esa-sceva/satcom-chunk-collection huggingface dataset:
## A New Meroditerpene and a New Tryptoquivaline Analog  
from the Algicolous Fungus _Neosartorya takakii_ KUFC 7898  
**[PERSON] 1,2, [PERSON] 1,2, [PERSON] 3, [PERSON] 1,4, [PERSON] 1, [PERSON] 2,5, [PERSON] 6 and [PERSON] 1,2,* 1**  
1 ICBAS--Instituto de Ciencias Biomedicas Abel Salazar, Universidade do Porto, [PERSON], 228, 4050-313 Porto, Portugal; E-Mails: [EMAIL_ADDRESS] (W.W.M.Z.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (L.G.); [EMAIL_ADDRESS] (J.A.P.)
2 Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), Rua [PERSON] 289, 4050-313 Porto, Portugal; E-Mail: [EMAIL_ADDRESS]
3 Division of Environmental Science, Faculty of Science, Ramkhamhaeng University, Bangkok 10240, Thailand; E-Mail: [EMAIL_ADDRESS]
4 Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, [PERSON], 228, 4050-313 Porto, Portugal
5 Laboratorio de Quimica Organica, Departamento de Ciencias Quimicas, Faculdade de Farmacia, Universidade do Porto, [PERSON], 228, 4050-313 Porto, Portugal
6 Departamento de Quimica & QOPNA, Universidade de Aveiro, 3810-193 Aveiro, Portugal; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +351-220428331; Fax: +351-220428090.  
Academic Editor: [PERSON]  
_Received: 29 April 2015 / Accepted: 4 June 2015 / Published: 15 June 2015_  
**Abstract:** A new meroditerpene sartorenol **(1)**, a new natural product takakiamide **(2)** and a new tryptoquivaline analog **(3)** were isolated, together with nine known compounds, including aszonapyrone A, chevalone B, aszonalenin, acetylaszonalenin, 3'-(4-oxoquinazolin-3-yl) spiro[_1H_-indole-3,5'-oxolane]-2,2'-dione, tryptoquivalines L, F and H, and the isocoumarin derivative, 6-hydroxymlelein, from the ethyl acetate extract of the culture of the algicolous fungus _Neosartorya takakii_ KUFC 7898. The structures of the new compounds were established based on 1D and 2D NMR spectral analysis, and, in the case of sartorenol **(1)** and tryptoquivaline U **(3)**, X-ray analysis was used to confirm their structures and to determine the absolute configuration of their stereogenic carbons. Compounds **1**, **2 and **3** were evaluated for their antimicrobial activity against Gram-positive and Gram-negative bacteria, and multidrug-resistant isolates from the environment; however, none exhibited antibacterial activity (MIC \(>\) 256 mg/mL). The three new compounds did not show any quorum sensing inhibition in the screening protocol based on the pigment production by _Chromobacterium violaceum_ (ATCC 31532).  
keywords:_Neosartorya takakii_; meroditerpene; sartorend; tryptoquivaline U; aszonapyrone A; chevalone B; aszonalenin; 6-hydroxymellein
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 3 Proposed Methods
### Method 2: Drowsiness Detection and Prediction Using Deep Learning  
In this method, the drowsiness detection using deep neural networks (DNNs) includes two phases of the training and testing, as illustrated in Figure 10. In the first phase, we train the proposed network models by training a dataset after pre-processing and feature extraction. In the second phase, we evaluate the network models with a test set for drowsiness detection.
# Evolution of the Complex Supply Chain Network Based on Deviation from the Power-Law Distribution
## 2 Evolutionary Characteristics and Model Analysis of Complex SCNs
### Complex SCN Evolution and Degree Distribution Model
However, due to the influence of internal and external factors in the SCN, the value of the power index \(\gamma\) will not decrease or expand indefinitely, and \(\gamma\) will eventually stabilize at a constant greater than 0, achieving a dynamic and stable distribution state in the structure. For example, the Tokyo Industrial Park Ohta-Ward includes 8311 companies, including TOSHIBA and other companies. The agglomerated SCN composed of these companies has a self-similar structure without a scale network degree distribution and a high degree of dispersion. The distribution of the degree of network nodes conforms to the power-law distribution of the power exponent \(\gamma=2.3\)[13], as shown in Figure 4.  
The power-law distribution is a sign of the transition of the self-organized critical system from the steady state to the chaotic state. The literature [9] found that the power-law distribution of the complex SCN of agricultural products will turn at \(\mathrm{kc}\), which is described by the single power-law distribution. This network degree distribution feature is not accurate, as shown in Figure 5. To accurately describe the power-law distribution of a complex SCN, a complementary distribution of degrees is proposed, which describes the power-law distribution from a turning point into two parts. The complementary distribution of the power-law distribution follows the following formula:  
\[F(k)=P(K>k)=\frac{\lambda}{\gamma-1}k^{-(\gamma-1)}, \tag{5}\]The formula for calculating the power index \(\gamma\) using maximum likelihood estimation is as follows:  
\[\hat{\gamma}=1+n\left[\sum_{i}^{n}\ln\left(\frac{k_{i}}{k_{\min}}\right)\right]^{ -1}, \tag{6}\]  
Among them, \(k_{\min}\) is the minimum degree value of the degree distribution conforming to the power rate characteristic part, and \(k_{\min}>0\). In addition, an increasing number of studies have found that a single power-law distribution does not fully describe the distribution law of real-world systems. The degree distribution of many complex SCNs will change during the evolution process and obey two power laws with different exponents. Distribution, such as China's airline network, social network, and SCNs such as automobiles and transportation ports. Figure 4: The Tokyo Industrial Park Ohta—Ward SCN degree distribution. Figure 5: The agglomerated SCN degree distribution. Therefore, the distribution of the complex network power-law distribution containing two sections of different power-law regions is called a two-stage power-law distribution, which has different power exponents. The function expression [14] is as follows:  
\[P(k)=\left\{ \begin{array}{l}k^{-\gamma_{1}},k<k_{c}\\ k^{-\gamma_{2}},k>k_{c}\end{array} \right., \tag{7}\]  
Among them, \(\gamma_{1}\) and \(\gamma_{2}\) are the indexes of the two-stage power-law distribution, and \(k_{c}\) is the turning point in the two-stage power-law distribution.
# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 2 Methods and Materials
### Subjective Evaluation  
Three neuro-radiologists, with from 6 to 26 years of professional experience, participated in this study. First, the DELPHI method was used among these three experts to establish the image evaluation criteria and the relative weightings of these criteria [22; 23; 24], as shown in Table 1. To obtain such a list, we proceeded as follows: a list of criteria of what was relevant in the image quality was obtained first by an external observation of the radiologists' method of reviewing each type of image, while they were verbally expressing their observations. This list was then submitted as a questionnaire to the each NR. Agreement on which criterion to use was obtained. Once the list of criteria was defined, agreement was easily obtained by the three NR on their respective weight. Each criterion is evaluated using a Likert scale, with scores corresponding to: 1--bad, 2--poor, 3--regular, 4--good, 5--excellent. According to this scale and the weight of each criterion, each exam obtains one grade per NR, and then a weighted Mean Opinion Score (wMOS) is calculated by averaging the scores given by the three NR.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Exam Type** & **Criterion Weight** & **Criterion** \\ \hline Sagittal \(T_{1}\) & 2/50 & Visualization of vertebral bodies \\  & 2/50 & Visualization of spinal cone \\  & 3/50 & Visualization of facet joints \\  & 2/50 & Signal from bone marrow \\  & 5/50 & Overall evaluation \\ \hline Sagittal \(T_{2}\) & 3/50 & Signal homogeneity in vertebral bodies \\  & 1/50 & Visualization of the entrance of basivertebral \\  & 3/50 & Contrast between vertebral body and \\  & & intervertebral disc \\  & 3/50 & Spinal cone visualization \\  & 3/50 & Homogeneity of spinal cord signal \\  & 5/50 & Distinction between spinal roots \\  & 5/50 & Overall evaluation \\ \hline Axial \(T_{1}\) & 1/50 & Similarity of signal between muscles: \\  & & paravertebral and psoas \\  & 5/50 & Definition of the edge of the intervertebral \\  & & discs \\  & 1/50 & Visualization of fascias or grooves of \\  & & subcutaneous fat \\  & 1/50 & Root path through epidural fat \\  & 5/50 & Overall evaluation \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of criteria used for the subjective image quality evaluation, elaborated by three neuro-radiologists, using the DELPHI method. Weighted Mean Opinion Score (wMOS) was calculated using the weights listed in the second column of this table. A higher criterion weight implies a greater importance for this specific criterion.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Multiscale Geometric Analysis  
The excellent characteristics of wavelet transform in one-dimensional data analysis cannot be simply extended to two-dimensional or multi-dimensional data, because it cannot make full use of the unique geometric features of the data itself in the case of higher dimensions. Thus, it is not the optimal or the sparsest method for function representation. The multi-scale geometric analysis (MGA) method arose in response to the proper time and conditions, and typical representations of multi-scale geometric analysis appeared. Ridgelet transform Candes [70], wedgelet Claypoole [71], beamlet Donoho [72], curvelet Candes [73], bandelet Pennec [74], and contourlet Do [75] were successively proposed.  
[PERSON] [27] studied a new image fusion method using bandelet transform based on MGA. In this method, a low-pass subband coefficient of a source image by bandelet transform is inputted into a pulse-coupled neural network (PCNN), and the fused image can then be obtained through inverse bandelet transform using the coefficient and geometric flow parameters. [PERSON] et al. [20] applied the curvelet transform to decompose the image combined with Fourier transform to extract features. [PERSON] et al. [76] explored a method of MGA based on the non-symmetry and anti-packing model (NAM). This method can adaptively be applied to three types of steel products--continuous casting slabs, hot-rolled steel, and cold-rolled steel--that cannot be assessed by the traditional method.  
It can be seen from the theoretical development of signal analysis methods that Fourier analysis is especially suitable for analyzing stable signals over a long period of time. The Gabor transform has its own application, but its effect depends on the window function. Wavelet analysis is especially suitable for analyzing mutated and singular signals. Multi-scale geometric analysis is suitable for the \"sparse\" function representation of high-dimension data.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 3 Proposed Scheme
### Overview of the Scheme
Moreover, the location update process for the agent is needed if the agent's location can be changed. To summarize, we derive the following requirements about an agent from above observation. * An agent should be within targeted continuous object. * An agent should be at fixed and unique location. We select the origin point of a continuous object as an agent. Generally, a continuous object tends to be originated at specific point and to diffuse slowly. With the features, designating the origin point as the location of an agent completely satisfies above requirements. First, the origin point is located within a continuous object because the continuous object diffuses toward its surroundings. Second, the origin point is fixed although the continuous object changes its shape and size. Additionally, there is no need to calculate the location of the origin point because it is surely detected by a specific source. With the origin point, we exploit overlaying virtual structure for supporting sink mobility. The structure should  
Figure 8: Mathematical models of individual location update (**left**) and integrated location update (**right**). Figure 7: Location updates to the sources detecting a continuous object: (**left**) Individual location updates and (**right**) Integrated location updates. satisfy the following design principles: construction of structure regardless of the number of sources and decentralization of messages for avoiding hot-spot problem. We exploit the origin-based structure for supporting sink mobility. The brief introduction of our design is as followings. The origin-based structure delivery location update messages from mobile sinks to the origin point. The node on the origin point is responsible for forwarding the messages to sources. The sources receiving the location update message report data to the sink's location in the messages directly. The origin-based structure is constructed regardless of the number of sources. Moreover, the direct data reporting prevents concentration of messages of the structure. With this, our design satisfies above principles. Additionally, we exploit clustering to reduce data report overhead.
## 5 Results  
SC data are continuous although generated from discrete data. A multi-tier centralized system was developed on Microsoft Azure, which reflects the hierarchical production nature of the manufacturer's supply chain. A systems engineering approach was applied to identify the interplay that exists between systems within the multi-hierarchical relationshipsamongst stakeholders. DDD was applied to develop an agile and modular SCM system, whereby departmental operations are built onto a microservice based on functionality. Since this is a tracing system, stateful microservices are applied. Each microservice is built with a database attached, and communication within the channels is event-driven, as illustrated in Figure 5 below.  
The microservices are deployed independently onto a K8s pod, resulting in a K8s cluster for the system. K8s offers a container orchestration platform, enabling an integrated vertical and horizontal SC orchestration, for inter-organization and intra-organization multi-level auto-scaling systems. A consumption-based payment model for cloud resources is a cost-efficient pricing model, especially for SMEs, due to the high capital expenditure in acquiring a local server, administration, and maintenance  
A blockchain-based SCM system was built on Fabric and deployed on K8s for easy container orchestration. A DT is deployed as a container under the microservice for engineering, whereby manufacturing specifications are shared within the IS using a smart contract, within select suppliers, and using a channel. CouchDB was selected as the default world state database. A private ledger was designed for permissioned multi-tier levels of access and authority. The Fabric network is centralized, based on the AIS system, to enable the tracing of material, information, and money flow within the environment. Chaincode is a centralized repository of information, based on an execute-order-validate ordering system across multiple channels. Multiple smart contracts are interlinked within the chaincode for data traceability for financial transactions and audit reliability. To further strengthen the system, a QR label is autogenerated by the BCMaaS system and is inscribed onto each produced boxed sheet metal part. This process creates an automated inventory management system for part production. Since the assembly process for railcars is defined, the master chaincode is continuously updated to trace the assembly process by the railcar manufacturer. CouchDB is queried using complex queries in order to identify different attributes such as inventory stock, production dates, purchase orders, etc. ML is applied to metadata to give supply chain analytics and give foresight on matching production activities to  
Figure 5: BCMaaS and SCM system architecture for boxed sheet metal manufacturing.  
assembly. As such, the system allows for a supplier performance audit, inventory analysis, the optimization of the assembly process, and the forecasting of part supply and part requirements for maintenance activities.
## 3 Results and Discussion
### AV Characterization of OGFC-5 UPFC Based on CT Scanning  
#### 3.3.1 Vertical Distribution  
The vertical distributions of the AV content and coefficient of variation (COV) are shown in Figure 11. The vertical distribution of the AV diameter is shown in Figure 12. As shown in Figures 11a and 12, the curves of the AV content and AV diameter are in a \"bathtub\" shape along the vertical direction. The AV content was homogeneous and stable in the middle part, whereas those in the bottom and top parts were much larger than the average AV content. In addition, except for the Marshall specimen, the AV content of the other simulated field cores exceeded the design range. The overall AV content and diameter of the MCM-designed UPFC samples was smaller than those of the simulated field cores. The results show that the MCM-designed UPFC did not correspond well with the field construction situation. Obviously, in order to achieve the AV content of the Marshall-designed UPFC during the on-site construction process, it is necessary to increase the compaction tonnage, which will inevitably lead to an increase in the aggregate crushing. Crushed aggregates further embed and fill voids, resulting in a decrease in porosity. However, this will have an adverse impact on the performance of the UPFC.  
Figure 10: Force chain distribution of different thickness UPFC models: (a) 20 mm; (b) 40 mm; (c) 60 mm.  
As shown in Figure 11b, the COV of the Marshall specimen was the largest among all test samples. This is because the SGC and RWC methods exert a kneading effect during the compaction process, such that the aggregates can be fully moved and arranged, thereby producing a relatively homogeneous internal structure. However, because the Marshall compaction method relies mainly on vertical impact, the homogeneity of the internal structure of the sample was relatively poor. The COV of the S150/15 specimen was the smallest, indicating that increasing the compaction area improved vertical uniformity [18, 40]. In addition, the COV values of the 15 mm specimens were all smaller than those of the 63.5 mm specimen, indicating that a small compaction thickness was conducive to uniform compaction. The reason that the 15 mm UPFC specimen could not reach the target height was not compaction. According to Figures 10 and 12, a concentrated force region existed, resulting in insufficient movement and packing between the aggregates when the compaction thickness of the UPFC was small. Thus, the AV size between the aggregates was large, and the thin UPFC specimen could not be compacted to the target thickness.
## 4 Experimental Setup
### The Musical Instrument Sound Dataset  
In total, 92 musical instrument sounds were selected. \"Popular\" and \"Keyboard\" musical instruments are from the RWC Music Database: Musical Instrument Sound [54]. All other sounds are from the Vienna Symphonic Library [55] database of musical instrument samples. Table 1 lists the musical instrument sounds used. The recordings were chosen to represent the range of musical instruments commonly found in traditional Western orchestras and in popular recordings. Some instruments feature different registers (alto, baritone, bass, _etc_). All sounds used belong to the same pitch class (C), ranging in pitch height from C2 (\(f0\approx 65\) Hz) to C6 (\(f0\approx 1046\) Hz). The dynamics is indicated as _forte_ (\"\(\ell\)\") or _fortissimo_ (\"\(\ell\)\"), and the duration of most sounds is less than 2 s. Normal attack (\"na\") and no vibrato (\"nv\") were chosen whenever available. Presence of vibrato (\"vib\"), progressive attack (\"pa\"), and slow attack (\"sa\") are indicated, as well as different playing modes such as _stacato_ (\"stacc\"), _sforzando_ (\"sforz\"), and _pizzicato_ (\"pz\"), achieved by plucking string instruments. Extended techniques were also included, such as _tongue ram_ (\"tr\") for the flute, _pres de la table_ (\"pdlt\") for the harp, muted (\"mu\") strings, and bowed idiphones (vibraphone, xylophone, _etc._) for short (\"sh\") and long (\"lg\") sounds.  
Different maltet materials such as metal (\"met\"), plastic (\"pl\"), and wood (\"wo\") and hardness such as soft (\"so\"), medium (\"med\"), and hard (\"ha\") are indicated.  
In what follows, we will present the results for 89 sounds because QHM failed to adapt for the three sounds marked * in Table 1. The estimation of parameters for QHM uses LS [45]. The matrix inversion fails numerically when the matrix is close to singular (see [44]). The fundamental frequency (C2 \(\approx\) 65 Hz) of these sounds determines a full-band harmonic spectral template whose frequencies are separated by C2, which results in singular matrices.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 2 Background
### Decentralized Learning  
In the current environment of information overload, in order to dig out valuable content from the massive amount of information for user, a large number of research has promoted the rapid development of personalized recommendation system. However, user data are normally distributed in various end devices. Due to privacy constraint, users and institutions are reluctant to share their personal data for centralized training. Decentralized learning [7] is proposed to solve this problem. Recently, it has been applied in many real-world applications, such as navigation [16] and localization [17]. [PERSON] et al. [18] propose a decentralized matrix factorization based on FunkSVD for point-of-interest recommendation. [PERSON] et al. [3] propose a general algorithmic framework for decentralized matrix factorization. However, it has a low efficiency problem. To the best of our knowledge, there is no existing work that achieves great performance in decentralizing a matrix factorization model using explicit and implicit feedback simultaneously.  
In this study, we propose a decentralized learning scheme where models are shared and learned without needing a server node, as shown in Figure 1. Specifically, we implement a decentralized CF model where a decomposing strategy is applied for users to exchange only non-private information, and thus collaboratively train a model.