times D\) downstream, and \(3.5\times D\) cross flow. The same grid, determined through a grid convergence analysis, was employed for all simulations. The cartesian, non-uniform grid features a spacing ranging from \(D/8\) at the boundaries, to \(D/64\) around the object, resulting in \(4.3\times 10^{4}\) elements. Boundary conditions were of uniform, constant velocity upstream, uniform and constant pressure downstream and symmetrical conditions crossflow. The turbulence closure model yielding best results in the validation phase was the \(k\)-\(\omega\) one [35], and it is then adopted in the simulations. In the analysed flow regimes, characterized by a Reynolds number \(Re=U_{\infty}D/v\simeq 10^{4}\), \(v\) being the kinematic viscosity, a regularly oscillating vortex shedding develops in the wake. The simulations were run until the wake attained a permanent oscillation. Drag and lift time histories acting on the VI were extracted and their time-averaged values, referred to respectively as \(F_{D},F_{L}\) in the following, passed to the beam structural model.  
Figure 2: 3D and 2D view (lower inset) of the non-uniform grid employed in the CFD code. Upper inset: example of flow field (velocity magnitude in m/s). The VI is depicted in red.
## 3 Network Model
### Lifetime Model of the Network  
In this study, the lifetime of the network is considered to start from its initialization until it cannot work effectively. Generally, some existing studies define the network lifetime as a period from network initialization until the energy of at least one sensor node is depleted, that is, one sensor node fails [8,41,42]. The region covered by sensor nodes may not be monitored continuously after the energy of some sensor nodes is depleted, especially when a small number of sensor nodes are deployed in the sensed region; this situation can result in data loss. For a WSN system with a large number of sensor nodes, the network may continue to work effectively until a certain percentage of sensor nodes fail; this percentage is then used to determine the network lifetime [32].  
Balancing the energy consumption of sensor nodes can extend the network lifetime of both small and large WSNs systems, because the energy consumption of sensor nodes become similar in this case. This can avoid the phenomenon where some sensor nodes consume energy faster while other sensors have a lot of residual energy, which is helpful to prolong the lifetime of WSNs.
# Predicting the Compressive Strength of Concrete Using an RBF-ANN Model
## 2 The Concrete Mix Proportioning  
According to [26], the 28-day compressive strength of concrete is mainly related to seven factors. They are the mass of water, cement, fine aggregate (sand), coarse aggregate (gravel), blast furnace slag, fly ash, and superplasticizer mixed in 1 m\({}^{3}\) concrete. Therefore, the 28-day compressive strength of concrete can be expressed as a mathematical function with seven arguments:  
\[y=f(x) \tag{1}\]  
in which  
\[x=\left[\begin{array}{cccc}x_{1}&x_{2}&\cdots&x_{7}\end{array}\right]^{T} \tag{2}\]  
where \(x_{1}\) to \(x_{7}\) are the abovementioned proportioning factors while \(y\) is the 28-day compressive strength of concrete. For the input and output of the ANN, all the data have to be normalized into the same range. In this paper, the raw data are transformed into the range of 0 to 1 linearly.  
\[\xi_{i}=\frac{x_{i}-x_{i,\min}}{x_{i,\max}-x_{i,\min}} \tag{3}\]  
\[\eta=\frac{y-y_{\min}}{y_{\max}-y_{\min}} \tag{4}\]  
The database in [26] which was also used by [27] for establishing their model is expanded by adding 89 pieces of data listed in [35; 36; 37; 38; 39]. Totally, there are 571 data in the database. The testing set is the same as in [27], so 72 pieces of data are for testing while 499 pieces of data are for training. The size of the testing set is about 12.6% of the database. Printing out all the data might make this paper redundant. Those who need the raw data may find them in the references [26; 27; 35; 36; 37; 38; 39]. The ranges from \(x_{1}\) to \(x_{7}\) and \(y\) are listed in Table 1. It is worthy to note that some of the values listed in Table 1 are slightly different from those in [27]. This is because the database has been expanded by adding data from other literature.
## 3 Results and Discussion
### Assessment of Anti-Bacterial Activity  
The activity of Aw-AgNPs against bacterial strains was observed at 100 ug/mL, as indicated in Figure 5. These results show that Aw-AgNPs display an evident significant effect against the tested bacterial strains. These finding are supported by an earlier study that showed that the highest antimicrobial activity was exhibited against _Staphylococcus epidermidis_, whereas the lowest antimicrobial activity was noted against _Bacillus cereus_ and _Escherichia coli_[2]. In this study Aw-AgNPs displayed evident anti-bacterial activity, with significant (\(p<0.05\), \(p<0.01\)) impacts on Gram-negative than Gram-positive bacteria. Similarly, a study by [PERSON] et al. showed that biosynthesized AgNPs from the leaf of two different plants presented very strong bactericidal effects against _Pseudomonas aeruginosa_, _Escherichia coli_, _Staphylococcus aureus_, and _Streptococcus viridians_[30]. Aw-AgNPs exhibited significantly higher inhibition of _Staphylococcus aureus_ and _Escherichia coli_ than the Sukkari dates seed extract AgNPs reported by [PERSON] [31]. Similarly, anti-bacterial activity of AgNPs assessed by broth dilution method shows that the MIC of AgNPs synthesized by date seed were found 1.56 ug/mL against A. baumannii (ATCC 1960) and K. pneumonia (PCI 602) [10]. In the present work, synthesized Aw-AgNPs showed a strong anti-bacterial effect and was similar to that observed with the use of penicillin, as indicated in Figure 5, highlighting its possible utility as an anti-bacterial agent. Our study exhibited significantly  
Figure 4: Characterization of AgNPs. (**A**) TEM image of AgNPs. The scale bar corresponds to 200 nm. (**B**) Closer view of the AgNPs. The scale bar corresponds to 50 nm. (**C**) Particle size distribution obtained by dynamic light scattering (DLS).  
higher 20, 18, 17, 19, 19 mm zone of inhibition against _Klebsiella pneumoniae_, _Pseudomonas aeruginosa_, _Escherichia coli_, _Staphylococcus aureus_ and _Enterococcus faecalis_ when compared with the [PERSON] et al. [32].
# Effect of Performance by Excessive Advanced Fuel Injection Timing on Marine Diesel Engine
## 5 Conclusions  
This study aimed to measure and diagnose the performance of an engine operating in commercially operated vessels, based on abnormal phenomena. Additionally, the study reported the results of readjusting the fuel injection timing to restore the engine's performance and remeasuring its performance. The findings of the study can be summarized as follows.  
1. The engine's starting fail, dull noise, and vibrations were attributed to excessively advanced fuel injection, resulting in premature ignition and peak pressure occurring before TDC, as well as significant variations in P\({}_{\text{max}}\) height among cylinders.
2. By retarding the injection timing by approximately 10 degrees, P\({}_{\text{max}}\) decreased by up to 15 bar and the ignition timing was delayed by 6.5 degrees.
3. Excessively advanced ignition reduced the work performed during the cycle by increasing compression work and decreasing expansion work.
4. Excessively advanced ignition and large variations in P\({}_{\text{max}}\) led to significant fluctuations in the crankshaft's angular velocity, causing engine vibrations.  
The accurate criteria for injection timing discussed in this study is a limitation of the study, as is the impact of injection timing on combustion in the NCR. These limitations need to be addressed in future research on engine normalization. Furthermore, research investigating the detection of injection timing and the combustion state through variations in engine angular velocity or vibration measurements could become very interesting in engine diagnostics.  
Conceptualization, G.-S.J. and J.-W.L.; Data curation, G.-S.J.; Formal analysis, Q.D.V.; Funding acquisition, J.-W.L. and J.-U.L.; Investigation, H.-M.B. and J.-U.L.; Methodology, G.-S.J.; Project administration, J.-W.L. and J.-U.L.; Resources, G.-S.J.; Software, H.-M.B.; Supervision, [PERSON]-W.L. and J.-U.L.; Validation, H.-M.B. and J.-U.L.; Visualization, Q.D.V.; Writing--original draft, H.-M.B. and J.-U.L.; Writing--review and editing, [PERSON]-U.L. and [PERSON]-W.L. All authors have read and agreed to the published version of the manuscript.  
Figure 10: (**a**) Crank shaft angular velocity at 43% (511 rpm) and 46% (499 rpm) load; (**b**) normalized angular velocity from (**a**); (**c**) fast Fourier transform result of (**a**) graph.  
**Funding:** This research was funded by the National Research Foundation of Korea (NRF), grant funded by the Korea government (MIST) (No. NFR-2021R1G1A1008612126 and No. NRF-2021R1F1A 1047115).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 4 Results  
In this section, we conduct experiments to validate the feasibility of simultaneous maize leaf disease classification and quantification using a single deep learning model. Visualizing the CNN class activation heatmaps provides support for the hypothesis that the areas covered by bright pixels represent the areas affected by lesions. Therefore, by extracting the affected areas (ROIs) and expressing them as a ratio of the total leaf area, it is possible to obtain a severity estimate as a percentage.
## 2 Methodology
### Comprehensive Sensing System Calibration
#### 2.3.2 Sensor Calibration  
Ideally, the sensing axes for each three-axis sensor should be parallel with the Cartesian coordinate, but there may be deviations due to package or welding quality. Based on the assumption that the sensing axes of the fresh-out-of-factory sensors are orthogonal, we can define a local sensor frame \(\{S_{i}\}\) to present the orientation of the \(i\)th sensor. Then, sensor orientation calibration for the \(i\)th sensor needs to figure out the rotational matrix \(\mathbf{R}_{i}\) between the local sensor frame \(\{S_{i}\}\) and Cartesian coordinate. Accordingly, the relationship between the on-axis magnetic field distributions \(\mathbf{B}_{c}\) of the coordinate system at sensor locations and that the \(\mathbf{B}_{m}\) value of the local sensor frames satisfies:  
\[\mathbf{B}_{c}=\mathbf{R}_{i}\mathbf{B}_{m}, \tag{8}\]  
\[\mathbf{R}_{i}=\mathbf{B}_{c}\mathbf{B}_{m}^{-1}, \tag{9}\]  
where \(\mathbf{R}_{i}\) is a orthogonal matrix and satisfies \(\mathbf{R}_{i}\mathbf{R}_{i}^{T}=\mathbf{E}\).  
There will be significant errors brought by interferences if we determine the rotational matrix \(\mathbf{R}_{i}\) with the inverse matrix of one-time sensor measurement \(\mathbf{B}_{m}\). Then, a large number of samples are employed for improved calibration accuracy, which posed an overdetermined problem. To solve the overdetermined problem brought from a large number of sampled sensor measurements, LS technology is implemented to find the rotational matrix that minimizes the objective function \(f_{R}\) in Equation (10):  
\[f_{R}(\mathbf{R}_{i})=\sum_{n=1}^{N}\|\mathbf{B}_{c}(n)-\mathbf{R}_{i} \mathbf{B}_{m}(n)\|^{2}, \tag{10}\]
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 4 IberSpeech-RTVE 2022 Submitted Systems
### Text and Speech Alignment Challenge
#### 4.3.1 Alignment of Re-Speaking Generated Subtitles (TaSAC-ST)
It consisted of a primary system based on a fusion of three ASR systems (two of them based on an encoder-decoder transformer architecture: XLS-R Conformer and Whisper large model, and the third one based on an RNN transducer architecture) and a contrastive system based on the best single ASR system (XLS-R Conformer). The audio processing pipeline started with Voice Activity Detection (VAD), based on a simple feed-forward DNN with 2 outputs (speech/non-speech) trained on the whole RTVE 2018 dataset, which produced segmented audio. Then, ASR was performed on this segmented audio and a 1-best hypothesis was obtained. This 1-best hypothesis was aligned with the original transcript, which produced segmented text, that is, the reference text was sequentially assigned to different audio segments. The alignment process involved filtering out words not recognized by ASR; still, if a word was not found in ASR but its surrounding words (two on each side) were found, the word was kept. Finally, forced alignment was performed between the segmented text and the segmented audio, which produced a sequence of aligned words (words with timestamps). Forced alignment was run on each audio segment separately, with 10-ms steps, using a GMM-HMM-based model (Kaldi) trained on RTVE datasets.