esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Factor Screening
#### 3.1.3 Dong93 Method  
The Dong93 method is a direct analysis technique that aims to solve the problem of unreplicated saturated factorial designs, which cannot be analyzed using standard variance analysis due to the lack of an independent error variance estimate of \(\tau\). The method utilizes an estimate \(\hat{\tau}\) of \(\tau\), which is used in the calculation of the statistics: \(\text{t}=|\hat{\beta}_{\text{i}}|/\hat{\tau}\) when conducting significance tests on the factor \(\hat{\beta}_{\text{i}}\). The rejection field takes the form of \(\text{t}_{\tau}>\text{t}_{\text{d}\hat{t};1-\alpha}\), where \(\text{t}_{\tau}\) is the observed test statistic, df represents the degrees of freedom, and \(\alpha\) is the significance level. However, due to the frequent non-independence of \(\tau\) and \(\hat{\beta}_{\text{i}}\), the distribution of \(\text{t}_{\tau}\) no longer follows an F-distribution, and its critical value is often obtained using Monte Carlo methods. To address this issue, [PERSON] proposed using the adjusted standard error (ASE) to estimate \(\tau\).  
\[\hat{\tau}_{\text{Dong}}=\text{ASE}=\sqrt{\frac{1}{\text{m}_{\text{ inactive}}}\sum_{|\hat{\beta}_{\text{i}}|\leq 2.5\text{s}_{0}}\hat{\beta}_{\text{i}}^{2}} \tag{11}\]  
where \(\text{S}_{0}=1.5\cdot\underset{1\leq i\leq m}{\text{median}}(|\hat{\beta}_{ \text{i}}|)\), and \(\text{m}_{\text{inactive}}\) is the number of factors that satisfy \(\left|\hat{\beta}_{\text{i}}\right|\leq 2.5\text{S}_{0}\). \(\left|\hat{\beta}_{\text{i}}\right|>\text{t}_{\gamma,\text{m}_{\text{ inactive}}}\times\text{ASE}\) can be used to test whether the factor \(\hat{\beta}_{\text{i}}\) is active or not, \(\gamma=(1+0.98^{1/\text{m}})/2\).  
Although the Dong93 method is a method intended for minimizing Error II [32], during the screening test phase, making Error I can result in the loss of important factors, which can significantly impact the subsequent optimization process. Therefore, this paper proposes an iterative approach to further improve the testing efficiency of the Dong93 method, as illustrated in Figure 10. By carrying out 10,000 iterations and counting the comparison results each time, the values of Error I and Error II before and after the ASE iteration were calculated based on the statistical results. The simulation results in Table 3  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Responses** & **Active Factors** \\ \hline SEA & \(\text{A}_{1}\), \(\text{A}_{2}\), \(\text{A}_{10}\) \\ \(\omega\) & \(\text{A}_{1}\), \(\text{A}_{5}\), \(\text{A}_{14}\) \\ In & \(\text{A}_{1}\), \(\text{A}_{2}\), \(\text{A}_{4}\), \(\text{A}_{14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Factor screening results of the semi-normal probability map.  
demonstrate that with the significance level \(\alpha\), the value of Error I decreased more significantly after each iteration. The factor screening results using the iterative Dong93 method are presented in Table 4. The iterative approach successfully avoided the loss error of the important factor A\({}_{2}\) when using the Dong93 method.
## 5 Results and Discussion
### Results of Seismic Ductility Analysis  
Seismic analysis by ductility is required to guarantee that the platform and its foundations can resist the equivalent of an earthquake of exceptional intensity; the aim is to  
Figure 8: Summary of resistance-level seismic analysis results.  
determine the seismic reserve capacity factor (Cr) by an incremental analysis of gravity and seismic loads (pushover), which must comply with the current regulations (Table 11.3 [45]). The collapse mechanism that will occur in the structure can be known by applying incremental pushover analysis in the gravity and seismic loading conditions.  
The seismic reserve capacity factor (Cr) represents the capacity of a structure to sustain ground motions due to an earthquake beyond the reference level. It is defined as the ratio of spectral acceleration that causes structural collapse or catastrophic failure of the system to the spectral acceleration. For fixed steel offshore structures, the representative value of Cr can be estimated from the general design characteristics of a structure according to Table 11.3-1 [45]. The analysis was performed for each of the 20 incident directions.  
The maximum shear (load) is reached by applying a load of 6.90 to the earthquake. This value is divided by the reference shear obtained in the earthquake resistance analysis (see Equation (3)), with the most unfavorable direction being 270\({}^{\circ}\) and the other directions obtaining higher factors.  
\[Cr=\frac{shear\ maximum}{shear\ reference} \tag{3}\]  
\[Cr=\frac{5993.6\ \text{ton}}{702.2\ \text{ton}}=8.31\]  
\[8.31>2.0\ (minimum\ required)\]  
It is observed in Figure 9 that by applying a load factor of 6.90, the earthquake reaches the maximum shear (5993 tons); this is because in this step, some elements in the topside near node 1B28 and the pile with node P002 begin to present plasticization, which causes the collapse mechanism to start. After applying a load factor of 7.10, the platform collapses due to the plasticization of elements. On the other hand, the behavior of the foundation and piles is as follows: the maximum load capacity of the piles for node P008 (axis B4) = 65.6% in tension and compression at node P001 (axis A1) = 72.8%; in both cases, they are less than 100% of their load capacity.  
The nodes P002 (at the base of the piles) and 1B28 (in the beam-column connection at the second level) were graphed, in which the behavior in terms of force-deformation  
Figure 9: Summary of ductility-level seismic analysis results (270\({}^{\circ}\)). Note: The symbol “E” denotes scientific notation, representing multiplication by 10 raised to the indicated power. For example, 1.5E3 indicates \(1.5\times 10^{3}\).  
is shown. The graphs have a stable behavior up to a load factor of 6.90, reaching a displacement of 60 cm for node P002 and 105 cm for node 1B28, followed by the displacement increasing very drastically until reaching structural collapse by applying a load factor of 7.10.
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 6 Conclusions  
In the conventional OCC system, only CNN has been used to detect LED regions irrespective of any unwanted LED regions or multiple LED regions from multiple users. In this article, we have designed a scheme by which the camera receiver can differentiate accurate data transmitting LED among unwanted LED leveraging SVM classifiers additionally for the first time. The FSOOK modulation scheme has been used to modulate data that is sent by blinking of the LED accordingly. We have evaluated the overall performance elaborately demonstrating the ROC, and other key performance parameters of the classifier, as well as other communication performance features, such as BER, data rate, and ISI. The ROC of the SVM classifier affirms its efficacy to accurately classify LED. The BER analysis exhibits 10 times lower BER at the maximum communication distance (60 cm). In addition, by the eye diagram, we have also investigated ISI of the decoded data from the recognized LED region. Moreover, the data rate against a distance varying no. of LEDs has been observed for our proposed scheme. In addition, BER against the data rate is also discussed to ensure the necessity of our proposed scheme. However, it is perceptible from the overall analysis that our scheme is a novel approach for reliable OCC communication. Based on our scheme, a new frontier is expected to be outstretched for the OCC to deploy in future communication technology.  
Conceptualization, M.H.R.; methodology, M.H.R. and M.S.; software, M.H.R.; validation, M.S. and M.O.A.; formal analysis, M.S. and M.K.H.; writing--original draft preparation, M.H.R.; writing--review and editing, M.K.H., M.S., and M.O.A.; supervision, Y.M.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program and in part by the Ministry of Science and ICT (MSIT), South Korea, under the  
Figure 14: BER against data rate comparison among our scheme based classifier, receiver using only CNN classifier, and receiver using only SVM classifier.  
Information Technology Research Center (ITRC) support program (IITP-2021-0-01396) supervised by the Institute for Information and Communications Technology Promotion (IITP).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** We have build our own data set in this study. Since further research are on processing, we cannot publish data set right now.  
**Conflicts of Interest:** The authors have declared that there is no conflict of interest regarding the publication of this paper.
###### Abstract  
Media authentication relies on the detection of inconsistencies that may indicate malicious editing in audio and video files. Traditionally, authentication processes are performed by forensics professionals using dedicated tools. There is rich research on the automation of this procedure, but the results do not yet guarantee the feasibility of providing automated tools. In the current approach, a computer-supported toolbox is presented, providing online functionality for assisting technically inexperienced users (journalists or the public) to investigate visually the consistency of audio streams. Several algorithms based on previous research have been incorporated on the backend of the proposed system, including a novel CNN model that performs a Signal-to-Reverberation-Ratio (SRR) estimation with a mean square error of 2.9%. The user can access the web application online through a web browser. After providing an audio/video file or a YouTube link, the application returns as output a set of interactive visualizations that can allow the user to investigate the authenticity of the file. The visualizations are generated based on the outcomes of Digital Signal Processing and Machine Learning models. The files are stored in a database, along with their analysis results and annotation. Following a crowdsourcing methodology, users are allowed to contribute by annotating files from the dataset concerning their authenticity. The evaluation version of the web application is publicly available online.  
t  
merging; authentication; misinformation; web application; news; machine learning; deep learning; crowdsourcing +
Footnote †: journal: _Future Internet_  
0000-0001-9999-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-  
today's digital forensics tools. However, these new capabilities can also be exploited for counter-/anti-forensic means, requiring constant and continuous effort.
# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 3 Methodologies  
Figure 1 shows the network framework of the proposed helmet-wearing detection algorithm, which consists of three main components: a backbone feature extraction network, a multi-scale segmentation and feature fusion network, and an improved lightweight residual convolutional attention network, namely LRCA-Netv2.  
GhostNet is the backbone feature extraction network to meet the network's overall lightweight. The multi-scale segmentation and feature fusion network (MSFFN) improves the algorithm's robustness in detecting objects at different scales. At the same time, feature fusion is also designed to facilitate the detection of helmets. In addition, the spatial attention module in LRCA-Net is improved. The original \(7\times 7\) convolution method, which cannot synthesize global information, is abandoned in the spatial dimension. The integrated features along the horizontal and vertical directions are fused and then weighted separately for attention, which is more effective in focusing on global features. The precise location information is used to make dependencies between more distant features. Experimental results show that such a design scheme can significantly improve helmet detection accuracy while greatly reducing computational costs.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 6 Conclusions  
Accurate identification of a shearer cutting state is the prerequisite of, and basic guarantee for, achieving intelligent shearer control. In this paper, infrared thermal imaging technology was utilized to capture the temperature changes of a shearer cutting a coal wall. The mathematical morphology and a spatio-temporal context algorithm were introduced to achieve the positioning and tracking of a shearer cutting unit. The related parameters were reasonably determined according to many simulations, and the experimental results indicate that the infrared thermal images can be used to accurately identify the cutting state of the shearer.  
In future work, the authors will analyze the influence of different traction speeds and drum speeds on the temperature of coal wall, and deeply study the feature extraction method of infrared images.  
Conceptualization, L.S. and Z.W.; Methodology, L.S. and Z.W.; Software, L.S. and Y.L.; Writing-Original Draft Preparation, L.S.; Writing-Review & Editing, C.T. and L.S.  
This research was funded by National Natural Science Foundation of China (grant numbers: 51605477 and U1510117) and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.  
The authors would like to thank the National Natural Science Foundation of China for their supports to the research work. The authors also thank the reviewers for their suggestions and corrections to the original manuscript.  
The authors declare no conflict of interest.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Results
#### 5.5.1 Varying the Number of Nodes
Figure 9(b) shows the power consumption for scenarios with 13, 25, and 37 nodes for the DYNASTI solution and _Static Instances_. The energy consumption pattern for both approaches is analogous, the difference is also significant, in which the highest energy consumption is achieved when _Static Instances_ is used, i.e., _Static Instances_ results in 68.48% (25 nodes) and 63.14% (37 nodes) more energy consumption than DYNASTI. Hence, the difference between approaches related to lost application messages and energy consumption increasing with the network size growth.
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 1 Introduction  
Unmanned aerial vehicles (UAVs) have been widely utilized in various industries due to their flexibility and rapid deployment capabilities. Consequently, the wireless communication of UAVs has garnered significant attention from researchers [1]. In this article, we address the issue of control signaling transmission for UAV clusters employed in mountain search and rescue operations. In this system, a ground base station (BS) is responsible for transmitting downlink signaling to control multiple UAVs, thereby facilitating the formation of a cluster to carry out missions. However, in mountainous environments, the line of sight between the BS and the UAV can often be obstructed. To overcome this challenge, we propose the use of an RIS-UAV, which involves a UAV hovering over the BS and equipped with multiple reconfigurable intelligent surfaces (RIS) serving as relays. The RIS-UAV stores position-based RIS coefficients and generates signal beams directed towards different spatial positions. By utilizing position information acquired fromeach UAV, the BS can select the appropriate RIS coefficient and form a highly directional beam that covers each individual UAV.  
The 3 rd Generation Partnership Project (3 GPP) Release 17 research project presents a class of communication demands specifically designed for machine communication within the industrial Internet of Things. These demands cater to applications requiring deterministic and periodic communication, with stringent requirements for low-transmission latency [2]. Deterministic periodic communication finds relevance in the transmission of control signals and feedback of UAV status information in UAV clusters. Within an RIS-aided UAV cluster system, the allocation of scheduling time slots for each UAV becomes a critical consideration. On one hand, an RIS can only generate a single group of coefficients within a time slot, and different UAVs necessitate distinct communication periodicity. As a result, the scheduling of time slots must concurrently cater to the communication resource requirements of all users. On the other hand, the number of UAVs that can be scheduled simultaneously in each time slot is limited by the number of RIS. Additionally, the beam formed by the RIS coefficient may potentially lead to interference with UAVs in adjacent clusters. Consequently, the scheduling of UAVs should strive to mitigate interference between adjacent UAV clusters.
## 3 Filling Precision Design
### Control Algorithm to Increase the Precision Level of Filling
#### 3.2.2 Filling Processing Algorithm  
The filling processing algorithm of the filler using the pulse flow rate method is as shown in the flow chart in Figure 3. According to the filling process algorithm in Figure 3, the filling valve counter pressure is a process where the pressure of the filler bowl and the bottle is equalized [5]. The filling valve and liquid valve is the process where the main valve, which fills up the bottle, is opened. The filling valve slow-fast fill valve is the process where the filling speed is slowed down from the initial high speed to a lower speed when the bottle is almost full (80% full, if the target volume is 500 gr, at 400 gr). The snifting valve is a process where the residual pressure inside the bottle is removed after filling is over [5]. When filling a bottle, the key element is controlling the level of the bottle and the pressure.  
Figure 2: Filling error analysis for filling targets.  
In a situation where the level and pressure should be controlled as in carbonate filling, a small amount of change in the pressure can significantly affect the filling precision.  
Figure 4 shows the interface, which shows and configures the PID (A piping and instrumentation diagram) of the level and pressure of the filling bottle. Furthermore, for the controller, the automatic tuning configuration and programs were implemented in order to optimize the filling bottle pressure and level with each pressure and level control PID value. Therefore, it is possible to find the optimized PID value with a single touch. The pressure control is implemented as shown above. Therefore, the system is capable of controlling the pressure up to \(\pm\)0.1 kg/cm\({}^{2}\) and the level \(\pm\)3 mm.
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 7 Conclusions  
This study addresses a virtual inertia control strategy from the DFIG-based WTG to increase the frequency stability without causing the rotor speed stall. To accomplish this, a kinetic energy-dependent control virtual inertia control gain is proposed.  
Simulation studies indicate that the suggested virtual inertia control strategy can increase the frequency stability more than that in the conventional strategy by releasing more kinetic energy from the DFIG-based WTG under the scenarios of full and partial loads. Moreover, the proposed control strategy can prevent the wind turbine from stalling, even when a large control gain is used.  
The advantages of the proposed virtual inertia control method are that the control gain varies with the wind speed conditions. The virtual inertia control factor is used to regulate the performance of the frequency stability. Further, the control gain reduces with the wind speed and is zero at the minimum rotor speed so that the proposed strategy is capable of preventing the DFIG-based WTG from stalling, thereby avoiding the rotor speed security issue.  
[PERSON], [PERSON], and [PERSON] mainly proposed the methodology of this paper. [PERSON] and [PERSON] carried out the simulation test. Writing, reviewing and editing were done by all the authors. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Application research program of Nantong City (JC2019092), Key project of smart grid technology and equipment of national key research and development plan of China (2016 YFB0900601).  
The authors declare no conflict of interest.
_Int. J. Mol. Sci._**2015**, _16_, 3035-3057; doi:10.3390/ijms16023035  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
**Transcriptome Analysis of Methyl Jasmonate-Elicited**  
_Panax ginseng_ **Advertitious Roots to Discover Putative**  
**Ginsenoside Biosynthesis and Transport Genes**  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON],**  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Molecular Biology Research Center, State Key Laboratory of Medical Genetics,  
School of Life Sciences, Central South University, Changsha 410078, China;  
E-Mails: caohongzhe1986(@hotmail.com (H.C.); [EMAIL_ADDRESS] (M.N.);  
haoxiu1990(@163.com (H.X.); [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (K.W.);  
[EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (L.W.);  
[EMAIL_ADDRESS] (J.-H.J.); [EMAIL_ADDRESS] (S.-J.P.); [PERSON](@csu.edu.cn (F.Y.);  
[EMAIL_ADDRESS] (J.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-731-8480-5025.  
Academic Editor: [PERSON]  
_Received: 12 December 2014 / Accepted: 22 January 2015 / Published: 29 January 2015_  
**Abstract:** The _Panax ginseng_ [PERSON]. [PERSON] belonging to the Araliaceae has long been used as an herbal medicine. Although public databases are presently available for this family, no methyl jasmonate (MeJA) elicited transcriptomic information was previously reported on this species, with the exception of a few expressed sequence tags (ESTs) using the traditional Sanger method. Here, approximately 53 million clean reads of adventitious root transcriptome were separately filtered via Illumina HiSeq(tm)2000 from two samples treated with MeJA (Pg-MeJA) and equal volumes of solvent, ethanol (Pg-Con). Jointly, a total of 71,095 all-unigenes from both samples were assembled and annotated, and based on sequence similarity search with known proteins, a total of 56,668 unigenes was obtained. Out of these annotated unigenes, 54,920 were assigned to the NCBI non-redundant protein (Nr) database, 35,448 to the Swiss-prot database, 43,051 to gene ontology (GO), and 19,986 to clusters of orthologous groups (COG). Searching in the Kyoto encyclopedia of genes and genomes (KEGG) pathway database indicated that 32,200 unigenes were mapped to 128 KEGG pathways. Moreover, we obtained several genes showing a wide range of expression levels. We also identified a total of 749 ginseng biosynthetic enzyme genes and 12 promising pleiotropic drug resistance (_PDR_) genes related to ginseng transport.  
**Keywords:**_Panax ginseng_; adventitious root; methyl jasmonate; Illumina/Solexa; transcriptome; ginseng biosynthesis; PDR (pleiotropic drug resistance) transporters; MVA (mevalonic acid) pathway