esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Salinity Origin and Its Impacts on Plants
### Primary Metabolism
#### Lipid Metabolism  
The fine tuning of plant lipid profile is a key tool in crop adaption to environmental changes, but the process by which lipid contouring modulates a respond to salt stress is not clear [101; 102]. Salinity causes structural changes in the lipid composition of the plasma membrane, like increasing the degree of free fatty acid saturation and enhancing concentration of free sterols, leading to a reduction in cellular membrane fluidity [103]. For example, the lipid composition of the root plasma membrane of _Phaseolus vulgaris_ grown under salinity differed in phospholipid groups, saturated and unsaturated fatty acids, and sterol groups from that of plants grown under non-saline conditions [104]. Furthermore, the intensity of salinity induced a considerable decrease in overall lipids derived from the black cumin seed [105].  
Salinity induced oxidative stress in _C. roseus_ cells, as seen by the rise in lipid peroxidation, increased lipoxygenase (LOX) function, following antioxidant enzyme activation [104]. Plant response to salinity may decrease the concentration of sterol ester, oleic acid, and free fatty acids, and reduces that of linoleic acid (C\({}_{18}\)H\({}_{32}\)O\({}_{2}\)) and triacylglycerolglycerol [106]. Under salinity, the expression of the genes involved in the lipid metabolism may suffer changes [107]. Novel discoveries have greatly expanded the importance of autophagy during lipid metabolism. An emerging model suggests that autophagy can operate in various aspects of lipid and membrane recycling. Current evidence also highlights the complexity of this system, given that autophagy is required for energy-producing organelle maintenance under stress conditions. This is of paramount importance because the removal of damaged and potentially ROS over producing energy organelles can circumvent cell death and promote lipid catabolism [108].
## 3 Numerical Analysis
### Mesh Sensitivity Study  
In finite element analysis, a suitable mesh size must be determined as it significantly affects the analysis result. In particular, because the mesh size significantly affects the pressure exerting on the steel box, the stress, and the corresponding permanent deformation on the steel box, the optimal size must be determined via a sensitivity study.  
In this study, a mesh sensitivity study was performed on water and steel box, as shown in Figures 5 and 6, respectively. For the water model, the 25 and 12.5 mm meshes showed similar results.  
As the number of Eulerian elements significantly affects the overall calculation time, element size 25 mm was used in consideration of the calculation efficiency. Meanwhile, similar results were obtained for plate mesh sizes of 25 and 12.5 mm. The plate mesh did not significantly affect the overall analysis time; hence, 12.5 mm was selected as the mesh size for an accurate stress analysis. The deformation of the plate is indicated by the relative  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Thickness** **(mm)** & **Elastic Modulus (MPa)** & **Poisson Ratio** & **Yield Strength (\(\sigma_{y}\))** & **Ultimate Strength (\(\sigma_{\Upsilon}\))** & **Ultimate Strain** \\
**(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(\(\varepsilon_{\Upsilon}\))** \\ \hline
3.00 & & & 299.5 & 448.7 & 0.1894 \\
5.00 & 205,800 & 0.3 & 312.4 & 455.0 & 0.1959 \\
8.00 & & & 280.8 & 433.2 & 0.2151 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties for steel box [4].  
Figure 4: Multilinear plasticity model for steel box.  
displacement, with both edges of the plate fixed at zero. The overall finite element model is shown in Figure 7.  
Figure 5: Mesh sensitivity study for water model.  
Figure 6: Mesh sensitivity study for steel box model.  
Figure 7: Finite element model for wet drop test.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 6 Perspectives
### Early Detection of Drought  
In contrast to many other natural hazards, drought does not happen immediately. Instead, it is the accumulated consequence of continuous water scarcity. It then becomes crucial whether we could detect agricultural drought at an early stage so that quick actions can be taken to prevent crops from being damaged. Traditional VIs such as NDVI can reflect crop water deficit, but only after it has lasted long enough to cause greenness changes. Solar-induced fluorescence is a very promising early indicator of agricultural drought [132], and as has been introduced in Section 3, much research has been carried out to investigate how to monitor agricultural drought with SIF [226; 279]. However, more work still needs to be done to elaborate on the physiological bond between water stress and SIF, especially at the spatial scale of satellite remote sensing [132; 134].
# Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector +
## 3 Methodology  
### AnFIS  
It has been reported that the majority of the cases related to multi-attribute decision-making are characterised by a definite degree of vagueness. Therefore, research on MADM and MCDM, respectively, incorporates models for dealing with this level of uncertainty such as fuzzy sets and fuzzy logic as a more generalised approach. Eliminating this unpredictability was the main goal of the approach, followed by a targeted calibration of the model's vagueness. Current research has determined that the ANFIS algorithm can be used in multi-criteria decision-making problems for supplier evaluation and selection with more precise and reliable results [39].  
Based on [PERSON] (1993) [3], ANFIS consists of five distinct layers, and the mathematical concept is explained as follows. For simplicity reasons, only two inputs were considered:  
* Layer 1: In the fuzzification layer, every node \(i\) in this layer is a square node with a node function: \[O_{i}^{1}=u_{A_{i}}(x)\] (1) where \(x\) is the input to node \(i\), \(A_{i}\) is the linguistic label, and \(O_{i}^{1}\) is the membership function of \(A_{i}\).
* Layer 2: Circle nodes in the normalisation layer multiply the incoming signals and send the product out. This represents the firing strength of a rule. \[w_{i}=u_{A_{i}}(x)u_{B_{i}}(y),i=1,2\] (2)
* Layer 3: Every node in this layer, labelled in Figure 1 with \(N\), calculates the average ratio of the ith rule's firing strength. \[\bar{w}=\frac{w_{i}}{w_{1}+w_{2}}\text{ or in a generic form: }O_{i}^{3}=\bar{w}_{i}=\frac{w_{i}}{\sum_{i=1}^{n}w_{i}},i=1,\ldots,n\] (3)
* Layer 4: Every node \"\(i\)\" in this layer is a square node with a node function, where \(w_{i}\) is the normalised firing strength from the output of Layer 3 and \(p_{i}\), \(q_{i}\), \(r_{i}\) are referred to as consequent parameters.
* Layer 5: The final layer represents the overall output y of the network as the summation of all incoming signals: \[O_{i}^{5}=f=\sum_{i}\bar{w}_{i}f_{i}=\frac{\sum_{i}w_{i}f_{i}}{\sum_{i}w_{i}}\] (4) where \(O_{i}^{5}\) is the output of the \(i^{th}\) node in Layer 5, \(\bar{w}_{i}\) is the normalised firing strength of the \(i^{th}\) rule calculated in Layer 3, and \(f_{i}\) is the output of the \(i^{th}\) node in Layer 4, which is given by the product of the normalised firing strength and the consequent parameters \(p_{i}\), \(q_{i}\), and \(r_{i}\). The overall output of the system is the summation of the incoming signals from all the nodes in Layer 4. The output of the ANFIS network in its final form can be expressed as follows: \[f=\frac{\sum_{i=1}^{n}w_{i}(p_{i}x+q_{i}y+r_{i})}{\sum_{i=1}^{n}w_{i}}\] (5)
## 3 Results and Discussion
### Flow Cytometry (FC) CD44 Evaluation  
Given the results obtained from the wound assay, we then evaluated the expression of CD44, which has been implicated in numerous cancer phenotypes, including enhanced cell proliferation, migration, and metastasis [30]. Recently, a meta-analysis study has demonstrated a negative correlation between CD44 expression and GBM patient survival [31,32]. GBM cells expressing CD44 can efficiently colonize and adhere to the extracellular matrix of the tissue environment. In this way, CD44 modulation influences GBM cell proliferation and migration by degrading hyaluronate and disrupting normal brain tissue [33]. These results showed that citrus peel extract reduces CD44 expression on the cell membrane of U87 and LN18 cell lines in terms of mean fluorescence intensity. After treatment with 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of citrus peel extract at different times, a reduction in CD44 expression level was observed at 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of citrus peel extract after 48 and 72 h in U87 cells, 1.6 and 2.2-fold, while a reduction was observed in LN18 cells after treatment with 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of extract to for 72 h. LN18 cells showed a 1.8-fold decrease in CD44 expression level compared to the control. The results indicated that the treatment for 72 h with 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of citrus peel extract reduced CD44 expression levels in both U87 and LN18 glioblastoma cell lines (Figure 7).
## Materials and Methods
### Analytic Approach  
To find which words are associated with switching, we used Word Association: a function that calculates the association of a word with another word in the TDM. We used the findAssocs() in R to calculate the association of a word with every other word in the TDM. The output scores range from 0 to 1, where a score of 1 means that two words always appear together, and a score of 0 means that they never appear together. To find associations, we set a minimum of 0.05, meaning that the program would look for all words that were associated in one tweet (that has a maximum of 140 characters) with \"premium\" at least 5% of the tweets. Since we were interested in attitudes to plan attributes, we tested three attributes of health insurance plans: \"premium,\" \"access,\" and \"network.\" We also looked for all the words that were associated with the word \"switch\" at least 5% of the time. We chose \"premium\" because we know from the literature that the premium matters when consumers buy health insurance, as well as \"access\" to doctors. Since we were particularly interested in whether provider networks, which refers to insurance coverage of doctors in-network, matters when consumers discuss health insurance, we also looked at \"network.\"  
To identify sentiments in the tweets, we use methods of classification. We used sentiment lexicons, a dictionary-based approach, which depends on finding opinion seed words, and then searches the dictionary for their synonyms. While various sentiment lexicons all have their advantages and disadvantages in the context of topic-specific subjectivity scores, interpretation, and completeness, the choice for a specific sentiment lexicon is context-specific. We used the NRC Emotion Lexicon (NRC Emolex) [39], which classifies words in a binary yes/no for classes of attitude \"positive\" and \"negative\"; and for classes of emotion: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. We wanted to not only find out the overall sentiment of tweets, but also what specific emotion they embodied, and identify which words represented those emotions.
## 6 Transceiver Architectures' Classification and Basic Concepts
### 4D-Based Architectures
PM: phase modulator; Tx-DSP: transmitter DSP; LO: local oscillator; PS: polarization stabilizer, MDL: matched delay line; C: circulator; T: tone; Sig. : signal; DP-Co-Rx: dual-polarized coherent receiver (adapted from [14; 129; 130]).
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Xanthone Glucosides  
#### 2.5.1. _Mangiferin_ and Its Derivatives  
Mangiferin (**9**, Figure 6A) can be found in _Mangifera indica_ mango leaves. It indicates anticancer activity on a few human brain cancer cell lines (**MTT assay**): **U-138 MG** (glioblastoma) [60], **U-87 MG** (glioblastoma astrocytoma) [61], and **U-118 MG** (glioblastoma multiforme)--by increasing apoptosis [62]--and among other cancer cells, **A549** **human lung carcinoma** cells (appoptosis induction) [62; 63], **K562 leukemia** cells (telomerase inhibition and apoptosis induction), and **MDA-MB231 breast cancer cells [64].  
It was also discovered that Mangiferin can make glioblastoma cells more sensitive to radiation. The in vitro results (**MTT assay**) showed that mangiferin administration (**59.2 \(\eta\)mol/mL**) followed by radiation causes DNA damage and decreases proliferation of glioblastoma cells **U-87 MG and U-118 MG**. The mock-treated cells served as a control in this experiment and the effect was more pronounced for cells treated with Mangiferin. Using a combination of mangiferin and radiation, the topic was further investigated on tumor-bearing mice. The obtained results were promising: reduction of the tumor mass: from 2.0 \(\pm\) 0.1 to 0.6 \(\pm\) 0.1 g, and the tumor volume: from 1 \(\pm\) 0.05 to 0.37 \(\pm\) 0.05 cm\({}^{3}\), as well as extended life span: in the group treated with Mangiferin  
Figure 5: Chemical structures of Garcinoxanthocin derivatives.  
Figure 6: Chemical structures of Xanthone glucosides.  
80% of mice were alive after 100 days, whereas in the control group, bearing the tumor without any treatment, all mice were dead after 80 days [60].  
Mangiferin was subject to acetylation, as this structural modification is known to increase the solubility and stability of some natural compounds (e.g., flavonoids) in lipophilic systems. Mangiferin (**9**, Figure 6A) and Mangiferin peracetate's (**10**, Figure 6B) growth inhibitory activity was tested on six human cell lines, including three glioblastoma astrocytoma lines: **U87 MG**, **U251**, **U373**, and **A375-C5** (malignant melanoma IL-1 insensitive); **MCF-7** (breast adenocarcinoma); and **NCI-H460** (non-small cell lung cancer). Mangiferin was active only on **A375-C5**, **MCF-7**, and **NCI-H460** with \(\text{GI}_{50}\) values higher than 150 \(\upmu\)M. \(\text{GI}_{50}\) is defined as the concentration of the tested compound that causes 50% cell growth inhibition. In contrast, Mangiferin peracetate displayed cell growth inhibitory activity on all of the tested cell lines. The best activity of mangiferin peracetate (the lowest \(\text{GI}_{50}\)) was observed for the **A375-C5** cell line (\(\text{GI}_{50}\) = 58.06 \(\upmu\)M). In the case of glioblastoma astrocytoma cell lines, the \(\text{GI}_{50}\) values were higher than 150 \(\upmu\)M [65].
# Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles
## 3 System Design of Intelligent Retrieval of Fault Information
### The Framework Design of the Fault Data Retrieval System
#### 3.2.2 Knowledge Graphs Module  
To establish an intelligent system that can truly solve the problem of NEV fault information retrieval, it is necessary to build a database of NEV faults and a relatively complete knowledge system. The realization of this function requires the knowledge body graph module. In the study, a combination of top-down and bottom-up methods was used to construct a knowledge graph of NEV fault information; on the basis of data preprocessing, the function was realized through knowledge modeling, knowledge extraction, and knowledge storage [39; 40]. The framework of the knowledge graph module is shown in Figure 6.  
(1) Knowledge modelingThis stage focuses on building the ontological framework structure for NEV fault issues. Through extensive research into real-world scenarios and data analysis, and based on the diagnostic characteristics of the fault tree model, the knowledge system for NEV faults is structured and layered. Relevant categories, attributes, and relationships are defined. Ontology modeling tools like Protege are used [41], and an improved seven-step top-down method is applied to establish the ontology database for NEV faults.  
(2) Knowledge extractionThis step involves extracting entities, relationships, and attributes from preprocessed data to construct and enrich the systematic knowledge graph for NEV faults. It primarily focuses on two types of triples: (entity, relationship, entity) and (entity, attribute, attribute value). To enhance the efficiency of knowledge extraction for fault information, extraction techniques are chosen based on the data structure. For semi-structured data, entities and attributes are extracted using rule-based scripts; for unstructured data, deep-learning models are employed to extract entity relationships [42; 43].  
(3) Knowledge storageCurrently, knowledge storage methods can be categorized into table-based and graph-based storage methods. Due to its strong expressiveness, ease of understanding, scalability, and ability to facilitate cross-domain knowledge integration, this study adopts a graph-based storage method using Neo4j [33]. This enables the storage, subsequent updates, and reasoning of knowledge related to NEVs.  
Figure 6: The knowledge graph module. (The arrows indicate the direction of data flow between the modules).
# A Comprehensive Review on Music Transcription
## 4 Challenges for Music Transcription
### Singing Techniques  
Vocalists can produce an extensive array of timbres and tonal qualities, ranging from breathy and soft to powerful and resonant [82; 83]. These timbral variations are influenced by factors such as vocal tract shaping, tension, and breath control [84]. Accurately capturing and representing these timbral changes in vocal transcription requires sophisticated analysis techniques. Singers often employ glissandi (continuous pitch slides) and microtonal inflections, which makes vocal transcription challenging in precisely determining discrete pitch values [85; 86]. The continuous nature of vocal pitch transitions requires advanced algorithms to track and transcribe these pitch changes accurately.
## 2 Materials and Methods
### Long Short-Term Memory Model  
The long short-term memory model (LSTM) is a type of recurrent neural network that directly learns from time-series data [42; 43]. However, although LSTM is a type of recurrent neural network (RNN), it adds a cell state structure to the hidden layer and stores information about the input data for a more extended period, thereby resolving the limitations of the RNN (Figure 4). The compact forms of equations calculate the LSTM cell with a forget gate [44].  
The LSTM cell contains three gates that regulate the data flow: a forget gate (\(f_{t}\)), an input gate (\(i_{t}\)), and an output gate (\(o_{t}\)). The forget gate (\(f_{t}\)) performs a calculation to determine which information to discard and applies the \(h_{t-1}\) of the previous step and \(x_{t}\) of the current step to the sigmoid function to obtain a value between 0 and 1. Moreover, the gate maintains and adjusts the cell state (\(\widetilde{C_{t}}\)) and the hidden state (\(h_{t}\)). The forget gate determines the information to be kept in the cell state (\(C_{t-1}\)) from the previous LSTM cell. The LSTM computes the algorithms from an input sequence \(x_{t}\) to output \(o_{t}\) by looping through Equations (2)-(7) with initial values of \(C_{o}=0\) and \(h_{o}=0\).  
\[f_{t}=\sigma\Big{(}W_{f}\times[h_{t-1},x_{t}]+b_{f}\Big{)}, \tag{2}\]  
\[i_{t}=\ \sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i}, \tag{3}\]  
\[\widetilde{C_{t}}=\tanh h(W_{\text{C}}\times[h_{t-1},x_{t}]+b_{\text{C}}, \tag{4}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C_{t}}, \tag{5}\]  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o}, \tag{6}\]  
\[h_{t}=o_{t}\times\tanh(C_{t}). \tag{7}\]
## 3 Overview of TSN Relevant Standards
In turn, \(B_{1}\) forwards frame \(f_{1,1}\) during window \(w_{i+1}\), and the frame reaches bridge \(B_{2}\) within that same window again. Finally, bridge \(B_{2}\) forwards frame \(f_{1,1}\) during window \(w_{i+2}\), and the frame reaches listeners \(L_{1,1}\) and \(L_{1,2}\) within window \(w_{i+2}\). Thus, the maximum end-to-end delay of a frame corresponds to the window size \(\times\) the number of hops, i.e., it takes three windows for frame \(f_{1,1}\) to be transmitted. When using CQF, we can define the period of any given stream as a multiple of communication windows, as long as all the windows in the path have the same duration. Thus, we can define the period \(T\) of a stream as \(T=n\times w\), where \(T\) is the period, \(n\) is the number of windows and \(w\) is the duration of a window. When using PTRF to replicate frames in the time domain, we must take into account that all the copies of a frame (replicas) must fit in the same window when defining \(w\), as we explain in Section 6.
## 3 System Model  
### Basic Assumptions  
We consider in this paper a wireless sensor network composed of a certain number of static sensor nodes and multiple mobile sink nodes. Each sensor node has a unique identifier (ID) that differs from other nodes. We make several basic assumptions, as follows:  
1. All sensor nodes are stationary after been deployed in the rectangular region.
2. Wireless links are symmetric.
3. Sensor nodes are location-aware.
4. Each sensor node can adjust their transmission power based on relative distance.
5. Sinks are energy-unconstrained and they can move and communicate anywhere in the network.
## 3 Simulation and Experimental Results  
This section deals with the simulation results of the proposed cycloconverter. Here, low frequency pulses are used for turning on the switching devices. The total simulation is done in the MATLAB/SIMULINK environment. Basically, the cycloconverter is used for controlling the speed of asynchronous motors such as a single phase induction motor which is simulated as an inductive load. The simulation parameters for the inductive load are illustrated in Table 2. Figures 8 and 9 display the output voltage and current of  
Figure 6: Firing pulses of different Triac of proposed cycloconverter for one\(-\)third conversion of 50 Hz.  
Figure 7: Firing pulses of different Triac of proposed cycloconverter for one\(-\)fourth conversion of 50 Hz.  
Figure 11: Output voltage of one\(-\)fourth frequency conversion of input frequency for resistive load.  
Figure 12: Output voltage of one\(-\)fourth frequency conversion of input frequency for motor load.  
Figure 10: Harmonics spectrum of one\(-\)third frequency conversion of input frequency for motor load: (**a**) voltage; (**b**) current.  
To verify the simulation results, two physical experiments took place for the proposed cycloconverter; the first one is achieving one-third conversion of 50 Hz while the second experiment is achieving one-fourth conversion of 50 Hz. The two experiments controlled the voltage of a resistive load. The equipment used in the two experiments are mentioned in Table 3 and the setup of the two experiments is shown in Figure 15:  
\begin{table}
\begin{tabular}{c c} \hline
**Title** & **Specifications** \\ \hline Input voltage (AC) & 220 V, 50 Hz \\ \hline Center trapped Transformer & 12-0-12,3 amp \\ \hline Triacs & BT136 \\ \hline Optoisolator & MOC30216 \\ \hline Zero crossing detector & Half wave rectifier with 4n35 IC \\ \hline Microcontroller Board & Arduino Uno (atmega328p) \\ \hline \end{tabular}
\end{table}
Table 3: Prototype Components.  
Figure 14: Harmonics spectrum for voltage and current of one\(-\)fourth frequency conversion of input frequency for motor load. (**a**) voltage; (**b**) current.  
Figure 13: Output current of one\(-\)fourth frequency conversion of input frequency for motor load.  
The results in Figure 16 show that the output voltage displayed by the Oscilloscope is the same as that of the expected results shown in Figure 4 and the simulation result in Figure 11.