esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ionic Liquids as Green Mobile Phase Additives  
Ionic liquids (ILs), which are also called room-temperature ionic liquids (RTILs), are liquid at ambient temperature, and are composed entirely of ions, most often organic cations, but also inorganic or organic anions [119]. These low-melting-point salts (below 100 \({}^{\circ}\)C) were first intensively used as solvents for organic synthesis but have more recently shown great potential as green solvents for analytical chemistry in academic and industrial fields. Indeed, they possess beneficial properties, such as high miscibility with water and organic solvents, capacity to solubilize organic and inorganic compounds, high thermal stability, negligible vapor pressure (low volatility), and non-flammability [120; 121]. However, their environmentally-friendly nature was recently questioned as aquatic toxicity is a matter of concern with respect to the environmental safety of ILs [16].  
A review of applications of ionic liquids in the whole field of analytical chemistry has been published [120]. Other reviews focus on applications in separation sciences [121; 122], and some specifically on liquid chromatography [119; 123; 124]. In chromatographic separations, ionic liquids are most common used as mobile phase additives, and sometimes as surface-bonded stationary phases for ion-exchange (IEX) or mixed mode IEX/RP-HPLC [119; 122]. ILs are usually used in small quantity in RP-HPLC mobile phases as a silanol-blocking agent to improve the chromatographic analysis of basic compounds. In fact, ILs can reduce or suppress interactions between positively charged compounds and anionic free silanols of the stationary phase at low pH mobile phases, which are responsible for peak tailing and peak broadening. ILs can also be added in higher proportions to totally aqueous mobile phases instead of polluting organic modifiers.  
Some applications of ILs as mobile phase additives in RP-HPLC have been reported for the analysis of pharmaceutical compounds [125; 126; 127; 128; 129; 130]. Different pharmaceutical compounds were analyzed such as \(\beta\)-blockers [125], antidepressants [126], \(\beta\)-lactam antibiotics [127], thiamine (vitamin B1) [128], urazamide [129], and ephedrines [130]. Among these, [PERSON] et al. [125] carried out a comparative study of the effect of 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) and triethylamine (TEA) added to the mobile phase on the analysis of seven \(\beta\)-blockers in RP-HPLC. They showed that, in terms of efficiency and asymmetry factor, BMIM BF4 was a significantly better additive compared to TEA. Even if this study highlights the benefits of using ILs, the greenness of the method can be questioned, as 30% ACN was used in the mobile phase. Also, [PERSON] et al. [129] developed and successfully validated a green method for the determination of urazamide in pharmaceutical preparation using an aqueous mobile phase modified with 1-ethyl-3-methyl-imidazolium tetrafluoroborate.
## 2 Materials and Methods  
### Reagents  
Phosphate-buffered saline (PBS) without Ca\({}^{2+}\) and Mg\({}^{2+}\), Dulbecco's Modified Eagle's Medium (DMEM) without L-glutamine and phenol red (17-205-CV), and Minimum Essential Medium Eagle (MEM) without L-glutamine and phenol red (17-305-CV) were purchased from Corning (Tewksbury, MA, USA). Trypsin, penicillin, streptomycin, resazurin sodium salt, dimethyl sulfoxide (DMSO), 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorohydrofloorescein diacetate (H\({}_{2}\)DCF-DA),caspase-3 substrate (Ac-DEVD-pNA), hydroxyethylpiperazineethanesulfonic acid (HEPES), sodium chloride (NaCl), 3-[(Scholamidopropyl)dimethylamino]-1-propanesulfonate hydrate (CHAPS), ethylenediaminetetraacetic acid (EDTA), Bradford reagent, methanol, acrylamide/bisacrylamide, Tris HCl, Tris Base, sodium dodecyl sulfide (SDS), N,N,N',N'-tetrametyloetylenediamine (TEMED), bovine serum albumin (BSA), ammonium persulfate (APS), glycerol, dithiothreitol (DTT), and 2-chloro-5-nitro-N-phenylbenzamide (GW9662--a selective PPAR\(\gamma\) antagonist) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The lactate dehydrogenase release assay was purchased from Takara Bio (Kusatsu, Japan). Fetal bovine serum (FBS), universal RNA purification kit, radioimmunoprecipitation assay buffer (RIPA), and Fast Probe qPCR Master Mix were purchased from EURx (Gdansk, Poland). The high-capacity cDNA reverse transcription kit, TaqMan(tm) probes and primers corresponding to specific genes encoding _ACTB_ (Hs01060665_g1), _AhR_ (Hs00169233_m1), _PPAR\(\gamma\)_ (Hs00234592_m1), _NF-kB1_ (Hs00765730_m1), and _PXR_ (Hs01114267_m1), and antimouse (#31430) and anti-rabbit (SH233595) HRP-conjugated secondary antibodies were purchased from ThermoFisher (Waltham, MA, USA). The primary anti-AhR antibodies (67785-1-lg) were kindly gifted by Proteintech (Duseldorf, Germany). Primary anti-PPARy antibodies (24355) were purchased from Cell Signalling (Danvers, MA, USA). Primary anti-GAPDH antibodies (sc-47724) and the 0.45 \(\upmu\)m pore-size PVDF membrane were obtained from Santa Cruz Biotechnology (Santa Cruz, CA, USA).
# Towards Digital-Twin Assisted Software-Defined Quantum Satellite Networks
## 1 Introduction
Each element composing the Data Plane (DP) is a QSR, which performs the BSMs and the swapping operations to establish an E2E entanglement between two GSs. Considering that QSBs need to be very accurately controlled, the SDN is recognized to be particularly appropriate to handle these kinds of architectures. In fact, the achievement of high rates requires efficient entanglement generation and swapping operations, a goal that can be easily achieved with the use of SDN [16]. In addition, on the basis that in specific cases, including distributed computing, the E2E operations should be minimized to mitigate the impact of decoherence that induces a deterioration of the entangled quantum states and in order to minimize the overhead that is due to the swapping operations, the paper also  
Figure 1: Representation of a DT data flow. proposes a dedicated Network Layer protocol. Specifically, the proposed protocol is utilized to monitor and manage the chain of QSRs according to the pattern given by the solutions processed by the DT. Moreover, the paper defines an optimization procedure to minimize the Round-Trip Time (RTT) of the quantum resource monitoring flows between the DT and the quantum satellites. Therefore, the Master Control Station (MCS) [28] interacts with a DT, which is a digital replica of the entire Quantum Satellite Network (QSN) and the involved QDs on the ground and operates on it through optimization procedures to generate optimal paths and achieve certain performance. The E2E paths are then configured by the SDN Controller integrated in the MCS based on the obtained results. Specifically, the controller performs the setup of the inter-satellite optimized path and coordinates the entanglement generation and swapping procedures between the QSRs and the GSs that compose the E2E path. In summary, this paper proposes an approach for the management of a QSB network that consists of multiple QSRs with an emphasis on the CP driven by the DT, with the contributions outlined below:  
* The design of a DT-assisted SDN-based CP including a specific TN [29; 30; 31] approach;
* A specifically designed Network Layer optimized protocol to monitor the QSN status and manage the generation of multiple entanglements on an E2E basis;
* A performance evaluation in terms of attenuation, E2E path configuration time, entanglement generation time, the RTT required to reach each satellite, and fidelity. The manuscript is composed of the following Sections: Section 2 reports a state-of-the-art analysis, focusing on the related scenario and experiments. Section 3 describes the system model and the proposed protocol. Next, Section 5 presents the validation of the results. Finally, Section 6 concludes the manuscript and outlines future developments.
## 3 Packet Detection Algorithms
### Energy-Based Algorithms  
Energy-based algorithms measure the received signal energy and do not require additional knowledge. When there is no packet, the received signal \(r_{n}\) only consists of noise. When the packet starts, the received energy increases due to the signal component. Therefore, there will be an obvious energy change when a signal arrives, and that change can be used for packet detection. The following are two energy-based detection algorithms: received signal energy detection and double sliding window packet detection.
## 2 Simulation Model
### Energy Management Strategy  
Given that FCHEVs utilize both fuel cells and battery packs as power sources, an energy management strategy is required to coordinate the outputs of these two power sources [23]. The goal is to ensure that the fuel cell or battery pack operates within its efficient output range while maintaining the system's economy and power. Rule-based control strategies are widely used in vehicle applications due to their simplicity and ease of implementation. However, traditional control methods often require precise mathematical models of the controlled objects. In engineering practice, a complex system can often achieve good control results based on practical operational experience. Based on a certain empirical basis, researchers have proposed the concept of fuzzy control. Fuzzy logic control consists of four main components: fuzzification, fuzzy inference, knowledge base, and defuzzification. The workflow diagram of fuzzy control is shown in Figure 4.  
Fuzzification is the primary task in designing fuzzy logic control. It involves converting the received data into easily understandable information using human-like thinking. For a specific problem, different membership functions are used to determine the membership degree of each precise input variable (such as the state of charge of the battery and the difference between the demanded power and the lower limit of power in the fuel cell's efficient range) within their respective domains. Then, based on which fuzzy subset the element belongs to, the accurate value of that variable is represented.  
The knowledge base primarily stores the fuzzy subsets of the input and output variables after fuzzification, as well as the fuzzy control rules generated based on expert experience. These two components are typically stored in a database and a rule base, respectively.  
Figure 4: Working principle diagram of fuzzy control.  
Figure 3: Fuel cell system architecture diagram.  
The main purpose of logical inference is to deduce the output values of a fuzzy control system based on fuzzy rules. It uses a relatively broad language variable value as a working criterion to represent specific numerical values.  
Defuzzification is primarily performed because the variables obtained after fuzzy inference are still in a fuzzy form. Therefore, the defuzzification process is used to convert these fuzzy variables into precise numerical values.
## 3 Methodology of FSACM
### Feature Selection Approach--FSAP  
The first version of the proposed FSAP approach was developed in our previous study. The first version of the FSAP approach consists of two stages: With the formula developed in the first stage, some of the features were eliminated. In the second stage, an algorithm was developed to determine which feature is more important by grouping with the remaining features [38].  
Figure 2: Architecture of the proposed methodology.  
In this new version of the FSAP approach, a new algorithm has been added that can further reduce the number of features to be used in the learning process. With this algorithm, the distribution of the normal/attack states within the groups formed from the features is examined. That is, this algorithm is based on the homogeneity condition. If there is a homogeneous distribution within a group, this feature is not selective; there must be a heterogeneous distribution. For example, if there is a DoS attack, it should be within a certain value range in the feature. If the feature can correspond to every value it contains, a homogeneous distribution has occurred and this feature cannot be an indicator in due diligence. Based on this situation, an algorithm has been developed. The developed algorithm is given in Algorithm 1.  
```
Input : \(g\) : Feature Groups List Output : \(f\) : Selected Features List Definitions \(\beta\) : state corresponding to data in the group \(a\) : state corresponding to data in the group \(c\) : counter of traffic situation distribution \(g_{1}\gets group1\), \(g_{2}\gets group2\),..., \(g_{N}\gets groupB\), \(s\gets u(g(i))\) for\(i\gets 10\) to \(s\)do if\(\beta(g(i))==normal\)then \(cNormal++\) else if\(\beta(g(i))==attackType_{1}\)then \(cType_{1}++\) else if\(\beta(g(i))==attackType_{2}\)then \(cType_{2}++\)...  else \(cType_{k}++\) end if \(c_{n}ormal/s_{i}<=a_{n}ormal\)then \(p++\) end if if\(c_{n}ve_{1}/s_{i}<=a_{i}ype_{1}\)then \(p++\) end if \(c_{n}ype_{2}/s_{i}<=a_{i}ype_{2}\)then \(p++\) end if \(c_{n}ype_{k}/s_{i}<=a_{i}ype_{k}\)then \(p++\) end if \(c_{n}ype_{k}/s_{i}<=a_{i}ype_{k}\)then \(p++\) end if \(p+k/2\)then \(add.f()\) else \(eliminatefeature\)
```  
**Algorithm 1**Feature Selection  
By utilizing feature selection algorithms with a predetermined threshold value, we were able to identify significant features. The proper selection of features is crucial for creating a successful machine learning model. Therefore, we utilized the FSAP approach to choose the best subset of features for the given datasets.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 3 Problem Statement and Approach
### Research Questions  
To address these shortcomings, the following four research questions are elaborated:  
1. What is a suitable architectural style of the desired model-based framework? How can this architecture be extended to deal with the (future) UASs?
2. How to define a domain model of the data sources suitable for detecting the effects of earthquakes?
3. How to compute the combined effectiveness, cost, and timing values as a result of data fusion? The algorithms for computation must be changeable and re-definable to satisfy different needs.
4. What kind of algorithms can be defined for automated synthesis of the optimal data fusion?
## 5 Discussion
### Dynamics of Day Events--Overwhelming Versus Calm and Surprising Versus Expected  
We were able to depict another factor that played an important role in affecting a user's notification experience and that was due to how their particular day was. Entrenched from the findings of the diary entries, we were able to determine four different days; overwhelming, calm, surprising, and expected. Depending on the type of day the user is experiencing, it can have an influence on how they perceive and interpret push notifications from their smartphones.  
A participant elaborated on their day by saying: _\"If I am having a day where I feel tired or have a lot of work to do then notifications usually get me more anxious and stressed. Sometimes I have a headache by the end of the day of trying to focus on work but yet dealing with the notifications. Could be useful to only have work notifications during the day and the rest during free time etc\"_. (P125) which indicates a relatively busy day, and notifications impacting the user's stress levels more than usual. Similar to this, another mentioned having an overwhelming day, _\"I had quite many messenger notification and it made me feel anxious because I had quite intense day, dealing with school and work at the same time and I knew that I will not be able to answer the messages right away thus I have to deal with them after I have finished my workday, which seemed like unwanted obligation at that time. Other notifications (reminder and bank money transfer) did not bother me\"_. (P12). On the contrary, a separate diary entry portrays a different story, _\"I feel like I had too few notifications today, if it makes sense. I was waiting for some replies here and there, but nothing was happening. And it felt boring and made me a bit sad like everyone forgot about me or didn't care to reply. Nothing too dramatic, but a bit of this unpleasant feeling\"_. (P128) where the participant mentioned a relatively calm day, however, associated with an unpleasant feeling of sadness or loneliness. This particular entry bolsters how daily contexts can vary, and depending on these circumstances, they can have very different impacts on users.  
Based on these findings, it is important to incorporate daily events and contexts users encounter into the bigger picture of push notifications. From our understanding, if a user is experiencing a busy schedule during their day, minimizing unnecessary notifications or delaying them could help mitigate the adverse effects. On the other hand, on calmer days, users could be more receptive and welcoming towards interruptions caused by notifications.
# Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
## 1 Introduction  
Geopolitical events are causing supply chain strains, and climatic events are impacting the food systems' resilience [1]. The challenges to ending hunger and food insecurities keep growing, and the COVID-19 pandemic has further highlighted fragilities in our agifood systems and inequalities in our societies [2]. This scenario becomes more urgent with the growing food demand. The Food and Agriculture Organization (FAO) has stated that by 2050 there will be around 10 billion people, and the food demand will grow by 70% [3]. Artificial intelligence (AI) techniques applied in agriculture can optimize agricultural processes by food system resilience increases.  
AI is an evolving set of technologies that are used to solve a variety of applied problems and has been extensively applied in farming recently [4]. This work's purpose is to make a systematic review of the current studies and research in agriculture that employ the recent practices of AI technologies to solve several relevant problems.  
Reviews of the literature are important for synthesizing the existing knowledge base: in [5], the authors conducted a review about crop yield prediction using machine learning; in [6], advanced agricultural disease image recognition technologies were explored; IoT solutions for smart farming were researched in [7]; big data in agriculture in [8]; and agriculture 4.0 in [9, 10]. This work presents extensive research on the latest application ofAI in agriculture to alleviate problems in the seven main agriculture domains identified: crop management, water management, soil management, fertirrigation, crop prediction, crop classification, disease, and pest management. In the 176 studies selected for descriptive analysis, more than 20 different artificial intelligence techniques were identified. After the qualitative analysis, 17 articles were selected and described their application in agriculture, challenges, and benefits.  
The outline of this paper proceeds with the research methodology, which follows the PRISMA steps with the selected criteria and data collected; Section 3 presents the bibliometric analysis results; Section 4 presents the relevant articles selected with a quality appraisal regarding their main agriculture domains and the AI technologies used; Section 5 presents the challenges, benefits, trends, and research directions identified. Finally, Section 6 shows some conclusions.
## 3 Income Model of Roadside Units
### Positive Income of Roadside RSU Coverage
#### 3.1.2 Construction of the Ferry Transmission Model under Different Permeability  
When ferry transmission occurs in a vehicle, the information carried by the intelligent connected vehicle flows along the moving direction of the vehicle until \(x=R_{v}\) and the information is transmitted. Based on the information flow-traffic flow coupling theory, ferry transmission is divided into four types, as shown in Figure 3. The initial state of the two types of ferry transmission is the same in that the traffic flow direction is the same as the information flow direction, as shown in Figure 3a,b. In Figure 3a, when the vehicle \(i\) catches up with the vehicle \(i+1\) in the same direction before encountering the reverse vehicle \(i-1\), this is the first type of ferry transmission. As for Figure 3b, when the vehicle \(i\) first encounters the reverse vehicle \(i-1\), it is the second type of ferry transmission. In the third and fourth types of ferry transmission, the ferry transmission direction is opposite to the information flow direction, as shown in Figure 3c,d. In Figure 3c, the vehicle \(i\) will be overtaken by the same rear vehicle \(i+1\), which is the third type of ferry transmission. In Figure 3d, the distance between two adjacent vehicles gradually increases and vehicle \(i\) has to complete information transmission before meeting, which is transformed into the second type of ferry transmission.  
Figure 3: Three types of ferry transmission.  
Therefore, the above four types are divided into three transmission methods. When calculating the expected transmission distance \(E(Y)\) of ferry transmission, Equations (13) and (14) can be obtained by weighting the above three transmission types as well as the corresponding transmission time Equation (15):  
\[E(Y)=\beta_{1}E(Y_{1})+\beta_{2}E(Y_{2})-\beta_{3}E(Y_{3}) \tag{13}\]  
\[E(Y_{n})=\frac{v_{i}}{\Delta v_{i}}\left[\frac{\int\limits_{R_{p}}^{+\infty}x \alpha\lambda e^{-\alpha\lambda x}dx}{1-\int\limits_{0}^{R_{p}}\alpha\lambda e ^{-\alpha\lambda x}dx}-R_{p}\right] \tag{14}\]  
\[t(Y)=\beta_{1}\frac{E(Y_{1})}{\Delta v_{1}}+\beta_{2}\frac{E(Y_{2})}{v_{1}+v_{ 2}}+\beta_{3}\frac{E(Y_{3})}{\Delta v_{2}} \tag{15}\]  
In the equation, \(E(Y_{n})\) represents the expected transmission distance of the above three methods, \(\beta_{n}\) represents the proportion of the three methods, \(v_{i}\) represents the speed of the vehicle carrying the information, and \(\Delta v_{i}\) represents the chasing speed.
## 3 CubeSats and Inter-Satellite Links (ISLs)
### Future Constellations  
In Table 5, a list of known planned mega-constellations of small satellites that indicate an ambition to incorporate optical communication links in LEO (i.e., catering for LEO-LEO or LEO-ground or both), are presented (NB This list may not be complete and subject to modification). The emergence of low-cost, fast turnaround launch capability to LEO is heralding a significant growth in the development of optical communication terminals for small satellites such as CubeSats and nanosats. The mass, power, and volume envelope restrictions now force manufacturers of optical communications terminals to adapt their payloads, applying novel approaches to optical design, while maintaining competitive data rates that greatly outstrip RF communications systems. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Corporation** & **Name** & \
\begin{tabular}{c} **Data Rate** \\ **(D:Down; UUp)** \\ \end{tabular} & **Link Type** & **Volume (cm\({}^{3}\))** &
\begin{tabular}{c} **Power** \\ **Consumption** \\ **(W)** \\ \end{tabular} & **Mass (kg)** & **Release (Year)** \\ \hline NEC/JAXA & LUCAS & 1.8 Gbps & LEO-GEO, LEO-LEO & - & - & - & 2020 \\ \hline NASA, MIT, & CLICK-A & 10 Mbps & LEO-LEO & 1.2 U & - & 1.2 & 2018 \\ Uni. of Florida & TBIRD [66] & TBIRD [66] & & U-5 kbps & LEO-GND & 1.8 U & - & 2.25 & 2022 \\ \hline DLR & OSIRISv1 & 200 Mbps & LEO-GND & - & 26 & 1.3 & 2017 \\ Uni. of & OSIRISv2 [68] & 1 Gbps & LEO-GND & - & 37 & 1.65 & 2016 \\ Stuttgart & OSIRISv3 & 10 Gbps & LEO-GND & - & 150 & 9 & 2021 \\  & OSIRIS 4 & 100 Mbps & LEO-GND & 0.3 U & 10 & 0.39 & 2021 \\  & PIXL1 [69] & & & & & & \\ \hline ESA (ARTES) & OCT & - & LEO-LEO & - & - & - & 2023 \\ Spire & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: R&D customized lasercom systems developed for CubeSats.