esa-sceva/satcom-chunk-collection huggingface dataset:
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 2 Oleaginous microorganisms
### FA Composition of SCOs  
SCOs represent a fascinating raw material for product development because they have a chemical profile suitable for biotechnological production of biodiesel and biochemicals. The FA profile of microbial oils varies according to the genus and species [1], but usually is similar to the oils produced by oleaginous plants such as soybean, rapeseed, sunflower and palm oil [58; 152; 153].  
SCOs produced by oleaginous yeasts and filamentous fungi mainly consist of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic, (C18:2) and \(\alpha\)- and \(\gamma\)-linolenic C18:3) acids, with C16:0, C18:1 and C18:2 usually being the most abundant Fas [2; 50; 57; 80]. Microalgae species synthesize long-chain Fas with a higher number of double bonds, such as docosahexaenoicenoic acid (C22:6), eicosapentaenoic acid (C20:5) and arachidonic acid (C20:4) [3; 28; 32; 154]. Most bacteria accumulate polyhydroxyalkanoates (usually poly 3-hydroxybutyric acid), instead of TGAs [155]. The oleaginous bacteria able to accumulate TAGs are characterized by the presence of more saturated Fas, such as lauric acid (C12:0), C14:0, C16:0 and C18:0, although unsaturated Fas are sometimes present [155; 156].  
The industrial applications of each oleaginous species are determined by its FA composition. Some microalgae and the most species of yeasts and bacteria synthesize common oils having a FA composition similar to plant oils, and thus are intended to produce biodiesel with similar physical and chemical properties to petroleum diesel [9; 28; 32; 66; 71; 154; 155; 157], while other species of microalgae and some fungi are able to synthesize polyunsaturated fatty acids and therefore are intended to produce cosmetics, infant formula additives and animal feed [28; 29].
## 1 Introduction
### Structure of the Paper  
The remainder of the paper is structured as follows: Section 2 contains a brief about the related work; Section 3 presents the foundational concepts; Section 4 presents the proposed architecture and construction of proposed scheme (i.e., CL-BS); Section 5 holds implementation of the proposed scheme in FANET; Section 6 outlines the AVISPA tool component of our proposed scheme for formal security verification as well as informal security analysis; Section 7 compares the proposed scheme with the existing schemes; and in the end, Section 8 succinctly culminates the manuscript by concluding the work.
Molecules2014198965-8980; doi:10.3390/molecules190789651014198965101420-3049  
_Article_  
Volatile Organic Compound Emissions from Different Stages of _Cananga odorata_ Flower Development  
[PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,3, [PERSON] 1, [PERSON] 1,2,3,*, [PERSON] 1,3 and [PERSON] 1,3  
1 Spice and Beverage Research Institute, Chinese Academy of Tropical Agricultural Science (CATAS), Wanning, Hainan 571533, China; E-Mails: [EMAIL_ADDRESS] (X.-W.Q.); [EMAIL_ADDRESS] (C.-Y.H.); [EMAIL_ADDRESS] (S.-Z.H.); [EMAIL_ADDRESS] (G.W.); [EMAIL_ADDRESS] (F.X.); [EMAIL_ADDRESS] (R.-S.H.)  
2 Key Laboratory of Genetic Resources Utilization of Spice and Beverage Crops, Ministry of Agriculture, Wanning, Hainan 571533, China  
3 Hainan Provincial Key Laboratory of Genetic Improvement and Quality Regulation for Tropical Spice and Beverage Crops, Wanning, Hainan 571533, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-898-6255-3687; Fax: +86-898-6256-1083.  
_Received: 14 April 2014; in revised form: 15 June 2014 / Accepted: 17 June 2014 / Published: 27 June 2014_  
**Abstract:** Headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used to identify the volatile organic compounds (VOCs) of the different flower development stages of _Cananga odorata_ for the evaluation of floral volatile polymorphism as a basis to determine the best time of harvest. Electronic nose results, coupled with discriminant factor analysis, suggested that emitted odors varied in different _C. odorata_ flower development stages, including the bud, display-petal, initial-flowering, full-flowering, end-flowering, wilted-flower, and dried flower stages. The first two discriminant factors explained 97.52% of total system variance. Ninety-two compounds were detected over the flower life, and the mean Bray-Curtis similarity value was 52.45% among different flower development stages. A high level of volatile polymorphism was observed during flower development. The VOCs were largely grouped as hydrocarbons, esters, alcohols, aldehydes, phenols, acids, ketones, and ethers, and the main compound was \(\beta\)-caryophyllene (15.05%-33.30%). Other identified compounds were \(\beta\)-cubebene, D-germacrene, benzyl benzoate, and \(\alpha\)-cubebene. Moreover, large numbers of VOCs were detected at intermediate times of flower development, and more hydrocarbons,esters, and alcohols were identified in the full-flowering stage. The full-flowering stage may be the most suitable period for _C. odorata_ flower harvest.  
**Keywords:**_Cananga odorata_; volatile organic compound; flower development; HS-SPME-GC-MS
## 4 Discussion  
### Feature Importance Analysis  
Figure 6 shows the feature importance and represents the relative importance of each feature in the final model. Feature importance is a metric used in machine learning models to evaluate the importance of each feature in predicting the outcome. It helps to understand which features have the most significant influence on the model's predictions. In general, a higher feature importance value indicates a greater impact of that feature  
Figure 5: Comparing the optimal models using (**a**) R\({}^{2}\); (**b**) RMSE; (**c**) MSE; (**d**) Delta RMSE; and (**e**) training time.  
on the model's predictions [87]. The statement regarding the importance of the \"Fine Aggregate/Total Material\" feature and its influence on compressive strength is consistent with previous studies (e.g., [88]). Similarly, the \"Water/Cement\" feature is identified as the second most important, aligning with the general understanding that reducing the water-cement ratio can improve the compressive strength of concrete [1, 43, 75, 89]. The third-largest impact is attributed to the \"Age\" feature, suggesting that curing time or the age of the concrete influences compressive strength, which of course is well known. However, the \"Fly Ash/Total Material\" feature is reported to have a value of '0', indicating no impact on compressive strength, which is attributed to limited data availability given that only 12 data in the 752 total dataset included fly ash.  
As the main motivator for this study, it can be seen that the moisture condition does not rank very high on the feature importance plot. Moisture condition of the RCA is the 8 th ranked feature, indicating that other features, including water-to-cement ratio, total cement content, and coarse RCA content, have a greater impact on the compressive strength of concrete.
# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
###### Abstract  
Tool condition forecasting (TCF) is a key technology for continuous drilling of CFRP/Ti stacks, as the tool wear is always rapid and severe, which may further induce unexpected drilling quality issues. However, for drilling CFRP/Ti stacks, the cutting spindle power and vibration signals change are complex, influenced by many factors due to the different materials properties. The TCF for drilling CFRP/Ti stacks remains challenging, as the sensitive features are difficult to extract, which decide the accuracy and robustness. Aiming to monitor and forecast tool wear of drilling CFRP/Ti stacks, an in-process TCF method based on residual neural network (ResNet) and long short-term memory (LSTM) network has been proposed in this paper. Using the cutting spindle power and vibration signals preprocessed by the proposed method, the LSTM network with the ResNet-based model integrated can forecast tool-wear values of the next drilling holes. A case study demonstrated the effectiveness of TCF, where the results using raw measured signals and preprocessed datasets are tested for comparison. The mean absolute error (MAE) using raw signals is 45.01 \(\upmu\)m, which is 2.20 times bigger than that using preprocess signals. With the proposed method, the data preprocessing for drilling CFRP/Ti stacks can improve the tool-wear forecasting accuracy to MAE 20.43\(\upmu\)m level, which meets the demand for online TCF.  
tool condition forecasting; CFRP/Ti stacks; tool wear; drilling
# Optical Injection Locking for Generation of Tunable Low-Noise Millimeter Wave and THz Signals
## 4 Results and Discussion
### Injection Locking with Two Lasers
This variance may be caused by the design and fabrication differences of different chips (see Table 1). The difference in the phase noise at high frequencies is explained by the different optical power incident on the photodiode and, as a consequence, by the different power of the analyzed RF signal. Measurement of the PN for 70 GHz spacing between selected lines of OFC shows an increase in the PN of below 100 kHz for approximately 3 dB as expected.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 2 Materials and Methods
### Measurements  
The wind velocities at the windward and leeward sides of the wind barriers were measured using an I-type hot wire anemometer (Dantec I-type Probe 55P11, sampling rate: 16.67 data s\({}^{-1}\)) and three-dimensional traverse equipment (Figure 1b). Wind velocity was measured at 3 points along the vertical (30, 60, and 90 cm) and 7 points along the horizontal (50 to 600 cm) points (i.e., 21 points in total leeward behind the wind barriers) (Figure 3). The wind velocity threshold required to move sand and dust in the coastal Saemangeum land of the Republic of Korea is assumed to be approximately 4.0 m s\({}^{-1}\) at 0.5 elevation [9]. Thus, we used a wind velocity of 5.0 m s\({}^{-1}\) at the windward side, which was approximately two-fold the 10-year monthly average wind speed for Gunsan city (approximately 2.2 m s\({}^{-1}\)) of the land [39]. After operating the wind turbine for several minutes, the wind velocities on the windward and leeward sides stabilized.  
The wind velocity of the wind tunnel was initially set to 5.0 m s\({}^{-1}\). The wind velocities at each single measuring point on the windward (\(n=3\)) and leeward (\(n=21\)) sides were measured for 60 s (i.e., 1 min mean wind speed), and the average wind velocity of each measuring point was determined (Figure 3). We analyzed the change in the average wind velocity at the measured distances. Wind speed reduction is determined by the ratio of windward and leeward wind speeds.  
\[U_{R}=\frac{U}{U_{0}}\times 100\ (\%) \tag{2}\]  
where \(U_{R}\) is the ratio of windward and leeward wind speeds, \(U\) is the average leeward wind speed (m s\({}^{-1}\)), and \(U_{0}\) is the average windward wind speed (m s\({}^{-1}\)).  
Wind flow visualization was conducted to quantitatively determine the movement of the wind velocity provided in Section 3.
## 1 Introduction  
With the development of the Internet, the amount of data is increasing exponentially. Therefore, it is difficult for users to quickly find information to meet their needs. The emergence of recommender systems has effectively alleviated this problem. However, the traditional recommendation system is seriously plagued by the problems of sparse interaction data and cold starts. Many researchers have introduced the knowledge graph (KG) [1, 2] into the recommendation model to solve this dilemma. Rich semantic associations can compensate for the sparseness of user historical interaction data. Therefore, the question of how to utilize the entity and relation information fully and effectively in KGs has become the focus of these studies.  
In recent years, generative adversarial networks (GANs) have developed rapidly in the field of deep learning due to their powerful ability to learn complex real data distributions [3, 4]. The purpose of a GAN is to conduct an adversarial battle between the generator (G) and the discriminator (D). The generator focuses on capturing the distribution of real data, generating adversarial samples, and fooling the discriminator; at the same time, the discriminator tries to distinguish whether the input samples come from the generator or not. Therefore, we proposed a KG recommendation model based on adversarial training (ATKGRM). This method can dynamically and adaptively adjust the aggregation weight of the knowledge graph, reasonably capture the content information of items, and better mine item features. Specifically, the model has two modules: G and D. G learns the real data distribution in the training data and then generates candidate items. D compares the user's scores on candidate items, positive items, and negative items and then identifies candidate items more accurately.  
This paper makes three main contributions:  
* In this paper, adversarial training is introduced into the KG recommendation model. Therefore, the knowledge graph aggregation weight can be dynamically and adaptively adjusted to obtain more reasonable user features and item features.
* The generator employs a long- and short-term interest model to discover the latent features of users and generates a high-quality set of candidate items. The discriminator uses a popularity-based sampling strategy to obtain more accurate positive items and negative items. It can more accurately identify candidate items by comparing the user's scores of positive items, negative items, and candidate items. The generator and discriminator continuously improve their performance through adversarial training.
* We present a comparison with multiple knowledge graph recommendation models and multiple adversarial training recommendation models on five public datasets: MovieLens-1M, MovieLens-10M, MovieLens-20M, MovieLens-20M, Book-Crossing, and Last. FM. Thus, the effectiveness of the ATKGRM proposed in this paper is verified.
# MINC-NRL: An Information-Based Approach for Community Detection
## 4 Community Detection Based on MINC
### Find the Peak Value of MINC through the Process  
The community evolution process is a topological miniature which covers the integration and fragmentation of the communities in real-world networks. The final step of the approach, is to find the state with the largest \(MINC\) value, as the final result of community detection.  
Figure 5 shows the \(MINC\) values of the first 20 states in the community evolution process constructed on Polbooks [26] network, with the abscissa indicating the corresponded CN values of the states. As shown in the figure, with the largest \(MINC\) value, the state with 3 communities will be output as the result partition.  
As a summary, the basic process of MINC-NRL is listed below:  
1. Perform random walks in the network, and obtain random-walking sequences by tracking the passing nodes;
2. Input the random-walking sequences into the Word2 vec [27] model, which outputs the vector representation of each node;
3. Perform a hierarchical clustering to the vectors. For non-overlapping community detection tasks, AHC is used; for overlapping community detection tasks, OHC is used. Each level of the dendrogram generated by the clustering algorithm will be convert to each state \(P_{i}\) of the community evolution process \(P=P_{0},P_{1},P_{2},\cdots,P_{N}\);
4. Calculate the \(MINC\) value of each states in \(P\);
5. Output the state with the largest \(MINC\) value as the result of community detection.  
Figure 4: Quasi-dendrogram generated by OHC.  
The pseudo-codes in Algorithm 1 gives a brief description of MINC-NRL.  
```
Input: Network \(G(E,V)\) Output: Communities detected in network \(G(E,V)\)
1 sequences \(\leftarrow\) List(\(R^{*}N\));
2 for\(r\gets 0\) to \(R\)do
3 for\(n\gets 0\) to \(N\)do
4\(seq\leftarrow\) List(\(\prime\));
5\(currentNode\gets n\);
6\(seq\).append(\(currentNode\));
7 for\(l\gets 0\) to \(L\)do
8\(currentNode\leftarrow\) randomSelect(\(currentNode\).neighbors);
9\(seq\).append(\(currentNode\));
10
11 end for
12\(sequences\).append(\(seq\));
13
14 end for
15
16 end for
17\(model\leftarrow\) Word2 Vec(\(sequences\), size \(\gets\) neu_size, window \(\gets\) window_size);
18\(vectors\gets model\).get_embeddings(\(0\));
19 ifoverlapping=Truethen
20\(endrogram\leftarrow\) OHC(\(vectors\));
21 else
22\(endrogram\leftarrow\) AHC(\(vectors\));
23
24 end for
25\(list\_partition\gets dendrogram\).levels ;
26\(list\_MINC\leftarrow\) List(\(N\));
27 for\(i\gets 2\) to \(N\)do
28\(list\_MINC[i]\leftarrow\) list_partition[\(i\)] ;
29
30 end for
31\(opt\_i\leftarrow\) index_of(max(\(list\_MINC\)));
32 returnlist_partition[\(opt\_i\)];
```  
**Algorithm 1**MINC-NRL
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 3 The Model of OSR of Interference Signals  
The OSR of interference signals means that, during the testing phase, the classifier can not only classify the known interference signal (KIS) but also reject the unknown interference signal (UIS), which does not appear in the training stage, i.e., to avoid the UIS being incorrectly classified as one of the known classes.  
A training dataset \(\left\{(x_{1},y_{1}),\dots,(x_{n},y_{n})\right\}\), including \(n\)-labeled KISs, and a test dataset \(\left\{t_{1},\dots,t_{u}\right\}\), including \(u\)-unlabeled samples, are given, where \(y_{i}\in\left\{1,\dots,N\right\}\) is the label of sample \(x_{1}\) and \(N\) is the number of known classes. In the OSR problem, test sample \(t_{i}\) may belong to either a known class \(\left\{1,\dots,N\right\}\) or unknown class \(\left\{N+1,\dots,N+U\right\}\), where \(U\) is the number of unknown classes in the testing phase.  
The interference signal is embedded into the feature space through a feature extractor. All KISs are embedded in the closed space, and the space far from all KISs is called the open space. From the problem description, the OSR of the interference signals can be decoupled into closed-set multiclassification and open-set binary classification.  
For closed-set multiclassification, labeling any KIS as the wrong class brings risk, and it can be defined as an empirical risk \(R_{e}\):  
\[R_{e}=\int_{C}g_{e}(x,y)dx \tag{1}\]  
where \(g_{e}(\cdot,\cdot)\) is a recognition function for closed-set multi-classification. \(g_{e}(x,y)=0\) indicates that the KIS is classified correctly, while \(g_{e}(x,y)=1\) indicates that \(x\) is classified erroneously.  
For the open-set binary classification, labeling any UIS as any class incurs risk, which is defined as the open space risk \(R_{o}\)[34]:  
\[R_{o}=\frac{\int_{O}g_{o}(x)dx}{\int_{C\cup O}g_{o}(x)dx} \tag{2}\]where \(g_{o}(\cdot)\) is a measurable recognition function for open-set binary classification. \(g_{o}(x)=1\) indicates that the sample \(x\) is recognized as KIS; otherwise, \(g_{o}(x)=0\). Therefore, open space risk is considered as a relative measure of open space compared to the overall measure space.  
Then, the goal of the OSR of interference signals is to find two suitable recognition functions, \(g_{e}(\cdot\,\cdot\,)\) and \(g_{o}(\cdot)\), to minimize the joint risk, which is composed of empirical risk and open space risk:  
\[\operatorname*{argmin}_{g_{e}(\cdot,\cdot),g_{o}(\cdot)}\left\{R_{e}+\lambda R _{o}\right\} \tag{3}\]  
where \(\lambda\) is a regularization constant.
## 4 Results and Discussion
### Direct Shear Test Results  
The results from the direct shear tests are presented in Figure 5, which are illustrated in the form of shear stress against normal stress. It can be seen that all soils had almost the same trend: a noticeable increase in the treated soil was observed compared with other soils.  
The direct shear test results showed that the internal friction angle (\(\Phi\)) decreased drastically from 28.56\({}^{\circ}\) to 12.7\({}^{\circ}\) for natural, treated (CEM II and OPC), and contaminated soils, respectively. [PERSON] et al. [41] stated that oil contamination causes a noticeable decrease in the \(\Phi\) value. [PERSON] [42], [PERSON] et al. [10], and [PERSON] et al. [17] reported that in the presence of a high crude oil content, the friction angle decreases. This inverse correlation might be explained by the coating of soil particles with crude oil, which acts as a lubricant that decreases the inter-granular contact force between the sand particles. However, [PERSON] et al. [43] reported that, for samples containing 2% to 20% oil, no significant difference in the frictional angle of the sand was detected, which indicates that the sand particles were totally covered with crude oil at a level of more than 2%, and their frictional angle remained unchanged. For the stabilized soils, the treated soils indicated an increment in the angle of internal friction by stabilizing with CEM II and OPC to 30.7\({}^{\circ}\) and 25.0\({}^{\circ}\), respectively, as shown in Figure 5. This increment could be related to the cement action that increases the agglomeration between grains and minimizes lubrication, increasing the contact force between particles. However, these results are different from the findings of [PERSON] et al. [32].  
The cohesion of the natural soil was 35 kPa. Crude oil contamination led to an increase in the cohesion value of this soil to 56 kPa. These findings match the results of [PERSON] et al. [17], but are incompatible with the findings of [PERSON] et al. [9]. It is clear that crude oil's ability to resist shear force is greater than water, since its viscosity is more. Therefore, during the application of shear force to the contaminated specimen, crude oil resists a portion of that shear force besides the soil particles and, in turn, increases the soil's apparent cohesion.  
In soil stabilized with CEM II, the rise in cohesion was dramatic, changing to 81 kPa, while the cohesion in soil treatment with OPC reached a low value of 27 kPa. This is due to the increase in the material's cohesiveness as a result of the cementing action caused by the hydration process. This is in line with the finding that CEM II hydrated more quickly  
Figure 5: Direct shear test results of natural, contaminated, and treated (CEM II and OPC) soils.  
and provided a higher strength than OPC. Since the C\({}_{2}\)S is responsible for the subsequent rise after the first week in the strength of the cement's hydraulic components, its value is 25.72% in CEM II, compared to 9.79% in OPC.
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Energy Optimization of Standalone UAV Deployments  
In this subsection, we consider the approaches that deal with optimization of the energy consumption of only the UAV-BSs, without considering the effect of UAV deployment on the overall energy consumption of the network. In this regard, only the communication or propulsion or both energy consumption components of the UAV-BS are considered. In addition, both single and multiple UAV-BS(s) deployments are considered as well as the various algorithms that have been proposed.