esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 3 BBN Design Considerations
### Mobility Management for BBN  
If accurate mobility management is crucial for mobile networks, it is more so for BBNs, especially to handle the ubiquitous service-oriented applications. For example, vital medical data should be routed seamlessly among mobile WBANs, without being affected by links disconnections, apart from the other critical tasks related to healthcare as well as other BBN ubiquitous applications. Mobility management is mandatory in a dynamic environment, such as BBNs, where the network topology is changing in a regular or irregular basis, and the mobility of coexisting WBANs could impact the performance of a WBAN of interest. Several works have investigated the mobility prediction for mobile networks [66; 67; 68; 69; 70; 71]. In the absence of previous works on mobility management and prediction for BBN scenarios, we review in this section the most relevant studies undertaken for WBANs and mobile ad hoc networks, with consideration of specific constraints of BBNs, since they have several aspects in common. In [66], we find a threefold classification of the mobility prediction methods according to the movement history, the physical topology and the logical topology. These methods are detailed and compared in Table 2, with their possible application in BBNs. An optimized handover mechanism for WBANs, based on mobility prediction, is proposed in [67], using stable routes and a hop-by-hop strategy to reach the sink in dynamic scenarios. Authors of [68] implemented a prediction-based secure and reliable routing framework (PSR) that relies on the regular aspects of body movements in some physical activities (jogging, swimming, etc. ), which may guarantee the link quality and predict topological changes. A WBAN-based heart monitoring application is implemented in [69], using a novel mobility handling routing protocol (MHRP). The WBAN topology considered by authors is somehow special, where a backup group of nodes composed of one sink, two relays and one acquisition node is placed symmetrically to another identical group of nodes, on the same WBAN. The redundant group of nodes would handle the data transmission and ensure reliability in case of link failures due to body movements. The major disadvantage of this solution is the discomfort inflicted to the human body, apart from the installation cost of such double infrastructure. A preferable alternative to deal with node's mobility could be found in [72], where a mobility-based temperature-aware routing protocol (MTR) is proposed. Indeed, in the WBAN topology, the nodes are classified into static and dynamic nodes, with respect to their location on the human body. Thus, the sensor data of dynamic nodes are saved in the node cache, for a prospective retransmission in case of link failure due to the node movement. Figure 5: Quality-of-service (QoS) modules of WBAN QoS protocols suitable for future BBNs. © [2019] IEEE. Reprinted and modified, with permission, from [4].
## The Investigation Framework
### Framework Overview
#### 4.2.3 External Module EM2-Data Splitter  
The pre-processed data set \(T_{p}\) is now split into training set \(TR\) and test set \(TE\). The training set becomes the input of module _M2-Feature Extractor_, while the test set \(TE\) will be used by module _M3-Multi-classifier_, for computing the accuracy. Notice that \(TE\) contains labeled messages: this is necessary for validating classification and compute the accuracy, by computing true positives, false positives, true negatives and false negatives simply by comparing the topic assigned by the classifier to the message and the label originally associated with the message.  
This is an external module of the investigation framework, if compared to modules M1, M2 and M3, that are the core modules of the framework. This choice is motivated by the need for flexibility. In fact, different techniques for splitting could be used; this way, the investigation framework is parametric with respect to data splitting.
# Hierarchical Gradient Similarity Based Video Quality Assessment Metric
## 3 Experimental Results
Figure 5: Scatter plot of DMOSs against scores predicted by HVQA (LIVE database). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Pearson CC** & **Spearman CC** \\ \hline VQM [24] & 0.843 & 0.838 \\ MOVIE [25] & 0.930 & 0.920 \\ PSNR [13] & 0.793 & 0.800 \\ SSIM [7] & 0.678 & 0.677 \\ paravqa [29] & 0.848 & 0.906 \\ MS-SSIM [10] & 0.915 & 0.922 \\ Proposed (HVQA) & **0.9653** & **0.9707** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison on the EPFL-PoliMI database. Figures 5 and 6 show the scatter plot of the DMOS (MOS) against the objective computational score performed on the LIVE database and EPFL-PoliMI database, respectively. It is observed that the proposed HVQA metric performs well on videos ranging from low quality to high quality for both databases.
## 3 Results and Discussion  
### SEM Analysis  
Figure 1a shows that the broken rice starch (C) granules present a regular polyhedral appearance. Their surfaces are smooth, flat, and integral, with a relatively complete structure and well-defined edges. Some starch granules of different sizes agglomerate together to form larger clusters. The starch granules in broken rice are non-uniform and their sizes also vary, with diameters approximately in the range of 3-8 \(\upmu\)m.  
In contrast, the ultrasonic treatment makes the starch surfaces become rough (Figure 1b). Depressions, pores, and channels can be observed on the starch granules, and distinct depressions of varying degrees appear at the edges, which is consistent with the previous research results [13, 33]. This may be due to the formation of cavitation bubbles during the ultrasonic process leading to the rupture of starch granules and mechanical damage. High-pressure gradients and high local velocities are generated in the nearby liquid layer, thereby creating shear forces that break the starch polymer chains and damage the granules [34].  
As shown in Figure 1c, the US-Q surface becomes smoother and more compact when starch is complexed with quercetin, which is in agreement with a previous study [35]. The composite material obtained via the combined modification methods of ultrasound and quercetin is in a lamellar form and relatively loose, and the connections are no longer close (Figure 1c). This is also because of the gelatinization process, resulting in the loss of granular integrity compared to the morphology of the starch granules in Figure 1a,b. This has been reported to disrupt the molecular ordering within the starch granules, leading to granule swelling, crystal melting, loss of birefringence, increased viscosity, and dissolution [36]. The partially solubilized quercetin acts on the starch and water molecules in the system through hydrogen bonding, and the undissolved portion may serve to dilute the starch matrix and reduce the formation and cross-linking of amylose double-helicalstructures. As a result, this hinders the re-polymerization process among starch molecules, forming a relatively loose structure.
# User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
## 3 Effective Capacity and Power Allocation  
### Effective Capacity  
To provide services with different delay QoS requirements, the concept of effective capacity is employed to characterize the effect of delay QoS limitation on achieved performance, characterized by \(\theta\ (\theta\geq 0)\)[10]. In this paper, the uncorrelated service process across different slots is further assumed and the normalized effective capacity is adopted. Under these conditions, given a delay QoS exponent \(\theta_{j}\), the normalized effective capacity of User \(j\) in bps/Hz is  
\[C_{j}(\theta_{j})=\frac{-1}{\theta_{j}T_{f}B}\ln\Bigl{(}\mathcal{E}\Bigl{\{}e ^{-\theta_{j}T_{f}BR_{j}}\Bigr{\}}\Bigr{)}=\frac{1}{\psi_{j}\mathrm{ln}2}\ln \Bigl{(}\mathcal{E}\Bigl{[}\bigl{(}1+\gamma_{j}\bigr{)}^{\psi_{j}}\Bigr{]} \Bigr{)}, \tag{8}\]  
where \(\psi_{j}=-\theta_{j}T_{f}B/\mathrm{ln}2\), with \(T_{f}\) and \(B\) being the frame duration and the occupied bandwidth, respectively, \(R_{j}=\log_{2}(1+\gamma_{j})\) is User \(j\)'s transmission rate, and \(\mathcal{E}\) is the expectation operator. We note that a larger/smaller delay QoS exponent \(\theta_{j}\) is required in a more critical/tolerant delay-limited scenario.
## 1 Introduction
Losses from blue water are counted when water can no longer be used in the defined analysis area, e.g., evaporation or pumped away. Green water is water stored in the root-zone of plants and is consumed by plants or evaporated away. Grey water is relevant to the concentration of pollutants released in the production of a good or service. It is the amount of water required to maintain the water quality standard in the pollutant release zone. The sum of these three values provides a metric of the true impact of a product or service on the water network. In addition to products and services, water footprints of nations can help identify the priority areas for the water security of a country. [PERSON] and [PERSON] [12] investigated the water footprints of Morocco and the Netherlands for agricultural production from domestic sources. [PERSON] et al. [13] concluded that water consumption for domestic, industrial, and agriculture sectors must be taken into account in planning water provision and promoting sustainable water consumption in a similar study for England at regional and national level. [PERSON] et al. [14] considered the spatial aspect of the internal and external water footprints for the UK. In recent years, a significant amount of detailed water efficiency, footprints, virtual trade, and accounting research has been carried out for China in the fields of food security, power generation, urban households, and agriculture sectors [15; 16; 17; 18; 19]. Unfortunately, Australian water footprints are concerning as the added value of water for industrial usage is lowest in the world, and water footprints for domestic and industrial usages are the fourth highest at the global level [20; 21]. Nevertheless, the country exports a large amount of virtual water annually through its trade. Considering the inefficient water usage in almost all sectors in Australia and the capabilities of water footprint assessments to address water security issues, the following research question triggered the current research:  
What are the major water consumption sectors and how can the water footprint help in reducing the water accounting-related vulnerabilities in Australia by identifying the most detrimental sector? The aim of this research is to analyze the substantial water consumption, efficiencies, and footprints in Australia to examine water conservation in different sectors, including trade. The undertaken research identifies possibilities to reduce the water footprints of different uses. A framework is developed to analyze the water security issues of a country/region. The work itself demonstrates an application of combining water footprint and virtual water trade techniques with traditional water accounting methods. The outcomes of this research not only provide guidance to improve water consumption in Australia but are equally beneficial for other countries/regions to review their water consumption practices considering the availability and economic value of water.
## 4 Results and Discussion
### Optimization Results
#### 4.2.2 The Impact of the RES Sizing  
This section tests the VPP model with the wind farm's maximum available power output of 20 MW. With the power curve (in p.u) given in Section 4.1, we have the forecasted wind and load capacity shown in Figure 12. It is easy to see that the wind capacity is not enough to supply the load most of the time. Therefore, ESS needs to increase the purchase of electricity with the off-peak price to store and provide to the local load during the peak-load period instead of focusing on reserve service. As a result, the reserve capacity and the number of hours of reserve service decreases, and the value of the reserve activation probability each hour has little effect on the optimal results (Figure 13). Note that we are only interested in the VPP's ability to provide reserve service when the RES power output is even less than the local load in this section. If the RES power output during most hours of the day is higher than the load, deciding whether to enter the BC market or not becomes much simpler.  
Figure 11: The VPP’s reserve bidding with reserve probability (**a**) \(p_{1}\), (**b**) \(2p_{1}\), (**c**) \(4p_{1}\), (**d**) \(6p_{1}\).
## 3 Results and Discussion
### Analysis of Volatile Compounds in High-Moisture Japonica Brown Rice during Storage  
Changes in the concentration of volatile compounds in high-moisture japonica brown rice during storage are shown in Table 1. A total of 38 volatile compounds were detected in all samples of high-moisture japonica brown rice, including alcohols (10), ketones (4), aldehydes (9), hydrocarbons (12), and others (3). Among these volatile compounds, esters were frequently characterized by a sweet, floral, and fruity aroma and aldehydes and alcohols contributed to green, grassy, fatty, fruity, and floral flavors [28]. Whereas most hydrocarbons exhibited no aroma, a few (such as dodecane and tridecane) produced an unpleasant gasoline odor. They may play a role in overall odor because of their high levels in rice [29]. Apparently, the high-moisture japonica brown rice had the greatest variety  
Figure 1: Changes in L\({}^{*}\) (**A**), a\({}^{*}\) (**B**), b\({}^{*}\) (**C**), appearance (**D**), FAV (**E**), and volatile compound types (**F**) of high-moisture japonica brown rice during storage. Different lowercase letters indicated significant differences (\(p\)\(<\) 0.05). and percentage concentration of volatile compounds (except hydrocarbons) after 90 days of storage at 15 \({}^{\circ}\)C (Figure 1E and Table 1). This suggested that storage at 15 \({}^{\circ}\)C better preserved the volatile compounds that contributed to the aroma of high-moisture japonica brown rice, such as nonanal, 1-octanol, and (E)-2-nonenal, which imparted fruity and floral aromas to the high-moisture japonica brown rice [30]. In addition, 6,10,14-trimethyl-2-pentadecanone could give the high-moisture japonica brown rice a herbal and jasmine aroma [31]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**NO.
# A Systematic Review on Blockchain Adoption
## 1 Introduction
The development of Blockchain applications can be classified into three generations: (i) Blockchain 1.0 is used for cryptocurrency transactions, (ii) Blockchain 2.0 is used for financial applications, and (iii) Blockchain 3.0 is used for other industrial applications, such as government, health, science, and Internet of Things (IoT) [13]. Blockchain assists in tracing and verifying multistep transactions that require traceability and verification. Blockchains minimize compliance costs and accelerate data transfer processing. Perhaps the worthiest value of adopting Blockchains is the enhanced security provided to users while making transactions. This feature builds confidence between consumers and industry partners, protects privacy, and increases transparency in tracing transactions. Despite the tremendous opportunities of Blockchain technologies, their adoption across many domains is still in short supply [15]. Their low adoption rates stem from inadequate knowledge regarding the factors affecting their use [16]. To draw a holistic view of the factors affecting Blockchain adoption, we need to understand the theories/models through which these factors are derived. Understanding those factors through the lenses of these theories/models would help scholars and practitioners prepare future policies and procedures for effectively employing Blockchain technologies across various sectors. By inspecting the existing reviews on Blockchain, it has been observed that there is inadequate knowledge about the main research methods used in Blockchain adoption and the primary domains involving Blockchain applications. To understand these issues, this systematic review aimed to provide a holistic view of Blockchain adoption through the lenses of technology adoption theories and models, and to identify the main research gaps that would guide future research. Therefore, this review study poses the following research questions:  
Q1: What are the main research methods and domains in the selected studies? * What are the main theories/models used for studying the use of Blockchain? * What are the most frequent external factors affecting the use of Blockchain? * What is the primary purpose of the reviewed studies? * Who are the target participants in the selected studies?
## Materials and Methods
### Apparatus and Gas Chromatography-Tandem Triple Quadrupole Mass Spectrometry Conditions  
The surface morphologies and particle sizes of the as-synthesized nanoparticles were observed by scanning electron microscopy (SEM, JSM-6300, JEOL, Tokyo, Japan) and transmission electron microscopy (TEM, JEM-200 CX, JEOL, Tokyo, Japan). The powder X-ray diffraction (XRD) patterns of the as-synthesized nanoparticles were obtained with an X-ray powder diffractometer (D8 Advance, Bruker, Karlsruhe, Germany). The Fourier-transform infrared (FT-IR) spectra of the as-synthesized nanoparticles were obtained with an FT-IR-8400 spectrometer (Shimadzu, Kyoto, Japan). A vibrating sample magnetometer (VSM, Lake Shore 7410, Columbus, OH, USA) was used to investigate the magnetic properties of all of the synthetic materials.  
Figure 1: Schematic illustration of the synthetic route to prepare zeolitic imidazolate framework based on magnetic multiwalled carbon nanotubes and the magnetic solid-phase extraction steps for organochlorine pesticides analysis.  
Gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS/MS) analysis was performed with a Shimadzu GC-2010 plus gas chromatograph coupled with an AOC-20s autosampler, a Shimadzu TQ8040 triple-quadrupole MS. The pesticides were separated on an Rtx-5 MS capillary column purchased from RESTEK (Bellefonte, PA, USA) (0.25 mm (id) \(\times\) 30 m, 0.25 \(\upmu\)m film thickness). Helium gas was used as the carrier gas at a constant flow rate of 1 mL min\({}^{-1}\). The column temperature was programmed as follows: the initial temperature of 40 \({}^{\circ}\)C was maintained for 4 min, the temperature was increased to 125 \({}^{\circ}\)C at 25 \({}^{\circ}\)C min\({}^{-1}\), the temperature was ramped to 300 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\), and the temperature was maintained at 300 \({}^{\circ}\)C for 6 min. The total run time was 30.9 min. The injector temperature was 250 \({}^{\circ}\)C, and the injection volume was 1.0 \(\upmu\)L in splitless mode. The specific multiple reaction monitoring (MRM) transitions for all the nine OCPs and the other parameters are given in Table 1.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
###### Abstract  
This paper presents a simulator of non-wide sense stationary uncorrelated scattering (non-WSSUS) multipath fading channels for the performance analysis of vehicle-to-everything (V2X) communication systems. The proposed simulator is constructed with the combination of the Monte Carlo and sum-of-cisoids (SOC) principles, and it is suitable for multicarrier transmission schemes such as those defined for dedicated short-range communications (DSRC) and cellular-based V2X (C-V2X) communications. The channel simulator provides an accurate and flexible solution to reproduce the time and frequency (TF) correlation properties of non-WSSUS vehicular channels under arbitrary isotropic and non-isotropic scattering conditions. Furthermore, the proposed simulator allows velocity variations and non-linear trajectories of the mobile stations (MSs). To demonstrate the practical value of the presented simulator, we evaluate the bit error rate (BER) performance of two channel estimation techniques that are considered for IEEE 802.11p transceivers, namely the least squares (LS) estimator and the spectral temporal averaging (STA) technique. The BER performance of both channel estimators was analyzed by considering three propagating scenarios for road safety applications. Our results show that the non-stationary characteristics of the vehicular multipath fading channel have nearly no effects on the LS estimator's BER performance. In contrast, the performance of the STA estimator is significantly affected by the channel's non-stationary characteristics. A variation of the original STA technique that applies only a temporal averaging is introduced in this work to improve the system's BER in non-WSSUS channels.  
channel simulation; dedicated short-range communications; IEEE 802.11p standard; vehicular communications 2020, 9, 1190; doi:10.3390/electronics9081190
## 3 The Application of Conventional ML Algorithms
### Dictionary Learning (DL)
In recent years, some researchers have used the subspace decomposition method to study the stratification of underground media. [PERSON], [PERSON] et al. used the multiple-signal classification (MUSIC) method to generate super-resolved velocity-depth spectra for underground multilayer analysis. The proposed method outperforms traditional SAR-based methods and has great potential for use [50]. Table 1 summarizes typical cited works and classifies them into the types of feature extraction methods, classifiers, and data form. \begin{table}
\begin{tabular}{c c c c} \hline
**Feature Extraction** & **Classifier** & **Year** & **Data Form** \\ \hline generic algorithm (GA) & SVM & 2008, 2009, 2009 & B-scan \\ \hline discrete cosine transform (DCT) & SVM & 2013 [8] & A-scan \\ \hline features extracted from forward & SVM & 2013 [9] & B-scan \\ simulation data & SVM & 2015 [53] & B-scan \\ \hline HOG & SVM & 2015 [54] & B-scan \\ \hline H-Alpha decomposition & SVM & 2018 [10] & B-scan \\ \hline some points of A-scan data & COSMM & 2018 [11] & B-scan \\ \hline the A-scan image with the & & & \\ highest energy in the & SVM & 2018 [12] & A-scan \\ B-scan image & & & \\ \hline HOG and nRAS & SVM and K-NN & 2018 [13] & B-scan \\ \hline FV, nRAS, BRT and their fusion & SVM and K-NN & 2020 [14] & B-scan \\ \hline EHD, SIFT, SURF, HOG, LBP, BRIEF, normalized pixel values, & PLSDA, linear SVM and non-linear SVM & 2017 [15] & B-scan \\ PCA and block PCA & & & \\ \hline EHD, Log Gabor, gprHOG & SVM and MSEK & 2019 [16] & B-scan and different sections of C-scan data \\ \hline PCA and down sampling & K-NN & 2000 [17] & A-scan \\ \hline EHD & K-NN & 2007 [18] & B-scan \\ \hline MA-BS, DC-offset removal, & & & \\ and SaW & & 2019 [19] & B-scan \\ \hline TGSS & K-NN & 2019 [20] & B-scan \\ \hline certain orientation extraction & HMM & 1999, 2001, 2005 & B-scan \\ \hline evolutionary algorithm (EA) & HMM & 2003 [55] & B-scan \\ \hline Gabor filter & HMM & 2007 [24] & B-scan \\ \hline a fusion of Gabor and EHD & MSDHMM & 2011 [25] & B-scan \\ \hline normalization, edge detection & SVM and HMM & 2014 [26] & B-scan \\ and other methods & & & \\ \hline EHD & eHMM & 2015 [27] & B-scan \\ \hline HOG & MiHMM & 2015 [28] & B-scan \\ \hline \end{tabular}
\end{table} 
Table 1: The application of conventional ML in GPR detection and classification.