esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation Analysis  
The relationship between total flavonoid content, phenolic content, antioxidant capacity, TAMB, and Y&M were evaluated using Spearman's Rank correlation. Spearman's correlation was used to determine the relationship between the mentioned variables. Other than that, the strength of the relationship between the relative movements of the variables was estimated using the general rule for evaluating the size of a correlation coefficient. The results of the correlation interaction between antioxidant capacity and total flavonoid showed a low positive correlation at 0.347 (Figure 6). This supports previous studies in which SAEW treatment improved the antioxidant capacity of fresh-cut kiwifruit by increasing the contents of flavonoids and total phenols [22]. Moreover, the correlation between antioxidant capacity and phenolic content showed a low positive correlation at 0.234 (Figure 6). On the other hand, the interaction between total flavonoid and phenolic content had a negative correlation (Figure 6).  
Antioxidant capacity and TPC showed a gradual increase for AEW-treated samples, while the TAMB and yeasts and moulds declined rapidly as storage time increased (Supplementary Figures S1 and S2). Correlation analysis indicated that the antioxidant capacity was significantly (\(p<0.01\)) negatively correlated with the TAMB (correlation coefficient \(r=-0.384\), in Supplementary Figure S1A) and yeasts and moulds (correlation coefficient \(r=-0.809\), Supplementary Figure S1B). Furthermore, phenolic content was significantly (\(p<0.05\)) negatively correlated with the TAMB (correlation coefficient \(r=-0.492\)) and yeasts and moulds were not significant (correlation coefficient \(r=-0.298\)), as shown in Supplementary Figure S2. For the changes in flavonoids, correlation to TAMB was negative and not significant (\(r=-0.199\)), while yeast and mould had a negative correlation and was not significant (\(r=-0.413\)). Therefore, this could be explained by the fact that the continuous decline in TAMB and yeasts and moulds was associated with the accumulation of total phenolics and the increase in antioxidant capacity. The flavonoid content from the AIEW-treated fresh-cut 'Granny Smith' apples was higher than the control and AEW-treated samples with a significant (\(p<0.01\)) difference from day 6 to day 9. These findings demonstrated that both AEW and AIEW treatment can efficiently delay degradation and inhibit microbial spoilage. This is in line with a previous work by [PERSON] et al. [17] and [PERSON] et al. [25] that by maintaining the bioactive content the incidence microbial spoilage in fresh-cut apples and longan fruit, respectively, in slowed down.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 2 Results
### Expression Profiling of Selected Genes in the Putative Rubellin Biosynthetic Clusters  
To confirm the involvement of the genes identified in the candidate clusters, their expression profile was investigated. Coregulation being a sine qua non condition to be part of a given cluster, the previously published RNA-seq data [43] were interrogated to quickly identify genes in the candidate cluster that exhibited a similar expression pattern to that of the core PKS gene _rccG_. Of the 15 genes present alongside _rccG_, only nine showed coregulation based on the RNA-seq data (Figure 4). Both genes located on the _rccL_ minicluster were expressed at the same time as _rccG_, and only _rccD_ in the _rccD_ minicluster was expressed at the same time as _rccG_. The expression of the putatively coregulated genes was further investigated during disease development in artificially inoculated barley seedling via qRT-PCR. Gene-specific primers exhibiting sufficient amplification efficiency were successfully designed for the nine genes present alongside _rccG_, for _rccD_ as well as for both genes present on the _rccL_ minicluster. Additionally, a primer set was also designed for a gene (_rcc_01325_) previously identified as sitting out with the cluster to confirm the validity of the RNA-seq data. Transcript levels of the _rcc_01325_ gene did not appear to follow that  
Figure 3: Homology between the rubellin candidate cluster and gene clusters involved in the biosynthesis of known secondary metabolites structurally related to rubellin; (**A**) The cladofulvin biosynthetic gene cluster in _C. fulbaum_ was described by [PERSON] et al.; (**B**) The rubellin candidate gene cluster in _R. collo-cysmi_ consists of gene clusters organised around the core gene _rccG_ as well as two miniclusters named _rccL_- and _rccD_ miniclusters organised around _rccL_ and _rccD_, respectively; (**C**) The monodictyphenone biosynthetic gene cluster in _A. nidulans_ was identified by [PERSON] et al. [42]. Homology was confirmed by reciprocal BLAST using the NCBI database.  
of the other genes in the cluster, therefore confirming the validity of the RNA-seq approach. All the genes investigated on the _rccG_ cluster as well as both genes on the _rccL_ minicluster and _rccD_ exhibited a similar expression profile with transcript levels being the highest at 5 and 7 dpi, decreasing after 10 dpi, and remaining low until 21 dpi (Figure 4).
# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
## 2 The RPL Protocol  
The IoT is a compound of multiple linked and interoperable devices, majorly framed with limited resources and lossy radio-links. To grant a successful management and routing control, two main protocols are used in IoT network layers: IPv6 over Low-Power 6 LoWPAN (WPAN) and RPL. Initially, the 6 LoWPAN protocol was standardized by The Internet Engineering Task Force (IETF), in order to allow IPv6 packets to be carried efficiently in small link layer frames in WSNs backgrounds [27].  
In its more recent adaptations, 6 LoWPAN has been used over broadened networking media, such as Low-Power Radio Frequency (LPRF), Bluetooth Smart (BS), Power Line Control (PLC), and Low-Power Wi-Fi (LPWIFI). To drive forward different routing paths on Low-power and Lossy Networks (LLNs), such as point-to-point (P2P), point-to-multipoint (P2 MP), and multipoint-to-point (MP2P), the RPL protocol has been established as a more resilient alternative, by creating a Destination-oriented Directed Acyclic Graph (DODAG) between the IoT nodes and 6 LoWPAN unidirectional traffic. To assemble incoming network nodes, DODAG senses and ranks each node by indicating its relative position with respect to others and with respect to the DODAG itself. As a case in point, in Figure 3, the sensor nodes A and C are parents of nodes D, E, and F, while node F acts as the parent of node G.  
As with any network protocol, RPL uses a control message medium to identify requested and forwarded packets. The message body is a DODAG object composed of ICMPv6 code fields, as well as source/destination addresses for all nodes in a hierarchical structure. In Table 1, the RPL control message types are listed [28].  
From Table 1, it is worth noticing two objects that play a principal role in DODAG organization: The DIS control message, which is responsible for sensing, discovering, and requesting neighbouring nodes to join a DODAG tree; and the DIO control message, for assigning an identity to a newly added node and hierarchically reconstructing the DODAG.  
Figure 3: Concept diagram of DODAG node organization.
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 4 Experimental Setup and Implementation
### System Architecture  
Figure 11 shows a block diagram of the proposed system. A mobile phone equipped with an 8 MP camera is used to capture images for blood samples. The images are transferred to a PC via USB cable, where they are processed using MATLAB [39]. However, the software can be also implemented completely on the mobile phone to provide a standalone solution. The software is described in Figure 12, where the input image is processed automatically by converting it from an RGB color model to grayscale and partitioned into three patches (three blood spots mixed with antibodies A, B, and D) in the preprocessing stage. The ORB algorithm is then used to detect the agglutination of the blood spots by matching it with a reference image. Finally, the blood type is determined by combining the results of the three blood spots. In each blood sample, any false detection in the agglutination in any of the three blood spots means that the resulting blood group does not match the  
\begin{table}
\begin{tabular}{c c} \hline
**Blood Type** & **\# of Collected Samples** \\ \hline A+ & 171 \\ \hline A\(-\) & 29 \\ \hline B\(+\) & 69 \\ \hline B\(-\) & 5 \\ \hline AB\(+\) & 37 \\ \hline AB\(-\) & 5 \\ \hline O\(+\) & 162 \\ \hline O\(-\) & 20 \\ \hline \end{tabular}
\end{table}
Table 3: Distribution of the collected blood samples.  
actual blood group of the tested sample. Otherwise, the system successfully determines the blood group. The accuracy of blood type determination is determined by the ratio of the correct decisions to the total number of samples. Another two systems were implemented by replacing the ORB algorithm in the agglutination detection stage with the SIFT and the SURF algorithms for the purpose of comparison as shown in Section 5. All systems were implemented using MATLAB.  
One of the most crucial steps in such system is to choose a good reference image that contains a large number of agglutination patterns such that it represents all possible variations of blood agglutination. Figure 13 shows the reference image that was used in our proposed system. It was selected carefully with a large number of detected keypoints; it also has different patterns of agglutination clusters (i.e., blood clotting). Figure 14 shows samples for different agglutination patterns. Hence, one can notice that the chosen reference image covers all of these different patterns. The matching algorithms try to find common keypoints between the input image and the reference image to detect the agglutination.  
Figure 11: The proposed system.  
Figure 12: Software processing steps.  
Figure 13: The referenced agglutination image used in the matching algorithms.
# Constitutive Expression of a Cytotoxic Anticancer Protein in Tumor-Colonizing Bacteria
## 2 Materials and Methods
### Cell Lines and Animal Experiments  
Female BALB/c mice (6-8 weeks, 18-20 g) were obtained from Orient Bio (South Korea). CT26 colon cancer cells and 4T1 murine mammary carcinoma cells were purchased from ATCC Korea and cultured in high-glucose DMEM supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells (1 \(\times\) 10\({}^{6}\)) in 30 \(\mu\)L of 1\(\times\) PBS were subcutaneously injected into the right thigh of each mouse. Bacterial injections were executed when tumors reached a size of 100-150 mm\({}^{3}\). For confirmation of rrnB P1 promoter activity in the tumor targeted bacteria, _E. coli_ K-12 MG1655 and \(\Delta\)ppGpp \(S\). Gallinarum, mice carrying CT26 xenografts were intravenously injected with bacteria carrying prrnBP1-gfpOVA (1 \(\times\) 10\({}^{8}\) colony forming units [CFU]/mouse). To examine the antitumor effects of the TP immunotoxin, the mice grafted with CT26 and 4T1 cells were injected with \(\Delta\)ppGpp \(\Delta\)_glm_S_. Gallinarum carrying prrnBP1-psp-TP through the tail vein. Tumor size was determined by measuring the length, width, and height of each tumor every 2 days after the injection (V = length \(\times\) width \(\times\) height \(\times\) 0.5). For bacterial distribution in vivo, solid tumors and other organs were extracted from mice and homogenized in 1\(\times\) PBS using a homogenizer (IKA, Ultra-Turrax T10). The bacteria counting method was previously described [25]. All mouse experiments were performed by following the guidelines of Chonnam National University-Institutional Animal Use and Care Committee (CNU IACUC-H-2020-7). The protocol requires sacrifice of the mice when the implanted tumor volume reaches > 1500 mm\({}^{3}\).
# An Anonymous Offline RFID Grouping-Proof Protocol
## 6 Description of EAGP
### Extension to \(n>2\) Tags
#### 6.2.2 Grouping-Proof Generation Phase  
The framework is shown in Figure 4. The solid arrow represents the direct communication, the dotted arrow represents the tag-to-tag communication via the reader.  
(1) Reader selects \(\mathit{Tag}_{1}\) as the first tag to calculate the grouping-proof. It generates message \(M=\{s,\mathit{C}_{0},\mathit{C}_{1}\}\) as Figure 3, and sends it to \(\mathit{Tag}_{1}\) with the \"_Start first_\" query.  
(2) \(\mathit{Tag}_{1}\) authorizes the reader, generates message \(m_{1}\) by Equation (2) and sends it to \(\mathit{Tag}_{2}\) by the reader.  
(3) \(\mathit{Tag}_{2}\) selects a random number \(k^{r}_{2}\), calculates \(r_{2}=x(k^{r}_{2}\cdot P),k^{t}_{2}=x(Y)\oplus r_{2}\),then sends \(m_{2}\) to the reader, \(T_{2}\) to \(\mathit{Tag}_{3}\) via the reader, where  
\[m_{2}=(k^{r}_{2})^{-1}(r_{s}+k_{2}\times r_{2}) \tag{10}\]  
Figure 4: The EAGP with \(n\) tags.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(r_{s},r_{i}\) & The random number generated by reader and \(\mathit{Tag}_{i}\). \\ \(p\) & The base point on the elliptic curve \(E(\mathbb{F}_{q})\). \\ \(Y_{i},y_{i}\) & The public/private Key of Group \(G\). \\ \(k^{t}_{i}\) & Temporary grouping-proof key of \(\mathit{Tag}_{i}\). \\ \(k_{i},PK_{i}\) & Secret/Public key of \(\mathit{Tag}_{i}\). \\ \(x(T)\) & The x-coordinate of point \(T\). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of notations in EAGP.  
\[T_{2}=(m_{2}+m_{1}\times k_{2}^{t})Y \tag{11}\]  
\(Tag_{3}\) generates \(k_{3}^{r}\) and \(r_{3}\) the same way as \(Tag_{2}\), calculates \(m_{3}\), \(T_{3}\) as below.  
\[m_{3}=(k_{3}^{r})^{-1}(r_{s}+k_{3}\times r_{3}) \tag{12}\]  
\[T_{3}=(m_{3}+x(T_{2})\times k_{3}^{t})Y \tag{13}\]  
\(Tag_{3}\) sends \(m_{3}\) to the reader, \(T_{3}\) to \(Tag_{4}\) via the reader.  
\(Tag_{i}|_{(3<i<n)}\) generates \(k_{i}^{r}\) and \(r_{i}\), calculates \(m_{i}\), \(T_{i}\) as below.  
\[m_{i}=(k_{i}^{r})^{-1}(r_{s}+k_{i}\times r_{i}) \tag{14}\]  
\[T_{i}=(m_{i}+x(T_{i-1})\times k_{i}^{t})Y \tag{15}\]  
Then \(Tag_{i}\) sends \(m_{i}\) to the reader, \(T_{i}\) to \(Tag_{i+1}\) via the reader.  
The last tag \(Tag_{n}\) calculates \(m_{n}\), \(T_{n}\), and sends \(T_{n}\) to \(Tag_{1}\) via the reader.  
\(Tag_{1}\) calculates \(T_{1}\) by Equation (16), and sends it to the reader.  
\[T_{1}=(m_{1}+x(T_{n})k_{1}^{t})Y \tag{16}\]  
The reader generates the grouping-proof \(G^{(n)}\) shown in Equation (17).  
\[G^{(n)}=\{\,m_{1},\,T_{1},\,r_{1},\,m_{2},\,T_{2},\,r_{2},\,\ldots,\,m_{n},\,T _{n},\,r_{n}\} \tag{17}\]
## 4 Simulation and Experimental Results
### Simulation Implementation Details  
We used the Ryu SDN controller framework [60] as the SDN controller implementation for our testbed due to its component-based implementation, which blends well with the microservices perspective. In particular, we foresee that the integration of detection and mitigation modules will be conducted in a microservices way as external functionalities (like plug-in functions) for the SDN controller core. This is because in resource constraint scenarios, such as in Industry 4.0, we do not want to overload the SDN controller with all functionalities if we do not need them. Furthermore, the management of those modules will be handled by a service orchestrator such as Open Source MANO [61] if the boundaries are 5G networks to dynamically (de)activate functionalities at the SDN controller.  
In particular, a Ryu application consists of a Python script that extends the RyuApp base class and implements the observable and observer interface. These interfaces allow the application to interact with the event-based communications in the Ryu framework. First, the Ryu framework starts the application manager that loads both the Ryu-based applications and external applications and registers the associated events. The most important application in the Ryu framework is _off_handler_, which allows the framework to interact with the OpenFlow protocol (_OpenFlowController_ class). To efficiently communicate with the switches, the Ryu framework creates a virtual representation of switches, called the _Datapath_. Figure 2 shows the logical architecture of the Ryu SDN framework in our proposed architecture. The _off_handler_ operates as the SDN controller in the IT layer. In particular, it manages the data paths that are at the OT layer. The Ryu apps, once invoked by an event, can reply directly to the data paths.
## 3 Experiment and Result Analysis
### Microstructural Analysis
Based on previous studies on the effects of temperature gradient and solidification rate on the solidification structure [30], it was inferred that the microstructure gradually transformed from columnar dendrites to cellular crystals. In addition, as the  
Figure 7: Microstructure in the melt pool parallel to the forming direction of Inconel 718 formed by SLM under different laser powers: (**a**) P = 285 W, (**b**) P = 310 W, (**c**) P = 335 W, (**d**) P = 360 W, (**e**–**h**) partial enlarged views of (**a**–**d**). laser power increased, the laser energy density gradually increased, and the heat input in the molten pool gradually increased, resulting in a longer solidification time. The increase in solidification time led to an increase in the arm spacing of secondary dendrites, which was reflected in the microstructure of the columnar crystals, as shown in Figure 7e-h.
## 2 Materials and Methods  
### Experimental System  
To simulate the actual satellite-based observations, it is necessary to observe the TIR change from the loaded rock in the outdoor condition. The experimental system for microwave and thermal infrared spectral radiation observations for horizontal loaded rock has been established in the outdoor condition [37; 38; 47]. Apart from the moveable load-testing machine, the multichannel temperature tester with Pt1000 probes, and the video camera in the system, an infrared thermal imager and an electrical resistance strain gauge were added.  
The infrared thermal imager ImageIR 3350, designed by the InfraTec Company, Germany, was used to detect the TIR variation of the sample surface during the loading process. The thermal infrared radiant energy of the target can be transferred into the electronic signal and recorded by the infrared thermal imager. Then, the spatial distribution field of the thermal radiant energy can be shown on the thermal infrared image [26; 48]. The images were recorded over the range of 3.7-5.0 \(\upmu\)m at a spatial resolution of 320 \(\times\) 240 pixels with an Indium Antimonide (InSb) detector. The noise equivalent temperature difference (NETD) was better than 25 mK at ambient temperature condition of 25 \({}^{\circ}\)C, and the acquisition rate was 10 frames per second (fps).  
An electrical resistance strain gauge was used to detect the strain data in the cyclic loading process. The strain measuring range was 0-\(\pm\)38,000 \(\upmu\varepsilon\) with the resolution of 1 \(\upmu\varepsilon\), and the accuracy was better than 0.1%.
## 4 Results
### Validation of ET Estimates  
The results were validated against measurements from ground-based EC stations. For each of the three days, nine EC stations were used as inputs to the SEB-A method to calculate the exotic ET, and the other eight EC stations were used to verify the estimates. Because three days' results were tested together, a total of twenty-four points were conducted for the validation. The comparison between estimates obtained by the SEB-A method and measurements from EC stations is shown in Figure 4. It shows that the estimates have an R\({}^{2}\) of 0.713, a MAE of 39.3 W/m\({}^{2}\) and an RMSE of 54.6 W/m\({}^{2}\). Except for three points that have absolute errors higher than 100 W/m\({}^{2}\) and two points that have absolute errors between 60 W/m\({}^{2}\) and 70 W/m\({}^{2}\), the other points have absolute errors lower than 20 W/m\({}^{2}\). Validation results are similar to previous studies. [PERSON] made an assessment of some 30 published validations that showed an RMSE value of approximately 50 W/m\({}^{2}\) and relative errors of 15%-30% [15]. The correlation analysis indicated that the simulated results were quite consistent with the available measurements, illustrating that ET estimates obtained by the SEB-A method were close to the 1:1 line and had high accuracy.
## 5 Results and Discussion  
Table 3 provides the initial values via the three methods and the optimal value for \(\alpha\) using a step search and Solver in Microsoft Excel, which were very similar. As an example for a small-sized dataset (S1), the optimal values for \(\alpha\) using a step search and Solver from Microsoft Excel were 0.369 and 0.3689, respectively, when setting the initial value using Method 1, whereas they were 0.316 and 0.3162, respectively, when setting the initial value using Method 2, and 0.323 and 0.3232, respectively, when setting the initial value using the proposed method. This trend was the same for all of the datasets. Figure 2 shows the results for each dataset when using different initialization methods; the optimal value for \(\alpha\) differs only slightly. Eventually, Solver from Microsoft Excel and the step search methods obtained very similar optimal values for \(\alpha\) for the same initialization method. Overall, the different sizes of small with S1-S5, medium with M1-M5, and large with L1-L5 datasets are represented by the bar chart of Figure 3, and it can be seen that both Solver in Microsoft Excel and step search obtained nearly the same optimal value for \(\alpha\) results. Hence, the Solver from Microsoft Excel is an alternative to a powerful method for obtaining the optimal value for \(\alpha\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset** & **M3 Competition Code** & **Time Period** & **Size** \\ \hline \multirow{5}{*}{Small} & S1 & N243 (Y) & 1947–1992 & 46 \\ \cline{2-5}  & S2 & N127 (Y) & 1947–1993 & 47 \\ \cline{2-5}  & S3 & N180 (Y) & 1947–1993 & 47 \\ \cline{2-5}  & S4 & N229 (Y) & 1947–1993 & 47 \\ \cline{2-5}  & S5 & N721 (Q) & 1984Q1–1994Q4 & 44 \\ \hline \multirow{5}{*}{Medium} & M1 & N1368 (Q) & 1974Q2–1991Q3 & 70 \\ \cline{2-5}  & M2 & N1449 (Q) & 1990Q1–2007Q1 & 69 \\ \cline{2-5}  & M3 & N1470 (Q) & 1990Q1–2007Q1 & 69 \\ \cline{2-5}  & M4 & N1472 (Q) & 1990Q1–2007Q1 & 69 \\ \cline{2-5}  & M5 & N903 (Q) & 1976Q1–1993Q4 & 72 \\ \hline \multirow{5}{*}{Large} & L1 & N2125 (M) & 1978M1–1989M12 & 144 \\ \cline{2-5}  & L2 & N1886 (M) & 1979M1–1990M12 & 144 \\ \cline{1-1} \cline{2-5}  & L3 & N2022 (M) & 1981M1–1992M12 & 144 \\ \cline{1-1} \cline{2-5}  & L4 & N2025 (M) & 1981M1–1992M12 & 144 \\ \cline{1-1} \cline{2-5}  & L5 & N2070 (M) & 1982M1–1993M12 & 144 \\ \hline \hline \end{tabular} \(Y,Q,\) and \(M\) are yearly, quarterly, and monthly, respectively.  
\end{table} 
Table 2: The M3 competition time series data used to compare the forecast methods. The results of a correlation analysis of the performances of the three methods are presented in Figure 3. It can be seen that the correlation coefficient values are close to +1