esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Model
This language includes the following features:  
_AL_ is an Attributive Language, a description logic that provides atomic concept, universal concept (_T_), bottom concept (\(\bot\)), atomic negation, intersection (\(\Pi\)), value restriction (\(\forall RC\)), and limited exist restriction (\(\exists R.T\));  
_E_--full extension quantification (existential restriction that has fillers other that owl:Thing);  
_Q_--qualified cardinality restriction;  
_D_--use of data type properties, data values, or data types. This description logic, among other characteristics, can use cardinality restriction and qualified cardinality restriction (available in OWL 1.1). Next, let us consider the relational part of the overall scheme of the AMSEC, stored in the hybrid repository. It is represented in Figure 6. The data scheme, which is used for the AMSEC, contains a set of the tables: _network_, _host_, _objectProperty_, _objectPropertyType_, _etc._ The data about the analyzed network and the analysis results are stored in these tables. The table _Network_ is for storage of the list of analyzed networks. It contains name of the network, network description, and the references to objects from the table _System_, which is intended for common description of analyzed system. The table _System_ can contain more complicated specification, which includes the description of the business process (this table enables integration with other SIEM components). The table _host_ includes the list of host descriptions. The table _objectProperty_ allows storing the properties of hosts and networks. The table _objectPropertyType_ is a directory of the properties' names and types for table _objectProperty_. Figure 6: Relational part of Attack Modeling and Security Evaluation Component (AMSEC) repository data scheme. The field _refTableName_ can store the name of additional directory table, and the field _value_ of the table _objectProperty_ contains the identifier of the record from external directory. It is used, for instance, to store the vulnerability list. The additional data scheme for vulnerabilities was created to store the NVD database. The table \"CVE_List\" is the root table which contains all vulnerability identifiers. To make a reference between hosts and vulnerabilities, the new property (id = 19, name = \"Vulnerability\", refTableName = \"CVE_List\") was created in _objectPropertyType_. Thus, it is possible to add new records to the table _objectProperty_ with the type identifier equal to 19, and with the vulnerability record id from the table \"CVE_List\" in the field _value_. The data schema contains also the tables _metric_, _metricType_, _event_, _fieldType_, _dataType_, _eventType_, _sourceType_. The table _metric_ includes various security metrics that are calculated by the AMSEC during attack graph analysis. The table _MetricType_ is a directory table for the metric types. The tables _event_ and _eventType_ store the alerts generated by the AMSEC. Thus, the presented data schema allows storing all information for attack graph building and analysis and to make interaction between the AMSEC and other components of the SIEM system.
###### Abstract  
With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed.  
Keywords:visual sensor networks; sensor placement problem; multi-objective optimization; coverage; reconstruction error +
Footnote †: journal: Applied sciences
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 4 RSTS Protocol
### Security Analysis  
FTSP is designed for a benign environment in which each node is valid. However, malicious attacks would destruct the synchronization process by injecting irregular time information or masquerading as a normal node. Therefore, in this subsection, we analyze the security of FTSP, FTSP with threshold detection, and RSTS.  
**Under manipulation attacks**. The manipulation attacker injects illegal timestamps into the network misleading its neighbor to synchronize to the wrong time. Obviously, unprotected FTSP quickly loses synchronization. Since the timestamps from attackers are without rules and two timestamps from a given valid neighbor are said to be conforming [21], many secure methods design checking mechanisms based on a preset threshold, such as delay and the difference of relative clock skew. If a node's timestamps cannot pass the threshold filtering, the node may be blacklisted. The network would exclude the suspicious node from the synchronous path and rebuild the topology. The feasibility of these methods has been proven. In RSTS, the attacker can act as a reference node or master node broadcasting illegal time. RSTS's security has been analyzed in the above illustration when the attacker acts as a reference node. The other scenario is similar to FTSP with threshold detection. Hence, it can integrate threshold detection to enhance RSTS's defense capability.  
**Under Sybil attacks**. Although the threshold detecting mechanisms are available in defending against manipulation attacks, they may lose defensive ability in facing Sybil attacks. Unlike manipulation attacks, a Sybil attacker masquerades as a normal node to broadcast illegal information instead of broadcasting illegal information directly. In this case, threshold-detecting mechanisms may render the disguised normal node out of synchronization. RSTS adopts a novel forwarding-based synchronization path and filters the attacker's time information by default in estimating the synchronization parameters, which results in a secure synchronization under Sybil attacks.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Data and Processing
#### 2.3.2 Data Preprocess  
We use the hourly foF2 data for training our models, so we need to preprocess the data obtained from websites. The original foF2 data are presented in text files, it is necessary to extract the parameters such as time, station name, latitude, longitude, and foF2 values to import to the database in hourly values. Besides, all input and output parameters need to be combined into a matrix as one of the sample datasets hourly before training the network. Also, those datasets with none numeric values or beyond reasonable values need to be excluded to make sure the sample datasets are effective.  
To standardize the sample data and accelerate the convergence of the network to improve the generalization ability for the network, it is necessary to normalize the input and output data to the range of \([-1,1]\) before training. We used MATLAB's mapminmax function to perform the normalization of the input parameters and the output parameter.
## 4 Analysis of Field Test Results
### Field Test Configuration  
The GNSS receiver antennas were set up as shown in Figure 3. Two identical NovAtel OEMV6 receivers were used to collect the data, with each receiver connected to a NovAtel pinwheel antenna. The training data were collected on 21 July 2020 for 24 h, and the test data were collected on 22 February 2021 for 24 h using the identical setup. The data were logged in the RINEX 3.1 format using NovAtel commercial software. To determine the true locations of each antenna, 24 h of GPS data were post-processed using Trimble Business Center (TBC). The relative vector between the two receiver antennas was used for the residual computation, not absolute coordinates. The length of the short baseline used for the DD process was 0.409 m. The MATLAB regression package was utilized for machine learning data processing and modeling.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Monitoring Weather Forecast  
Combining radar and Global Positioning System (GPS) technologies, one can obtain useful information regarding atmospheric water vapor, operational weather forecasting, atmospheric storm systems, and global climate changes [61; 94]. [PERSON] et al. reported a modern approach for the remote sensing of hail using an S-band dual linear polarization radar. The process employs a new hail signal HDR, which can be used for measurements of raindrop size distribution.
###### Abstract  
The advent of ultrafast X-ray free-electron lasers (XFELs) opens the tantalizing possibility of the atomic-resolution imaging of reproducible objects such as viruses, nanoparticles, single molecules, clusters, and perhaps biological cells, achieving a resolution for single particle imaging better than a few tens of nanometers. Improving upon this is a significant challenge which has been the focus of a global single particle imaging (SPI) initiative launched in December 2014 at the Linac Coherent Light Source (LCLS), SLAC National Accelerator Laboratory, USA. A roadmap was outlined, and significant multi-disciplinary effort has since been devoted to work on the technical challenges of SPI such as radiation damage, beam characterization, beamline instrumentation and optics, sample preparation and delivery and algorithm development at multiple institutions involved in the SPI initiative. Currently, the SPI initiative has achieved 3D imaging of rice dwarf virus (RDV) and coliphage PR772 viruses at \(\sim\)10 nm resolution by using soft X-ray FEL pulses at the Atomic Molecular and Optical (AMO) instrument of LCLS. Meanwhile, diffraction patterns with signal above noise up to the corner of the detector with a resolution of \(\sim\)6 Angstrom (A) were also recorded with hard X-rays at the Coherent X-ray Imaging (CXI) instrument, also at LCLS. Achieving atomic resolution is truly a grand challenge and there is still a long way to go in light of recent developments in electron microscopy. However, the potential for studying dynamics at physiological conditions and capturing ultrafast biological, chemical and physical processes represents a tremendous potential application, attracting continued interest in pursuing further method development. In this paper, we give a brief introduction of SPI developments and look ahead to further method development.  
X-ray free-electron lasers; XFEL; coherent diffraction imaging; single particle imaging; resolution 2018 8, 132; doi:10.3390/app8010132
# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
###### Abstract  
The clinical decision support system provides an automatic diagnosis of human diseases using machine learning techniques to analyze features of patients and classify patients according to different diseases. An analysis of real-world electronic health record (EHR) data has revealed that a patient could be diagnosed as having more than one disease simultaneously. Therefore, to suggest a list of possible diseases, the task of classifying patients is transferred into a multi-label learning task. For most multi-label learning techniques, the class imbalance that exists in EHR data may bring about performance degradation. Cross-Coupling Aggregation (COCOA) is a typical multi-label learning approach that is aimed at leveraging label correlation and exploring class imbalance. For each label, COCOA aggregates the predictive result of a binary-class imbalance classifier corresponding to this label as well as the predictive results of some multi-class imbalance classifiers corresponding to the pairs of this label and other labels. However, class imbalance may still affect a multi-class imbalance learner when the number of a coupling label is too small. To improve the performance of COCOA, a regularized ensemble approach integrated into a multi-class classification process of COCOA named as COCOA-RE is presented in this paper. To provide disease diagnosis, COCOA-RE learns from the available laboratory test reports and essential information of patients and produces a multi-label predictive model. Experiments were performed to validate the effectiveness of the proposed multi-label learning approach, and the proposed approach was implemented in a developed system prototype.  
clinical decision support system (CDSS); decision-making; electronic health records (EHRs); multi-label learning +
Footnote †: journal: Applied Sciences
###### Abstract  
During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (h\({}_{c}\)) to detect changes in leaf physiology of soybean (C\({}_{3}\)) and maize (C\({}_{4}\)) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (T\({}_{r}\)), leaf stomatal conductance (g\({}_{s}\)), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (h\({}_{c}\) and LAI), as well as vegetation cover reflectance and radiometric temperature (T\({}_{L,Rad}\)). Our results showed that water stress caused significant ET decreases in both crops. This reduction was linked to tighter stomatal control for soybean plants, whereas LAI changes were the primary control on maize ET. Spectral vegetation indices (VIs) and T\({}_{L,Rad}\) were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of g\({}_{s}\), T\({}_{r}\), and A using hyperspectral data was more accurate when pooling data from both crops together rather than individually. Nonetheless, separated PLS-R crop models are useful to identify the most relevant variables in each crop such as T\({}_{L,Rad}\) for soybean and h\({}_{c}\) for maize under our experimental conditions. Interestingly, the most important spectral bands sensitive to drought, derived from PLS-R analysis, were not exactly centered at the same wavelengths of the studied VIs sensitive to drought, highlighting the benefit of having contiguous narrow spectral bands to predict leaf physiology and suggesting different wavelength combinations based on crop type. Our results are only a first but a promising step towards larger scale remote sensing applications (e.g., airborne and satellite). PLS-R estimates of leaf physiology could help to parameterize canopy level GPP or ET models and to identify different photosynthetic paths or the degree of stomatal closure in response to drought.  
**Keywords:** crop phenotyping; hydraulic traits; leaf conductance; phenology; photosynthetic CO\({}_{2}\) assimilation rate; PLSR; shortwave radiation; transpiration rate; temperature; water stress
## 3 Results
### Experiment B  
While the control showed no significant change in aliphatic hydrocarbon concentrations compared to the initial values, a significant decrease in aliphatic hydrocarbon concentrations was observed in five test variants in just 45 days, a period one order of magnitude shorter than that in Experiment A (Table 3 and Supplementary Table S1). The highest reduction of aliphatic hydrocarbon was achieved by _G. rubripertinca_ CWB2 in Fe\({}^{2+}\)-containing BSM (variant BG1), the results being significantly lower (LSD test, \(p\) < 0.1) than the other four variants (Table 3). Significant values (LSD test, \(p\) < 0.1 to <0.001) were also obtained for _G. rubripertinca_ CWB2 in Fe-free BSM (variant BG2), _R. erythropolis_ S43 in Fe\({}^{2+}\)-containing BSM (variant BR1), _R. erythropolis_ S43 in Fe-free BSM (variant BR2) and through biostimulation with DFOB (variant BD). Variants BG3 and BR3, both with Fe\({}^{3+}\) addition, showed no significant biodegradation.  
Initial hydrocarbon concentrations in the test soil corresponded to \(\sim\)7% TOC and despite the significant decrease of aliphatic hydrocarbons, no significant change in TOC was observed in any variant. Supposing complete mineralization of aliphatic hydrocarbons, the decrease to 64.9 \(\pm\) 8.8 (i.e., average of successful variants BD, BG1, BG2, BR1 and BR2) should have corresponded to a decrease in TOC of 1.3% (i.e., the final TOC value of 12.2%). In fact, the final TOC value from the successful variants (13.0 \(\pm\) 0.6%) corresponded to only around 40% of theoretical mineralization, roughly confirmed by the amount of respier CO\({}_{2}\) throughout the experiment. Hence, approximately 60% of the hydrocarbons removed were either transformed into biomass or only partially transformed to other soil components; a combination of both most likely.  
While both actinobacterial strains proved successful at biodegradation of the soil hydrocarbons, some differences were observed. _G. rubripertinca_ CWB2 proved slightly better at biodegradation than _R. erythropolis_ S43 (final values of 65.2 \(\pm\) 10.5 and 70.2 \(\pm\) 5.9 mg.kg\({}^{-1}\) dwt, respectively; significant at \(p\) < 0.095). On the other hand, the use of _R. erythropolis_ S43 led to significantly higher microbial activity, i.e., DHA (_t_-test, \(p\) < 1.2 \(\times\) 10\({}^{-5}\)) and respiration (_t_-test, \(p\) < 3.9 \(\times\) 10\({}^{-7}\)), suggesting improved regeneration of soil functioning following bioaugmentation by _R. erythropolis_ S43.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 2 Materials and Methods  
### Sample Preparation  
Methanol (MeOH) was used three times to extract 600 g of dried _M. fragrans_ seeds. Ethyl acetate (EtOAc) was used to partition the solvent after the MeOH extract (114.38 g) had been suspended in water, yielding 98.05 g. The EtOAc fraction (96.78 g) was subjected to silica gel column chromatography using a gradient of n-hexane/EtOAc (100:0 to 1:1) as the solvent to obtain five subfractions (MFE1-MFE5). The MFE3 (34.50 g) fraction was separated using preparative RP-MPLC with 50-90% MeOH to yield compound **1** (16.50 g, macelignan; Figure 1a). Nuclear magnetic resonance (NMR) spectra were recorded using a JNM-ECA AVANCE 400 MHz spectrometer (JEOL Ltd., Tokyo, Japan). The NMR solvent (CDCl\({}_{3}\)) was acquired from Cambridge Isotope Laboratories Inc. (Andover, MA, USA).  
Compound **1** (macelignan): \({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\)) \(\delta_{\rm H}\) 6.82 (1H, d, \(J\) = 7.9 Hz, H-5), 6.72 (1H, d, \(J\) = 7.9 Hz, H-5), 6.65 (1H, s, H-2), 6.64 (1H, d, \(J\) = 7.9 Hz, H-6), 6.62 (1H, s, H-2), 6.59 (1H, d, \(J\) = 1.7 Hz, H-6), 5.92 (1H, d, \(J\) = 1.4 Hz, OCH\({}_{2}\)O), 5.91 (1H, d, \(J\) = 1.4 Hz, OCH\({}_{2}\)O), 5.45 (1H, s, 4\({}^{\prime}\)-OH), 3.86 (3H, s, OCH\({}_{3}\)), 2.76 (2H, dd, \(J\) = 13.8, 4.9 Hz, H-7), 2.26 (2H, m, H-7), 1.72 (2H, m, H-8 and 8), 0.83 (6H, m, H-9 and 9); \({}^{13}\)C-NMR (100 MHz, CDCl\({}_{3}\)) \(\delta_{\rm C}\) 147.5 (C-3), 146.3 (C-3), 145.5 (C-4), 143.6 (C-4), 135.7 (C-1), 133.8 (C-1), 121.8 (C-6), 121.7 (C-6), 114.0 (C-5), 111.5 (C-2), 109.4 (C-5), 108.0 (C-2),100.7 (OCH\({}_{2}\)O), 55.9 (OCH\({}_{3}\)), 39.4 (C-8), 39.3 (C-8\({}^{\prime}\)), 39.1 (C-7), 38.9 (C-7\({}^{\prime}\)), 16.2 (C-9), 16.1 (C-9\({}^{\prime}\)); HRESIMs _m/z_ 329.1737 [M + H] \({}^{+}\) (calcd. for C\({}_{20}\)H\({}_{25}\)O\({}_{4}\), 329.1753).
# Use of Vine Shoot Waste for Manufacturing Innovative Reinforced Cement Composites
## 1 Introduction
Using coir fiber reinforcement in masonry mortar materials results in lower densities. The lignocellulosic fragments with shorter lengths are better for reinforcement, and the water absorption capacity of 15% decreases with the fiber size [23]. [PERSON] et al. reported using various lignocellulosic wastes (rice straw, hazelnut shell, coir, oil palm residues, wheat straw, bark, bagasse, corn granules and achar stalks) in construction. The advantages of their use are the reduction of carbon dioxide emissions, safe and efficient waste disposal, low thermal conductivity, enhanced bio and fire resistance and a lightweight composite cement-based material [15]. Vine shoot waste (VSW) is another type of lignocellulosic biomass generated in large quantities worldwide and is commonly burned on the field or used as compost [24; 25]. To the best of our knowledge, apart from studies conducted on the use of vine shoot waste as a source of bioenergy [26], cellulose nanocrystals [27], particleboards [28] and clay bricks [29], no such renewable raw material has previously been used to enhance the properties of cement. In this regard, the study aims to obtain an innovative cement-based material by adding 10 wt.% vine shoot waste. Thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDX), Fourier-transformed infrared (FT-IR) spectroscopy and solid-state \({}^{27}\)Al and \({}^{29}\)Si nuclear magnetic resonance (NMR) spectroscopy evidenced the improved structural and hydration properties of the cement paste with 10 wt.% vine shoot waste. The obtained results contribute to the enhancement of knowledge in the field of waste management and the progress toward the circular economy by providing sustainable ways of waste disposal.
## 5 Semantic Understanding of Concept Layer
### Visual Features Based Method  
The concept layer semantics of the geographic scene can be directly described by low-level visual feature attributes. The scene classification algorithm based on visual features extracts the low-level visual features (such as color, shape and texture), then describes the features and designs the classifier to infer the semantic information of the geographic scene. According to the different sources of low-level feature extraction, scene classification based on low-level features includes two categories: global-feature-based methods and local-feature-based methods. The extraction methods of global features and local features in visual features are detailed in Section 3 (Table 2). Common classifiers used for visual features include maximum likelihood [95], minimum distance [96] and K-means clustering [97].  
A single low-level visual feature is not suitable for the complicated task of geographic scene classification, and more methods of multi-feature fusion are applied. Feature fusion combines color, texture and other features into high-dimensional feature descriptors, and then uses a neural network to achieve feature dimension reduction [98]. In addition, on the basis of local features, the image is divided into local blocks, and the low-level visual features of each block are taken to establish the multi-feature fusion descriptors [99; 100]. Nevertheless, the method based on local or global visual features and their fusion of visual features is not effective. The core problem is that the concept layer semantics need to infer from the low-level features to obtain the high-level semantic representation, while the visual-features-based method just lacks this semantic representation.