esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fruit Matrix** & **Analyte** & **Content [\(\mu\)g/g]** & **County of Origin** & **Source** \\ \hline \multirow{2}{*}{Barberry} & EA & n.d. & Poland & [29] \\ \cline{2-5}  & Q & 21.0–22.0 \(\mu\)g/g & China & [40] \\ \hline \multirow{4}{*}{Blackthorn} & EA & 94 \(\mu\)g/g & Romania & [41] \\ \cline{2-5}  & Q & 53 \(\mu\)g/g & Romania & [42] \\ \cline{2-5}  & Q & 207.0 \(\mu\)g/g & fw & Sweden & [43] \\ \hline \multirow{4}{*}{Chokeberry} & \multirow{4}{*}{EA} & n.d. & Bulgaria & [43] \\  & & 15.7 \(\mu\)g/g & fw & Canada & [44] \\ \cline{1-1}  & & 4.0 \(\pm\) 0.1 \(\mu\)g/g & Croatia & [45] \\ \cline{1-1}  & & n.d. & Croatia & [46] \\ \cline{1-1}  & & 129 \(\mu\)g/g dw * & Romania & [47] \\ \cline{1-1}  & & n.d. & Spain & [48] \\ \cline{1-1} \cline{2-5}  & & 148 \(\mu\)g/g dw & Bulgaria & [43] \\ \cline{1-1}  & & 2.1 \(\mu\)g/g fw & Canada & [44] \\ \cline{1-1}  & & 92.2 \(\mu\)g/g * & Croatia & [45] \\ \cline{1-1}  & & 437.8 \(\mu\)g/g fw & Croatia & [46] \\ \cline{1-1}  & & 17.4 \(\mu\)g/g fw & Croatia & [49] \\ \cline{1-1}  & & 89.0 \(\mu\)g/g * & Finland & [50] \\ \cline{1-1}  & & 348.0 \(\mu\)g/g fw & Finland & [42] \\ \cline{1-1}  & & 7 \(\mu\)g/g dw * & Romania & [47] \\ \hline \end{tabular}
\end{table} 
Table 4: Literature data on EA and Q content in studied medical fruits. Data on EA content in other tested fruits remain limited. It shows that the fruits of these medicinal plants are still an undervalued and untested source of EA. [PERSON] and [PERSON] [46] also measured the EA and Q content in various fruit samples and reported that EA contents in chokeberry and elderberry (expressed in fresh weight) were below the method's LOD. In another study, the same researcher also quantified EA in chokeberry at 3.97 \(\pm\) 0.1 ug/g fresh weight [45]. Quercetin has been more extensively studied in various berries compared to EA (Table 4). Among the tested medicinal fruits in our study, linguonberry exhibited the highest Q amounts, with a mean concentration of 159.81 ug/g dw. Our findings correspond with results obtained for Finnish linguonberries (74-146 ug/g) [42; 50]. In contrast, lower Q concentrations have been observed in American and Canadian linguonberries [44; 54].
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 4 Challenges and Limitations
### Multiplexed Detection and Device Integration  
While GFET sensors are highly sensitive, their integration with microfluidic systems for sample delivery, fluid handling, and multiplexed detection adds complexity. Successful integration necessitates careful design to guarantee that fluid dynamics do not disrupt the interaction between target biomolecules and the graphene surface and that samples are quickly supplied to the detecting area.  
Substantial progress was achieved in multiplexed neurochemical monitoring by creating soft neural probes with GFETs that detect DA and SER with high sensitivity and selectivity [163]. Site-selective functionalisation effectively reduced crosstalk between sensors. The probes remained effective in cerebrospinal fluid for 96 h, indicating promising stability for future in vivo applications. The sensors' degraded response at high NT concentrations following exposure to the fluid raises questions about their long-term durability. Ex vivo investigations in harvested mouse brains demonstrated the ability of these probes to detect a triggered NT release. Modifying the aptamers allows for the detection of various neurochemicals. The spatially defined photopolymerisation of hydrogel gate GFET devices allowed for the encapsulation of AChE and penicillinase [176]. The hydrogel-mediated integration permitted enzymatic processes in a restricted microenvironment at the FET surface, overcoming obstacles such as charge screening in high ionic strength physiological fluids. This method, along with on-chip microfluidic control, allowed for real-time, label-free detection.  
There are other approaches used in integrating GFET devices during biosensing that can be relevant for amine NT detection. The integration of a GFET biosensor onto a CMOS multiplexer platform resulted in over 99% device yield with minimal variability amongst sensors [285]. This technology allows for scalability from thousands to millions of devices based on ASIC design, increasing assay development with large-scale statistics and increased dependability. CMOS multiplexing addresses sensor reliability and repeatability issues, allowing malfunctioning devices or faults to be removed from the data. Multiplexedsensor arrays can also provide a greater dynamic range and improved performance by supporting a variety of sensor types with varied sensitivity [286, 287].
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
###### Abstract  
With the rapid evolution in wireless communications and autonomous vehicles, intelligent and autonomous vehicles will be launched soon. Vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and road information in real-time in vehicular networks. V2X has evolved by integrating cellular 5G and New Radio (NR) access technology in V2X communications (i.e., 5G NR V2X); it can fulfill the ever-evolving vehicular application, communication, and service demands of connected vehicles, such as ultra-low latency, ultra-high bandwidth, ultra-high reliability, and security. However, with the increasing number of intelligent and autonomous vehicles and their safety requirements, there is a backlash in deployment and management because of scalability, poor security and less flexibility. Multi-access Edge Computing (MEC) plays a significant role in bringing cloud services closer to vehicular nodes, which reduces the scalability and flexibility issues. In addition, blockchain has evolved as an effective technology enabler to solve several security, privacy, and networking issues faced by the current 5G-based MEC systems in vehicular networks. Blockchain can be integrated as a strong security mechanism for securing and managing 5G V2X along with MEC. In this survey, we discuss, in detail, state-of-the-art V2X, its evolution based on cellular 5G technology and non-cellular 802.11 bd. We investigate the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. We present the issues and challenges in existing edge computing and 5G V2X and, then, we shed some light on future research directions in these integrated and emerging technologies.  
connected vehicles; security; blockchain; edge computing; privacy +
Footnote †: journal: Electronics Electronics  
1
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 3 The AUKF Based on the Second-Order RC Equivalent Circuit Model
To ensure that the covariance matrix is definitely positive, we must take \(t\geq 0\); \(\alpha\) controls the distance of the selected points, with \(10^{-2}\leq\alpha\leq 1\), and \(\beta\) is used to reduce the error of the higher-order terms. For a Gaussian [30], the optimal choice is \(\beta=2\) in this paper, along with \(t=0\) and \(\alpha=1\). \(\hat{X}_{k-1,i}\) consists of \(\hat{X}_{k-1,i}^{x}\), \(\hat{X}_{k-1,i}^{w}\) and \(\hat{X}_{k-1,i}^{w}\). Based on this, the time update of the state estimation is performed:  
\[ \begin{array}{ll}\hat{x}_{k|k-1}&=E[\big{[}f(x_{k-1},\;u_{k-1})+w_{k-1}]| \Psi_{k-1}\big{\}}\\ &=\sum\limits_{i=0}^{2L}z_{i}^{(m)}\Big{[}A_{k-1}X_{k-1,i}^{x}+B_{k-1}u_{k-1}+ X_{k-1,i}^{w}\Big{]}\\ &=\sum\limits_{i=0}^{2L}z_{i}^{(m)}X_{k|k-1,i}^{x}\end{array}  \tag{22}\](2) Time update of mean square error:  
\[ \begin{array}{ll}P_{x,k|k-1}&=E\bigg{[}\Big{(}x_{k}-\hat{x}_{k|k-1}\Big{)} \Big{(}x_{k}-\hat{x}_{k|k-1}\Big{)}^{T}\bigg{]}\\ &=\sum\limits_{i=0}^{2L}z_{i}^{(c)}\Big{(}X_{k|k-1,i}^{x}-\hat{x}_{k|k-1}\Big{)} \Big{(}X_{k|k-1,i}^{x}-\hat{x}_{k|k-1}\Big{)}^{T}\end{array}  \tag{23}\]  
(3) A priori estimate of system output:  
\[ \begin{array}{ll}\hat{y}_{k}&=E[[h(x_{k},\ u_{k})+v_{k}]|\Psi_{k-1}\}\\ &=\sum\limits_{i=0}^{2L}z_{i}^{(m)}\Big{[}h\Big{(}X_{k|k-1}^{x},\ u_{k}\Big{)} +X_{k-1,i}^{x}\Big{]}\\ &=\sum\limits_{i=0}^{2L}z_{i}^{(m)}y_{k|k-1}\end{array}  \tag{24}\]  
(4) Calculation of the filter gain matrix:  
\[ \begin{array}{ll}L_{k}&=P_{xy,k}P_{y,k}^{-1}\\ &=\sum\limits_{i=0}^{2L}z_{i}^{(c)}\Big{(}X_{k|k-1,i}^{x}-\hat{x}_{k|k-1}\Big{)} \Big{(}y_{k|k-1,i}-\hat{y}_{k}\Big{)}^{T}\bigg{[}\sum\limits_{i=0}^{2L}z_{i}^{ (c)}\Big{(}y_{k|k-1,i}-\hat{y}_{k}\Big{)}\Big{(}y_{k|k-1,i}-\hat{y}_{k}\Big{)} ^{T}\bigg{]}^{-1}\end{array}  \tag{25}\]  
(5) Optimal state estimation:  
\[x_{k}=\hat{x}_{k|k-1}+L_{k}(y_{k}-\hat{y}_{k}) \tag{26}\]  
where \(y_{k}\) is the actual measured value of the system output. (6) Estimation of mean square error:  
\[P_{x,k}=P_{x,k|k-1}-L_{k}P_{y,k}L_{k}^{T} \tag{27}\]  
As the process noise and measurement noise are measured in real-time, and to update the covariance of the process noise and measurement noise in real time, the following is needed:  
\[\left\{ \begin{array}{ll}\mu_{k}=y_{k}-h[\hat{x}_{k},u_{k}]\\ F_{k}=\mu_{k}u_{k}^{T}\\ R_{k}^{v}=\bigg{(}F_{k}+\sum\limits_{i=0}^{2L}z_{i}^{c}\Big{(}y_{k|k-1,i}-y_{k }\Big{)}\Big{(}y_{k|k-1,i}-y_{k}\Big{)}^{T}\bigg{)}/2\\ R_{k}^{w}=L_{k}F_{k}L_{k}^{T}\end{array} \right. \tag{28}\]  
where \(\mu_{k}\) is the residual error of the system measured output, and \(y_{k|k-1,i}\) is the residual error of the system measured output estimated by the sigma points. Real-time updates of the process noise and measurement noise can be achieved by Equation (28). Thus, the establishment of the AUKF is completed.
## 5 Performance Evaluation
### Experimental Results
#### 5.4.3 Experiment 3
Here, the difference between the DR before and after the attacks also reflects the severity of the attacks \(ASR\). In No Defense, the \(DR\) shows a minimal decrease from 97.48% to 47.96% under BIM attack. However, with the proposed defense, the \(DR\) values remain above 90% for all attacks across clusters C1-XAI to C5-XAI. It is evident from those \(DR\) values that the proposed anomaly detector achieves a promising level of robustness. This is attributed to the use of clustering, which results in a lower level of generalization, and XAI, which facilitates the separation of benign and anomalous consumption patterns, as shown in Figure 5 and Figure
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 4 Evaluation of the Proposed Protection Levels for PPP-RTK
### Experimental Data Processing  
To evaluate the performance of the proposed protection level, we used the GNSS observation data provided by the website of the Geospatial Information Authority of Japan [25] and the L6 data provided by the website of the QZSS [26]. CLASLIB, an open-source software tool [26], was used to extract quality messages from the L6 broadcast data and generate the GNSS measurements for the VRS-RTK process.  
CLASLIB is an open-source library that parses the CLAS L6 messages, and the process is shown in Figure 1. It converts the CLAS L6 messages to the observation space representation of correction messages or provides parsed L6 message Type 4073 in comma-separated value formats. CLASLIB can also generate VRS-RTK GNSS observation data from the given GNSS navigation data and the corresponding L6 broadcast messages.  
To evaluate the proposed protection-level equations, GNSS data were obtained from a GEONET base station located in Chiyoda City, Tokyo, Japan, as shown in Figure 2. The test site is located within the CLAS service volume. The GNSS dataset consisted of the GPS and Galileo RINEX observation and navigation files collected over 7 days from 00:00:00 on 11 April 2021, to 23:59:30 on 17 April 2021, with 30 s intervals. The base station uses a Trimble NET89 receiver and a Trimble TPSCR.GSC antenna. The numbers of visible GPS and Galileo satellites during this period are shown in Figure 3. Because the satellite constellation exhibits a trend similar to that of a daily cycle, only the number of visible satellites in 1 day is shown. Figures 4 and 5 show the time series of the \(\sigma_{i,jeno}\) and \(\sigma_{i,tropo}\), respectively, extracted from the dataset. Figure 6 shows the time series of the \(\sigma_{i,sis}\) for the GPS and Galileo satellites. The user receiver multipath and noise uncertainty of the code  
Figure 1: Data extraction of CLAS L6 broadcast messages using CLASLIB.  
and carrier-phase measurements are modelled as \(\sigma_{user,code}=\frac{15}{\sin(EI)}\) in centimeters and \(\sigma_{user,carrier}=\frac{3}{\sin(EI)}\) in millimeters, respectively, and are the typically used values.
## 2 Materials and Methods  
The PPMSDB should fulfil the following:  
1. Define a structural architecture of hierarchical databases suitable to represent the complex interplay between outcomes of interest, attributes investigated, and levels considered. So that, on the side and in synergy with information such as the variables considered, preferences metrics' measures and weights, and all the relevant meta-data regarding the PPS linked.
2. Collect and harmonise data from PPS based on quantitative methods (elicitation preference).
3. Allow researchers to insert their PPS data in autonomy (based on quantitative methods) manually and programmatically [39].
4. Allow for peer-review of the data inserted, with a \"not-checked\" label/meta-information for PPS inserted from public resources that still miss an external and independent confirmation of the record's contents [39].
5. Permit the extraction and download of preferences' weights based on categorical and textual criteria and filters selected or inputted by the users. Textual search should allow at least for exact word matching and the more flexible regular expression searching on the PPS textual fields, such as title, abstract/description, inclusion/exclusion criteria, attributes, outcomes, etcetera. Users should refine the final selection provided by criteria and filters, including the possibility of manually excluding single studies of interest [40].
6. Permit the computation and reporting of pooled PPI of interest on the PPS selected, such as a simple list of weights and outcomes, their summary statistics of localisation and dispersion (i.e., minimum, maximum, mean, median, standard deviation, and inter-quartile range), and regression analyses on them (possibly allowing interactions and non-linear effects) [41].
7. In the input phase, permit to compute derived metrics of interest unreported in the original PPS [41].
8. Allow the download of a report, including the selections to obtain the information retrieved, giving a formal context to the data extracted [42].
9. Possibly, track the number of filtering explored up to the exported data to permit correction for multiple tests in selecting proper data [43].
10. Designed to allow for horizontal (number of instances) easily and vertical (computational power) scalability [40].
11. Adhere to the four open science principles: open access, open methodology, open data, and open source [44].  
The aim of the PPMSDB is to enable the evaluation of previous studies and the continuous inclusion of new studies. As such, it will respond to the need for cumulative approaches for data aggregation in addition to a retrospective summary of past studies.  
Nowadays, several databases are dedicated to patients and others to clinical trials. However, none of these collect information on patient preference studies, although the use of these data can be beneficial throughout the MPLC (Figure 1).
## 2 System Model
### Wind Power Management  
Windmills generate power 24 h per day. The generated power is utilized to meet local energy demands and to charge the hydrogen storage system. Any excess power is either fed into the grid or used to supply non-essential loads. A total of 1.5 MW power is generated by a variable pitch wind model. To determine the output power of the wind farm, the calculated wind speed by anemometer height is transformed to match the wind turbine hub height by means of the power law Equation (4) [36].  
\[\frac{V_{b}}{V_{a}}=(\frac{h}{h_{reference}})^{\partial} \tag{4}\]  
Here, \(V_{a}\) and \(V_{b}\) are the wind velocities in m/s at the wind turbine hub height and at reference height \(h_{reference}\), respectively, whereas \(\partial\) shows the exponential of power law, which is known as roughness factor. The roughness factor depends entirely upon the landscape, temperature, and wind speed at that specific time; either it's the daytime only or its year time. Therefore, it would be different on plain land and in deeply woodland landscapes. So, the output of wind turbine \(P_{W}\) can be stated by set of Equation (5):  
\[P_{W}=\left\{\begin{array}{cc}(xV^{3}-y)&P_{RW},&V_{Cin}\leq V\leq\ V_{Ra}\\ P_{RW},&V_{Ra}\leq V\leq V_{CO}\\ 0,&Otherwise\end{array}\right. \tag{5}\]  
where the output of WT is dependent upon two significant features: the rated power \(P_{W}\) of the turbine and airstream speed \(V\). Moreover, other vital wind velocity standards are used to control the turbine's action. The cut-in velocity showed by \(V_{Cin}\), the rated velocity of turbine denoted by \(V_{Ra}\), and finally, the cut-off speed, can be measure by \(V_{CO}\). Now the remaining constant values \(x\) and \(y\) can be calculated by some expressions:  
\[x=\frac{1}{{V_{Ra}}^{3}-{V_{Cin}}^{3}} \tag{6}\]  
\[y=\frac{{V_{Cin}}^{3}}{{V_{Ra}}^{3}-{V_{Cin}}^{3}} \tag{7}\]  
Typically, the \(V_{Cin}\) has the choice in the values of 2.5-3.5 m/s while the \(V_{CO}\) has the range of 20-25 m/s. Figure 5 depicts the wind turbine characteristics with respect to power.  
When the wind speed exceeds the specified threshold values, the output power must be limited to these thresholds to prevent damage to the generator and its associated power electronics. If the wind speed exceeds the cut-off limit, the system needs to be shut down and removed from the circuit to safeguard its components. Thus, the proposed optimized EMS enhances the efficiency and reduces the cost of energy produced from wind.
## 4 V2H and V2G
### Benefits and Business Opportunities  
As demonstrated in Figure 5, with the V2H system, owners could charge their EVs during the day via solar energy or during non-peak times with lower electricity prices, and then power household appliances at night or during peak hours using the stored energy [62]. The onboard battery can also provide emergency power in case of grid breakdown. Using the V2G system, owners could charge during non-peak hours and feed the stored energy back to the grid to boost its electricity capacity during peak hours [63]. This charging strategy could relieve grid loading pressure during peak hours and stabilize grid operation by reducing the range of loading fluctuations. Meanwhile, EVs can serve as sizeable offline storage devices for renewable energy, facilitating the dispatch of electricity across time scales.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Challenges** & **Opportunities** \\ \hline \multirow{3}{*}{Market framework} & New market models that enable active and reactive EV distribution service systems, such as load shifting, peak shaving, valley filling, voltage regulation, and reactive power control at the distribution system level \\ \cline{2-3}  & Economic aspects & Economic evaluations, including aspects such as benefit analysis for all stakeholders and possible remuneration strategies for service providers \\ \hline \multirow{2}{*}{Battery degradation} & Integration of battery degradation costs with changes in charging/discharging strategies \\ \end{tabular}
\end{table}
Table 2: Summary of challenges and opportunities of application of smart power distribution for EVs [59].
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 5 Experimental Results  
In this section, we thoroughly study the effectiveness of AccFlow on the ns-3 platform in four major simulation setups. We use the network topology illustrated in Figure 1b in all simulation setups whereas the traversing flows are different under different setups (AccFlow is not limited to the simple dumbbell network topology. It is effective to protect both the inter-domain and intra-domain traffic). The first setup regards the distributed low-rate TCP DoS attack, in which we consider different types of legitimate traffic and different attacking strategies. The second setup is about another DoS attack derived from the low-rate TCP DoS attack. We call it the Short Selfish TCP Flow (SSTF) attack because the attackers selfishly consume nearly the whole network resources by generating excessive numbers of short TCP flows. The third setup is designed to verify that AccFlow does not falsely drop packets from legitimate periodic flows. Finally, we consider the general DoS attacks in the fourth setup.
## 3 Results and Discussion
### Cascade Protocol for Grape Stalk Valorization
Overall, liquid fractions after alkaline delignification at 60 min and with ChLAGly at 120 min could be considered as the richest in antioxidant compounds. Therefore, these two liquid fractions were more deeply characterized by GC-MS to investigate the presence of volatile phenolic compounds. Furthermore, despite the lower DPPH results, ChLevA liquid fraction after 120 min treatment was also analyzed because of the engaging delignification observed. Chromatograms mainly showed the selective extraction of 2,6-di-tert-butyl-4-methylphenol for all the systems (Figure S1), endowed with antioxidant properties and useful in some biological in vitro and in vivo assays, as reported [49]. The alkaline extract contained also other minor volatile phenolic compounds. The two liquid fractions remaining after delignification with NaDESs (ChLAGly and ChLevA) were also further analyzed by HPLC-DAD. As shown in Figure S2, the former contained more phenolic compounds than the extract from ChLevA treatment. This result could explain the lower antioxidant activity of ChLevA liquid fraction. Finally, the production of LevA via MW-assisted flash conversion of the cellulose-rich solid fraction remaining after delignification was accomplished (see Figure 2), on the basis of the good results achieved in a previous work [25]. Due to the highest delignification, and considering the shorter residence times, the cellulose-rich fraction obtained after ChLevA treatment at 60 min was selected for this further valorization step. Moreover, the obtained LevA could be reused to prepare ChLevA again, which can efficiently extract lignin from GS (Figure 1), paving the way for a circular and sustainable protocol towards full-biomass valorization. Results are summarized in Table 3.  
As observed, delignification notably improves LevA yields compared to the raw GS (Table 3, entries 1-2), as expected. Some literature works reported that after delignification treatment the accessibility of cellulose is enhanced by removing both lignin and hemicellulose, which favors the further conversion of the remaining fraction into valuable products [22,23]. Therefore, 85% LevA in terms of molar yield was achieved in a 2 min MW treatment at 225 \({}^{\circ}\)C, which corresponds to 60% in terms of ponderal yield and 68% conversion. These results were similar to those obtained from post-harvest tomato plant (PHTP) in our previous work (Table 3, entry 3) [25], demonstrating these optimized conditions being efficient also for other types of biomasses. Furthermore, it should be noted that LevA was obtained in a 100%-selective transformation, since it was the only product detected in the organic phase, as observed by GC-MS and NMR analyses (Figure S3). Overall, delignification was demonstrated as a valuable tool in terms of valorization, not only for antioxidants and lignin recovery, but also for enhancing the yields achieved by the conversion of cellulose-rich fraction. In parallel, the employment of process intensification technologies could improve the efficiency and sustainability of the cascade protocol.