esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi Infeed High Voltage Direct Current-Based WAMPAC Control
### Mildc Control Modeling  
In this study, we proposed LCC and VSC models from PSS/E software for HVDC operation [26]. There are several LCC model types in PSS/E which are classified according to additional protection control. CDC4T has a standard operational characteristic that can be feasibly applied to the other models. Three distinct sorts of action controls in CDC4T modeling are described. The first action control is called normal regulation, which works by maintaining the rectifier and inverter's set operating range value to a constant current or power. The second option is a momentary overriding of a dc converter, which maintains the performance as feedback control when AC system voltages are disrupted following malfunctions. The last action control involves modulating the DC power system response, which aims to eliminate the AC system's rotor angle swings. Moreover, the correlation between frequency stability and the rotor angle was explored in [27].  
Figure 5: Phasor data processing stages in the WAMPAC Platform.  
Figure 6: WAMPAC communication infrastructure.  
On the other hand, VSC modeling is carried out using the VSCDYN control. The VSCDYN has three separate sorts of action via the controls, almost similar to LCC. The first action is reactive power control, also known as AC voltage control. The next is active power control, frequently referred to as DC voltage control, which is associated with two procedures, including as converter blocking and power ramping, if an unexpected event such as contingency takes place while the system is operating normally. The last action is a limitation of the current output, which seeks to maintain the current value while VSCDYN is applied.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 2 Related Work  
### Vehicular Ad Hoc Networks  
Since vehicular networks could be a data infrastructure in both urban and rural areas, multi-hop vehicular ad hoc networks have been deeply studied so far. Once geographic routing has been found to be suitable in vehicular networks, several effective techniques have been developed based on geographic routing. [PERSON] et al. proposed a DTN routing scheme called GPCR (Greedy Perimeter Coordinator Routing) and showed that the geometric routing is one of the suitable choices in routing strategies in VANET [4]. [PERSON] et al. proposed GPSRJ+ (Greedy Perimeter Stateless Routing Junction+) that improves the road segment selection method based on geographic routing to achieve higher packet delivery ratio [5]. [PERSON] et al. presented GSR (Geographical Source Routing) that introduces an anchor-based routing, in which Dynamic Source Routing (DSR) based on anchors on map is applied into vehicular networks [6]. [PERSON] et al. proposed an anchor-based routing called A-STAR (Anchor based Street and Traffic Aware Routing) that considers streets on maps and vehicular traffic amount on streets in making routing decisions [7]. [PERSON] et al. proposed VADD (Vehicle-Assisted Date Delivery) in which they introduced the _carry-and-forward_ behavior into geometric routing that holds packets until they find adequate next-hop vehicles that are likely to forward packets to the destinations [8]. [PERSON] et al. proposed MDDV (Mobility-centric Data Dissemination Algorithm for Vehicular Networks) that forwards packets along the trajectory precomputed from static road information [9]. [PERSON] et al. proposed GyTAR (Greedy Traffic Aware Routing), which considers dynamic traffic information such as density of vehicles to select delay-optimal outgoing roads at each intersection [10]. [PERSON] et al. proposed a street-centric routing that introduces the concept of micro topologies to consider the wireless connections among vehicles on each street [11]. However, although those techniques certainly improve the delivery ratio of packets in sparse scenarios of VANETs, the delivery ratio is still too low in order to support practical applications over VANETs.
## 5 Result and Discussion
The fact that the particle residence time varies with the degree of mixing is important in determining the performance of flotation columns. Intensity of axial mixing is related to vessel dispersion number, residence time distribution, superficial gas velocity, and the column diameter, etc. (2018). The effect of vessel dispersion number, \(N_{d}\), which is a measure of the degree of mixing and the particle residence time (\(\tau_{p}\)), are affected by both superficial flowrate of gas and slurry/water as shown in the Figure 5. As can be seen, it increases with \(J_{g}\) and decreases with \(J_{sl}\). An increase in the vessel dispersion number with an increase in the superficial gas velocity is because of the increase in the gas holdup. The increased gas holdup results in the phenomenon of cloud bubbles generated from an increase in the aeration rate, and it then increases the axial mixing due to the turbulence (2018; 2018). The superficial slurry velocity deceases the gas holdup, and then liquid/slurry volume of downstream leads to a reduction in the axial mixing. Figure 4: Ratio of particle mean residence time to liquid mean residence time (\(\tau_{p}/\tau_{L}\)) versus particle size. dispersion and mixing are the most important characteristics in terms of design and scale up of the flotation columns. It may be useful to establish the variation of \(N_{d}\) with the above significant variables along with the aspect ratio of the column given by \((H_{c}/d_{c})\), to estimate the extent of mixing. For the coal cleaning operation described here, it may be given by a regression relationship of the form:  
\[N_{d}\ =0.079\ U_{s}^{0.288}\ \left(\ \frac{J_{g}}{J_{sl}}\ \right)^{0.615} \left(\ \frac{H_{c}}{d_{c}}\ \right)^{-0.019}, \tag{20}\]  
Figure 5: Effect of (**a**) superficial gas flowrate and (**b**) superficial slurry flowrate on vessel dispersion number. where \(H_{c}\) is the height of the collection zone and \(d_{c}\) the diameter of the column. The variation in \(H_{c}\) arise due to variations in gas holdup in the column. Relationship between calculated and observed vessel dispersion number (\(N_{d}\)) are shown in Figure 6. The relationship can be used with 94% accuracy for the coal cleaning application within the range of operating variables tested in superficial gas velocity (0.7-1.6 cm/s), superficial wash water velocity (0.1-0.4 cm/s), and \(H_{c}/d_{c}\) (26.8-32.7). As shown in Figure 7, a regression equation may also be developed to determine the variation of the flotation rate constant with vessel dispersion number to estimate the former in terms of the operating variables. Flotation efficiency of coal can be predicted by adjusting the dispersion number according to the conditions of the above mixing characteristic variables. Figure 6: Relationship between calculated and observed vessel dispersion number, \(N_{d}\). Figure 7: Plot of collection rate constant (\(k_{c}\)) versus vessel dispersion number (\(N_{d}\)).
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 2 The Emergence of Privacy Impact Assessment
### Understanding the Differences between PIAs and the GDPR's DPIAs  
In addition to the aforementioned regulatory efforts, amendments on the DPIA processes has also been proposed from the academia. These efforts include making the DPIA process more systematic and structured by using formal modeling techniques for privacy threats [19]. LINDDUN is a privacy threat analysis framework that can support analysts in systematically eliciting and mitigating privacy threats and consists of six main steps [25]. The acronym stands for Linkability, Identifiability, Non-repudiation, Detectability, Disclosure of information, Unawareness and Non-compliance. LINDDUN and the CNIL method [26] have common principles. LINDDUN, however, has the functionality to visualise data flow diagrams and privacy threat tree patterns [27] compared to the CNIL. However, LINDDUN is missing assessment steps from a legal perspective and also is not integrated with a risk assessment process [21]. The work in [28] presents a methodology, firstly introduced in [24], to reinforce the privacy enhancement of a system design model, since there is a luck of a common a methodology concerning the design of IT systems [24]. More precisely, a systematic model-based cost estimation methodology is proposed that takes into consideration a range of privacy controls, including privacy-design strategies, patterns, and PETs as well as the interrelations and dependencies among them. More recently, the authors in [29] propose a detailed methodology for identifying and quantifying data privacy risks, while the risk values are calculated at two different levels for helping the senior management and the operational personnel to assess and mitigate privacy risks. In addition, the work in [19] present a systematic privacy-related information security risk assessment (pISRA) model, which combines both a privacy impact analysis and a risk assessment. In [30], the authors present an empirically evaluated privacy risk assessment framework, the DPIA Data Wheel. This framework considers the contextual integrity that practitioners can use to take decision around the privacy risks of Cyber Physical Systems (CPS). Unfortunately, most of the research efforts do not implement their proposed method/model.
###### Abstract  
The issue of mycotoxin contamination in food is particularly important for consumer safety. The consumption of these fungi can cause genetic mutations or lead to the formation of cancer cells. For years, foods have been studied for this problem, but for table olives, there is currently a significant lack of scientific literature. This study aimed to point out the possible application of the Enzyme-Linked Immunosorbent Assay (ELISA) method for the detection of mycotoxins in table olives, assessing the presence of aflat and ochratoxin A. Different tests were carried out on commercial and inoculated olives to assess the efficacy of the method by comparison with the conventional one, based on Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). Initial unsatisfactory results led us to refine the extraction method via the substitution of the chemical extractant 70% methanol with dichloromethane; this procedure allowed for improving the recovery of inoculated mycotoxins. The results show the effectiveness of the ELISA test, especially in mycotoxin recovery, after the modification of the extraction method. The recoveries increased for aflat from 55% to over 70% and for ochratoxin A from 40% to over 55%. When samples were analyzed with commercial kits, the results showed some false positives, especially for the first sample of ochratoxin A, but the reliability of the ELISA method test was largely verified in the rest of the samples. The validity, the reduced cost, and the speed of application of the ELISA test make this method suitable as a control program for mycotoxin detection in table olives, which can be supported by LC-MS/MS analysis in case of positive results.  
table olives; ochratoxin A; aflat; ELISA; LC-MS/MS Article
# MEC: A Mesoscale Events Classifier for Oceanographic Imagery
### Proposed Approach  
This work introduces _Mesoscale Events Classifier_ (MEC), a new automatic method capable of accepting massive datasets of oceanographic SST imagery as input and returning the classified images as output. It is suitable to point out that the objective pursued through this method slightly differs from the previous approaches: the aim here is not to replicate the implementation of another tool to optimally separate upwelling and non-upwelling areas, but to analyse systematically and in a broader perspective the several mesoscale phenomena that occur within the observed EBUE and to identify and accordingly label the dominant occurring configurations of the system. The output classification labels reflect the different regimes of observable upwelling patterns. The identification of a specific temperature pattern is based on the extraction of quantitative features from the SST maps. Particular attention is devoted to those features that reflect the signal variability (e.g., gradient-based indicators). Indeed the emergence of a certain pattern is usually highly correlated with peculiarities in the temperature spatial arrangement at time fixed (e.g., the presence of abrupt variations in the temperature values within a certain neighbourhood), as well as with the observation of specific temperature trends at fixed locations, providing insights about the flowing of water masses between points at different temperature values. In a previous work devoted to this topic [41], a dedicated tool was developed to extract and visualise the time series of the SST signals related to a given number of fixed locations within an area of interest. Based on this result, a novel step is introduced in the pipeline, with the objective of processing the mentioned signal series to extract quantitative descriptors of the signals trend. The computed quantities are finally used to fulfil the classification task through the implementation of a set of rules that, according to the numerical values of the computed features, assign each set of time series to a specific class.  
MEC will be applied to the southern Iberian region, contributing to the understanding of the formation of upwelling filaments and the effects of climate change in this particular EBUE. The metrics currently used in MEC (e.g., the signal variation rate and its deviation from the mean value) are able to identify different types of mesoscale features, but different variables and arrangements still need to be tested, as discussed in the text.  
The paper is arranged as follows: Sections 2 and 3 concern a detailed description of the employed dataset and the related ground truth classification; Section 4 thoroughly reports about the developed processing pipeline and describes a relevant use case; Sections 5 and 6 conclude the paper by discussing the outcomes of this work and providing a few considerations about future perspectives.
# MAC Protocols for mmWave Communication: A Comparative Survey +
###### Abstract  
With the increase in the number of connected devices, to facilitate more users with high-speed transfer rate and enormous bandwidth, millimeter-wave (mmWave) technology has become one of the promising research sectors in both industry and academia. Owing to the advancements in 5G communication, traditional physical (PHY) layer-based solutions are becoming obsolete. Resource allocation, interference management, anti-blockage, and deafness are crucial problems needing resolution for designing modern mmWave communication network architectures. Consequently, comparatively new approaches such as medium access control (MAC) protocol-based utilization can help meet the advancement requirements. A MAC layer accesses channels and prepares the data frames for transmission to all connected devices, which is even more significant in very high frequency bands, i.e., in the mmWave spectrum. Moreover, different MAC protocols have their unique limitations and characteristics. In this survey, to deal with the above challenges and address the limitations revolving around the MAC layers of mmWave communication systems, we investigated the existing state-of-the-art MAC protocols, related surveys, and solutions available for mmWave frequency. Moreover, we performed a categorized qualitative comparison of the state-of-the-art protocols and finally examined the probable approaches to alleviate the critical challenges in future research.  
MAC protocols; millimeter-wave; resource allocation; scheduling +
Footnote †: This paper is an extended version of our paper published in International Conference on Information and Communication Technology Convergence (CCTC), Jeju, Korea, 20-22 October 2021.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 2 SSA Forecasting  
There are two different algorithms for forecasting with SSA, namely recurrent forecasting and vector forecasting [12, 28]. Those interested in a comparison of the performance of both algorithms are referred to [25]. Both of these forecasting algorithms require that one follows two common steps of SSA, the decomposition and reconstruction of a time series [12, 28]. In what follows, we provide a brief description of forecasting processes in SSA.
## 3 Results and Discussion
### Fatty Acid Content in Paprika Oil Varieties  
However, the cis (oleic acid) and trans (elaidic acid) isomers of C18:1 and cis (linoleic acid) and trans (linolaidic acid) isomers of C18:2 were not separated and were quantified together. The lipid content varied in the following order: P5 (\(16.0\pm 1.1\%\)) > P2 (15.5 \(\pm\) 1.0%) > P6 (13.7 \(\pm\) 1.1%) > P7 (13.0 \(\pm\) 1.0%) > P8 (12.4 \(\pm\) 0.89%) > P4 (12.1 \(\pm\) 0.90) > P1 (10.01 \(\pm\) 0.90%) > P3 (4.2 \(\pm\) 0.2%). The fatty acid methyl esters found in all oil from paprika powder varieties are presented in Table 2 and Figure 4. All oil samples analyzed contain SFAs, MUFAs and PUFAs in different quantities within sample types. The SFAs found in paprika oil are C12:0, C14:0, C15:0, C16:0, C17:0, C18:0, C20:0, C21:0, C22:0 and C23:0. Palmitic acid (C16:0) is the predominant SFA in all paprika samples. The highest content of C16:0 was found in the P1 sample. The identified MUFAs are C14:1, C16:1, C17:1, C18:1(c + t) and C20:1. The highest oleic and octadecenoic acid (C18:1(c + t)) content was found in P8 (11.31%) and P4 (10.38%).  
The PUFAs are divided into omega-6 (u-6) and omega-3 (u-3). The highest PUFA (u-6) quantities found in all samples are reported for linoleic acid (C18:2(c + t)) and varied in the following order: P4 (64.7%) > P8(61.9%) > P6(61.4%) > P7 (56.8%) > P3(24.3%) > P1, P5 and P2 (approx.20%). The highest PUFA content was found in spicy samples. According to Figure 4, the highest content of SFA was found in golden pepper (P3), due to a high content of tricosanoic acid (C23:0). Red paprika varieties were found to be free of saturated tricosanoic acid. [PERSON] and [PERSON] (2009) reported a content of 59.4% C18:2 and 5.1% C18:3 in red paprika powder oil. The method by which the oil was extracted was Soxhlet with hexane/ethyl ether (1:1) [37]. Several studies reported that food rich in PUFAs reduces the risk of cardiovascular diseases, and they were recently shown to prevent the risk of SARS-CoV-2 infection [38]. Omega-3 was found in high quantities in samples P2 (20.6%), P1 and P3 (19.5%) and in lower amounts in samples P7, P4, P8 and P6 (lower than  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Acid Type** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** \\ \hline  
4%). The conclusion is that spicy paprika has a high omega-3 content and is appropriate for consumption. It is essential that the consumption of SFAs is replaced with the consumption of PUFAs. It is recommended that the consumption of SFAs should not exceed 10%, according to the Food and Agricultural Organization of United Nations [39]. A lower \(\omega\)-6/\(\omega\)-3 ratio is desirable due to the reduction in the risk of cardiovascular problems. The free fatty acids were analyzed in all samples (as acid values). All the oil samples contained below 1%, demonstrating the solubility of fatty acids in used solvents in the presence of the ultrasound method. The fatty acid profile of paprika varieties is important for food chemistry due to differences in variety chemistry, assigned quality, origin, food taste and color.
###### Abstract  
This study examined the effects of alternative sweeteners--allulose, stevia, and xylose--on the physicochemical, textural, and sensory properties of Yanggaeng fortified with 2% _Corni fruct_ powder (CF). A proximate composition analysis revealed that Yanggaeng treated with alternative sweeteners had a significantly higher moisture content and a lower carbohydrate content than that treated with sugar. Colorimetric analysis showed that the xylose treated Yanggaeng decreased lightness (\(L^{*}\)) and increased redness (\(a^{*}\)) and yellowness (\(b^{*}\)), likely due to enhanced browning reactions. The xylose treated Yanggaeng exhibited the highest Brix values and antioxidant activities, including DPPH and ABTS radical scavenging and ferric reducing antioxidant power (FRAP). Although the allulose treated group had the highest total phenolic content, its radical scavenging activity was lower than the xylose treated Yanggaeng. Xylose produced a firmer and more cohesive gel matrix, increasing hardness, gumminess, and chewiness, whereas stevia resulted in a softer, less chewy texture. In sensory evaluations, the xylose treated Yanggaeng scored higher for color, flavor, sweetness, overall acceptance, and purchase intent than other groups. These findings suggest that xylose is the most suitable alternative sweetener for CF-enhanced Yanggaeng, offering reduced caloric content, enhanced antioxidant properties, improved texture, and superior consumer acceptance.  
alternative sweeteners; _Corni fruct_; Yanggaeng; physicochemical properties;sensory attributes
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 4 Future Research Directions
### Leverage AI and Machine Learning for Detection  
In the constantly evolving Web tracking landscape, trackers frequently adopt new and sophisticated evasion techniques that diminish the effectiveness of traditional blocklists. A promising solution is to integrate AI and machine learning (ML) models, rather than relying solely on domain-specific blocklists, to detect tracking behaviors. By analyzing patterns in network traffic, script execution, and browser interactions, ML models can identify abnormal behaviors indicative of tracking, even when trackers employ new or dynamically generated techniques. Additionally, ML models can continuously learn from real-time data, enhancing their ability to detect and block emerging tracking strategies. Adversarial machine learning techniques can also be utilized to predict future evasion tactics, enabling the system to more effectively counter upcoming threats. These AI-driven systems provide adaptive and dynamic protection, ensuring users are safeguarded from previously unknown tracking methods.