esa-sceva/satcom-chunk-collection huggingface dataset:
# 108 m Underwater Wireless Optical Communication Using a 490 nm Blue VECSEL and an AOM
## 4 Results and Discussion
### Comparison of Modulation  
It is vital to prioritize modulation schemes with high transmission rates or high transmission efficiency while assuring dependability [21; 22; 23]. Most UWOC studies have been carried out utilizing PPM, or on-off keying (OOK), as the modulation format in the water channel. OOK and PPM are basic intensity modulation methods that are frequently employed in long-distance UWOC systems. OOK modulation is a switch-keyed modulation that performs signal transmission by switching the carrier amplitude. PPM modulation utilizes pulse position modulation to achieve signal transmission by changing the position of the carrier pulse. When the PPM system is in operation, the laser can function at high peak power and low average power, resulting in improved transmission efficiency. Due to the complexity of the underwater environment, different modulation methods have their own advantages. As depicted in Figure 7, the y-axis represents the optical  
Figure 5: (**a**) Schematic of acousto-optic Bragg modulation. RF in the picture means radio frequency. (**b**) Normalized signal modulation depth corresponding to pulse width.  
Figure 6: Eye diagrams of signals with frequencies of 10 MHz (**a**), 20 MHz (**b**), 50 MHz (**c**), and 100 MHz (**d**).  
power requirement normalized to OOK, and the x-axis represents the ratio of bandwidth required for PPM and OOK after normalization. The number indicates the number of PPM time slots. As the modulation order increases, the bandwidth requirement of the PPM gradually increases, but the power efficiency also increases. The OOK structure is comparatively uncomplicated and possesses a higher bandwidth efficiency compared to the PPM modulation for transmitting the same communication capacity. Nevertheless, it also requires a higher average optical power.  
We evaluated the communication performance of the UWOC system at different received optical powers to better understand the difference between OOK and PPM. The use of hard-decision demodulation in OOK modulation leads to larger-than-expected errors during the demodulation process. The eye diagram of the PPM modulation is already blurred at a received optical power of \(-19.58\) dBm, but since the PPM modulation has no DC component and the PPM pulse width is fixed, it may be easily demodulated by filtering out the noise. As shown in Figure 8a, the signal waveform at the received optical power was \(-19.58\) dBm, and the SNR at this time was 1.79 dB. Figure 8b shows the 64 PPM signal after synchronous equalization and filtering, and its pulse position is clearly visible. This also proves that PPM has better anti-jamming performance compared to OOK modulation.  
Figure 8: (**a**) Unfiltered 64 PPM signal. (**b**) Filtered and sampled 64 PPM signal.  
Figure 7: Power requirements versus bandwidth requirements with on-off keying (OOK) and pulse position modulation (PPM).
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 3 Results  
### The Grassland Area Analysis of the TP  
The percentages of grasslands on the TP varied among the five land cover products. The CCI-LC and CLCD demonstrated that the percentages of grassland area on the TP reached more than 60% from 2000 to 2020, while the percentages were about 50% and 45% for MCD12Q1 and CNLUCC, respectively. For Globaland30, the grassland area in 2000 only accounted for 33.08% of the TP, while the percentages exceeded 50% in 2010 and 2020 (Figure 3a).  
The five land cover products were superimposed to obtain a more accurate area of grassland degradation on the TP. The grassland fusion product showed that the percentages of grassland area on the TP in 2000, 2010 and 2020 were 55.16%, 60.40% and 60.15%, respectively. The grassland area increased by 9.43% from 2000 to 2010 and decreased by 0.54% from 2010 to 2020 (Figure 3b). Thus, the grassland area of the TP increased by 8.84% during the period from 2000 to 2020. From 2000 to 2010, the greening area of grassland (219,317 km\({}^{2}\)) was much larger than the degradation area (84,387 km\({}^{2}\)), which means that the grassland area has increased by 134,930 km\({}^{2}\). While the grassland greening area has not been able to compensate for the degradation area, which has led to a decrease of 8,455 km\({}^{2}\) of grassland on the TP from 2010 to 2020. The total degraded grassland area (mean values of grassland area change in five land cover products) on the TP reached 128,544.52 km\({}^{2}\) from 2000 to 2020. Although the area of grassland was expanding from 2000 to 2020, the grassland greening rate is slowing down (from 219,317 km\({}^{2}\)/10a of 2000-2010 to 126,056 km\({}^{2}\)/10a of 2010-2020), while the grassland degradation rate is increasing (from  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\beta\) & \(Z\) & **Trend Features** \\ \hline \multirow{2}{*}{\(\beta>0\)} & \(1.96<\mid\!Z\!\mid\) & significant greening \\  & \(0<\mid\!Z\!\mid\leq 1.96\) & greening \\ \hline \multirow{2}{*}{\(\beta=0\)} & \(Z\) & no change \\ \hline \multirow{2}{*}{\(\beta<0\)} & \(0<\mid\!Z\!\mid\leq 1.96\) & degradation \\  & \(1.96<\mid\!Z\!\mid\) & significant degradation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mann-Kendall test trend categories.  
219,317 km\({}^{2}\)/10a of 2000-2010 to 126,056 km\({}^{2}\)/10a of 2010-2020). The rate of grassland degradation exceeded the rate of greening during 2010-2020. Regions with degraded grassland of more than 10,000 km\({}^{2}\) included Changdu City, Linzhi City, Ganzi Tibetan Autonomous Prefecture, A'Ii Prefecture and Naqu City (Figure 4).
## 3 Proposed Distributed Secondary Control Technique
### Case Five: Proposed Control Capability during Communication Time Delay  
Distributed secondary controllers rely on communication links to establish interaction and share information within the microgrid to accomplish control objectives. Consequently, it is crucial to examine the influence of time delays in the communication protocol on the response of the proposed FDSC technique. Utilizing Topology \(Y\) in our analysis in this case study, we explore different time delays between the secondary controls of the DERs; specifically, we set \(k_{34}=150\) ms, \(k_{23}=250\) ms, and \(k_{12}=100\) ms. The simulation was divided into three stages and lasted for 10 s. During the first stage, \(t\in[0,3)\), both the primary droop and secondary controls of the DERs were enabled, with load \(R_{L1}\), \(R_{L2}\), and \(R_{L3}\) connected. In the second stage, \(t\in(3,7)\), the load \(R_{L3}\) was disconnected at \(t=3\) and reconnected at \(t=7\) during the third stage, \(t\in(7,10)\). Figures 22 and 23 shows the results obtained. Based on the observation in Figure 23a, it can be noted that the starting times of the output currents from the DERs vary during the first stage. This disparity is caused by the varying time delays in the microgrid's communication protocol. However, the proposed FDSC guarantees achieving consensus in current sharing within a time frame of fewer than 0.7 s in operation. Likewise, during the second and third stages, when a load is disconnected and reconnected, respectively, the current output of each DER adjusts accordingly under both circumstances to meet the varying demands. The impact of time delay becomes apparent in the current output responses, yet the total current demand is evenly distributed among all the DERs.  
Figure 23b illustrates the DC bus voltage response in this case study. The time delay effect is not easily noticeable when juxtaposed with the current output responses of the DERs. This can be attributed to the fact that voltage restoration is a global objective that can be achieved with the control action of at least one active DER. In contrast, current sharing is a consensus objective that necessitates the involvement of all operational DERs within the MG. Figure 22 illustrates the control action, represented by the correction signals, of the proposed FDSC employed within the MG. The observation reveals that each secondary control system within the MG responds to fulfill the control objectives, which involve maintaining the bus voltage at 48 V and achieving equal load current distribution among the DERs. Therefore, it can be inferred that the proposed FDSC remains beneficial in aiding system restoration despite communication latency. Similar results were also obtained in [16; 32; 37], where the effect of communication delay was considered.
## 3 Sorbates and Benzoates

Table 1: _Cont._Moreover, the antimicrobial effect of PS depends on the dissociation of sorbic acid and consequently on the pH. However, the antimicrobial mechanism of the preservative is not yet fully understood [6]. The minimum inhibitory concentration (MIC) values of potassium sorbate and sodium benzoate are 5 mg/mL for _Escherichia coli_, 10 mg/mL for _Bacillus mucoides_, _Bacillus subtilis_, and _Staphylococcus aureus_, and 50 mg/mL for _Aspergillus flavus_[6]. In the case of _Candida albicans_, the MIC is 50 and 2.5 mg/mL for potassium sorbate and sodium benzoate, respectively. In other words, microorganisms with a lower MIC are more sensitive to preservatives, meaning a small amount is enough to inhibit their growth. In turn, the higher the MIC, the greater the microorganism's ability to resist the preservative, and a greater amount is needed to inhibit it, as it the case of _Aspergillus flavus_ and _Candida albicans_ for PS. While potassium sorbate is generally regarded as a safe and low-toxicity food additive [53], there is significant concern about its potential negative impact on human health. The lethal oral dose of PS is 500 mg/kg body weight. Prolonged ingestion of PS can lead to shortness of breath, headache, chest pain, mucosal irritation, and pulmonary edema [6]. Also, when the consumption of sodium benzoate goes beyond the recommended limits, it can cause health problems like nausea, vomiting, abdominal pain, and central nervous system depression [54]. Therefore, several studies have been conducted on the risks of this compound and other food additives to human health. [PERSON] et al. [52] investigated the cell toxicity and genetic toxicity of potassium sorbate in human umbilical vein endothelial cells. Based on the results, this food additive does not present significant cyto-genotoxic effects at low concentrations. Other studies have reported the importance of the interaction between food preservatives and gut microbiota. Gut microbiota play an important role in helping to regulate several physiological processes in the host organism. According to [PERSON] et al. [53], PS affected the gut health of zebrafish, reducing the amount and diversity of bacteria in the gut, and hampering their regular metabolism. [PERSON] et al. [54] evaluated the effect on mice gut microbiota diversity and potassium sorbate, benzoic acid, and sodium nitrite composition in doses equivalent to those indicated as safe. This study revealed that PS resulted in lower gut microbiota diversity in the host that consumed it compared to the other two preservatives. [PERSON] et al. [7] observed negative effects of PS intake, such as infiltration of inflammatory cells in the liver and effects on gut microbiota, when this compound was taken for 10 weeks. These effects were abolished after a 5-week washout period, and a healthy gut microbiota was again established [7]. Thus, since potassium sorbate presents a potential health risk, searching for natural components to replace this chemical preservative in various foods is crucial.
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 2 The Network Model
In the process of vehicle-ground communication, the main role of trackside nodes is to forward data information from adjacent nodes and achieve multi-hop communications. 3. Sink Nodes. It can also be called the gateway node. This node is mainly located next to the station, and it is mainly responsible for sending and receiving data information forwarded by the mobile nodes or trackside nodes and, finally, transmitting the data to the control server of the station through fiber, Ethernet, and other basic network facilities.
## 4 Conclusions  
In this paper, a non-destructive diagnosis strategy for the standing tree MC level based on a multi-sensory information fusion system was studied. The mRMR was used to select optimal features from the root-soil and AE signal parameters. Feature fusion was carried out based on the weight of conditional information entropy, and the FA-ISSA-SVM deduction model was constructed. The system optimized the SVM operation parameters based on the fusion feature subset and achieved an accurate identification rate. By comparison and evaluation of the long-term measurement, the following conclusions can be drawn:  
1. The mRMR algorithm was highly efficient for the hybrid features' optimal selection procedure. When the first nine features were input into the recognition model, the accuracy of the SVM for the collected data was more than 92%.
2. After the conditional entropy weight information fusion, the diagnostic model dynamically adjusts the data fusion weighting coefficient of the corresponding sensor characteristic parameters according to the variance changes of the measured values of each sensor, and finally obtains a minimum root-mean-square error of 2.23% and a maximum goodness-of-fit of 97.64%.
3. The proposed system performed an excellent steady-state precision and generalization performance for the online MC diagnosis of living trees' heartwood. When applied on the metasequoia, lodgepole pine, hovenia acerba, and cherry-apple trees, respectively, the diagnosis accuracy reached 96.4%, 96.7%, 96.3%, and 95.5%.
4. By comparison with the commercial MC measuring instrument, the proposed system can effectively represent the average MC level of the trunk and has stronger applicability to the living trees in the field with a high MC value.  
**Author Contributions:** [PERSON] and [PERSON] devised the programs together; [PERSON] drafted the initial manuscript and designed the project; [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China by grant 32171788 and 31700478, and China's Jiangsu Provincial Government Scholarship for Overseas Study by grant JS-2018-043.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Overall Equipment Effectiveness: Systematic Literature Review and Overview of Different Approaches
## 2 Materials and Methods
### Selection of Relevant Papers  
The selection of relevant articles was standardized as per [20] to avoid information bias. Hence, inclusion (I) and exclusion (E) criteria were defined to ensure that the selected papers were the least subjective.  
The I and E criteria were defined as follows:  
* I1: The paper is a literature review and/or is related specifically to OEE and its application;
* I2: The study mentions an OEE-based model;
* I3: The study only uses OEE to verify an improvement or change in any process;
* E1: The paper cannot be obtained and/or is not written in English;
* E2: The term \"OEE\" is only mentioned; no OEE-based model is calculated or applied;
* E3: The paper is not an article, e.g., proceedings papers, magazines, books, editorial material and letters.  
This review was adapted from the preferred reporting items for systematic review and meta-analysis (PRISMA) statement [23]. Figure 1 shows a PRISMA flow chart that illustrates the different phases of the systematic literature review.  
\begin{table}
\begin{tabular}{c c} \hline
**Research Questions** & **Motivation** \\ \hline RQ1. What is the focus of the current research effort in the OEE domain? & Present a descriptive finding that exhibits interest in the topic and identify trends signified by keywords. \\ \hline RQ2. What models based on OEE have been developed? & Generate a list of different models that have been developed based on the original OEE, to determine fields of study that have applied the indicator as an effectiveness measure. \\ \hline RQ3. What are the principal contributions in OEE and what are the future trends? & Summarize main contributions from different authors and establish future trends to propose new research activities. \\ \hline \end{tabular}
\end{table}
Table 1: Research questions and motivation.  
First, a general search was performed using the search string (Section 2.2) in the selected scientific databases. Subsequently, using an Excel spreadsheet, articles were filtered to eliminate duplicates. Finally, the articles were examined, and the I and E criteria were applied to retain the selected articles for answering the RQs.
# Knowledge Sharing in Security-Sensitive Communities
## 2 Literature Review
### Trust, Social Behavior, and Network Structure  
[PERSON] et al. [24] define trust as a behavioral intention and link it to social behavior, including interactions and risk-taking in relationships. Cultural values affect trust and information privacy [25] and affect how people use social platforms [26; 27]. Trust and privacy concerns in computer-mediated communication (CMC) also vary by culture, especially in how people obtain and interpret information: direct and formal sources are preferred in cultures like the USA, UK, and Australia, while personal networks and contextual cues are more common in Japan, China, and Korea [28].  
Trust is vital for knowledge sharing in organizations, serving as an antecedent and influencing other variables [29]. Trust among employees fosters knowledge exchange and the creation of new knowledge, enabling organizations to adapt to change [30]. Various types of trust, including trust in leadership, colleagues, and technologies, enhance knowledge sharing [31].  
A graph-type data structure represents the relationship between a member, the information source, and factors affecting understanding. We refer to this as a network. There is a reciprocal relationship between network structure and behavior. Structure influences behavior [32], with network analysis revealing human behavior, such as patterns in email correspondence [33]. The network structure also depends on member behavior; for example, structured versus open forums lead to different network configurations [34; 35]. Participants often design their networks to share news with only the \"necessary, but sufficient\" recipients [36]. The social behavior of intelligence personnel influences the network's structure.  
Social Network Analysis (SNA) measures and analyzes community structure and interactions [37; 38]. Social networks represent relationships between social units, such as student friendships [39; 40; 41], interactions between users during disasters via social media [42], football movement among players [43; 44], or corporate email communication [45; 46]. Modeling knowledge sharing as networks, with actors as nodes and messages as arcs, allows analysis of behavior and interactions. Networks can be directional, like citation networks, or undirected, like co-authorship [35].
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 4 Experiments and Results  
### Experiment Setup  
It is essential to evaluate the proposed VM consolidation strategy on a large-scale experiment environment with a real infrastructure since the target system is an IaaS Cloud. Yet to conduct repeatable experiments on a real infrastructure is extremely difficult due to the requirements of evaluating and comparing the proposed algorithm. In contrast to alternative simulation toolkits (SimGrid or GangSim), CloudSim [13] allows the modeling of virtualized environments, supporting on-demand resource management and configuration.  
Therefore, we choose the CloudSim toolkit 4.0 to simulate our proposed strategy, as it is a modern simulation framework aimed at Cloud computing environments. The experiments selected 50 heterogeneous physical hosts. Each physical node is allocated with 3 to 5 virtual machines initially. When a node is determined as overload or underload compared to our defined dynamic multi-thresholds, the proposed self-adaptive VM consolidation strategy will be triggered. The CPU MIPS (millions instrument per seconds) ratings are equivalent to Amazon EC2 instance types. The users submit requests for provisioning of 200 heterogeneous VMs. Each VM is randomly assigned a workload trace from one of the servers from the workload data. Initially, VMs are allocated according to their parameters assuming 100% utilization. The configuration is shown in Table 4.  
Our proposed dynamic multi-thresholds strategy (DMT) is compared with four threshold adjustment strategies. The first strategy uses a static threshold (ST) in which the threshold never changes during the consolidation. The threshold sets to 70% for detecting an over-loaded host and 30% for detecting a low-loaded one. The next two strategies adjust the utilization threshold dynamically based on the statistic method by the median absolute deviation (MAD) and the interquartile range (IQR) while the fourth method (LiRCUP) considers the prediction of CPU utilization on each PM, as it selects the higher one to be the next source PMs instead of adjusting the threshold [14]. Among this strategy, the first three are the benchmark algorithms presented in CloudSim (ST, MAD, IQR) [1] and the last one (LiRCUP) is an improved single-parameter prediction algorithm.  
To compare the performance of the proposed dynamic multi-thresholds strategy, the algorithm on VM selection and placement of the above five strategies (ST, MAD, IQR, LiCUP, DMT) are the same, with Minimum VM Migration algorithm (MVM) for VM selection and Best Fit algorithm (BF) for VM placement. For the sixth strategy DMT (MW) in our experiment, its VM selection algorithm is MW-MWM, a modified algorithm of MVM shown in Section 3.2, and its VM placement algorithm is MW-BF shown in Section 3.3. The DMT (MW) strategy is added to further compare the performance with DMT, in terms of the improved VM selection algorithm (MW-MWM) and placement algorithm (MW-BF).