esa-sceva/satcom-chunk-collection huggingface dataset:
_Int. J. Mol. Sci._**2013**, _14_, 9080-9098; doi:10.3390/ijms14059080  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
**Transcriptome Analysis of _Thapsia laciniata_ Rouy Provides**  
**Insights into Terpenoid Biosynthesis and Diversity in Apiaceae**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 3 and [PERSON] 1,* 1 Department of Plant and Environmental Sciences, Faculty of Sciences, University of Copenhagen, Frederiksberg DK-1871, Denmark; E-Mails: [EMAIL_ADDRESS] (D.P.D.);**  
[EMAIL_ADDRESS] (B.D.); [EMAIL_ADDRESS] (C.W.)
2 Wine Science and Business, School of Agriculture Food and Wine, University of Adelaide, South Australia, SA 5064, Australia
3 Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada; E-Mails: [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (C.W.S.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +45-353-33328.  
_Received: 22 February 2013; in revised form: 21 March 2013 / Accepted: 17 April 2013 / Published: 25 April 2013_  
**Abstract:**_Thapsia laciniata_ Rouy (Apiaceae) produces irregular and regular sesquiterpenoids with thapsane and guaiene carbon skeletons, as found in other Apiaceae species. A transcriptomic analysis utilizing Illumina next-generation sequencing enabled the identification of novel genes involved in the biosynthesis of terpenoids in _Thapsia_. From 66.78 million HQ paired-end reads obtained from _T. laciniata_ roots, 64.58 million were assembled into 76,565 contigs (N50: 1261 bp). Seventeen contigs were annotated as terpene synthases and five of these were predicted to be sesquiterpene synthases. Of the 67 contigs annotated as cytochromes P450, 18 of these are part of the CYP71 clade that primarily performs hydroxylations of specialized metabolites. Three contigs annotated as aldehyde dehydrogenase grouped phylogenetically with the characterized ALDH1 from _Artemisia annua_ and three contigs annotated as alcohol dehydrogenases grouped with the recently described ADH1 from _A. annua_. ALDH1 and ADH1 were characterized as part of the artemisinin biosynthesis. We have produced a comprehensive EST dataset for _T. laciniata_ roots, which contains a large sample of the _T. laciniata_ transcriptome. These transcriptome data provide the foundation for future research into the molecular basis for terpenoid biosynthesis in _Thapsia_ and on the evolution of terpenoids in Apiaceae.  
_Int. J. Mol. Sci._**2013**, _14_ **9081**  
**Keywords:**_Thapsia laciniata_; guaianolides; thapsane; Apiaceae; sesquiterpenoids
## 2 Materials and Methods
### Method Validation  
The analytical performance of the MAE-LC-MS/MS methodology was assessed by evaluating the linearity, accuracy (bias and precision), limit of detection (LOD), and limit of quantification (LOQ). The bias of the method was assessed by means of relative percentage of recovery (RE%) from spiked samples at 0.01, 0.03, 0.1, and 1 \(\mathrm{\SIUnitSymbolMicro g}\) fortification level, according to the following equation:  
\[\mathrm{RE\%}=\frac{\mathrm{C\ spiked\ sample-C\ neat\ sample}}{\mathrm{C\ standard}}\times 100 \tag{1}\]  
where C spiked sample is the concentration of the analyte in a, C neat sample is the concentration of the extracted sample (control sample), and C standard is the concentration of the spike standard.  
For each level of fortification, five replicates were analyzed (n = 5).  
Precision was expressed as repeatability in terms of the relative standard deviation (RSD%) at 0.03 and 0.1 \(\mathrm{\SIUnitSymbolMicro g}\)/g fortification levels. Repeatability, expressed as within-day precision (RSD%), was assessed in ten replicates (n = 10), and reproducibility, expressed as between-days precision (RSD%), was assessed after analyzing four spiked samples at 0.1 \(\mathrm{\SIUnitSymbolMicro g}\)/g fortification level within one month (n = 4 \(\times\) 3), according to the following equation:  
\[\mathrm{RSD\%}=\frac{\mathrm{RE\%\ standard\ deviation}}{\mathrm{RE\%\ mean}}\times 100 \tag{2}\]  
where RE% standard deviation is the standard deviation (SD) of the RE% of each analyte and RE% mean is the mean concentration of each spiked sample, analyzed in three replicates.  
LODs were calculated according to EURACHEM guidelines (EURACHEM 1998). Ten independent blank samples were fortified at the lowest acceptable concentration and processed in the chromatographic system. The LOD for each analyte was expressed as the analyte concentration corresponding to 3 times the SD. LOQs were determined as described in the European Commission (EC) Document No. SANTE/12682/2019 [26]. The LOQs were set as the lowest fortification levels for each analyte with acceptable bias (RE% = 70-120) and precision (RSD%\(\leq\) 20).  
Matrix effect (ME) was estimated as the ratio of the slopes of the calibration curves prepared with matrix-matched standards to those of solvent-based standards, according to the following equation:  
\[\mathrm{ME\%}=\frac{\mathrm{slope\ of\ matrix\ matched\ calibration\ curve}}{\mathrm{slope\ of\ solvent\ based\ standards\ calibration\ curve}}\times 100 \tag{3}\]
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 4 Experimental Results
### Proof-of-Concept Implementation  
The implementation of the architecture has been carried out using two main elements: a smartphone application and a customized version of The ONE Simulator. These elements are detailed below:  
* Smartphone application. The proposed application is based on a modification of the tool provided in the work [4]. For this proof-of-concept, the application manages communications in the physical 5G-MANET, acting as a simple message chat between local devices. The application generates random new messages at fixed intervals of 5 s, with a random destination selected among the nodes in the 5G-MANET. The selected routing algorithm is Epidemic Routing [33], due to its intensive use of encounters and data duplication. Local communications are handled using BLE to discover surrounding devices and Bluetooth to transmit information. These technologies are applied as alternatives to WiFi, as WiFi-Direct and WiFi Ad Hoc impose challenging restrictions:WiFi-Direct requires a centralized node as a group manager, which impedes the application of dynamic and ad-hoc topologies [34], while WiFi Ad Hoc requires root access for smartphones [35]. As a result, Bluetooth provides a flexible wireless interface for ad-hoc transmissions [36]. Finally, the application logs all individual events in the network, enabling the eventual performance assessment of QoS metrics. It is also responsible for communicating updates to the MQTT interface using 5G.
* DT. As Figure 5 shows, the proof-of-concept integrates the minimal components to provide a functional version of the DT. It has been implemented using The ONE Simulator to enable quick integration of visualization and simulation tasks into the proposal. Two main customized elements have been implemented, utilizing the real-time information received by the MQTT interface: a real-time mobility model, responsible for updating node positions, and a real-time routing protocol, which manages messages. Finally, the graphical representation enables the visualization of the scenario. On the other hand, simulation tasks are handled by the default simulation core of The ONE Simulator. In this way, users can configure contextual variables such as the mobility model, topology, and routing protocol.  
The proposed proof-of-concept represents an initial implementation to assess the platform's capabilities and its performance in an evaluation scenario. The following section describes the evaluation setup of the proposal.
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 3 Proposed Crypto Processor Architecture
### RegFile  
The yellow color in Figure 1 shows the memory unit. It consists of an \(8\times m\) register array (RegFile). The purpose of this unit is to store the initial, intermediate and final output. It contains two \(8\times 1\) multiplexers and one \(1\times 8\) demultiplexer. The objective of (RegFile) multiplexers is to read two \(m\)-bit operands for further computations. The demultiplexer is utilized to update the value of the specified register. Finally, for read/write operations, the control signals (shown with red color lines in Figure 1) are generated by the control unit.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input:**\(k=(k_{n-1},\ldots,k_{1},k_{0})\) with \(k_{n-1}=1\), \(P=(x_{p},y_{p})\in GF(2^{m})\) \\
**Output:**\(Q=(x_{q},y_{q})=k\cdot P\) \\
1 Affine to projective conversions: \(X_{1}=x_{p}\), \(Z_{1}=1\), \(Z_{2}=x_{p}^{2}\) and \(X_{2}=x_{p}^{4}+b\) \\
2 PM in projective coordinates: \\
3 for (\(i\) from \(m-2\) down to \(0\)) do \\
4 if (\(k_{i}=1\)) then \\
5\(PA(X_{1},Z_{1})=(X_{1},Z_{1},X_{2},Z_{2})\) and \(PD(X_{2},Z_{2})=(X_{2},Z_{2})\) \\
6 else \\
7\(PA(X_{2},Z_{2})=(X_{2},Z_{2},X_{1},Z_{1})\) and \(PD(X_{1},Z_{1})=(X_{1},Z_{1})\) \\
8 endif \\
9 endfor \\
10 Reconversion from projective to affine: \(x_{q}=\frac{X_{1}}{Z_{1}},y_{q}=x_{p}+(\frac{X_{1}}{Z_{1}})[(X_{1}+x_{p}\times Z _{1})(X_{2}+x_{p}\times Z_{2})+(x_{p}^{2}+y_{p})(Z_{1}\times Z_{2})](x_{p}\times Z _{1}\times Z_{2})^{-1}+y_{p}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Point addition and double instructions.
## 2 Background Knowledge
### Karl Pearson Coefficient and Cosine Similarity  
The two most popular measures for measuring similarities quantitatively between different users based on their ratings are the Karl Pearson (KP) coefficient and cosine similarity function and their variants. They are statistical measures of the degree of association between two vectors. Both have been applied successfully for calculating correlations in a variety of scenarios (2018; 2019; 2020; 2020).  
The Pearson correlation similarity of two users \(x,y\) is defined as:  
\[\textit{simil}\left(x,y\right)=\frac{\sum_{i\in I_{x,y}}\left(r_{x,i}-\ \overline{r_{x}}\right)\left(r_{y,i}-\ \overline{r_{y}}\right)}{\sqrt{\sum_{i\in I_{x,y}}\left(r_{x,i}- \overline{r_{x}}\right)^{2}\ \sum_{i\in I_{x,y}}\left(r_{y,i}-\overline{r_{y}}\right)^{2}}} \tag{1}\]  
where \(I_{x,y}\) is the set of items rated by both user \(x\) and user \(y\). For the KP correlation, the magnitude of the value determines to what degree the users are correlated, and the sign indicates the direction of the relation. The value ranges from \(-1\) to \(1\). Values near one indicate a high degree of correlation between users, signifying that the pattern of students' grades is similar. Values near minus one indicate a high degree of negative correlation between users, so the pattern of grades would be the opposite. The courses with the crests of grades for one student would be the troughs for the other. Zero indicates that the pattern of grades of the students is hardly related.  
The cosine-based approach defines the cosine-similarity between two users \(x\) and \(y\) as:  
\[\textit{simil}\left(x,y\right)=\cos\left(\overset{\rightarrow}{x},\overset{ \rightarrow}{y}\right)=\ \frac{\overset{\rightarrow}{x}\cdot\overset{ \rightarrow}{y}}{\mid\mid\overset{\rightarrow}{x}\mid\mid\times\ \mid\overset{\rightarrow}{y}\mid\mid\overset{\rightarrow}{1}}=\frac{\sum_{i\in I _{xy}}r_{x,i}r_{y,i}}{\sqrt{\sum_{i\in I_{x}}r_{x,i}^{2}\sqrt{\sum_{i\in I_{y} }r_{y,i}^{2}}}} \tag{2}\]  
For the cosine, if all of the vector elements are positive, as in the case of course grades, the magnitude behaves as a scalar, with high values indicating high correlation, and low values indicatinghigh negative correlation. Values near one denote high correlation; values near zero indicate negative correlation. Less related data would have cosine values somewhere in between.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
###### Abstract  
Quality is an essential aspect of public transport. In the case of regular public passenger transport by road, punctuality and regularity are criteria used to assess quality of service. Calculating metrics related to these criteria continuously over time and comprehensively across the entire transport network requires the handling of large amounts of data. This article describes a system for continuously and comprehensively monitoring punctuality and regularity. The system uses location data acquired continuously in the vehicles and automatically transferred for analysis. These data are processed intelligently by elements that are commonly used by transport operators: GPS-based tracking system, onboard computer and wireless networks for mobile data communications. The system was tested on a transport company, for which we measured the punctuality of one of the routes that it operates; the results are presented in this article.  
intelligent transport systems; public transport systems; operation control; automatic vehicle location +
Footnote †: journal: Sensors
## 2 Materials and Methods
### SPME-GC-MS Analysis  
Precisely 5.00 g of the treated sample was weighed in a 40 mL headspace vial, and then 2 \(\mu\)L of 2,4,6-trimethylpyridine standard solution was added. The vial was sealed using a cap. A solid-phase microextraction (SPME) needle (DVB/CAR/PDMS, 1 cm, 50/30 \(\upmu\)m; Supelco, Bellefonte, PA, USA) was inserted into the vial containing the sample in the headspace. The sampling process was performed for 40 min in a water bath operating at a constant temperature of 65 \({}^{\circ}\)C. The needle was subsequently dispatched to the GC injection  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Array Number** & **Sensor Name** & **Description of Sensitivity** & **Sensitive Gas** & **Threshold (mL/m3)** \\ \hline
1 & W1C & Aromatic components and & & C\({}_{7}\)H\({}_{8}\) & 10 \\
2 & W5S & Nitrogen oxides & NO\({}_{2}\) & 1 \\
3 & W3C & Aromatic components & & C\({}_{6}\)H\({}_{6}\) & 10 \\
4 & W6S & Selective for hydrides & H\({}_{2}\) & 100 \\
5 & W5C & Aromatic components of & & C\({}_{3}\)H\({}_{8}\) & 1 \\
6 & W1S & Methyl groups & CH\({}_{4}\) & 100 \\
7 & W1W & Sulfides & H\({}_{2}\)S & 1 \\
8 & W2S & Alcohols, aldehydes, and & & CO & 100 \\
9 & W2W & Aromatic components and & & H\({}_{3}\)S & 1 \\  & & organosulfides & & H\({}_{4}\) & 100 \\
10 & W3S & Long-chain alkanes & CH\({}_{4}\) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensors and corresponding representative sensitive material types of the PEN3 electronic nose.  
port for thermal desorption, which was performed at 250 \({}^{\circ}\)C for 5 min. The instrument was activated to retrieve the detection data [14]. A TQ8050 NX gas chromatograph and mass spectrometer (Shimadzu, Kyoto, Japan) equipped with an InertCap(r) Pure-WAX quartz capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) was used for GC-MS analysis. The carrier gas He (99.999% purity) was added at a flow rate of 1.0 mL/min. The flow rate was also maintained at 1.0 mL/min. The column was heated initially to 40 \({}^{\circ}\)C for 3 min, gradually increased to 100 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min for 2 min, and finally increased to 230 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min for 5 min. The electron ionization energy was 70 eV, the interface temperature was maintained at 250 \({}^{\circ}\)C, and the temperature of the ion source was 230 \({}^{\circ}\)C. The mass scanning range was between 33 and 550 \(m/z\), and the acquisition mode was Q3 [15]. Notably, \(m/z\) represents the mass-to-charge ratio, which is a commonly used parameter in mass spectrometry.
## 3 Experiments
### Transfer Results
#### 3.2.2 Ablation Investigations  
To ascertain the superiority of the key components of RASAN-W, when the remaining parameters of RASAN-W are the same, the accuracy of the transfer task is used as a comparison standard, and the method is evaluated by discarding components from our method each time. Specifically, these components include: cross-entropy loss (\(L_{cls}\) ), cross-entropy loss and maximum mean discrepancy loss (\(L_{cls}+L_{mmd}\) ), cross-entropy loss and local maximum mean discrepancy loss (\(L_{cls}+L_{lmmd}\) ), and combinations of cross-entropy loss, local maximum mean discrepancy loss, and pseudo label loss (\(L_{cls}+L_{lmmd}+L_{pseudo}\)). A comprehensive ablation analysis is performed by performing the transfer task on (\(C_{0}\to C_{2}\) ) on the CWRU dataset, and the results are shown in Figure 4, showing the effectiveness of all proposed modules in our method.  
Figure 4: Ablation study on CWRU dataset (**a**) and the JNU dataset (**b**) with different losses.
# Comprehensive Analysis of Maritime Cybersecurity Landscape Based on the NIST CSF v2.0
## 5 NIST CSF-Based Analysis of Maritime Cybersecurity
### Protect
#### 5.3.4 Platform Security  
The platform security category focuses on managing hardware, software, and services in harmony with an organization's risk strategy. As indicated by the NIST CSF v2.0, it encompasses the application of configuration management practices, preventive measures against unauthorized software, and the integration of secure software development practices. The aim is to maintain the integrity, confidentiality, and availability of both virtual and physical platforms, resulting in a more resilient and reliable cybersecurity environment. In the realm of Automatic Identification Systems (AIS) and radar technologies, several studies have been carried out. [PERSON] et al. [96] proposed a mechanism for the protection of AIS and radar systems on ships to deter cyber threats. Meanwhile, [PERSON] et al. [88] identified vulnerabilities in AIS systems and introduced methods to combat these using RF from Software-Defined Radio (SDR). [PERSON] et al. [97] made significant contributions by proposing SecureAIS, a key agreement scheme designed to enhance the efficiency and security within AIS systems. The significance of security protocols and algorithms in enhancing maritime cybersecurity has been highlighted in several studies. For instance, [PERSON] et al. [64; 98] introduced two distinct algorithms aimed at enhancing protective mechanisms in maritime systems. One of these algorithms leverages the principles of forensic science for incident detection, while the other ensures the secure transmission of data to safeguard against potential cyber threats. In a different vein, [PERSON] et al.'s [77] research adopted a systems perspective on cybersecurity management in cyberspace, indicating a more comprehensive approach towards mitigating digital threats. Additionally, [PERSON] et al. [99] proposed an innovative algorithm designed for deployment in ship position monitoring systems, anticipating that their algorithm could enhance decision-making processes and improve collision avoidance among vessels, thereby contributing to safer and more secure maritime operations.
## 1 Introduction  
Septic arthritis (SA) following anterior cruciate ligament (ACL) reconstruction (ACL-R) is a rare but serious complication, with an incidence rate that ranges from 0.14% to 1.7% [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Early diagnosis is difficult, and late diagnosis and treatment can lead to joint cartilage and graft damage, and to early development of osteoarthritis (OA) [1, 2, 5, 7, 8, 16, 18, 19]. The current recommended treatment for SA following ACL-R is arthroscopic debridement and lavage repeated 1-3 times on average, depending on the clinical condition, combined with targeted antibiotic therapy [5, 6, 20, 21]. The aim of repeated knee arthroscopy is the lavage of the joint, leading to removal of the pathogen and proteolytic enzymes it produces, as well as the toxic pro-inflammatory molecules and metalloproteinases responsible for cartilage degradation [22, 23]. According to animal studies, a joint infection lasting 7 days leads to the degradation of >50% of glycosaminoglycans and collagen present in the articular cartilage [24]. Recent experimental publications by [PERSON] et al. indicate that a 1% loss of cartilage proteoglycans causes a 1.1% decrease in the biomechanical properties of rabbit medial tibial condyle cartilage [25].  
The development of OA after SA is multifactorial because its presence is confirmed 10 years after uncomplicated ACL-R, and its severity depends on coexisting pathologies such as meniscus and cartilage damage [26; 27; 28; 29; 30]; nevertheless, the results of experimental studies and the long-term results of SA treatment indicate that pathogen eradication time may be the key to preventing the development of OA caused by these factors [24; 25; 31]. The issue has not, however, been covered in previous publications [3; 4; 5; 13; 14; 15; 16; 17; 31; 32]. Moreover, despite the passage of time, SA therapeutic recommendations based on meta-analyses have not addressed this issue, and there are no studies in the available literature that quantify the articular cartilage on MRI during the long-term follow-up period after SA treatment following ACL-R [3; 4; 5; 13; 14; 15; 16; 17; 31; 32]. In addition, the speed of pathogen eradication may affect the cost of treatment and the prevention of graft removal. Recognizing the time of eradication as a key factor in preventing articular cartilage degradation resulting in earlier development of degenerative lesions, the senior author of this study ([PERSON]) has, since 2002, been successfully using arthroscopic debridement and lavage with closed flow drainage of the knee until C-reactive protein (CRP) normalization in patients treated for SA after hamstring ACL-R [33].  
This study therefore aimed to prospectively evaluate the effect of the applied management of SA after ACL-R on knee function, pathogen eradication time as expressed by CRP normalization time, knee articular cartilage status on MR imaging and treatment costs.
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 2 Related Background  
### Entropy and the Edge Entropy  
To acquire the satisfactory level imperceptibility and robustness, different techniques are utilized to find optimal watermarking parameters [9]. Entropy has also been used extensively to support data-hiding algorithms. For an image, disturbances are much less visible in highly textured regions than in uniform areas, and the entropy can be used to describing the texture of it. The edge is regarded as another important image characteristic. But the edge points are not the suitable site for watermark embedding. Therefore, the edge entropy is an important factor that determines an image block to be selected for embedding whether or not. The entropy and the edge entropy were utilized to determine the embedding positions in the cover data so as to cause minimal perceptual distortion.  
The entropy of an n-state system can be represented as follows [21]:  
\[ETP=-\sum\limits_{i=1}^{n}p_{i}\log p_{i}. \tag{1}\]  
where \(p_{i}\) indicates the probability of occurrence of the event \"\(i\)\" with \(0\leq\mathrm{pi}\leq 1\) and \(\sum\limits_{i=1}^{n}p_{i}=1\).  
The edge entropy can be described as follows [21].  
\[ETPE=\sum\limits_{i=1}^{n}p_{i}\exp(\mu_{i})=\sum\limits_{i=1}^{n}p_{i}\exp(1-p _{i}). \tag{2}\]  
where \(\mu_{i}=1-p_{i}\) is the ignorance or uncertainty of the pixel value.