esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Fatty Acid Methyl Esters (FAMEs)  
All chemicals and analytical reagents were of high-performance liquid chromatography grade unless stated otherwise (Sigma-Aldrich, Dorset, UK). On the day of analysis, freeze-fried algal biomass was weighed (\(\sim\)1 to 2 mg) followed by direct transesterification as described elsewhere [30; 31; 32]. A total of 290 \(\upmu\)L of toluene, 300 \(\upmu\)L of 0.5 M sodium methoxide, and 10 \(\upmu\)L of hexane containing C13:0 as an internal standard were added to the weighed samples, followed by incubation at 80 \({}^{\circ}\)C for 20 min. After cooling at room temperature, 300 \(\upmu\)L of 10% boron tri-fluoride in methanol was added and the mixture was incubated at 80 \({}^{\circ}\)C for 20 min. After cooling to room temperature, 300 \(\upmu\)L of water and 600 \(\upmu\)L of hexane were added. The mixture was vortexed for 1 min and centrifuged at 18,000\(\times\)\(g\) at 4 \({}^{\circ}\)C for 10 min. The organic phase was recovered, measured, and evaporated to dryness under inert nitrogen gas using a six-port mini-vap evaporator (Sigma-Aldrich, Dorset, UK).  
The dried fatty acid methyl esters (FAMEs) were reconstituted in 300 \(\upmu\)L hexane prior to identification and quantification on a GC-ToF-MS (Waters Corporation, Milford, MA, USA) using a TR-FAME capillary column (25 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m). The sample volume of 1 \(\upmu\)L was injected in split injection mode at 250 \({}^{\circ}\)C. The split ratio was 1:20, split flow 30 mL min\({}^{-1}\), and helium carrier gas flow 1.5 mL min\({}^{-1}\). GC oven temperature was held at an initial temperature of 150 \({}^{\circ}\)C for 1 min, then increased by 10 \({}^{\circ}\)C min\({}^{-1}\) up to 250 \({}^{\circ}\)C and held at 250 \({}^{\circ}\)C for 1 min; total run time was 17 min. The GC-ToF-MS was operated in EI mode at 70 eV, scanning \(m/z\) 50-700 with an initial 2 min solvent delay. EI source temperature was held at 200 \({}^{\circ}\)C and GC re-entrant temperature at 250 \({}^{\circ}\)C. Fatty acids were identified by comparing the obtained retention times with that of known standards (37 component FAME mix, Supelco(tm), Bellefonte, PA, USA). A representative GC-ToF-MS chromatogram of 37 FAME Mix (C8-C24) standard is available in the Supplementary Material. Data acquisition and post-acquisition processing for peak identification were performed using MassLynx (version 4.1) software (Waters Corporation, Milford, MA, USA). A six-point calibration curve was generated using one internal (C13:0) and two external standards (C17:0 and C19:0). Quantitation of FAMEs in the biomass extract was then determined by comparing experimentally derived component peak areas with the calibration curve generated by the reference internal and external standards (Supplementary Material, Figure S2). In total, \(n\) = 3 replicates (biological) were run, among which, FAMEs identified in only 2 or more replicates were considered as true hits. The data were later normalised to the dry weight of the biomass and FAMEs were reported on a percentage dry weight basis.
# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 2 Artificial Intelligence Techniques
### Deep Learning  
Deep learning can be defined as providing knowledge to computers through data, observations and interactions with the world [36]. DL is a relatively new advancement in NN programming and represents a way to train deep neural networks (DNNs), as traditional NN-based methods might be affected by problems such as overfitting, diminishing gradients, etc. [36]. As defined in [35], a NN is a massively parallel-distributed processor made up of simple processing units that has a natural propensity for storing experiential knowledge and making it available for use. A typical NN comprises several layers of interconnected neurons, each one connected to the others in the ensuing layer. Data are presented to the neural network via an input layer, while an output layer holds the response of the network to the input. One or more hidden layers may exist between the input layer and the output layer. Essentially, any NN with more than two layers is called \"deep\". On the other hand, DL is a semi-supervised training approach, suitable for DNN training [36].  
In the last few years, DL has led to very good performance on a variety of problems, such as speech recognition, visual recognition, natural language processing, pattern recognition, automatic translations, self-driving cars, medical diagnosis, financial prediction, automatic trading, etc. On the contrary, the application of DL in photovoltaics is still limited. DNNs are able to automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs [36]. The main DL methods are [32]: convolutional neural network (CNN), long short-term memory (LSTM), and other hybrid combinations especially used when dealing with multistep forecasting.  
CNNs have a grid-like topology and are a special type of NNs where the convolution is used in place of a general matrix multiplication [37]. Like common NNs, CNNs are made of an input layer, an output layer, and many hidden layers in between. The hidden layers consist of three layers: the convolution, the activation or rectified linear unit (ReLU) layer, and the pooling layer. The ability of CNNs to learn and automatically extract features from raw input data can be applied to time series forecasting problems. A sequence of observations can be treated like a one-dimensional image that a CNN model can read and distill into the most salient elements.  
LSTM is a kind of RNN with a memory cell, an input gate, an output gate, and a forget gate in addition to the hidden state always present in traditional RNNs. The main drawback of RNNs is that they practically fail to handle long-term dependencies. As the gap between the output and the input data point increases, RNNs fail in connecting the information between the two. In the last decades, researchers have proposed a number of new recurrent units (RU) to solve this problem, and the most effective solution are LSTM [38] and gated recurrent units (GRU) [39].
# Framework for Social Media Analysis Based on Hashtag Research
## 2 Theoretical Background  
Nowadays, synonymous terms for social networks are heavily intertwined and interchangeable, which can be seen in relation to Facebook, as indicated in references [33; 34], where it is a social medium platform, and in references [35; 36], where it is a social network. Facebook is an example of an overlap between the two areas. Reference [37] defines social networks as \"web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.\" Reference [38] defines social media as \"Internet-based channels that allow users to interact opportunistically and selectively self-present, either in real-time or asynchronously, with both broad and narrow audiences who derive value from user-generated content and the perception of interaction with others.\"  
In connection with these concepts, the term social network will be used in this article to describe a platform that enables a connection between individual users and social media platforms as possibilities of this network in the field of mass information sharing. We follow the research [39], which defines online social networking as a process whose goal is to allow people to connect with each other. Social networks are tools for sharing content [40].  
More on the issue of defining the difference between social media and social network can be found in the research of [PERSON] (2021) [41], which focuses only on this issue.  
Social Network Analysis vs. Social Media Analysis  
Following the definition of the terms social network and social media, it is also necessary to define the difference between the terms of social network analysis (SNA) and social media analysis (SMA).  
Social network analysis examines social structures using network theory and graph theory [42]. The importance of social network analysis can be seen in the number of articles with a title containing the keywords \"Social Network Analysis\" in the ScienceDirect database, which increased from 251 articles in 2010 to 1259 in 2020.  
Social media analysis aims to collect, monitor, analyse, summarise, and visualise data from social media, from which useful patterns and information can be identified [43]. It is a process of collecting data from communication on digital media and processing them into structured views, which leads to better business decisions based on the obtained information [44]. Reference [45] defines social media analysis as a process of obtaining unstructured data from social media and analysing these data for areas of business decision-making based on the information obtained. This analysis is useful for product development, product innovation, product utilisation, brand engagement, competitive intelligence, and general marketing [46].
## 4 Discussion
### Crop Productivity
This is due to the P absorbed by the rotation crops and the accumulation of a large volume of plant material, which, during decomposition, releases the P necessary for the proper development of the crop. In addition to nutrient availability, soil organic matter (SOM) can retain more water than its own mass, meaning that small increases in SOM can lead to significant gains in the amount of water available for the crop, thus improving drought tolerance. Both the increased water retention and availability due to organic content and the lower soil density, which allows for deeper root growth, enhance the crop's ability to withstand dry periods with minimal impact on harvest yields [38]. Given that there was little precipitation during this intercropping study (Figure 1), the minimal impact on productivity can also be attributed to the quantity and quality of SOM. The inoculation of _A. brasilense_ also has positive effects on drought tolerance, as it improves plant morphological characteristics such as root branching, increased root biomass, and higher root hair density, which result in better soil profile exploration for water uptake, in addition to biological nitrogen fixation (BNF) and phytohormone production [47]. All these factors contributed to an increase in sorghum grain yield and panicle dry matter. However, for the production of Piata grass in intercropping, a decrease was observed in inoculated areas, likely due to competition between crops, as only the sorghum was inoculated, leading to its dominance over the forage. Conversely, the residual phosphorus doses led to the increased production of Piata grass. Phosphorus is a limiting nutrient for achieving higher productivity, and, according to [PERSON]'s Law of the Minimum, this effect is even more pronounced in pastures. A deficiency of this macronutrient results in reduced root development, making plants less drought-tolerant and less capable of absorbing soil nutrients, leading to a lower tillering rate and, consequently, reduced forage mass production [48].
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results
### Impact on Quality
#### 4.3.4 SSIM Decrease  
Table 5 shows the average decrease in SSIM. The SSIM values of \(\text{K}_{\text{src}}\) are slightly higher than those of \(\text{K}_{\text{INS}}\), which is in contrast to their relation in the VMAF scores and PSNR values. However, the differences in SSIM are negligible. The other observations from Tables 2 and 4 regarding the QP values are also observed in Table 5.
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 4 Results and Discussion
### Packet Delivery Ratio  
The values for \(PDR\) have a high median (0.97), and 76% of those are above 0.80. Prediction results for \(PDR\) are presented in Figure 4. True values and predicted values are plotted which lie in a close proximity to the ideal diagonal line. To have a closer look, predicted values for three portions of selected data are plotted against actual values in Figure 5 from three different ranges (0.44 and 0.57, 0.68 and 0.78, 0.93 and 0.94). It is evident that the range shrinks for the same number of data points as \(PDR\) gets higher. It is also visible from the plots in Figure 5 that the predicted values are highly aligned with the actual values, in general. In order to better understand the prediction performance, further results are revealed in Figure 6. \(RMSE\) for individual features are shown in Figure 6a, where \(OF\), \(AT\), and \(AQS\) yield better prediction accuracy compared to other pre-configured features. This is because \(OF\) directly translates into queuing losses with \(AQS\) as an implicit indicator for the potential of the same, whereas \(AT\) directly defines the other (primarily radio) losses that occurred. Therefore, \(OF\) and \(AT\) produce a minimum error because not only do they directly define losses but they also have fine-grained per-packet values compared to the values for pre-configured features. Results for the combination of features are presented in Figure 6b, where five features, namely: \(OF\), \(AT\), \(TP\), \(DT\) and \(MT\), result in a minimum \(RMSE\) of 0.012 achieving the overall best prediction accuracy of 98.8% over the range of \(PDR\) values. The \(x\)-axis label codes, used in Figure 6b, are listed in Figure 6d. Finally, the prediction error is characterized in Figure 6d, where 90% of the predictions have an error of 4.2% or less of the actual \(PDR\) values. It is important to note that the addition of any further features after \(OF\), \(AT\), \(TP\), \(DT\), and \(RMSE\) does not result in any significant improvement in the accuracy.  
Figure 4: Actual values vs. predictions for \(PDR\).  
Figure 5: Actual values vs. predictions for \(PDR\) (selected data).
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Link Characteristics Validation  
To check our experimental validity partly, the performance of one single link on the platform is tested. The required levels of the link characteristics are listed in the first columns of Tables 2-4. In each measurements, we repeated data transmission ten times and averaged the results. As can be seen in the tables, no matter which required level we adopted, it achieved approximate performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Required Level (ms)**} & \multirow{2}{*}{**Measured Level (ms)**} & \multicolumn{2}{c}{**Absolute Deviation**} & \multicolumn{1}{c}{**Relative Deviation**} \\  & & **(ms)** & **(\%)** \\ \hline
5.000 & 5.130 & \(-\)0.130 & 2.600 \\
50.000 & 50.135 & \(-\)0.135 & 0.270 \\
100.000 & 100.136 & \(-\)0.136 & 0.136 \\
500.000 & 500.150 & \(-\)0.150 & 0.030 \\
1000.000 & 1000.147 & \(-\)0.147 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of measurement of delay.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 4 Performance Analysis
The energy efficiency of AI-DRX is 70% higher than Poisson arrival rate (\(\lambda=1/20\)). The corresponding mean delay of AI-DRX is 100 ms (on an average) higher as compared to Poisson arrival for \(\lambda=1/20\). The gain in energy efficiency is achieved as AI-DRX considers real traffic arrival rate for selection of sleep cycles and inactivity timer, while Poisson arrival considers mean arrival rate. The energy efficiency and mean delay both are zero for higher Poisson arrival rate \(\lambda=1\). The reason lies in the fact that for higher arrival rate, UE could not transit to sleep state to save the power. Various services in a wireless network can tolerate different delay levels while not compromising quality of service (QoS). QoS class identifier (QCI) is a metric that is used to identify the characteristics of traffic. QCI measures the QoS with two parameters; (1) packet loss rate (PLR) and (2) packet delay budget (PDB). PDB can be defined as maximum tolerable waiting time by a packet during its delivery from eNB to UE. Standardized QCI characteristics are shown in Table 2[6,30,31]. In  
Figure 12: AI-DRX energy efficiency and mean delay with varying \(T_{ON}\) (\(Th_{Min}=300\), \(Th_{Max}=1600\)). Figure 13: AI-DRX energy efficiency and mean delay comparison with Poisson Arrival with varying \(T_{ON}\) (\(Th_{Min}=300\), \(Th_{Max}=1600\)). various kinds of non-real-time services like email, web browsing, after a certain time period, a UE does not require to monitor PDCCH continuously [6; 32]. Hence, these types of services can tolerate higher delays up to 300 ms [6; 30; 31]. These types of services require DRX with higher values of sleep time (\(T_{DY}>Th_{Min}\) & \(T_{DY}>Th_{Max}\)) and smaller values of ON timer for better energy efficiency. Whereas, the real-time services like voice and live video streaming cannot tolerate delay [33]. Therefore, the delay should be a higher priority than energy saving. If we limit the mean delay to 125 ms, the energy efficiency will be 95.02%. For this energy efficiency we have to select \(Th_{Min}=200\),\(Th_{Max}=1000,T_{ON}=6\) ms. If we increase the \(T_{ON}\) the energy efficiency of UE decreases as UE remains in active state. The mean delay also increases with increase in \(T_{ON}\) as UE does not receive the data during ON period but only monitors the Physical Downlink Control Channel (PDCCH). The mean delay observed by UE increases to 150 ms when \(T_{ON}=80\), with energy efficiency of 80.2%. The network can maximize the energy efficiency of UE by selecting optimum value of \(Th_{Min}\), \(Th_{Max},\ T_{ON}\) depending on QCI value of different services.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 2 Basic Structure of the Proposed RFID-WSN Hybrid System  
The proposed RFID-WSN hybrid system is supposed to be used in applications ranging from public service and manufacturing environments to logistic organizations, in which employees, patients, products, machinery etc. are to be identified, tracked, or localized. The environment variables of a medium or large space should also be captured and transmitted to the service management control center. So both RFID tags and WSN sensor nodes are used as end nodes to cover the whole industrial or service area. The RFID reader/router is able to obtain both kinds of information from the two kinds of end nodes as well as transmit the information from one to another and finally to the coordinator.  
For the communication network, ZigBee is selected for its characteristics of range, auto routing capability, low power consumption and sufficient bandwidth [11]. The basic structure of the hybrid system is shown in Figure 2. The RFID reader/routers work as the receiver and transmitter of both identity and sensing information, and self-organize a network to relay the information to the coordinator and then to the service management control center.  
The network communication structure of the system is shown in Figure 2, including service management control center, coordinator, RFID reader/routers, sensor nodes and RFID tags. (1) RFID tags record the label card number and data information of the item. (2) Sensor nodes are responsible for collecting the environmental information of the entire area, and then transferring the information to RFID reader/router nodes. (3) RFID reader/routers are responsible for sending information from sensor nodes to coordinator, simultaneously receiving the instruction information sent by coordinator and transferring to sensor nodes. (4) Coordinator is responsible for starting the entire ZigBee system network, sending gathered information through the serial port to the host computer application system, and simultaneously transmitting the control information that derived from the service management control center to RFID reader/routers. (5) Service management control center gathers all the identity and sensing information for further processing, and is responsible for providing human-computer interaction.  
Compared with the existing RFID reader that communicates with the host computer through RS232 or Ethernet interface, the RFID reader/routers offer two wireless communication links. The RFID reader part works at 13.56 MHz with passive RFID tags. The router part works at 2.4 GHz with sensor nodes, other RFID reader/routers and the coordinator.  
The hybrid system has the following benefits: (1) The communication range for 13.56 MHz passive RFID tags is enlarged; and (2) keeps the simplicity of the sensor node. There is no additional power consumption for sensor nodes; (3) sensor nodes, coordinator and RFID reader/routers can communicate with each other by creating a robust self-organizing networks.
# The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator
###### Abstract  
Both X-ray imaging and neutron imaging are essential methods in non-destructive testing. In this work, a bimodal imaging method combining neutron and X-ray imaging is introduced. The experiment is based on a small electron accelerator-based photoneutron source that can simultaneously generate the following two kinds of radiations: X-ray and neutron. This identification method utilizes the attenuation difference of the two rays' incidence on the same material to determine the material's properties based on dual-imaging fusion. It can enhance the identification of the materials from single ray imaging and has the potential for widespread use in on-site, non-destructive testing where metallic materials and non-metallic materials are mixed.  
bimodal imaging; compact neutron source; neutron imaging; X-ray imaging; image fusion Article
# Bioactive Compounds from Herbal Medicine Targeting Multiple Myeloma
## 4 Plant Extracts in Multiple Myeloma  
In recent years, research has focused on the discovery of new plant extract metabolites that act as antitumor agents in various cancers, including hematological cancers [461]. Plant extracts target different signaling pathways in cancer cells, such as proliferation, differentiation, and apoptosis [462], acting through synergistic and/or additive effects [463; 464; 465]. Concomitant administration of plant extracts and anti-cancer drugs has increased the therapeutic efficacy of treatment by increasing the sensitivity of cancer cells to drugs and overcoming drug-induced resistance to cancer [466]. Currently in clinical practice a significant number of plant-isolated compounds are used in the treatment of cancer in combination with other drugs [467], against hematological malignancies [468; 469; 470; 471; 472]. The main pharmacological effects exerted by herbal extracts are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Herbal Extracts**} & \multirow{2}{*}{**In Vitro/In Vivo**} & \ \begin{tabular}{c} **Cancer Cell Line and** \\ **Animal Model** \\ \end{tabular}  & \multirow{2}{*}{**Bioactive Effect**} & \multirow{2}{*}{**References**} \\ \hline _Azorella cglabra_ & \multirow{2}{*}{In vitro} & \begin{tabular}{c} SKMM1, RPMI-8226, \\ and MMIS cells \\ PBMCs \\ \end{tabular}  & \(\uparrow\) antioxidant activity & \multirow{2}{*}{[461]} \\  & & & \(\downarrow\) cell viability, induced apoptosis, and cell & \\ \hline _Corchorus olivotrius_ & \multirow{2}{*}{In vitro} & \multirow{2}{*}{ARH-77 cells} & - cytotoxic effects on cells (LE and SE) & \multirow{2}{*}{[473]} \\ leaf extracts (LE) and seed extracts (SE) & & & -SE had higher cytotoxicity on cells & \\  & & & -S genotoxicity effects in dose-dependent & \\  &