esa-sceva/satcom-chunk-collection huggingface dataset:
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 2 CAP-Based UVLC System
### Underwater-Optical-Turbulence Channel  
In this paper, GG statistical distribution was adopted to model the UOT, i.e., the fading coefficient \(\alpha\) had a probability density function (PDF) given by [12]  
\[f_{\alpha}(\alpha)=\frac{2\
u}{\left(\Omega/m\right)^{m}\Gamma(m)}\alpha^{2 \
u m-1}\exp\left(-\frac{m\alpha^{2\
u}}{\Omega}\right),\ \alpha\geq 0 \tag{10}\]  
where \(m,\ \Omega,\ \text{and}\ \
u\) represent the fading, scaling, and shape parameters, respectively, and \(\Gamma(\cdot)\) is the gamma function defined as Equation (8.310/1) in [27].  
Scintillation index \(\delta_{\text{I}}^{2}\) is defined as [8]  
\[\delta_{I}^{2}=\frac{\text{E}\Big{[}\alpha^{2}\Big{]}-\text{E}^{2}[\alpha]}{ \text{E}^{2}[\alpha]} \tag{11}\]  
where the first and second moments of \(\alpha\) are expressed as [11]:\[\mathrm{E}[\alpha]=\frac{\Gamma\Big{(}m+\frac{1}{2v}\Big{)}}{\big{(}\frac{\Omega}{m} \big{)}^{2v}\Gamma(m)} \tag{12}\]  
\[\mathrm{E}\Big{[}\alpha^{2}\Big{]}=\frac{\Gamma\Big{(}m+\frac{1}{v}\Big{)}}{ \big{(}\frac{\Omega}{m}\big{)}^{v}\Gamma(m)} \tag{13}\]  
Thus, the scintillation index was given by  
\[\delta_{I}^{2}=\frac{\Gamma(m)\Gamma\Big{(}m+\frac{1}{v}\Big{)}}{\Gamma^{2} \Big{(}m+\frac{1}{2v}\Big{)}}-1 \tag{14}\]  
To ensure that no energy loss or gain occurs during the turbulence-induced fading process, the fading coefficient was normalised, i.e., \(\mathrm{E}[\alpha]=1\). Then, we had  
\[\Omega=m\Bigg{(}\frac{\Gamma(m)}{\Gamma\Big{(}m+\frac{1}{2v}\Big{)}}\Bigg{)}^ {2v} \tag{15}\]  
Optical-intensity fluctuation due to UOT was characterised by multiplicative fading coefficient \(\alpha\)[3].
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 4 Results and Discussion
### KNN Model  
Accuracy-wise, the KNN model performs well on our self-made dataset achieving results of 99.9% on the 64 \(\times\) 64 images and 97.2% on the 32 \(\times\) 32 images. The results also show the effectiveness of the pre-processing, segmentation, and feature extraction performed on the dataset. However, the results in the table illustrate some of the problems of the KNN classification model, as the model that used the 64 \(\times\) 64 images took 188 s to make predictions on the unseen test set. When we resized the images to 32 \(\times\) 32, the prediction time significantly decreased to only 18 s, 1/10 th of the one trained on 64 \(\times\) 64 images. These findings align with the general literature of sign language recognition, as KNN quickly loses its efficiency when the dataset becomes too big or has many features.
# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Image Formats
#### 2.2.1 Header and Image Objects  
HypPy's header module is used to read and write ENVI header files. ENVI images and spectral libraries typically consist of two files, the data and the header. The data files contain raw binary data. The header files contain human-readable text.  
Inside HypPy, the ENVI header is represented as a Python object of the class Header. Objects of this class contain attributes that reflect the attributes as they are found in the ENVI header files. In the Python code, there are a few differences. First, the names of the attributes in the ENVI header can contain spaces, which is inconvenient in a programming language like Python because variable names are not allowed to contain spaces. A choice was made to replace the spaces in ENVI attribute names with an underscore. For instance, the 'data-type' attribute in the ENVI file header will be renamed to 'data-type' in Python. Second, some of the values inside the ENVI header may change their appearance in Python. For instance, IDL records, as they appear inside ENVI headers, such as values of the wavelength attribute, are changed into the standard Python list data type. The translation between IDL records and Python lists is handled by HypPy internally.  
HypPy supports all IDL/ENVI raw binary data types from 8-bit unsigned integers to 64-bit signed integers and from 32-bit single floating point numbers to 64-bit double-precision complex numbers with a real-imaginary pair of double precision. Furthermore, through the use of the 'byte order' field name, HypPy supports both big-endian and little-endian byte order files.  
Figure 1: The top-level menu of HypPy.  
HypPy has two factory functions in its image module, the Open() function for opening existing images, and the New() function for setting up new (empty) images.  
The factory function Open() returns one of the image objects of class ImageBIP, ImageBIL, ImageBSQ, Classification, or ImageSL (a spectral library mapped as an image). Images accessed using the Open() function can only be read, not written. To protect existing images, these are always opened as read-only.  
The factory function New() returns one of the image objects of class ImageBIP, ImageBIL, ImageBSQ, or Classification. In addition to these classes, the class ImageSL is also supported, which is an image representation of an ENVI spectral library.
## 4 Synthetic Data Generation
### Dataset Acquisition from the Generated Scenes
#### 4.4.2 Three-Dimensional Point Cloud and Annotations  
Blender software provides tools to export point clouds from meshes in the scene. However, common aggregate point clouds are either collected by 3D scanners or reconstructed from a set of multi-view 2D RGB images, both of which are incapable of containing invisible object points in the output point clouds. To obtain scanned/reconstructed 3D point clouds, rendering was conducted from multiple camera perspectives, each producing three outputs: RGB colors (Figure 7a), object index map (Figure 7b), and depth map (Figure 7c). The depth map output format should be single-channel EXR images to retain the precision of 3D coordinates.  
When the rendering is completed, camera parameters are retrieved from each view to perform an inverse projection of 2D pixels from the color image and object index map back into 3D world coordinates using the depth map. This approach facilitates the generation of a textured point cloud (Figure 7d) and its corresponding annotations (Figure 7e). Within the output dataset, each 3D point within the point cloud is stored in a 10-dimensional array describing its 3-dimensional location in the world coordinate system, 3-dimensional RGB colors, 3-dimensional normal direction computed from depth maps, and the identification of the associated ballast particle as a 1-dimensional integer.  
Figure 7: Data collected from various camera perspectives include: (**a**) color image, (**b**) 2D segmentation labels (object indices), and (**c**) depth map. Through projecting all captured data into the world coordinate system, (**d**) the ground truth point cloud and (**e**) the 3D ground truth labels (different colors indicate different ballast particles) are obtained [23].
## 3 Results
### Structural Equation Modeling for Effluent T-N
#### 3.1.1 Path Model of Effluent T-N
Through the iterative process of finding a suitable model, airflow rate (B_Airflow) was omitted from the causal network, and BOD, COD, and T-N inflow concentration (B_in_BOD, B_in_COD, B_in_T-N) were introduced into the model. The fitness of this modified path model for effluent T-N was acceptable for all indices. There are four paths interpreted as meaningless by having p-values greater than 0.05, and they are represented by dashed lines in Figure 3b. For the significant paths, there are three paths that are from influent BOD concentration to MLSS concentration in the bioreactor, from influent T-N concentration to effluent T-N showing effluent T-N variation depending on influent loading, and the path from influent T-N to the aeration-tank nitrate. These two paths related to influent and effluent T-N concentration means that, even with stable nitrogen-removal performance, fluctuation of effluent T-N is due to influent fluctuation. It can also be interpreted as showing the limitation of nitrogen removal using inner nitrate return from the aeration tank to the anoxic. This is in agreement with the well-known theory and the processing mechanism, and it is meaningful to find these relationships embedded in the measured variable numbers. MLSS concentration remains in the model because influent BOD is included in it as an influencing factor on effluent T-N. There was an attempt to build a path model that includes influent BOD as an essential internal carbon source for denitrification and excludes MLSS concentration, but the model could not meet the fitness standard. The insignificant path from B_DO to Oxic_NH4-N can be estimated as having the same theory as the remaining DO concentration mentioned above. As the validation results of the modified path model for effluent T-N using the datasets prepared by random extraction for model verification, the model fitness was acceptable with the index values of 2.111 for Q, 0.958 for GFI, 0.881 for AGFI, 0.082 for RMSEA, and 0.980 for CFI.
## 3 Results
### Chemical Characterization of the Three Propolis Extracts
#### 3.1.1 Chemical Characterization of Propolis Extracts by HPLC  
The PPF, PSDE and MPE extracts contained 94, 80 and 40% \(w/w\) of propolis dry matter, respectively. In the PPF, only caffeic and \(p\)-coumaric acids were detected (Table 1, Figure 1). These two biomarkers were also detected in PSDE and MPE; however, in PPF these compounds were detected at much higher concentrations (\(p\leq 0.05\)) (Table 1, Figure 1).  
PSDE and MPE extract constitutions were more complex than that of PPF. PSDE was found to contain seven, while MPE contained all of the 10 chemical markers of propolis that were assayed. Both of these two extracts contained the components 3,5-dicaffeoylquinic acid, 4,5-dicaffeoylquinic acid, drupanin, galangin, and artepillin C; this latter was the predominant compound in both extracts. All compounds were in significantly different concentrations in PSDE versus MPE (\(p\leq 0.05\)). Aromodenrin, chrysin and baccharin were detected only in MPE (Table 1, Figure 1). Among the four propolis components tested for immunomodulation in the macrophage model, caffeic and \(p\)-coumaric acid were detected in all three extracts, artepillin C was found in PSDE and MPE, and baccharin was detected only in MPE. All four compounds were in different concentrations in each extract (\(p\leq 0.05\)) (Table 1, Figure 1). These compounds were selected to include two from the more polar fraction (caffeic and \(p\)-coumaric acids) and two that are prenylated compounds (artepillin and baccharin); the latter are characteristic of green propolis and more apolar than the phenolic acids. Caffeic and \(p\)-coumaric acids were present in all four extracts, and were reported to have antioxidant activity in a previous analysis [PERSON] et al. (2019). Additionally, considering that artepillin C, drupanin, and baccharin are characteristic of Brazilian green propolis ([PERSON], 2019), along with artepillin C, baccharin was selected because this substance differs structurally from artepillin C and drupanin, as it has an additional cinnamic acid group in its composition, and because it was detected only in MPE.
# A Phase Correction Model for Fourier Transform Spectroscopy
## 6 Conclusions  
In FTS, phase correction techniques typically utilize the well-established Mertz algorithm. However, this method exhibits limited effectiveness in correcting nonlinear phase errors. The all-pass filter technique based on the principles of IIR filtering demonstrates superior performance in terms of both correction accuracy and handling nonlinear phase errors, as convincingly demonstrated in [14]. Building upon this foundation, this paper introduces the use of the phase correlation method, commonly employed in signal delay estimation and image registration, to calculate the linear component of phase errors. The residual phase errors are then corrected using the all-pass filters. Additionally, the improved GS-SABO algorithm employed in the optimization of the filter parameters exhibits favorable properties, particularly rapid convergence.  
Overall, through the theoretical analysis and simulation validation presented in this paper, it is evident that our proposed PCM-APF model for phase correction in FTS is a viable and effective approach.  
Conceptualization, methodology, software, formal analysis, investigation, writing--original draft preparation, visualization, H.C.; writing--review and editing, supervision, H.S.; resources, L.M.; data curation, C.B.; project administration, P.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the innovation project of the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CXJ)-22S014).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 7 Placement of Nodes  
The placement of nodes largely influences the operations and performance of WSNs, as sensor nodes must be able to observe events of interest, and transmit the information to data collection centres. Moreover, sensor placement also affects the resource management in WSNs [111]. Sensor networks often monitor specific points, e.g., monitoring intersections or animal nesting sites, which constrain the node placement. In such cases, the placement issue is highly application-specific. Our survey is only of cases where the aim is to monitor an entire area, either continuous or on a discrete grid.  
According to the roles that the deployed nodes play, node placement can be classified into placement of ordinary nodes, and placement of relay nodes, respectively. The former focus on the deployment of normal sensors, while the latter places a special type of nodes, which are responsible for forwarding packets.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 6 Conclusions  
The growth of IoT devices forms a huge security threat to data privacy, increasing the number of malware attacks. Thus, developing an algorithm that meets the IoT devices' requirements is crucial. AIS methods are not expensive to implement, which makes them a good fit to secure the IoT. Even though the AIS solutions are promising in this context, the published results are based on computer simulations only. In this paper, we first validate the malware detection results of AIS solutions using multiple datasets with different types of malware attacks. Next, we presented the first implementation of AIS solutions using a real-time IoT platform. We used AWS to create different IoT system architectures mirroring real-life scenarios. We demonstrated the lightweight, transfer-learning, and detection capabilities of the AIS methods. The results showed that increasing the size of the IoT device's memory allowed us to increase the size of the dataset to train the module, which led to better detection performance. The results also showed that running an AIS solution on one of the IoT devices could secure the device itself from malware attacks and any other IoT devices connected to the same network. Finally, we validated the claim that AIS security solutions should be tested in a real setup to obtain accurate results.  
In future work, we will connect more IoT devices to the network to further investigate the AIS solutions' robustness and fault tolerance. Also, using different datasets with different malware attack files might improve the learning curve, which might lead to better classification results.  
Conceptualization, H.A., G.G. and M.J.; methodology, H.A.; software, H.A.; validation, H.A., G.G. and M.J.; formal analysis, H.A., G.G. and M.J.; investigation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, H.A., G.G. and M.J.; visualization, H.A.; supervision, G.G. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We used the NSL-KDD dataset to run the performance analysis in this research. The dataset is available at [[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)), accessed on 25 March 2023.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AIS & Artificial immune systems \\ AWS & Amazon Web Services \\ IoT & Internet of things \\ NSSN & Neural networks methods \\ OS & Operating system \\ IoMT & Internet of medical things \\ \end{tabular}
## 4 Results and Discussion
### Thermal Shock in Trochoidal and Side Milling  
Plenty of researchers found that thermal shock played an important role in cutter life. To investigate the thermal shock in trochoidal milling, first, the thermal shock \(T_{shock}\) is calculated based on the maximum and the minimum cutting temperature in one cutter revolution (see Figure 16)  
\[T_{shock}=T_{\text{max}}-T_{\text{min}} \tag{24}\]  
Then, the cutting temperature between 31.55 s and 46.70 s in Figure 15 is designated to conduct the investigation, for it is stable and covered one trochoidal cycle. The thermal shock of trochoidal and side milling are calculated and plotted in Figure 17.  
For side milling, its thermal shock keeps a constant of 253.7 \({}^{\circ}\)C. While in a cycle of trochoidal milling, the thermal shock gradually increases but lower than the side milling between 31.55 s and 32.76 s. Then, the thermal shock of trochoidal milling is larger than that in side milling between 32.76 s and 34.93 s, with a maximum of 14 \({}^{\circ}\)C. Thereafter, the thermal shock of trochoidal milling is less than that in side milling and keeps decreasing, down to zero finally. By calculation, in the 86.1% of the whole trochoidal cycle time, the thermal shock of trochoidal milling is less than that of side milling. Therefore, we cautiously put forward an argument that the lesser thermal shock is a potential  
Figure 16: Definition of thermal shock.  
Figure 17: Thermal shock of trochoidal and side milling.  
reason for trochoidal milling to prolong the cutter life. The argument is going to be examined with experiments in our further work.
## 3 Results
### Spatiotemporal Trends of PM\({}_{2.5}\) Concentrations  
Using the above Model-I, we can create daily, weekly, monthly, seasonal, and annual mean PM\({}_{2.5}\) maps which can be applied in health effect analysis, air pollution management, and mitigation policy decision-making support. Figure 8 illustrates the annual mean maps of MAIAC AOT-derived PM\({}_{2.5}\) estimations at 1 km spatial resolution in the study area from 2013 to 2017. The annual mean estimated PM\({}_{2.5}\) concentrations are 63.93, 69.09, 65.28, 59.17, and 48.56 \(\upmu\)g/m\({}^{3}\) from 2013 to 2017, respectively. These maps revealed the significantly similar spatial patterns of PM\({}_{2.5}\) concentrations across the Beijing metropolitan area during the study period: high PM\({}_{2.5}\) levels are located in the southern parts which are closer to the major pollution sources from the Hebei Province and in the upwind direction of Hebei, while low PM\({}_{2.5}\) levels appear in rural and mountainous areas in the northern parts. This corresponds well with the spatial patterns demonstrated by the ground-based monitoring shown in Figure 1c.  
Figure 7: The differences between estimated and measured PM\({}_{2.5}\) (\(\upmu\)g/m\({}^{3}\)) at each monitoring site during 2013–2017.  
The area within the Fifth Ring Road is the downtown area of Beijing with the highest population density and heaviest traffic flow. Figure 9 illustrates the annual mean PM\({}_{2.5}\) estimates in the downtown area from 2013 to 2017, showing that PM\({}_{2.5}\) levels are apparently reduced, especially in 2017 with much lower PM\({}_{2.5}\) levels. The annual mean PM\({}_{2.5}\) estimates in this downtown area are 81.56, 77.50, 80.04, 67.38, and 55.90 \(\mu\)g/m\({}^{3}\) from 2013 to 2017, respectively. Within the downtown area, PM\({}_{2.5}\) levels in the northern part are much lower than those in the southern part.  
The seasonal mean PM\({}_{2.5}\) estimates (Figure 10) for the entire Beijing metropolitan area are 66.10, 45.33, 60.17, and 75.59 \(\mu\)g/m\({}^{3}\) for spring (March, April, and May), summer (June, July, and August), autumn (September, October, and November), and winter (December, January, and February), respectively, indicating that PM\({}_{2.5}\) levels exhibit distinct seasonal variations, with the highest PM\({}_{2.5}\) levels in winter and the lowest in summer. Such seasonal variations can be attributed to the mixed  
Figure 8: Annual mean PM\({}_{2.5}\) concentration (\(\mu\)g/m\({}^{3}\)) calculated from daily PM\({}_{2.5}\) estimations.  
contributions from meteorological conditions (planetary boundary layer height (PBLH), relative humidity, seasonal wind, etc.) and local pollution sources such as coal combustion for domestic heating in winter. Moreover, the seasonal mean PM\({}_{2.5}\) has similar spatial patterns with the annual mean PM\({}_{2.5}\); that is, higher concentrations occur in the southern part and lower concentrations occur in the northern part.  
Figure 9: Mean PM\({}_{2.5}\) concentration (\(\upmu\)g/m\({}^{3}\)) calculated from daily PM\({}_{2.5}\) estimations for each year and a city map over the downtown area of Beijing.
## 4 Materials and Methods
### Aroma Profiling  
The analysis of the stem chemical composition was performed with the use of a gas chromatograph coupled with a mass spectrometer (single quadrupole mass spectrometer, gas chromatograph Shimadzu GC-MS QP 2020, Shimadzu, Kyoto, Japan) with using solid-phase micro extraction (SPME) technique. Each sample (-500 mg of fresh plant material respectively) was placed in a vial of the head-space type, volume of 20 mL. The polymer coating of the fibre was a mixture of divinylbenzene, WR carboxene and polydimethylsiloxane (DVB/C-WR/PDMS). The volatile extraction was performed at 80 \({}^{\circ}\)C for 10 min; before the extraction sample was preincubated for 10 min at extraction temperature. The desorption of extracted volatile was carried out in the apparatus injector for 3 min. The injector temperature was 250 \({}^{\circ}\)C. As the carrier gas, helium at flow 1.0 mL/min, with split 5, was applied. Separation was reached using a capillary column Zebron ZB-5 (30 m, 0.25 mm, 0.25 \(\upmu\)m of stationary phase; Phenomenex, Torrance, CA, USA). The temperature program of the column was as follows: 50 \({}^{\circ}\)C, an increase by 4.0 \({}^{\circ}\)C min\({}^{-1}\) to 130 \({}^{\circ}\)C, then 10 \({}^{\circ}\)C min\({}^{-1}\) to 180 \({}^{\circ}\)C then 20 \({}^{\circ}\)C min\({}^{-1}\) to 280 \({}^{\circ}\)C. The MS analysis was performed using scans from 40 to 300 \(m/z\) with the application of electron ionization (EI) at 70 eV. The analysis was carried out in four replications.  
The compounds were identified with the help of two different analytic methods in order to compare the retention times of authentic chemical compounds (standard of saturated alkanes Supelco C\({}_{6}\)-C\({}_{30}\)) with the mass spectra acquired from the available library (Willey NIST 17, match indicator; 90%). The identification of compounds was performed through a comparison of the experimentally obtained linear retention indices calculated relative to the mixture of \(n\)-alkanes C\({}_{6}\)-C\({}_{30}\) (SigmaAldrich, Saint Louis, MO, USA) and mass spectra with the ones available at libraries (NIST 17 Mass Spectral and Retention Index Library (NIST17) and NIST WebBook) or in literature [95].