esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 3 Literature Review  
In the field of flight delay prediction, a variety of methods have been employed in the literature. These methods can be categorized into regression models, probability-based models, operational research models, network-based models, and machine learning methods. Regression models have been used extensively, such as a hybrid regression and time series models applied to Frankfurt Airport, explaining 60-69% of variability [14], and a multivariate regression model achieving a 5.3 min precision [15]. Probability-based approaches have been utilized, including a probabilistic model combined with genetic algorithms to estimate delay distributions at Denver International Airport [16], and survival analysis with the Cox model to analyze flight delays probabilistically [17]. Operations research techniques have also played a role, with dynamic optimization models for flight operations [18], optimization of aircraft routes and departure times using mixed-integer programming [19], and multi-objective optimization leading to a 12-31% improvement in flight delays [20]. Network-based models, like the clustered airport modeling approach of optimizing flight operations [21], the minimum-cost flow problem for system design [22], and the investigation of delay propagation among airports with Granger causality [23; 24] are also used. Despite the historical utilization of these methods, their practical application has not been widely adopted. In recent years, there has been a notable shift towards the embrace of machine learning techniques. This transition has been driven by the abundance of big datasets in the aviation industry and the demonstrated success of machine learning in pattern recognition. The earliest documented use of machine learning methods in flight delay prediction from our literature review dates to 2014. Since 2016, research employing machine learning techniques has been on the rise, with an evolving array of methods. Table 1 provides a concise summary of findings from these studies, encompassing information on data sources, datasets used, and selected techniques. Among the methods employed, Random Forest (RF) stands out as the most utilized technique in the papers reviewed. Following closely, Decision Trees (DT), Gradient Boosting (GB), and Artificial Neural Networks (ANN) have been preferred in successive order. In terms of dataset origins, the United States predominantly serves as the source, with select studies encompassing data from Egypt, Iran, Brazil, and various European countries or individual airline companies. While most publications cover information on multiple airports, some studies focus on just one or a few specific airports.
## 2 Materials and Methods
### Description of Data Analysis  
The analysis of SIF, Tr, and environmental factors in this research is based on data collected daily from 7:00 to 17:30 during the period from 2021 to 2022. We assume that Tr predominantly dominates ET during the observation period. To mitigate the effects of soil evaporation and interception evaporation, we selected data from June to September, when the leaves are fully expanded, the canopy structure is stable, and changes in LAI are relatively small. Additionally, we excluded data from the days of rainfall and the following day.
## 3 Data Analysis and Results
### The Kos Aftershock Sequence  
An earthquake with magnitude \(M_{w}\) = 6.5 at a depth of 7.1 km, which had a normal faulting mechanism striking about east-west (Figure 1), happened on 20 July 2017 in Gokova Bay, in the Aegean Sea, at 22:31:10 GMT between Bodrum town, Turkey, and Kos Island, Greece. As stated in the data, the mainshock epicenter was given as 27.41\({}^{\circ}\) E and 36.97\({}^{\circ}\) N located 12 km ENE to Kos in Greece and 8 km SE to Bodrum in Mugla in Turkey. The earthquake generated a tsunami that affected the coast of the Bodrum peninsula and the northeast coast of Kos. A tide gauge in Bodrum, close to the earthquake's epicenter, recorded the tsunami [37].  
The data were obtained from the Boun Koeri Regional Earthquake-Tsunami Monitoring Center, Kandilli Observatory and Earthquake Research Institute (RETMC) (the Turkish Disaster and Emergency Management Presidency, AFAD; Bogazici University (KOERI), [[http://www.koeri.boun.edu.tr/](http://www.koeri.boun.edu.tr/)]([http://www.koeri.boun.edu.tr/](http://www.koeri.boun.edu.tr/)), accessed on 27 September 2022). This study's goal is to give a thorough region-time analysis with a variety of aftershock attributes such as the parameter \(q\) by Tsallis for 6492 aftershocks identified in six months after the mainshock.  
In terms of Tsallis Entropy the value of \(q\) is equal to \(q\) = 1.63 (Figure 8a). Following, the transition estimated to be at \(T_{c}\approx\) 15,005 s (Figure 8b). The parameter \(T_{c}\) (red dashed line in Figure 7c) shows that the NESP describes the early part of the aftershocks while as time goes on, BG statistics are revealed.  
Figure 7: (a) The interevent times CDF of the 2018 \(M_{w}\)6.6 Zakynthos Earthquake, for the events with \(M\) > \(M_{c}\). The scarlet stroke is the \(Q\)-exponential fitting with \(q\) = 1.68. The change of colors indicates the crossover between the NESP (blue circles) and BG statistics (green triangles). (b) The \(ln_{Q}\)(\(>\)7) as a function of \(T\), where the red line is the fitting with \(q\) = 1.68 and correlation coefficient 0.9966 up to \(T_{c}\). \(T_{c}\) value close to 5607 s is suggested by the deviation from linearity. (c) Interevent time \(T\) evolution with time \(t\) since the main shock. The scarlet line illustrates the \(T_{c}\) value.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Localization Result Fusion  
\(\hat{\mathbf{u}}_{a}\) and \(\hat{\mathbf{u}}_{t}\) are asymptotically unbiased estimates which, respectively, obey the following distribution:  
\[\hat{\mathbf{u}}_{a}\sim\mathbb{N}(\mathbf{u},\mathbf{cov}(\hat{\mathbf{u}}_{a})),\hat{\mathbf{u}} _{t}\sim\mathbb{N}(\mathbf{u},\mathbf{cov}(\hat{\mathbf{u}}_{t})) \tag{84}\]  
Based on Kalman filtering [23], the DOA-based localization results and TDOA-based localization results can be fused to obtain  
\[\hat{\mathbf{u}}=\hat{\mathbf{u}}_{a}+K(\hat{\mathbf{u}}_{a}-\hat{\mathbf{u}}_{t}) \tag{85}\]  
where \(K\) denotes the Kalman gain, which is defined as [24; 25]  
\[K=\frac{trace(\mathbf{cov}(\hat{\mathbf{u}}_{a}))}{trace(\mathbf{cov}(\hat{\mathbf{u}}_{a}))+ trace(\mathbf{cov}(\hat{\mathbf{u}}_{t}))} \tag{86}\]  
The details of the proposed method are given in Figure 3. The computational complexity of the proposed method can be obtained by combining Tables 3 and 5, although  
\begin{table}
\begin{tabular}{l} \hline
**Step 1:** Set \(k=0\) and choose appropriate \(\delta_{t}>0\) and \(\kappa_{t}\). Let \(\mathbf{W}_{t}=\mathbf{Q}_{t}^{-1}\) to obtain the initial result; **Step 2:** Set \(k=k+1\). Substituting \(\hat{\mathbf{q}}_{t}^{k-1}\) and \(\hat{\mathbf{u}}_{t}^{k-1}\) into (73) and (75) yields \(\mathbf{W}_{t}^{k-1}\), \(\mathbf{D}_{t}^{k-1}\), and \(\hat{\mathbf{u}}_{t}^{k-1}\), respectively; **Step 3:** Calculate \(\hat{\mathbf{q}}_{t}^{k}\) based on (77) and update the iteration result by (78); **Step 4:** If \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\ \end{tabular}
\end{table}
Table 4: Summary of satellite-based localization.  
publicly available short-wave dataset has been found. The method proposed in this paper can not be tested on public datasets. Therefore, this paper uses simulation to verify the performance of the proposed method._  
Assuming that the noise between each parameter is uncorrelated, the covariance matrix can be set as \(\mathbf{Q}_{\mathbf{\theta}}=\sigma_{\mathbf{\theta}}^{2}\mathbf{I}_{N_{1}}\), \(\mathbf{Q}_{\mathbf{\varphi}}=\sigma_{\mathbf{\theta}}^{2}\mathbf{I}_{N_{1}}\), \(\mathbf{Q}_{\mathbf{h}}=\sigma_{\mathbf{h}}^{2}\mathbf{I}_{N_{1}}\), \(\mathbf{Q}_{\mathbf{s}}=\sigma_{\mathbf{s}}^{2}\mathbf{I}_{3N_{2}}\), \(\mathbf{Q}_{\mathbf{r}}=\sigma_{\mathbf{r}}^{2}\mathbf{R}_{N_{2}-1}\), where \(\mathbf{R}_{N_{2}-1}\) denotes the \((N_{2}-1)\times(N_{2}-1)\) matrix with a diagonal element of 1 and the rest of the elements are 0.5.  
The root mean square error (RMSE) is used as the localization accuracy criterion and is defined as follows:  
\[RMSE(\mathbf{\hat{u}}_{a})=\sqrt{\frac{\sum_{j=1}^{M}||\mathbf{\hat{u}}_{a}-\mathbf{u}||_{2 }^{2}}{M}},RMSE(\mathbf{\hat{u}}_{t})=\sqrt{\frac{\sum_{j=1}^{M}||\mathbf{\hat{u}}_{t}- \mathbf{u}||_{2}^{2}}{M}} \tag{87}\]  
where \(M\) denotes the number of Monte Carlo experiments. Set \(\delta_{a}=\delta_{t}=10^{-6}\), \(x_{a}=0.9^{k}\), and \(x_{t}=0.9^{k}\), where \(k\) denotes the number of iterations. The maximum number of iterations is set to 50.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 4 Evaluation
### Computational Cost  
Tables 2 and 3 show an upper bound of the number of CPU cycles needed by COMPASS to execute each line of Algorithms 1 and 2. These algorithms are executed by COMPASS to derive the ICS and adapt the communication parameters, respectively. The Repetitions column represents how many times each algorithm line needs to be executed (many are inside for loops). It is an upper bound because some lines are only executed when a certain condition is satisfied (If-Else sentences). The number of CPU cycles has been computed considering Intel CPU architectures [30]. For example, the addition or subtraction require 1 CPU cycle, the multiplication of two floating point numbers requires 5 CPU cycles, and their division requires 39 cycles. Using these tables, we can estimate an upper bound of the total number of CPU cycles needed to run COMPASS. Figure 11 plots the upper bound of the CPU time needed to run COMPASS for different CPU speeds and a varying number of neighboring vehicles. The results shown consider \(C=3\) communication parameters and \(P=3\) interacting protocols for varying number of neighboring vehicles, \(N\). The parameters considered represent realistic scenarios. For these scenarios, Figure 11 shows that the time needed to execute COMPASS is below 3 us. These results provide clear evidences that COMPASS has a low computational cost, and it is hence feasible to implement it in real systems without introducing significant delays.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm Line** & **CPU Cycles** & **Repetitions** \\ \hline
1 & 1 & 2\(\cdot\)C \\
2 & 1 & 1 \\
3 & 1 & \(P\) \\
4 & 1 & _P_-C \\
5 & 1 & _P_-C \\
6 & 1 & _P_-C \\
8 & 1 & _P_-C \\
9 & 1 & _P_-C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational cost of Algorithm 1.  
Figure 10: Time evolution of the percentage of vehicles with a required minimum transmission power established by the awareness control protocol MINT. The results are obtained with COMPASS coordinating the operation of A-DCC, MINT and TOWER. The results are depicted for different traffic densities and two possible intervals of the communication range (CR) requirements: between 50–200 m and between 10–50 m.
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 4 Numerical Analysis Methods
### K-Means Clustering for Placement of Hovering Points  
K-means clustering is known as a non-hierarchical cluster analysis method, classifying multiple elements into \(k\) clusters. K-means has been adopted by related studies to UAV communication networks, such as [36], where it is used to solve the placement problem of multiple UAV base stations. In this article, K-means is considered feasible to solve the placement problem of the UAV hovering points.  
We now apply K-means to the UE distribution shown in Figure 4; the UE clustering by K-means with a varying number of clusters \(k\) is shown in Figure 7. The dotted circles represent the required coverage area to cover all UEs belonging to each cluster, while the locations of the hovering points are assumed as the center points of the clusters. However, it is suggested from [36] that the center points of the clusters are not the optimal locations for the hovering points. As shown in Figure 7, there are empty areas with no UE covered in the coverage areas. To enhance the transmission efficiency, a geometric method, the minimum inclusion circle, was introduced by [36], but it only works when the UEs are all on the same plane.  
In this article, we proposed searching for the optimal location of hovering points through numerical computation. The hill climbing algorithm (HC), a simple local search method, is adopted. Minimum SNRs between the UAV and the UEs from each candidate point are calculated. And then the location of the hovering points is updated to the new location with the largest minimum SNR, until the minimum SNR does not change any more. However, the K-means clustering has 2 critical problems:  
* For realistic problems, the number of clusters \(k\) is often difficult to determine in advance.
* Conditions such as the limitation of the UAV's flight altitude, which is 150 m according to the law in Japan, are hard to include. As shown in Figure 7, the flight altitude is too high when \(k\) is small.  
Therefore, we proposed a sequential K-means clustering method to search for the minimum \(k\). If the result of the UE clustering satisfies the condition of the UAV's flight altitude, the present \(k\) will be adopted. Otherwise, the operation will continue by increasing \(k\).
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### High-Resolution Melting Analysis (HRMA)  
HRMA is a simple, rapid, and inexpensive tool useful in the identification of a broad range of clinically relevant _Candida_ species. It is combined with RT-PCR, providing an alternative for directly analyzing genetic variations [94]. [PERSON] and colleagues (2014) used HRMA using the _ITS_ region of rDNA to classify relevant _Candida_ spp. from oral samples [46]. The authors identified all species in their list and four different genotypes of _C. albicans_[46]. Another author suggests real-time PCR followed by HRMA directly in the biological samples as an efficient method that takes only 6 h to result [54]. HRMA followed by RT-PCR was more rapid and efficient than the classic biochemical methods used in the study [95].  
HRMA has some advantages over other genotyping methods owing to the inexpensive single-step procedure, reducing the risk of contamination when compared to a multistep procedure (such as RFLP or nested PCR). Despite this, the technique does not distinguish between some _Candida_ spp. due to Tm (Melting Temperature) ranges overlapping [54].
## 2 Materials and Methods
### Setting, Virtual Reality, and Eye-Tracking Apparatus  
The experiment took place in a sound-proofed room of the Laboratory of Cognitive Science and Immersive Virtual Reality (CS-IVR; Dept. of Psychology, University of Campania L. Vanvitelli Caserta, Italy). The workstation comprised a computer with a large screen (23.8\({}^{\prime\prime}\)), a comfortable chair to simulate the driver's seat and the IVR equipment. This last included the HTC Vive (HTC, Corporation, Taipei, Taiwan) head-mounted display (HMD) having two OLED display panels with a resolution of 1080 \(\times\) 1200 per eye, with a refresh rate of 120 Hz and a 110-degree field of view and integrated with the Tobii Eye-tracking Retrofit system ([[https://www.tobii.com/](https://www.tobii.com/)]([https://www.tobii.com/](https://www.tobii.com/)), Tobii Technology, Stockholm, Sweden, accessed on 3 January 2023). The IVR system continuously tracked participants' head orientation through two cameras connected to the HMD. Visual information was updated in real-time.
## 2 System Architecture and Assumptions
### Receiving Terminal  
We started by considering the Micius receiving characteristics. We, thus, started by considering a receiving aperture diameter of 1 m, with a focal length of 10 m and an obscuration ratio of 0.1, which was located at an 850 m altitude (as was the OGS in Xinglong). Moving forward, our study will focus on employing telescopes with diverse design parameters (such as aperture diameter, obscuration ratio, focal length, etc.) as receivers by varying the parameters crucial for establishing communication in the photon starve regime. Fiber coupling will also be considered by modeling the coupling efficiency in terms of Strehl ratio metrics.  
The choice between Superconducting Nanowire Single-Photon Detectors (SNSPDs) and SPADs for signal detection is pivotal given their distinct operational characteristics. Focusing our attention on 1550 nm, we explored the potential of establishing robust quantum communication links by employing both SNSPDs and SPADs for signal detection. SNSPDs are acknowledged for their superior quantum efficiency across a broad wavelength range, albeit with higher complexity and cost due to the necessitated cryogenic cooling. Conversely, SPADs present a more cost-effective solution, albeit with certain limitations in detection efficiency and timing jitter.
# Detection of Household Furniture Storage Space in Depth Images
## 3 Methods
### Empty Storage Volume Detection Algorithm
#### 3.1.3 Empty Storage Volume Candidate Pruning  
Depending on the resolution of the rasterization of the mesh model to the voxels and the smoothness of the CAD models, some irregularities may appear in the voxelized model. Uneven furniture surfaces resulting from the voxelization process seemingly divide the free space into several segments. That leads to oversegmentation of the actual free space core into multiple connected components, referred to as _free space core segments_, ultimately resulting in multiple empty storage volumes instead of one.  
To avoid this, the algorithm performs pruning of the empty storage volume candidates. Free space core segments representing the same empty space result in multiple storage volume candidates located above the same supporting surface. However, due to the rasterization problem described above, it is possible that these storage volumes have height differences of one voxel. Therefore, the algorithm analyzes sets of bounding boxes that have the supporting surface at the same height with a deviation of one voxel allowed, referred to as _neighboring height_. The storage volume candidates grouped by the neighboring height are then compared in pairs. If one of the storage volume candidates is completely contained within another, the algorithm prunes the inner candidate.  
```
Input: free space core Output: set of storage volume candidates \(C\) \(\leftarrow\) empty set for each free spaces cores do \(C^{\prime}\)\(\leftarrow\) empty set for each axis do for each FSC layer do ifFSClayer has only one connected component then ifFSClayer is not rectangularly shaped then substitute FSC layer by its bounding box endif  compute storage volume candidate by dilating FSC layer by \(\delta\) if minimum size of storage volume candidate \(\geq\)\(d_{bb}\)then  detect storage surface  adjust storage volume candidate to storage surface if this is the largest storage volume candidate for the current axis then  add storage volume candidate to \(C^{\prime}\) endif endif endif endfor endfor  add the largest storage volume candidate contained in \(C^{\prime}\) to set C endfor
```  
**Algorithm 2** An algorithm for detection of usable empty storage volumes in the occupancy grid.  
Figure 4: Detected storage surface (green voxels) of a FSC from Figure 3 and its corresponding storage volume (orange bounding box): (**a**) Front view, (**b**) Side view.
## 3 Results and Discussion
### Proximate Composition
#### 3.1.2 Proteins  
In comparison with two remaining _Stevia_ sources, the dried leaves of _S. rehudiana_ originating from Polish cultivation contained significantly (\(p\leq 0.05\)) less protein (11.2 g/100 g DM). However, in the material derived from Paraguay and Brazilian cultivation, the content of this component did not differ significantly and amounted to 17.1 and 15.5 g/100 g DM, respectively (Table 1).  
Protein contents in dried leaves of S. _rehudiana_ reported by other authors were 11.4, 11.2, 10.0, 11.2, and 9.8 g/100 g DM [6; 7; 45; 46; 48]. In view of this, only the value determined  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ingredient**} & \multicolumn{3}{c}{_Stevia rehudiana_} \\ \cline{2-4}  & Polish & Paraguayan & Brazilian \\ \hline Dry matter (g/100 g) & 99.6 \(\pm\) 0.06 a  & 99.4 \(\pm\) 0.27 a  & 99.3 \(\pm\) 0.05 a  \\ Protein (g/100 g DM) & 11.2 \(\pm\) 0.97 a  & 17.1 \(\pm\) 1.85 b  & 15.5 \(\pm\) 0.51 b  \\ Fat (g/100 g DM) & 3.33 \(\pm\) 0.10 b  & 1.73 \(\pm\) 0.01 a  & 4.90 \(\pm\) 0.32 c  \\ Digestible carbohydrates (g/100 g DM) & 61.7 \(\pm\) 0.67 b  & 60.4 \(\pm\) 1.52 b  & 55.0 \(\pm\) 0.39 a  \\ Dietary fiber (g/100 g DM) & 15.7 \(\pm\) 0.18 c  & 12.3 \(\pm\) 0.3 a  & 14.1 \(\pm\) 0.22 b  \\ Total ash (g/100 g DM) & 7.65 \(\pm\) 0.40 a  & 7.85 \(\pm\) 0.03 b  & 9.78 \(\pm\) 0.05 c  \\ \hline \hline \end{tabular}  
* b and c indicate statistically significant differences (\(p\leq 0.05\)); data are shown as the mean \(\pm\) SD (standard deviation).  
\end{table}
Table 1: Proximate composition of _Stevia rehudiana_ Bertoni dried leaves depending on the country of origin.  
in the dried _Stevia_ leaves from Poland was within the range of the values reported by these authors. In turn, the highest protein content, amounting to 20.4 g/100 g DM, was found in this plant grown in India [49]. Taking into account the upper threshold cited above, the dry matter contents determined in dried _Stevia_ leaves from Paraguay and Brazil were within the broad limits identified by the aforementioned authors.  
According to [PERSON] et al. [47], protein content depended on the harvest date and was 12.0 g/100 g DM in the plants harvested in summer season and 12.9 g/100 g DM in those from the autumn period. Our study revealed that the results obtained for the dried _Stevia_ leaves from foreign crops were higher than those cited above. These differences may depend primarily on growing conditions (climate, soil composition, fertilization), storage conditions and the drying method used [5].  
Additionally, in some scientific papers the amino acids in _Stevia_ leaves were determined. [PERSON] et al. [50] isolated nine amino acids from _Stevia_ leaves, including glutamic acid, aspartic acid, lysine, serine, isoleucine, alanine, proline, tyrosine and methionine. In turn, [PERSON] et al. [6] supplemented these data, in which seventeen amino acids, both exo- and endogenous, were identified.