esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions
_RQ3: Is the Representational category of DQDs negatively affected by the variety characteristic and the fact that the quality of data would depend upon the aims of data analysis?_  
The importance of the Representational DQ category is quite low relative to the two previous categories with a total count of only 48. The authors were of the opinion that this category could be relatively unknown due to the _variety_ characteristic of Big Data. However, most of the research data involved in the IHC conducted in this work concerned mostly text-based data, including numbers, and the results were analyzed and used by well-trained personnel. This could warrant future work concerning data quality with other kinds of data (images, charts, and videos) used in the health industry. On the other hand, even if some authors, such as [23], point toward the final goals of data analysis and how data quality is therefore important, most other research studies as part of the IHC do not give enough indication pertaining to the rationale behind the data analysis. Therefore, there is the inherent assumption in most research studies that data would be used in one or very few use cases; as discussed in the earlier sections of this paper, this assumption might not hold true, especially in the Big Data context. _RQ4: Does the fact that health datasets are very often publicly available and voluminous data slows down access and retrieval of data negatively impact the Accessibility category of DQDs?_  
Finally, the Accessibility category ranks unsurprisingly last in the findings. Many datasets are publicly available for analysis and research; hence, previous authors have undertaken their work within a context in which accessibility was easy. Thus, the volume aspect could be deduced not to have affected the accessibility to data, but care should be taken to probe the investigation of the relationship between _volume_ and _accessibility_ for Big Data applications. Furthermore, for private and industry-based applications of Big Data within the health industry, it could be argued that DQDs such as Security, Privacy, and Compliance would have a higher impact. Thus, future Big Data governance frameworks specifically for the health industry should further explore this Accessibility category.
# Research on Seismic Signal Analysis Based on Machine Learning
## 1 Introduction
In this paper, we applied several powerful machine learning techniques to automatically classify noise, earthquake, and blast events, including a variety of classical convolutional neural networks and unsupervised learning methods. In particular, we advocated a two-step approach where feature learning is decoupled from classification or clustering. By adopting the efficient unsupervised method MiniRocket [24] for time series feature extraction, we could utilize the obtained features as prior for the consequent task, such as supervised classification and unsupervised clustering. Finally, we comprehensively evaluated the advantages of various models in terms of accuracy and computational efficiency. The main contributions of this study are as follows:  
(1) The main goals of the paper were to provide a novel unsupervised classification method with high accuracy for real-time earthquake catalog elimination, such as blasting interference events, that expands the technical framework for real-time earthquake prediction, enriches the purification means of the earthquake catalog, and can also be reversed to provide an induced earthquake catalog for industrial production, such as coal mine and shale gas mining, filtering natural earthquakes and only retaining artificial earthquakes. Then, the risk of earthquake induced by industrial mining was analyzed to protect the safe mining. (2) This paper proposed a two-step approach of pre-training feature learning and target task learning which can be either supervised or unsupervised--a lightweight collaborative learning model for seismic sensing signal classification (LCL-SSS). This method only uses a simple one-dimensional convolutional neural network structure to replace the multilayerconvolutional neural network, which greatly improves the computational efficiency while the accuracy is consistent with other multilayer convolutional neural networks. (3) Classical unsupervised methods, such as K-means, perform poorly in seismic sensing signal classification due to the complex time domain. In this paper, we used a one-dimensional convolutional neural network combined with an unsupervised classification method to extract the features of the seismic waveform, and a good classification effect was achieved. This provides a new reference for the study of seismic waveform classification in the future.
## 3 Hydrological Applications
### Mapping of watersheds and hydrologic features  
Different sensors aboard airplane or satellites have been used extensively in providing imaging, photographing and mapping information for different purposes. Mapping of wetlands, floodplains, disaster areas, coastal shores, river banks, snow pack, fire damage, drainage basins and others that show the areal extent of a given land feature distinct from others due to the difference in the spectral signature fall under this category. Different studies have used aerial photos, satellite images, lidar and radar data to map and visualize land surfaces for planning, resource mapping, hazard assessment and emergency operations.  
Landsat images were used to capture the expansion of a hydrologically-closed lake, Devils Lake, North Dakota. Results of the study are presented below.
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 4 Conclusions  
In this paper, a novel problem was proposed: how to use the measured multi-point vibration response to predict the unmeasured multi-point vibration response in the frequency domain under unknown uncorrelated multi-source loads using data-driven multiple regression analysis. After completing the problem description, the solution of this problem was presented, which was an MIMO LS-SVR-based multi-point vibration response prediction model using the measured multi-point vibration response and the unmeasured multi-point vibration response history training dataset. The experimental verification results for acoustic and vibration sources on union loads experimental devices on a cylindrical shell demonstrated that this proposed approach is more effective and has higher accuracy than transmissibility matrix-based methods and multiple linear regression model-based multi-point vibration response prediction methods.  
Because this method cannot be used when the measurement position is unknown, in the next step, we can study how to predict the position and response simultaneously. If the multiple loads excitations are correlated with each other in the frequency domain, the relationship between the multiple loads excitation and vibration responses is much more complex, and the phase becomes very important. Moreover, due to the phase loss of this method, the predicted vibration response power spectrum of unknown points cannot be converted to the time domain. Therefore, phase information containing the multi-point dynamic random vibration response prediction algorithm in the frequency domain is a further research direction. Additionally, validating this method on nonlinear time invariant structures is also future work.  
Conceptualization, C.W., D.C., H.H. and W.Z.; methodology, C.W., D.C., H.H. and W.Z.; software, D.C., H.H. and W.Z.; validation, D.C., H.H. and W.Z.; writing--original draft preparation, D.C., H.H. and W.Z.; writing--review and editing, C.W., D.C., H.H. and W.Z.; supervision, C.W., X.L. and J.C.; project administration, C.W.; funding acquisition, C.W., X.L. and J.C.; Correspondence, C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China grant numbers 51305142 and 51305143, the China Postdoctoral Science Foundation grant number 2014M552429 and the Promotion Program for Young and Middle-aged Teachers in Science and Technology Research at Huaqiao University grant number ZQN-PY212.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Extracellular Vesicles for Regenerative Medicine Applications
## 3 Pre-Clinical Applications of EVs in Tissue Engineering and Regenerative Medicine
### Skin and Wound Healing  
The application of TERM in skin and topical wound healing has been extensively studied; the result is that there is an extensive number of products available and in development for this purpose. EVs are a logical supplement to these TERM approaches to skin and wound healing and have shown initial promise in pre-clinical studies. Across various applications of EVs in skin and wound healing, results have consistently shown that EVs derived from a variety of cell sources promote skin and topical wound healing through promoting re-epithelialization, angiogenesis, ECM remodeling, and the migration of keratinocytes and fibroblasts to the wound region [101; 102; 103; 104; 105; 106; 107; 108; 109; 110]. Specific applications of note include adipose MSC-derived exosomes and gingival MSC-derived exosomes embedded in a hydrogel formulation [109; 110]. In addition, there has been extensive interest in using EVs derived from adipose-derived stem cells for application in skin regeneration and hair follicle regeneration [111].
# Impact of Climate Change on the Major Eco-Hydrological Parameters in the Dongting Lake Basin
###### Abstract  
Quantifying the impacts of climate change on evapotranspiration (ET) and gross primary production (GPP) in the Dongting Lake Basin is essential for assessing water scarcity and implementing sustainable development strategies. Premised on actual measurements and remote sensing data from 47 stations, the impact of climate change on eco-hydrological parameters in the Dongting Lake Basin was analyzed in the present study using the BESS model (The Breathing Earth System Simulator), ridge regression analysis, stepwise regression model and time-lag analysis. The results reveal that: (1) the Dongting Lake Basin has been warm and arid over the last four decades, with the frequent occurrence of extreme climate events. Vegetation carbon sequestration capacity exhibited a slightly upward trend with 0.0081 g C m\({}^{-2}\) d\({}^{-1}\)/year from 2000-2017. ET changed with rates of \(-3.309\) mm/year, with possible risk conflicts between water demand and supply in the future. (2) The increasing temperature was the main driver of ET enhancement in the Dongting Lake Basin. Meanwhile, both temperature and precipitation were found to be the dominant drivers of GPP enhancement. The effect of temperature on GPP was found to be greater in the areas covered by crops and tree vegetation, and natural vegetation was more strongly influenced by precipitation than radiation. (3) Extreme temperature events have had a significant impact on evapotranspiration (ET) and gross primary production (GPP) in the Dongting Lake Basin. Specifically, the cold index in extreme temperature events was found to significantly affect ET, while the heat index in extreme temperature events significantly affected GPP. Additionally, both ET and GPP were found to respond to extreme precipitation events in the region. The results of the study established that vegetation is highly sensitive to temperature, especially temperature extremes, and that precipitation also has a stressful effect. Increasing temperatures and precipitation within a range benefit vegetation productivity. (4) In the Dongting Lake Basin, we found that different climatic factors produced different time lag effects on GPP and ET by time lag analysis. This study highlights the lag effects of climate factors and extreme climate events on eco-hydrological parameters. We suggest incorporating the effects into simulation models of eco-hydrological parameters. This will lead to a better understanding of the variation of eco-hydrological parameters under climate change.  
eco-hydrological parameters; extreme climate events; temperature; precipitation; Dongting Lake Basin +
Footnote †: journal: Applied Sciences
# A Synchronous Spin-Exchange Optically Pumped NMR-Gyroscope
## 4 Discussion  
We demonstrated a novel spin-exchange pumped \({}^{131}\)Xe-\({}^{129}\)Xe NMR gyroscope. The production of longitudinal spin-exchange fields and the systematic uncertainty inherent to them is greatly suppressed by synchronous spin-exchange optical pumping. The simultaneous precession of each transversely polarized noble gas is continuously monitored using the optically pumped Rb atoms. The measured Larmor resonance frequencies are highly correlated with bias magnetic field fluctuations.  
Computing the stationary sensor's perceived inertial rotation when oriented East-West and assuming a scale factor determined solely by \(\rho\) allows for an ARW sensitivity of \(\sim\)16 \(\mu\)Hz\(/\sqrt{\mathrm{Hz}}\) and bias instability of \(\sim\)800 nHz. The finite short term sensitivity appears limited by the detection SNR. What limits the SNR is not known. The photon shot noise limited magnetometer performance should support an SNR more than 100x greater than we measure. It is possible that fluctuations in pump frequency and intensity could limit our SNR by producing noise in the AC Stark shift and the Rb polarization. We confirmed that the fidelity of modulation produced by the EOMs is sufficient to support 4x the measured SNR. The source of drift which limits the bias instability is also unknown. Cell temperature, which was not stabilized during these measurements, could be an important contribution to long term stability. We see no direct indications of first order quadrupole from \({}^{131}\)Xe interactions with electric field gradients at the cell walls. We showed that cross talk exists between phase sensitive detection channels for each noble gas. Such cross talk influences the device's effective scale factor. Future work will include detecting \(S_{z}\) in a way such that gain modulation is suppressed as demonstrated in [1; 2]. This device's scale factor can be verified by orienting its sensitive axis along North-South and measuring Earth's rate of rotation.  
Conceptualization, S.S.S., D.A.T. and T.G.W.; methodology, T.G.W., S.S.S. and D.A.T.; software, D.A.T.; validation, S.S.S., D.A.T. and T.G.W.; formal analysis, S.S.S.; investigation, S.S.S. and D.A.T.; resources, T.G.W.; data curation, S.S.S. and D.A.T.; writing--original draft preparation, D.A.T.; writing--review and editing, S.S.S. and T.G.W.; visualization, S.S.S. and D.A.T.; supervision, T.G.W.; project administration, T.G.W.; funding acquisition, T.G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation grant numbers PHY-1607439 and PHY-1912543 and Northrop Grumman Mission Systems' University Research Program.  
We would like to thank [PERSON] for insightful discussions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Automation Application Areas
#### 3.2.1 Automated Cyber Threat Intelligence (CTI) Information  
With the rise of threat information-sharing platforms, vulnerability databases, and internet-based cybersecurity information, SOCs are challenged with converting large volumes of threat information into threat intelligence. Considering big data, automated solutions are needed to collect and classify information accordingly. Such processes can aid in quickly educating SOC analysts. A commonly mentioned use case in this area is automated rule generation for intrusion detection systems (IDS), whereby tools automatically collect CTI, generate indicators of compromise (IOCs), and update IDS for the detection of malicious activity [26, 29, 30, 31, 52]. In one study, more than 70% of analysts stated that automated tools must improve the quality of the information they collect [31]. Presenting security analysts with comprehensive information that can be effectively leveragedrepresents a core goal of these tools. Another study recommended collecting threat intelligence from malware information-sharing platforms (MISP) instead of general web data to achieve data quality and developed SmartValidator, a tool that continuously and automatically collects CTI and validates internal security alerts against it (i.e., proving their legitimacy) [30]. They showcased how SmartValidator performed better when MISPs were the primary data source. Ref. [52] posited that the manual generation of IOCs is more interpretable and human-friendly, allowing SOC analysts to understand them sufficiently. Thus, they compared their automated IOCs to manually generated ones and noted that security analysts found no difference between the two sources. The notion of threat intelligence interpretability was also discussed by [29] who stated that security analysts must efficiently identify and comprehend all the relevant information when analyzing automated threat reports.  
**Findings:** Overall, we classify automated CTI tools operating in four sequential phases:  
1. Collect threat information.
2. Analyze and convert threat information into threat intelligence.
3. Validate the cyber threat intelligence against internal alert information to determine the presence or absence of intrusions.
4. Generate indicators of compromise (IOCs).  
By scanning the external threat landscape for information that can assist SOCs in efficiently detecting threats, automated CTI tools permit security analysts to channel their efforts into effective decision-making strategies [30]. These four phases illustrate that the automation of CTI information can be implemented to varying degrees--with the recommendation of all four phases being adhered to.
## 2 Route Generation Methodology
### Feasible Path Generation: Turn Radius Consideration  
The routes created by the lower and higher-level path generation methods do not consider the turn radius of the vehicle. As a result, the paths are initially difficult for the vehicles to follow. Therefore, additional methods were applied to make the optimal paths feasible with respect to the vehicle's kinematics. Feasible path generation is a post-processing event that modifies the paths produced by the lower and higher-level path generation methods. Feasible path generation consists of two methods that can be applied in different use cases: the corner-cutting method and the Reeds-Shepp method.
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 4 Non-Thermal Atmospheric Plasma--NTAP  
### General Description of NTAP Technology  
In 1972, [PERSON] [132] reported that the generated plasma is the fourth and unique state of matter. Treatment of foods with generated plasma is a novel processing technology gaining more industrial interest in recent years [133]. Plasma treatment can be categorized in thermal and non-thermal atmospheric plasma (NTAP) based on the conditions in which it is generated. When the plasma is generated at ambient pressure and temperature, it is termed cold plasma (CP), atmospheric cold plasma (ACP), or non-thermal atmospheric plasma (NTAP), while higher power and pressures need to obtain thermal plasma [134]. To generate NTAP, any kind of energy (electrical, thermal, optical, radioactive, and electromagnetic radiation) can be used to ionize the gases, but mainly electric and electromagnetic fields are used [135; 136]. Plasma has a neutral ionized gaseous form constituted by ions, free electrons, gas atoms, and molecules, as well as UV photons depending on the process parameters and the gas employed [133; 136; 137]. Plasma occurs after the gas subjection through an electric field created between two electrodes (cathode and anode) separated by a small distance of 1 cm [7; 138]. The gases mainly used for plasma generation and that can also affect its properties are air, oxygen (O\({}_{2}\)), nitrogen (N\({}_{2}\)), carbon dioxide (CO\({}_{2}\)), and noble gases, individually or in combinations for optimal results.  
NTAP has proved to be an additional tool for the successfully decontamination of abiotic food surfaces such as packaging materials and various food and seafood products [136]. NTAP can even be used toward the decontamination of food in their final package, with a dielectric barrier discharge (DBD) equipment [139; 140]. NTAP can be used to inactivate many types of common foodborne pathogens [141; 142; 143; 144], spoilage microorganisms [145; 146] including yeasts and fungi [147; 148], spores [147; 149], and viruses [150]. A parameter that impacts the efficiency of NTAP is its low penetration efficiency making the method able to inactivate microorganisms only on the surface of solid foods [151]. In association with the previous parameter, when a foodstuff has high microbial loads forming multiple layers of bacteria on the surface, it cannot be destroyed because the upper layers of cells protect the underling from the NTAP [133]. [PERSON] et al. (2013) [151] proposed three mechanisms of action for the inactivation of microorganisms with the use of NTAP: (a) the direct disruption of the cell membrane or wall, pushing to leakage of cellular components, (b) the oxidative damage to membranes or intracellular components, such as proteins and carbohydrates, and (c) the cellular DNA damage. Instead of the microbial inactivation, NTAP can extend the shelf life of foods, but more research is necessary before its adoption by the industry [76].