esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS.  
L-band digital aeronautical communication system; cyclostationary characteristics; direction of arrival; interference plus noise covariance matrix; beamforming +
Footnote †: journal: Electronics Electronics  
**Citation:** [PERSON]; [PERSON]; [PERSON] Advanced Interference Mitigation Method Based on Joint Direction of Arrival Estimation and Adaptive Beamforming for L-Band Digital Aeronautical Communication System  
**Electronics 2024**, _13_, 1600. [[https://doi.org/10.3390/electronics13081600](https://doi.org/10.3390/electronics13081600)]([https://doi.org/10.3390/electronics13081600](https://doi.org/10.3390/electronics13081600))  
Academic Editor: [PERSON]  
Received: 13 March 2024  
Received: 18 April 2024  
Accepted: 19 April 2024  
Published: 22 April 2024
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### Envision  
Envision excels in modelling dynamic community interactions and defining agents and their behaviours [72], which is crucial for understanding community dynamics in adaptation planning. This tool supports agent learning and adaptation, allowing the modelling of adaptive strategies based on experiences or changing conditions. Envision integrates socio-economic variables, enabling the economic impact modelling of adaptation strategies. It considers demographic factors through policies, guiding agent decision-making [73]. The tool handles diverse data types, including climate, geographical, and socio-economic data, enhancing model comprehensiveness and accuracy [73]. Envision is scalable for different community sizes, addressing challenges in both larger and smaller contexts. It assesses and models uncertainties and risks associated with adaptation strategies, supporting scenario testing and sensitivity analysis for stakeholder decision-making. The tool incorporates feedback loops to represent the long-term impact of adaptation measures, enabling the monitoring and evaluation of strategy effectiveness over time [72; 73]. In conclusion, Envision is a potent tool for community-based adaptation modelling, offering dynamic interaction modelling, learning processes, socio-economic integration, demographic considerations, data handling, scalability, risk assessment, sensitivity analysis, scenario testing, and feedback mechanisms. It serves as a versatile platform for integrated planning and environmental assessments in community-based adaptation.
## 3 Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization
### DTG-LFA Algorithm
Therefore, selecting a resource block with a small \(F_{E}\) value can make the occupied resources on the candidate path more continuous in the time slot, which reduces resource fragmentation and makes idle resources more adjacent. Taking Figure 6 as an example, we elaborate on the specific methods for calculating the LFA of the candidate resource block in different situations. The two-dimensional grid in the figure represents the time slot resources of all links along the alternative path, assuming that the link of the selected path is \((link1,link2,link3,link4)\). The total number of each small grid represents this link's total number of time slots, in which the color grid represents the occupied time slot resources. The gray grid represents the time slot resources allocated by the service request \(r\). According to the value of \(t_{abr}\), the calculation method of \(F_{E}\) can be divided into four cases. As shown in (a) and (c), when the selected candidate resource block does not include the first and last time slots, the value of \(F_{E}\) is 20 and 10, respectively. As shown in (b) and (d), when the selected candidate resource block includes first and last time slots, the value of \(F_{E}\) is 15 and 14, respectively. We select the resource block with the lowest \(F_{E}\) value to allocate resources to the request. According to the above analysis, the DTG-LFA algorithm is put forward to lower the blocking probability of requests for optical satellite networks further and improve resource utilization. The specific description is shown in Algorithm 2. Figure 6: LFA algorithm scheme. (**a**) Candidate resource block is located in the middle and near the first time slot. (**b**) Candidate resource block includes first time slots. (**c**) Candidate resource block is located in the middle and near the last time slot. (**d**) Candidate resource block includes last time slots. ```
for\(reR\)do  Update the resource matrix and calculate the path set \(P\) for \(r\). for\(P_{i,r}\epsilon P\)do  Select \(B_{can}\) based on Equation (8). Set \(init\_F_{E}=inf\) for each \(B_{can}\)do  Save the beginning time slot \(t_{abr}\). Pre-allocation range \([t_{abr},t_{abr}+t_{sr}-1]\). Calculate the value of \(pre\_F_{E}\) using Equations (9)-(12). if\(pre\_F_{E}<init\_F_{E}\)then \(init\_F_{E}=pre\_F_{E}\). Save \(t_{abr}\). endif endfor endfor if\(init\_F_{E}<inf\)then  Allocate resources on the link to request \(r\) and the network resource matrix based on the saved \(t_{abr}\) is updated. else  Reject request \(r\). endif endfor
```  
**Algorithm 2** DTG-LFA algorithm
## 3 Results
### Identification and Analysis of the DEGs  
Based on the determination of physiological composition, petals in full bloom were selected for RNA-seq analysis of differentially expressed genes (DEGs) in the two _Dendrobium_ species. In three independent experiments, 24,199 genes were matched to the _Dendrobium_ genome, of which 16,735 were expressed. Of these expressed genes, 841 genes were unique to _D._ 'H1', 1912 genes were unique to _D. moniliforme_, and 13,982 genes were found in both species (Figure 3A). DESeqR and Q-value were applied to determine if the genes in _D._ 'H1' and _D. moniliforme_ species were significantly different based on 16,735 expressed genes with a 1% false discovery rate (FDR). According to fold change (FC) > 1 and \(p\) < 0.01, 5790 differentially expressed genes (DEGs) showed dynamic changes between _D._ \"H1\" and _D. moniliforme_. If compared with _D._ \"H1\", 3210 DEGs were up-regulated, and 2580 DEGs were down-regulated in _D. moniliforme_ (Figure 3B).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cultivar and Flower Part** & **Compound Name** & **Variety** & **Relative Content (\%)** \\ \hline _D. moniliforme_ Petals & Terpene & 15 & 77.25 \\  & Aromatic & 2 & 2.12 \\  & Fatty acids & 2 & 9.99 \\  & Nitrogenous compound & 6 & 4.94 \\  & Ketone & 0 & 0 \\  & Alcohol & 1 & 1.94 \\  & Alkanes & 0 & 0 \\  & Total & 26 & 96.24 \\ \hline _D. moniliforme_ Gynandrium & Terpene & 7 & 81.75 \\  & Aromatic & 3 & 5.8 \\  & Fatty acids & 0 & 0 \\  & Nitrogenous compound & 4 & 3.03 \\  & Ketone & 3 & 6.19 \\  & Alcohol & 0 & 0 \\  & Alkanes & 0 & 0 \\  & Total & 17 & 96.77 \\ \hline _D._ H1 Petals & Terpene & 13 & 50.38 \\  & Aromatic & 7 & 18.16 \\  & Fatty acids & 4 & 11.24 \\  & Nitrogenous compound & 7 & 13.1 \\  & Ketone & 1 & 1.5 \\  & Alcohol & 1 & 0.58 \\  & Alkanes & 3 & 2.88 \\  & Total & 36 & 97.84 \\ \hline _D._ 'H1' Gynandrium & Terpene & 7 & 63.31 \\  & Aromatic & 4 & 18.12 \\  & Fatty acids & 1 & 3.36 \\  & Nitrogenous compound & 4 & 7.58 \\  & Ketone & 1 & 1.29 \\  & Alcohol & 1 & 1.67 \\  & Alkanes & 1 & 2.21 \\  & Total & 19 & 97.54 \\ \hline \end{tabular}
\end{table}
Table 1: Classification of scent compositions of different fluorescence in two flower parts of two _Dendobium_ species.  
Using the pHEATMAP package in R, we performed hierarchical clustering to show a comprehensive overview of the two species (Figure 3C). These results indicated that the three replicates of each sample from each species belong to the same respective clades, demonstrating the reproducibility and reliability of our RNA-Seq data.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Detection Learning Modes  
Anomaly detection learning modes can be classified into three types based on the availability of labels in the datasets used to create baselines [48], including supervised, semi-supervised, and unsupervised anomaly detection, as shown in Figure 6.
# Study on the Ozone Gas Sensing Properties of rf-Sputtered Al-Doped NiO Films
## 2 Materials and Methods
### Gas Sensing  
In order to investigate the gas sensing performance in ozone, the NiO:Al films deposited on IDTs were placed in a homemade stainless steel test chamber. A mechanical pump was used to initially evacuate the chamber, while an FP-400 temperature controller was employed to regulate the operating temperature. Ozone of different concentrations was produced by an ozone analyzer (Thermo, Model 49i, Thermofisher scientific, Waltham, MA, USA). A Keithley 6517A electrometer was used to apply a constant voltage on the films and measure the electrical current variations upon the interaction between the film and the target gas. The chamber was initially evacuated at a pressure of 1 mbar. Prior to the ozone exposure, the films were treated with synthetic (dry) air for 45 min in order to be stabilized in the testing environment. After that, the films were exposed to ozone of specific concentrations for a constant time duration in which the electrical current was increased, while the recovery of the films in its initial state was done by exposing them in synthetic air for a specific time period. Both ozone and synthetic air were introduced in the chamber with a flow of 200 sccm (standard cubic centimeters per minute) regulated by mass flow controllers, while the pressure in the chamber was kept constant of 700 mbar. The whole procedure was monitored by a PC through an appropriate LabVIEW program. The sensitivity (S) of the sensor is defined by the following Equation (4)  
\[\rm{S(\%)=[(I_{gas}-I_{air})/I_{air}]\cdot 100\%} \tag{4}\]  
where \(\rm{I_{gas}}\) is the maximum value of electrical current in the presence of ozone, while \(\rm{I_{air}}\) is the minimum value of electrical current in the presence of synthetic air (absence of ozone), as depicted in Figure 1. Moreover, the response time (\(\rm{t_{resp}}\)) is defined as the time that is needed for the electrical current to become equal to the 90% of the value of \(\rm{I_{gas}}\) in the presence of ozone, while the recovery time (\(\rm{t_{rec}}\)) is defined as the required time for the electrical current to be equal to 10% of the value of \(\rm{I_{gas}}\) in the absence of ozone (in the presence of synthetic air).  
All measurements were taken under the room relative humidity (RH) which was about 60%.  
Figure 1: Typical sensing measurement of an O\({}_{3}\)-synthetic air cycle.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 2 Materials and Methods
### Hyperspectral Imaging and Data Analysis
#### 2.3.3 Hyperspectral Data Analysis and Waveband Selection  
The tomato leaf ROI was \(15\times 15\) pixels (225 pixels) and it was selected from the tip of each terminal leaflet. For each sample, the spectral data were calculated using the average ROI of the spectral data. The relative reflectance (Figure 4) and the standardized spectrum of the tomato leaf were calculated for each leaf sample by using Formulas (3) and (4), respectively [10].  
\[R=\frac{r_{leaf}}{r_{refer}}, \tag{3}\]  
\[N_{i}=\log_{10}(\frac{1}{R}). \tag{4}\]  
where  
\(r_{leaf}\): Spectrum of the tomato leaf sample, raw spectral data;  
\(r_{refer}\): Spectrum of the standard reflector;  
R : Relative reflectance of the standard reflector;  
\(N_{i}\) : Standardized spectrum of the tomato leaf (\(i=1\)-\(81\) wavelength bands).  
Figure 4: Relative reflectance of the tomato leaf samples.  
Figure 3: Hyperspectral image processing and data analysis. (**a**) Extraction of the leaf image (R: 1000 nm, G: 1430 nm, B: 1530 nm). (**b**) Leaf area calculation.  
The first-order derivative of spectral reflectance (DR) and second-order derivative of spectral reflectance (SR), based on the raw reflectance, were calculated using Formulas (5) and (6) with the Norris gap method [24].  
\[\rho^{\prime}(\lambda_{i})=\frac{\rho(\lambda_{i+1})-\rho(\lambda_{i-1})}{2 \Delta\lambda}, \tag{5}\]  
\[\rho^{\prime\prime}(\lambda_{i})=\frac{\rho(\lambda_{i+1})-2\rho(\lambda_{i})+ \rho(\lambda_{i-1})}{\Delta\lambda^{2}}. \tag{6}\]  
where \(\rho\) is the spectral reflectance, \(\lambda_{i}\) denotes the wavelength of each band, and \(\Delta\lambda\) represents the interval from \(\lambda_{i-1}\) to \(\lambda_{i}\).  
Partial least squares regression analysis was employed to assess the tomato leaf water status (WC and MC) by using all wavelengths from 900 nm to 1700 nm. For this purpose, Unscrambler X software (CAMO Software Inc., Oslo, Norway) was used because it is effective for multivariate analysis. Kernel partial least squares and cross-validation were employed in the regression analysis. NDVI and TBI were also adopted to assess the tomato leaf water status by using Formulas (7) and (8), respectively.  
\[NDVI=\frac{\lambda_{1}-\lambda_{2}}{\lambda_{1}+\lambda_{2}}, \tag{7}\]  
\[TBI=\frac{\lambda_{1}+\lambda_{2}}{\lambda_{1}-\lambda_{2}}. \tag{8}\]  
where \(\lambda_{1}\) is the wavelength of Band 1, from 900 nm to 1700 nm, and \(\lambda_{2}\) denotes the wavelength of Band 2, from 900 nm to 1700 nm. In the wavelength range from 900 nm to 1700 nm, 81 bands were identified by the 10 nm spectral resolution. All 6561 combinations of \(\lambda_{1}\) with \(\lambda_{2}\) were calculated for NDVI and TBI modeling by using MATLAB. Autocorrelation analysis was employed to improve the model accuracy and ensure model stability.
## 3 Results and Discussion  
In our experiments, low-pH buffers and SDS were used. The magnitude of the EOF was determined by the surface charge on the capillary wall, and a small EOF was observed at low pH owing to the suppressed dissociation of silanol groups. Owing to the suppressed EOF, negatively charged SDS micelles migrated toward the anode. Under these conditions, analytes strongly partitioning into the micelles (lipophilic compounds) migrated to the detector more rapidly compared to the less partitioned compounds (hydrophilic solutes). Scheme 1 shows the illustration of the experiment. Three experimental parameters that affect the separation efficiency of analytes include the phosphate buffer concentration and pH and SDS concentration, respectively. These three factors were utilized to improve the MEKC separation efficiency.
## 3 Improvement of the Retinex Algorithm
### Improvement of the Luminance Component V
#### 3.2.3 Detail Enhancement  
After the Retinex transform is applied to the underground mine image, there may still be some halo artefacts in the image, and some areas are overexposed, resulting in loss of image detail; therefore, subsequent detail enhancement of the image is required to help achieve effective image enhancement.  
The reflective component of the image contains the high-frequency details of the image, also known as the detail layer of the image, and can be seen as the difference between the output and input image. Adding detail enhancement can be an excellent way to improve the halo phenomenon in the image while adding more detail to the resulting image.  
A modified Retinex algorithm was used to process the luminance component V and gamma-correct the incident component, followed by a histogram equalisation correction for its reflected component [24]. The histogram equalisation function increases the global contrast of the image when contrast stretching is applied to the reflectance image, especially when the user data in the image are of similar contrast. The method allows for a better distribution of luminance across the histogram and an increase in local contrast.  
The Laplace operator was used for image detail enhancement [25]. The Laplace enhancement operator can preserve the greyscale differences between pixels while preserving the background of the image, enhance the contrast at abrupt greyscale changes and highlight the edge detail information contained in the image. Therefore, the Laplace enhancement operator was used to enhance the edge details in the image. The Laplace enhancement operator is the most straightforward isotropic differential operator with rotational invariance. Its enhancement principle is shown in Equation (15):  
\[g(x,y)=f(x,y)+c[\
abla^{2}f(x,y)] \tag{15}\]  
where \(g(x,y)\) is the output image, \(f(x,y)\) is the original image, and \(c\) is the transform enhancement factor.  
The continuous Laplace operator cannot be used directly for image enhancement and requires a template for implementation. The principle of image edge enhancement is to convolve the Laplace enhancement operator template with the original luminance image. The result of the convolution operation is the edge image obtained by subtracting the result of the convolution operation from the original luminance image. There are various Laplacian templates, with the most common being the 4-neighbourhood Laplacian template [26] and the 8-neighbourhood Laplacian template [27]. The essential requirement is that the coefficients of the central pixel are positive, while the coefficients of the pixels surrounding the central pixel are harmful, and all coefficients sum to zero.  
A schematic diagram of the structure for processing the luminance component V is shown in Figure 6.  
Figure 5: Gamma curves with different parameters.
## 6 Target Classification
### Binary (UAV vs. Bird) Classification  
Subsequently, bird micro-Doppler images were included in the classification efforts. Initially, a binary classification task for distinguishing UAVs and birds was conducted. The sklearn.svm toolkit was utilized with the linear SVC classifier with a maximum iteration of 1000. The linear SVC uses the l2 penalty and a squared-hinge loss function with 0.0001 tolerance.  
With a 2/3 training and 1/3 testing split, 152 drone images and 86 bird images were used for testing. The confusion matrix in Figure 26 shows high accuracy in classifying between drones and birds with 96% accuracy. The precision for drones is 98% and for birds is 92%.
## 2 Research Method
### Mathematical Modeling
#### 2.1.4 Quarter-Car Model  
The equation of motion of the two-degree-of-freedom (2-DOF) suspension system shown in Figure 8 is expressed as a linear spring constant \(k_{s}\) and a linear damping rate \(c_{s}\). The tire was modeled using the spring constant \(k_{t}\). The linear equation of motion is expressed as follows:  
Figure 8: Quarter-car model.  
Figure 7: Damping force in the proposed MR damper.  
\[-c_{s}\big{(}\dot{z}_{s}-\dot{z}_{u}\big{)}-k_{s}(z_{s}-z_{u})=m_{s}\ddot{z}_{s} \tag{21}\]  
\[c_{s}\big{(}\dot{z}_{s}-\dot{z}_{u}\big{)}+k_{s}(z_{s}-z_{u})-k_{l}(z_{u}-z_{r})=m _{u}\ddot{z}_{u} \tag{22}\]  
\[\ddot{z}_{s}+\frac{c_{s}}{m_{s}}(\dot{z}_{s}-\dot{z}_{u})+\frac{k_{s}}{m_{s}}(z_ {s}-z_{u})=0 \tag{23}\]  
\[\ddot{z}_{u}-\frac{c_{s}}{m_{u}}\big{(}\dot{z}_{s}-\dot{z}_{u}\big{)}-\frac{k_{ s}}{m_{u}}(z_{s}-z_{u})+\frac{k_{l}}{m_{u}}(z_{u}-z_{r})=0 \tag{24}\]  
The Laplace transform of Equations (23) and (24) are as follows:  
\[s^{2}Z_{s}(s)+s\frac{c_{s}}{m_{s}}(Z_{s}(s)-Z_{u}(s))+\frac{k_{s}}{m_{s}}(Z_{s }(s)-Z_{u}(s))=0 \tag{25}\]  
\[s^{2}Z_{u}(s)-s\frac{c_{s}}{m_{u}}(Z_{s}(s)-Z_{u}(s))-\frac{k_{s}}{m_{u}}(Z_{ s}(s)-Z_{u}(s)+\frac{k_{l}}{m_{u}}(Z_{u}(s)-Z_{r}(s))=0 \tag{26}\]  
This can be written in matrix form as in Equation (27).  
\[\begin{bmatrix}s^{2}+s\frac{c_{s}}{m_{u}}+\frac{k_{u}}{m_{u}}+\frac{k_{l}}{m_ {u}}&-s\frac{c_{s}}{m_{u}}-\frac{k_{u}}{m_{u}}\\ -s\frac{c_{s}}{m_{s}}-\frac{k_{u}}{m_{s}}&s^{2}+s\frac{c_{s}}{m_{s}}+\frac{k_{ s}}{m_{s}}\end{bmatrix}\begin{bmatrix}Z_{u}(s)\\ Z_{s}(s)\end{bmatrix}+\begin{bmatrix}\frac{k_{l}}{m_{u}}\\ 0\end{bmatrix}Z_{r}(s) \tag{27}\]  
The transfer function can be obtained using Cramer's formula.  
\[\frac{Z_{s}(s)}{Z_{r}(s)}=\frac{\frac{k_{l}c_{s}}{m_{u}m_{s}}\Big{(}s+\frac{k _{s}}{c_{s}}\Big{)}}{s^{4}+\Big{(}\frac{c_{s}}{m_{u}}+\frac{c_{s}}{m_{s}} \Big{)}s^{3}+\Big{(}\frac{k_{s}}{m_{u}}+\frac{k_{u}}{m_{s}}+\frac{k_{l}}{m_{u }}\Big{)}s^{2}} \tag{28}\]
## 3 Experimental
### Chemistry
#### 3.1.1 Synthesis of Tetra-Substituted Pyridine Derivatives (**4a-f** and **6a-f**)
Brown solid, (69% yield), mp 141-143 \({}^{\circ}\)C; IR (KBr) \(\
u_{\rm max}\) 3425 (NH), 1721, 1682, 1657 (3C=O) cm\({}^{-1}\); \({}^{1}\)H NMR (DMSO-\(d_{6}\)) 8 1.00 (t, \(J\) = 7.2 Hz, 3H, CH\({}_{3}\)), 2.58 (s, 3H, CH\({}_{3}\)), 2.99 (s, 6H, 2 CH\({}_{3}\)), 4.11 (q, \(J\) = 7.2 Hz, 2H, CH\({}_{2}\)), 6.78-7.39 (m, 14H, Ar-H), 7.72 (s, 1H, Pyridine-H5), 9.73 (s, br, 1H, NH); MS \(m/z\) (%) 546 (M\({}^{+}\), 14), 406 (36), 349 (55), 241 (49), 121 (36), 76 (30), 43 (100). Anal. Calcd. for C\({}_{33}\)H\({}_{30}\)N\({}_{4}\)O\({}_{4}\) (546.63): C, 72.51; H, 5.53; N, 10.25. Found: C, 72.39; H, 5.38; N, 10.02%. _Ethyl 3-(5-acetyl-4-(4-chlorophenylophenyl)-6-oxo-1,6-dihydropyridin-2-yl)-1,5-diphenyl-\(1H\)-pyrazole-4-carboxylate_ **(6e)**. Brown solid, (68% yield), mp 170-172 \({}^{\circ}\)C; IR (KBr) \(\
u_{\rm max}\) 3366 (NH), 1720, 1680, 1663 (3C=O) cm\({}^{-1}\); \({}^{1}\)H NMR (DMSO-\(d_{6}\)) 8 1.06 (t, \(J\) = 7.2 Hz, 3H, CH\({}_{3}\)), 2.58 (s, 3H, CH\({}_{3}\)), 4.14 (q, \(J\) = 7.2 Hz, 2H, CH\({}_{2}\)), 7.24-7.59 (m, 14H, Ar-H), 7.78 (s, 1H, Pyridine-H5), 10.06 (s, br, 1H, NH); MS \(m/z\) (%) 540 (M\({}^{+}\), 2), 1), 538 (M\({}^{+}\), 3), 368 (53), 214 (100), 120 (55), 40 (79). Anal. Calcd. for C\({}_{31}\)H\({}_{24