esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Mineralogical Composition  
The mineralogical composition of dust was largely dominated by phyllosilicates and tectosilicates, with minor proportions of carbonates and iron oxides (Table 6, Figure 4). Sample M1 had a major proportion of clay minerals (59%), previously corroborated with a finer granulometry (Table 4), and the longest distance travelled (Table 2). In contrast, sample M2 was richer in K-feldspar (13%) and calcite (11%). Both samples contained the same proportion of quartz, and the rest of the mineral phases were almost constant.  
Smectite, palygorskite, illite, kaolinite and mixed-layer clay minerals were identified within the group of clay minerals. Paragonite was not detected in our samples, in contrast to other studies of aeolian dust in the Iberian Peninsula; this mineral is absent in African soils but is typically found in Betic materials from southern Spain [18]. The presence of kaolinite was corroborated by SEM-EDX (Figures 11 and 12). Other mineral phases such asplaygorskite and smectite had previously been detected in Saharan dust [40], corroborating the African provenance.  
The proportion of quartz considerably differed from other studies in the Canary Islands, which in turn reported a minor percentage of clay minerals [19; 37]. Quartz has been described in dust of Saharan origin [41].  
Carbonates and feldspars were found in intermediate proportions. Mineral dust containing a high abundance of carbonates (>10%, calcite > dolomite) is likely to have a North African provenance [42]. According to these authors, plagioclase and K-feldspars are also commonly found in minor proportions in African dust.  
Haematites and gypsum were detected in minor proportions. Haematites are present in low proportions in topsoil of the Sahara Desert and the Sahel region [43]; this mineral species, along with goethite, would be responsible for the yellowish-reddish colour of desert soils [44]. The iron phases might also be involved in atmosprogenesis processes, as will be discussed in Section 4.5. Gypsum has previously been described in soils from the Sahara and Sahel region [45]. This mineral could also be a product of atmospheric neformation due to the attack of atmospheric H\({}_{2}\)SO\({}_{4}\) on some primary minerals, such as smectites and calcite (both detected in our samples) (Table 6 and Figure 4) [17].
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Architecture of AMG-Buffer
By keeping the hot tuples of the victim page in the S-Buffer, we can release the page space in the F-Buffer but keep a high hit ratio. The reason for migrating dirty tuples is to reduce the write operations to disks. In such a case, the S-Buffer can be regarded as an update buffer for dirty tuples. The dirty tuples in the S-Buffer will be written back to disks in a batch (for example, when the S-Buffer is full). The buffer replacement process of AMG-Buffer can be described as follows. When a page in the F-Buffer is replaced, AMG-Buffer checks the clustering rate of the page. If the clustering rate is over a pre-defined threshold, we move the page to the P-Buffer and release the page space in the F-Buffer; otherwise, we move the hot or dirty tuples in the evicted page (in the F-Buffer) to the S-Buffer. The S-Buffer uses a dynamic memory allocation strategy to allocate memory space to tuples. The tuples extracted from the same page are logically grouped as a virtual page named _Sparse Page_, as shown in Figure 1. Moreover, to access the tuples in the S-Buffer, we maintain a page descriptor for each sparse page to store its memory address. Each page in the F-Buffer and the P-Buffer also has a corresponding page descriptor. As Figure 1 shows, we use a mapping table to index all page descriptors. In addition, we use an LRU list to organize all the pages in each sub-buffer. Figure 2 shows the data structure of the page descriptor in AMG-Buffer. The meanings of elements in the page descriptor are as follows. (1) _PageID_ is the unique identifier of a page. (2) _BuffID_ indicates the buffer frame identifier in the F-Buffer. _BuffID_ is only valid when the page is in the F-Buffer. (3) _VisitBitmap_ marks the tuples that have been accessed since the page was read into the F-Buffer. Figure 1: The architecture of AMG-Buffer. (4) _DirtyBitmap_ is used to mark dirty tuples. (5) _DirtyFlag_ is used to indicate whether the page contains dirty tuples. (6) _PageType_ indicates whether the page is in the F-Buffer or the S-Buffer. (7) _SlotArray_ is a set of <SlotID, Addr>, which stores the pointers pointing to the tuples in the S-Buffer. Note that the pages in the P-Buffer can be directly migrated from the F-Buffer by changing the _PageType_ (see Figure 2) and moving the descriptor from the LRU list of the F-Buffer to the LRU list of the P-Buffer. Thus, we need not perform tuple migrations for those pages.
## 2 Materials and Methods
### The Total Content and Essential Oil Composition  
Gas chromatography-mass spectrometry (GC-MS) analysis and gas chromatography with flame-ionization detection (GC-FID) were applied to determine the essential oil profile, quantity and concentration in freshly picked leaves. Fresh leaves of all variants were extracted with hexane. The extraction was carried out in 100 mL flasks with a plant material fresh mass to solvent volume ratio of 1 g per 3 mL, respectively. The flask with the extract was placed in an ultrasonic stirrer Elmasonic S60H (Singen, Germany) (exposure time was 15 min). The samples were shaken gently with added sodium sulfate anhydrous (Na\({}_{2}\)SO\({}_{4}\)) for dehydration [42]. After the extraction process was completed, the solution was centrifuged and filtered through a syringe filter.  
For a qualitative definition a 1 mL volume of the extract was injected into GC-MS Agilent 8890 GC (Santa Clara, CA, USA), equipped with a 30 m 0.25 mm fused silica capillary column Agilent Carbowax (Santa Clara, CA, USA) (i.d. 0.25 \(\upmu\)m). The measurements were carried out according to the established technique [42]. Identification of sample components was performed by comparing their comparison of mass spectra of the electron ionization compounds of the samples with the GC-MS library NIST MS Library Bundle 2020.  
For the quantitative oil determination, fresh leaves of one variant were similarly extracted with hexane, and hexadecane was added as an internal standard for quantitative calculations. The relative percentage of the oil constituents was calculated from the GC peak areas. The identification of compounds was performed by comparison of the NIST Chemistry WebBook. A volume of 1 \(\upmu\)L of the extract was injected into gas chromatograph with a flame ionization detector (FID) Shimadzu (Kyoto, Japan) (model LC-20, software GCSolution v. 2.3 (Kyoto, Japan)). Nitrogen was used as a carrier gas at a constant rate of 1 mL/min. Samples were injected in splitless mode. The injector temperature was 250 \({}^{\circ}\)C and the temperature of the transmission line and the detector was 270 \({}^{\circ}\)C. The temperature gradient increased from 50 \({}^{\circ}\)C to 260 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 2 Modeling and Simulation Methods
### Material Parameters and Boundary Conditions
In light of Equations (4)-(6), the cooling air mass flow \(W_{\text{c}}\), mean temperature \(T_{\text{mean}}\) at the cascade inlet, and inlet total pressure \(P_{\text{in}}^{*}\) are computed by  
\[W_{\text{c}}=W_{\text{eng}}[(1-w)(1+F)+w] \tag{7}\]  
\[T_{\text{mean}}=\frac{C_{\text{g}}W_{\text{eng}}T^{*}_{\text{com}}}{C_{\text{ pg}}W_{\text{c}}l_{\text{sh}}}+T^{*}_{\text{ex}} \tag{8}\]  
\[P^{*}_{\text{in}}=P^{*}_{\text{com}}\cdot\eta_{\text{rs}} \tag{9}\]  
where \(w\) = 1.88% is the percentage of coolant flow; \(F\) = \(W_{\text{fu}}\)/\(W_{\text{eng}}\) is the gas-oil ratio, where \(W_{\text{fu}}\) is the oil mass and \(W_{\text{eng}}\) is the gas mass flow; \(C_{\text{g}}\) is the specific heat capacity of air; \(C_{\text{pg}}\) is the specific heat capacity of hot gas; \(\eta_{\text{sh}}\) = 99% is the mechanical efficiency of aeroengine shaft; \(\eta_{\text{rs}}\) = 0.97 is the total pressure recovery coefficient of combustor [34]; \(T^{*}_{\text{ex}}\) indicates exhaust temperature; and \(P^{*}_{\text{com}}\) expresses the inlet total pressure of compressor. In terms of Equations (5)-(9), the conditions of the NGV simulation are shown in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** \\ \hline Inlet total pressure, \(P_{\text{in}}^{*}\) & 522 KPa \\ Inlet average temperature, \(T_{\text{mean}}\) & 1310 K \\ Outlet mass flow, \(W_{\text{out}}\) & 148.9 g/s \\ Cooling air mass flow, \(W_{\text{ca}}\) & 2.8 g/s \\ Cooling air inlet total temperature, \(T^{*}_{\text{com}}\) & 491 K \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation conditions of the NGV. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Inlet average total temperature \(T^{*}_{\text{eng}}\), K & 290.29 K \\ Exhaust nozzle total temperature \(T^{*}_{\text{ex}}\), K & 917.33 K \\ Inlet static pressure \(P_{\text{eng}}\), KPa & 95.22 KPa \\ Compressor outlet total pressure \(P^{*}_{\text{com}}\), KPa & 538.39 KPa \\ Inlet air flow of the engine \(W_{\text{eng}}\), g/s & 3520 g/s \\ Fuel mass flow \(W_{\text{fu}}\), g/s & 54.48 g/s \\ Mach number of engine inlet \(Ma_{\text{eng}}\) & 0.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Engine performance parameters [26]. All boundaries were set as no-slip walls, and the periodic boundary method was used to predict periodic flow. The influence of gas kinetic energy on heat transfer is considered by the total energy model. The selected turbulence model SST \(\gamma\)-\(\theta\), which is usually used in the simulation of cascades, has a high prediction accuracy [35]. The solution scheme of the model is the second-order backward Euler method [36]. When the residue error of mass flow is less than \(10^{-6}\), the calculation is considered to be convergent. The bottom surface of the NGV was restrained in the \(z\) direction and the top surface was constrained in the \(x\) and \(y\) directions.
## 4 Flight Control Algorithm  
### UAV Dynamic Model  
The 6 DOF motion of a rigid quadrotor is described in Figure 11.  
Figure 10: Tracking of a human target moving at pedestrian speed. (**a**) video streams with no target selected; (**b**) tracking of the selected target with its error pixels displayed on screen; (**c**) autonomously lock on the target in close range using the gimbal system; (**d**) real-time adjustment to the scale changes of the target.  
Let \(m\) denote the mass of the quadrotor and \(J\) the moment of inertia. The external forces and torques which act on the quadrotor platform are primarily caused by propellers and gravity. A local NED frame \(N\) and body-fixed frame \(B\) are introduced to describe the motion of the quadrotor. \({}^{n}p=\left[\begin{array}{cc}{}^{n}p_{x}&{}^{n}p_{y}&{}^{n}p_{z}\end{array} \right]^{T}\) and \({}^{n}v=\left[\begin{array}{cc}{}^{n}v_{x}&{}^{n}v_{y}&{}^{n}v_{z}\end{array} \right]^{T}\) are the position and linear velocity of the quadrotor's mass center relative to \(N\). \(\Theta=\left[\phi\ \theta\ \Psi\right]^{T}\) is the roll/pitch/yaw angles, which represents the orientation of the quadrotor in \(N\). The rotation matrix \(R_{b}^{n}\) from \(B\) to \(N\) is expressed as:  
\[{}^{N}R_{B}=\left[\begin{array}{ccc}\cos\theta\cos\psi&\sin\phi\sin\theta \cos\psi-\cos\phi\sin\psi&\cos\phi\sin\theta\cos\psi+\sin\phi\sin\psi\\ \cos\theta\sin\psi&\sin\phi\sin\theta\sin\psi+\cos\phi\cos\psi&\cos\phi\sin \theta\sin\psi-\sin\phi\cos\psi\\ -\sin\theta&\sin\phi\cos\theta&\cos\phi\cos\theta\end{array}\right] \tag{57}\]  
The equations of motion can be described as:  
\[{}^{n}\dot{p}={}^{n}v \tag{58}\]  
\[{}^{n}\dot{v}=gn_{3}-\frac{f_{b}}{m}R_{b}^{n}n_{3} \tag{59}\]  
\[J\cdot{}^{b}\dot{\omega}=-{}^{b}\omega\times\left(J\cdot{}^{b}\omega\right)+G _{a}+\tau \tag{60}\]  
where \(f_{b}\) is the force applied to the quadrotor given in \(B\), \(\tau\) is the torque, \(g\) is the gravitational acceleration and \({}^{b}\omega\) is the angular velocity of the quadrotor in \(B\). The gyroscopic moment \(G_{a}\) is mainly produced by propellers, which can be neglected. In addition, the translational dynamics shown in Equations (58) and (59) can be simplified as:  
\[{}^{n}\ddot{p}_{x}=-\frac{f_{b}}{m}(\sin\phi\sin\psi+\cos\phi\sin\theta\cos\psi) \tag{61}\]  
\[{}^{n}\ddot{p}_{y}=-\frac{f_{b}}{m}(-\sin\phi\cos\psi+\cos\phi\sin\theta\sin \psi) \tag{62}\]  
\[{}^{n}\ddot{p}_{z}=g-\frac{f_{b}}{m}\cos\phi\cos\theta \tag{63}\]  
Furthermore, it can be assumed that \(\sin\phi\approx\phi\), \(\cos\phi\approx 1\), \(\sin\theta\approx\theta\), and \(\cos\theta\approx 1\) for small angle approximation, which leads to a simplified dynamic model as described in [59].  
Figure 11: The quadrotor with corresponding frames.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### User Impersonation Attack  
In our scheme, if an adversary wants to impersonate the user, he must know the message, \(M_{2}\), which can verify the legitimacy of the user. However, \(M_{2}\) is protected by the user's identity and password, and the adversary cannot verify whether his guessed identity and password are correct or not. Therefore, our scheme can withstand the impersonation attack.
## 4 Conclusions  
The strong correlation between spectral data and the reference values (r\({}_{\text{pred}}\geq\) 0.81) as well as low standard errors found in this work are evidence of the potential of a portable NIR spectrometer to determine tomato quality parameters. The non-invasive analyses performed in approximately 20 s show that a NIR portable spectrometer can be a useful tool to help farmers make fast management decisions (for example, about harvest time). We obtained improved model performances by employing a handheld spectrometer equipped with a MEMS-based interferometer and InGaAs detector arrays that takes advantage of the unique spectral information in the long-wave NIR (LWNIR) region, showing improved performance over other commercial handheld devices using the SWNIR region. Furthermore, the handheld device allowed the simultaneous quantification of six quality parameters in different types of tomato using a PLSR algorithm. Furthermore, we obtained good prediction performance of an independent validation set measured in the field without the need to transport the tomatoes to a laboratory.  
Figure 6: Regression vectors for SSC, round and coma tomatoes (**A**) and grape and cherry (**B**); total acidity for round and Roma tomatoes (**C**) and grape and cherry (**D**).  
**Author Contributions:** K.R.B.: methodology, formal analysis, data curation, and writing-original draft preparation. D.P.A.: data curation, validation, writing-review and editing. M.I.M.: formal analysis and writing-review and editing. M.D.F.: validation and writing--review and editing. L.A.C.: validation and writing--review and editing. L.E.R.-S.: conceptualization, methodology, data curation, validation, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by CAPES institution providing the scholarship, grant number \"88881.187998/2018-01\" and \"CNPq-Research Productivity Fellowship\", grant \"309043/2016-6 and 302866/2017-5 \".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank Lipman Family Farms (Florida, US) for providing tomato samples.  
**Conflicts of Interest:** Authors declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects.
## 3 Image Processing Procedure
### Morphological Operation  
The binary image after thresholding may contain noise and small residual spots which mask the useful information to be interpreted. To eliminate these artefacts, morphological processing can be applied. Dilation and erosion are two basic morphological operations [29]. Dilatations and erosions are usually utilised in pairs to constitute morphological opening and closing. Opening by a disk-structuring element can smooth the boundary, break narrow parts and eliminate small objects. Closing by a disk-structuring element can smooth the boundary, fill narrow bays and eliminate small holes. In view of our purpose in this paper, i.e., smoothing the binary image after thresholding to remove small residual spots, it is appropriate to use an opening operation.
###### Abstract  
Autonomous driving technology and vehicle-to-vehicle communication technology make the hybrid driving of connected and automated vehicles (CAVs) and regular vehicles (RVs) a long-existing phenomenon in the coming future. Among the existing studies, IDM models are mostly used to study the performance of homogeneous traffic flow. To explore the stability of mixed traffic flow, an extended intelligent driver model (IDM) based car-following model was proposed for mixed traffic flow (MTF) with both CAVs and RVs, considering the headway, the speed and acceleration of multiple front vehicles, as well as the response characteristics of RV drivers. Through the linear stability analysis, the criterion for the stability of MTFs was derived, and the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF are discussed. Based on the above theoretical model, a numerical simulation was conducted in two typical scenarios of starting and braking. The results showed that, at the microscopic scale, the vehicle in the Cooperative Adaptive Cruise Control (CACC) mode could significantly decelerate in response to the interference from other vehicles in the same traffic environment. At the macroscopic scale, as the penetration rate of CAVs increased, the overall acceleration fluctuation of the traffic flow decreased. At the same penetration rate of CAVs, the higher density of CAVs coincided with the higher stability of the MTF. When the penetration rate of CAVs was 50%, the degree of distribution had the greatest impact on the MTF. When the penetration rate of CAVs exceeded 70%, the degree of distribution had little impact on the MTF. This research can provide basic theoretical support for the management and control of MTF in the future.  
car-following model; mixed traffic flow (MTF); stability analysis; numerical simulation 2022, 14, 11010. [[https://doi.org/10.3390/su14711010](https://doi.org/10.3390/su14711010) [https://www.mdpi.com/journal/sustainability](https://www.mdpi.com/journal/sustainability)]([https://doi.org/10.3390/su14711010](https://doi.org/10.3390/su14711010) [https://www.mdpi.com/journal/sustainability](https://www.mdpi.com/journal/sustainability))
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 2 Related Work
### Works Based on Balanced Datasets  
[PERSON] et al., focused on designing a model for phishing email detection and classification using biogeography-based optimization with deep learning [24]. The dataset used in this article is sourced from the CLAIR dataset [25], which comprises 3685 phishing emailsand 4894 legitimate emails. The ratio of legitimate emails to phishing emails is 1.3:1. However, it is important to note that the dataset is quite old, dating back to 2008, and therefore may have limited relevance to current trends in email phishing. [PERSON] et al. proposed a three-classifier system based on deep learning to classify emails into legitimate, spam, and phishing categories based on content characteristics in the dataset [26]. The dataset used in the experiment consisted of 2758 legitimate emails and 2432 phishing emails, which is a ratio of almost 1:1.  
[PERSON] et al., proposed a model for classifying phishing emails that combines graph convolutional network (GCN) and natural language processing techniques [27]. The dataset used in the experiment is the fraud dataset [25], which includes 3685 phishing emails and 4894 legitimate emails. Two-thirds of the dataset was used for training. The remaining data are for testing purposes. The accuracy of detecting phishing emails in this dataset is 98.2%, with a false-positive rate of only 0.015.  
[PERSON] et al., proposed a method for classifying phishing emails using a combination of word embedding and machine learning algorithms [28]. They used three datasets for the experiment, one of which was a balanced dataset containing 6295 benign emails and 9135 phishing emails, resulting in an accuracy of 99.5%. [PERSON] et al. [29] proposed a model for detecting phishing emails that utilizes Word2 vec [30] and four machine learning classifiers, which takes into account gain and loss clues present in the emails. The dataset included 19,153 legitimate emails and 17,902 phishing emails. It achieved the highest accuracy for gain (96.52%), loss (96.16%), and a combined accuracy of 95.97%. It is evident that the datasets used in the mentioned work are close to balance or outdated, which is far from the actual scenario.
## 2 Materials and Methods
### Data
#### 2.2.2 Reference Samples  
In this study, the samples were obtained from China Meteorological Science Data Sharing Service (CMSDSS) ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 15 March 2023), which includes crop type labels, longitude, and latitude and provides 196 agricultural weather stations, commonly utilized as a reference map for crop identification [22]. Moreover, the team conducted field surveys in Hebei Province in 2012 and 2014, selecting sample sites for the winter wheat-summer corn class and using a handheld Global Position System (GPS) to determine the latitude and longitude coordinates of the sample. According to the agricultural station, field survey, and visual interpretation, a total of 12,750 sample pixels were obtained from 2003 to 2015 (Figure 2).  
The pixel sample points were randomly divided into three datasets: training, validation, and test, following the ratios of 60%, 20%, and 20% (Table 1). The training datasets were utilized to train the classification algorithms. The optimal hyper-parameters were selected with the validation datasets. The test datasets were used to assess the model's ability to generalize and the performance of the model for unseen data.  
Figure 3: Crop calendars in North China. Blue represents seeding, green represents crop growth and development, and yellow represents maturity. Blank indicates the time of planting for crops that did not fall under any category.