esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The detection accuracy and model building time of machine learning (ML) classifiers are vital aspects for an intrusion detection system (IDS) to predict attacks in real life. Recently, researchers have introduced feature selection methods to increase the detection accuracy and minimize the model building time of a limited number of ML classifiers. Therefore, identifying more ML classifiers with very high detection accuracy and the lowest possible model building time is necessary. In this study, the authors tested six supervised classifiers on a full NSL-KDD training dataset (a benchmark record for Internet traffic) using 10-fold cross-validation in the Weka tool with and without feature selection/reduction methods. The authors aimed to identify more options to outperform and secure classifiers with the highest detection accuracy and lowest model building time. The results show that the feature selection/reduction methods, including the wrapper method in combination with the discretize filter, the filter method in combination with the discretize filter, and the discretize filter, can significantly decrease model building time without compromising detection accuracy. The suggested ML algorithms and feature selection/reduction methods are automated pattern recognition approaches to detect network attacks, which are within the scope of the Symmetry journal.  
IDS; ML classifiers; information security; network; Weka; NSL-KDD
# Raman Spectroscopy Analysis of Free Fatty Acid in Olive Oil
###### Abstract  
Free fatty acid (FFA) is one of the most critical parameters for evaluating the quality of olive oil. In this paper, we present a simple and rapid Raman spectroscopy method for analyzing free fatty acid in olive oil. First, FFA degradation of carotenoids in olive oil is confirmed by analyzing the relative intensity of characteristic vibrational modes and introducing an intensity decrease factor. Second, it is demonstrated that the relative intensity ratio of the two characteristic vibrational modes at 1525 cm\({}^{-1}\) and 1655 cm\({}^{-1}\) presents a good and rapid analysis of FFA content in olive oil; the relative intensity ratio decreases linearly with FFA content. In addition, resonance Raman scattering of carotenoid is discussed, showing that a green laser should be utilized to study FFA in olive oil.  
free fatty acid; olive oil; Raman spectroscopy; relative intensity analysis; resonance Raman Raman Spectroscopy in Olive Oil  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], H., [PERSON], H.  
experiment (i.e., absolute Raman intensity) were analyzed. For practical applications, the analysis of absolute Raman intensity would be difficult, since comparison of absolute intensity would require same experimental conditions.  
In this paper, relative Raman intensity analysis -- the study of relative intensity ratios of characteristic vibrational modes, is applied to investigate FFA in olive oils. Relative intensity ratios are more reliable parameters than absolute Raman intensities, thus would make practical application of Raman method possible. Our relative intensity analysis shows that in the spectral window of 945-1600 cm\({}^{-1}\), the two characteristic vibrational modes at 1525 cm\({}^{-1}\) and 1155 cm\({}^{-1}\) are correlated with FFA content in olive oil. In addition, the relative intensity ratio of vibrational modes at 1525 cm\({}^{-1}\) and 1655 cm\({}^{-1}\) presents a good and rapid analysis of FFA content in extra virgin olive oil; the intensity ratio decreases linearly with FFA content. Portable Raman system operating at 785 nm had been applied for prediction of fatty acid composition in vegetable oils and identification of waste cooking oils [22; 23]. The relative intensity analysis is not dependent on experimental conditions, thus applying it in portable Raman system operating with a visible laser would be very helpful for quick on-site quality evaluation of olive oils.
## 2 Methodology
### Feature Extraction  
MFCC is one of the most common feature extraction techniques used in automatic VPD systems. The steps are as follows: (1) Preprocessing: the pre-emphasis, framing, and windowing of the voice signal. (2) Fast Fourier Transform: a short-time Fourier analysis is performed to obtain the magnitude spectrum. (3) Mel filter: The magnitude spectrum is wrapped as a Mel spectrum using 24 overlapping triangular windows with an equal distribution of centre frequencies of the Mel scale windows. (4) Log power: calculates each filter bank output's log power, which is the square of the Mel spectrum. (5) Discrete Cosine Transform (DCT): The 13 th-order MFCC coefficients are obtained by log power to apply DCT. (6) Perform cepstral liftering: extract the first and second-order differences of MFCCs.  
In the feature extraction step, we can compute a set of MFCCs for each speech frame of one-second audio to obtain approximately 64 MFCC samples (window size = 32 ms, frame overlapping rate = 50%). Finally, we use their multi-frame averaging as the MFCC vector for a voice. A total of thirty-six MFCC parameters includes MFCCs' original feature parameters and their first-order and second-order derivatives.
## 3 Technologies: Internet of Medical Things
### Technologies for IoMT Edge Devices
\begin{table}
\begin{tabular}{c c c} \hline
**Processor** & **Manufacturer** & **Key Applications** \\ \hline \multirow{6}{*}{LPC1800 Series} & NXP & building automation, \\  & & IoT gateways \\  & & Industrial IoT (IIoT), \\  & & smart-grid, \\  & & e-health \\ \hline \multirow{6}{*}{CC2650} & TI & Home automation, \\  & & proximity tags, \\  & & tracking, \\  & & smart metering, \\  & & e-health, \\  & & retail \\ \hline ST32-M & STMicroelectronics & HVAC, IIoT, e-health, \\  & & m2m, VANET \\ \hline SmartFusion2 & Microsemi & IIoT, e-health, \\   SoC FPGA Family & & IoT gateway \\ \hline Atmel SAM3 & Atmel & IIoT and smart-grid \\ \hline \end{tabular}
\end{table} 
Table 4: Market survey of the cortex-M3 processor in the IoT domain. Figure 3: Fog-based e-health system. Recently, multicore technology has received ample attention in the realization of IoT edge devices. It is well-known that in low-power design, the most effective way for a substantial power reduction is to reduce the supply voltage. However, the speed of the logic devices deteriorates. Parallel processing is one approach that alleviates the speed-degradation. In processor design, increasing the number of low-power cores is an effective approach for low-power design. In the nanometer regime, near-threshold computing combined with parallel processing is an effective way for reducing the power consumption of digital circuitry. This approach has been used in [26] to design a quad-core RISC-V processor for IoT endpoints. The processor is designed with compressed instructions, deploys an L0-buffer to reduce cache access, uses a logarithmic interconnect for the inter-core communication and core-memory data transfer. Cache memories are accessed via a dedicated interconnect. The reported efficiency of the processor implemented using 65 nm bulk CMOS is 67 MOPS/mW. Figure 4 illustrates a simplified architecture of the Parallel Ultra-Low-Power Processor (PULP). The processor was used to design wearables that collect ECG and EMG signals [27]. The wearable has higher energy efficiency compared with similar wearables designed using ARM-Cortex M4F and M7.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 6 Modeling Marine Transportation as Social Network
### Applying SNA to Marine Traffic Data  
As previously described, all nodes were coded using the name of the port (in case of static nodes) and the name of the closest island or inland point for the dynamic nodes. In all cases, the latitude and longitude values were obtained. The network was formed by the aforementioned nodes and edges were placed by monitoring the ship movement over time. More specifically, when a ship was passing from a specific node a trigger was initiated that was alert until the ship passed from another node in the network for a given period and if not, the trigger was set to the initial value. For example, when a ship was found initially in the node that corresponds to the port of Piraeus, a trigger was initialized, waiting for the ship to enter another node in the network during a manually predefined period. In case it did, the counter of ships form Piraeus to the other node was increased by one and at the end of the day, if these counts exceeded a threshold value, an edge was established. From a technology perspective, the trigger was implemented using Oracle database triggers in PL/SQL language. The triggers were fired during INSERT/UPDATE operation within the framework's database.  
Figure 15 portrays a visualization based on the authority level of nodes, for the area of the Aegean Sea, followed by the same network layered on a map in order to clearly depict the areas of interest, see Figure 16. As we can observe, node colors correspond to the modularity class of the network, that is the different node communities that are formed, while network sizes reflect the closeness centrality. The label size is also proportional to this metric, helping visualization of the status of ship movements between these points of interests (i.e., nodes). The edges are weighed according to the number of ships moving between two nodes. Note that these edges could be weighted with other metrics such as average speed, type of cargo, weather conditions, etc., since all of the above are available from our database.  
This figure can reveal and visualize important knowledge to domain experts from the AIS data. For instance, the green arcs portray significant marine traffic within the island group of Cyclades, and the island of Crete, some of the most touristic parts of Greece. Since there are both commercial and passenger ships that form the network, domain experts could examine imposing regulated waterways to the former since the effects of a potential accident including vessels with dangerous cargo would have catastrophic consequences for the economy and the environment.  
In the figure below, the same graph as above is simplified by removing the edges and mapped onto a Google Maps' layer of the Aegean Sea.
# AS-OCT and Ocular Hygrometer as Innovative Tools in Dry Eye Disease Diagnosis
## 3 Anterior Segment Optical Coherence Tomography  
AS-OCT produces cross-sectional images of the anterior structures of the eye by low-coherence interferometry [50]. It allows for the precise measurement of certain parameters of the tear meniscus, such as the tear meniscus area (TMA) and the tear meniscus height (TMH), without the interference reflex tearing thanks to the rapidity of the image acquisition and its non-contact nature [51]. TMH acquired by time domain OCT (TD-OCT) has been shown to correlate well with the Schirmer test, corneal staining scores and strip meniscometry [52]. Subsequently, the introduction of spectral domain OCT (SD-OCT) allowed for the improvement of the sensitivity and specificity of the diagnostic biomarkers of DED, TMH and TMA, compared to those achieved with TD-OCT [53]. One study reported that the diagnostic accuracy of SD-OCT was the highest for Sjogren syndrome and acceptable for non-Sjogren aqueous-deficient DED, but the lowest efficiency was found for the diagnosis of evaporative DED [54; 55]. Finally, the advent of swept source OCT (SS-OCT) has made it possible to measure the tear meniscus volume (TMV) in addition to the TMH and TMA, thanks to its ability to acquire three-dimensional images as well as the greater imaging depth and enhanced scanning speed [56]. Moreover, these parameters, TMH, TMV and TMA, measured with SS-OCT showed a correlation with the Schirmer test, TBUT and the corneal staining score [57]. AS-OCT can capture the tear lake in a single moment in time, but as there may be changes in TMV throughout the day, the acquired value of a single measurement has questionable significance [58]. Therefore, it may be necessary to perform serial OCT measurements to establish an average tear film thickness and tear lake volume. In cases of evaporative DED, AS-OCT can aid in the diagnosis of MGD due to the introduction of a 3D imaging method for the MGs [59; 60]. However, OCT is expensive,time-consuming and often not reimbursed by insurance. In addition, further studies in larger populations need to be performed to assess the specificity and the sensitivity of this technology in the diagnosis of DED.
# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 3 Introduction of the Interaction Curve, the \(\triangle_{y}(x)\) and \(\triangle_{y}x\)  
The aim of this study is to visually reveal the effects of interactions between parameters on MRR. In traditional statistical methods, ANOVA judges the significance of interaction terms using p-values, while RSM is required for more complex experimental designs [22]. In contrast, interaction curves visualise whether there is an interaction between different parameter combinations and the significance of the interaction between parameter combinations for MRR changes by means of two-dimensional graphs. Therefore, using the mathematical model of MRR in Equation (1), the interaction curve is plotted, and the interaction and significance of two factors on MRR are investigated using Origin software.  
To study the interaction and significance of voltage and current on MRR, for example, a new curve, which is called the interaction curve of current and voltage on MRR, is drawn and analysed. Two curves are included. One curve shows the change in MRR with variation in current under a certain value of voltage. The other curve shows the change in MRR with variation in voltage under a certain value of current. During plotting, the value of pulse on time and the value of pulse off time are fixed at their centre levels, respectively.  
To obtain the curve showing the change in MRR with variation on current under a certain value of voltage, the voltage is first set at a certain value within the experimental interval [30 V, 70 V], and then the current is varied within its experimental interval to calculate the change in MRR caused by the change of current. An orthogonal coordinate system is established, as shown in Figure 2. The horizontal axis at the top is the value of voltage. The vertical axis on the right side is the change in MRR due to the change in current when the voltage is set at the corresponding value shown by the horizontal axis. The curve is marked as the influence of current. Using the same method, the curve showing the change in MRR with variation in voltage under a certain value of current is drawn and also shown in Figure 2, marked as the influence of voltage.  
The curve shown in Figure 2 is called the interaction curve of current and voltage on MRR. It could be used to analyse the interaction between voltage and current on MRR.  
In order to obtain the significance, the variables \(\triangle_{y}(x)\) and \(\triangle_{y\,x}\) are defined. The variation of MRR caused by a change of factor is marked as \(\triangle_{y}(x)\), where y represents the factor that is set at a certain value and \(x\) represents the factor that varies to change the MRR. For example, in the interaction of current and voltage on MRR (namely _U-I_), the variation of MRR due to a change in current is expressed as \(\triangle_{U}(I)\) when the voltage is fixed. The change of change is marked as \(\triangle_{y\,x}\). For example, in the _U-I_ mentioned above, the change of in \(\triangle_{U}(I)\) due to a change in voltage is expressed by \(\triangle_{U\,I}\).
## 3 Results  
### Evaluation of ORCHIDEE iWUE* and LUE Average  
#### 3.1.1 Average iWUE* during 1982-2010  
Figure 1 shows the multi-year averaged iWUE* during 1982-2010. High values of iWUE* from the MTE product are found over savannahs and rain-green woodland areas (northern savannahs and the southern edge of African central forests). The average MTE-iWUE* over these regions is 18 gChPa \(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\). The maximum MTE-iWUE* value reaches up to \(30\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\) in the southern edge of the central African forest and more than \(35\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\) in some grid cells over the northern savannahs. In contrast, lower MTE-iWUE* values are found in the central African forest with values of less than \(12\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\) (the vapor pressure deficit is low in the central African forest).  
The ORCHIDEE 11-layer version significantly underestimates iWUE* over the grasslands and savannah zones in comparison with the MTE product derived iWUE* (iWUE* \(<20\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\) over a large part of Africa). In contrast, the iWUE* from the 11-layer run is higher than the MTE-iWUE* over the central African forest (\(15\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\)_vs._ 10\(\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\)). Unlike the 11-layer version, the 2-layer version overestimates iWUE* over the whole continent, especially over the grasslands and savannah zones (iWUE* \(\approx 28\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\) against \(15\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\)_vs._ 18\(\,\mathrm{gC}\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\) for MTE-iWUE* averaged in grassland areas).  
Note that the difficulty of ORCHIDEE to reproduce the high iWUE* of arid and desert transition zones is probably due to problems in the land cover map used in this study and to the lack of a shrub-land PFT in the ORCHIDEE model [47, 63].
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.4.1 Ablation Studies  
In this subsection, we experimentally show the effectiveness of our design choice. We utilize the CVC-ClinicDB dataset to train and compare the performance of variants of channel attention and spatial attention. We sequentially combine the spatial and channel modules. We only change the reduction ratio of channel attention module, the size of kernel and the 2D descriptor of spatial attention module.  
**Channel attention:** we utilize both of pooling methods ( max-pooling and average pooling) to share information. Subsequently, we apply standard \(1\times 1\) convolution to reduce the number of channel. Finally, we add both of them to yield the channel attention module. We compare the effectiveness of the reduction ratio that conducts on three ratios: 16, 8, and 2.  
Table 2 shows the experimental results. We observe that the dice score and recall score achieve best performance on ratio of 16 as compared to other ratios. In this experiment, we concentrate on Dice metric so that we choose ratio of 16 for getting best performance on Dice score.  
**Spatial attention:** to generate a 2D spatial attention map, we first apply a convolution layer that encodes information obtained from raw feature map. Subsequently, we utilize max-pooling, average pooling, or standard \(1\times 1\) convolution. Finally, we use Sigmoid function to normalize the spatial map. We compare the effectiveness of a kernel size that conducts on four kernel sizes: 7, 5, and 3. In addition, we also investigate on concatenating or using only one of three modules: max-pooling, average-pooling, and \(1\times 1\) convolution.  
Experimental results with different ratios are shown in Table 3. I find that the combination of pooling modules and standard \(1\times 1\) convolution achieves better performance. Max-pooling extracts the most salient information, average-pooling encodes global statistics softly, and \(1\times 1\) convolution slightly improves accuracy in the context of deep axis. Additionally, we also observe that the dice score and recall score of kernel size of 7 are higher than other kernel sizes. Inclusion, we use the joint of all three modules and a convolution with kernel size of 7 to generate spatial attention map.
## 2 Materials and Methods
### DEM Validation
Other measures of the accuracy of the depth extracted from the DEM are shown in the scatterplots of Figure 8 and in the results of the Pearson's correlation tests in Table 4. The resulting \(p\)-value (<<0.05) and correlation coefficients (>0.9) indicate a good positive correlation between the values derived from field and DEM measurements, with an excellent correlation coefficient of 0.97 for the dataset without outliers. Furthermore, the linear regression best-fit models calculated for the entire population (Figure 9A) and the subset without outliers (Figure 9B) show high R\({}^{2}\) values of 0.83 and 0.95, respectively. The slope of the trend lines (a value of 1 would mean a perfect match between field and DEM derived measurements) is 0.91 confirming a good accuracy of our DEM. The dolines with the highest errors are those presenting dense vegetation cover or human-built structures that obscure the view of the real bottom surface (Figure 10). Figure 8: Boxplots of the difference between depth (D) values from field- and DEM-derived measurements for the entire population (**left**) and a subset without outliers (**right**). The boxplots are grouped according to doline shape. The limits of the boxes and whiskers represent the first and third quartiles, respectively. The straight lines inside the boxes represent the median values and the crosses represent the mean values.
## 2 Materials and Methods
### Amplicon Metagenomic Sequencing  
To analyze non-culturable microbiome composition, 100 mg of freeze-dried BSG solid sidestream was added directly to the lysis tube from the DNA extraction kit, DNeasy PowerFood Microbial Kit (Qiagen, Hilden, Germany), and lysed using TissueLyser II (Qiagen, Hilden, Germany) at 25 Hz for 15 min \(\times\) 2 cycles. DNA clean-up was performed using Mag-Bind(r) TotalPure NGS (Omega Bio-tek, Norcross, GA, USA). DNA yield and purity were determined using a NanoDrop(tm) 2000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA). SCG samples were sent for extraction at a commercial lab (NovogeneAIT Genomics, Singapore), where 200 mg freeze-dried solid of each SCG sample was used for extraction using the FastDNA Spin kit (MP Biomedical, Santa Ana, CA, USA). Extracted DNA samples from BSG and SCG were submitted for 16S rRNA and fungal ITS amplicon metagenomics sequencing (NovogeneAIT Genomics, Singapore). 16S rRNA and ITS gene amplicon library construction and sequencing were carried out on the Illumina Novaseq PE250 platform (San Diego, CA, USA) at 100 K raw tags/sample with a focus on the V3-V4 hypervariable region and ITS1-5F region for bacteria and fungi, respectively. Data QC and analysis were carried out using QIIME 2.