esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Engineering Approaches for the Microbial Production of Vanillin
## 4 Genetic Engineering Approaches
### Genetic Engineering in Vanillin-Producing Microorganisms
#### 4.2.2 Introduction of Heterologous Genes Directing the Synthesis of Vanillin Precursors  
As mentioned before, ferulic acid is quite expensive, and its availability (and price) can vary, so it is desirable to implement pathways that allow the use of economically viable substrates. However, the lack of efficient genetic tools in most vanillin-producing microorganisms makes the genetic manipulations more complex. So far, only the introduction of a ferulic acid biosynthetic pathway from the cheap metabolite eugenol has been described. Thus, the heterologous expression of the _vaO_ gene, from _Penicillium simplicissimum_, under the control of the lac promoter in _Amyscolatopsis_ sp. HR167 (Figure 6(A1)), using a replicative vector, enables the production of trace amounts of vanillin from eugenol [82]. The expression of the _vaO_ gene under the control of _ermE*p_ in _Streptomyces_ sp. (Figure 6(A2)) was also reported for vanillin production from eugenol [83]. These studies showed that the strains used possess endogenous genes encoding enzymes able to convert coniferyl alcohol into ferulic acid and vanillin. However, the _Amyscolatopsis_ sp. HR167 strain expressing _vaO_ accumulated coniferyl alcohol and coniferyl aldehyde in the medium, suggesting the low specificity of native enzymes for the conversion of coniferyl alcohol into vanillin. To improve vanillin production from eugenol, the authors suggested the additional heterologous expression of the _caLA_ and _calB_ genes in this _Amyscolatopsis_ sp. HR167 recombinant strain [82].  
The introduction of the full biosynthetic pathway from eugenol to ferulic acid has also been tested in other bacteria. For instance, the expression of the _vaO_ gene, from _Penicillium simplicissimum_, and the _caLA_ and _calB_ genes from _Pseudomonas_ sp. HR199 in _Rhodococcus_ opacus_ PD630 allowed the production and transie  
Figure 6: **Genetic engineering approaches allowing the use of eugenol as a precursor of ferulic acid for vanillin bioproduction. (A) Schematic representation of the biosynthetic pathway and the applied constructions. (B) Table summarising the function and origin of the genes/enzymes used.**  
acid from eugenol (Figure 6(A3)) [36]. Similar results were obtained using a recombinant strain of _Ralstonia eutropha_ H16 expressing the _ehyA_, _ehyB_, and _calA_ and _calB_ genes from _Pseudomonas_ sp. HR199 (Figure 6(A4)) [84]. Ferulic acid could be purified and then converted into vanillin by vanillin-producing microorganisms.
## 3 Results and Discussion
### Nanocomposite Electrical Properties  
It has been observed that the composite sample with a 4 wt% GNP content as well as the hybrid nanocomposite 2 wt%GNP2 wt%MWCNT/HDPE exhibit a positive temperature coefficient (see Figure 6). It should be noted that the bifiller composite shows a percolation above 80 degrees, whereafter the resistance rises sharply. The reason of this leap in resistivity can be the joint work of the GNPs and MWCNTs in the hybrid material, establishing a more effective conductive network in the polymer matrix. The graphene particles, with their flat, plate-like structures, provide a large surface area for the conductive network, while the carbon nanotubes, with their tubular structures, provide the necessary interconnections between the graphene nanoplatelets. This combination results in an interconnected conductive network that is more sensitive to the thermal expansion of the HDPE matrix. Compared to the bifiller composite, the composition 4 wt%GNP/HDPE demonstrated a smooth increase in resistance as well as higher electrical resistivity due to the not-well-established conductive network. In contrast, at the nanocomposite 4 wt%MWCNT/HDPE, MWCNTs are the primary conductive particles, and their structures allow them to form a more interconnected conductive network in the polymer die, which is more stable and was not affected by thermal expansion; therefore, its resistivity hardly changes in the studied temperature range.  
Figure 6: Resistivity measurements of the nanocomposite structures in the temperature range of 20–100 \({}^{\circ}\)C.
# Testing Concrete for the Construction of Winemaking Tanks
## 2 Materials and Methods
### Leaching Test  
The two selected compositions were part-immersed in white and red wine for 30 days with the aim of testing the leachability of metals from the concrete into the wine and the stability of the concrete structure under the influence of wine, the latter being an acidic solution. The concrete was characterized before and after part-immersion. As an alternative for the construction of the interiors of the tanks, three coatings were also tested for leachability in wine. Two of them were commercially available epoxy resin coatings for edible goods' tanks, coded D and S. Another coating, coded A, was produced by mixing waterglass with soil from the area of the winery, to introduce aspects of the locality (terroir) of the crop to the wine through the maturation process in the tank.  
The leaching test consisted of the following steps:  
* Large containers (60 \(\times\) 30 \(\times\) 30) cm were filled with red and white wine.
* The pH value of the wines and the pH value of the concrete samples were determined prior to immersion.
* Coated (three different coatings) and uncoated concrete specimens (150 \(\times\) 150 \(\times\) 150) mm, at 28 days of age, were partially immersed in red and white wine.
* After 30 days, 50 mL of wine quantity was removed from every container and the pH value of the wine was determined.
* To continue, 1 mL of 1 M HNO\({}_{3}\) acid was added to the vials to preserve the wines until the determination of total metals. The samples were heated in a sand bath at 95 \({}^{\circ}\)C for 1 h. At the end of this period, 5 mL of concentrated nitric acid was added again and left for a further hour at the same temperature. The pre-treated sample is then removed from the sand bath and allowed to come to room temperature. The procedure is completed by adding it to a 100 mL volumetric flask and filling it up with deionized water. The pre-treated samples were filtered with a vacuum pump before the determination of the metals.
* Total metal oxides determination was conducted with flame atomic absorption spectroscopy (FAAS) for metals reported at the mg/L level and graphite (GFAAS) for metals reported at the ug/L level. The determination of mercury was carried out with a cold vapor atomic absorption apparatus (CVAAS).
## 3 Simulation Results and Discussion
### Effect of Thermal Conductivity
As observed, the mean minimum difference values for all the thermal conductivities was under 10%, whilst the mean differences were under 25% for all the conductivities relative to the reference. In turn, the mean maximum differences compared with the reference were under 40% for all thermal conductivities. This shows that, although greater thermal conductivity results in shorter start-up times, cases exist in which low thermal conductivity does not mean excessive increases in these times. Thus, a wooden flooring should not be rejected solely on the basis of its conductivity value.
###### Abstract  
Proper management of waste is one of the major challenges for the food industry. Fruit seeds are an example of by-products that are rich in bioactive substances and generated in significant amounts during fruit processing. Oils, including those obtained from seeds, should meet certain requirements to be allowed for human consumption. The aim of this study was to determine the quality of oils from black current, strawberry and cranberry seeds by determining the following parameters: fatty acid composition by gas chromatography, distribution of fatty acids between sn-2 and sn-1,3 positions of triacylglycerlevels by partial enzymatic hydrolysis, oxidative stability by pressure differential scanning calorimetry and melting characteristics by differential scanning calorimetry. Additionally, health indices of atherogenicity (IA), thrombogenicity (IT) and hypologicrapeutic/hypercholesterolemic (HH) ratio were calculated. It was found that the oils studied were a rich source of unsaturated fatty acids. Linoleic acid was found to occupy the internal position of triacylglycer levels in the highest proportion. Black currant and strawberry seed oils were of lower oxidative stability and, in the case of cranberry seed oil, high oxidative stability was determined. The shape of melting curves for black currant and strawberry seed oils indicated the presence of low-melting fractions of triacylglycer levels containing polyunsaturated fatty acids. It can be concluded that cranberry seed oil, with low values of IA and IT, high values of HH index and the highest oxidative stability, can be considered the most favorable from a nutritional point of view concerning human health.  
black currant seed oil; strawberry seed oil; cranberry seed oil; quality assessment; fatty acid composition; fatty acid distribution; oxidative stability; melting profile; health indices Article
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 6 Evaluation
### Protection from Malicious Content  
The spread of fake or harmful content is a serious threat that is not addressed by previously-proposed advert distribution systems that make use of opportunistic technologies. As data are propagated through temporary connections among peers, a malicious Agent could easily replace an advert with his/her own content before forwarding it. Our system overcomes this threat through the use of cryptography on both the ARMs, as well as the ads themselves. As previously stated in Section 5, ARMs are encrypted with \(EK_{System}\), ads are encrypted with \(EK_{i}^{AD}\) and the corresponding \(DK_{i}^{AD}\) is composed into an ADM, which is encrypted with \(K_{i}^{user}\).  
In order for an attacker to replace an advert within a Bundle, he/she would need to encrypt the new malicious advert with the same \(EK_{Z}^{AD}\), which he/she does not have. If the attacker were to simply use a new encryption key \(EK_{Attacker}\) that he/she generates himself/herself, the other users would not be able to decrypt the malicious advert since they do not have the corresponding decryption key \(DK_{Attacker}\). For this attack to work, the attacker would first need to create a new ADM that contains the appropriate \(DK_{Attacker}\), and then, the new ADM would need to be encrypted with \(K_{i}^{user}\). Obtaining \(K_{i}^{user}\) however is impossible, as it is enclosed in the ARM that has been encrypted with \(EK_{System}\) and can therefore only be accessed by the Ad-Dealer who knows \(DK_{System}\).  
One last possibility that needs to be considered is that of the user being tricked into creating ARMs that are not encrypted with \(EK_{System}\). Since \(EK_{System}\) is installed within the client software, this could only be possible if the attacker managed to compromise the user's mobile device. If the attacker were to have access to the user's client, then this attack would be trivial since the attacker could easily bypass the system by simply replacing the locally stored adverts. We can therefore conclude that for as long as \(DK_{System}\) and \(EK_{i}^{AD}\) are kept secret, it is infeasible for anyone other than an Ad-Dealer to send encrypted adverts to users.
## 3 Scalable On-line Anomaly Detection System (SOADS)
### Architecture of SOADS
#### 3.1.2 Sequence Extractor  
The Sequence Extractor groups recognized read points for each object ID from the flat formatted event logs delivered from the Event Log Preprocessor, and generates [object-(read point list)] pairs that list read points in the order of time. The generated object-(read point list) pairs are delivered to the Anomaly Evaluator. Figure 3 shows examples of object-(read point list) pairs generated from event logs.
## 2 Materials and Methods
### Design and Participants
#### 2.1.5 Anxiety  
Two levels of anxiety were measured using the State-Trait Anxiety Inventory (STAI) questionnaire. The questionnaire consists of 40 items, 20 pertaining to state anxiety and 20 to trait anxiety. State anxiety (S-A) is conceptualized as a transient emotional condition of the human organism, characterized by consciously perceived subjective feelings of tension and apprehension, as well as hyperactivity of the autonomic nervous system. It can vary over time and fluctuate in intensity. Trait anxiety (T-A), on the other hand, refers to a relatively stable anxious disposition in which individuals differ in their tendency to perceive situations as threatening, leading to an increase in state anxiety. Anxiety levels were measured using a four-point Likert scale (0 = not at all to 3 = very much). Finally, it is important to note that some of the 40 questions are reversed, while others are direct. For trait anxiety, there are 7 reversed items (21, 26, 27, 30, 33, 36, 39) and 13 direct items (22, 23, 24, 25, 28, 29, 31, 32, 34, 35, 37, 38, 40). For state anxiety, there are 10 reversed items (1, 2, 5, 8, 10, 11, 15, 16, 19, 20) and 10 direct items (3, 4, 6, 7, 9, 12, 13, 14, 17, 18). Reversed items are scored inversely, where 0 becomes 3, 1 becomes 2, 2 becomes 1, and 3 becomes 0. The total score for each concept ranges from 0 to 60 points, with higher scores indicating greater anxiety [32]. As for the reliability of the questionnaire, [PERSON]'s alpha scores of 0.90 were observed for trait anxiety and 0.93 for state anxiety [33].
## Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering  
**[PERSON] 1,\(\star\), [PERSON] 2, [PERSON] 1 and [PERSON] 1**  
1 Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou Street, GR-185-34  
Piraeus, Greece; E-Mails: [EMAIL_ADDRESS] (D.D.V.); [EMAIL_ADDRESS] (C.D.)  
2 Department of Electronics, Technological Educational Institute (TEI) of Athens, Agiou Spiridonos, GR-12210 Egaleo, Athens, Greece; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +30-697-481-8999; Fax: +30-210-414-2458.  
_Received: 14 September 2012; in revised form: 4 January 2013 / Accepted: 14 January 2013 / Published: 18 January 2013_  
**Abstract:** The wide utilization of Wireless Sensor Networks (WSNs) is obstructed by the severely limited energy constraints of the individual sensor nodes. This is the reason why a large part of the research in WSNs focuses on the development of energy efficient routing protocols. In this paper, a new protocol called Equalized Cluster Head Election Routing Protocol (ECHERP), which pursues energy conservation through balanced clustering, is proposed. ECHERP models the network as a linear system and, using the Gaussian elimination algorithm, calculates the combinations of nodes that can be chosen as cluster heads in order to extend the network lifetime. The performance evaluation of ECHERP is carried out through simulation tests, which evince the effectiveness of this protocol in terms of network energy efficiency when compared against other well-known protocols.  
**Keywords:** WSNs; energy efficiency; hierarchical routing; Gaussian elimination
# A Force Measurement Method Based on Flexible PDMS Grating
## 2 Property of PDMS and fabrication of PDMS grating
### Property of PDMS  
The material studied in this paper is polydimethylsiloxane (PDMS), which is a silicon-based polymer. This material is optically transparent and generally considered inert, non-toxic and non-flammable [13]. PDMS has the characteristics of elasticity and adjustable elastic modulus. The brief formula of PDMS is shown in Figure 1.  
Generally, PDMS is composed of an elastomer base and elastomer curing agent. By increasing the proportion of curing agent, the cross-linking structure of the material increases, leading to reduction of the elasticity and raise of the hardness. Moreover, heating treatment accelerates the cross-linking reaction [14]. In 2011, [PERSON] et al. applied macro compression and nano indentation technology to measure the mechanical properties of PDMS [15]. In recent years, the most widely used mixing ratio  
Figure 1: PDMS chemical formula.  
of PDMS is 10:1, that is, 10 Sylgard 184 silicone elastomer base and 1 Sylgard 184 silicone elastomer curing agent [16].  
Elastic modulus is an important property of PDMS, which is the quotient of stress to strain. Tensile test determines the strength and plasticity feature of a material by the relationship between stress and strain [17]. The first stage in the tensile test is so-called elastic deformation where the sample deforms linearly with the stress. With the rise of stress until the sample is yielded, the plastic deformation will occur. The following stage is the strain hardening, where the increasing stress leads to further deformation. Finally, the sample breaks and the stress is called fracture stress [18]. Particularly, the proposed force sensor works in the elastic deformation stage, where the elastic modulus determines the linear relationship between stress and strain.  
To measure the elastic modulus of PDMS samples under different processing parameters, a tension tester is often used. The stress in the stretching direction is the ratio of tension force to cross-sectional area of the sample [19; 20]. Meanwhile, the strain in the stretching direction is the ratio of tension displacement to the length of the sample. The elastic modulus of the PDMS grating sample can be calculated according to the stress and strain data recorded [21].
## 3 Data and Methodology
### Modeling Methods
#### 3.4.4 Naive Bayes  
Naive Bayes (NB) is a statistical classifier based on the Bayesian theorem [73]. To simplify the calculation of the posterior probability of observed cases in training data, the NB assumes that each attribute independently affects the classification results [74]. Landslide spatial prediction using NB can be expressed as following equation:  
\[y_{NB}=\text{P}(y_{i})\prod_{i=1}^{n}\text{P}(x_{i}|y_{i}) \tag{6}\]  
where \(\text{P}(y_{i})\) is prior probability of target class \(y_{i}\) (landslide), and \(\text{P}(x_{i}|y_{i})\) is the conditional probability of each attribute.
## 2 Related Work
### ASD Recent Research  
Given the broad array of early audio-centered approaches, and their lingering success with nonvisual data, current efforts have turned more towards vision-oriented issues. This is not to say audio has stopped being a key aspect of current approaches to ASD. In fact, multi-modal state-of-the-art techniques have used machine learning to realize audiovisual correlations in data. Such techniques are successful, for instance, as a means to recognize speech related facial motion while also discerning it from other unrelated movements. In [16; 19], [PERSON] et al. used the outputs of a face detector, along with audio-derived voice activity detection (VAD) labels, to train a CNN task specific feature extractor together with a Perceptron classifier. For transfer learning comparison, known CNN architectures were used as pre-trained feature extractors whose outputs were employed in training temporal (LSTM net) and nontemporal (Perceptron) classifiers. In a related work [20], [PERSON] et al. tackled clutter and unrelated motion fallible situations by extending conventional LSTM models to share learned weights across the audio and visual modalities. Thus learning audiovisual correlations to improve model robustness. With a real implementation in a NAO robot, [PERSON] et al. [21] used time difference of arrival (TDOA) over a set of microphone pairs to simultaneously evince potential sound sources in 2D space. A facial detector then estimated mouth locations in 3D space, to be statistically mapped to the former 2D sound sources and TDOAs. In a similar fashion, [PERSON] et al. [22] mapped located sound sources to the 2D image plane as well as estimated potential speaker lip positions. However, this cross-modal weighting approach employed a GMM variation to assign weights to audio observations based on their spatial proximity to visual observations, and vice-versa. Audiovisual clustering then achieved active speaker detection. To give another example, [PERSON] et al. [23] attempted correlating the degree of occurrence of individual faces in a video with single speaker clusters of speech segments, obtained using vectors of locally aggregated descriptors (VLADs) [24].  
In addition to the usual reliance on high-end equipment or ideal settings (i.e. consistent number of speakers, frontal facing and nonoverlapping speech, mic/camera location) which is common to clustering and other methods, further issues also occur with current state-of-the-art techniques. Particularly, unidirectional dependency between modalities is recurrent among machine learning approaches. The lack of carefully labeled big data on both the audio and visual modalities for ASD evidently forces resourcing to alternatives which transfer labels generated from one modality to its sibling (typically from audio to video). Such processes unavoidably lead to propagation of error, originating from the single modality prediction techniques, which could be avoided were large ASD datasets existent and applicable to robust machine learning architectures.
## 5 Proposed Method
### Hop Count  
Hop count is defined as the average number of intermediate nodes in the routing path between the source node and the destination node. The first scenario evaluates the number of hops based on the packet-sending rate. It is represented in Figure 14. Based on this figure, we can deduce that the number of hops has a direct relationship with the packet sending rate (PSR), meaning that high PSR leads to a high number of hops in the routing path because high PSR causes congestion in the network and has a negative impact on the routing process. QFHR has lowered the number of hops compared to IRQ, IV2 XQ, QGrid, and GPSR, by 6.74%, 20.09%, 2.68%, and 12.22%, respectively. The second scenario evaluates the number of hops based on the signal transmission radius (STR). It is represented in Figure 15. According to this figure, when STR has a large value, the number of intermediate nodes in the path decreases. QFHR lowers the number of hops by 14.13%, 32.58%, 7.71%, and 21.39% compared to IRQ, IV2 XQ, QGrid, and GPSR, respectively.  
Figure 12: Average delay in different approaches according to PSR.  
Figure 13: Average delay in different routing approaches according to STR.