esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this article, we consider an UAV (unmanned aerial vehicle)-assisted free space optical (FSO) secure communication network. Since FSO signal is impossible to detect by eavesdroppers without proper beam alignment and security authentication, a BS employs FSO technique to transfer information to multiple authenticated sensors, to improve the transmission security and reliability with the help of an UAV relay with decode and forward (DF) mode. All the sensors need to first send information to the UAV to obtain security authentication, and then the UAV forwards corresponding information to them. Successive interference cancellation (SIC) is used to decode the information received at the UAV and all authenticated sensors. With consideration of fairness, we introduce a statistical metric for evaluating the network performance, i.e., the maximum decoding outage probability for all authenticated sensors. In particular, applying an intelligent approach, we obtain a near-optimal scheme for secure transmit power allocation. With a well-trained allocation scheme, approximate closed-form expressions for optimal transmit power levels can be obtained. Through some numerical examples, we illustrate the various design trade-offs for such a system. Additionally, the validity of our approach was verified by comparing with the result from exhaustive search. In particular, the result with DRL was only 0.3% higher than that with exhaustive search. These results can provide some important guidelines for the fairness-aware design of UAV-assisted secure FSO communication networks.  
Keywords:secure FSO communication; security authentication; successive interference cancellation (SIC); decoding outage probability; UAV relay +
Footnote †: journal:
## 3 Results and Discussions
### Artificial Neural Network (ANN)
#### 3.4.5 Comparison of Experimental Data with ANN Predicted Data  
In this section of the study, the results of the comparison of the experimentally obtained TL glow curves at radiation doses of 0.5, 4, 120, 250, and 450 Gy, which were not used in the training with ANN, and the TL glow curves produced by the ANN model are presented.  
The TL glow curve obtained experimentally as a result of the irradiation of the plagioclase mineral with a radiation dose of 120 Gy and the glow curves produced by the LM, BR, and SCG optimizers in the ANN model are shown in Figure 9.  
As can be seen in Figure 9, the glow curves obtained from the LM and BR optimizers overlap with the experimental glow curve, while the glow curve obtained from the SCG optimizer shows deviations. Therefore, in the following comparisons, only the data generated by the LM and BR optimizer are compared with the experimental data. While making these comparisons, graphs obtained at low, medium, and high radiation doses were used to observe the effects of different levels of doses.  
The TL glow curves obtained with different radiation doses (0.5, 4, 250, and 450 Gy) experimentally and produced by the LM and BR optimizers are shown in Figure 10.  
When all the graphs in Figure 10 are examined, it can be said that both the LM and BR optimizers are able to predict the TL glow curves in accordance with the experimental data.  
Figure 9: TL glow curves obtained experimentally and with different optimizers (radiation dose, D = 120 Gy).  
As can be seen in Figure 10a,b, the experimental TL glow curves at low dose values are not consistent in both low- and high-temperature regions. In many studies, it has been shown that quantum emission in the visible region by the heating unit of the thermoluminescence reader causes a spurious TL signal at low radiation dose levels [32; 33; 34]. Apart from these spurious signals, room temperature, noise, and background radiation can also affect the data obtained at low dose levels.  
Therefore, these experimental deviations at low radiation doses in both low- and high-temperature regions should be considered normal. Due to these deviations, it can be seen in Figure 10a,b that there are some deviations in the TL glow curve predictions made with the LM and BR optimizers at low dose values, both in the low-temperature (<50 \({}^{\circ}\)C) and high-temperature (>325 \({}^{\circ}\)C) regions. It is also seen in Figure 10c,d that these deviations disappear towards high dose values and the TL glow curve predictions give more qualitative results.
# Oil Press-Cakes and Meals Valorization through Circular Economy Approaches: A Review
## 5 Possible Ways to Capitalize the by-Products Obtained from the Oil Industry
### Extraction of Bioactive Compounds
#### Antioxidants
These modulate the enzymatic decomposition of carbohydrates by inhibiting glucosidases and amylases. Natural phenolic antioxidants (flavonoids, phenolic acids) from sesame and coconut oilcake and synthetic butylated hydroxytoluene (BHT) were introduced in vanilla cake composition to compare their potential in improve the oxidative stability and both microbial and chemical shelf-life, without changing consumer acceptability. Both oilcakes improved chemical and microbiological stability up to 13 days, and only up to 11 days for BHT. The sensory quality in cakes with by-products from the oil industry was retained from the first day to day 12. Moreover, the activity of natural antioxidants is stable thermally [137]. Sesame oilseed cake extract (lignans) at a lower amount (150 ppm) can be used as a food additive with a role in oil stability improvement. In butter, it can reduce the lipid oxidation time much better than BHT without changing the sensory properties [57; 136]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Antioxidants** & **Oilcake** & **Reference** \\ \hline Gallic acid & Canola & [111; 136; 138] \\ Tannic acid & Linseed & [111] \\ p-coumaric & Flax, Peanut, Sunflower, Linseed, Sesame, & \\ Catech & Mustard, Rapeseed, Palm, Olive & [111; 136; 138] \\ Caffeic acid & Canola/Rapeseed, Pamp, Peanut, Mustard, Palm & [111; 136; 138] \\ Epicatechin & Canola/Rapeseed, Sunflower & [111; 136; 138] \\ Ferulic acid & Canola/Rapeseed, Flax, Linseed, Sesame, & [111; 136; 138] \\ Quercetin & Hemp, Canola/Rapeseed, Cottonseed, Olive & [111; 136; 138] \\ Luteolin & Hemp, Canola/Rapeseed, Olive & [111; 136; 138] \\ Lignans & Sesame, Olive, Linseed & [136; 136; 136] \\ Chlorogenic acid & Sunflower & [136; 139] \\ Sinapic acid & Rapeseed, Mustard & [125] \\ p-hydroxybenzoic acid & Linseed, Olive, Palm & [136] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Antioxidants in oil cakes.
## 3 Motivation  
Previously, many works have shown that deep learning techniques tend to perform better for disk failure prediction [8; 9]. Thus, this work was motivated by failure prediction experiments based on deep learning models, where two observations were found. To simplify, we show some examples using a CNN model.  
Observation 1: For deep learning models, more training epochs lead to lower training losses but higher latency.Figure 1 shows the change in modeling loss with the increase in training epochs, where the modeling loss displays a decreasing tendency. This reflects that sufficient epochs are needed to obtain a more accurate model. At the same time, Figure 1 shows the normalized modeling latency; from 10 to 1000 epochs, the latency increases rapidly; therefore, a more accurate model always needs a sufficiently long time for training. Thus, this would impact the efficiency of some applications that require timeliness. Unfortunately, disk failure prediction is one of these, where if timeliness is not considered, the modeling latency may be too long to replace failed disks in time, wasting computing resources and storage, and what is worse, resulting in data and economic losses.  
Observation 2: Using fewer attributes does not degrade the prediction quality but reduces the modeling latency. Figure 2 shows the prediction effect for different models, and these models were trained using different numbers of attributes. To evaluate the effect, precision, recall, and F1-score were used, which is explained in Section 5; and for all of them, the higher the value, the better. The epoch of this CNN model was set to 50 for simplicity, and the attributes were selected randomly without putting them back. Generally, training models with more attributes may allow a better prediction effect; however, the trend for the three metrics is up and down, as shown in the figure. This reflects that more attributes cannot result in better prediction effects; even with 33 attributes, the result was not better than that with 8 attributes, in which, the precision, recall and F1-score of the former and the later were 0.81, 0.24, 0.38, and 0.65, 0.39, 0.49, respectively. Additionally, Figure 2 shows the normalized modeling latency when training with 33 to 3 attributes; the latency decreased significantly with fewer attributes. Thus, in this example, if only eight attributes are used for training, the prediction results would be close to training with all attributes; meanwhile, the model latency would be reduced significantly.  
Therefore, to maintain both timeliness and model quality for disk failure prediction, it is necessary to training models with enough epochs and at the same time reduce the modeling latency as much as possible. It is necessary to design a good feature selection scheme to improve the efficiency of this task.  
Figure 1: Observation 1: (**a**) The loss of a CNN model decreases with the increase in epochs; (**b**) The latency increases with the increase in epochs.
## 3 Result Analysis
### Pore Analysis  
In order to understand the pore content and size of rocks, pores need to be extracted for analysis, as shown in Figure 8.  
For the sample scanning data, the pores were extracted through threshold segmentation (as shown in Figures 9 and 10), and the volume percentage of pores \(\geq\)34 \(\upmu\)m in the total volume of the scanned samples was calculated under the current resolution (34 \(\upmu\)m), that is, the porosity was 2.3% and 1.5%, respectively.  
Figure 8: Crack extraction by threshold segmentation. (**a**) Sample 1. (**b**) Sample 2.  
Figure 7: Sample 2 3D display. (**a**) Sample 3D display. (**b**) Pore 3D display. (**c**) Sample + Pore 3D display.  
Figure 11: _Cont._  
Figure 10: Sample 2—crack extraction and display. (**a**) A 3D map of pore structure. (**b**) Pore labeling.  
Figure 9: Sample 1—crack extraction and display. (**a**) A 3D map of pore structure. (**b**) Pore labeling.  
Count the number of pores in different equivalent diameter ranges, before and after \"/\" are sample 1 and sample 2, respectively, as shown in Table 2, and pie charts are drawn for a more intuitive comparison of the proportion of each diameter range, as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Range of EqD** & **EqD \(\leq\) 50 um** & **50 \(\leq\) EqD \(<\) 100 um** & **100 um \(\leq\) EqD \(<\) 150 um** & **150 um \(\leq\) EqD \(<\) 200 um** & **200 um \(<\) EqD** \\ \hline Quantity & 798,519/387,918 & 1,441,575/1,159,424 & 618,214/658,976 & 265,479/297,246 & 162,830/1,125,884 \\ Percentage of total pore volume & 0.7\%/0.6\% & 7.1\%/9.8\% & 14.3\%/24.4\% & 16.5\%/29.3\% & 61.4\%/36.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pore equivalent diameter statistics.  
Figure 11: Pore equivalent diameter screening. (**a**) Sample 1. (**b**) Sample 2.  
Figure 12: Pore equivalent diameter pie chart (%). (**a**) Sample 1. (**b**) Sample 2.  
With the increase in the equivalent diameter of the pore, the pore number showed a rapid decline, and the equivalent diameter of 50-100 \(\upmu\)m was the greatest. From the pie chart of pore volume fraction, it can be seen that pores with equivalent diameter >200 \(\upmu\)m occupy the largest volume ratio (61.4% and 36.0%).  
In response to the high porosity and high volume of large-aperture pores in the Luohe Formation sandstone, which leads to on-site issues such as easy rock fragmentation, excessive water inflow, significant deformation, and challenging support, improvement measures have been proposed. It is recommended to replace the top anchor rod with anchor cables by extending the anchoring section into the deep surrounding rock to control roof collapses. Additionally, reinforcing the mesh panels with steel straps can enhance the synergy between anchor.
## 4 Non-Parametric Methods
### K-Nearest Neighbour  
K-nearest neighbours (kNN) anomaly detection schemes are generally classified into two broad categories, those that are density-based and those that are distance-based [41]. The former is ideal for multilevel detection applications and can process unevenly dense training data. Two popular metric parameters in these schemes are the local outlier factor (LOF) and the connectivity-based outlier factor (COF) which both have large computational complexities that may not make them feasible for IWSN applications without some optimisation. Distance based schemes classify a data point as anomalousto a dataset if less than a predetermined number of points in that set are a predetermined distance from that set. These schemes also have high computational complexities but can be optimised for IWSN applications.  
A density-based kNN scheme that uses LOF as a metric for anomaly detection on uncertain data was proposed by the authors in [42]. Local density and uncertainty information is used to calculate uncertain local outlier factors (ULOF) in an uncertain dataset. The distance distribution function is found by using the least squares algorithm on multi-times curve fitting and a pruning algorithm is used to reduce the set of potential \"nearest-neighbours\". Experiments yielded favourable results when compared to state-of-the-art techniques, but dimensionality was found to be a major drawback.  
The lazy-learning problem of the kNN algorithm was addressed by the authors in [41] who proposed a distance-based scheme that makes online detection a possibility. The scheme is based on a hypergrid that consists of several fixed-size hypercubes in which data points are mapped in order to make an anomaly detectable. Hypergrid schemes have low computational complexities making them ideal for IWSN applications. In fact, the complexity of the proposed scheme increases logarithmically with sample size as opposed to other schemes that have quadratic increase.  
The effectiveness of a kNN algorithm lies with the optimal selection of the k-value. A larger k-value makes the algorithm less susceptible to noise but could also result in a neighbourhood that is too large with indistinct boundaries and cause short circuit errors. The opposite could inadvertently lead to data points in the same class being separated due the weakened association between the nodes in the same neighbourhood. In [43] the authors propose a weighted natural neighbour graph (WNaNG) which is an adaptive algorithm that does not make use of the k-value. The algorithm makes use of the principle of implicit trust to find all the \"friendly\" nodes in a particular cluster. The rest of the nodes are then considered \"outsiders\". The algorithm has generally favourable results when compared traditional kNN algorithms depending on the data distribution and selected k-value.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Classification of MFCs  
An MFC cell consists of an anaerobic, biotic anode component and an aerobic biotic/abiotic cathode component. Depending on the external environment, MFCs can broadly be classified into lab-scale (inside lab) and in situ (outside lab) configurations. In situ lab configurations can be further classified into aquatic and terrestrial configurations. Aquatic MFCs consist of sediment docked and non-sediment docked configurations and sediment docked configuration can be further classified into open water configurations and closed water configurations. A hierarchy of this placement dependent MFC classification can be found in Figure 7.
## 3 Modelling of Three-Phase Orbital Manoeuvring Method
### Three-Phase Orbital Manoeuvring Model  
There is a common feature in all previous studies of constellation deployment: energy fuel consumption minimization was one of the most significant criteria [23]. Therefore, low-thrust technology is used to reduce the fuel consumption of orbital manoeuvring in this paper [24]. A three-phase orbital manoeuvre model is used in this paper. The manoeuvring strategy corresponding to the model is to use the natural orbital motion of the satellite to achieve the desired changes in the observation of the satellite's subsatellite points. The natural disturbance size of satellite orbit is related to the satellite height; therefore, the disturbance is used to achieve the desired change of satellite orbit and change the satellite height, to achieve the change of ground orbit. Figure 2 shows the three-phase orbital manoeuvre model.  
* Phase 1: the satellite maintains a constant acceleration using constant low thrust, causing the satellite orbit to increase or decrease in altitude relative to the initial orbit to a transfer orbit.
* Phase 2: satellites maintain a constant altitude in transfer orbit by counteracting the effects of atmospheric drag with thrust.
* Phase 3: the satellite is moved to the desired final altitude by using the same constant low thrust as in the first phase to give the satellite constant acceleration.  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Unit** \\ \hline Altitude & 703 & km \\ Inclination & 40 & deg \\ Right ascension of the ascending node at epoch & 0 & deg \\ Argument of latitude at epoch & 0 & deg \\ \hline \end{tabular}
\end{table}
Table 1: Spacecraft orbit parameters.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Damage Detection
#### 4.4.2 Damage Wall Comparison  
In Table 5, the top predictions for ResNet, MobileNet, and EfficientNet correctly classify the object as a _damaged wall_, and MobileNet again achieves the highest confidence score of 97.58% for that predicted class. The bounding box location in Figure 7b predicted by MobileNet more accurately encompasses the entire _damaged wall_ structure in comparison to the bounding box location in Figure 7a predicted by MobileNet and Figure 7c predicted by EfficientNet. However, the second and third predictions made by ResNet and MobileNet are inaccurately classified as either _structural damage_ or a _damaged roof_, likely due to similar features of a _damaged wall_ in this image that the model has learned to extract in predicting the _structural damage_ and _damaged roof_ classes as well.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ResNet** & \multicolumn{2}{c}{**MobileNet**} & \multicolumn{2}{c}{**EfficientNet**} \\ \hline  & **Score** & **Type** & **Score** & **Type** & **Score** & **Type** \\ \hline \#1 & 75.00\% & damaged wall & 97.58\% & damaged wall & 55.22\% & damaged wall \\ \#2 & 23.44\% & structural damage & 15.07\% & structural damage & 18.41\% & damaged wall \\ \#3 & 20.54\% & damaged roof & 11.56\% & structural damage & 13.46\% & damaged wall \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of confidence scores and associated object categories for the top three predictions made on the image of a _damaged wall_ for each of the three models.  
Figure 6: Inference carried out on the image of a _damaged roof_ for each of the three models. The green bounding boxes correspond to the correct predictions of a _damaged roof_, and the grey bounding box corresponds to the incorrect prediction of _structural damage_ from the first row of Table 4.
## 3 Results and Discussion
### EO Profile of the Two Cultivars
The different terpene profiles could potentially impact the aroma and therapeutic properties of the essential oils. These compounds have a wide range of biological activities; some of them are interesting in view of the potential use of the essential oils in cosmetic applications, as they exhibit antioxidant (beta-myrcene), anti-inflammatory (beta-myrcene, alpha-pinene, E-beta-caryophyllene), and antibacterial effects (alpha- and beta-pinene, E-beta-caryophyllene, (+)-limonene) [23]. It is worth noting that there are differences from other essential oils of _Cannabis_ described in the literature; for instance, the cannabidiol amounts were lower compared to the values reported in the literature for other registered cultivars [18]. On the other hand, these differences should not surprise, because it is widely documented that the composition of _Cannabis_ EOs depends on several intrinsic and extrinsic factors, such as the cultivar type, pedoclimatic conditions, harvesting time, processing of plant material before extraction, and extraction techniques [24; 25; 26].
# Effects of Oxidized Pyrenes on the Biological Responses in the Human Bronchial Epithelial Cells
###### Abstract  
Although polycyclic aromatic hydrocarbons (PAHs) are toxic, the effects of oxidized PAHs on health and biological responses remain unclear. In this study, we examined the in vitro effects of varying concentrations of pyrene, a type of PAH, and its quinone forms, namely 4,5-pyrenequinone (PyQ) and 1,8-PyQ + 1,6-PyQ on human lung epithelial (BEAS-2B) cells. We evaluated cell viability, apoptosis, and the production of interleukin (IL)-6, IL-8, soluble intercellular adhesion molecule-1 (sICAM-1), and reactive oxygen species (ROS). Exposure to 1 uM 4,5-PyQ or 1,8-PyQ + 1,6-PyQ increased the cellular activity. At 3 uM, 4,5-PyQ increased the number of late apoptotic and/or necrotic cells compared with those in the control, whereas 1,8-PyQ + 1,6-PyQ increased the number of dead cells. Exposure to 4,5-PyQ at 10 uM decreased IL-6 production and exposure to both 4,5-PyQ and 1,8-PyQ + 1,6-PyQ at 3 or 10 uM decreased IL-8 production. sICAM-1 production was increased after 1,8-PyQ + 1,6-PyQ exposure at 10 uM. In the presence of cells, 4,5-PyQ and 1,8-PyQ + 1,6-PyQ increased ROS production significantly in a concentration-dependent manner; similar results were observed with 1,8-PyQ + 1,6-PyQ without cells. Overall, our results suggest that oxidized PAHs induce stronger respiratory toxicity/inflammatory responses than PAHs.  
pyrene; quinone forms; BEAS-2B cells; respiratory toxicity +
Footnote †: journal: Applied Sciences