esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Of these, some compounds e.g., trimethylamine, 3-methyl-1-butanol, 3-methylbutanal and ethyl acetate have been reported as bacterial metabolites, while other compounds e.g., 1-octen-3-ol, 1-penten-3-ol, hexanal, nonanal, heptanal, as products of chemical oxidation of fatty acids or other chemical reactions [9]. In other studies, some bacterial metabolites e.g., ethanol, acetic acid, 2-methylbutanal, 3-methylbutanal, 1-propanol, 2-methyl-1-butanol, 3-methyl-1-butanol, 3-hydroxy-2-butanone, 2-butanone, and ethyl esters, were found to increase during fish storage because of _Pseudomonas_, _Shewanella_, LAB, Enterobacteriaceae, _Brochthrix thermosphata_, _Photobacterium phosphoreum_ activity [61; 62; 63; 64]. These compounds have been proposed as potential spoilage markers of fish [13; 14; 61; 63; 65; 66; 67; 68; 69; 70]. In our case, the most interesting compounds (i.e., those with high Spearman's \(p\) and VIP), such as trimethylamine, 3-methylbutanoic acid and 3-methyl-1-butanol were found to increase during cold storage of meagre, where _Pseudomonas_ and _Psychrobacter_ or _Shewanella_ dominated. Indeed, these compounds have been linked with metabolic activity of the aforementioned bacteria, mainly of _Pseudomonas_, during storage of fish or inoculated fish model systems in other studies [64; 71; 72; 73; 74; 65]. It is interesting that trimethylamine was found as a candidate for spoilage marker in meagre, a fish from Greek seawaters. Despite the fact that trimethylamine is produced in large amounts in fish from Northern seas as metabolic product of _Shewanella_ and _Photobacterium_[10], it is produced at negligible amounts in fish caught in Mediterranean waters even after long storage [51; 76]. This can be ascribed to the low level of precursor compound trimethylamine oxide in fish from such water [51; 76; 77]. With the exception of aquacultured sea-bass where trimethylamine oxide concentration was 16 mmol/kg [77], there is a lack of such data and it would be interesting to determine trimethylamine oxide content in aquacultured meagre in a future study. Regarding compounds associated with chemical activity, no such VOCs were found to increase in the whole or filleted meagre. On the other hand, the compounds with a declining trend, such as alkanes, could be potentially used as indicators of freshness loss. Finally, to the best of our knowledge, no previous work occurs in the volatile profile of meagre during storage to compare our findings. Therefore, further research must be done in the field to enrich our knowledge on such quality characteristics of this much promising fish from the Mediterranean aquaculture.
## 2 Materials and Methods  
### Materials and Sample Preparation  
The epoxy resin DER-331 based on Bisphenol A (Dow Chemical, Germany) was used as a polymer matrix. The epoxy resin DER-331 contains 22.4-23.6% epoxy groups; the average molecular weight of the resin is 340 g/mol. Polyethylenepolyamine (PEPA) was used as a curing agent (ZAO Uralkhimplast, Nizhny Tagil, Russia).  
The multi-walled carbon nanotubes used in this work were supplied by Shenzen Nano-Tech Port Co. (MWCNT-4060 grade).  
Thermally-expanded graphite was produced from commercial intercalated graphite (EG-350-50 brand, \"Khimicheskie systemy\"Co., Russia) using the method of programmable heating [39]. According to this method, expanded graphite was obtained from intercalated graphite by heating it at a rate of 20 \({}^{\circ}\)C/min to a temperature of 500 \({}^{\circ}\)C.  
The epoxy resin was heated to 55 \({}^{\circ}\)C from room temperature (heating rate was 10 \({}^{\circ}\)C/min), and then the required amount of fillers was added into the epoxy resin using mechanical stirring for 10 min; afterwards, PEPA was added into mixture. The ratio of epoxy resin and PEPA was 6:1 by weight.  
The curing of the samples was conducted at room temperature (\(25\pm 2\)\({}^{\circ}\)C) for 24 h. The formulations of the studied epoxy composites are given in Table 1. The loading of MWCNTs ranged from 0.01 to 0.5 wt.%, whereas the loading of EG was in the range from 0.5 to 2.5 wt.%, and the upper limit of filler concentration was taken according to its bulk density. It was not possible to obtain high-quality samples of epoxy/MWCNTs with the loading of the latter above 0.5 wt.%.
# Laser Irradiation on Limestone and Cracking: An Experimental Approach
## 3 Results
### Temperature Distribution under Different Laser Power  
During the experiment, the infrared thermal imager was used for real-time recording. The upper limit of temperature measurement of an infrared thermal imager is 2000 \({}^{\circ}\)C, and the recording frame rate is 6 Hz. To facilitate analysis, the video was saved every 180 frames (30 s). The selected samples were irradiated with a power of 90 W power for 180 s and 135 W power for 180 s, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Laser Power/W** & **Irradiation Time/s** & **Specimen** \\ \hline  & & 90 & 1 \\  & 90 & 120 & 2 \\  & & 150 & 3 \\  & & 180 & 4 \\ \cline{2-4} Dried rock & & 90 & 5 \\  & 135 & 120 & 6 \\  & & 150 & 7 \\  & & 180 & 8 \\ \hline  & & 90 & 9 \\  & 90 & 120 & 10 \\  & & 150 & 11 \\ Soaked rock & & 180 & 12 \\ \cline{2-4}  & & 90 & 13 \\  & 135 & 120 & 14 \\  & & 150 & 15 \\  & & 180 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scheme of rock-breaking by laser irradiation.  
Figure 3: Temperature distribution after laser irradiation with different laser power (**a**) 90 W; and (**b**) 135 W.  
Figure 3a shows the temperature distribution of the rock with the irradiation time from 30 s to 180 s at a laser power of 90 W. The irradiation area above 100 \({}^{\circ}\)C increases with the increase of irradiation time, and the maximum temperature of the rock remains above 1200 \({}^{\circ}\)C. Figure 3b shows the temperature distribution of the rock with the irradiation time from 30 s to 180 s at a laser power of 135 W. Compared with the power of 90 W, the irradiation area range above 100 \({}^{\circ}\)C is larger. This is because the higher the laser power for the same irradiation time is, the more energy the rock absorbed, causing more damage to the limestone sample.  
Figure 4 shows the trend of the area of the temperature region (over 100 \({}^{\circ}\)C) with increasing laser irradiation time at different laser power. The high temperature area increases from 2.27 cm\({}^{2}\) to 7.07 cm\({}^{2}\) with the increase in irradiation time when the sample is irradiated with a power of 90 W. The high temperature area increases from 3.46 cm\({}^{2}\) to 13.20 cm\({}^{2}\) with the increase in irradiation time when the sample is irradiated with a power of 135 W. In addition, when the power is 90 W, the growth trend of the high-temperature region over 100 \({}^{\circ}\)C becomes slower after 90 s, while the high-temperature region still maintains a clear growth trend during the entire irradiation period at a power of 135 W. This is because the subsequent laser energy is almost completely dissipated or consumed in the repeated melting of the rock when the irradiation time reaches 90 s at a power of 90 W. For the power of 135 W, the time of 180 s has not reached equilibrium, and there is still a trend of continuous increase.
# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
## Results
### Seasonal Phenology Patterns and Analysis of Forest Canopy Gaps  
Annual EVI was positively correlated to leaf litter mass (r = 0.55; \(p<0.05\)). We observed one month lag between peaks in leaf litter mass (June and November) and EVI (July and December) (Figure 5). These observations suggest the months with greater leaf renewal would be June and November, which correspond to high peaks of seasonal GPP.  
The canopy structure of our studied area had a mean height of 10.2 m (sd = 3.08), where most values from the canopy surface model are between the range of 4.8 and 14.9 m (5 th and 95 th percentile),  
Figure 4: Scatterplots between the daily mean of eddy covariance GPP compared to climate drivers from2011 to 2013: Net radiation (**a**), ET (**b**), VPD (**c**), Average soil moisture for layers up to 200 cm depth (**d**), Rainfall (**e**), Ta (**f**) and the monthly average of GPP compared to CWD (**g**) and EVI (**h**). Both lines represent the regression fit between variables during flooded (blue) and non-flooded (yellow) months.  
and up to 38 m maximum height (Figure 6b). The canopy surface model shows the canopy is mostly closed with only 0.51% of the canopy area exhibiting gaps (height \(<\) 2 m), meaning that only a very small fraction of areas within the forest canopy would allow direct observation of the underlying understory. Although the LiDAR transect is 7 km away from the tower (Figure 6a), field measurements of tree height support our assumptions that the forest structure around the tower is similar to those of the LiDAR transect, as described in Table 2.  
Figure 5: Monthly average leaf litter mass rate (solid black line) collected at the surrounding area of the tower from April 2004 to May 2005 [49], and monthly average EVI-multi-angle implementation correction (MAIAC) (black dashed line) for the same period. The flooded period is represented by the blue shading area (**a**). Scatterplot between EVI and leaf litter mass with the fitted regression line (red) (**b**).  
Figure 6: (**a**) Spatial and temporal correlation between tower EVI and pixels with less than 10% of permanent water channels [52] inside the Cantão State Park. (**b**) LiDAR canopy height model (CHM) transect and gaps (\(<\) 2m height) in red.  
The EVI spatial correlation also showed that pixels within this transect are positively correlated to the tower EVI response through the time series analysis (\(r\geq 0.6\), \(p<0.01\)). Hence, we argue there is no strong influence of free standing water on the EVI from MODIS-MAIAC observed during the flooded period, which could skew the observed phenological patterns.
## 3 Results and Discussion
### Improvement of Membrane Properties
#### 3.4.2 LEP and Contact Angle  
A comparison of the membrane properties of the LEP, contact angle and pore size between the as-spun and post-treated membranes was investigated. Consistent with the previous study [24], Figure 6 shows that the LEP of the neat membrane was the lowest, contributing to the rapid wetting in DCMD. After the membrane was heat-pressed, the LEP increased dramatically from 73 kPa to 91 kPa owing to the vastly decreased average pore diameter and increased crystallinity, although the contact angle decreased from 153\({}^{\circ}\) to 142\({}^{\circ}\) owing to the reduced roughness [24]. Annealing the HP membranes does not result in a significant change of the LEP as the effect of the increased pore sizes is offset by the increase in the hydrophobicity of the membranes, indicated by the increased contact angle. A2 shares a nearly identical LEP as the HP while it had the highest pore size (1.3 \(\upmu\)m) owing to a greatly increased contact angle of 147\({}^{\circ}\). Two days of annealing is proved to be sufficient for the improvement of the properties as A3 shares a nearly identical LEP, contact angle and average pore diameter as A2. It is worth mentioning that support layers had minimal effects on the LEP owing to the hydrophilicity and large pore size. The introduction of support layers into the DCMD test mainly aimed to improve the mechanical strength of the electrospun membranes. However, the use of the support layer increased the mass transfer resistance and caused additional temperature polarization, which requires further study [6].  
Figure 5: Pore diameter distribution of as-spun and post-treated electrospun membranes.
## 2 Wettability Measurement Methods
### Methods Based on Spontaneous Absorption of Liquid by Rock and Capillary Pressure
#### 2.1.2 United States Bureau of Mines (USBM) Method  
The USBM wettability evaluation method follows the same steps as the Amott-Harvey test. The USBM index is defined as the logarithmic ratio between the area under the capillary pressure curve of the oil displacement curve \(A_{O}\) and the area under the water displacement curve \(A_{W}\) (see Figure 2) using Equation (3) [9] as follows:  
\[I_{USBM}=log\bigg{(}\frac{A_{O}}{A_{W}}\bigg{)} \tag{3}\]  
The USBM wettability index values are most often in the range from \(-2\) to \(2\)[10]. The results of the assessment of rock wettability according to the USBM wettability index value are presented in Table 2. The USBM method as well as the Amott-Harvey method allow for the determination of core wettability, and they are reliable, but the former allows for the determination of wettability values close to neutral. The limitation of this method is its cost, complexity, and relative slowness, as it takes 1-2 days.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Amott-Harvey wettability Index** \\ \hline water-wet & 0.3–1 \\ \hline mixed-wet & \(-0.3\)–0.3 \\ \hline oil-wet & \(-1\)—\(-0.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock wettability and the Amott–Harvey index.  
Figure 2: The capillary pressure curves for the USBM method [9].  
The USBM method differs from the Amott-Harvey test in that it does not involve the spontaneous imbibition of the sample. This accelerates the process of obtaining wettability results and makes the method more sensitive to samples with neutral wettability [75]. It cannot be definitively stated that one method is superior to the other, as each has its own limitations. Therefore, in practice, both methods are often used simultaneously to provide a more accurate assessment of sample wettability [76; 77].
## Variable-Diameter Drum with Concentric Threshing Gap and Performance Comparison Experiment
###### Abstract  
The threshing gap of the thresher device for rice combine harvester has to be adjusted in real time based on different feed rates to ensure the operation efficiency in the harvesting process. However, adjusting the threshing gap by changing the position of concave grid may result in unevenness of threshing gap of the thresher device and further impact on the fluidity of material in the thresher device; in addition, it is also unavailable to adjust the threshing gap by changing the drum diameter when the rice combine harvester is in operation. In view of the above and based on axial flow threshing drum, the design of a variable-diameter threshing drum available for overall and rapid drum diameter adjustment and the research on diameter adjustment device as well as electronic control self-locking device were introduced in this study. Besides, stress analysis was implemented to the diameter adjustment device to ensure the stability of the variable-diameter threshing drum. Field experiment was implemented to identify the difference between the impacts brought to the threshing performance (grain-entrainment loss rate, damage rate, threshing efficiency, and threshing power consumption) by both methods for threshing gap adjustment. The experiment result shows that the drum adjustment method with variable-diameter drum features higher grain-entrainment loss rate, threshing efficiency, and threshing power consumption, yet stable in terms of consumption fluctuation, but a lower damage rate than their counterparts with concave adjustment method.  
**Keywords:** combine harvester; variable-diameter threshing drum; threshing gap; field experiment; threshing performance
## 4 Benchmark Databases and Performance Evaluation Metrics
### Dataset Used
#### 4.1.12 CICIDS2018  
This dataset was produced in cooperation with the CIC and the Communications Security Establishment (CSE) [201] as an expansion of the CICIDS2017 dataset. The objective was to develop a new dataset that was scalable and more accurate. The authors used the Amazon Web Services (AWS) platform instead of the obsolete network architecture used in the CICDIS2017 dataset to build the normal and attack classes using the same notion of network profiles. The network traffic in the CICIDS2018 dataset was compiled over a 10-day period. It has attacks such as brute force, Heartbleed, botnets, DoS, DDoS, web attacks, and infiltration of the network. There are a total of 1,046,845 records in this dataset. There are 360,833 records classified as \"normal traffic\", which amounts to 34.5%, and 686,012 (65.5%) classified as \"attack traffic\". This is freely accessible to researchers in flow and packet formats.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 3 Methods  
### Convolutional Neural Network Architecture  
The neural network design utilized in this work is based on the basic architecture used for some of the first successful convolutional neural networks proposed by [PERSON] [17]. Instead of using two-dimensional convolutional layers, as one would use for an image, this network uses one-dimensional convolutional layers. The input layer takes in the real and imaginary parts of the CIR as separate channels. Early investigations found that a real/imaginary representation performs better than a magnitude/phase representation, probably due to the lack of a jump introduced by the phase wrap [18].  
The CNN is constructed using the Tensorflow library [19]. The CNN architecture is depicted in Figure 4. Each convolutional layer consists of 40 learned filters and uses a rectified linear unit [20] as the activation function. This is followed by a dropout layer to help prevent overfitting [21] and a maximum pooling layer. The pooling reduces the dimensionality, improves invariance to translation, decreases the processing time, and changes the scale of features being learned by the next convolutional layer. After four layers of this, the output of the pooling layer is flattened and attached to a fully-connected 100-element layer. To facilitate the use of this network for featurizing, a second eight-element layer is included before the regression output layer.
## 4 Case Study
### IEEE RTS-24 Node Case Study System
#### 4.2.2 Planning Results under Different Scenarios
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & \multirow{2}{*}{\begin{tabular}{c} **Transmission Line Planning Scheme:** \\ **Corridor (Number of New Lines)** \\ \end{tabular} } & \begin{tabular}{c} **Energy Storage** \\ **Planning** \\ **Schedme: Nordic** \\ **(MW/MWh)** \\ \end{tabular} & \begin{tabular}{c} **Transmission** \\ **Line Investment Cost (\(\times 10^{8}\) CNY)** \\ \end{tabular} & \begin{tabular}{c} **Energy Storage** \\ **Investment Cost** \\ **(\(\times 10^{8}\) CNY)** \\ \end{tabular} & \
\begin{tabular}{c} **Wind** \\ **Curaliment Cost** \\ **(\(\times 10^{8}\) CNY)** \\ \end{tabular} &
\begin{tabular}{c} **Solar** \\ **Curaliment Cost** \\ **(\(\times 10^{8}\) CNY)** \\ \end{tabular} \\ \hline \hline \multirow{4}{*}{3} & 1 & 2-6(1), 7-8(1), 11-13(1) & 20(16.84/100.0) & 1.32 & 0.88 & 1.38 & 0.01 \\  & 2-4(1), 4-9(1), 7-8(1), 20-23(1) & 20(40.46/240.20) & 0.88 & 1.60 & 1.52 & 0 \\  & 2-4(1), 4-9(1), 9-12(1), 12-23(1) & 4(113.43/808.17) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multi-stage coordinated expansion planning scheme for transmission network and energy storage, and associated costs. By comparing Tables 2 and 3, it can be found that the planning results of Single multi-stage transmission expansion planning and the method proposed in this paper are mainly different in the third stage. In the multi-stage coordinated expansion planning scheme of transmission network and energy storage, as shown in Table 3, the number of new lines in the three stages is 3, 4, and 6 respectively, and the number of new lines in the three stages gradually increases. This is because the grid-connected capacity of new energy gradually increases, and the construction of energy storage is mainly concentrated at nodes 4 and 23, which are the grid-connected nodes of new energy stations. In the first stage, the grid-connected capacity of wind power plants and photovoltaic power stations is small, and the expansion of lines is mainly to meet the load demand of system load nodes (6 and 8) and the power output of thermal power plants (nodes 2, 7, and 13); In the second stage, when the grid-connected capacity of new energy increases, the newly built lines are mainly around node 4 (grid-connected node of photovoltaic power station) and node 23 (grid-connected node of wind power plant) to achieve low-cost power output of wind and photovoltaic power generation; In the third stage, the grid-connected capacity of new energy increases significantly. To alleviate the transmission congestion of the system, the number of newly built lines increases significantly, and they are still concentrated around the grid-connected nodes of new energy and related nodes.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 12 Boot-Strapping  
Obtaining measurement data for constructing the observation model--and for locating the APs--takes some effort. There is a limit to how many distinctive landmarks one may want to make measurements from. Once an approximate observation model--and approximate positions for the APs--have been determined in this fashion, one can generate a lot of synthetic data: the estimate of the location of the UE can then be used as a proxy for the true position of the UE. This way, instead of a few dozen measurements obtained from known landmarks, one has available thousands of RTT measurements that can be used to improve the model and refine the positions of the APs. This boot-strapping process for fine-tuning the model can be repeated, although the return-on-effort tends to be lower the second time around.