esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Regulation of Intracellular Ap4A Levels
### Biosynthesis of Ap4A  
Ap4A is synthesized primarily through the action of the aminoacyl-tRNA synthetase (aaRS) during the process of tRNA charging. This enzyme catalyzes the formation of Ap4A by transferring the adenylate moiety from ATP to an already adenylated nucleoside diphosphate, such as ADP, resulting in the production of Ap4A [11]. This reaction typically occurs as a by-product of the tRNA aminoacylation process, where amino acids are attached to their corresponding tRNA molecules. In proliferating cells and cancer tissues, aaRS expression and activity are frequently upregulated, correlating it with increased Ap4A synthesis. In immune cells, lysyl-tRNA synthetase (LysRS) can be phosphorylated upon immune activation (e.g., in mast cells or macrophages), causing a shift from translation-related functions to Ap4A synthesis [5]. This process is critical for regulating immune gene expression, such as MITF and other transcription factors [17]. In neurons and platelets, Ap4A is synthesized in response to stimulation and stored in synaptic vesicles or dense granules, respectively. These specialized cells rely on stimulus-dependent exocytosis to release Ap4A into the extracellular environment, where it can act as a purinergic signaling molecule.  
In addition to aaRS, other enzymes are also capable of synthesizing Ap4A, including DNA ligases and certain enzymes involved in DNA repair pathways [18]. For instance, DNA ligases can catalyze the synthesis of Ap4A from ATP and ADP during DNA replication and repair processes [10]. The production of \({}_{\text{AP4A}}\) in these contexts is often associated with cellular stress responses, where the molecule functions as a signaling mediator involved in regulating cell growth, apoptosis, and oxidative stress responses [19]. Ap4A synthesis is tightly regulated and varies depending on the cell's metabolic state and external stress conditions. Under normal physiological conditions, intracellular Ap4A levels remain low. However, they can rise significantly in response to environmental stressors such as heat shock, oxidative damage, or genotoxic stress [19]. In Drosophila cell cultures, for instance, heat shock and heavy metal exposure led to a 2-3-fold increase in Ap4A levels compared to unstressed controls [20]. Similarly, in the slime mold Physarum, hypoxia induced a 3-7-fold rise in Ap4A, which rapidly returned to baseline upon reoxygenation--illustrating its dynamic sensitivity to metabolic stress [21]. Conversely, during growth-inhibitory conditions such as serum deprivation or amino acid starvation, Ap4A levels can decline dramatically--by up to 100-fold--despite stable intracellular ATP concentrations. This divergence suggests that Ap4A operates as a distinct metabolic \"alarmone,\" finely tuned to signal adverse conditions and initiate adaptive responses. Therefore, the predominant mode of Ap4A synthesis--whether constitutive or inducible, enzymatic or stress-regulated--varies according to cell type and functional context.
# Research and Development of Automatic Monitoring System for Livestock Farms
## 2 Overview of the System Architecture
### Experimental Software Architecture  
In this research, the Mitsubishi GX developer software was used for editing PLC control program. Furthermore, the schematic design for the human-machine interface was carried out by Shihlin Electric's EU Editor2 software for the purpose of instantly controlling or monitoring the environmental status of temperature and humidity. The wireless communication has adopted the Modbus serial communication protocol for connecting monitoring computers and Remote Terminal Unit (RTU) from the Supervisory Control And Data Acquisition (SCADA) system. As to the database for historical recording, the MX Sheet application software developed by Mitsubishi Corporation has been used, which can directly export the data to Microsoft Excel from PLC, so that the variations in the collected environmental data can be analyzed from Excel. The software architecture diagram is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device name (product model)** & **Supplier** \\ \hline PLC 1 (FX3U-32 MR) & Mitsubishi Electric CO., Tokyo Japan \\ Left communication module (FX3U-485 ADP-MB) & Mitsubishi Electric CO., Tokyo Japan \\ Wireless moden (RFU-400) & ICP DAS CO., Hinshu Taiwan \\ Temperature \& humidity sensor (EYC-THS307) & YUDEN-TECH CO., New Taipei Taiwan \\ Aviation plug - Wind speed sensor (CWS-013A) & JETEC Electronics CO., Taipei Taiwan \\ Photo sensor (LXT-401S) & Rixen Technology CO., New Taipei Taiwan \\ \hline \hline \end{tabular} \({}^{1}\) PLC, Programmable Logic Controller.  
\begin{tabular}{c} \hline \hline
**Illuminance Sensor** \\ \hline Temperature sensor \\ \hline Humidity sensor \\ \hline Wind speed sensor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental hardware in cowshed device listed.  
Figure 3: The flow chart of experimental hardware architecture.  
Figure 2: The floor plan of experimental device in dairy housing. Where A is wind speed sensors (A1,A2,A3,A4), F is fans (F1,F2,... F14), I represents illuminance sensors (I1,I2,I3,I4), M denotes the humidity sensors (M1,M2), T is temperature sensors (T1,T2), D denotes distribution box.
# Power Management Strategy for an Autonomous DC Microgrid
###### Abstract  
Owing to the intermittent nature of renewable energy, microgrids in islanding operation mode require backup power sources. The diesel generator is the most popular backup source, but does not offer an instantaneous start-up and cannot immediately provide the necessary power. Therefore, supercapacitors are used to assist the power balance during diesel generator start-up thanks to their responsiveness and high-power density. This paper proposed a power management strategy for an autonomous DC microgrid based on a photovoltaic source, electrochemical storage, a supercapacitor, and a diesel generator. The proposed control system aimed at power balance while accounting for the slow start-up characteristic of the diesel generator, the self-discharge of the supercapacitor, the dynamic load management, and the economic operating mode of the diesel generator. The main contribution of this paper centered on a power management strategy solving the above issues integrally, and economic analysis for the diesel generator and microgrid. Experimental studies were carried out for different scenarios and the results obtained confirmed the effectiveness of the proposed strategy. Furthermore, the study provided a comparison between the economic operating and load-following modes of the diesel generator and demonstrated that the economic operating mode of the diesel generator can reduce the total energy cost of the DC microgrid.  
DC microgrid; power management strategy; diesel generator; supercapacitor; power balance; energy cost +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Mass and Energy Balance Overview  
To conclude this section, Figure 11 shows an energy flow diagram of the gasification CHP plant for olive cake pellets with a moisture content of 10% at full load operation (80 kW\({}_{\text{e}}\)). The diagram illustrates how the input biomass provides 445 kW, which is then fed into the gasifier. During the gasification process, there are heat losses of 47 kW, which correspond to 10.6% of the total energy input, in addition to 108 kW of thermal losses due to the producer gas conditioning, which represent about 24.4% of the input energy flow. After this stage, 289 kW are left at the input of the power generation unit, of which 86 kW are delivered as mechanical power--92 kW to cooling water, and 82 kW to the exhaust gases. The remaining 29 kW are thermal losses from the engine. Finally, the total CHP efficiency of the system is 35.6%, with an electrical efficiency of 18.0%. The energy flow diagram of the gasification CHP plant for olive pruning was not included, because a very similar distribution of the energy flows was obtained.  
Figure 10: Hot water flow at 45 \({}^{\circ}\)C (**left**), electrical, and CHP performance of the power generation unit (**right**).
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 3 Materials and Methods
### Exploratory Data Analysis and Statistical Analysis
#### 3.3.3 K-Means Clustering  
In this subsection, K-means clustering was performed to group by administrative dongs. K-means clustering is a widely adopted unsupervised clustering algorithm that partitions a dataset into K clusters based on the similarity of the data points' features. Specifically, the algorithm works by minimizing the sum of squared distances between data points and their respective cluster centers. In this study, K-means clustering was performed by using variables derived from the PCA.  
Interestingly, the results indicated that number of crimes and number of emergency bells, respectively, were the most discriminating factors in clustering data. Figure 5 illustrates the clusters divided into low-level, intermediate-level, and high-level based on the variables of number of crimes and number of emergency bells. While East and South districts were mainly located at the intermediate level, they did not appear at the high level.  
Figure 4: Biplot of principal component analysis. First component: Num.Bell, Area.CCTV, Num.CCTV, Float.pop.D, Float.pop.N; Second component: Area.Building; Third component: RequestCCTV; Variables that were under zero (Dim2): Num.Crime, Fac.Build, Population, Pop.Woman.
## 3 Results
### RQ1: Which Electrophysiological Signals May Index Emotional Processing?
#### 3.1.2 Ecg  
ECG is an electrophysiological measurement used to record the electrical activity of the heart caused by the repolarization of ventricular walls and activation of the atrium. This activity is recorded by placing electrodes on specific areas of the body [14]. The features that were mostly extracted were the amplitude of Respiratory Sinus Arrhythmia (RSA) [29; 33] and the inter-beat R-R wave interval [36; 50; 61]. In a study concerning R-R intervals, data in the frequency domain was extracted using the fast Fourier and Wavelet transform [62]. The ratio between EDA and integration of the spectrum of R-R intervals was reported in [27]. High-frequency variations in beat-to-beat intervals was also reported [30]. Only one study reported computing the root mean square of successive differences of Heart Rate Variability (HRV) [48].  
A contingent trend between cardiac activity and emotions was observed. An increase in HR after stimulus was associated with fear in control groups [50] and population with conditions such as bipolar disorder type I [36] was observed. Patients with anterior cruciate ligament reconstruction showed decreased HR [50]. Lower values of HR were also observed in control subjects while watching scenes inducing tenderness [29]. The ability to enhance sad emotion was associated with higher HRV [48]. One study reported no significant effect of emotional stimuli on RSA [33]. Patients with borderline personality disorder and post-traumatic stress disorder showed lower values of the high frequency component in HRV in positive, negative, and neutral stimulations [30]. A decrease in the wavelet transformation of high frequency component in HRV after negative stimuli was observed in patients with anorexia nervosa, whereas an increase was observed in healthy control subjects [62].
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 4 Key Technologies
### Multi-Data Organization and Management
#### 4.1.1 Special Data Pool for Landslide Monitoring  
With regard to data content, the special topics data of landslide monitoring were placed into one of the following five categories (Table 1), while the technological procedures were integrated into the database construction (Figure 3).  
With the spatial database and spatial data engine, the spatial data form an object oriented database management system, which has integrated management over spatial data and attribute data. For the design of spatial databases, previous studies have investigated landslide features in-depth using an object-oriented information model and a design method for a structured UML static view. Using this approach, they obtained the relationships between the patterns of landslide information management, countermeasures, and limitations. The spatial data of potential landslides include dotted and polygon maps, unified serial numbers (id) for monitoring sites, influential areas, and boundaries.  
The attribute data of potential landslides involve large volumes and contain complex relationships, primarily for those relating to real-time monitoring, forecasting and early warning, historical information, and monitoring management over relevant staff. The real-time monitoring data include landslide monitoring information, real-time monitoring sites, and sensors, among which various relationships exist (Figure 4).  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Categories** & \multicolumn{2}{c}{**Data Contents**} \\ \hline \multirow{4}{*}{Static data} & \multirow{4}{*}{Basic geographical data} & Basic arrow data on geological information, including big scale administration division, lake and water systems, residential area, and lattice spatial data for the digital elevation model \\ \cline{2-3}  & Remote sensing image data & Aerial image and satellite-borne remote sensing image data \\ \cline{2-3}  & Survey data about potential landslide sites & Plane graph, profile, location map of landslides, rock and soil mass characteristics data \\ \cline{2-3}  & Documentary information & Relevant statistical materials recorded about historical disasters, photos, and technical reports. \\ \hline \multirow{2}{*}{Dynamic data} & Sensor monitoring data from potential landslide sites & Ground surface displacement, depth displacement, groundwater level, inclination and rainfall \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landslide Monitoring Data Categories.  
Figure 3: Flow-chart of database construction.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 6 Simulation Results
### Comparison of Velocity Field  
The blade loads are affected by the local flow around the blade, and the forces consequently modify the flow around the blade and further in the wake. The flowfield simulated by the AD/NS demonstrates how the tip loss corrections affect the velocity field. In order to clearly show the influence of using different tip loss corrections, instead of directly showing the velocity field, the following velocity difference is defined,  
\[\delta u_{z}=\frac{\left|u_{z,new}-u_{z,G}\right|}{u_{z,new}}\times 100\%, \tag{60}\]  
where \(u_{z,new}\) and \(u_{z,G}\) are the axial velocity obtained from the simulations using the new tip loss correction and the Glauert-C tip loss correction, respectively.  
Figure 9 displays the contours of \(\delta u_{z}\) in the flow field of the studied two rotors, which highlights the influence of the tip loss corrections in terms of changing the velocity field not only around the blade tip, but also in the wake. The influence of \(\delta u_{z}\) is more concentrated near the blade tip in the rotor plane, whereas, the inner part is hardly affected. In the wake, the influence range of \(\delta u_{z}\) tends to cover a wider radial space downstream. The magnitude is even increased in the wake of the _NREL 5 MW_ rotor.  
Figure 8: Normal force \(F_{n}\) and tangential force \(F_{t}\) along the _NREL 5 MW_ blade.
## 3 Results
### The FEM Model Results  
#### 3.2.1 The Load Step History  
In the FEM model, the elements 10,801, 10,426, 9426 and 14,401 were close to the measuring points from MP 1 to MP 4. Besides, element 10,076 and element 9451 were close to crack 1 and crack 2. Therefore, the results of the elements mentioned above were summarized. The strain of vertical and circumferential direction is shown in Figure 10. During the time from 0 to 20 h, the strain in two directions had little change. At the prestress load step (time from 20 to 40 h), the strain in two directions was compression strain with huge difference in magnitude. At the end time of prestress force, the vertical and circumferential direction strains were \(-482\) and \(-166~{}\upmu\varepsilon\). At the time of maximum inner pressure, the vertical and circumferential direction strain came to \(-250\) and \(-93~{}\upmu\varepsilon\). It was clear that the concrete cylindrical wall was still subjected to the compression strain at maximum inner pressure. This meant that there was no tension strain occurring at the concrete surface. The prestress tendons were still working to protect the concrete from cracking and the material was still elastic. Because the FEM model was ideal, especially in that the concrete material property difference was not considered, there was little difference between each measuring point.
## 2 Background  
### Substance-Field Model (SF)  
The substance-field model (SF) is a model in the form of a minimal workable technical system consisting of two substance elements (S1--tool, function carrier and S2--function object) and their mutual interactions, specified to the physical nature of a corresponding field (F) [26; 27; 28; 37]. The method for modelling the system or its parts as a single SF or an SF network is called SF analysis (SFA).  
A graphic presentation of the model is a triangle with vertices corresponding to the model components (S1, S2, and F) and edges representing the links between the components (Figure 1). The graphic symbols (different types of arrows), which represent interactions between the different components of an SF model, are shown in Table 2.  
From the perspective of the problem-oriented approach, the actual object of SF modelling is the problem function or functions. In fact, the model is a graphic representation of an unsatisfactory interaction between two elements, or a set of interactions between them, with at least one of them being undesirable. The following functions may be considered to be problem functions: a missing useful function; a harmful (undesirable) function, the occurrence of which accompanies the effective realisation of a useful function under given conditions; and an ineffective useful function, whose realisation quality indicators cannot be improved under the existing conditions within the limits of a known technical solution. SFA provides rules for problem setting and solving, which present ways of transforming the generic SFs into useful effective SFs by adding new elements or eliminating existing elements [26; 27; 28]. These rules are organised and specified according to the generic SF form and the terms of useful function realisation in a system of standards for engineering problem solving [26; 28; 29; 31; 38].  
Figure 1: SF models of the problems presented as (**a**) ineffective useful SF; (**b**) harmful SF; (**c**) combined harmful SF (developed by the authors based on: [28; 37]).
###### Abstract  
As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10-15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.  
online social network (OSNs); social network analysis; cybersecurity; spam detection; neural word embeddings; RNN; contextual word embeddings; LSTM; BERT; EMLO
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
### Challenges in Development of Separation Methods for Lithium Isotopes Using Electrochemical
#### 2.3.4 Methods Involving Ion-Exchange Membranes  
Employing a lower working pressure, the membrane chromatography can provide a similar lithium isotope separation efficiency as column chromatography, but avoiding the intra-particle diffusions as a mass transfer-determining step. It is believed that the separation of lithium isotopes with membrane chromatography takes place by multilayer adsorption, surface diffusion, as well by ion-pore electrostatic interaction between Li ions, and the crown ether moieties on the membranes. In the case of membrane chromatography, the convective transport through membrane pores is the transfer-determining step. A PSF-g-AB15C5-type polymer porous membrane with porosity of 80.4% and average pore size of 62.7 nm was prepared by [PERSON] et al. [44], while a concentration of 0.52 mmol immobilized AB15C5/g polymer was used. Discs with 24 mm-diameter were produced by cutting the PSF-g-AB15C5 membranes with an average thickness of 100 \(\upmu\)m, while three layers of porous polyester (PET) filter were used as support between every two membranes (pore size of 300 \(\upmu\)m and thickness of 200 \(\upmu\)m), so a stationary phase packed in a chromatography column (\(\mathcal{O}\) 25 \(\times\) 100 mm) was built, resulting in a four-stage tandem membrane chromatography system [44]. In such a chromatographic system, the eluate depleted in \({}^{6}\)Li obtained from the previous stage is used as the feed solution for the next stage [44]. The separation factor obtained from the single-stage membrane chromatography was up to 1.0232, while the relative abundances of \({}^{6}\)Li in the four-stage tandem membrane chromatography increased by 0.2% (from 7.60 to 7.80%) [44].  
Challenges in Development of Analysis Methods for Lithium Isotopes Using Spectrometric and Spectroscopic Methods